[ { "path": ".appveyor.yml", "content": "version: 4.6.0.99.{build}\n\nimage: Visual Studio 2017\nplatform: x64\nconfiguration:\n - '3.9'\n\n# only build on 'master' and pull requests targeting it\nbranches:\n only:\n - master\n\nenvironment:\n matrix:\n - COMPILER: MSVC\n TASK: python\n - COMPILER: MINGW\n TASK: python\n\nclone_depth: 5\n\ninstall:\n - git submodule update --init --recursive # get `external_libs` folder\n - set PATH=C:\\mingw-w64\\x86_64-8.1.0-posix-seh-rt_v6-rev0\\mingw64\\bin;%PATH%\n - set PYTHON_VERSION=%CONFIGURATION%\n - ps: |\n $env:APPVEYOR = \"true\"\n $env:CMAKE_BUILD_PARALLEL_LEVEL = 4\n $env:MINICONDA = \"C:\\Miniconda3-x64\"\n $env:PATH = \"$env:MINICONDA;$env:MINICONDA\\Scripts;$env:PATH\"\n $env:BUILD_SOURCESDIRECTORY = \"$env:APPVEYOR_BUILD_FOLDER\"\n # tell scripts where to put artifacts\n # (this variable name is left over from when jobs ran on Azure DevOps)\n $env:BUILD_ARTIFACTSTAGINGDIRECTORY = \"$env:APPVEYOR_BUILD_FOLDER/artifacts\"\n\nbuild: false\n\ntest_script:\n - conda config --remove channels defaults\n - conda config --add channels nodefaults\n - conda config --add channels conda-forge\n - conda config --set channel_priority strict\n - conda init powershell\n - powershell.exe -ExecutionPolicy Bypass -File %APPVEYOR_BUILD_FOLDER%\\.ci\\test-windows.ps1\n" }, { "path": ".ci/README.md", "content": "Helper Scripts for CI\r\n=====================\r\n\r\nThis folder contains scripts which are run on CI services.\r\n\r\nDockerfile used on CI service is maintained in a separate [GitHub repository](https://github.com/guolinke/lightgbm-ci-docker) and can be pulled from [Docker Hub](https://hub.docker.com/r/lightgbm/vsts-agent).\r\n" }, { "path": ".ci/append-comment.sh", "content": "#!/bin/bash\n#\n# [description]\n# Post a comment to a pull request.\n#\n# [usage]\n# append-comment.sh \n#\n# PULL_REQUEST_ID: ID of PR to post the comment on.\n#\n# BODY: Text of the comment to be posted.\n\nset -e -E -u -o pipefail\n\nif [ -z \"$GITHUB_ACTIONS\" ]; then\n echo \"Must be run inside GitHub Actions CI\"\n exit 1\nfi\n\nif [ $# -ne 2 ]; then\n echo \"Usage: $0 \"\n exit 1\nfi\n\npr_id=$1\nbody=$2\n\nbody=${body/failure/failure ❌}\nbody=${body/error/failure ❌}\nbody=${body/cancelled/failure ❌}\nbody=${body/timed_out/failure ❌}\nbody=${body/success/success ✔️}\ndata=$(\n jq -n \\\n --argjson body \"\\\"$body\\\"\" \\\n '{\"body\": $body}'\n)\ncurl -sL \\\n --fail \\\n -X POST \\\n -H \"Accept: application/vnd.github.v3+json\" \\\n -H \"Authorization: token ${GITHUB_TOKEN}\" \\\n -d \"$data\" \\\n \"${GITHUB_API_URL}/repos/lightgbm-org/LightGBM/issues/${pr_id}/comments\"\n" }, { "path": ".ci/build-docs.sh", "content": "#!/bin/bash\n\nset -e -E -u -o pipefail\n\nconda env create \\\n --name test-env \\\n --file ./docs/env.yml \\\n|| exit 1\n\n# shellcheck disable=SC1091\nsource activate test-env\n\nmake -C docs html || exit 1\n" }, { "path": ".ci/check-dynamic-dependencies.sh", "content": "#!/bin/bash\n#\n# [description]\n# Helper script for checking versions in the dynamic symbol table.\n# This script checks that LightGBM library is linked to the appropriate symbol versions.\n# Linking to newer symbol versions at compile time is problematic because it could result\n# in built artifacts being unusable on older platforms.\n#\n# Version history for these symbols can be found at the following:\n# * GLIBC: https://sourceware.org/glibc/wiki/Glibc%20Timeline\n# * GLIBCXX: https://gcc.gnu.org/onlinedocs/libstdc++/manual/abi.html\n# * OMP/GOMP: https://github.com/gcc-mirror/gcc/blob/master/libgomp/libgomp.map\n#\n# [usage]\n# check-dynamic-dependencies.sh \n#\n# PATH: Path to the file.\n# Path to the file with the dynamic symbol table entries of the file\n# (result of `objdump -T` command).\n\nset -e -E -u -o pipefail\n\nif [ \"$#\" -ne 1 ]; then\n echo \"Usage: $0 \"\n exit 1\nfi\n\nINPUT_FILE=\"$1\"\n\nif [ ! -f \"$INPUT_FILE\" ]; then\n echo \"Error: File '$INPUT_FILE' not found.\"\n exit 1\nfi\n\nawk '\nBEGIN {\n glibc_count = 0\n glibcxx_count = 0\n gomp_count = 0\n\n has_error = 0\n}\n\n# --- Check GLIBC ---\n/0{16}[ \\t\\(]+GLIBC_[0-9]+\\.[0-9]+/ {\n match($0, /GLIBC_([0-9]+)\\.([0-9]+)/, parts)\n if (RSTART > 0) {\n match($0, /GLIBC_[0-9]+\\.[0-9]+/)\n ver_str = substr($0, RSTART+6, RLENGTH-6) # skip \"GLIBC_\"\n split(ver_str, v, \".\")\n major = v[1] + 0\n minor = v[2] + 0\n\n glibc_count++\n\n if (major > 2 || (major == 2 && minor > 28)) {\n print \"Error: found unexpected GLIBC version: \\x27\" major \".\" minor \"\\x27\"\n has_error = 1\n }\n }\n}\n\n# --- Check GLIBCXX ---\n/0{16}[ \\t\\(]+GLIBCXX_[0-9]+\\.[0-9]+/ {\n match($0, /GLIBCXX_[0-9]+\\.[0-9]+(\\.[0-9]+)?/)\n if (RSTART > 0) {\n ver_str = substr($0, RSTART+8, RLENGTH-8) # skip \"GLIBCXX_\"\n n = split(ver_str, v, \".\")\n major = v[1] + 0\n minor = v[2] + 0\n patch = (n >= 3) ? v[3] + 0 : 0\n patch_str = (n >= 3) ? v[3] : \"\"\n\n glibcxx_count++\n\n msg_ver = major \".\" minor\n if (n >= 3) msg_ver = msg_ver \".\" patch\n\n if (major != 3 || minor != 4) {\n print \"Error: found unexpected GLIBCXX version: \\x27\" msg_ver \"\\x27\"\n has_error = 1\n }\n if (n >= 3 && patch > 22) {\n print \"Error: found unexpected GLIBCXX version: \\x27\" msg_ver \"\\x27\"\n has_error = 1\n }\n }\n}\n\n# --- Check OMP/GOMP ---\n/0{16}[ \\t\\(]+G?OMP_[0-9]+\\.[0-9]+/ {\n match($0, /G?OMP_[0-9]+\\.[0-9]+/)\n if (RSTART > 0) {\n full_match = substr($0, RSTART, RLENGTH)\n us_idx = index(full_match, \"_\")\n ver_str = substr(full_match, us_idx + 1)\n\n split(ver_str, v, \".\")\n major = v[1] + 0\n minor = v[2] + 0\n\n gomp_count++\n\n if (major > 4 || (major == 4 && minor > 5)) {\n print \"Error: found unexpected OMP/GOMP version: \\x27\" major \".\" minor \"\\x27\"\n has_error = 1\n }\n }\n}\n\nEND {\n if (glibc_count <= 1) {\n print \"Error: Not enough GLIBC symbols found (found \" glibc_count \", expected > 1)\"\n has_error = 1\n }\n if (glibcxx_count <= 1) {\n print \"Error: Not enough GLIBCXX symbols found (found \" glibcxx_count \", expected > 1)\"\n has_error = 1\n }\n if (gomp_count <= 1) {\n print \"Error: Not enough OMP/GOMP symbols found (found \" gomp_count \", expected > 1)\"\n has_error = 1\n }\n\n if (has_error == 1) {\n exit 1\n }\n}\n' \"$INPUT_FILE\"\n" }, { "path": ".ci/check-omp-pragmas.sh", "content": "#!/bin/bash\n\nset -e -u\n\necho \"checking that all OpenMP pragmas specify num_threads()\"\nget_omp_pragmas_without_num_threads() {\n grep \\\n -n \\\n -R \\\n --include='*.c' \\\n --include='*.cc' \\\n --include='*.cpp' \\\n --include='*.h' \\\n --include='*.hpp' \\\n 'pragma omp parallel' \\\n | grep -v ' num_threads'\n}\n\n# 'grep' returns a non-0 exit code if 0 lines were found.\n# Turning off '-e -o pipefail' options here so that bash doesn't\n# consider this a failure and stop execution of the script.\n#\n# ref: https://www.gnu.org/software/grep/manual/html_node/Exit-Status.html\nset +e\nPROBLEMATIC_LINES=$(\n get_omp_pragmas_without_num_threads\n)\nset -e\nif test \"${PROBLEMATIC_LINES}\" != \"\"; then\n get_omp_pragmas_without_num_threads\n echo \"Found '#pragma omp parallel' not using explicit num_threads() configuration. Fix those.\"\n echo \"For details, see https://www.openmp.org/spec-html/5.0/openmpse14.html#x54-800002.6\"\n exit 1\nfi\necho \"done checking OpenMP pragmas\"\n" }, { "path": ".ci/check-python-dists.sh", "content": "#!/bin/sh\n\nset -e -u\n\nDIST_DIR=${1}\n\n# defaults\nMETHOD=${METHOD:-\"\"}\nTASK=${TASK:-\"\"}\n\necho \"checking Python-package distributions in '${DIST_DIR}'\"\n\npip install \\\n -qq \\\n check-wheel-contents \\\n twine || exit 1\n\necho \"twine check...\"\ntwine check --strict \"$(echo \"${DIST_DIR}\"/*)\" || exit 1\n\nif { test \"${TASK}\" = \"bdist\" || test \"${METHOD}\" = \"wheel\"; }; then\n echo \"check-wheel-contents...\"\n check-wheel-contents \"$(echo \"${DIST_DIR}\"/*.whl)\" || exit 1\nfi\n\nPY_MINOR_VER=$(python -c \"import sys; print(sys.version_info.minor)\")\nif [ \"$PY_MINOR_VER\" -gt 7 ]; then\n echo \"pydistcheck...\"\n pip install 'pydistcheck>=0.9.1'\n if { test \"${TASK}\" = \"cuda\" || test \"${METHOD}\" = \"wheel\"; }; then\n pydistcheck \\\n --inspect \\\n --ignore 'compiled-objects-have-debug-symbols'\\\n --ignore 'distro-too-large-compressed' \\\n --max-allowed-size-uncompressed '500M' \\\n --max-allowed-files 800 \\\n \"$(echo \"${DIST_DIR}\"/*)\" || exit 1\n elif { test \"$(uname -m)\" = \"aarch64\"; }; then\n pydistcheck \\\n --inspect \\\n --ignore 'compiled-objects-have-debug-symbols' \\\n --max-allowed-size-compressed '5M' \\\n --max-allowed-size-uncompressed '15M' \\\n --max-allowed-files 800 \\\n \"$(echo \"${DIST_DIR}\"/*)\" || exit 1\n else\n pydistcheck \\\n --inspect \\\n --max-allowed-size-compressed '5M' \\\n --max-allowed-size-uncompressed '15M' \\\n --max-allowed-files 800 \\\n \"$(echo \"${DIST_DIR}\"/*)\" || exit 1\n fi\nelse\n echo \"skipping pydistcheck (does not support Python 3.${PY_MINOR_VER})\"\nfi\n\necho \"done checking Python-package distributions\"\n" }, { "path": ".ci/check-workflow-status.sh", "content": "#!/bin/bash\n\n# [description]\n#\n# Look for the last run of a given GitHub Actions workflow on a given branch.\n# If there's never been one (as might be the case with optional workflows like valgrind),\n# exit with 0.\n#\n# Otherwise, check the status of that latest run.\n# If it wasn't successful, exit with a non-0 exit code.\n#\n# [usage]\n#\n# check-workflow-status.sh \n#\n# BRANCH: name of a branch involved in a pull request.\n#\n# WORKFLOW_FILE: filename (e.g. 'r_valgrind.yml') defining the GitHub Actions workflow.\n#\n\nset -e -u -o pipefail\n\nBRANCH=\"${1}\"\nWORKFLOW_FILE=\"${2}\"\nPR_NUMBER=\"${3}\"\n\n# Limit how much data is pulled from the API and needs to be parsed locally.\nOLDEST_ALLOWED_RUN_DATE=$(date --date='7 days ago' '+%F')\n\necho \"Searching for latest run of '${WORKFLOW_FILE}' on branch '${BRANCH}' \"\n\nLATEST_RUN_ID=$(\n gh run list \\\n --repo 'lightgbm-org/LightGBM' \\\n --event 'workflow_dispatch' \\\n --created \">= ${OLDEST_ALLOWED_RUN_DATE}\" \\\n --workflow \"${WORKFLOW_FILE}\" \\\n --json 'createdAt,databaseId,name' \\\n --jq \"sort_by(.createdAt) | reverse | map(select(.name | contains (\\\"pr=${PR_NUMBER}\\\"))) | .[0] | .databaseId\"\n)\n\nif [[ \"${LATEST_RUN_ID}\" == \"\" ]]; then\n echo \"No runs of '${WORKFLOW_FILE}' found on branch from pull request ${PR_NUMBER} (on or after ${OLDEST_ALLOWED_RUN_DATE}).\"\n exit 0\nfi\n\necho \"Checking status of workflow run '${LATEST_RUN_ID}'\"\ngh run view \\\n --repo \"lightgbm-org/LightGBM\" \\\n --exit-status \\\n \"${LATEST_RUN_ID}\"\n" }, { "path": ".ci/conda-envs/README.md", "content": "# conda-envs\n\nThis directory contains files used to create `conda` environments for development\nand testing of LightGBM.\n\nThe `.txt` files here are intended to be used with `conda create --file`.\n\nFor details on that, see the `conda` docs:\n\n* `conda create` docs ([link](https://conda.io/projects/conda/en/latest/commands/create.html))\n* \"Managing Environments\" ([link](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html))\n" }, { "path": ".ci/conda-envs/ci-core-py39.txt", "content": "# [description]\n#\n# Similar to ci-core.txt, but specific to Python 3.9.\n#\n# Unlike ci-core.txt, this includes a Python version and uses\n# `=` and `<=` pins to make solves faster and prevent against\n# issues like https://github.com/lightgbm-org/LightGBM/pull/6370.\n#\n# [usage]\n#\n# conda create \\\n# --name test-env \\\n# --file ./.ci/conda-envs/ci-core-py39.txt\n#\n\n# python\npython=3.9.*\n\n# direct imports\ncffi=1.15.*\n# dask and distributed versions below are the first versions that support tornado >=6.2\n# which is required for testing\ndask=2022.12.*\ndistributed=2022.12.*\njoblib=1.3.*\nmatplotlib-base=3.5.*\nnumpy=1.22.*\npandas=1.3.*\npyarrow=9.0.*\npython-graphviz=0.20.*\nscikit-learn=1.0.*\nscipy=1.7.*\n\n# testing-only dependencies\ncloudpickle=2.2.*\npluggy=1.0.*\npsutil=5.9.3\npytest=7.4.*\n\n# https://github.com/lightgbm-org/LightGBM/issues/6990\ngraphite2=1.3.14=*_0\n\n# other recursive dependencies, just\n# pinned here to help speed up solves\nbokeh=2.4.*\nfsspec=2023.1.*\nmsgpack-python=1.0.*\npytz=2024.1\nsetuptools=59.8.*\nsnappy=1.1.*\ntomli=2.0.*\ntornado=6.2.*\nwheel=0.42.*\nzict=2.2.*\nzipp=3.15.*\n" }, { "path": ".ci/conda-envs/ci-core.txt", "content": "# [description]\n#\n# Core dependencies used across most LightGBM continuous integration (CI) jobs.\n#\n# 'python' constraint is intentionally omitted, so this file can be reused across\n# Python versions.\n#\n# These floors are not the oldest versions LightGBM supports... they're here just to make conda\n# solves faster, and should generally be the latest versions that work for all CI jobs using this.\n#\n# [usage]\n#\n# conda create \\\n# --name test-env \\\n# --file ./.ci/conda-envs/ci-core.txt \\\n# python=3.10\n#\n\n# direct imports\ncffi>=1.16\ndask>=2023.5.0,<2024.12\njoblib>=1.3.2\nmatplotlib-base>=3.7.3\nnumpy>=1.24.4\npandas>2.0\npyarrow-core>=6.0\npython-graphviz>=0.20.3\nscikit-learn>=1.3.2\nscipy>=1.1\n\n# testing-only dependencies\ncloudpickle>=3.0.0\npsutil>=5.9.8\npytest>=8.1.1\n\n# other recursive dependencies, just\n# pinned here to help speed up solves\npluggy>=1.4.0\nsetuptools>=69.2\nwheel>=0.43\n" }, { "path": ".ci/create-nuget.py", "content": "# coding: utf-8\n\"\"\"Script for generating files with NuGet package metadata.\"\"\"\n\nimport datetime\nimport sys\nfrom pathlib import Path\nfrom shutil import copyfile\n\nif __name__ == \"__main__\":\n source = Path(sys.argv[1])\n nuget_dir = Path(__file__).absolute().parent / \"nuget\"\n print(f\"Creating nuget directory '{nuget_dir}'\")\n linux_folder_path = nuget_dir / \"runtimes\" / \"linux-x64\" / \"native\"\n linux_folder_path.mkdir(parents=True, exist_ok=True)\n osx_folder_path = nuget_dir / \"runtimes\" / \"osx-x64\" / \"native\"\n osx_folder_path.mkdir(parents=True, exist_ok=True)\n windows_folder_path = nuget_dir / \"runtimes\" / \"win-x64\" / \"native\"\n windows_folder_path.mkdir(parents=True, exist_ok=True)\n build_folder_path = nuget_dir / \"build\"\n build_folder_path.mkdir(parents=True, exist_ok=True)\n print(f\"Looking for libraries in '{source}'\")\n copyfile(source / \"lib_lightgbm.so\", linux_folder_path / \"lib_lightgbm.so\")\n copyfile(source / \"lib_lightgbm.dylib\", osx_folder_path / \"lib_lightgbm.dylib\")\n copyfile(source / \"lib_lightgbm.dll\", windows_folder_path / \"lib_lightgbm.dll\")\n copyfile(source / \"lightgbm.exe\", windows_folder_path / \"lightgbm.exe\")\n version = (nuget_dir.parents[1] / \"VERSION.txt\").read_text(encoding=\"utf-8\").strip().replace(\"rc\", \"-rc\")\n print(f\"Setting version to '{version}'\")\n nuget_str = rf\"\"\"\n \n \n LightGBM\n {version}\n Guolin Ke\n Guolin Ke\n MIT\n https://github.com/lightgbm-org/LightGBM\n false\n A fast, distributed, high performance gradient boosting framework\n Copyright {datetime.datetime.now().year} @ Microsoft\n machine-learning data-mining distributed native boosting gbdt\n \n \n \n \n \n \n \n \"\"\"\n prop_str = r\"\"\"\n \n \n \n PreserveNewest\n false\n \n \n PreserveNewest\n false\n \n \n \n \"\"\"\n target_str = r\"\"\"\n \n \n true\n \n \n \n \n \n \"\"\"\n (nuget_dir / \"LightGBM.nuspec\").write_text(nuget_str, encoding=\"utf-8\")\n (nuget_dir / \"build\" / \"LightGBM.props\").write_text(prop_str, encoding=\"utf-8\")\n (nuget_dir / \"build\" / \"LightGBM.targets\").write_text(target_str, encoding=\"utf-8\")\n print(\"Done creating NuGet package\")\n" }, { "path": ".ci/download-artifacts.sh", "content": "#!/bin/bash\n\n# [description]\n# Collect and download artifacts from all workflow runs for a commit.\n#\n# [usage]\n# ./download-artifacts.sh \n#\n\nset -e -u -E -o pipefail\n\nCOMMIT_ID=\"${1}\"\nOUTPUT_DIR=\"./release-artifacts\"\n\nget-latest-run-id() {\n gh run list \\\n --repo \"lightgbm-org/LightGBM\" \\\n --commit \"${1}\" \\\n --workflow \"${2}\" \\\n --json 'createdAt,databaseId' \\\n --jq 'sort_by(.createdAt) | reverse | .[0] | .databaseId'\n}\n\n# ensure directory for storing artifacts exists\necho \"preparing to download artifacts for commit '${COMMIT_ID}' to '${OUTPUT_DIR}'\"\nmkdir -p \"${OUTPUT_DIR}\"\n\n# get core artifacts\necho \"downloading core artifacts\"\ngh run download \\\n --repo \"lightgbm-org/LightGBM\" \\\n --dir \"${OUTPUT_DIR}\" \\\n \"$(get-latest-run-id \"${COMMIT_ID}\" 'build.yml')\"\necho \"done downloading core artifacts\"\n\n# get python-package artifacts\necho \"downloading python-package artifacts and NuGet package\"\ngh run download \\\n --repo \"lightgbm-org/LightGBM\" \\\n --dir \"${OUTPUT_DIR}\" \\\n \"$(get-latest-run-id \"${COMMIT_ID}\" 'python_package.yml')\"\necho \"done downloading python-package artifacts and NuGet package\"\n\n# get R-package artifacts\necho \"downloading R-package artifacts\"\ngh run download \\\n --repo \"lightgbm-org/LightGBM\" \\\n --dir \"${OUTPUT_DIR}\" \\\n \"$(get-latest-run-id \"${COMMIT_ID}\" 'r_package.yml')\"\necho \"done downloading R-package artifacts\"\n\n# get SWIG artifacts\necho \"downloading SWIG artifacts\"\ngh run download \\\n --repo \"lightgbm-org/LightGBM\" \\\n --dir \"${OUTPUT_DIR}\" \\\n \"$(get-latest-run-id \"${COMMIT_ID}\" 'swig.yml')\"\necho \"done downloading SWIG artifacts\"\n\n# 'gh run download' unpackages into nested directories like {artifact-name}/{file}.\n#\n# This moves all files to the top level and then deletes those {artifact-name}/ directories,\n# to make it easier to bulk upload all files to a release.\necho \"flattening directory structure\"\nfind \"${OUTPUT_DIR}\" -type f -mindepth 2 -exec mv -i '{}' \"${OUTPUT_DIR}\" \\;\nfind \"${OUTPUT_DIR}\" -type d -mindepth 1 -exec rm -r '{}' \\+\n\necho \"downloaded artifacts:\"\nfind \"${OUTPUT_DIR}\" -type f\n" }, { "path": ".ci/install-opencl.ps1", "content": "Write-Output \"Installing OpenCL CPU platform\"\n\n$installer = \"AMD-APP-SDKInstaller-v3.0.130.135-GA-windows-F-x64.exe\"\n\nWrite-Output \"Downloading OpenCL platform installer\"\n$ProgressPreference = \"SilentlyContinue\" # progress bar bug extremely slows down download speed\n$params = @{\n OutFile = \"$installer\"\n Uri = \"https://github.com/lightgbm-org/LightGBM/releases/download/v2.0.12/$installer\"\n}\nInvoke-WebRequest @params\n\nif (Test-Path \"$installer\") {\n Write-Output \"Successfully downloaded OpenCL platform installer\"\n} else {\n Write-Output \"Unable to download OpenCL platform installer\"\n Write-Output \"Setting EXIT\"\n $host.SetShouldExit(-1)\n exit 1\n}\n\n# Install OpenCL platform from installer executable\nWrite-Output \"Running OpenCL installer\"\nInvoke-Command -ScriptBlock {\n Start-Process \"$installer\" -ArgumentList '/S /V\"/quiet /norestart /passive /log opencl.log\"' -Wait\n}\n\n$property = Get-ItemProperty -Path Registry::HKEY_LOCAL_MACHINE\\SOFTWARE\\Khronos\\OpenCL\\Vendors\nif ($null -eq $property) {\n Write-Output \"Unable to install OpenCL CPU platform\"\n Write-Output \"OpenCL installation log:\"\n Get-Content \"opencl.log\"\n Write-Output \"Setting EXIT\"\n $host.SetShouldExit(-1)\n exit 1\n} else {\n Write-Output \"Successfully installed OpenCL CPU platform\"\n Write-Output \"Current OpenCL drivers:\"\n Write-Output $property\n}\n" }, { "path": ".ci/install-r-deps.R", "content": "# Install R dependencies, using only base R.\n#\n# Supported arguments:\n#\n# --all Install all the 'Depends', 'Imports', 'LinkingTo', and 'Suggests' dependencies\n# (automatically implies --build --test).\n#\n# --build Install the packages needed to build.\n#\n# --exclude= Comma-delimited list of packages to NOT install.\n#\n# --include= Comma-delimited list of additional packages to install.\n# These will always be installed, unless also used in \"--exclude\".\n#\n# --test Install packages needed to run tests.\n#\n\n\n# [description] Parse command line arguments into an R list.\n# Returns a list where keys are arguments and values\n# are either TRUE (for flags) or a vector of values passed via a\n# comma-delimited list.\n.parse_args <- function(args) {\n out <- list(\n \"--all\" = FALSE\n , \"--build\" = FALSE\n , \"--exclude\" = character(0L)\n , \"--include\" = character(0L)\n , \"--test\" = FALSE\n )\n for (arg in args) {\n parsed_arg <- unlist(strsplit(arg, \"=\", fixed = TRUE))\n arg_name <- parsed_arg[[1L]]\n if (!(arg_name %in% names(out))) {\n stop(sprintf(\"Unrecognized argument: '%s'\", arg_name))\n }\n if (length(parsed_arg) == 2L) {\n # lists, like \"--include=roxygen2,testthat\"\n values <- unlist(strsplit(parsed_arg[[2L]], \",\", fixed = TRUE))\n out[[arg_name]] <- values\n } else {\n # flags, like \"--build\"\n out[[arg]] <- TRUE\n }\n }\n return(out)\n}\n\nargs <- .parse_args(\n commandArgs(trailingOnly = TRUE)\n)\n\n# which dependencies to install\nALL_DEPS <- isTRUE(args[[\"--all\"]])\nBUILD_DEPS <- ALL_DEPS || isTRUE(args[[\"--build\"]])\nTEST_DEPS <- ALL_DEPS || isTRUE(args[[\"--test\"]])\n\n# force downloading of binary packages on macOS\nCOMPILE_FROM_SOURCE <- \"both\"\nPACKAGE_TYPE <- getOption(\"pkgType\")\n\n# CRAN has precompiled binaries for macOS and Windows... prefer those,\n# for faster installation.\nif (Sys.info()[[\"sysname\"]] == \"Darwin\" || .Platform$OS.type == \"windows\") {\n COMPILE_FROM_SOURCE <- \"never\"\n PACKAGE_TYPE <- \"binary\"\n}\noptions(\n install.packages.check.source = \"no\"\n , install.packages.compile.from.source = COMPILE_FROM_SOURCE\n)\n\n# always use the same CRAN mirror\nCRAN_MIRROR <- Sys.getenv(\"CRAN_MIRROR\", unset = \"https://cran.r-project.org\")\n\n# we always want these\ndeps_to_install <- c(\n \"data.table\"\n , \"jsonlite\"\n , \"Matrix\"\n , \"R6\"\n)\n\nif (isTRUE(BUILD_DEPS)) {\n deps_to_install <- c(\n deps_to_install\n , \"knitr\"\n , \"markdown\"\n )\n}\n\nif (isTRUE(TEST_DEPS)) {\n deps_to_install <- c(\n deps_to_install\n , \"RhpcBLASctl\"\n , \"testthat\"\n )\n}\n\n# add packages passed through '--include'\ndeps_to_install <- unique(c(\n deps_to_install\n , args[[\"--include\"]]\n))\n\n# remove packages passed through '--exclude'\ndeps_to_install <- setdiff(\n x = deps_to_install\n , args[[\"--exclude\"]]\n)\n\nmsg <- sprintf(\n \"[install-r-deps] installing R packages: %s\\n\"\n , toString(sort(deps_to_install))\n)\ncat(msg)\n\ninstall.packages( # nolint: undesirable_function.\n pkgs = deps_to_install\n , dependencies = c(\"Depends\", \"Imports\", \"LinkingTo\")\n , lib = Sys.getenv(\"R_LIB_PATH\", unset = .libPaths()[[1L]])\n , repos = CRAN_MIRROR\n , type = PACKAGE_TYPE\n , Ncpus = parallel::detectCores()\n)\n" }, { "path": ".ci/lint-all.sh", "content": "#!/bin/bash\n\nset -e -E -u -o pipefail\n\npwsh -command \"Install-Module -Name PSScriptAnalyzer -Scope CurrentUser -SkipPublisherCheck\"\necho \"Linting PowerShell code\"\npwsh -file ./.ci/lint-powershell.ps1 || exit 1\n\nconda create -q -y -n test-env \\\n \"python=3.13[build=*_cp*]\" \\\n 'pre-commit>=3.8.0' \\\n 'r-lintr>=3.3.0'\n\n# shellcheck disable=SC1091\nsource activate test-env\n\necho \"Running pre-commit checks\"\npre-commit run --all-files || exit 1\n\necho \"Linting R code\"\nRscript ./.ci/lint-r-code.R \"$(pwd)\" || exit 1\n" }, { "path": ".ci/lint-powershell.ps1", "content": "$ErrorActionPreference = 'Stop'\r\n\r\n$settings = @{\r\n Severity = @(\r\n 'Information',\r\n 'Warning',\r\n 'Error'\r\n )\r\n IncludeDefaultRules = $true\r\n # Additional rules that are disabled by default\r\n Rules = @{\r\n PSAvoidExclaimOperator = @{\r\n Enable = $true\r\n }\r\n PSAvoidLongLines = @{\r\n Enable = $true\r\n MaximumLineLength = 120\r\n }\r\n PSAvoidSemicolonsAsLineTerminators = @{\r\n Enable = $true\r\n }\r\n PSPlaceCloseBrace = @{\r\n Enable = $true\r\n NoEmptyLineBefore = $true\r\n IgnoreOneLineBlock = $true\r\n NewLineAfter = $false\r\n }\r\n PSPlaceOpenBrace = @{\r\n Enable = $true\r\n OnSameLine = $true\r\n NewLineAfter = $true\r\n IgnoreOneLineBlock = $true\r\n }\r\n PSUseConsistentIndentation = @{\r\n Enable = $true\r\n IndentationSize = 4\r\n PipelineIndentation = 'IncreaseIndentationAfterEveryPipeline'\r\n Kind = 'space'\r\n }\r\n PSUseConsistentWhitespace = @{\r\n Enable = $true\r\n CheckInnerBrace = $true\r\n CheckOpenBrace = $true\r\n CheckOpenParen = $true\r\n CheckOperator = $true\r\n CheckSeparator = $true\r\n CheckPipe = $true\r\n CheckPipeForRedundantWhitespace = $true\r\n CheckParameter = $true\r\n IgnoreAssignmentOperatorInsideHashTable = $false\r\n }\r\n PSUseCorrectCasing = @{\r\n Enable = $true\r\n }\r\n }\r\n}\r\n\r\nInvoke-ScriptAnalyzer -Path ./ -Recurse -EnableExit -Settings $settings\r\n" }, { "path": ".ci/lint-r-code.R", "content": "loadNamespace(\"lintr\")\n\nargs <- commandArgs(\n trailingOnly = TRUE\n)\nSOURCE_DIR <- args[[1L]]\n\nFILES_TO_LINT <- list.files(\n path = SOURCE_DIR\n , pattern = \"\\\\.r$|\\\\.rmd$\"\n , all.files = TRUE\n , ignore.case = TRUE\n , full.names = TRUE\n , recursive = TRUE\n , include.dirs = FALSE\n)\n\n# text to use for pipe operators from packages like 'magrittr'\npipe_text <- paste0(\n \"For consistency and the sake of being explicit, this project's code \"\n , \"does not use the pipe operator.\"\n)\n\n# text to use for functions that should only be called interactively\ninteractive_text <- paste0(\n \"Functions like '?', 'help', and 'install.packages()' should only be used \"\n , \"interactively, not in package code.\"\n)\n\nLINTERS_TO_USE <- list(\n \"absolute_path\" = lintr::absolute_path_linter()\n , \"any_duplicated\" = lintr::any_duplicated_linter()\n , \"any_is_na\" = lintr::any_is_na_linter()\n , \"assignment\" = lintr::assignment_linter()\n , \"backport\" = lintr::backport_linter()\n , \"boolean_arithmetic\" = lintr::boolean_arithmetic_linter()\n , \"braces\" = lintr::brace_linter()\n , \"class_equals\" = lintr::class_equals_linter()\n , \"commas\" = lintr::commas_linter()\n , \"conjunct_test\" = lintr::conjunct_test_linter()\n , \"duplicate_argument\" = lintr::duplicate_argument_linter()\n , \"empty_assignment\" = lintr::empty_assignment_linter()\n , \"equals_na\" = lintr::equals_na_linter()\n , \"fixed_regex\" = lintr::fixed_regex_linter()\n , \"for_loop_index\" = lintr::for_loop_index_linter()\n , \"function_left\" = lintr::function_left_parentheses_linter()\n , \"function_return\" = lintr::function_return_linter()\n , \"implicit_assignment\" = lintr::implicit_assignment_linter()\n , \"implicit_integers\" = lintr::implicit_integer_linter()\n , \"infix_spaces\" = lintr::infix_spaces_linter()\n , \"inner_combine\" = lintr::inner_combine_linter()\n , \"is_numeric\" = lintr::is_numeric_linter()\n , \"lengths\" = lintr::lengths_linter()\n , \"length_levels\" = lintr::length_levels_linter()\n , \"length_test\" = lintr::length_test_linter()\n , \"line_length\" = lintr::line_length_linter(length = 120L)\n , \"literal_coercion\" = lintr::literal_coercion_linter()\n , \"matrix\" = lintr::matrix_apply_linter()\n , \"missing_argument\" = lintr::missing_argument_linter()\n , \"non_portable_path\" = lintr::nonportable_path_linter()\n , \"numeric_leading_zero\" = lintr::numeric_leading_zero_linter()\n , \"outer_negation\" = lintr::outer_negation_linter()\n , \"package_hooks\" = lintr::package_hooks_linter()\n , \"paren_body\" = lintr::paren_body_linter()\n , \"paste\" = lintr::paste_linter()\n , \"quotes\" = lintr::quotes_linter()\n , \"redundant_equals\" = lintr::redundant_equals_linter()\n , \"regex_subset\" = lintr::regex_subset_linter()\n , \"routine_registration\" = lintr::routine_registration_linter()\n , \"scalar_in\" = lintr::scalar_in_linter()\n , \"semicolon\" = lintr::semicolon_linter()\n , \"seq\" = lintr::seq_linter()\n , \"spaces_inside\" = lintr::spaces_inside_linter()\n , \"spaces_left_parens\" = lintr::spaces_left_parentheses_linter()\n , \"sprintf\" = lintr::sprintf_linter()\n , \"string_boundary\" = lintr::string_boundary_linter()\n , \"todo_comments\" = lintr::todo_comment_linter(c(\"todo\", \"fixme\", \"to-do\"))\n , \"trailing_blank\" = lintr::trailing_blank_lines_linter()\n , \"trailing_white\" = lintr::trailing_whitespace_linter()\n , \"true_false\" = lintr::T_and_F_symbol_linter()\n , \"undesirable_function\" = lintr::undesirable_function_linter(\n fun = c(\n \"cbind\" = paste0(\n \"cbind is an unsafe way to build up a data frame. merge() or direct \"\n , \"column assignment is preferred.\"\n )\n , \"dyn.load\" = \"Directly loading or unloading .dll or .so files in package code should not be necessary.\"\n , \"dyn.unload\" = \"Directly loading or unloading .dll or .so files in package code should not be necessary.\"\n , \"help\" = interactive_text\n , \"ifelse\" = \"The use of ifelse() is dangerous because it will silently allow mixing types.\"\n , \"install.packages\" = interactive_text\n , \"is.list\" = paste0(\n \"This project uses data.table, and is.list(x) is TRUE for a data.table. \"\n , \"identical(class(x), 'list') is a safer way to check that something is an R list object.\"\n )\n , \"rbind\" = \"data.table::rbindlist() is faster and safer than rbind(), and is preferred in this project.\"\n , \"require\" = paste0(\n \"library() is preferred to require() because it will raise an error immediately \"\n , \"if a package is missing.\"\n )\n )\n )\n , \"undesirable_operator\" = lintr::undesirable_operator_linter(\n op = c(\n \"%>%\" = pipe_text\n , \"%.%\" = pipe_text\n , \"%..%\" = pipe_text\n , \"|>\" = pipe_text\n , \"?\" = interactive_text\n , \"??\" = interactive_text\n )\n )\n , \"unnecessary_concatenation\" = lintr::unnecessary_concatenation_linter()\n , \"unnecessary_lambda\" = lintr::unnecessary_lambda_linter()\n , \"unreachable_code\" = lintr::unreachable_code_linter()\n , \"unused_import\" = lintr::unused_import_linter()\n , \"vector_logic\" = lintr::vector_logic_linter()\n , \"whitespace\" = lintr::whitespace_linter()\n)\n\nnoquote(paste0(length(FILES_TO_LINT), \" R files need linting\"))\n\nresults <- NULL\n\nfor (r_file in FILES_TO_LINT) {\n\n this_result <- lintr::lint(\n filename = r_file\n , linters = LINTERS_TO_USE\n , cache = FALSE\n )\n\n print(\n sprintf(\n \"Found %i linting errors in %s\"\n , length(this_result)\n , r_file\n )\n , quote = FALSE\n )\n\n results <- c(results, this_result)\n\n}\n\nissues_found <- length(results)\n\nnoquote(paste0(\"Total linting issues found: \", issues_found))\n\nif (issues_found > 0L) {\n print(results)\n}\n\nquit(save = \"no\", status = issues_found)\n" }, { "path": ".ci/parameter-generator.py", "content": "# coding: utf-8\n\"\"\"Helper script for generating config file and parameters list.\n\nThis script generates LightGBM/src/io/config_auto.cpp file\nwith list of all parameters, aliases table and other routines\nalong with parameters description in LightGBM/docs/Parameters.rst file\nfrom the information in LightGBM/include/LightGBM/config.h file.\n\"\"\"\n\nimport re\nfrom collections import defaultdict\nfrom pathlib import Path\nfrom typing import Dict, List, Tuple\n\n\ndef get_parameter_infos(config_hpp: Path) -> Tuple[List[Tuple[str, int]], List[List[Dict[str, List]]]]:\n \"\"\"Parse config header file.\n\n Parameters\n ----------\n config_hpp : pathlib.Path\n Path to the config header file.\n\n Returns\n -------\n infos : tuple\n Tuple with names and content of sections.\n \"\"\"\n is_inparameter = False\n cur_key = None\n key_lvl = 0\n cur_info: Dict[str, List] = {}\n keys = []\n member_infos: List[List[Dict[str, List]]] = []\n with open(config_hpp) as config_hpp_file:\n for line in config_hpp_file:\n if line.strip() in {\"#ifndef __NVCC__\", \"#endif // __NVCC__\"}:\n continue\n if \"#pragma region Parameters\" in line:\n is_inparameter = True\n elif \"#pragma region\" in line and \"Parameters\" in line:\n key_lvl += 1\n cur_key = line.split(\"region\")[1].strip()\n keys.append((cur_key, key_lvl))\n member_infos.append([])\n elif \"#pragma endregion\" in line:\n key_lvl -= 1\n if cur_key is not None:\n cur_key = None\n elif is_inparameter:\n is_inparameter = False\n elif cur_key is not None:\n line = line.strip()\n if line.startswith(\"//\"):\n key, _, val = line[2:].partition(\"=\")\n key = key.strip()\n val = val.strip()\n if key not in cur_info:\n if key == \"descl2\" and \"desc\" not in cur_info:\n cur_info[\"desc\"] = []\n elif key != \"descl2\":\n cur_info[key] = []\n if key == \"desc\":\n cur_info[\"desc\"].append((\"l1\", val))\n elif key == \"descl2\":\n cur_info[\"desc\"].append((\"l2\", val))\n else:\n cur_info[key].append(val)\n elif line:\n has_eqsgn = False\n tokens = line.split(\"=\")\n if len(tokens) == 2:\n if \"default\" not in cur_info:\n cur_info[\"default\"] = [tokens[1][:-1].strip()]\n has_eqsgn = True\n tokens = line.split()\n cur_info[\"inner_type\"] = [tokens[0].strip()]\n if \"name\" not in cur_info:\n if has_eqsgn:\n cur_info[\"name\"] = [tokens[1].strip()]\n else:\n cur_info[\"name\"] = [tokens[1][:-1].strip()]\n member_infos[-1].append(cur_info)\n cur_info = {}\n\n return keys, member_infos\n\n\ndef get_names(infos: List[List[Dict[str, List]]]) -> List[str]:\n \"\"\"Get names of all parameters.\n\n Parameters\n ----------\n infos : list\n Content of the config header file.\n\n Returns\n -------\n names : list\n Names of all parameters.\n \"\"\"\n names = []\n for x in infos:\n for y in x:\n names.append(y[\"name\"][0])\n return names\n\n\ndef get_alias(infos: List[List[Dict[str, List]]]) -> List[Tuple[str, str]]:\n \"\"\"Get aliases of all parameters.\n\n Parameters\n ----------\n infos : list\n Content of the config header file.\n\n Returns\n -------\n pairs : list\n List of tuples (param alias, param name).\n \"\"\"\n pairs = []\n for x in infos:\n for y in x:\n if \"alias\" in y:\n name = y[\"name\"][0]\n alias = y[\"alias\"][0].split(\",\")\n for name2 in alias:\n pairs.append((name2.strip(), name))\n return pairs\n\n\ndef parse_check(check: str, reverse: bool = False) -> Tuple[str, str]:\n \"\"\"Parse the constraint.\n\n Parameters\n ----------\n check : str\n String representation of the constraint.\n reverse : bool, optional (default=False)\n Whether to reverse the sign of the constraint.\n\n Returns\n -------\n pair : tuple\n Parsed constraint in the form of tuple (value, sign).\n \"\"\"\n try:\n idx = 1\n float(check[idx:])\n except ValueError:\n idx = 2\n float(check[idx:])\n if reverse:\n reversed_sign = {\"<\": \">\", \">\": \"<\", \"<=\": \">=\", \">=\": \"<=\"}\n return check[idx:], reversed_sign[check[:idx]]\n else:\n return check[idx:], check[:idx]\n\n\ndef set_one_var_from_string(name: str, param_type: str, checks: List[str]) -> str:\n \"\"\"Construct code for auto config file for one param value.\n\n Parameters\n ----------\n name : str\n Name of the parameter.\n param_type : str\n Type of the parameter.\n checks : list\n Constraints of the parameter.\n\n Returns\n -------\n ret : str\n Lines of auto config file with getting and checks of one parameter value.\n \"\"\"\n ret = \"\"\n univar_mapper = {\"int\": \"GetInt\", \"double\": \"GetDouble\", \"bool\": \"GetBool\", \"std::string\": \"GetString\"}\n if \"vector\" not in param_type:\n ret += f' {univar_mapper[param_type]}(params, \"{name}\", &{name});\\n'\n if len(checks) > 0:\n check_mapper = {\"<\": \"LT\", \">\": \"GT\", \"<=\": \"LE\", \">=\": \"GE\"}\n for check in checks:\n value, sign = parse_check(check)\n ret += f\" CHECK_{check_mapper[sign]}({name}, {value});\\n\"\n ret += \"\\n\"\n else:\n ret += f' if (GetString(params, \"{name}\", &tmp_str)) {{\\n'\n type2 = param_type.split(\"<\")[1][:-1]\n if type2 == \"std::string\":\n ret += f\" {name} = Common::Split(tmp_str.c_str(), ',');\\n\"\n else:\n ret += f\" {name} = Common::StringToArray<{type2}>(tmp_str, ',');\\n\"\n ret += \" }\\n\\n\"\n return ret\n\n\ndef gen_parameter_description(\n sections: List[Tuple[str, int]], descriptions: List[List[Dict[str, List]]], params_rst: Path\n) -> None:\n \"\"\"Write descriptions of parameters to the documentation file.\n\n Parameters\n ----------\n sections : list\n Names of parameters sections.\n descriptions : list\n Structured descriptions of parameters.\n params_rst : pathlib.Path\n Path to the file with parameters documentation.\n \"\"\"\n params_to_write = []\n lvl_mapper = {1: \"-\", 2: \"~\"}\n for (section_name, section_lvl), section_params in zip(sections, descriptions):\n heading_sign = lvl_mapper[section_lvl]\n params_to_write.append(f\"{section_name}\\n{heading_sign * len(section_name)}\")\n for param_desc in section_params:\n name = param_desc[\"name\"][0]\n default_raw = param_desc[\"default\"][0]\n default = default_raw.strip('\"') if len(default_raw.strip('\"')) > 0 else default_raw\n param_type = param_desc.get(\"type\", param_desc[\"inner_type\"])[0].split(\":\")[-1].split(\"<\")[-1].strip(\">\")\n options = param_desc.get(\"options\", [])\n if len(options) > 0:\n opts = \"``, ``\".join([x.strip() for x in options[0].split(\",\")])\n options_str = f\", options: ``{opts}``\"\n else:\n options_str = \"\"\n aliases = param_desc.get(\"alias\", [])\n if len(aliases) > 0:\n aliases_joined = \"``, ``\".join([x.strip() for x in aliases[0].split(\",\")])\n aliases_str = f\", aliases: ``{aliases_joined}``\"\n else:\n aliases_str = \"\"\n checks = sorted(param_desc.get(\"check\", []))\n checks_len = len(checks)\n if checks_len > 1:\n number1, sign1 = parse_check(checks[0])\n number2, sign2 = parse_check(checks[1], reverse=True)\n checks_str = f\", constraints: ``{number2} {sign2} {name} {sign1} {number1}``\"\n elif checks_len == 1:\n number, sign = parse_check(checks[0])\n checks_str = f\", constraints: ``{name} {sign} {number}``\"\n else:\n checks_str = \"\"\n main_desc = f'- ``{name}`` :raw-html:`🔗︎`, default = ``{default}``, type = {param_type}{options_str}{aliases_str}{checks_str}'\n params_to_write.append(main_desc)\n params_to_write.extend([f\"{' ' * 3 * int(desc[0][-1])}- {desc[1]}\" for desc in param_desc[\"desc\"]])\n\n with open(params_rst) as original_params_file:\n all_lines = original_params_file.read()\n before, start_sep, _ = all_lines.partition(\".. start params list\\n\\n\")\n _, end_sep, after = all_lines.partition(\"\\n\\n.. end params list\")\n\n with open(params_rst, \"w\") as new_params_file:\n new_params_file.write(before)\n new_params_file.write(start_sep)\n new_params_file.write(\"\\n\\n\".join(params_to_write))\n new_params_file.write(end_sep)\n new_params_file.write(after)\n\n\ndef gen_parameter_code(\n config_hpp: Path, config_out_cpp: Path\n) -> Tuple[List[Tuple[str, int]], List[List[Dict[str, List]]]]:\n \"\"\"Generate auto config file.\n\n Parameters\n ----------\n config_hpp : pathlib.Path\n Path to the config header file.\n config_out_cpp : pathlib.Path\n Path to the auto config file.\n\n Returns\n -------\n infos : tuple\n Tuple with names and content of sections.\n \"\"\"\n keys, infos = get_parameter_infos(config_hpp)\n names = get_names(infos)\n alias = get_alias(infos)\n names_with_aliases = defaultdict(list)\n str_to_write = r\"\"\"/*!\n * Copyright (c) 2018-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2018-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n *\n * \\note\n * This file is auto generated by LightGBM\\.ci\\parameter-generator.py from LightGBM\\include\\LightGBM\\config.h file.\n */\n\"\"\"\n str_to_write += \"#include \\n\\n\"\n str_to_write += \"#include \\n\"\n str_to_write += \"#include \\n\"\n str_to_write += \"#include \\n\"\n str_to_write += \"#include \\n\\n\"\n str_to_write += \"namespace LightGBM {\\n\"\n\n # alias table\n str_to_write += \"const std::unordered_map& Config::alias_table() {\\n\"\n str_to_write += \" static std::unordered_map aliases({\\n\"\n\n for pair in alias:\n str_to_write += f' {{\"{pair[0]}\", \"{pair[1]}\"}},\\n'\n names_with_aliases[pair[1]].append(pair[0])\n str_to_write += \" });\\n\"\n str_to_write += \" return aliases;\\n\"\n str_to_write += \"}\\n\\n\"\n\n # names\n str_to_write += \"const std::unordered_set& Config::parameter_set() {\\n\"\n str_to_write += \" static std::unordered_set params({\\n\"\n\n for name in names:\n str_to_write += f' \"{name}\",\\n'\n str_to_write += \" });\\n\"\n str_to_write += \" return params;\\n\"\n str_to_write += \"}\\n\\n\"\n # from strings\n str_to_write += \"void Config::GetMembersFromString(const std::unordered_map& params) {\\n\"\n str_to_write += ' std::string tmp_str = \"\";\\n'\n for x in infos:\n for y in x:\n if \"[no-automatically-extract]\" in y:\n continue\n param_type = y[\"inner_type\"][0]\n name = y[\"name\"][0]\n checks = []\n if \"check\" in y:\n checks = y[\"check\"]\n tmp = set_one_var_from_string(name, param_type, checks)\n str_to_write += tmp\n # tails\n str_to_write = f\"{str_to_write.strip()}\\n}}\\n\\n\"\n str_to_write += \"std::string Config::SaveMembersToString() const {\\n\"\n str_to_write += \" std::stringstream str_buf;\\n\"\n for x in infos:\n for y in x:\n if \"[no-save]\" in y:\n continue\n param_type = y[\"inner_type\"][0]\n name = y[\"name\"][0]\n if \"vector\" in param_type:\n if \"int8\" in param_type:\n str_to_write += f' str_buf << \"[{name}: \" << Common::Join(Common::ArrayCast({name}), \",\") << \"]\\\\n\";\\n'\n else:\n str_to_write += f' str_buf << \"[{name}: \" << Common::Join({name}, \",\") << \"]\\\\n\";\\n'\n else:\n str_to_write += f' str_buf << \"[{name}: \" << {name} << \"]\\\\n\";\\n'\n # tails\n str_to_write += \" return str_buf.str();\\n\"\n str_to_write += \"}\\n\\n\"\n\n str_to_write += \"\"\"const std::unordered_map>& Config::parameter2aliases() {\n static std::unordered_map> map({\"\"\"\n for name in names:\n str_to_write += '\\n {\"' + name + '\", '\n if names_with_aliases[name]:\n str_to_write += '{\"' + '\", \"'.join(names_with_aliases[name]) + '\"}},'\n else:\n str_to_write += \"{}},\"\n str_to_write += \"\"\"\n });\n return map;\n}\n\n\"\"\"\n str_to_write += \"\"\"const std::unordered_map& Config::ParameterTypes() {\n static std::unordered_map map({\"\"\"\n int_t_pat = re.compile(r\"int\\d+_t\")\n # the following are stored as comma separated strings but are arrays in the wrappers\n overrides = {\n \"categorical_feature\": \"vector\",\n \"ignore_column\": \"vector\",\n \"interaction_constraints\": \"vector>\",\n }\n for x in infos:\n for y in x:\n name = y[\"name\"][0]\n if name == \"task\":\n continue\n if name in overrides:\n param_type = overrides[name]\n else:\n param_type = int_t_pat.sub(\"int\", y[\"inner_type\"][0]).replace(\"std::\", \"\")\n str_to_write += '\\n {\"' + name + '\", \"' + param_type + '\"},'\n str_to_write += \"\"\"\n });\n return map;\n}\n\n\"\"\"\n\n str_to_write += \"} // namespace LightGBM\\n\"\n with open(config_out_cpp, \"w\") as config_out_cpp_file:\n config_out_cpp_file.write(str_to_write)\n\n return keys, infos\n\n\nif __name__ == \"__main__\":\n current_dir = Path(__file__).absolute().parent\n config_hpp = current_dir.parent / \"include\" / \"LightGBM\" / \"config.h\"\n config_out_cpp = current_dir.parent / \"src\" / \"io\" / \"config_auto.cpp\"\n params_rst = current_dir.parent / \"docs\" / \"Parameters.rst\"\n sections, descriptions = gen_parameter_code(config_hpp, config_out_cpp)\n gen_parameter_description(sections, descriptions, params_rst)\n" }, { "path": ".ci/pip-envs/requirements-latest.txt", "content": "# nightlies for: matplotlib, numpy, pandas, pyarrow, scipy, scikit-learn\n--extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple\n\n# runtime dependencies (using `.dev0` suffix to allow nightlies)\n#\n# pinning rules:\n#\n# * latest versions of lightgbm's dependencies,\n# * including pre-releases and nightlies\n#\ncffi>=1.17.1\nmatplotlib>=3.11.0.dev0\nnumpy>=2.4.0.dev0\npandas>=3.0.0.dev0\npyarrow>=21.0.0.dev0\nscikit-learn>=1.8.dev0\nscipy>=1.17.0.dev0\n\n# testing-only dependencies\ncloudpickle>=3.1.1\npsutil>=7.0\npytest>=8.4.1\n" }, { "path": ".ci/pip-envs/requirements-oldest.txt", "content": "# oldest versions of dependencies published after\n# minimum supported Python version's first release,\n# for which there are wheels compatible with the\n# python:{version} image\n#\n# see https://devguide.python.org/versions/\n#\ncffi==1.15.1\nnumpy==1.19.3\npandas==1.1.3\npyarrow==6.0.1\nscikit-learn==0.24.2\nscipy==1.6.0\n" }, { "path": ".ci/rerun-workflow.sh", "content": "#!/bin/bash\n#\n# [description]\n# Rerun specified workflow for given pull request.\n#\n# [usage]\n# rerun-workflow.sh \n#\n# WORKFLOW_ID: Identifier (config name of ID) of a workflow to be rerun.\n#\n# PR_BRANCH: Name of pull request's branch.\n\nset -e -E -u -o pipefail\n\nif [ -z \"$GITHUB_ACTIONS\" ]; then\n echo \"Must be run inside GitHub Actions CI\"\n exit 1\nfi\n\nif [ $# -ne 2 ]; then\n echo \"Usage: $0 \"\n exit 1\nfi\n\nworkflow_id=$1\npr_branch=$2\n\n# --branch for some GitHub GLI commands does not respect the difference between forks and branches\n# on the main repo. While some parts of the GitHub API refer to the branch of a workflow\n# as '{org}:{branch}' for branches from forks, others expect only '{branch}'.\n#\n# This expansion trims a leading '{org}:' from 'pr_branch' if one is present.\npr_branch_no_fork_prefix=\"${pr_branch/*:/}\"\n\nRUN_ID=$(\n gh run list \\\n --repo 'lightgbm-org/LightGBM' \\\n --workflow \"${workflow_id}\" \\\n --event \"pull_request\" \\\n --branch \"${pr_branch_no_fork_prefix}\" \\\n --json 'createdAt,databaseId' \\\n --jq 'sort_by(.createdAt) | reverse | .[0] | .databaseId'\n)\n\nif [[ -z \"${RUN_ID}\" ]]; then\n echo \"ERROR: failed to find a run of workflow '${workflow_id}' for branch '${pr_branch}'\"\n exit 1\nfi\n\necho \"Re-running workflow '${workflow_id}' (run ID ${RUN_ID})\"\n\ngh run rerun \\\n --repo 'lightgbm-org/LightGBM' \\\n \"${RUN_ID}\"\n" }, { "path": ".ci/run-r-cmd-check.sh", "content": "#!/bin/bash\n\nset -e -u -o pipefail\n\nPKG_TARBALL=\"${1}\"\ndeclare -i ALLOWED_CHECK_NOTES=${2}\n\n# 'R CMD check' redirects installation logs to a file, and returns\n# a non-0 exit code if ERRORs are raised.\n#\n# The '||' here gives us an opportunity to echo out the installation\n# logs prior to exiting the script.\ncheck_succeeded=\"yes\"\nR CMD check \"${PKG_TARBALL}\" \\\n --as-cran \\\n --run-donttest \\\n|| check_succeeded=\"no\"\n\nCHECK_LOG_FILE=lightgbm.Rcheck/00check.log\nBUILD_LOG_FILE=lightgbm.Rcheck/00install.out\n\necho \"R CMD check build logs:\"\ncat \"${BUILD_LOG_FILE}\"\n\nif [[ $check_succeeded == \"no\" ]]; then\n echo \"R CMD check failed\"\n exit 1\nfi\n\n# WARNINGs or ERRORs should be treated as a failure\nif grep -q -E \"WARNING|ERROR\" \"${CHECK_LOG_FILE}\"; then\n echo \"WARNINGs or ERRORs have been found by R CMD check\"\n exit 1\nfi\n\n# Allow a configurable number of NOTEs.\n# Sometimes NOTEs are raised in CI that wouldn't show up on an actual CRAN submission.\nset +e\nNUM_CHECK_NOTES=$(\n grep -o -E '[0-9]+ NOTE' \"${CHECK_LOG_FILE}\" \\\n | sed 's/[^0-9]*//g'\n)\nif [[ ${NUM_CHECK_NOTES} -gt ${ALLOWED_CHECK_NOTES} ]]; then\n echo \"Found ${NUM_CHECK_NOTES} NOTEs from R CMD check. Only ${ALLOWED_CHECK_NOTES} are allowed\"\n exit 1\nfi\n" }, { "path": ".ci/set-commit-status.sh", "content": "#!/bin/bash\n#\n# [description]\n# Set a status with a given name to the specified commit.\n#\n# [usage]\n# set-commit-status.sh \n#\n# NAME: Name of status.\n# Status with existing name overwrites a previous one.\n#\n# STATUS: Status to be set.\n# Can be \"error\", \"failure\", \"pending\" or \"success\".\n#\n# SHA: SHA of a commit to set a status on.\n\nset -e -E -u -o pipefail\n\nif [ -z \"$GITHUB_ACTIONS\" ]; then\n echo \"Must be run inside GitHub Actions CI\"\n exit 1\nfi\n\nif [ $# -ne 3 ]; then\n echo \"Usage: $0 \"\n exit 1\nfi\n\nname=$1\n\nstatus=$2\nstatus=${status/error/failure}\nstatus=${status/cancelled/failure}\nstatus=${status/timed_out/failure}\nstatus=${status/in_progress/pending}\nstatus=${status/queued/pending}\n\nsha=$3\n\ndata=$(\n jq -n \\\n --arg state \"${status}\" \\\n --arg url \"${GITHUB_SERVER_URL}/lightgbm-org/LightGBM/actions/runs/${GITHUB_RUN_ID}\" \\\n --arg name \"${name}\" \\\n '{\"state\":$state,\"target_url\":$url,\"context\":$name}'\n)\n\ncurl -sL \\\n --fail \\\n -X POST \\\n -H \"Accept: application/vnd.github.v3+json\" \\\n -H \"Authorization: token ${GITHUB_TOKEN}\" \\\n -d \"$data\" \\\n \"${GITHUB_API_URL}/repos/lightgbm-org/LightGBM/statuses/$sha\"\n" }, { "path": ".ci/setup.sh", "content": "#!/bin/bash\n\nset -e -E -u -o pipefail\n\n# defaults\nIN_UBUNTU_BASE_CONTAINER=${IN_UBUNTU_BASE_CONTAINER:-\"false\"}\nSETUP_CONDA=${SETUP_CONDA:-\"true\"}\n\nARCH=$(uname -m)\n\n\nif [[ $OS_NAME == \"macos\" ]]; then\n # Check https://github.com/actions/runner-images/tree/main/images/macos for available\n # versions of Xcode\n macos_ver=$(sw_vers --productVersion)\n if [[ \"${macos_ver}\" =~ 15. ]]; then\n xcode_path=\"/Applications/Xcode_16.0.app/Contents/Developer\"\n else\n xcode_path=\"/Applications/Xcode_15.0.app/Contents/Developer\"\n fi\n sudo xcode-select -s \"${xcode_path}\" || exit 1\n if [[ $COMPILER == \"clang\" ]]; then\n brew install libomp\n else # gcc\n brew install 'gcc@14'\n fi\n if [[ $TASK == \"mpi\" ]]; then\n brew install open-mpi\n fi\n if [[ $TASK == \"swig\" ]]; then\n brew install swig\n fi\nelse # Linux\n if type -f apt > /dev/null 2>&1; then\n sudo apt-get update\n sudo apt-get install --no-install-recommends -y \\\n ca-certificates \\\n curl\n else\n sudo yum update -y\n sudo yum install -y \\\n ca-certificates \\\n curl\n fi\n CMAKE_VERSION=\"3.30.0\"\n curl -O -L \\\n \"https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}-linux-${ARCH}.sh\" \\\n || exit 1\n sudo mkdir /opt/cmake || exit 1\n sudo sh \"cmake-${CMAKE_VERSION}-linux-${ARCH}.sh\" --skip-license --prefix=/opt/cmake || exit 1\n sudo ln -sf /opt/cmake/bin/cmake /usr/local/bin/cmake || exit 1\n\n if [[ $IN_UBUNTU_BASE_CONTAINER == \"true\" ]]; then\n # fixes error \"unable to initialize frontend: Dialog\"\n # https://github.com/moby/moby/issues/27988#issuecomment-462809153\n echo 'debconf debconf/frontend select Noninteractive' | sudo debconf-set-selections\n\n sudo apt-get update\n sudo apt-get install --no-install-recommends -y \\\n software-properties-common\n\n sudo apt-get install --no-install-recommends -y \\\n build-essential \\\n git \\\n libcurl4 \\\n libicu-dev \\\n libssl-dev \\\n locales \\\n locales-all || exit 1\n if [[ $COMPILER == \"clang\" ]]; then\n sudo apt-get install --no-install-recommends -y \\\n clang \\\n libomp-dev\n elif [[ $COMPILER == \"clang-17\" ]]; then\n sudo apt-get install --no-install-recommends -y \\\n wget\n wget -qO- https://apt.llvm.org/llvm-snapshot.gpg.key | sudo tee /etc/apt/trusted.gpg.d/apt.llvm.org.asc\n sudo apt-add-repository deb http://apt.llvm.org/jammy/ llvm-toolchain-jammy-17 main\n sudo apt-add-repository deb-src http://apt.llvm.org/jammy/ llvm-toolchain-jammy-17 main\n sudo apt-get update\n sudo apt-get install -y \\\n clang-17 \\\n libomp-17-dev\n fi\n\n export LANG=\"en_US.UTF-8\"\n sudo update-locale LANG=${LANG}\n export LC_ALL=\"${LANG}\"\n fi\n if [[ $TASK == \"r-package\" ]] && [[ $COMPILER == \"clang\" ]]; then\n sudo apt-get install --no-install-recommends -y \\\n libomp-dev\n fi\n if [[ $TASK == \"mpi\" ]]; then\n if [[ $IN_UBUNTU_BASE_CONTAINER == \"true\" ]]; then\n sudo apt-get update\n sudo apt-get install --no-install-recommends -y \\\n libopenmpi-dev \\\n openmpi-bin\n else # in manylinux image\n sudo yum update -y\n sudo yum install -y \\\n openmpi-devel \\\n || exit 1\n fi\n fi\n if [[ $TASK == \"gpu\" ]]; then\n if [[ $IN_UBUNTU_BASE_CONTAINER == \"true\" ]]; then\n sudo apt-get update\n sudo apt-get install --no-install-recommends -y \\\n libboost1.74-dev \\\n libboost-filesystem1.74-dev \\\n ocl-icd-opencl-dev\n else # in manylinux image\n sudo yum update -y\n sudo yum install -y \\\n boost-devel \\\n ocl-icd-devel \\\n opencl-headers \\\n || exit 1\n fi\n fi\n if [[ $TASK == \"gpu\" || $TASK == \"bdist\" ]]; then\n if [[ $IN_UBUNTU_BASE_CONTAINER == \"true\" ]]; then\n sudo apt-get update\n sudo apt-get install --no-install-recommends -y \\\n patchelf \\\n pocl-opencl-icd\n elif [[ $(uname -m) == \"x86_64\" ]]; then\n sudo yum update -y\n sudo yum install -y \\\n ocl-icd-devel \\\n opencl-headers \\\n || exit 1\n fi\n fi\n if [[ $TASK == \"cuda\" ]]; then\n echo 'debconf debconf/frontend select Noninteractive' | debconf-set-selections\n if [[ $COMPILER == \"clang\" ]]; then\n apt-get update\n apt-get install --no-install-recommends -y \\\n clang \\\n libomp-dev\n fi\n fi\nfi\n\nif [[ \"${TASK}\" != \"cpp-tests\" ]] && [[ \"${TASK}\" != \"r-package\" ]] && [[ \"${TASK}\" != \"swig\" ]]; then\n if [[ $SETUP_CONDA != \"false\" ]]; then\n curl \\\n -sL \\\n -o miniforge.sh \\\n \"https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-${ARCH}.sh\"\n sh miniforge.sh -b -p \"${CONDA}\"\n fi\n conda config --set always_yes yes --set changeps1 no\n conda update -q -y conda\n\n # print output of 'conda info', to help in submitting bug reports\n echo \"conda info:\"\n conda info\nfi\n" }, { "path": ".ci/test-python-latest.sh", "content": "#!/bin/bash\n\nset -e -E -u -o pipefail\n\necho \"installing lightgbm and its dependencies\"\npip install \\\n --prefer-binary \\\n --upgrade \\\n -r ./.ci/pip-envs/requirements-latest.txt \\\n dist/*.whl\n\necho \"installed package versions:\"\npip freeze\n\necho \"\"\necho \"running tests\"\npytest tests/c_api_test/\npytest tests/python_package_test/\n" }, { "path": ".ci/test-python-oldest.sh", "content": "#!/bin/bash\n\nset -e -E -u -o pipefail\n\necho \"installing lightgbm and its dependencies\"\npip install \\\n --prefer-binary \\\n --upgrade \\\n -r ./.ci/pip-envs/requirements-oldest.txt \\\n dist/*.whl\n\necho \"installed package versions:\"\npip freeze\n\necho \"\"\necho \"checking that examples run without error\"\n\n# run a few examples to test that Python-package minimally works\necho \"\"\necho \"--- advanced_example.py ---\"\necho \"\"\npython ./examples/python-guide/advanced_example.py || exit 1\n\necho \"\"\necho \"--- logistic_regression.py ---\"\necho \"\"\npython ./examples/python-guide/logistic_regression.py || exit 1\n\necho \"\"\necho \"--- simple_example.py ---\"\necho \"\"\npython ./examples/python-guide/simple_example.py || exit 1\n\necho \"\"\necho \"--- sklearn_example.py ---\"\necho \"\"\npython ./examples/python-guide/sklearn_example.py || exit 1\n\necho \"\"\necho \"done testing on oldest supported Python version\"\n" }, { "path": ".ci/test-r-package-valgrind.sh", "content": "#!/bin/bash\n\nset -e -E -u -o pipefail\n\nRDscriptvalgrind ./.ci/install-r-deps.R --test || exit 1\nsh build-cran-package.sh \\\n --r-executable=RDvalgrind \\\n --no-build-vignettes \\\n || exit 1\n\nRDvalgrind CMD INSTALL --preclean --install-tests lightgbm_*.tar.gz || exit 1\n\ncd R-package/tests\n\nALL_LOGS_FILE=\"out.log\"\nVALGRIND_LOGS_FILE=\"valgrind-logs.log\"\n\nRDvalgrind \\\n --no-readline \\\n --vanilla \\\n -d \"valgrind --tool=memcheck --leak-check=full --track-origins=yes --gen-suppressions=all\" \\\n -f testthat.R \\\n > ${ALL_LOGS_FILE} 2>&1 || exit 1\n\ncat ${ALL_LOGS_FILE}\n\necho \"writing valgrind output to ${VALGRIND_LOGS_FILE}\"\ncat ${ALL_LOGS_FILE} | grep -E \"^\\=\" > ${VALGRIND_LOGS_FILE}\n\nbytes_definitely_lost=$(\n cat ${VALGRIND_LOGS_FILE} \\\n | grep -E \"definitely lost\\: .*\" \\\n | sed 's/^.*definitely lost\\: \\(.*\\) bytes.*$/\\1/' \\\n | tr -d \",\"\n)\necho \"valgrind found ${bytes_definitely_lost} bytes definitely lost\"\nif [[ ${bytes_definitely_lost} -gt 0 ]]; then\n exit 1\nfi\n\nbytes_indirectly_lost=$(\n cat ${VALGRIND_LOGS_FILE} \\\n | grep -E \"indirectly lost\\: .*\" \\\n | sed 's/^.*indirectly lost\\: \\(.*\\) bytes.*$/\\1/' \\\n | tr -d \",\"\n)\necho \"valgrind found ${bytes_indirectly_lost} bytes indirectly lost\"\nif [[ ${bytes_indirectly_lost} -gt 0 ]]; then\n exit 1\nfi\n\n# one error caused by a false positive between valgrind and openmp is allowed\n# ==2063== 352 bytes in 1 blocks are possibly lost in loss record 153 of 2,709\n# ==2063== at 0x483DD99: calloc (in /usr/lib/x86_64-linux-gnu/valgrind/vgpreload_memcheck-amd64-linux.so)\n# ==2063== by 0x40149CA: allocate_dtv (dl-tls.c:286)\n# ==2063== by 0x40149CA: _dl_allocate_tls (dl-tls.c:532)\n# ==2063== by 0x5702322: allocate_stack (allocatestack.c:622)\n# ==2063== by 0x5702322: pthread_create@@GLIBC_2.2.5 (pthread_create.c:660)\n# ==2063== by 0x56D0DDA: ??? (in /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0)\n# ==2063== by 0x56C88E0: GOMP_parallel (in /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0)\n# ==2063== by 0x1544D29C: LGBM_DatasetCreateFromCSC (c_api.cpp:1286)\n# ==2063== by 0x1546F980: LGBM_DatasetCreateFromCSC_R (lightgbm_R.cpp:91)\n# ==2063== by 0x4941E2F: R_doDotCall (dotcode.c:634)\n# ==2063== by 0x494CCC6: do_dotcall (dotcode.c:1281)\n# ==2063== by 0x499FB01: bcEval (eval.c:7078)\n# ==2063== by 0x498B67F: Rf_eval (eval.c:727)\n# ==2063== by 0x498E414: R_execClosure (eval.c:1895)\nbytes_possibly_lost=$(\n cat ${VALGRIND_LOGS_FILE} \\\n | grep -E \"possibly lost\\: .*\" \\\n | sed 's/^.*possibly lost\\: \\(.*\\) bytes.*$/\\1/' \\\n | tr -d \",\"\n)\necho \"valgrind found ${bytes_possibly_lost} bytes possibly lost\"\nif [[ ${bytes_possibly_lost} -gt 1104 ]]; then\n exit 1\nfi\n\n# ensure 'grep --count' doesn't cause failures\nset +e\n\necho \"checking for invalid reads\"\ninvalid_reads=$(\n cat ${VALGRIND_LOGS_FILE} \\\n | grep --count -i \"Invalid read\"\n)\nif [[ ${invalid_reads} -gt 0 ]]; then\n echo \"valgrind found invalid reads: ${invalid_reads}\"\n exit 1\nfi\n\necho \"checking for invalid writes\"\ninvalid_writes=$(\n cat ${VALGRIND_LOGS_FILE} \\\n | grep --count -i \"Invalid write\"\n)\nif [[ ${invalid_writes} -gt 0 ]]; then\n echo \"valgrind found invalid writes: ${invalid_writes}\"\n exit 1\nfi\n" }, { "path": ".ci/test-r-package-windows.ps1", "content": "# Download a file and retry upon failure. This looks like\n# an infinite loop but CI-level timeouts will kill it\nfunction Get-File-With-Tenacity {\n param(\n [Parameter(Mandatory = $true)][string]$url,\n [Parameter(Mandatory = $true)][string]$destfile\n )\n $ProgressPreference = \"SilentlyContinue\" # progress bar bug extremely slows down download speed\n do {\n Write-Output \"Downloading ${url}\"\n sleep 5\n Invoke-WebRequest -Uri $url -OutFile $destfile\n } while (-not $?)\n}\n\n# External utilities like R.exe / Rscript.exe writing to stderr (even for harmless\n# status information) can cause failures in GitHub Actions PowerShell jobs.\n# See https://github.community/t/powershell-steps-fail-nondeterministically/115496\n#\n# Using standard PowerShell redirection does not work to avoid these errors.\n# This function uses R's built-in redirection mechanism, sink(). Any place where\n# this function is used is a command that writes harmless messages to stderr\nfunction Invoke-R-Code-Redirect-Stderr {\n param(\n [Parameter(Mandatory = $true)][string]$rcode\n )\n $decorated_code = \"out_file <- file(tempfile(), open = 'wt'); sink(out_file, type = 'message'); $rcode; sink()\"\n Rscript --vanilla -e $decorated_code\n}\n\n# Remove all items matching some pattern from PATH environment variable\nfunction Remove-From-Path {\n [CmdletBinding(SupportsShouldProcess)]\n param(\n [Parameter(Mandatory = $true)][string]$pattern_to_remove\n )\n if ($PSCmdlet.ShouldProcess($env:PATH, \"Removing ${pattern_to_remove}\")) {\n $env:PATH = ($env:PATH.Split(';') | Where-Object { $_ -notmatch \"$pattern_to_remove\" }) -join ';'\n }\n}\n\n# remove some details that exist in the GitHub Actions images which might\n# cause conflicts with R and other components installed by this script\n$env:RTOOLS40_HOME = \"\"\nRemove-From-Path \".*Amazon.*\"\nRemove-From-Path \".*Anaconda.*\"\nRemove-From-Path \".*android.*\"\nRemove-From-Path \".*Android.*\"\nRemove-From-Path \".*chocolatey.*\"\nRemove-From-Path \".*Chocolatey.*\"\nRemove-From-Path \".*cmake.*\"\nRemove-From-Path \".*CMake.*\"\nRemove-From-Path \".*\\\\Git\\\\.*\"\nRemove-From-Path \"(?!.*pandoc.*).*hostedtoolcache.*\"\nRemove-From-Path \".*Microsoft SDKs.*\"\nRemove-From-Path \".*mingw.*\"\nRemove-From-Path \".*msys64.*\"\nRemove-From-Path \".*PostgreSQL.*\"\nRemove-From-Path \".*\\\\R\\\\.*\"\nRemove-From-Path \".*R Client.*\"\nRemove-From-Path \".*rtools40.*\"\nRemove-From-Path \".*rtools42.*\"\nRemove-From-Path \".*rtools43.*\"\nRemove-From-Path \".*rtools44.*\"\nRemove-From-Path \".*rtools45.*\"\nRemove-From-Path \".*shells.*\"\nRemove-From-Path \".*Strawberry.*\"\nRemove-From-Path \".*tools.*\"\n\nRemove-Item C:\\rtools40 -Force -Recurse -ErrorAction Ignore\nRemove-Item C:\\rtools42 -Force -Recurse -ErrorAction Ignore\nRemove-Item C:\\rtools43 -Force -Recurse -ErrorAction Ignore\nRemove-Item C:\\rtools44 -Force -Recurse -ErrorAction Ignore\nRemove-Item C:\\rtools45 -Force -Recurse -ErrorAction Ignore\n\n# Get details needed for installing R components\n#\n# NOTES:\n# * some paths and file names are different on R4.0\n$env:R_MAJOR_VERSION = $env:R_VERSION.split('.')[0]\nif ($env:R_MAJOR_VERSION -eq \"4\") {\n $RTOOLS_INSTALL_PATH = \"C:\\rtools43\"\n $env:RTOOLS_BIN = \"$RTOOLS_INSTALL_PATH\\usr\\bin\"\n $env:RTOOLS_MINGW_BIN = \"$RTOOLS_INSTALL_PATH\\x86_64-w64-mingw32.static.posix\\bin\"\n $env:RTOOLS_EXE_FILE = \"rtools43-5550-5548.exe\"\n $env:R_WINDOWS_VERSION = \"4.3.1\"\n} else {\n Write-Output \"[ERROR] Unrecognized R version: $env:R_VERSION\"\n Assert-Output $false\n}\n$env:CMAKE_VERSION = \"3.30.0\"\n\n$env:R_LIB_PATH = \"$env:BUILD_SOURCESDIRECTORY/RLibrary\" -replace '[\\\\]', '/'\n$env:R_LIBS = \"$env:R_LIB_PATH\"\n$env:CMAKE_PATH = \"$env:BUILD_SOURCESDIRECTORY/CMake_installation\"\n$env:PATH = @(\n \"$env:RTOOLS_BIN\",\n \"$env:RTOOLS_MINGW_BIN\",\n \"$env:R_LIB_PATH/R/bin/x64\",\n \"$env:CMAKE_PATH/cmake-$env:CMAKE_VERSION-windows-x86_64/bin\",\n \"$env:PATH\"\n) -join \";\"\n$env:CRAN_MIRROR = \"https://cran.rstudio.com\"\n$env:MIKTEX_EXCEPTION_PATH = \"$env:TEMP\\miktex\"\n\n# don't fail builds for long-running examples unless they're very long.\n# See https://github.com/lightgbm-org/LightGBM/issues/4049#issuecomment-793412254.\nif ($env:R_BUILD_TYPE -ne \"cran\") {\n $env:_R_CHECK_EXAMPLE_TIMING_THRESHOLD_ = 30\n}\n\nif (($env:COMPILER -eq \"MINGW\") -and ($env:R_BUILD_TYPE -eq \"cmake\")) {\n $env:CXX = \"$env:RTOOLS_MINGW_BIN/g++.exe\"\n $env:CC = \"$env:RTOOLS_MINGW_BIN/gcc.exe\"\n}\n\nSet-Location \"$env:BUILD_SOURCESDIRECTORY\"\ntzutil /s \"GMT Standard Time\"\n[Void][System.IO.Directory]::CreateDirectory(\"$env:R_LIB_PATH\")\n[Void][System.IO.Directory]::CreateDirectory(\"$env:CMAKE_PATH\")\n\n# download R, RTools and CMake\nWrite-Output \"Downloading R, Rtools and CMake\"\n$params = @{\n url = \"$env:CRAN_MIRROR/bin/windows/base/old/$env:R_WINDOWS_VERSION/R-$env:R_WINDOWS_VERSION-win.exe\"\n destfile = \"R-win.exe\"\n}\nGet-File-With-Tenacity @params\n\n$params = @{\n url = \"https://github.com/lightgbm-org/LightGBM/releases/download/v2.0.12/$env:RTOOLS_EXE_FILE\"\n destfile = \"Rtools.exe\"\n}\nGet-File-With-Tenacity @params\n\n$params = @{\n url = \"https://github.com/Kitware/CMake/releases/download/v{0}/cmake-{0}-windows-x86_64.zip\" -f $env:CMAKE_VERSION\n destfile = \"$env:CMAKE_PATH/cmake.zip\"\n}\nGet-File-With-Tenacity @params\n\n# Install R\nWrite-Output \"Installing R\"\n$params = @{\n FilePath = \"R-win.exe\"\n NoNewWindow = $true\n Wait = $true\n ArgumentList = \"/VERYSILENT /DIR=$env:R_LIB_PATH/R /COMPONENTS=main,x64,i386\"\n}\nStart-Process @params ; Assert-Output $?\nWrite-Output \"Done installing R\"\n\nWrite-Output \"Installing Rtools\"\n$params = @{\n FilePath = \"Rtools.exe\"\n NoNewWindow = $true\n Wait = $true\n ArgumentList = \"/VERYSILENT /SUPPRESSMSGBOXES /DIR=$RTOOLS_INSTALL_PATH\"\n}\nStart-Process @params; Assert-Output $?\nWrite-Output \"Done installing Rtools\"\n\nWrite-Output \"Installing CMake\"\nAdd-Type -AssemblyName System.IO.Compression.FileSystem\n[System.IO.Compression.ZipFile]::ExtractToDirectory(\"$env:CMAKE_PATH/cmake.zip\", \"$env:CMAKE_PATH\") ; Assert-Output $?\n# Remove old CMake shipped with RTools\nRemove-Item \"$env:RTOOLS_MINGW_BIN/cmake.exe\" -Force -ErrorAction Ignore\nWrite-Output \"Done installing CMake\"\n\nWrite-Output \"Installing dependencies\"\nRscript.exe --vanilla \".ci/install-r-deps.R\" --build --include=processx --test ; Assert-Output $?\n\nWrite-Output \"Building R-package\"\n\n# R CMD check is not used for MSVC builds\nif ($env:COMPILER -ne \"MSVC\") {\n\n $PKG_FILE_NAME = \"lightgbm_$env:LGB_VER.tar.gz\"\n $LOG_FILE_NAME = \"lightgbm.Rcheck/00check.log\"\n\n if ($env:R_BUILD_TYPE -eq \"cmake\") {\n if ($env:TOOLCHAIN -eq \"MINGW\") {\n Write-Output \"Telling R to use MinGW\"\n $env:BUILD_R_FLAGS = \"c('--skip-install', '--use-mingw', '-j4')\"\n } elseif ($env:TOOLCHAIN -eq \"MSYS\") {\n Write-Output \"Telling R to use MSYS\"\n $env:BUILD_R_FLAGS = \"c('--skip-install', '--use-msys2', '-j4')\"\n } elseif ($env:TOOLCHAIN -eq \"MSVC\") {\n $env:BUILD_R_FLAGS = \"'--skip-install'\"\n } else {\n Write-Output \"[ERROR] Unrecognized toolchain: $env:TOOLCHAIN\"\n Assert-Output $false\n }\n Invoke-R-Code-Redirect-Stderr \"commandArgs <- function(...){$env:BUILD_R_FLAGS}; source('build_r.R')\"\n Assert-Output $?\n } elseif ($env:R_BUILD_TYPE -eq \"cran\") {\n $params = -join @(\n \"result <- processx::run(command = 'sh', args = 'build-cran-package.sh', \",\n \"echo = TRUE, windows_verbatim_args = FALSE, error_on_status = TRUE)\"\n )\n Invoke-R-Code-Redirect-Stderr $params ; Assert-Output $?\n Remove-From-Path \".*msys64.*\"\n # Test CRAN source .tar.gz in a directory that is not this repo or below it.\n # When people install.packages('lightgbm'), they won't have the LightGBM\n # git repo around. This is to protect against the use of relative paths\n # like ../../CMakeLists.txt that would only work if you are in the repoo\n $R_CMD_CHECK_DIR = \"tmp-r-cmd-check\"\n New-Item -Path \"C:\\\" -Name $R_CMD_CHECK_DIR -ItemType \"directory\" > $null\n Move-Item -Path \"$PKG_FILE_NAME\" -Destination \"C:\\$R_CMD_CHECK_DIR\\\" > $null\n Set-Location \"C:\\$R_CMD_CHECK_DIR\\\"\n }\n\n Write-Output \"Running R CMD check\"\n if ($env:R_BUILD_TYPE -eq \"cran\") {\n # CRAN packages must pass without --no-multiarch (build on 64-bit and 32-bit)\n $check_args = \"c('CMD', 'check', '--as-cran', '--run-donttest', '$PKG_FILE_NAME')\"\n } else {\n $check_args = \"c('CMD', 'check', '--no-multiarch', '--as-cran', '--run-donttest', '$PKG_FILE_NAME')\"\n }\n $params = -join (\n \"result <- processx::run(command = 'R.exe', args = $check_args, \",\n \"echo = TRUE, windows_verbatim_args = FALSE, error_on_status = TRUE)\"\n )\n Invoke-R-Code-Redirect-Stderr $params ; $check_succeeded = $?\n\n Write-Output \"R CMD check build logs:\"\n $INSTALL_LOG_FILE_NAME = \"lightgbm.Rcheck\\00install.out\"\n Get-Content -Path \"$INSTALL_LOG_FILE_NAME\"\n\n Assert-Output $check_succeeded\n\n Write-Output \"Looking for issues with R CMD check results\"\n if (Get-Content \"$LOG_FILE_NAME\" | Select-String -Pattern \"NOTE|WARNING|ERROR\" -CaseSensitive -Quiet) {\n Write-Output \"NOTEs, WARNINGs, or ERRORs have been found by R CMD check\"\n Assert-Output $False\n }\n} else {\n $INSTALL_LOG_FILE_NAME = \"$env:BUILD_SOURCESDIRECTORY\\00install_out.txt\"\n Invoke-R-Code-Redirect-Stderr \"source('build_r.R')\" 1> $INSTALL_LOG_FILE_NAME ; $install_succeeded = $?\n Write-Output \"----- build and install logs -----\"\n Get-Content -Path \"$INSTALL_LOG_FILE_NAME\"\n Write-Output \"----- end of build and install logs -----\"\n Assert-Output $install_succeeded\n # some errors are not raised above, but can be found in the logs\n if (Get-Content \"$INSTALL_LOG_FILE_NAME\" | Select-String -Pattern \"ERROR\" -CaseSensitive -Quiet) {\n Write-Output \"ERRORs have been found installing lightgbm\"\n Assert-Output $False\n }\n}\n\n# Checking that the correct R version was used\nif ($env:TOOLCHAIN -ne \"MSVC\") {\n $checks = Select-String -Path \"${LOG_FILE_NAME}\" -Pattern \"using R version $env:R_WINDOWS_VERSION\"\n $checks_cnt = $checks.Matches.length\n} else {\n $checksParams = @{\n Path = \"${INSTALL_LOG_FILE_NAME}\"\n Pattern = \"R version passed into FindLibR.* $env:R_WINDOWS_VERSION\"\n }\n $checks = Select-String @checksParams\n $checks_cnt = $checks.Matches.length\n}\nif ($checks_cnt -eq 0) {\n Write-Output \"Wrong R version was found (expected '$env:R_WINDOWS_VERSION'). Check the build logs.\"\n Assert-Output $False\n}\n\n# Checking that we actually got the expected compiler. The R-package has some logic\n# to fail back to MinGW if MSVC fails, but for CI builds we need to check that the correct\n# compiler was used.\nif ($env:R_BUILD_TYPE -eq \"cmake\") {\n $checks = Select-String -Path \"${INSTALL_LOG_FILE_NAME}\" -Pattern \"Check for working CXX compiler.*$env:COMPILER\"\n if ($checks.Matches.length -eq 0) {\n Write-Output \"The wrong compiler was used. Check the build logs.\"\n Assert-Output $False\n }\n}\n\n# Checking that we got the right toolchain for MinGW. If using MinGW, both\n# MinGW and MSYS toolchains are supported\nif (($env:COMPILER -eq \"MINGW\") -and ($env:R_BUILD_TYPE -eq \"cmake\")) {\n $checks = Select-String -Path \"${INSTALL_LOG_FILE_NAME}\" -Pattern \"Trying to build with.*$env:TOOLCHAIN\"\n if ($checks.Matches.length -eq 0) {\n Write-Output \"The wrong toolchain was used. Check the build logs.\"\n Assert-Output $False\n }\n}\n\n# Checking that MM_PREFETCH preprocessor definition is actually used in CI builds.\nif ($env:R_BUILD_TYPE -eq \"cran\") {\n $checks = Select-String -Path \"${INSTALL_LOG_FILE_NAME}\" -Pattern \"checking whether MM_PREFETCH work.*yes\"\n $checks_cnt = $checks.Matches.length\n} elseif ($env:TOOLCHAIN -ne \"MSVC\") {\n $checks = Select-String -Path \"${INSTALL_LOG_FILE_NAME}\" -Pattern \".*Performing Test MM_PREFETCH - Success\"\n $checks_cnt = $checks.Matches.length\n} else {\n $checks_cnt = 1\n}\nif ($checks_cnt -eq 0) {\n Write-Output \"MM_PREFETCH preprocessor definition wasn't used. Check the build logs.\"\n Assert-Output $False\n}\n\n# Checking that MM_MALLOC preprocessor definition is actually used in CI builds.\nif ($env:R_BUILD_TYPE -eq \"cran\") {\n $checks = Select-String -Path \"${INSTALL_LOG_FILE_NAME}\" -Pattern \"checking whether MM_MALLOC work.*yes\"\n $checks_cnt = $checks.Matches.length\n} elseif ($env:TOOLCHAIN -ne \"MSVC\") {\n $checks = Select-String -Path \"${INSTALL_LOG_FILE_NAME}\" -Pattern \".*Performing Test MM_MALLOC - Success\"\n $checks_cnt = $checks.Matches.length\n} else {\n $checks_cnt = 1\n}\nif ($checks_cnt -eq 0) {\n Write-Output \"MM_MALLOC preprocessor definition wasn't used. Check the build logs.\"\n Assert-Output $False\n}\n\n# Checking that OpenMP is actually used in CMake builds.\nif ($env:R_BUILD_TYPE -eq \"cmake\") {\n $checks = Select-String -Path \"${INSTALL_LOG_FILE_NAME}\" -Pattern \".*Found OpenMP: TRUE.*\"\n if ($checks.Matches.length -eq 0) {\n Write-Output \"OpenMP wasn't found. Check the build logs.\"\n Assert-Output $False\n }\n}\n\nif ($env:COMPILER -eq \"MSVC\") {\n Write-Output \"Running tests with testthat.R\"\n Set-Location R-package/tests\n # NOTE: using Rscript.exe intentionally here, instead of Invoke-R-Code-Redirect-Stderr,\n # because something about the interaction between Invoke-R-Code-Redirect-Stderr\n # and testthat results in failing tests not exiting with a non-0 exit code.\n Rscript.exe --vanilla \"testthat.R\" ; Assert-Output $?\n}\n\nWrite-Output \"No issues were found checking the R-package\"\n" }, { "path": ".ci/test-r-package.sh", "content": "#!/bin/bash\n\nset -e -E -u -o pipefail\n\n# defaults\nARCH=$(uname -m)\n\n# set up R environment\nexport CRAN_MIRROR=\"https://cran.rstudio.com\"\nexport R_LIB_PATH=~/Rlib\nmkdir -p $R_LIB_PATH\nexport R_LIBS=$R_LIB_PATH\nexport PATH=\"$R_LIB_PATH/R/bin:$PATH\"\n\n# don't fail builds for long-running examples unless they're very long.\n# See https://github.com/lightgbm-org/LightGBM/issues/4049#issuecomment-793412254.\nif [[ $R_BUILD_TYPE != \"cran\" ]]; then\n export _R_CHECK_EXAMPLE_TIMING_THRESHOLD_=30\nfi\n\n# Get details needed for installing R components\nR_MAJOR_VERSION=\"${R_VERSION%.*}\"\nif [[ \"${R_MAJOR_VERSION}\" == \"4\" ]]; then\n export R_MAC_VERSION=4.3.1\n export R_MAC_PKG_URL=${CRAN_MIRROR}/bin/macosx/big-sur-${ARCH}/base/R-${R_MAC_VERSION}-${ARCH}.pkg\n export R_LINUX_VERSION=\"4.3.1-1.2204.0\"\n export R_APT_REPO=\"jammy-cran40/\"\nelse\n echo \"Unrecognized R version: ${R_VERSION}\"\n exit 1\nfi\n\n# installing precompiled R for Ubuntu\n# https://cran.r-project.org/bin/linux/ubuntu/#installation\n# adding steps from https://stackoverflow.com/a/56378217/3986677 to get latest version\n#\n# `devscripts` is required for 'checkbashisms' (https://github.com/r-lib/actions/issues/111)\nif [[ $OS_NAME == \"linux\" ]]; then\n mkdir -p ~/.gnupg\n echo \"disable-ipv6\" >> ~/.gnupg/dirmngr.conf\n sudo apt-key adv \\\n --homedir ~/.gnupg \\\n --keyserver keyserver.ubuntu.com \\\n --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 || exit 1\n sudo add-apt-repository \\\n \"deb ${CRAN_MIRROR}/bin/linux/ubuntu ${R_APT_REPO}\" || exit 1\n sudo apt-get update\n sudo apt-get install \\\n --no-install-recommends \\\n -y \\\n devscripts \\\n r-base-core=${R_LINUX_VERSION} \\\n r-base-dev=${R_LINUX_VERSION} \\\n texinfo \\\n texlive-latex-extra \\\n texlive-latex-recommended \\\n texlive-fonts-recommended \\\n texlive-fonts-extra \\\n tidy \\\n qpdf \\\n || exit 1\n\n if [[ $R_BUILD_TYPE == \"cran\" ]]; then\n sudo apt-get install \\\n --no-install-recommends \\\n -y \\\n \"autoconf=$(cat R-package/AUTOCONF_UBUNTU_VERSION)\" \\\n automake \\\n || exit 1\n fi\nfi\n\n# Installing R precompiled for Mac OS 10.11 or higher\nif [[ $OS_NAME == \"macos\" ]]; then\n brew update-reset --auto-update\n brew update --auto-update\n if [[ $R_BUILD_TYPE == \"cran\" ]]; then\n brew install automake || exit 1\n fi\n brew install \\\n checkbashisms \\\n qpdf || exit 1\n brew install basictex || exit 1\n export PATH=\"/Library/TeX/texbin:$PATH\"\n sudo tlmgr --verify-repo=none update --self || exit 1\n sudo tlmgr --verify-repo=none install inconsolata helvetic rsfs || exit 1\n\n curl -sL \"${R_MAC_PKG_URL}\" -o R.pkg || exit 1\n sudo installer \\\n -pkg \"$(pwd)/R.pkg\" \\\n -target / || exit 1\n\n # install tidy v5.8.0\n # ref: https://groups.google.com/g/r-sig-mac/c/7u_ivEj4zhM\n TIDY_URL=https://github.com/htacg/tidy-html5/releases/download/5.8.0/tidy-5.8.0-macos-x86_64+arm64.pkg\n curl -sL ${TIDY_URL} -o tidy.pkg\n sudo installer \\\n -pkg \"$(pwd)/tidy.pkg\" \\\n -target /\n\n # ensure that this newer version of 'tidy' is used by 'R CMD check'\n # ref: https://cran.r-project.org/doc/manuals/R-exts.html#Checking-packages\n export R_TIDYCMD=/usr/local/bin/tidy\nfi\n\n# {Matrix} needs {lattice}, so this needs to run before manually installing {Matrix}.\n# This should be unnecessary on R >=4.4.0\n# ref: https://github.com/lightgbm-org/LightGBM/issues/6433\nRscript --vanilla -e \"install.packages('lattice', repos = '${CRAN_MIRROR}', lib = '${R_LIB_PATH}')\"\n\n# manually install {Matrix}, as {Matrix}=1.7-0 raised its R floor all the way to R 4.4.0\n# ref: https://github.com/lightgbm-org/LightGBM/issues/6433\nRscript --vanilla -e \"install.packages('https://cran.r-project.org/src/contrib/Archive/Matrix/Matrix_1.6-5.tar.gz', repos = NULL, lib = '${R_LIB_PATH}')\"\n\n# Manually install dependencies to avoid a CI-time dependency on devtools (for devtools::install_deps())\nRscript --vanilla ./.ci/install-r-deps.R --build --test --exclude=Matrix || exit 1\n\ncd \"${BUILD_DIRECTORY}\"\nPKG_TARBALL=\"lightgbm_$(head -1 VERSION.txt).tar.gz\"\nBUILD_LOG_FILE=\"lightgbm.Rcheck/00install.out\"\nLOG_FILE_NAME=\"lightgbm.Rcheck/00check.log\"\nif [[ $R_BUILD_TYPE == \"cmake\" ]]; then\n Rscript build_r.R -j4 --skip-install || exit 1\nelif [[ $R_BUILD_TYPE == \"cran\" ]]; then\n\n # on Linux, we recreate configure in CI to test if\n # a change in a PR has changed configure.ac\n if [[ $OS_NAME == \"linux\" ]]; then\n ./R-package/recreate-configure.sh\n\n num_files_changed=$(\n git diff --name-only | wc -l\n )\n if [[ ${num_files_changed} -gt 0 ]]; then\n echo \"'configure' in the R-package has changed. Please recreate it and commit the changes.\"\n echo \"Changed files:\"\n git diff --compact-summary\n echo \"See R-package/README.md for details on how to recreate this script.\"\n echo \"\"\n exit 1\n fi\n fi\n\n ./build-cran-package.sh || exit 1\n\n # Test CRAN source .tar.gz in a directory that is not this repo or below it.\n # When people install.packages('lightgbm'), they won't have the LightGBM\n # git repo around. This is to protect against the use of relative paths\n # like ../../CMakeLists.txt that would only work if you are in the repo\n R_CMD_CHECK_DIR=\"${HOME}/tmp-r-cmd-check/\"\n mkdir -p \"${R_CMD_CHECK_DIR}\"\n mv \"${PKG_TARBALL}\" \"${R_CMD_CHECK_DIR}\"\n cd \"${R_CMD_CHECK_DIR}\"\nfi\n\nif [[ $PRODUCES_ARTIFACTS == \"true\" ]]; then\n cp \"${PKG_TARBALL}\" \"${BUILD_ARTIFACTSTAGINGDIRECTORY}/lightgbm-${LGB_VER}-r-cran.tar.gz\"\nfi\n\ndeclare -i allowed_notes=0\nbash \"${BUILD_DIRECTORY}/.ci/run-r-cmd-check.sh\" \\\n \"${PKG_TARBALL}\" \\\n \"${allowed_notes}\"\n\n# ensure 'grep --count' doesn't cause failures\nset +e\n\nused_correct_r_version=$(\n cat $LOG_FILE_NAME \\\n | grep --count \"using R version ${R_VERSION}\"\n)\nif [[ $used_correct_r_version -ne 1 ]]; then\n echo \"Unexpected R version was used. Expected '${R_VERSION}'.\"\n exit 1\nfi\n\nif [[ $R_BUILD_TYPE == \"cmake\" ]]; then\n passed_correct_r_version_to_cmake=$(\n cat $BUILD_LOG_FILE \\\n | grep --count \"R version passed into FindLibR.cmake: ${R_VERSION}\"\n )\n if [[ $passed_correct_r_version_to_cmake -ne 1 ]]; then\n echo \"Unexpected R version was passed into cmake. Expected '${R_VERSION}'.\"\n exit 1\n fi\nfi\n\n# this check makes sure that CI builds of the package actually use OpenMP\nif [[ $OS_NAME == \"macos\" ]] && [[ $R_BUILD_TYPE == \"cran\" ]]; then\n omp_working=$(\n cat $BUILD_LOG_FILE \\\n | grep --count -E \"checking whether OpenMP will work .*yes\"\n )\nelif [[ $R_BUILD_TYPE == \"cmake\" ]]; then\n omp_working=$(\n cat $BUILD_LOG_FILE \\\n | grep --count -E \".*Found OpenMP: TRUE.*\"\n )\nelse\n omp_working=1\nfi\nif [[ $omp_working -ne 1 ]]; then\n echo \"OpenMP was not found\"\n exit 1\nfi\n\n# this check makes sure that CI builds of the package\n# actually use MM_PREFETCH preprocessor definition\n#\n# _mm_prefetch will not work on arm64 architecture\n# ref: https://github.com/lightgbm-org/LightGBM/issues/4124\nif [[ $ARCH != \"arm64\" ]]; then\n if [[ $R_BUILD_TYPE == \"cran\" ]]; then\n mm_prefetch_working=$(\n cat $BUILD_LOG_FILE \\\n | grep --count -E \"checking whether MM_PREFETCH work.*yes\"\n )\n else\n mm_prefetch_working=$(\n cat $BUILD_LOG_FILE \\\n | grep --count -E \".*Performing Test MM_PREFETCH - Success\"\n )\n fi\n if [[ $mm_prefetch_working -ne 1 ]]; then\n echo \"MM_PREFETCH test was not passed\"\n exit 1\n fi\nfi\n\n# this check makes sure that CI builds of the package\n# actually use MM_MALLOC preprocessor definition\nif [[ $R_BUILD_TYPE == \"cran\" ]]; then\n mm_malloc_working=$(\n cat $BUILD_LOG_FILE \\\n | grep --count -E \"checking whether MM_MALLOC work.*yes\"\n )\nelse\n mm_malloc_working=$(\n cat $BUILD_LOG_FILE \\\n | grep --count -E \".*Performing Test MM_MALLOC - Success\"\n )\nfi\nif [[ $mm_malloc_working -ne 1 ]]; then\n echo \"MM_MALLOC test was not passed\"\n exit 1\nfi\n\n# this check makes sure that no \"warning: unknown pragma ignored\" logs\n# reach the user leading them to believe that something went wrong\nif [[ $R_BUILD_TYPE == \"cran\" ]]; then\n pragma_warning_present=$(\n cat $BUILD_LOG_FILE \\\n | grep --count -E \"warning: unknown pragma ignored\"\n )\n if [[ $pragma_warning_present -ne 0 ]]; then\n echo \"Unknown pragma warning is present, pragmas should have been removed before build\"\n exit 1\n fi\nfi\n" }, { "path": ".ci/test-windows.ps1", "content": "function Assert-Output {\n param( [Parameter(Mandatory = $true)][bool]$success )\n if (-not $success) {\n $host.SetShouldExit(-1)\n exit 1\n }\n}\n\n$env:CONDA_ENV = \"test-env\"\n$env:LGB_VER = (Get-Content $env:BUILD_SOURCESDIRECTORY\\VERSION.txt).trim()\n# Use custom temp directory to avoid\n# > warning MSB8029: The Intermediate directory or Output directory cannot reside under the Temporary directory\n# > as it could lead to issues with incremental build.\n# And make sure this directory is always clean\n$env:TMPDIR = \"$env:USERPROFILE\\tmp\"\nRemove-Item $env:TMPDIR -Force -Recurse -ErrorAction Ignore\n[Void][System.IO.Directory]::CreateDirectory($env:TMPDIR)\n\n# create the artifact upload directory if it doesn't exist yet\n[Void][System.IO.Directory]::CreateDirectory($env:BUILD_ARTIFACTSTAGINGDIRECTORY)\n\nif ($env:TASK -eq \"r-package\") {\n & .\\.ci\\test-r-package-windows.ps1 ; Assert-Output $?\n exit 0\n}\n\nif ($env:TASK -eq \"cpp-tests\") {\n cmake -B build -S . -DBUILD_CPP_TEST=ON -DUSE_DEBUG=ON -A x64\n cmake --build build --target testlightgbm --config Debug ; Assert-Output $?\n .\\Debug\\testlightgbm.exe ; Assert-Output $?\n exit 0\n}\n\nif ($env:TASK -eq \"swig\") {\n $env:JAVA_HOME = $env:JAVA_HOME_8_X64 # there is pre-installed Eclipse Temurin 8 somewhere\n $ProgressPreference = \"SilentlyContinue\" # progress bar bug extremely slows down download speed\n $params = @{\n Uri = \"https://sourceforge.net/projects/swig/files/latest/download\"\n OutFile = \"$env:BUILD_SOURCESDIRECTORY/swig/swigwin.zip\"\n UserAgent = \"curl\"\n }\n Invoke-WebRequest @params\n Add-Type -AssemblyName System.IO.Compression.FileSystem\n [System.IO.Compression.ZipFile]::ExtractToDirectory(\n \"$env:BUILD_SOURCESDIRECTORY/swig/swigwin.zip\",\n \"$env:BUILD_SOURCESDIRECTORY/swig\"\n ) ; Assert-Output $?\n $SwigFolder = Get-ChildItem -Name -Path \"$env:BUILD_SOURCESDIRECTORY/swig\" -Attributes Directory\n $env:PATH = @(\"$env:BUILD_SOURCESDIRECTORY/swig/$SwigFolder\", \"$env:PATH\") -join \";\"\n $BuildLogFileName = \"$env:BUILD_SOURCESDIRECTORY\\cmake_build.log\"\n cmake -B build -S . -A x64 -DUSE_SWIG=ON *> \"$BuildLogFileName\" ; $build_succeeded = $?\n Write-Output \"CMake build logs:\"\n Get-Content -Path \"$BuildLogFileName\"\n Assert-Output $build_succeeded\n $checks = Select-String -Path \"${BuildLogFileName}\" -Pattern \"-- Found SWIG.*${SwigFolder}/swig.exe\"\n $checks_cnt = $checks.Matches.length\n if ($checks_cnt -eq 0) {\n Write-Output \"Wrong SWIG version was found (expected '${SwigFolder}'). Check the build logs.\"\n Assert-Output $False\n }\n cmake --build build --target ALL_BUILD --config Release ; Assert-Output $?\n if ($env:PRODUCES_ARTIFACTS -eq \"true\") {\n cp ./build/lightgbmlib.jar $env:BUILD_ARTIFACTSTAGINGDIRECTORY/lightgbmlib_win.jar ; Assert-Output $?\n }\n exit 0\n}\n\n# setup for Python\nconda activate ; Assert-Output $?\nconda config --set always_yes yes --set changeps1 no ; Assert-Output $?\nconda config --remove channels defaults ; Assert-Output $?\nconda config --add channels nodefaults ; Assert-Output $?\nconda config --add channels conda-forge ; Assert-Output $?\nconda config --set channel_priority strict ; Assert-Output $?\nconda install -q -y conda \"python=$env:PYTHON_VERSION[build=*_cp*]\" ; Assert-Output $?\n\n# print output of 'conda info', to help in submitting bug reports\nWrite-Output \"conda info:\"\nconda info\n\nif ($env:PYTHON_VERSION -eq \"3.9\") {\n $env:CONDA_REQUIREMENT_FILE = \"$env:BUILD_SOURCESDIRECTORY/.ci/conda-envs/ci-core-py39.txt\"\n} else {\n $env:CONDA_REQUIREMENT_FILE = \"$env:BUILD_SOURCESDIRECTORY/.ci/conda-envs/ci-core.txt\"\n}\n\n$condaParams = @(\n \"-y\",\n \"-n\", \"$env:CONDA_ENV\",\n \"--file\", \"$env:CONDA_REQUIREMENT_FILE\",\n \"python=$env:PYTHON_VERSION[build=*_cp*]\"\n)\nconda create @condaParams ; Assert-Output $?\n\n# print output of 'conda list', to help in submitting bug reports\nWrite-Output \"conda list:\"\nconda list -n $env:CONDA_ENV\n\nif ($env:TASK -ne \"bdist\") {\n conda activate $env:CONDA_ENV\n}\n\nSet-Location \"$env:BUILD_SOURCESDIRECTORY\"\nif ($env:TASK -eq \"regular\") {\n cmake -B build -S . -A x64 ; Assert-Output $?\n cmake --build build --target ALL_BUILD --config Release ; Assert-Output $?\n sh ./build-python.sh install --precompile ; Assert-Output $?\n cp ./Release/lib_lightgbm.dll \"$env:BUILD_ARTIFACTSTAGINGDIRECTORY\"\n cp ./Release/lightgbm.exe \"$env:BUILD_ARTIFACTSTAGINGDIRECTORY\"\n} elseif ($env:TASK -eq \"sdist\") {\n sh ./build-python.sh sdist ; Assert-Output $?\n sh ./.ci/check-python-dists.sh ./dist ; Assert-Output $?\n Set-Location dist; pip install @(Get-ChildItem *.gz) -v ; Assert-Output $?\n} elseif ($env:TASK -eq \"bdist\") {\n # Import the Chocolatey profile module so that the RefreshEnv command\n # invoked below properly updates the current PowerShell session environment.\n $module = \"$env:ChocolateyInstall\\helpers\\chocolateyProfile.psm1\"\n Import-Module \"$module\" ; Assert-Output $?\n RefreshEnv\n\n Write-Output \"Current OpenCL drivers:\"\n Get-ItemProperty -Path Registry::HKEY_LOCAL_MACHINE\\SOFTWARE\\Khronos\\OpenCL\\Vendors\n\n conda activate $env:CONDA_ENV\n # TODO: restore --integrated-opencl as part of https://github.com/lightgbm-org/LightGBM/issues/6968\n sh \"build-python.sh\" bdist_wheel ; Assert-Output $?\n sh ./.ci/check-python-dists.sh ./dist ; Assert-Output $?\n Set-Location dist; pip install @(Get-ChildItem *py3-none-win_amd64.whl) ; Assert-Output $?\n cp @(Get-ChildItem *py3-none-win_amd64.whl) \"$env:BUILD_ARTIFACTSTAGINGDIRECTORY\"\n} elseif (($env:APPVEYOR -eq \"true\") -and ($env:TASK -eq \"python\")) {\n if ($env:COMPILER -eq \"MINGW\") {\n sh ./build-python.sh install --mingw ; Assert-Output $?\n } else {\n sh ./build-python.sh install; Assert-Output $?\n }\n}\n\nif (($env:TASK -eq \"sdist\") -or (($env:APPVEYOR -eq \"true\") -and ($env:TASK -eq \"python\"))) {\n # cannot test C API with \"sdist\" task\n $tests = \"$env:BUILD_SOURCESDIRECTORY/tests/python_package_test\"\n} else {\n $tests = \"$env:BUILD_SOURCESDIRECTORY/tests\"\n}\nif ($env:TASK -eq \"bdist\") {\n # Make sure we can do both CPU and GPU; see tests/python_package_test/test_dual.py\n # TODO: set LIGHTGBM_TEST_DUAL_CPU_GPU back to \"1\" as part of https://github.com/lightgbm-org/LightGBM/issues/6968\n $env:LIGHTGBM_TEST_DUAL_CPU_GPU = \"0\"\n}\n\npytest $tests ; Assert-Output $?\n\nif (($env:TASK -eq \"regular\") -or (($env:APPVEYOR -eq \"true\") -and ($env:TASK -eq \"python\"))) {\n Set-Location \"$env:BUILD_SOURCESDIRECTORY/examples/python-guide\"\n @(\"import matplotlib\", \"matplotlib.use('Agg')\") + (Get-Content \"plot_example.py\") | Set-Content \"plot_example.py\"\n # Prevent interactive window mode\n (Get-Content \"plot_example.py\").replace(\n 'graph.render(view=True)',\n 'graph.render(view=False)'\n ) | Set-Content \"plot_example.py\"\n conda install -y -n $env:CONDA_ENV \"h5py>=3.10\" \"ipywidgets>=8.1.2\" \"notebook>=7.1.2\"\n # Run all examples\n foreach ($file in @(Get-ChildItem *.py)) {\n @(\n \"import sys, warnings\",\n -join @(\n \"warnings.showwarning = lambda message, category, filename, lineno, file=None, line=None: \",\n \"sys.stdout.write(warnings.formatwarning(message, category, filename, lineno, line))\"\n )\n ) + (Get-Content $file) | Set-Content $file\n python $file ; Assert-Output $?\n }\n # Run all notebooks\n Set-Location \"$env:BUILD_SOURCESDIRECTORY/examples/python-guide/notebooks\"\n (Get-Content \"interactive_plot_example.ipynb\").replace(\n 'INTERACTIVE = False',\n 'assert False, \\\"Interactive mode disabled\\\"'\n ) | Set-Content \"interactive_plot_example.ipynb\"\n jupyter nbconvert --ExecutePreprocessor.timeout=180 --to notebook --execute --inplace *.ipynb ; Assert-Output $?\n}\n" }, { "path": ".ci/test.sh", "content": "#!/bin/bash\n\nset -e -E -o -u pipefail\n\n# defaults\nCONDA_ENV=\"test-env\"\nIN_UBUNTU_BASE_CONTAINER=${IN_UBUNTU_BASE_CONTAINER:-\"false\"}\nMETHOD=${METHOD:-\"\"}\nPRODUCES_ARTIFACTS=${PRODUCES_ARTIFACTS:-\"false\"}\nSANITIZERS=${SANITIZERS:-\"\"}\n\nARCH=$(uname -m)\n\nLGB_VER=$(head -n 1 \"${BUILD_DIRECTORY}/VERSION.txt\")\n\n# create the artifact upload directory if it doesn't exist yet\nmkdir -p \"${BUILD_ARTIFACTSTAGINGDIRECTORY}\"\n\nif [[ $OS_NAME == \"macos\" ]] && [[ $COMPILER == \"gcc\" ]]; then\n export CXX=g++-14\n export CC=gcc-14\nelif [[ $OS_NAME == \"linux\" ]] && [[ $COMPILER == \"clang\" ]]; then\n export CXX=clang++\n export CC=clang\nelif [[ $OS_NAME == \"linux\" ]] && [[ $COMPILER == \"clang-17\" ]]; then\n export CXX=clang++-17\n export CC=clang-17\nfi\n\nif [[ $IN_UBUNTU_BASE_CONTAINER == \"true\" ]]; then\n export LANG=\"en_US.UTF-8\"\n export LC_ALL=\"en_US.UTF-8\"\nfi\n\n# Setting MACOSX_DEPLOYMENT_TARGET prevents CMake from building against too-new\n# macOS features, and helps tools like Python build tools determine the appropriate\n# wheel compatibility tags.\n#\n# ref:\n# * https://cmake.org/cmake/help/latest/envvar/MACOSX_DEPLOYMENT_TARGET.html\n# * https://github.com/scikit-build/scikit-build-core/blob/acb7d0346e4a05bcb47a4ea3939c705ab71e3145/src/scikit_build_core/builder/macos.py#L36\nif [[ $ARCH == \"x86_64\" ]]; then\n export MACOSX_DEPLOYMENT_TARGET=10.15\nelse\n export MACOSX_DEPLOYMENT_TARGET=12.0\nfi\n\nif [[ \"${TASK}\" == \"r-package\" ]]; then\n bash \"${BUILD_DIRECTORY}/.ci/test-r-package.sh\" || exit 1\n exit 0\nfi\n\ncd \"${BUILD_DIRECTORY}\"\n\nif [[ $TASK == \"swig\" ]]; then\n cmake -B build -S . -DUSE_SWIG=ON\n cmake --build build -j4 || exit 1\n if [[ $OS_NAME == \"linux\" ]] && [[ $COMPILER == \"gcc\" ]]; then\n objdump -T ./lib_lightgbm.so > ./objdump.log || exit 1\n objdump -T ./lib_lightgbm_swig.so >> ./objdump.log || exit 1\n ./.ci/check-dynamic-dependencies.sh ./objdump.log || exit 1\n fi\n if [[ $PRODUCES_ARTIFACTS == \"true\" ]]; then\n cp ./build/lightgbmlib.jar \"${BUILD_ARTIFACTSTAGINGDIRECTORY}/lightgbmlib_${OS_NAME}.jar\"\n fi\n exit 0\nfi\n\nif [[ \"$TASK\" == \"cpp-tests\" ]]; then\n cmake_args=(\n -DBUILD_CPP_TEST=ON\n -DUSE_DEBUG=ON\n )\n if [[ $METHOD == \"with-sanitizers\" ]]; then\n cmake_args+=(\"-DUSE_SANITIZER=ON\")\n if [[ -n $SANITIZERS ]]; then\n cmake_args+=(\"-DENABLED_SANITIZERS=$SANITIZERS\")\n fi\n fi\n cmake -B build -S . \"${cmake_args[@]}\"\n cmake --build build --target testlightgbm -j4 || exit 1\n ./testlightgbm || exit 1\n exit 0\nfi\n\n# including python=version=[build=*_cp*] to ensure that conda prefers CPython and doesn't fall back to\n# other implementations like pypy\nCONDA_PYTHON_REQUIREMENT=\"python=${PYTHON_VERSION}[build=*_cp*]\"\n\nif [[ $TASK == \"if-else\" ]]; then\n conda create -q -y -n \"${CONDA_ENV}\" \"${CONDA_PYTHON_REQUIREMENT}\" numpy\n # shellcheck disable=SC1091\n source activate \"${CONDA_ENV}\"\n cmake -B build -S . || exit 1\n cmake --build build --target lightgbm -j4 || exit 1\n cd \"$BUILD_DIRECTORY/tests/cpp_tests\"\n ../../lightgbm config=train.conf convert_model_language=cpp convert_model=../../src/boosting/gbdt_prediction.cpp\n ../../lightgbm config=predict.conf output_result=origin.pred\n ../../lightgbm config=predict.conf output_result=ifelse.pred\n python test.py\n exit 0\nfi\n\nif [[ $PYTHON_VERSION == \"3.9\" ]]; then\n CONDA_REQUIREMENT_FILE=\"${BUILD_DIRECTORY}/.ci/conda-envs/ci-core-py39.txt\"\nelse\n CONDA_REQUIREMENT_FILE=\"${BUILD_DIRECTORY}/.ci/conda-envs/ci-core.txt\"\nfi\n\nconda create \\\n -y \\\n -n \"${CONDA_ENV}\" \\\n --file \"${CONDA_REQUIREMENT_FILE}\" \\\n \"${CONDA_PYTHON_REQUIREMENT}\" \\\n|| exit 1\n\n# print output of 'conda list', to help in submitting bug reports\necho \"conda list:\"\nconda list -n ${CONDA_ENV}\n\n# shellcheck disable=SC1091\nsource activate $CONDA_ENV\n\ncd \"${BUILD_DIRECTORY}\"\n\nif [[ $TASK == \"sdist\" ]]; then\n sh ./build-python.sh sdist || exit 1\n sh .ci/check-python-dists.sh ./dist || exit 1\n pip install \"./dist/lightgbm-${LGB_VER}.tar.gz\" -v || exit 1\n if [[ $PRODUCES_ARTIFACTS == \"true\" ]]; then\n cp \"./dist/lightgbm-${LGB_VER}.tar.gz\" \"${BUILD_ARTIFACTSTAGINGDIRECTORY}\" || exit 1\n fi\n pytest ./tests/python_package_test || exit 1\n exit 0\nelif [[ $TASK == \"bdist\" ]]; then\n if [[ $OS_NAME == \"macos\" ]]; then\n sh ./build-python.sh bdist_wheel || exit 1\n sh .ci/check-python-dists.sh ./dist || exit 1\n if [[ $PRODUCES_ARTIFACTS == \"true\" ]]; then\n cp \"$(echo \"dist/lightgbm-${LGB_VER}-py3-none-macosx\"*.whl)\" \"${BUILD_ARTIFACTSTAGINGDIRECTORY}\" || exit 1\n fi\n else\n sh ./build-python.sh bdist_wheel --integrated-opencl || exit 1\n\n # print some debugging logs about the wheel's GLIBC version and dependencies on shared libraries\n pip install 'auditwheel>=6.5.1'\n auditwheel show ./dist/lightgbm*.whl\n\n # pass through 'auditwheel repair' to set the appropriate wheel tags.\n #\n # intentionally avoid vendoring libgomp, to reduce the risk of multiple OpenMP libraries\n # being loaded in the same process.\n auditwheel repair \\\n --exclude 'libgomp.so*' \\\n --lib-sdir '' \\\n --wheel-dir dist-fixed/ \\\n ./dist/lightgbm*.whl\n\n # overwrite the original wheel with the new one\n rm ./dist/lightgbm*.whl\n mv ./dist-fixed/lightgbm*.whl ./dist\n\n # check wheel properties\n sh .ci/check-python-dists.sh ./dist || exit 1\n\n if [[ $PRODUCES_ARTIFACTS == \"true\" ]]; then\n # hard-code expected tag so CI will fail if 'auditwheel repair' has a surprising result (e.g. newer\n # manylinux tag than we intended)\n if [[ $ARCH == \"x86_64\" ]]; then\n PLATFORM=\"manylinux_2_27_x86_64.manylinux_2_28_x86_64\"\n else\n PLATFORM=\"manylinux2014_aarch64.manylinux_2_17_aarch64\"\n fi\n cp \"dist/lightgbm-${LGB_VER}-py3-none-${PLATFORM}.whl\" \"${BUILD_ARTIFACTSTAGINGDIRECTORY}\" || exit 1\n fi\n # Make sure we can do both CPU and GPU; see tests/python_package_test/test_dual.py\n export LIGHTGBM_TEST_DUAL_CPU_GPU=1\n fi\n pip install -v ./dist/*.whl || exit 1\n pytest ./tests || exit 1\n exit 0\nfi\n\nif [[ $TASK == \"gpu\" ]]; then\n sed -i'.bak' 's/std::string device_type = \"cpu\";/std::string device_type = \"gpu\";/' ./include/LightGBM/config.h\n grep -q 'std::string device_type = \"gpu\"' ./include/LightGBM/config.h || exit 1 # make sure that changes were really done\n if [[ $METHOD == \"pip\" ]]; then\n sh ./build-python.sh sdist || exit 1\n sh .ci/check-python-dists.sh ./dist || exit 1\n pip install \\\n -v \\\n --config-settings=cmake.define.USE_GPU=ON \\\n \"./dist/lightgbm-${LGB_VER}.tar.gz\" \\\n || exit 1\n pytest ./tests/python_package_test || exit 1\n exit 0\n elif [[ $METHOD == \"wheel\" ]]; then\n sh ./build-python.sh bdist_wheel --gpu || exit 1\n sh ./.ci/check-python-dists.sh ./dist || exit 1\n pip install \"$(echo \"./dist/lightgbm-${LGB_VER}\"*.whl)\" -v || exit 1\n pytest ./tests || exit 1\n exit 0\n elif [[ $METHOD == \"source\" ]]; then\n cmake -B build -S . -DUSE_GPU=ON\n fi\nelif [[ $TASK == \"cuda\" ]]; then\n sed -i'.bak' 's/std::string device_type = \"cpu\";/std::string device_type = \"cuda\";/' ./include/LightGBM/config.h\n grep -q 'std::string device_type = \"cuda\"' ./include/LightGBM/config.h || exit 1 # make sure that changes were really done\n # by default ``gpu_use_dp=false`` for efficiency. change to ``true`` here for exact results in ci tests\n sed -i'.bak' 's/gpu_use_dp = false;/gpu_use_dp = true;/' ./include/LightGBM/config.h\n grep -q 'gpu_use_dp = true' ./include/LightGBM/config.h || exit 1 # make sure that changes were really done\n if [[ $METHOD == \"pip\" ]]; then\n sh ./build-python.sh sdist || exit 1\n sh ./.ci/check-python-dists.sh ./dist || exit 1\n pip install \\\n -v \\\n --config-settings=cmake.define.USE_CUDA=ON \\\n \"./dist/lightgbm-${LGB_VER}.tar.gz\" \\\n || exit 1\n pytest ./tests/python_package_test || exit 1\n exit 0\n elif [[ $METHOD == \"wheel\" ]]; then\n sh ./build-python.sh bdist_wheel --cuda || exit 1\n sh ./.ci/check-python-dists.sh ./dist || exit 1\n pip install \"$(echo \"./dist/lightgbm-${LGB_VER}\"*.whl)\" -v || exit 1\n pytest ./tests || exit 1\n exit 0\n elif [[ $METHOD == \"source\" ]]; then\n cmake -B build -S . -DUSE_CUDA=ON\n fi\nelif [[ $TASK == \"mpi\" ]]; then\n if [[ $METHOD == \"pip\" ]]; then\n sh ./build-python.sh sdist || exit 1\n sh ./.ci/check-python-dists.sh ./dist || exit 1\n pip install \\\n -v \\\n --config-settings=cmake.define.USE_MPI=ON \\\n \"./dist/lightgbm-${LGB_VER}.tar.gz\" \\\n || exit 1\n pytest ./tests/python_package_test || exit 1\n exit 0\n elif [[ $METHOD == \"wheel\" ]]; then\n sh ./build-python.sh bdist_wheel --mpi || exit 1\n sh ./.ci/check-python-dists.sh ./dist || exit 1\n pip install \"$(echo \"./dist/lightgbm-${LGB_VER}\"*.whl)\" -v || exit 1\n pytest ./tests || exit 1\n exit 0\n elif [[ $METHOD == \"source\" ]]; then\n cmake -B build -S . -DUSE_MPI=ON -DUSE_DEBUG=ON\n fi\nelse\n cmake -B build -S .\nfi\n\ncmake --build build --target _lightgbm -j4 || exit 1\n\nsh ./build-python.sh install --precompile || exit 1\npytest ./tests || exit 1\n\nif [[ $TASK == \"regular\" ]]; then\n if [[ $PRODUCES_ARTIFACTS == \"true\" ]]; then\n if [[ $OS_NAME == \"macos\" ]]; then\n cp ./lib_lightgbm.dylib \"${BUILD_ARTIFACTSTAGINGDIRECTORY}/lib_lightgbm.dylib\"\n else\n if [[ $COMPILER == \"gcc\" ]]; then\n objdump -T ./lib_lightgbm.so > ./objdump.log || exit 1\n ./.ci/check-dynamic-dependencies.sh ./objdump.log || exit 1\n fi\n cp ./lib_lightgbm.so \"${BUILD_ARTIFACTSTAGINGDIRECTORY}/lib_lightgbm.so\"\n fi\n fi\n cd \"$BUILD_DIRECTORY/examples/python-guide\"\n sed -i'.bak' '/import lightgbm as lgb/a\\\nimport matplotlib\\\nmatplotlib.use\\(\\\"Agg\\\"\\)\\\n' plot_example.py # prevent interactive window mode\n sed -i'.bak' 's/graph.render(view=True)/graph.render(view=False)/' plot_example.py\n # requirements for examples\n conda install -y -n $CONDA_ENV \\\n 'h5py>=3.10' \\\n 'ipywidgets>=8.1.2' \\\n 'notebook>=7.1.2'\n for f in *.py **/*.py; do python \"${f}\" || exit 1; done # run all examples\n cd \"$BUILD_DIRECTORY/examples/python-guide/notebooks\"\n sed -i'.bak' 's/INTERACTIVE = False/assert False, \\\\\"Interactive mode disabled\\\\\"/' interactive_plot_example.ipynb\n jupyter nbconvert --ExecutePreprocessor.timeout=180 --to notebook --execute --inplace ./*.ipynb || exit 1 # run all notebooks\n\n # importing the library should succeed even if all optional dependencies are not present\n conda uninstall -n $CONDA_ENV --force --yes \\\n cffi \\\n dask \\\n distributed \\\n joblib \\\n matplotlib-base \\\n pandas \\\n psutil \\\n pyarrow \\\n python-graphviz \\\n scikit-learn || exit 1\n python -c \"import lightgbm\" || exit 1\nfi\n" }, { "path": ".editorconfig", "content": "root = true\n\n[*]\ncharset = utf-8\ntrim_trailing_whitespace = true\ninsert_final_newline = true\nend_of_line = lf\nindent_style = space\nindent_size = 2\n\n[*.{py,sh,ps1,js,json}]\nindent_size = 4\nmax_line_length = 120\nskip = external_libs\nknown_first_party = lightgbm\n\n# Tabs matter for Makefile and .gitmodules\n[{makefile*,Makefile*,*.mk,*.mak,*.makefile,*.Makefile,GNUmakefile,BSDmakefile,make.bat,Makevars*,*.gitmodules}]\nindent_style = tab\n" }, { "path": ".git-blame-ignore-revs", "content": "# introduce ruff-format (#6308)\n6330d6269c81dfd4c96e664b99239b8ff39ccf91\n# enable ruff format on tests and examples (#6317)\n1b792e716682254c33ddb5eb845357e84018636d\n# enable ruff-format on main library Python code (#6336)\ndd31208ab7a7aea86762830697b00666f843ded9\n# enable whitespace/indent_namespace rule from cpplint (#7056)\n50f11a9f3c066eadee475ea567f9e9d49e6bb827\n" }, { "path": ".github/CODEOWNERS", "content": "# This file controls default reviewers for LightGBM code.\n# See https://help.github.com/en/articles/about-code-owners\n# for details\n#\n# Maintainers are encouraged to use their best discretion in\n# setting reviewers on PRs manually, but this file should\n# offer a reasonable automatic best-guess\n\n# catch-all rule (this only gets matched if no rules below match)\n* @guolinke @jameslamb @shiyu1994 @jmoralez @borchero @StrikerRUS\n" }, { "path": ".github/ISSUE_TEMPLATE/BUG_REPORT.md", "content": "---\nname: Bug Report 🐞\nabout: Something isn't working as expected? Here is the right place to report.\n---\n\n## Description\n\n\n## Reproducible example\n\n\n## Environment info\n\nLightGBM version or commit hash:\n\nCommand(s) you used to install LightGBM\n\n```shell\n\n```\n\n\n\n\n## Additional Comments\n\n" }, { "path": ".github/ISSUE_TEMPLATE/FEATURE_REQUEST.md", "content": "---\nname: Feature Request 💡\nabout: Suggest a new idea for the project.\nlabels: enhancement, feature-request\n---\n\n\n\n## Summary\n\n\n\n## Motivation\n\n\n\n## Description\n\n\n\n## References\n\n\n" }, { "path": ".github/dependabot.yml", "content": "version: 2\nupdates:\n - package-ecosystem: github-actions\n directory: /\n schedule:\n interval: monthly\n groups:\n ci-dependencies:\n patterns:\n - \"*\"\n cooldown:\n # only accept releases that have been out for at least 15 days\n default-days: 15\n commit-message:\n prefix: \"[ci]\"\n labels:\n - maintenance\n" }, { "path": ".github/release-drafter.yml", "content": "name-template: 'v$NEXT_PATCH_VERSION'\ntag-template: 'v$NEXT_PATCH_VERSION'\ncategories:\n - title: '💡 New Features'\n label: 'feature'\n - title: '🔨 Breaking'\n label: 'breaking'\n - title: '🚀 Efficiency Improvement'\n label: 'efficiency'\n - title: '🐛 Bug Fixes'\n label: 'fix'\n - title: '📖 Documentation'\n label: 'doc'\n - title: '🧰 Maintenance'\n label: 'maintenance'\nchange-template: '- $TITLE @$AUTHOR (#$NUMBER)'\ntemplate: |\n ## Changes\n\n $CHANGES\n" }, { "path": ".github/workflows/build.yml", "content": "# builds core artifacts, intended to be attached to releases\n# or used by other workflows\nname: Build\n\non:\n push:\n branches:\n - master\n pull_request:\n branches:\n - master\n\n# automatically cancel in-progress builds if another commit is pushed\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\nenv:\n # tell scripts where to put artifacts\n BUILD_ARTIFACTSTAGINGDIRECTORY: '${{ github.workspace }}/artifacts'\n\njobs:\n archive:\n runs-on: ubuntu-latest\n timeout-minutes: 15\n steps:\n - name: Checkout repository\n uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2\n with:\n fetch-depth: 5\n persist-credentials: false\n submodules: true\n - name: Create source archive\n run: |\n mkdir -p \"${BUILD_ARTIFACTSTAGINGDIRECTORY}\"\n tar \\\n -czvf \\\n /tmp/LightGBM-complete_source_code_tar_gz.tar.gz \\\n .\n mv \\\n /tmp/LightGBM-complete_source_code_tar_gz.tar.gz \\\n ${BUILD_ARTIFACTSTAGINGDIRECTORY}/\n - name: Create commit.txt\n shell: bash\n run: |\n # for pull requests, github.sha refers to the merge commit from merging the PR and\n # target branch... we want the actual commit that was pushed\n if [[ \"${{ github.event_name }}\" == \"pull_request\" ]]; then\n COMMIT_SHA=\"${{ github.event.pull_request.head.sha }}\"\n else\n COMMIT_SHA=\"${{ github.sha }}\"\n fi\n echo \"${COMMIT_SHA}\" > \"${BUILD_ARTIFACTSTAGINGDIRECTORY}/commit.txt\"\n - name: Upload artifacts\n uses: actions/upload-artifact@bbbca2ddaa5d8feaa63e36b76fdaad77386f024f # v7.0.0\n with:\n name: source-archive\n path: |\n ${{ env.BUILD_ARTIFACTSTAGINGDIRECTORY }}/commit.txt\n ${{ env.BUILD_ARTIFACTSTAGINGDIRECTORY }}/LightGBM-complete_source_code_tar_gz.tar.gz\n if-no-files-found: error\n all-build-jobs-successful:\n if: always()\n runs-on: ubuntu-latest\n needs:\n - archive\n steps:\n - name: Note that all tests succeeded\n uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2\n with:\n jobs: ${{ toJSON(needs) }}\n" }, { "path": ".github/workflows/cpp.yml", "content": "name: C++\n\non:\n push:\n branches:\n - master\n pull_request:\n branches:\n - master\n\n# automatically cancel in-progress builds if another commit is pushed\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\nenv:\n # tell scripts where to put artifacts\n # (this variable name is left over from when jobs ran on Azure DevOps)\n BUILD_ARTIFACTSTAGINGDIRECTORY: '${{ github.workspace }}/artifacts'\n # where repo sources are cloned to\n BUILD_DIRECTORY: '${{ github.workspace }}'\n # in CMake-driven builds, parallelize compilation\n CMAKE_BUILD_PARALLEL_LEVEL: 4\n\njobs:\n test:\n name: ${{ matrix.task }} (${{ matrix.os-display-name || matrix.os }}, ${{ matrix.compiler }})\n runs-on: ${{ matrix.os }}\n container: ${{ matrix.container }}\n timeout-minutes: 60\n strategy:\n fail-fast: false\n matrix:\n include:\n #############\n # C++ tests #\n #############\n - os: ubuntu-latest\n task: cpp-tests\n compiler: clang-17\n method: with-sanitizers\n container: 'ubuntu:22.04'\n os-display-name: 'ubuntu22.04'\n - os: macos-15-intel\n task: cpp-tests\n compiler: clang\n method: with-sanitizers\n sanitizers: \"address;undefined\"\n container: null\n - os: windows-2022\n task: cpp-tests\n compiler: msvc\n container: null\n ###########\n # if-else #\n ###########\n # These test CLI-generated C++ inference code.\n #\n # They need Python because they're written with pytest, but they\n # do not need the LightGBM Python package\n - os: ubuntu-latest\n task: if-else\n compiler: clang-17\n container: 'ubuntu:22.04'\n os-display-name: 'ubuntu22.04'\n python_version: '3.13'\n setup-conda: 'true'\n - os: macos-15-intel\n task: if-else\n compiler: clang\n python_version: '3.10'\n container: null\n - os: ubuntu-latest\n task: if-else\n compiler: gcc\n python_version: '3.11'\n container: 'lightgbm.azurecr.io/vsts-agent:manylinux_2_28_x86_64'\n os-display-name: 'manylinux_2_28'\n steps:\n - name: Install packages used by third-party actions\n if: matrix.container == 'ubuntu:22.04'\n shell: bash\n run: |\n apt-get update -y\n apt-get install --no-install-recommends -y \\\n dirmngr \\\n gpg \\\n gpg-agent \\\n software-properties-common \\\n sudo\n # install newest version of git\n # ref:\n # - https://unix.stackexchange.com/a/170831/550004\n # - https://git-scm.com/download/linux\n add-apt-repository ppa:git-core/ppa -y\n apt-get update -y\n apt-get install --no-install-recommends -y \\\n git\n - name: Trust git cloning LightGBM\n if: startsWith(matrix.os, 'ubuntu')\n run: |\n git config --global --add safe.directory \"${GITHUB_WORKSPACE}\"\n - name: Checkout repository\n uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2\n with:\n fetch-depth: 5\n persist-credentials: false\n submodules: true\n - name: Setup and run tests on Linux and macOS\n if: startsWith(matrix.os, 'macos') || startsWith(matrix.os, 'ubuntu')\n shell: bash\n run: |\n export COMPILER=\"${{ matrix.compiler }}\"\n export CONDA=\"${HOME}/miniforge\"\n export METHOD=\"${{ matrix.method }}\"\n export PATH=${CONDA}/bin:${PATH}\n export PYTHON_VERSION=\"${{ matrix.python_version }}\"\n export SANITIZERS=\"${{ matrix.sanitizers }}\"\n export SETUP_CONDA=\"${{ matrix.setup-conda }}\"\n export TASK=\"${{ matrix.task }}\"\n if [[ \"${{ matrix.os }}\" =~ ^macos ]]; then\n export OS_NAME=\"macos\"\n elif [[ \"${{ matrix.os }}\" == \"ubuntu-latest\" ]]; then\n export OS_NAME=\"linux\"\n fi\n if [[ \"${{ matrix.container }}\" == \"ubuntu:22.04\" ]]; then\n export DEBIAN_FRONTEND=noninteractive\n export IN_UBUNTU_BASE_CONTAINER=\"true\"\n fi\n $GITHUB_WORKSPACE/.ci/setup.sh\n $GITHUB_WORKSPACE/.ci/test.sh\n - name: Setup and run tests on Windows\n if: startsWith(matrix.os, 'windows')\n shell: pwsh -command \". {0}\"\n run: |\n $env:BUILD_SOURCESDIRECTORY = $env:BUILD_DIRECTORY\n $env:TASK = \"${{ matrix.task }}\"\n & \"$env:GITHUB_WORKSPACE/.ci/test-windows.ps1\"\n all-cpp-jobs-successful:\n if: always()\n runs-on: ubuntu-latest\n needs:\n - test\n steps:\n - name: Note that all tests succeeded\n uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2\n with:\n jobs: ${{ toJSON(needs) }}\n" }, { "path": ".github/workflows/cuda.yml", "content": "name: CUDA Version\n\non:\n # Run manually by clicking a button in the UI\n workflow_dispatch:\n\n# automatically cancel in-progress builds if another commit is pushed\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\njobs:\n test:\n # yamllint disable-line rule:line-length\n name: ${{ matrix.task }} ${{ matrix.cuda_version }} ${{ matrix.method }} (${{ matrix.linux_version }}, ${{ matrix.compiler }}, Python ${{ matrix.python_version }})\n # yamllint disable-line rule:line-length\n # ref: https://docs.github.com/en/actions/concepts/runners/about-larger-runners#specifications-for-gpu-larger-runners\n runs-on: github-hosted-linux-gpu-amd64\n container:\n image: nvcr.io/nvidia/cuda:${{ matrix.cuda_version }}-devel-${{ matrix.linux_version }}\n env:\n CMAKE_BUILD_PARALLEL_LEVEL: 4\n COMPILER: ${{ matrix.compiler }}\n CONDA: /tmp/miniforge\n DEBIAN_FRONTEND: noninteractive\n METHOD: ${{ matrix.method }}\n OS_NAME: linux\n PYTHON_VERSION: ${{ matrix.python_version }}\n TASK: ${{ matrix.task }}\n SKBUILD_STRICT_CONFIG: true\n options: --gpus all\n timeout-minutes: 30\n strategy:\n fail-fast: false\n matrix:\n include:\n - method: wheel\n compiler: gcc\n python_version: \"3.11\"\n cuda_version: \"12.8.0\"\n linux_version: \"ubuntu22.04\"\n task: cuda\n - method: source\n compiler: gcc\n python_version: \"3.13\"\n cuda_version: \"12.2.2\"\n linux_version: \"ubuntu22.04\"\n task: cuda\n - method: pip\n compiler: clang\n python_version: \"3.12\"\n cuda_version: \"11.8.0\"\n linux_version: \"ubuntu20.04\"\n task: cuda\n steps:\n - name: Install latest git and sudo\n run: |\n apt-get update\n apt-get install --no-install-recommends -y \\\n ca-certificates \\\n software-properties-common\n add-apt-repository ppa:git-core/ppa -y\n apt-get update\n apt-get install --no-install-recommends -y \\\n git \\\n sudo\n - name: Checkout repository\n uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2\n with:\n fetch-depth: 5\n persist-credentials: false\n submodules: true\n - name: Setup and run tests\n run: |\n export BUILD_ARTIFACTSTAGINGDIRECTORY=\"${{ github.workspace }}/artifacts\"\n export BUILD_DIRECTORY=\"$GITHUB_WORKSPACE\"\n export PATH=$CONDA/bin:$PATH\n\n # check GPU usage\n nvidia-smi\n\n # build and test\n $GITHUB_WORKSPACE/.ci/setup.sh\n $GITHUB_WORKSPACE/.ci/test.sh\n all-cuda-jobs-successful:\n if: always()\n runs-on: ubuntu-latest\n needs: [test]\n steps:\n - name: Note that all tests succeeded\n uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2\n with:\n jobs: ${{ toJSON(needs) }}\n" }, { "path": ".github/workflows/lock.yml", "content": "name: 'Lock Inactive Threads'\n\non:\n schedule:\n # midnight UTC, every Wednesday, for Issues\n - cron: '0 0 * * 3'\n # midnight UTC, every Thursday, for PRs\n - cron: '0 0 * * 4'\n # allow manual triggering from GitHub UI\n workflow_dispatch:\n\npermissions:\n issues: write\n pull-requests: write\n\nconcurrency:\n group: lock\n\njobs:\n action:\n runs-on: ubuntu-latest\n steps:\n - uses: dessant/lock-threads@7266a7ce5c1df01b1c6db85bf8cd86c737dadbe7 # v6.0.0\n with:\n github-token: ${{ github.token }}\n # after how many days of inactivity should a closed issue/PR be locked?\n issue-inactive-days: '365'\n pr-inactive-days: '365'\n # do not close feature request issues...\n # we close those but track them in https://github.com/lightgbm-org/LightGBM/issues/2302\n exclude-any-issue-labels: 'feature request'\n # what labels should be removed prior to locking?\n remove-issue-labels: 'awaiting response,awaiting review,blocking,in progress'\n remove-pr-labels: 'awaiting response,awaiting review,blocking,in progress'\n # what message should be posted prior to locking?\n issue-comment: >\n This issue has been automatically locked\n since there has not been any recent activity since it was closed.\n\n To start a new related discussion,\n open a new issue at https://github.com/lightgbm-org/LightGBM/issues\n including a reference to this.\n pr-comment: >\n This pull request has been automatically locked\n since there has not been any recent activity since it was closed.\n\n To start a new related discussion,\n open a new issue at https://github.com/lightgbm-org/LightGBM/issues\n including a reference to this.\n # what should the locking status be?\n issue-lock-reason: 'resolved'\n pr-lock-reason: 'resolved'\n process-only: ${{ github.event.schedule == '0 0 * * 3' && 'issues' || 'prs' }}\n" }, { "path": ".github/workflows/lychee.yml", "content": "name: Link checks\n\non:\n # Run manually by clicking a button in the UI\n workflow_dispatch:\n # Run once a day at 8:00am UTC\n schedule:\n - cron: '0 8 * * *'\n\nenv:\n COMPILER: gcc\n OS_NAME: 'linux'\n TASK: 'check-links'\n\njobs:\n check-links:\n timeout-minutes: 60\n runs-on: ubuntu-latest\n steps:\n - name: Checkout repository\n uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2\n with:\n fetch-depth: 5\n persist-credentials: false\n submodules: false\n - name: Build docs\n run: |\n export CONDA=${HOME}/miniforge\n export PATH=${CONDA}/bin:${HOME}/.local/bin:${PATH}\n $GITHUB_WORKSPACE/.ci/setup.sh || exit 1\n $GITHUB_WORKSPACE/.ci/build-docs.sh || exit 1\n - name: Check links\n uses: lycheeverse/lychee-action@8646ba30535128ac92d33dfc9133794bfdd9b411 # v2.8.0\n with:\n args: >-\n --config=./docs/.lychee.toml\n --\n \"**/*.rst\"\n \"**/*.md\"\n \"./R-package/**/*.Rd\"\n \"./docs/_build/html/*.html\"\n fail: true\n failIfEmpty: true\n" }, { "path": ".github/workflows/no_response.yml", "content": "name: No Response Bot\n\npermissions:\n issues: write\n pull-requests: write\n\non:\n issue_comment:\n types: [created]\n schedule:\n # \"every day at 04:00 UTC\"\n - cron: '0 4 * * *'\n\njobs:\n noResponse:\n runs-on: ubuntu-latest\n steps:\n - uses: lee-dohm/no-response@9bb0a4b5e6a45046f00353d5de7d90fb8bd773bb # v0.5.0\n with:\n closeComment: >\n This issue has been automatically closed\n because it has been awaiting a response for too long.\n\n When you have time to to work with the maintainers to resolve this issue,\n please post a new comment and it will be re-opened.\n If the issue has been locked for editing by the time you return to it,\n please open a new issue and reference this one.\n\n Thank you for taking the time to improve LightGBM!\n daysUntilClose: 30\n responseRequiredLabel: awaiting response\n token: ${{ github.token }}\n" }, { "path": ".github/workflows/optional_checks.yml", "content": "name: Optional checks\n\non:\n pull_request:\n branches:\n - master\n\njobs:\n all-optional-checks-successful:\n timeout-minutes: 30\n runs-on: ubuntu-latest\n env:\n GITHUB_TOKEN: ${{ github.token }}\n steps:\n - name: Checkout repository\n uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2\n with:\n fetch-depth: 5\n persist-credentials: false\n submodules: false\n - name: Check valgrind workflow\n shell: bash\n run: |\n # ref: https://docs.github.com/en/actions/reference/workflows-and-actions/contexts#github-context\n PR_BRANCH=\"${GITHUB_HEAD_REF:-${GITHUB_REF#refs/heads/}}\"\n echo \"checking status for branch '${PR_BRANCH}'\"\n ./.ci/check-workflow-status.sh \\\n \"${PR_BRANCH}\" \\\n 'r_valgrind.yml' \\\n ${{ github.event.number }}\n" }, { "path": ".github/workflows/python_package.yml", "content": "name: Python-package\n\non:\n push:\n branches:\n - master\n pull_request:\n branches:\n - master\n\n# automatically cancel in-progress builds if another commit is pushed\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\nenv:\n # tell scripts where to put artifacts\n # (this variable name is left over from when jobs ran on Azure DevOps)\n BUILD_ARTIFACTSTAGINGDIRECTORY: '${{ github.workspace }}/artifacts'\n # where repo sources are cloned to\n BUILD_SOURCESDIRECTORY: '${{ github.workspace }}'\n CMAKE_BUILD_PARALLEL_LEVEL: 4\n # avoid interactive prompts in Debian-based distributions\n DEBIAN_FRONTEND: noninteractive\n # On runners using nvidia-container-runtime with Docker, ensure GPUs are available to running processes.\n #\n # ref: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/docker-specialized.html\n NVIDIA_VISIBLE_DEVICES: 'all'\n SKBUILD_STRICT_CONFIG: true\n\njobs:\n test-linux-aarch64:\n name: bdist wheel (manylinux2014-arm, gcc, Python 3.13)\n runs-on: ubuntu-24.04-arm\n timeout-minutes: 60\n steps:\n - name: Checkout repository\n uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2\n with:\n fetch-depth: 5\n persist-credentials: false\n submodules: true\n - name: Setup and run tests\n shell: bash\n # this uses 'docker run' instead of just setting 'container:'\n # because actions/checkout requires GLIBC 2.28 and that is too\n # new for manylinux2014\n env:\n BUILD_DIRECTORY: /LightGBM\n run: |\n cat > ./docker-script.sh <> tests.log 2>&1 || exit_code=-1\n cat ./tests.log\n exit ${exit_code}\n test-r-extra-checks:\n name: r-package (${{ matrix.image }}, R-devel)\n timeout-minutes: 60\n strategy:\n fail-fast: false\n matrix:\n # references:\n # * CRAN \"additional checks\": https://cran.r-project.org/web/checks/check_issue_kinds.html\n # * images: https://r-hub.github.io/containers/containers.html\n image:\n - clang16\n - clang17\n - clang18\n - clang19\n - clang20\n - gcc14\n - intel\n - rchk\n runs-on: ubuntu-latest\n container: ghcr.io/r-hub/containers/${{ matrix.image }}:latest\n steps:\n - name: Checkout repository\n uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2\n with:\n fetch-depth: 5\n persist-credentials: false\n submodules: true\n - name: Install pandoc\n uses: r-lib/actions/setup-pandoc@6f6e5bc62fba3a704f74e7ad7ef7676c5c6a2590 # v2.11.4\n - name: Install LaTeX\n shell: bash\n run: |\n if type -f apt 2>&1 > /dev/null; then\n apt-get update\n apt-get install --no-install-recommends -y \\\n devscripts \\\n texinfo \\\n texlive-latex-extra \\\n texlive-latex-recommended \\\n texlive-fonts-recommended \\\n texlive-fonts-extra \\\n tidy \\\n qpdf\n else\n yum update -y\n yum install -y \\\n devscripts \\\n qpdf \\\n texinfo \\\n texinfo-tex \\\n texlive-latex \\\n tidy\n fi\n - name: Install packages and run tests\n shell: bash\n run: |\n Rscript ./.ci/install-r-deps.R --build --test --exclude=testthat\n sh build-cran-package.sh\n\n # 'rchk' isn't run through 'R CMD check', use the approach documented at\n # https://r-hub.github.io/containers/local.html\n if [[ \"${{ matrix.image }}\" =~ \"rchk\" ]]; then\n r-check \"$(pwd)\" \\\n | tee ./rchk-logs.txt 2>&1\n\n # the '-v' exceptions below are from R/rchk itself and not LightGBM:\n # https://github.com/kalibera/rchk/issues/22#issuecomment-656036156\n if grep -E '\\[PB\\]|ERROR' ./rchk-logs.txt \\\n | grep -v 'too many states' \\\n > /dev/null; \\\n then\n echo \"rchk found issues\"\n exit 1\n else\n echo \"rchk did not find any issues\"\n exit 0\n fi\n fi\n\n # 'testthat' is not needed by 'rchk', so avoid installing it until here\n Rscript -e \"install.packages('testthat', repos = 'https://cran.rstudio.com', Ncpus = parallel::detectCores())\"\n\n if [[ \"${{ matrix.image }}\" =~ \"clang\" ]]; then\n # allowing the following NOTEs (produced by default in the clang images):\n #\n # * checking compilation flags used ... NOTE\n # Compilation used the following non-portable flag(s):\n # ‘-Wp,-D_FORTIFY_SOURCE=3’\n #\n # even though CRAN itself sets that:\n # https://www.stats.ox.ac.uk/pub/bdr/Rconfig/r-devel-linux-x86_64-fedora-clang\n #\n declare -i allowed_notes=1\n else\n declare -i allowed_notes=0\n fi\n\n bash .ci/run-r-cmd-check.sh \\\n \"$(echo lightgbm_$(head -1 VERSION.txt).tar.gz)\" \\\n \"${allowed_notes}\"\n all-r-package-jobs-successful:\n if: always()\n runs-on: ubuntu-latest\n needs: [test, test-r-sanitizers, test-r-extra-checks]\n steps:\n - name: Note that all tests succeeded\n uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2\n with:\n jobs: ${{ toJSON(needs) }}\n" }, { "path": ".github/workflows/r_valgrind.yml", "content": "name: R valgrind tests\n\n# 'run-name' is used here to distinguish in the API between different runs from forks.\n#\n# When this was added, it was the only way to feed workflow inputs into something that\n# would show up in the output of 'gh run list'.\n#\n# See https://github.com/orgs/community/discussions/73223#discussioncomment-11862624\nrun-name: R valgrind tests (pr=${{ inputs.pr-number }})\n\non:\n workflow_dispatch:\n inputs:\n pr-branch:\n type: string\n description: |\n Branch the PR was submitted from.\n Branches from forks should be prefixed with the user/org they originate from,\n like '{user}:{branch}'.\n pr-number:\n type: string\n description: Pull request ID, found in the PR URL.\n\npermissions:\n actions: write\n checks: write\n contents: read\n deployments: none\n discussions: none\n id-token: write\n issues: none\n packages: none\n pages: none\n pull-requests: write\n repository-projects: none\n security-events: none\n statuses: write\n\njobs:\n test-r-valgrind:\n name: r-package (ubuntu-latest, R-devel, valgrind)\n timeout-minutes: 360\n runs-on: ubuntu-latest\n container: wch1/r-debug\n env:\n GITHUB_TOKEN: ${{ github.token }}\n steps:\n - name: Install essential software before checkout\n shell: bash\n run: |\n apt-get update\n apt-get install --no-install-recommends -y \\\n curl \\\n jq\n - name: Install GitHub CLI\n run: |\n GH_CLI_VERSION=\"2.83.0\"\n curl \\\n --fail \\\n -O \\\n -L \\\n https://github.com/cli/cli/releases/download/v${GH_CLI_VERSION}/gh_${GH_CLI_VERSION}_linux_amd64.tar.gz\n tar -xvf ./gh_${GH_CLI_VERSION}_linux_amd64.tar.gz\n mv ./gh_${GH_CLI_VERSION}_linux_amd64/bin/gh /usr/local/bin/\n - name: Trust git cloning LightGBM\n run: |\n git config --global --add safe.directory \"${GITHUB_WORKSPACE}\"\n - name: Checkout repository\n uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2\n with:\n fetch-depth: 5\n submodules: true\n persist-credentials: false\n repository: lightgbm-org/LightGBM\n ref: \"refs/pull/${{ inputs.pr-number }}/merge\"\n - name: Run tests with valgrind\n shell: bash\n run: ./.ci/test-r-package-valgrind.sh\n - name: Send final status\n if: ${{ always() }}\n run: |\n $GITHUB_WORKSPACE/.ci/set-commit-status.sh \\\n \"${{ github.workflow }}\" \\\n \"${{ job.status }}\" \\\n \"${{ github.sha }}\"\n comment=\"Workflow **${{ github.workflow }}** has been triggered! 🚀\\r\\n\"\n comment=\"${comment}\\r\\n${GITHUB_SERVER_URL}/lightgbm-org/LightGBM/actions/runs/${GITHUB_RUN_ID} \\r\\n\"\n comment=\"${comment}\\r\\nStatus: ${{ job.status }}\"\n $GITHUB_WORKSPACE/.ci/append-comment.sh \\\n \"${{ inputs.pr-number }}\" \\\n \"${comment}\"\n - name: Rerun workflow-indicator\n if: ${{ always() }}\n run: |\n bash $GITHUB_WORKSPACE/.ci/rerun-workflow.sh \\\n \"optional_checks.yml\" \\\n \"${{ inputs.pr-branch }}\" \\\n || true\n" }, { "path": ".github/workflows/release_drafter.yml", "content": "name: Release Drafter\n\npermissions:\n contents: read\n\non:\n push:\n branches:\n - master\n\njobs:\n updateReleaseDraft:\n permissions:\n contents: write\n pull-requests: read\n runs-on: ubuntu-latest\n steps:\n - uses: release-drafter/release-drafter@6a93d829887aa2e0748befe2e808c66c0ec6e4c7 # v6.4.0\n with:\n config-name: release-drafter.yml\n disable-autolabeler: true\n env:\n GITHUB_TOKEN: ${{ github.token }}\n" }, { "path": ".github/workflows/static_analysis.yml", "content": "# contains non-functional tests, like checks on docs\n# and code style\nname: Static Analysis\n\non:\n push:\n branches:\n - master\n pull_request:\n branches:\n - master\n\n# automatically cancel in-progress builds if another commit is pushed\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\nenv:\n COMPILER: 'gcc'\n MAKEFLAGS: '-j4'\n OS_NAME: 'linux'\n\njobs:\n lint:\n name: lint\n runs-on: ubuntu-latest\n timeout-minutes: 60\n steps:\n - name: Checkout repository\n uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2\n with:\n fetch-depth: 5\n persist-credentials: false\n submodules: false\n - name: Setup and run tests\n shell: bash\n run: |\n export TASK=lint\n export CONDA=${HOME}/miniforge\n export PATH=${CONDA}/bin:$HOME/.local/bin:${PATH}\n $GITHUB_WORKSPACE/.ci/setup.sh || exit 1\n $GITHUB_WORKSPACE/.ci/lint-all.sh || exit 1\n check-docs:\n name: check-docs\n runs-on: ubuntu-latest\n timeout-minutes: 60\n steps:\n - name: Checkout repository\n uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2\n with:\n fetch-depth: 5\n persist-credentials: false\n submodules: false\n - name: Setup and run tests\n shell: bash\n run: |\n export TASK=check-docs\n export CONDA=${HOME}/miniforge\n export PATH=${CONDA}/bin:$HOME/.local/bin:${PATH}\n $GITHUB_WORKSPACE/.ci/setup.sh || exit 1\n $GITHUB_WORKSPACE/.ci/build-docs.sh || exit 1\n r-check-docs:\n name: r-package-check-docs\n timeout-minutes: 60\n runs-on: ubuntu-latest\n container: rocker/verse\n steps:\n - name: Trust git cloning LightGBM\n run: |\n git config --global --add safe.directory \"${GITHUB_WORKSPACE}\"\n - name: Checkout repository\n uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2\n with:\n fetch-depth: 5\n persist-credentials: false\n submodules: true\n - name: Install packages\n shell: bash\n run: |\n Rscript ./.ci/install-r-deps.R --build --include=roxygen2\n sh build-cran-package.sh || exit 1\n R CMD INSTALL --with-keep.source lightgbm_*.tar.gz || exit 1\n - name: Test documentation\n shell: bash --noprofile --norc {0}\n run: |\n Rscript --vanilla -e \"roxygen2::roxygenize('R-package/', load = 'installed')\" || exit 1\n num_doc_files_changed=$(\n git diff --name-only | grep --count -E \"\\.Rd|NAMESPACE\"\n )\n if [[ ${num_doc_files_changed} -gt 0 ]]; then\n echo \"Some R documentation files have changed. Please re-generate them and commit those changes.\"\n echo \"\"\n echo \" sh build-cran-package.sh\"\n echo \" R CMD INSTALL --with-keep.source lightgbm_*.tar.gz\"\n echo \" Rscript -e \\\"roxygen2::roxygenize('R-package/', load = 'installed')\\\"\"\n echo \"\"\n exit 1\n fi\n all-static-analysis-jobs-successful:\n if: always()\n runs-on: ubuntu-latest\n needs: [lint, check-docs, r-check-docs]\n steps:\n - name: Note that all tests succeeded\n uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2\n with:\n jobs: ${{ toJSON(needs) }}\n" }, { "path": ".github/workflows/swig.yml", "content": "name: SWIG\n\non:\n push:\n branches:\n - master\n pull_request:\n branches:\n - master\n\n# automatically cancel in-progress builds if another commit is pushed\nconcurrency:\n group: ${{ github.workflow }}-${{ github.ref }}\n cancel-in-progress: true\n\nenv:\n # tell scripts where to put artifacts\n # (this variable name is left over from when jobs ran on Azure DevOps)\n BUILD_ARTIFACTSTAGINGDIRECTORY: '${{ github.workspace }}/artifacts'\n # in CMake-driven builds, parallelize compilation\n CMAKE_BUILD_PARALLEL_LEVEL: 4\n # all SWIG jobs produce artifacts\n PRODUCES_ARTIFACTS: 'true'\n # all jobs here have the same 'TASK'\n TASK: swig\n\njobs:\n test-swig:\n name: swig (${{ matrix.os-display-name || matrix.os }}, ${{ matrix.compiler }})\n runs-on: ${{ matrix.os }}\n container: ${{ matrix.container }}\n timeout-minutes: 60\n strategy:\n fail-fast: false\n matrix:\n include:\n - os: ubuntu-latest\n compiler: gcc\n container: 'lightgbm.azurecr.io/vsts-agent:manylinux_2_28_x86_64'\n artifact-name: swig-linux-x86_64-jar\n os-display-name: 'manylinux_2_28'\n - os: macos-15-intel\n compiler: clang\n container: null\n artifact-name: swig-macos-x86_64-jar\n # Visual Studio 2022\n - os: windows-2022\n compiler: msvc\n container: null\n artifact-name: swig-windows-x86_64-jar\n steps:\n - name: Checkout repository\n uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2\n with:\n fetch-depth: 5\n persist-credentials: false\n submodules: true\n - name: Setup and run tests on Linux and macOS\n if: startsWith(matrix.os, 'macos') || startsWith(matrix.os, 'ubuntu')\n shell: bash\n run: |\n export BUILD_DIRECTORY=\"${GITHUB_WORKSPACE}\"\n export COMPILER=\"${{ matrix.compiler }}\"\n export CONDA=\"${HOME}/miniforge\"\n export PATH=${CONDA}/bin:${PATH}\n if [[ \"${{ matrix.os }}\" =~ ^macos ]]; then\n export OS_NAME=\"macos\"\n elif [[ \"${{ matrix.os }}\" == \"ubuntu-latest\" ]]; then\n export OS_NAME=\"linux\"\n fi\n $GITHUB_WORKSPACE/.ci/setup.sh\n $GITHUB_WORKSPACE/.ci/test.sh\n - name: Setup and run tests on Windows\n if: startsWith(matrix.os, 'windows')\n shell: pwsh -command \". {0}\"\n run: |\n $env:BUILD_DIRECTORY = $env:GITHUB_WORKSPACE\n $env:BUILD_SOURCESDIRECTORY = $env:BUILD_DIRECTORY\n & \"$env:GITHUB_WORKSPACE/.ci/test-windows.ps1\"\n - name: Upload artifacts\n uses: actions/upload-artifact@bbbca2ddaa5d8feaa63e36b76fdaad77386f024f # v7.0.0\n with:\n name: ${{ matrix.artifact-name }}\n path: ${{ env.BUILD_ARTIFACTSTAGINGDIRECTORY }}/*.jar\n if-no-files-found: error\n all-swig-jobs-successful:\n if: always()\n runs-on: ubuntu-latest\n needs:\n - test-swig\n steps:\n - name: Note that all tests succeeded\n uses: re-actors/alls-green@05ac9388f0aebcb5727afa17fcccfecd6f8ec5fe # v1.2.2\n with:\n jobs: ${{ toJSON(needs) }}\n" }, { "path": ".gitignore", "content": "## Ignore Visual Studio temporary files, build results, and\n## files generated by popular Visual Studio add-ons.\n\n# User-specific files\n*.suo\n*.user\n*.userosscache\n*.sln.docstates\n\n# User-specific files (MonoDevelop/Xamarin Studio)\n*.userprefs\n\n# Build results\n[Dd]ebug/\n[Dd]ebugPublic/\n[Rr]elease/\n[Rr]eleases/\nx64/\nx86/\nbld/\n[Bb]in/\n[Oo]bj/\n[Ll]og/\n[Bb]uild/\n\n# Visual Studio 2015 cache/options directory\n.vs/\n# Uncomment if you have tasks that create the project's static files in wwwroot\n#wwwroot/\n\n# MSTest test Results\n[Tt]est[Rr]esult*/\n[Bb]uild[Ll]og.*\n\n# NUNIT\n*.VisualState.xml\nTestResult.xml\n\n# Build Results of an ATL Project\n[Dd]ebugPS/\n[Rr]eleasePS/\ndlldata.c\n\n# DNX\nproject.lock.json\nartifacts/\n\n*_i.c\n*_p.c\n*_i.h\n*.ilk\n*.meta\n*.obj\n*.pch\n*.pdb\n*.pgc\n*.pgd\n*.rsp\n*.sbr\n*.tlb\n*.tli\n*.tlh\n*.tmp\n*.tmp_proj\n*.log\n*.vspscc\n*.vssscc\n.builds\n*.pidb\n*.svclog\n*.scc\n\n# Chutzpah Test files\n_Chutzpah*\n\n# Visual C++ cache files\nipch/\n*.aps\n*.ncb\n*.opendb\n*.opensdf\n*.sdf\n*.cachefile\n\n# Visual Studio profiler\n*.psess\n*.vsp\n*.vspx\n*.sap\n\n# TFS 2012 Local Workspace\n$tf/\n\n# Guidance Automation Toolkit\n*.gpState\n\n# ReSharper is a .NET coding add-in\n_ReSharper*/\n*.[Rr]e[Ss]harper\n*.DotSettings.user\n\n# JustCode is a .NET coding add-in\n.JustCode\n\n# TeamCity is a build add-in\n_TeamCity*\n\n# DotCover is a Code Coverage Tool\n*.dotCover\n\n# NCrunch\n_NCrunch_*\n.*crunch*.local.xml\nnCrunchTemp_*\n\n# MightyMoose\n*.mm.*\nAutoTest.Net/\n\n# Web workbench (sass)\n.sass-cache/\n\n# Installshield output folder\n[Ee]xpress/\n\n# DocProject is a documentation generator add-in\nDocProject/buildhelp/\nDocProject/Help/*.HxT\nDocProject/Help/*.HxC\nDocProject/Help/*.hhc\nDocProject/Help/*.hhk\nDocProject/Help/*.hhp\nDocProject/Help/Html2\nDocProject/Help/html\n\n# Click-Once directory\npublish/\n\n# Publish Web Output\n*.[Pp]ublish.xml\n*.azurePubxml\n*.pubxml\n*.publishproj\n\n# NuGet Packages\n*.nupkg\nnuget/\n# The packages folder can be ignored because of Package Restore\n**/packages/*\n# except build/, which is used as an MSBuild target.\n!**/packages/build/\n# Uncomment if necessary however generally it will be regenerated when needed\n#!**/packages/repositories.config\n# NuGet v3's project.json files produces more ignoreable files\n*.nuget.props\n*.nuget.targets\n\n# Microsoft Azure Build Output\ncsx/\n*.build.csdef\n\n# Microsoft Azure Emulator\necf/\nrcf/\n\n# Microsoft Azure ApplicationInsights config file\nApplicationInsights.config\n\n# Windows Store app package directory\nAppPackages/\nBundleArtifacts/\n\n# Visual Studio cache files\n# files ending in .cache can be ignored\n*.[Cc]ache\n# but keep track of directories ending in .cache\n!*.[Cc]ache/\n\n# Others\nClientBin/\n~$*\n*~\n.*.swp\n*.dbmdl\n*.dbproj.schemaview\n*.pfx\n*.publishsettings\nnode_modules/\norleans.codegen.cs\n\n# RIA/Silverlight projects\nGenerated_Code/\n\n# Backup & report files from converting an old project file\n# to a newer Visual Studio version. Backup files are not needed,\n# because we have git ;-)\n_UpgradeReport_Files/\nBackup*/\nUpgradeLog*.XML\nUpgradeLog*.htm\n\n# SQL Server files\n*.mdf\n*.ldf\n\n# Business Intelligence projects\n*.rdl.data\n*.bim.layout\n*.bim_*.settings\n\n# Microsoft Fakes\nFakesAssemblies/\n\n# GhostDoc plugin setting file\n*.GhostDoc.xml\n\n# Node.js Tools for Visual Studio\n.ntvs_analysis.dat\n\n# Visual Studio 6 build log\n*.plg\n\n# Visual Studio 6 workspace options file\n*.opt\n\n# Visual Studio LightSwitch build output\n**/*.HTMLClient/GeneratedArtifacts\n**/*.DesktopClient/GeneratedArtifacts\n**/*.DesktopClient/ModelManifest.xml\n**/*.Server/GeneratedArtifacts\n**/*.Server/ModelManifest.xml\n_Pvt_Extensions\n\n# Paket dependency manager\n.paket/paket.exe\n\n# FAKE - F# Make\n.fake/\n\n\n# Compiled Object files\n*.slo\n*.lo\n*.o\n*.obj\n\n# Precompiled Headers\n*.gch\n*.pch\n\n# Compiled Dynamic libraries\n*.so\n*.dylib\n*.dll\n\n# Fortran module files\n*.mod\n\n# Compiled Static libraries\n*.lai\n*.la\n*.a\n*.lib\n\n# Executables\n*.exe\n*.out\n*.app\n/windows/LightGBM.VC.db\n/lightgbm\n/testlightgbm\n\n# Created by https://www.gitignore.io/api/python\n\n### Python ###\n!/python-package/lightgbm/\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\nprof/\n*.prof\ncoverage.xml\n*.cover\n*.py.cover\n.hypothesis/\n.pytest_cache/\ncover/\n**/coverage.html\n**/coverage.html.zip\n**/Rplots.pdf\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\ndocs/pythonapi/\n*.flag\n\n# Doxygen documentation\ndocs/doxyoutput/\n\n# PyBuilder\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\nUntitled*.ipynb\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# dotenv\n.env\n\n# virtualenv\n.venv/\nvenv/\nENV/\n\n# Spyder project settings\n.spyderproject\n\n# Rope project settings\n.ropeproject\n\n# R testing artefact\nlightgbm.model\n\n# saved or dumped model/data\n*.model\n*.pkl\n*.bin\n*.h5\n\n# macOS\n**/.DS_Store\n\n# VSCode\n.vscode\n\n# IntelliJ/CLion\n.idea\n*.iml\n/cmake-build-debug/\n\n# Files from local Python install\nlightgbm-python/\npython-package/LICENSE\npython-package/build_cpp/\npython-package/compile/\npython-package/lightgbm/VERSION.txt\n\n# R build artefacts\n**/autom4te.cache/\nR-package/conftest*\nR-package/config.status\n!R-package/data/agaricus.test.rda\n!R-package/data/agaricus.train.rda\n!R-package/data/bank.rda\nR-package/docs\nR-package/src/CMakeLists.txt\nR-package/src/Makevars\nR-package/src/lib_lightgbm.so.dSYM/\nR-package/src/src/\nR-package/src-x64\nR-package/src-i386\nR-package/**/VERSION.txt\n**/Makevars.win\nlightgbm_r/*\nlightgbm*.tar.gz\nlightgbm*.tgz\nlightgbm.Rcheck/\nmiktex*.zip\n*.def\n\n# Files created by examples and tests\n*.buffer\n**/lgb-Dataset.data\n**/lgb.Dataset.data\n**/model.txt\n**/lgb-model.txt\nexamples/**/*.txt\ntests/distributed/mlist.txt\ntests/distributed/train*\ntests/distributed/model*\ntests/distributed/predict*\n\n\n# Files from interactive R sessions\n.Rproj.user\n**/.Rapp.history\n**/.Rhistory\n*.rda\n*.RData\n*.rds\n\n# Files generated by aspell\n**/*.bak\n\n# GraphViz artifacts\n*.gv\n*.gv.*\n\n# Files from local Dask work\ndask-worker-space/\n\n# credentials and key material\n*.env\n*.pem\n*.pub\n*.rdp\n*_rsa\n\n# hipify-perl -inplace leaves behind *.prehip files\n*.prehip\n\n# pixi environments\n.pixi\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# files created by release tasks\n/release-artifacts\n" }, { "path": ".gitmodules", "content": "[submodule \"include/boost/compute\"]\n\tpath = external_libs/compute\n\turl = https://github.com/boostorg/compute\n[submodule \"eigen\"]\n\tpath = external_libs/eigen\n\turl = https://gitlab.com/libeigen/eigen.git\n[submodule \"external_libs/fmt\"]\n\tpath = external_libs/fmt\n\turl = https://github.com/fmtlib/fmt.git\n[submodule \"external_libs/fast_double_parser\"]\n\tpath = external_libs/fast_double_parser\n\turl = https://github.com/lemire/fast_double_parser.git\n" }, { "path": ".pre-commit-config.yaml", "content": "# exclude files which are auto-generated by build tools\nexclude: |\n (?x)^(\n build|\n external_libs|\n lightgbm-python|\n lightgbm_r|\n )$\n |R-package/configure$\n |R-package/inst/Makevars$\n |R-package/inst/Makevars.win$\n |R-package/man/.*Rd$\n\nrepos:\n - repo: https://github.com/pre-commit/pre-commit-hooks\n rev: v6.0.0\n hooks:\n - id: check-toml\n - id: check-xml\n - id: end-of-file-fixer\n - id: trailing-whitespace\n - repo: https://github.com/cmake-lint/cmake-lint\n rev: '1.4.3'\n hooks:\n - id: cmakelint\n args: [\"--linelength=120\"]\n - repo: https://github.com/cpplint/cpplint\n rev: '2.0.2'\n hooks:\n - id: cpplint\n args:\n - --root=.. # workaround to get correct header guard pattern\n - --recursive\n - --filter=-build/include_subdir,-whitespace/line_length\n - repo: local\n hooks:\n - id: check-omp-pragmas\n name: check-omp-pragmas\n entry: sh\n args:\n - .ci/check-omp-pragmas.sh\n language: system\n pass_filenames: false\n - repo: https://github.com/adrienverge/yamllint\n rev: v1.37.1\n hooks:\n - id: yamllint\n args: [\"--strict\"]\n - repo: local\n hooks:\n - id: regenerate-parameters\n name: regenerate-parameters\n entry: python\n args:\n - ./.ci/parameter-generator.py\n language: python\n pass_filenames: false\n - repo: https://github.com/rstcheck/rstcheck\n rev: v6.2.5\n hooks:\n - id: rstcheck\n args: [\"--config\", \"./python-package/pyproject.toml\"]\n additional_dependencies:\n - breathe>=4.36.0\n - sphinx>=8.1.3\n - sphinx_rtd_theme>=3.0.1\n - repo: https://github.com/astral-sh/ruff-pre-commit\n rev: v0.14.10\n hooks:\n - id: ruff-check\n args: [\"--config\", \"python-package/pyproject.toml\"]\n types_or: [python, jupyter]\n - id: ruff-format\n args: [\"--config\", \"python-package/pyproject.toml\"]\n types_or: [python, jupyter]\n - repo: https://github.com/biomejs/pre-commit\n rev: v2.3.10\n hooks:\n - id: biome-ci\n args:\n - --config-path=./biome.json\n - --diagnostic-level=info\n - --error-on-warnings\n - repo: https://github.com/shellcheck-py/shellcheck-py\n rev: v0.11.0.1\n hooks:\n - id: shellcheck\n - repo: https://github.com/crate-ci/typos\n rev: v1.40.0\n hooks:\n - id: typos\n args: [\"--force-exclude\"]\n exclude: (\\.gitignore$)|(^\\.editorconfig$)\n - repo: https://github.com/henryiii/validate-pyproject-schema-store\n rev: 2025.11.21\n hooks:\n - id: validate-pyproject\n files: python-package/pyproject.toml$\n - repo: https://github.com/pre-commit/mirrors-mypy\n rev: v1.19.1\n hooks:\n - id: mypy\n args: [\"--config-file\", \"python-package/pyproject.toml\", \"python-package/\"]\n pass_filenames: false\n verbose: true\n additional_dependencies:\n - matplotlib>=3.9.1\n - pandas>=2.0\n - pyarrow>=17.0\n - scikit-learn>=1.5.2\n" }, { "path": ".readthedocs.yaml", "content": "version: 2\nbuild:\n os: \"ubuntu-24.04\"\n tools:\n python: \"mambaforge-23.11\"\nconda:\n environment: docs/env.yml\nformats:\n - pdf\nsphinx:\n builder: html\n configuration: docs/conf.py\n fail_on_warning: true\nsubmodules:\n include: all\n recursive: true\n" }, { "path": ".typos.toml", "content": "default.extend-ignore-re = [\n \"/Ot\",\n \"mis-alignment\",\n \"mis-spelled\",\n \"posix-seh-rt\",\n]\n\n[default.extend-words]\nMAPE = \"MAPE\"\ndatas = \"datas\"\nindx = \"indx\"\ninterprete = \"interprete\"\nmape = \"mape\"\nsplitted = \"splitted\"\n\n[default.extend-identifiers]\nERRORs = \"ERRORs\"\nGAM = \"GAM\"\nND24s = \"ND24s\"\nWARNINGs = \"WARNINGs\"\nfullset = \"fullset\"\nthess = \"thess\"\n" }, { "path": ".yamllint.yml", "content": "# default config: https://yamllint.readthedocs.io/en/stable/configuration.html#default-configuration\nextends: default\n\nrules:\n document-start: disable\n line-length:\n max: 120\n truthy:\n # prevent treating GitHub Workflow \"on\" key as boolean value\n check-keys: false\n" }, { "path": "CMakeLists.txt", "content": "option(USE_MPI \"Enable MPI-based distributed learning\" OFF)\noption(USE_OPENMP \"Enable OpenMP\" ON)\noption(USE_GPU \"Enable GPU-accelerated training\" OFF)\noption(USE_SWIG \"Enable SWIG to generate Java API\" OFF)\noption(USE_TIMETAG \"Set to ON to output time costs\" OFF)\noption(USE_CUDA \"Enable CUDA-accelerated training \" OFF)\noption(USE_ROCM \"Enable ROCm-accelerated training \" OFF)\noption(USE_DEBUG \"Set to ON for Debug mode\" OFF)\noption(USE_SANITIZER \"Use sanitizer flags\" OFF)\nset(\n ENABLED_SANITIZERS\n \"address\" \"leak\" \"undefined\"\n CACHE\n STRING\n \"Semicolon separated list of sanitizer names, e.g., 'address;leak'. \\\nSupported sanitizers are address, leak, undefined and thread.\"\n)\noption(USE_HOMEBREW_FALLBACK \"(macOS-only) also look in 'brew --prefix' for libraries (e.g. OpenMP)\" ON)\noption(BUILD_CLI \"Build the 'lightgbm' command-line interface in addition to lib_lightgbm\" ON)\noption(BUILD_CPP_TEST \"Build C++ tests with Google Test\" OFF)\noption(BUILD_STATIC_LIB \"Build static library\" OFF)\noption(INSTALL_HEADERS \"Install headers to CMAKE_INSTALL_PREFIX (e.g. '/usr/local/include')\" ON)\noption(__BUILD_FOR_PYTHON \"Set to ON if building lib_lightgbm for use with the Python-package\" OFF)\noption(__BUILD_FOR_R \"Set to ON if building lib_lightgbm for use with the R-package\" OFF)\noption(__INTEGRATE_OPENCL \"Set to ON if building LightGBM with the OpenCL ICD Loader and its dependencies included\" OFF)\n\ncmake_minimum_required(VERSION 3.28)\n\n# If using Visual Studio generators, always target v10.x of the Windows SDK.\n# Doing this avoids lookups that could fall back to very old versions, e.g. by finding\n# outdated registry entries.\n# ref: https://cmake.org/cmake/help/latest/variable/CMAKE_VS_WINDOWS_TARGET_PLATFORM_VERSION.html\nif(CMAKE_GENERATOR MATCHES \"Visual Studio\")\n set(CMAKE_SYSTEM_VERSION 10.0 CACHE INTERNAL \"target Windows SDK version\" FORCE)\nendif()\n\nproject(lightgbm LANGUAGES C CXX)\n\nset(CMAKE_CXX_STANDARD 17)\nset(CMAKE_CXX_STANDARD_REQUIRED ON)\n\nlist(APPEND CMAKE_MODULE_PATH \"${PROJECT_SOURCE_DIR}/cmake/modules\")\n\n#-- Sanitizer\nif(USE_SANITIZER)\n if(MSVC)\n message(FATAL_ERROR \"Sanitizers are not supported with MSVC.\")\n endif()\n include(cmake/Sanitizer.cmake)\n enable_sanitizers(\"${ENABLED_SANITIZERS}\")\nendif()\n\nif(__INTEGRATE_OPENCL)\n set(__INTEGRATE_OPENCL ON CACHE BOOL \"\" FORCE)\n set(USE_GPU OFF CACHE BOOL \"\" FORCE)\n message(STATUS \"Building library with integrated OpenCL components\")\nendif()\n\nif(__BUILD_FOR_PYTHON OR __BUILD_FOR_R OR USE_SWIG)\n # the SWIG wrapper, the Python and R packages don't require the CLI\n set(BUILD_CLI OFF)\n # installing the SWIG wrapper, the R and Python packages shouldn't place LightGBM's headers\n # outside of where the package is installed\n set(INSTALL_HEADERS OFF)\nendif()\n\nif(CMAKE_CXX_COMPILER_ID STREQUAL \"GNU\")\n if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS \"4.8.2\")\n message(FATAL_ERROR \"Insufficient gcc version (${CMAKE_CXX_COMPILER_VERSION})\")\n endif()\nelseif(CMAKE_CXX_COMPILER_ID STREQUAL \"Clang\")\n if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS \"3.8\")\n message(FATAL_ERROR \"Insufficient Clang version (${CMAKE_CXX_COMPILER_VERSION})\")\n endif()\nelseif(CMAKE_CXX_COMPILER_ID STREQUAL \"AppleClang\")\n if(CMAKE_CXX_COMPILER_VERSION VERSION_LESS \"8.1.0\")\n message(FATAL_ERROR \"Insufficient AppleClang version (${CMAKE_CXX_COMPILER_VERSION})\")\n endif()\nelseif(MSVC)\n if(MSVC_VERSION LESS 1900)\n message(FATAL_ERROR \"Insufficient MSVC version (${MSVC_VERSION})\")\n endif()\nendif()\n\nif(USE_SWIG)\n find_package(SWIG REQUIRED)\n find_package(Java REQUIRED)\n find_package(JNI REQUIRED)\n include(UseJava)\n include(UseSWIG)\n set(SWIG_CXX_EXTENSION \"cxx\")\n set(SWIG_EXTRA_LIBRARIES \"\")\n set(SWIG_JAVA_EXTRA_FILE_EXTENSIONS \".java\" \"JNI.java\")\n set(SWIG_MODULE_JAVA_LANGUAGE \"JAVA\")\n set(SWIG_MODULE_JAVA_SWIG_LANGUAGE_FLAG \"java\")\n set(CMAKE_SWIG_OUTDIR \"${CMAKE_CURRENT_BINARY_DIR}/java\")\n include_directories(Java_INCLUDE_DIRS)\n include_directories(JNI_INCLUDE_DIRS)\n include_directories($ENV{JAVA_HOME}/include)\n if(WIN32)\n set(LGBM_SWIG_DESTINATION_DIR \"${CMAKE_CURRENT_BINARY_DIR}/com/microsoft/ml/lightgbm/windows/x86_64\")\n include_directories($ENV{JAVA_HOME}/include/win32)\n elseif(APPLE)\n set(LGBM_SWIG_DESTINATION_DIR \"${CMAKE_CURRENT_BINARY_DIR}/com/microsoft/ml/lightgbm/osx/x86_64\")\n include_directories($ENV{JAVA_HOME}/include/darwin)\n else()\n set(LGBM_SWIG_DESTINATION_DIR \"${CMAKE_CURRENT_BINARY_DIR}/com/microsoft/ml/lightgbm/linux/x86_64\")\n include_directories($ENV{JAVA_HOME}/include/linux)\n endif()\n file(MAKE_DIRECTORY \"${LGBM_SWIG_DESTINATION_DIR}\")\nendif()\n\nset(EIGEN_DIR \"${PROJECT_SOURCE_DIR}/external_libs/eigen\")\ninclude_directories(${EIGEN_DIR})\n\n# See https://gitlab.com/libeigen/eigen/-/blob/master/COPYING.README\nadd_definitions(-DEIGEN_MPL2_ONLY)\nadd_definitions(-DEIGEN_DONT_PARALLELIZE)\n\nset(FAST_DOUBLE_PARSER_INCLUDE_DIR \"${PROJECT_SOURCE_DIR}/external_libs/fast_double_parser/include\")\ninclude_directories(${FAST_DOUBLE_PARSER_INCLUDE_DIR})\n\nset(FMT_INCLUDE_DIR \"${PROJECT_SOURCE_DIR}/external_libs/fmt/include\")\ninclude_directories(${FMT_INCLUDE_DIR})\n\nif(__BUILD_FOR_R)\n find_package(LibR REQUIRED)\n message(STATUS \"LIBR_EXECUTABLE: ${LIBR_EXECUTABLE}\")\n message(STATUS \"LIBR_INCLUDE_DIRS: ${LIBR_INCLUDE_DIRS}\")\n message(STATUS \"LIBR_LIBS_DIR: ${LIBR_LIBS_DIR}\")\n message(STATUS \"LIBR_CORE_LIBRARY: ${LIBR_CORE_LIBRARY}\")\n include_directories(${LIBR_INCLUDE_DIRS})\n add_definitions(-DLGB_R_BUILD)\nendif()\n\nif(USE_TIMETAG)\n add_definitions(-DTIMETAG)\nendif()\n\nif(USE_DEBUG)\n add_definitions(-DDEBUG)\nendif()\n\nif(USE_MPI)\n find_package(MPI REQUIRED)\n add_definitions(-DUSE_MPI)\nelse()\n add_definitions(-DUSE_SOCKET)\nendif()\n\nif(USE_CUDA)\n set(CMAKE_CUDA_HOST_COMPILER \"${CMAKE_CXX_COMPILER}\")\n enable_language(CUDA)\n set(USE_OPENMP ON CACHE BOOL \"CUDA requires OpenMP\" FORCE)\nendif()\n\nif(USE_ROCM)\n enable_language(HIP)\n set(USE_OPENMP ON CACHE BOOL \"ROCm requires OpenMP\" FORCE)\nendif()\n\nif(USE_OPENMP)\n if(APPLE)\n find_package(OpenMP)\n if(NOT OpenMP_FOUND)\n if(USE_HOMEBREW_FALLBACK)\n # libomp 15.0+ from brew is keg-only, so have to search in other locations.\n # See https://github.com/Homebrew/homebrew-core/issues/112107#issuecomment-1278042927.\n execute_process(COMMAND brew --prefix libomp\n OUTPUT_VARIABLE HOMEBREW_LIBOMP_PREFIX\n OUTPUT_STRIP_TRAILING_WHITESPACE)\n set(OpenMP_C_FLAGS \"-Xpreprocessor -fopenmp -I${HOMEBREW_LIBOMP_PREFIX}/include\")\n set(OpenMP_CXX_FLAGS \"-Xpreprocessor -fopenmp -I${HOMEBREW_LIBOMP_PREFIX}/include\")\n set(OpenMP_C_LIB_NAMES omp)\n set(OpenMP_CXX_LIB_NAMES omp)\n set(OpenMP_omp_LIBRARY ${HOMEBREW_LIBOMP_PREFIX}/lib/libomp.dylib)\n endif()\n find_package(OpenMP REQUIRED)\n endif()\n else()\n find_package(OpenMP REQUIRED)\n endif()\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}\")\nendif()\n\nif(USE_GPU)\n set(BOOST_COMPUTE_HEADER_DIR ${PROJECT_SOURCE_DIR}/external_libs/compute/include)\n include_directories(${BOOST_COMPUTE_HEADER_DIR})\n find_package(OpenCL REQUIRED)\n include_directories(${OpenCL_INCLUDE_DIRS})\n message(STATUS \"OpenCL include directory: \" ${OpenCL_INCLUDE_DIRS})\n if(WIN32)\n set(Boost_USE_STATIC_LIBS ON)\n endif()\n find_package(Boost 1.56.0 COMPONENTS filesystem system REQUIRED)\n if(WIN32)\n # disable autolinking in boost\n add_definitions(-DBOOST_ALL_NO_LIB)\n endif()\n include_directories(${Boost_INCLUDE_DIRS})\n add_definitions(-DUSE_GPU)\nendif()\n\nif(__INTEGRATE_OPENCL)\n if(APPLE)\n message(FATAL_ERROR \"Integrated OpenCL build is not available on macOS\")\n else()\n include(cmake/IntegratedOpenCL.cmake)\n add_definitions(-DUSE_GPU)\n endif()\nendif()\n\nif(BUILD_CPP_TEST AND MSVC)\n # Use /MT flag to statically link the C runtime\n set(CMAKE_MSVC_RUNTIME_LIBRARY \"MultiThreaded$<$:Debug>\")\nendif()\n\nif(USE_CUDA)\n find_package(CUDAToolkit 11.0 REQUIRED)\n find_package(NCCL REQUIRED)\n include_directories(${CUDAToolkit_INCLUDE_DIRS})\n set(CMAKE_CUDA_FLAGS \"${CMAKE_CUDA_FLAGS} -Xcompiler=${OpenMP_CXX_FLAGS} -Xcompiler=-fPIC -Xcompiler=-Wall\")\n\n # reference for mapping of CUDA toolkit component versions to supported architectures (\"compute capabilities\"):\n # https://en.wikipedia.org/wiki/CUDA#GPUs_supported\n set(CUDA_ARCHS \"60\" \"61\" \"62\" \"70\" \"75\")\n if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL \"11.0\")\n list(APPEND CUDA_ARCHS \"80\")\n endif()\n if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL \"11.1\")\n list(APPEND CUDA_ARCHS \"86\")\n endif()\n if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL \"11.5\")\n list(APPEND CUDA_ARCHS \"87\")\n endif()\n if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL \"11.8\")\n list(APPEND CUDA_ARCHS \"89\")\n list(APPEND CUDA_ARCHS \"90\")\n endif()\n if(CUDAToolkit_VERSION VERSION_GREATER_EQUAL \"12.8\")\n list(APPEND CUDA_ARCHS \"100\")\n list(APPEND CUDA_ARCHS \"120\")\n endif()\n # Generate PTX for the most recent architecture for forwards compatibility\n list(POP_BACK CUDA_ARCHS CUDA_LAST_SUPPORTED_ARCH)\n list(TRANSFORM CUDA_ARCHS APPEND \"-real\")\n list(APPEND CUDA_ARCHS \"${CUDA_LAST_SUPPORTED_ARCH}-real\" \"${CUDA_LAST_SUPPORTED_ARCH}-virtual\")\n message(STATUS \"CUDA_ARCHITECTURES: ${CUDA_ARCHS}\")\n if(USE_DEBUG)\n set(CMAKE_CUDA_FLAGS \"${CMAKE_CUDA_FLAGS} -g\")\n else()\n set(CMAKE_CUDA_FLAGS \"${CMAKE_CUDA_FLAGS} -O3 -lineinfo\")\n endif()\n message(STATUS \"CMAKE_CUDA_FLAGS: ${CMAKE_CUDA_FLAGS}\")\n\n add_definitions(-DUSE_CUDA)\n\n if(NOT DEFINED CMAKE_CUDA_STANDARD)\n set(CMAKE_CUDA_STANDARD 17)\n set(CMAKE_CUDA_STANDARD_REQUIRED ON)\n endif()\nendif()\n\nif(USE_ROCM)\n find_package(HIP)\n include_directories(${HIP_INCLUDE_DIRS})\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -D__HIP_PLATFORM_AMD__\")\n set(CMAKE_HIP_FLAGS \"${CMAKE_HIP_FLAGS} ${OpenMP_CXX_FLAGS} -fPIC -Wall\")\n\n # avoid warning: unused variable 'mask' due to __shfl_down_sync work-around\n set(DISABLED_WARNINGS \"${DISABLED_WARNINGS} -Wno-unused-variable\")\n # avoid warning: 'hipHostAlloc' is deprecated: use hipHostMalloc instead\n set(DISABLED_WARNINGS \"${DISABLED_WARNINGS} -Wno-deprecated-declarations\")\n # avoid many warnings about missing overrides\n set(DISABLED_WARNINGS \"${DISABLED_WARNINGS} -Wno-inconsistent-missing-override\")\n # avoid warning: shift count >= width of type in feature_histogram.hpp\n set(DISABLED_WARNINGS \"${DISABLED_WARNINGS} -Wno-shift-count-overflow\")\n\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} ${DISABLED_WARNINGS}\")\n set(CMAKE_HIP_FLAGS \"${CMAKE_HIP_FLAGS} ${DISABLED_WARNINGS}\")\n\n if(USE_DEBUG)\n set(CMAKE_HIP_FLAGS \"${CMAKE_HIP_FLAGS} -g -O0\")\n else()\n set(CMAKE_HIP_FLAGS \"${CMAKE_HIP_FLAGS} -O3\")\n endif()\n message(STATUS \"CMAKE_HIP_FLAGS: ${CMAKE_HIP_FLAGS}\")\n\n # Building for ROCm almost always means USE_CUDA.\n # Exceptions to this will be guarded by USE_ROCM.\n add_definitions(-DUSE_CUDA)\n add_definitions(-DUSE_ROCM)\nendif()\n\ninclude(CheckCXXSourceCompiles)\ncheck_cxx_source_compiles(\"\n#include \nint main() {\n int a = 0;\n _mm_prefetch(&a, _MM_HINT_NTA);\n return 0;\n}\n\" MM_PREFETCH)\n\nif(${MM_PREFETCH})\n message(STATUS \"Using _mm_prefetch\")\n add_definitions(-DMM_PREFETCH)\nendif()\n\ninclude(CheckCXXSourceCompiles)\ncheck_cxx_source_compiles(\"\n#include \nint main() {\n char *a = (char*)_mm_malloc(8, 16);\n _mm_free(a);\n return 0;\n}\n\" MM_MALLOC)\n\nif(${MM_MALLOC})\n message(STATUS \"Using _mm_malloc\")\n add_definitions(-DMM_MALLOC)\nendif()\n\nif(UNIX OR MINGW OR CYGWIN)\n set(\n CMAKE_CXX_FLAGS\n \"${CMAKE_CXX_FLAGS} -pthread -Wextra -Wall -Wno-ignored-attributes -Wno-unknown-pragmas -Wno-return-type\"\n )\n if(MINGW)\n # ignore this warning: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=95353\n set(\n CMAKE_CXX_FLAGS\n \"${CMAKE_CXX_FLAGS} -Wno-stringop-overflow\"\n )\n endif()\n if(USE_DEBUG)\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -g -O0\")\n else()\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -O3\")\n endif()\n if(USE_SWIG)\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -fno-strict-aliasing\")\n endif()\n if(NOT USE_OPENMP)\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -Wno-unknown-pragmas -Wno-unused-private-field\")\n endif()\n if(__BUILD_FOR_R AND CMAKE_CXX_COMPILER_ID STREQUAL \"GNU\")\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -Wno-cast-function-type\")\n endif()\nendif()\n\nif(WIN32 AND MINGW)\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -static-libstdc++\")\nendif()\n\n# Check if inet_pton is already available, to avoid conflicts with the implementation in LightGBM.\n# As of 2022, MinGW started including a definition of inet_pton.\nif(WIN32)\n include(CheckSymbolExists)\n list(APPEND CMAKE_REQUIRED_LIBRARIES \"ws2_32\")\n check_symbol_exists(inet_pton \"ws2tcpip.h\" WIN_INET_PTON_FOUND)\n if(WIN_INET_PTON_FOUND)\n add_definitions(-DWIN_HAS_INET_PTON)\n endif()\n list(REMOVE_ITEM CMAKE_REQUIRED_LIBRARIES \"ws2_32\")\nendif()\n\nif(MSVC)\n # compiling 'fmt' on MSVC: \"Unicode support requires compiling with /utf-8\"\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} /W4 /MP /utf-8\")\n if(__BUILD_FOR_R)\n # MSVC does not like this commit:\n # https://github.com/wch/r-source/commit/fb52ac1a610571fcb8ac92d886b9fefcffaa7d48\n #\n # and raises \"error C3646: 'private_data_c': unknown override specifier\"\n add_definitions(-DR_LEGACY_RCOMPLEX)\n endif()\n if(USE_DEBUG)\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} /Od\")\n else()\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} /O2 /Ob2 /Oi /Ot /Oy\")\n endif()\nelse()\n if(NOT BUILD_STATIC_LIB)\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -fPIC\")\n endif()\n if(NOT USE_DEBUG)\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} -funroll-loops\")\n endif()\nendif()\n\nset(LightGBM_HEADER_DIR ${PROJECT_SOURCE_DIR}/include)\n\nset(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR})\nset(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR})\n\ninclude_directories(${LightGBM_HEADER_DIR})\n\nif(USE_MPI)\n include_directories(${MPI_CXX_INCLUDE_PATH})\nendif()\n\nset(\n LGBM_SOURCES\n src/boosting/boosting.cpp\n src/boosting/gbdt_model_text.cpp\n src/boosting/gbdt_prediction.cpp\n src/boosting/gbdt.cpp\n src/boosting/prediction_early_stop.cpp\n src/boosting/sample_strategy.cpp\n src/io/bin.cpp\n src/io/config_auto.cpp\n src/io/config.cpp\n src/io/dataset_loader.cpp\n src/io/dataset.cpp\n src/io/file_io.cpp\n src/io/json11.cpp\n src/io/metadata.cpp\n src/io/parser.cpp\n src/io/train_share_states.cpp\n src/io/tree.cpp\n src/metric/dcg_calculator.cpp\n src/metric/metric.cpp\n src/network/linker_topo.cpp\n src/network/linkers_mpi.cpp\n src/network/linkers_socket.cpp\n src/network/network.cpp\n src/objective/objective_function.cpp\n src/treelearner/data_parallel_tree_learner.cpp\n src/treelearner/feature_histogram.cpp\n src/treelearner/feature_parallel_tree_learner.cpp\n src/treelearner/gpu_tree_learner.cpp\n src/treelearner/gradient_discretizer.cpp\n src/treelearner/linear_tree_learner.cpp\n src/treelearner/serial_tree_learner.cpp\n src/treelearner/tree_learner.cpp\n src/treelearner/voting_parallel_tree_learner.cpp\n src/utils/openmp_wrapper.cpp\n)\nset(\n LGBM_CUDA_SOURCES\n src/boosting/cuda/cuda_score_updater.cpp\n src/boosting/cuda/cuda_score_updater.cu\n src/boosting/cuda/nccl_gbdt.cpp\n src/metric/cuda/cuda_binary_metric.cpp\n src/metric/cuda/cuda_pointwise_metric.cpp\n src/metric/cuda/cuda_regression_metric.cpp\n src/metric/cuda/cuda_pointwise_metric.cu\n src/objective/cuda/cuda_binary_objective.cpp\n src/objective/cuda/cuda_multiclass_objective.cpp\n src/objective/cuda/cuda_rank_objective.cpp\n src/objective/cuda/cuda_regression_objective.cpp\n src/objective/cuda/cuda_binary_objective.cu\n src/objective/cuda/cuda_multiclass_objective.cu\n src/objective/cuda/cuda_rank_objective.cu\n src/objective/cuda/cuda_regression_objective.cu\n src/treelearner/cuda/cuda_best_split_finder.cpp\n src/treelearner/cuda/cuda_data_partition.cpp\n src/treelearner/cuda/cuda_histogram_constructor.cpp\n src/treelearner/cuda/cuda_leaf_splits.cpp\n src/treelearner/cuda/cuda_single_gpu_tree_learner.cpp\n src/treelearner/cuda/cuda_best_split_finder.cu\n src/treelearner/cuda/cuda_data_partition.cu\n src/treelearner/cuda/cuda_gradient_discretizer.cu\n src/treelearner/cuda/cuda_histogram_constructor.cu\n src/treelearner/cuda/cuda_leaf_splits.cu\n src/treelearner/cuda/cuda_single_gpu_tree_learner.cu\n src/io/cuda/cuda_column_data.cu\n src/io/cuda/cuda_tree.cu\n src/io/cuda/cuda_column_data.cpp\n src/io/cuda/cuda_metadata.cpp\n src/io/cuda/cuda_row_data.cpp\n src/io/cuda/cuda_tree.cpp\n src/cuda/cuda_utils.cpp\n src/cuda/cuda_algorithms.cu\n)\n\nif(USE_CUDA OR USE_ROCM)\n list(APPEND LGBM_SOURCES ${LGBM_CUDA_SOURCES})\nendif()\n\nif(USE_ROCM)\n set(CU_FILES \"\")\n foreach(file IN LISTS LGBM_CUDA_SOURCES)\n string(REGEX MATCH \"\\\\.cu$\" is_cu_file \"${file}\")\n if(is_cu_file)\n list(APPEND CU_FILES \"${file}\")\n endif()\n endforeach()\n set_source_files_properties(${CU_FILES} PROPERTIES LANGUAGE HIP)\nendif()\n\nadd_library(lightgbm_objs OBJECT ${LGBM_SOURCES})\n\nif(BUILD_CLI)\n add_executable(lightgbm src/main.cpp src/application/application.cpp)\n target_link_libraries(lightgbm PRIVATE lightgbm_objs)\nendif()\n\nset(API_SOURCES \"src/c_api.cpp\")\n# Only build the R part of the library if building for\n# use with the R-package\nif(__BUILD_FOR_R)\n list(APPEND API_SOURCES \"src/lightgbm_R.cpp\")\nendif()\n\nadd_library(lightgbm_capi_objs OBJECT ${API_SOURCES})\n\nif(BUILD_STATIC_LIB)\n add_library(_lightgbm STATIC)\nelse()\n add_library(_lightgbm SHARED)\nendif()\n\n# R expects libraries of the form .{dll,dylib,so}, not lib_.{dll,dylib,so}\nif(__BUILD_FOR_R)\n set_target_properties(\n _lightgbm\n PROPERTIES\n PREFIX \"\"\n OUTPUT_NAME \"lightgbm\"\n )\nendif()\n\n# LightGBM headers include openmp, cuda, R etc. headers,\n# thus PUBLIC is required for building _lightgbm_swig target.\ntarget_link_libraries(_lightgbm PUBLIC lightgbm_capi_objs lightgbm_objs)\n\nif(MSVC AND NOT __BUILD_FOR_R)\n set_target_properties(_lightgbm PROPERTIES OUTPUT_NAME \"lib_lightgbm\")\nendif()\n\nif(USE_SWIG)\n set_property(SOURCE swig/lightgbmlib.i PROPERTY CPLUSPLUS ON)\n list(APPEND swig_options -package com.microsoft.ml.lightgbm)\n set_property(SOURCE swig/lightgbmlib.i PROPERTY SWIG_FLAGS \"${swig_options}\")\n swig_add_library(_lightgbm_swig LANGUAGE java SOURCES swig/lightgbmlib.i)\n swig_link_libraries(_lightgbm_swig _lightgbm)\n set_target_properties(\n _lightgbm_swig\n PROPERTIES\n # needed to ensure Linux build does not have lib prefix specified twice, e.g. liblib_lightgbm_swig\n PREFIX \"\"\n # needed in some versions of CMake for VS and MinGW builds to ensure output dll has lib prefix\n OUTPUT_NAME \"lib_lightgbm_swig\"\n )\n if(WIN32)\n set(LGBM_SWIG_LIB_DESTINATION_PATH \"${LGBM_SWIG_DESTINATION_DIR}/lib_lightgbm_swig.dll\")\n if(MINGW OR CYGWIN)\n set(LGBM_LIB_SOURCE_PATH \"${PROJECT_SOURCE_DIR}/lib_lightgbm.dll\")\n set(LGBM_SWIG_LIB_SOURCE_PATH \"${PROJECT_SOURCE_DIR}/lib_lightgbm_swig.dll\")\n else()\n set(LGBM_LIB_SOURCE_PATH \"${PROJECT_SOURCE_DIR}/Release/lib_lightgbm.dll\")\n set(LGBM_SWIG_LIB_SOURCE_PATH \"${PROJECT_SOURCE_DIR}/Release/lib_lightgbm_swig.dll\")\n endif()\n elseif(APPLE)\n set(LGBM_LIB_SOURCE_PATH \"${PROJECT_SOURCE_DIR}/lib_lightgbm.dylib\")\n set(LGBM_SWIG_LIB_SOURCE_PATH \"${PROJECT_SOURCE_DIR}/lib_lightgbm_swig.jnilib\")\n set(LGBM_SWIG_LIB_DESTINATION_PATH \"${LGBM_SWIG_DESTINATION_DIR}/lib_lightgbm_swig.dylib\")\n else()\n set(LGBM_LIB_SOURCE_PATH \"${PROJECT_SOURCE_DIR}/lib_lightgbm.so\")\n set(LGBM_SWIG_LIB_SOURCE_PATH \"${PROJECT_SOURCE_DIR}/lib_lightgbm_swig.so\")\n set(LGBM_SWIG_LIB_DESTINATION_PATH \"${LGBM_SWIG_DESTINATION_DIR}/lib_lightgbm_swig.so\")\n endif()\n add_custom_command(\n TARGET _lightgbm_swig\n POST_BUILD\n COMMAND \"${Java_JAVAC_EXECUTABLE}\" -d . java/*.java\n COMMAND\n \"${CMAKE_COMMAND}\"\n -E\n copy_if_different\n \"${LGBM_LIB_SOURCE_PATH}\"\n \"${LGBM_SWIG_DESTINATION_DIR}\"\n COMMAND\n \"${CMAKE_COMMAND}\"\n -E\n copy_if_different\n \"${LGBM_SWIG_LIB_SOURCE_PATH}\"\n \"${LGBM_SWIG_LIB_DESTINATION_PATH}\"\n COMMAND \"${Java_JAR_EXECUTABLE}\" -cf lightgbmlib.jar com\n )\nendif()\n\nif(USE_MPI)\n target_link_libraries(lightgbm_objs PUBLIC ${MPI_CXX_LIBRARIES})\nendif()\n\nif(USE_OPENMP)\n if(CMAKE_CXX_COMPILER_ID MATCHES \"Clang\")\n target_link_libraries(lightgbm_objs PUBLIC OpenMP::OpenMP_CXX)\n # c_api headers also includes OpenMP headers, thus compiling\n # lightgbm_capi_objs needs include directory for OpenMP.\n # Specifying OpenMP in target_link_libraries will get include directory\n # requirements for compilation.\n # This uses CMake's Transitive Usage Requirements. Refer to CMake doc:\n # https://cmake.org/cmake/help/v3.16/manual/cmake-buildsystem.7.html#transitive-usage-requirements\n target_link_libraries(lightgbm_capi_objs PUBLIC OpenMP::OpenMP_CXX)\n endif()\nendif()\n\nif(USE_GPU)\n target_link_libraries(lightgbm_objs PUBLIC ${OpenCL_LIBRARY} ${Boost_LIBRARIES})\nendif()\n\nif(USE_CUDA)\n target_link_libraries(lightgbm_objs PUBLIC ${NCCL_LIBRARY})\nendif()\n\nif(USE_ROCM)\n find_package(rccl)\n target_link_libraries(lightgbm_objs PUBLIC ${RCCL_LIBRARY})\nendif()\n\nif(__INTEGRATE_OPENCL)\n # targets OpenCL and Boost are added in IntegratedOpenCL.cmake\n add_dependencies(lightgbm_objs OpenCL Boost)\n # variables INTEGRATED_OPENCL_* are set in IntegratedOpenCL.cmake\n target_include_directories(lightgbm_objs PRIVATE ${INTEGRATED_OPENCL_INCLUDES})\n target_compile_definitions(lightgbm_objs PRIVATE ${INTEGRATED_OPENCL_DEFINITIONS})\n target_link_libraries(lightgbm_objs PUBLIC ${INTEGRATED_OPENCL_LIBRARIES} ${CMAKE_DL_LIBS})\nendif()\n\nif(USE_CUDA)\n\n set_target_properties(\n lightgbm_objs\n PROPERTIES\n CUDA_ARCHITECTURES \"${CUDA_ARCHS}\"\n CUDA_SEPARABLE_COMPILATION ON\n )\n\n set_target_properties(\n _lightgbm\n PROPERTIES\n CUDA_ARCHITECTURES \"${CUDA_ARCHS}\"\n CUDA_RESOLVE_DEVICE_SYMBOLS ON\n )\n\n if(BUILD_CLI)\n set_target_properties(\n lightgbm\n PROPERTIES\n CUDA_ARCHITECTURES \"${CUDA_ARCHS}\"\n CUDA_RESOLVE_DEVICE_SYMBOLS ON\n )\n endif()\nendif()\n\nif(USE_ROCM)\n target_link_libraries(lightgbm_objs PUBLIC hip::host)\nendif()\n\nif(WIN32)\n if(MINGW OR CYGWIN)\n target_link_libraries(lightgbm_objs PUBLIC ws2_32 iphlpapi)\n endif()\nendif()\n\nif(__BUILD_FOR_R)\n # utils/log.h and capi uses R headers, thus both object libraries need to link\n # with R lib.\n if(MSVC)\n set(R_LIB ${LIBR_MSVC_CORE_LIBRARY})\n else()\n set(R_LIB ${LIBR_CORE_LIBRARY})\n endif()\n target_link_libraries(lightgbm_objs PUBLIC ${R_LIB})\n target_link_libraries(lightgbm_capi_objs PUBLIC ${R_LIB})\nendif()\n\n#-- Google C++ tests\nif(BUILD_CPP_TEST)\n find_package(GTest CONFIG)\n if(NOT GTEST_FOUND)\n message(STATUS \"Did not find Google Test in the system root. Fetching Google Test now...\")\n include(FetchContent)\n# lint_cmake: -readability/wonkycase\n FetchContent_Declare(\n# lint_cmake: +readability/wonkycase\n googletest\n GIT_REPOSITORY https://github.com/google/googletest.git\n GIT_TAG v1.14.0\n )\n# lint_cmake: -readability/wonkycase\n FetchContent_MakeAvailable(googletest)\n# lint_cmake: +readability/wonkycase\n add_library(GTest::GTest ALIAS gtest)\n endif()\n\n set(LightGBM_TEST_HEADER_DIR ${PROJECT_SOURCE_DIR}/tests/cpp_tests)\n include_directories(${LightGBM_TEST_HEADER_DIR})\n\n set(\n CPP_TEST_SOURCES\n tests/cpp_tests/test_array_args.cpp\n tests/cpp_tests/test_arrow.cpp\n tests/cpp_tests/test_byte_buffer.cpp\n tests/cpp_tests/test_chunked_array.cpp\n tests/cpp_tests/test_common.cpp\n tests/cpp_tests/test_main.cpp\n tests/cpp_tests/test_serialize.cpp\n tests/cpp_tests/test_single_row.cpp\n tests/cpp_tests/test_stream.cpp\n tests/cpp_tests/testutils.cpp\n )\n if(MSVC)\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} /permissive-\")\n endif()\n add_executable(testlightgbm ${CPP_TEST_SOURCES})\n target_link_libraries(testlightgbm PRIVATE lightgbm_objs lightgbm_capi_objs GTest::GTest)\nendif()\n\nif(BUILD_CLI)\n install(\n TARGETS lightgbm\n RUNTIME DESTINATION ${CMAKE_INSTALL_PREFIX}/bin\n )\nendif()\n\nif(__BUILD_FOR_PYTHON)\n set(CMAKE_INSTALL_PREFIX \"lightgbm\")\nendif()\n\n# The macOS linker puts an absolute path to linked libraries in lib_lightgbm.dylib.\n# This block overrides that information for LightGBM's OpenMP dependency, to allow\n# finding that library in more places.\n#\n# This reduces the risk of runtime issues resulting from multiple {libgomp,libiomp,libomp}.dylib being loaded.\n#\nif(APPLE AND USE_OPENMP AND NOT BUILD_STATIC_LIB)\n # store path to {libgomp,libiomp,libomp}.dylib found at build time in a variable\n get_target_property(\n OpenMP_LIBRARY_LOCATION\n OpenMP::OpenMP_CXX\n INTERFACE_LINK_LIBRARIES\n )\n # get just the filename of that path\n # (to deal with the possibility that it might be 'libomp.dylib' or 'libgomp.dylib' or 'libiomp.dylib')\n get_filename_component(\n OpenMP_LIBRARY_NAME\n ${OpenMP_LIBRARY_LOCATION}\n NAME\n )\n # get directory of that path\n get_filename_component(\n OpenMP_LIBRARY_DIR\n ${OpenMP_LIBRARY_LOCATION}\n DIRECTORY\n )\n # get exact name of the library in a variable\n get_target_property(\n __LIB_LIGHTGBM_OUTPUT_NAME\n _lightgbm\n OUTPUT_NAME\n )\n if(NOT __LIB_LIGHTGBM_OUTPUT_NAME)\n set(__LIB_LIGHTGBM_OUTPUT_NAME \"lib_lightgbm\")\n endif()\n\n if(CMAKE_SHARED_LIBRARY_SUFFIX_CXX)\n set(\n __LIB_LIGHTGBM_FILENAME \"${__LIB_LIGHTGBM_OUTPUT_NAME}${CMAKE_SHARED_LIBRARY_SUFFIX_CXX}\"\n CACHE INTERNAL \"lightgbm shared library filename\"\n )\n else()\n set(\n __LIB_LIGHTGBM_FILENAME \"${__LIB_LIGHTGBM_OUTPUT_NAME}.dylib\"\n CACHE INTERNAL \"lightgbm shared library filename\"\n )\n endif()\n\n # Override the absolute path to OpenMP with a relative one using @rpath.\n #\n # This also ensures that if a {libgomp,libiomp,libomp}.dylib has already been loaded, it'll just use that.\n add_custom_command(\n TARGET _lightgbm\n POST_BUILD\n COMMAND\n install_name_tool\n -change\n ${OpenMP_LIBRARY_LOCATION}\n \"@rpath/${OpenMP_LIBRARY_NAME}\"\n \"${__LIB_LIGHTGBM_FILENAME}\"\n WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}\n COMMENT \"Replacing hard-coded OpenMP install_name with '@rpath/${OpenMP_LIBRARY_NAME}'...\"\n )\n # add RPATH entries to ensure the loader looks in the following, in the following order:\n #\n # - (R-only) ${LIBR_LIBS_DIR} (wherever R for macOS stores vendored third-party libraries)\n # - ${OpenMP_LIBRARY_DIR} (wherever find_package(OpenMP) found OpenMP at build time)\n # - /opt/homebrew/opt/libomp/lib (where 'brew install' / 'brew link' puts libomp.dylib)\n # - /opt/local/lib/libomp (where 'port install' puts libomp.dylib)\n #\n\n # with some compilers, OpenMP ships with the compiler (e.g. libgomp with gcc)\n list(APPEND __omp_install_rpaths \"${OpenMP_LIBRARY_DIR}\")\n\n # with clang, libomp doesn't ship with the compiler and might be supplied separately\n if(CMAKE_CXX_COMPILER_ID MATCHES \"Clang\")\n list(\n APPEND __omp_install_rpaths\n \"/opt/homebrew/opt/libomp/lib\"\n \"/opt/local/lib/libomp\"\n )\n # It appears that CRAN's macOS binaries compiled with -fopenmp have install names\n # of the form:\n #\n # /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libomp.dylib\n #\n # That corresponds to the libomp.dylib that ships with the R framework for macOS, available\n # from https://cran.r-project.org/bin/macosx/.\n #\n # That absolute-path install name leads to that library being loaded unconditionally.\n #\n # That can result in e.g. 'library(data.table)' loading R's libomp.dylib and 'library(lightgbm)' loading\n # Homebrew's. Having 2 loaded in the same process can lead to segfaults and unpredictable behavior.\n #\n # This can't be easily avoided by forcing R-package builds in LightGBM to use R's libomp.dylib\n # at build time... LightGBM's CMake uses find_package(OpenMP), and R for macOS only provides the\n # library, not CMake config files for it.\n #\n # Best we can do, to allow CMake-based builds of the R-package here to continue to work\n # alongside CRAN-prepared binaries of other packages with OpenMP dependencies, is to\n # ensure that R's library directory is the first place the loader searches for\n # libomp.dylib when clang is used.\n #\n # ref: https://github.com/lightgbm-org/LightGBM/issues/6628\n #\n if(__BUILD_FOR_R)\n list(PREPEND __omp_install_rpaths \"${LIBR_LIBS_DIR}\")\n endif()\n endif()\n set_target_properties(\n _lightgbm\n PROPERTIES\n BUILD_WITH_INSTALL_RPATH TRUE\n INSTALL_RPATH \"${__omp_install_rpaths}\"\n INSTALL_RPATH_USE_LINK_PATH FALSE\n )\nendif()\n\ninstall(\n TARGETS _lightgbm\n RUNTIME DESTINATION ${CMAKE_INSTALL_PREFIX}/bin\n LIBRARY DESTINATION ${CMAKE_INSTALL_PREFIX}/lib\n ARCHIVE DESTINATION ${CMAKE_INSTALL_PREFIX}/lib\n)\n\nif(INSTALL_HEADERS)\n install(\n DIRECTORY ${LightGBM_HEADER_DIR}/LightGBM\n DESTINATION ${CMAKE_INSTALL_PREFIX}/include\n )\n install(\n FILES ${FAST_DOUBLE_PARSER_INCLUDE_DIR}/fast_double_parser.h\n DESTINATION ${CMAKE_INSTALL_PREFIX}/include/LightGBM/utils\n )\n install(\n DIRECTORY ${FMT_INCLUDE_DIR}/\n DESTINATION ${CMAKE_INSTALL_PREFIX}/include/LightGBM/utils\n FILES_MATCHING PATTERN \"*.h\"\n )\nendif()\n" }, { "path": "CODE_OF_CONDUCT.md", "content": "# Microsoft Open Source Code of Conduct\n\nThis code of conduct outlines expectations for participation in Microsoft-managed open source communities, as well as steps for reporting unacceptable behavior. We are committed to providing a welcoming and inspiring community for all. People violating this code of conduct may be banned from the community.\n\n## Our open source communities strive to:\n\n* Be friendly and patient: Remember you might not be communicating in someone else's primary spoken or programming language, and others may not have your level of understanding.\n* Be welcoming: Our communities welcome and support people of all backgrounds and identities. This includes, but is not limited to members of any race, ethnicity, culture, national origin, color, immigration status, social and economic class, educational level, sex, sexual orientation, gender identity and expression, age, size, family status, political belief, religion, and mental and physical ability.\n* Be respectful: We are a world-wide community of professionals, and we conduct ourselves professionally. Disagreement is no excuse for poor behavior and poor manners. Disrespectful and unacceptable behavior includes, but is not limited to:\n 1. Violent threats or language.\n 2. Discriminatory or derogatory jokes and language.\n 3. Posting sexually explicit or violent material.\n 4. Posting, or threatening to post, people's personally identifying information (\"doxing\").\n 5. Insults, especially those using discriminatory terms or slurs.\nBehavior that could be perceived as sexual attention.\nAdvocating for or encouraging any of the above behaviors.\n* Understand disagreements: Disagreements, both social and technical, are useful learning opportunities. Seek to understand the other viewpoints and resolve differences constructively.\n* This code is not exhaustive or complete. It serves to capture our common understanding of a productive, collaborative environment. We expect the code to be followed in spirit as much as in the letter.\n\n## Scope\n\nThis code of conduct applies to all repos and communities for Microsoft-managed open source projects regardless of whether or not the repo explicitly calls out its use of this code. The code also applies in public spaces when an individual is representing a project or its community. Examples include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.\n\nNote: Some Microsoft-managed communities have codes of conduct that pre-date this document and issue resolution process. While communities are not required to change their code, they are expected to use the resolution process outlined here. The review team will coordinate with the communities involved to address your concerns.\n\n## Reporting Code of Conduct Issues\n\nWe encourage all communities to resolve issues on their own whenever possible. This builds a broader and deeper understanding and ultimately a healthier interaction. In the event that an issue cannot be resolved locally, please feel free to report your concerns by contacting opencode@microsoft.com. Your report will be handled in accordance with the issue resolution process described in the Code of Conduct FAQ.\n\nIn your report please include:\n\n* Your contact information.\n* Names (real, usernames or pseudonyms) of any individuals involved. If there are additional witnesses, please include them as well.\n* Your account of what occurred, and if you believe the incident is ongoing. If there is a publicly available record (e.g. a mailing list archive or a public chat log), please include a link or attachment.\n* Any additional information that may be helpful.\n\nAll reports will be reviewed by a multi-person team and will result in a response that is deemed necessary and appropriate to the circumstances. Where additional perspectives are needed, the team may seek insight from others with relevant expertise or experience. The confidentiality of the person reporting the incident will be kept at all times. Involved parties are never part of the review team.\n\nAnyone asked to stop unacceptable behavior is expected to comply immediately. If an individual engages in unacceptable behavior, the review team may take any action they deem appropriate, including a permanent ban from the community.\n\nThis code of conduct is based on the template established by the TODO Group and used by numerous other large communities (e.g., Facebook, Yahoo, Twitter, GitHub) and the Scope section from the Contributor Covenant version 1.4.\n" }, { "path": "CONTRIBUTING.md", "content": "# contributing\n\nLightGBM has been developed and used by many active community members.\n\nYour help is very valuable to make it better for everyone.\n\n## How to Contribute\n\n- Check the [Feature Requests Hub](https://github.com/lightgbm-org/LightGBM/issues/2302), and submit pull requests to address chosen issue. If you need development guideline, you can check the [Development Guide](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Development-Guide.rst) or directly ask us in Issues/Pull Requests.\n- Contribute to the [tests](https://github.com/lightgbm-org/LightGBM/tree/master/tests) to make it more reliable.\n- Contribute to the [documentation](https://github.com/lightgbm-org/LightGBM/tree/master/docs) to make it clearer for everyone.\n- Contribute to the [examples](https://github.com/lightgbm-org/LightGBM/tree/master/examples) to share your experience with other users.\n- Add your stories and experience to [Awesome LightGBM](https://github.com/lightgbm-org/LightGBM/blob/master/examples/README.md). If LightGBM helped you in a machine learning competition or some research application, we want to hear about it!\n- [Open an issue](https://github.com/lightgbm-org/LightGBM/issues) to report problems or recommend new features.\n\n## Development Guide\n\n### Linting\n\nEvery commit in the repository is tested with multiple static analyzers.\n\nWhen developing locally, run some of them using `pre-commit` ([pre-commit docs](https://pre-commit.com/)).\n\n```shell\npre-commit run --all-files\n```\n\nThat command will check for some issues and automatically reformat the code.\n" }, { "path": "LICENSE", "content": "The MIT License (MIT)\n\nCopyright (c) Microsoft Corporation\nCopyright (c) The LightGBM developers\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "MAINTAINING.md", "content": "# maintaining\n\nThis document is for LightGBM maintainers.\n\n## Releasing\n\n### Step 1: Put up a Release PR\n\nCreate a pull request into `master` which prepares the source code for release.\n\nCopy the description and checklist from the previous release PR (for example: https://github.com/lightgbm-org/LightGBM/pull/6796).\n\nThis should usually also include a checklist of other issues and PRs that should be completed for the release,\nand the PR should be used to discuss what makes it into the release.\n\n### Step 2: Merge the Release PR\n\nOnce the PR is approved, merge it.\n\nDo not merge any other PRs into `master` until the rest of the release is complete.\n\n### Step 3: Wait for a New CI Run on `master`\n\nWait for all CI runs triggered by the merge to `master` to complete successfully.\n\nThese runs build and test the official artifacts that will be attached to the GitHub release and published to package managers.\n\n### Step 4: Create a Release\n\nNavigate to https://github.com/lightgbm-org/LightGBM/releases.\n\nClick \"edit\" on the draft release that `release-drafter` has created there.\n\n* update the tag and release title to match the version of LightGBM, in the format `v{major}.{minor}.{patch}`\n* ensure that tag points at the commit on ``master`` created by merging the release PR\n\nWhen you're satisfied with the state of the release, click \"Publish release\".\n\n### Step 5: Upload Artifacts\n\nAfter creating a release, run the following from the root of the repo to populate it with artifacts.\n\n```shell\n# download all artifacts to a local directory\n./.ci/downloads-artifacts.sh ${COMMIT_ID}\n\n# attach them to the GitHub release\ngh release upload \\\n --repo lightgbm-org/LightGBM \\\n \"${TAG}\" \\\n ./release-artifacts/*\n```\n\nWhere:\n\n* `COMMIT_ID` = full commit hash of the commit on `master` corresponding to the release\n* `TAG` = the tag for the release (e.g. `v4.6.0`)\n\n### Step 6: Complete All Other Post-merge Release Steps\n\nThese include things like publishing to package managers, updating build configs for repackagers like ``conda-forge``, and many other steps.\n\nSee the release checklist on the PR for details.\n" }, { "path": "R-package/.Rbuildignore", "content": "\\.appveyor\\.yml\nAUTOCONF_UBUNTU_VERSION\n^autom4te.cache/.*$\n^.*\\.bin\n^build_r.R$\n\\.clang-format\n^.*\\.clusterfuzzlite$\n^cran-comments\\.md$\n^docs$\n^.*\\.dll\n\\.drone\\.yml\n^.*\\.dylib\n\\.git\n\\.gitkeep$\n^.*\\.history\n^Makefile$\n^.*\\.o\n^.*\\.out\n^pkgdown$\n^recreate-configure\\.sh$\n^.*\\.so\n^src/build/.*$\n^src/CMakeLists.txt$\n^src/external_libs/compute/.appveyor.yml$\n^src/external_libs/compute/.coveralls.yml$\n^src/external_libs/compute/.travis.yml$\n^src/external_libs/compute/test/.*$\n^src/external_libs/compute/index.html$\n^src/external_libs/compute/.git$\n^src/external_libs/compute/.gitignore$\n^src/external_libs/compute/CONTRIBUTING.md$\n^src/external_libs/compute/README.md$\nsrc/external_libs/fast_double_parser/benchmarks\nsrc/external_libs/fast_double_parser/Makefile\nsrc/external_libs/fast_double_parser/.*\\.md\nsrc/external_libs/fast_double_parser/tests\nsrc/external_libs/fast_double_parser/.*\\.yaml\nsrc/external_libs/fast_double_parser/.*\\.yml\nsrc/external_libs/fmt/.*\\.md\nsrc/external_libs/fmt/.travis.yml\nsrc/external_libs/fmt/doc\nsrc/external_libs/fmt/support/Android\\.mk\nsrc/external_libs/fmt/support/bazel/.bazel.*\nsrc/external_libs/fmt/support/.*\\.gradle\nsrc/external_libs/fmt/support/.*\\.pro\nsrc/external_libs/fmt/support/.*\\.py\nsrc/external_libs/fmt/support/rtd\nsrc/external_libs/fmt/support/.*sublime-syntax\nsrc/external_libs/fmt/support/Vagrantfile\nsrc/external_libs/fmt/support/.*\\.xml\nsrc/external_libs/fmt/support/.*\\.yml\nsrc/external_libs/fmt/test\n" }, { "path": "R-package/AUTOCONF_UBUNTU_VERSION", "content": "2.71-2\n" }, { "path": "R-package/DESCRIPTION", "content": "Package: lightgbm\nType: Package\nTitle: Light Gradient Boosting Machine\nVersion: ~~VERSION~~\nDate: ~~DATE~~\nAuthors@R: c(\n person(\"Yu\", \"Shi\", email = \"yushi2@microsoft.com\", role = c(\"aut\")),\n person(\"Guolin\", \"Ke\", email = \"guolin.ke@outlook.com\", role = c(\"aut\")),\n person(\"Damien\", \"Soukhavong\", email = \"damien.soukhavong@skema.edu\", role = c(\"aut\")),\n person(\"James\", \"Lamb\", email=\"jaylamb20@gmail.com\", role = c(\"aut\", \"cre\")),\n person(\"Qi\", \"Meng\", role = c(\"aut\")),\n person(\"Thomas\", \"Finley\", role = c(\"aut\")),\n person(\"Taifeng\", \"Wang\", role = c(\"aut\")),\n person(\"Wei\", \"Chen\", role = c(\"aut\")),\n person(\"Weidong\", \"Ma\", role = c(\"aut\")),\n person(\"Qiwei\", \"Ye\", role = c(\"aut\")),\n person(\"Tie-Yan\", \"Liu\", role = c(\"aut\")),\n person(\"Nikita\", \"Titov\", role = c(\"aut\")),\n person(\"Yachen\", \"Yan\", role = c(\"ctb\")),\n person(\"Microsoft Corporation\", role = c(\"cph\")),\n person(\"Dropbox, Inc.\", role = c(\"cph\")),\n person(\"Alberto\", \"Ferreira\", role = c(\"ctb\")),\n person(\"Daniel\", \"Lemire\", role = c(\"ctb\")),\n person(\"Victor\", \"Zverovich\", role = c(\"cph\")),\n person(\"IBM Corporation\", role = c(\"ctb\")),\n person(\"David\", \"Cortes\", role = c(\"aut\")),\n person(\"Michael\", \"Mayer\", role = c(\"ctb\"))\n )\nDescription: Tree based algorithms can be improved by introducing boosting frameworks.\n 'LightGBM' is one such framework, based on Ke, Guolin et al. (2017) .\n This package offers an R interface to work with it.\n It is designed to be distributed and efficient with the following advantages:\n 1. Faster training speed and higher efficiency.\n 2. Lower memory usage.\n 3. Better accuracy.\n 4. Parallel learning supported.\n 5. Capable of handling large-scale data.\n In recognition of these advantages, 'LightGBM' has been widely-used in many winning solutions of machine learning competitions.\n Comparison experiments on public datasets suggest that 'LightGBM' can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. In addition, parallel experiments suggest that in certain circumstances, 'LightGBM' can achieve a linear speed-up in training time by using multiple machines.\nEncoding: UTF-8\nLicense: MIT + file LICENSE\nURL: https://github.com/lightgbm-org/LightGBM\nBugReports: https://github.com/lightgbm-org/LightGBM/issues\nNeedsCompilation: yes\nBiarch: true\nVignetteBuilder: knitr\nSuggests:\n knitr,\n markdown,\n processx,\n RhpcBLASctl,\n testthat\nDepends:\n R (>= 4.0)\nImports:\n R6 (>= 2.4.0),\n data.table (>= 1.9.6),\n graphics,\n jsonlite (>= 1.0),\n Matrix (>= 1.1-0),\n methods,\n parallel,\n utils\nSystemRequirements:\n C++17\nRoxygenNote: 7.3.3\n" }, { "path": "R-package/LICENSE", "content": "YEAR: 2016\nCOPYRIGHT HOLDER: Microsoft Corporation\n" }, { "path": "R-package/NAMESPACE", "content": "# Generated by roxygen2: do not edit by hand\n\nS3method(\"dimnames<-\",lgb.Dataset)\nS3method(dim,lgb.Dataset)\nS3method(dimnames,lgb.Dataset)\nS3method(get_field,lgb.Dataset)\nS3method(predict,lgb.Booster)\nS3method(print,lgb.Booster)\nS3method(set_field,lgb.Dataset)\nS3method(summary,lgb.Booster)\nexport(getLGBMthreads)\nexport(get_field)\nexport(lgb.Dataset)\nexport(lgb.Dataset.construct)\nexport(lgb.Dataset.create.valid)\nexport(lgb.Dataset.save)\nexport(lgb.Dataset.set.categorical)\nexport(lgb.Dataset.set.reference)\nexport(lgb.configure_fast_predict)\nexport(lgb.convert_with_rules)\nexport(lgb.cv)\nexport(lgb.drop_serialized)\nexport(lgb.dump)\nexport(lgb.get.eval.result)\nexport(lgb.importance)\nexport(lgb.interprete)\nexport(lgb.load)\nexport(lgb.make_serializable)\nexport(lgb.model.dt.tree)\nexport(lgb.plot.importance)\nexport(lgb.plot.interpretation)\nexport(lgb.restore_handle)\nexport(lgb.save)\nexport(lgb.slice.Dataset)\nexport(lgb.train)\nexport(lightgbm)\nexport(setLGBMthreads)\nexport(set_field)\nimport(methods)\nimportClassesFrom(Matrix,CsparseMatrix)\nimportClassesFrom(Matrix,RsparseMatrix)\nimportClassesFrom(Matrix,dgCMatrix)\nimportClassesFrom(Matrix,dgRMatrix)\nimportClassesFrom(Matrix,dsparseMatrix)\nimportClassesFrom(Matrix,dsparseVector)\nimportFrom(Matrix,Matrix)\nimportFrom(R6,R6Class)\nimportFrom(data.table,\":=\")\nimportFrom(data.table,as.data.table)\nimportFrom(data.table,data.table)\nimportFrom(data.table,rbindlist)\nimportFrom(data.table,set)\nimportFrom(data.table,setnames)\nimportFrom(data.table,setorder)\nimportFrom(data.table,setorderv)\nimportFrom(graphics,barplot)\nimportFrom(graphics,par)\nimportFrom(jsonlite,fromJSON)\nimportFrom(methods,is)\nimportFrom(methods,new)\nimportFrom(parallel,detectCores)\nimportFrom(stats,quantile)\nimportFrom(utils,modifyList)\nimportFrom(utils,read.delim)\nuseDynLib(lightgbm , .registration = TRUE)\n" }, { "path": "R-package/R/aliases.R", "content": "# Central location for parameter aliases.\n# See https://lightgbm.readthedocs.io/en/latest/Parameters.html#core-parameters\n\n# [description] List of respected parameter aliases specific to lgb.Dataset. Wrapped in a function to\n# take advantage of lazy evaluation (so it doesn't matter what order\n# R sources files during installation).\n# [return] A named list, where each key is a parameter relevant to lgb.Dataset and each value is a character\n# vector of corresponding aliases.\n.DATASET_PARAMETERS <- function() {\n all_aliases <- .PARAMETER_ALIASES()\n return(all_aliases[c(\n \"bin_construct_sample_cnt\"\n , \"categorical_feature\"\n , \"data_random_seed\"\n , \"enable_bundle\"\n , \"feature_pre_filter\"\n , \"forcedbins_filename\"\n , \"group_column\"\n , \"header\"\n , \"ignore_column\"\n , \"is_enable_sparse\"\n , \"label_column\"\n , \"linear_tree\"\n , \"max_bin\"\n , \"max_bin_by_feature\"\n , \"min_data_in_bin\"\n , \"pre_partition\"\n , \"precise_float_parser\"\n , \"two_round\"\n , \"use_missing\"\n , \"weight_column\"\n , \"zero_as_missing\"\n )])\n}\n\n# [description] Non-exported environment, used for caching details that only need to be\n# computed once per R session.\n.lgb_session_cache_env <- new.env()\n\n# [description] List of respected parameter aliases. Wrapped in a function to take advantage of\n# lazy evaluation (so it doesn't matter what order R sources files during installation).\n# [return] A named list, where each key is a main LightGBM parameter and each value is a character\n# vector of corresponding aliases.\n.PARAMETER_ALIASES <- function() {\n if (exists(\"PARAMETER_ALIASES\", where = .lgb_session_cache_env)) {\n return(get(\"PARAMETER_ALIASES\", envir = .lgb_session_cache_env))\n }\n params_to_aliases <- jsonlite::fromJSON(\n .Call(\n LGBM_DumpParamAliases_R\n )\n )\n for (main_name in names(params_to_aliases)) {\n aliases_with_main_name <- c(main_name, unlist(params_to_aliases[[main_name]]))\n params_to_aliases[[main_name]] <- aliases_with_main_name\n }\n # store in cache so the next call to `.PARAMETER_ALIASES()` doesn't need to recompute this\n assign(\n x = \"PARAMETER_ALIASES\"\n , value = params_to_aliases\n , envir = .lgb_session_cache_env\n )\n return(params_to_aliases)\n}\n\n# [description]\n# Per https://github.com/lightgbm-org/LightGBM/blob/master/docs/Parameters.rst#metric,\n# a few different strings can be used to indicate \"no metrics\".\n# [returns]\n# A character vector\n.NO_METRIC_STRINGS <- function() {\n return(\n c(\n \"na\"\n , \"None\"\n , \"null\"\n , \"custom\"\n )\n )\n}\n\n.MULTICLASS_OBJECTIVES <- function() {\n return(\n c(\n \"multi_logloss\"\n , \"multiclass\"\n , \"softmax\"\n , \"multiclassova\"\n , \"multiclass_ova\"\n , \"ova\"\n , \"ovr\"\n )\n )\n}\n\n.BINARY_OBJECTIVES <- function() {\n return(\n c(\n \"binary_logloss\"\n , \"binary\"\n , \"binary_error\"\n )\n )\n}\n" }, { "path": "R-package/R/callback.R", "content": "# constants that control naming in lists\n.EVAL_KEY <- function() {\n return(\"eval\")\n}\n.EVAL_ERR_KEY <- function() {\n return(\"eval_err\")\n}\n\n#' @importFrom R6 R6Class\nCB_ENV <- R6::R6Class(\n \"lgb.cb_env\",\n cloneable = FALSE,\n public = list(\n model = NULL,\n iteration = NULL,\n begin_iteration = NULL,\n end_iteration = NULL,\n eval_list = list(),\n eval_err_list = list(),\n best_iter = -1L,\n best_score = NA,\n met_early_stop = FALSE\n )\n)\n\n# Format the evaluation metric string\n.format_eval_string <- function(eval_res, eval_err) {\n\n # Check for empty evaluation string\n if (is.null(eval_res) || length(eval_res) == 0L) {\n stop(\"no evaluation results\")\n }\n\n # Check for empty evaluation error\n if (!is.null(eval_err)) {\n return(sprintf(\"%s\\'s %s:%g+%g\", eval_res$data_name, eval_res$name, eval_res$value, eval_err))\n } else {\n return(sprintf(\"%s\\'s %s:%g\", eval_res$data_name, eval_res$name, eval_res$value))\n }\n\n}\n\n.merge_eval_string <- function(env) {\n\n # Check length of evaluation list\n if (length(env$eval_list) <= 0L) {\n return(\"\")\n }\n\n # Get evaluation\n msg <- list(sprintf(\"[%d]:\", env$iteration))\n\n # Set if evaluation error\n is_eval_err <- length(env$eval_err_list) > 0L\n\n # Loop through evaluation list\n for (j in seq_along(env$eval_list)) {\n\n # Store evaluation error\n eval_err <- NULL\n if (isTRUE(is_eval_err)) {\n eval_err <- env$eval_err_list[[j]]\n }\n\n # Set error message\n msg <- c(msg, .format_eval_string(eval_res = env$eval_list[[j]], eval_err = eval_err))\n\n }\n\n return(paste(msg, collapse = \" \"))\n\n}\n\ncb_print_evaluation <- function(period) {\n\n # Create callback\n callback <- function(env) {\n\n # Check if period is at least 1 or more\n if (period > 0L) {\n\n # Store iteration\n i <- env$iteration\n\n # Check if iteration matches moduo\n if ((i - 1L) %% period == 0L || is.element(i, c(env$begin_iteration, env$end_iteration))) {\n\n # Merge evaluation string\n msg <- .merge_eval_string(env = env)\n\n # Check if message is existing\n if (nchar(msg) > 0L) {\n cat(.merge_eval_string(env = env), \"\\n\")\n }\n\n }\n\n }\n\n return(invisible(NULL))\n\n }\n\n # Store attributes\n attr(callback, \"call\") <- match.call()\n attr(callback, \"name\") <- \"cb_print_evaluation\"\n\n return(callback)\n\n}\n\ncb_record_evaluation <- function() {\n\n # Create callback\n callback <- function(env) {\n\n if (length(env$eval_list) <= 0L) {\n return()\n }\n\n # Set if evaluation error\n is_eval_err <- length(env$eval_err_list) > 0L\n\n # Check length of recorded evaluation\n if (length(env$model$record_evals) == 0L) {\n\n # Loop through each evaluation list element\n for (j in seq_along(env$eval_list)) {\n\n # Store names\n data_name <- env$eval_list[[j]]$data_name\n name <- env$eval_list[[j]]$name\n env$model$record_evals$start_iter <- env$begin_iteration\n\n # Check if evaluation record exists\n if (is.null(env$model$record_evals[[data_name]])) {\n env$model$record_evals[[data_name]] <- list()\n }\n\n # Create dummy lists\n env$model$record_evals[[data_name]][[name]] <- list()\n env$model$record_evals[[data_name]][[name]][[.EVAL_KEY()]] <- list()\n env$model$record_evals[[data_name]][[name]][[.EVAL_ERR_KEY()]] <- list()\n\n }\n\n }\n\n # Loop through each evaluation list element\n for (j in seq_along(env$eval_list)) {\n\n # Get evaluation data\n eval_res <- env$eval_list[[j]]\n eval_err <- NULL\n if (isTRUE(is_eval_err)) {\n eval_err <- env$eval_err_list[[j]]\n }\n\n # Store names\n data_name <- eval_res$data_name\n name <- eval_res$name\n\n # Store evaluation data\n env$model$record_evals[[data_name]][[name]][[.EVAL_KEY()]] <- c(\n env$model$record_evals[[data_name]][[name]][[.EVAL_KEY()]]\n , eval_res$value\n )\n env$model$record_evals[[data_name]][[name]][[.EVAL_ERR_KEY()]] <- c(\n env$model$record_evals[[data_name]][[name]][[.EVAL_ERR_KEY()]]\n , eval_err\n )\n\n }\n\n return(invisible(NULL))\n\n }\n\n # Store attributes\n attr(callback, \"call\") <- match.call()\n attr(callback, \"name\") <- \"cb_record_evaluation\"\n\n return(callback)\n\n}\n\ncb_early_stop <- function(stopping_rounds, first_metric_only, verbose) {\n\n factor_to_bigger_better <- NULL\n best_iter <- NULL\n best_score <- NULL\n best_msg <- NULL\n eval_len <- NULL\n\n # Initialization function\n init <- function(env) {\n\n # Early stopping cannot work without metrics\n if (length(env$eval_list) == 0L) {\n stop(\"For early stopping, valids must have at least one element\")\n }\n\n # Store evaluation length\n eval_len <<- length(env$eval_list)\n\n # Check if verbose or not\n if (isTRUE(verbose)) {\n msg <- paste0(\n \"Will train until there is no improvement in \"\n , stopping_rounds\n , \" rounds.\\n\"\n )\n cat(msg)\n }\n\n # Internally treat everything as a maximization task\n factor_to_bigger_better <<- rep.int(1.0, eval_len)\n best_iter <<- rep.int(-1L, eval_len)\n best_score <<- rep.int(-Inf, eval_len)\n best_msg <<- list()\n\n # Loop through evaluation elements\n for (i in seq_len(eval_len)) {\n\n # Prepend message\n best_msg <<- c(best_msg, \"\")\n\n # Internally treat everything as a maximization task\n if (!isTRUE(env$eval_list[[i]]$higher_better)) {\n factor_to_bigger_better[i] <<- -1.0\n }\n\n }\n\n return(invisible(NULL))\n\n }\n\n # Create callback\n callback <- function(env) {\n\n # Check for empty evaluation\n if (is.null(eval_len)) {\n init(env = env)\n }\n\n # Store iteration\n cur_iter <- env$iteration\n\n # By default, any metric can trigger early stopping. This can be disabled\n # with 'first_metric_only = TRUE'\n if (isTRUE(first_metric_only)) {\n evals_to_check <- 1L\n } else {\n evals_to_check <- seq_len(eval_len)\n }\n\n # Loop through evaluation\n for (i in evals_to_check) {\n\n # Store score\n score <- env$eval_list[[i]]$value * factor_to_bigger_better[i]\n\n # Check if score is better\n if (score > best_score[i]) {\n\n # Store new scores\n best_score[i] <<- score\n best_iter[i] <<- cur_iter\n\n # Prepare to print if verbose\n if (verbose) {\n best_msg[[i]] <<- as.character(.merge_eval_string(env = env))\n }\n\n } else {\n\n # Check if early stopping is required\n if (cur_iter - best_iter[i] >= stopping_rounds) {\n\n if (!is.null(env$model)) {\n env$model$best_score <- best_score[i]\n env$model$best_iter <- best_iter[i]\n }\n\n if (isTRUE(verbose)) {\n cat(paste0(\"Early stopping, best iteration is: \", best_msg[[i]], \"\\n\"))\n }\n\n # Store best iteration and stop\n env$best_iter <- best_iter[i]\n env$met_early_stop <- TRUE\n }\n\n }\n\n if (!isTRUE(env$met_early_stop) && cur_iter == env$end_iteration) {\n\n if (!is.null(env$model)) {\n env$model$best_score <- best_score[i]\n env$model$best_iter <- best_iter[i]\n }\n\n if (isTRUE(verbose)) {\n cat(paste0(\"Did not meet early stopping, best iteration is: \", best_msg[[i]], \"\\n\"))\n }\n\n # Store best iteration and stop\n env$best_iter <- best_iter[i]\n env$met_early_stop <- TRUE\n }\n }\n\n return(invisible(NULL))\n\n }\n\n attr(callback, \"call\") <- match.call()\n attr(callback, \"name\") <- \"cb_early_stop\"\n\n return(callback)\n\n}\n\n# Extract callback names from the list of callbacks\n.callback_names <- function(cb_list) {\n return(unlist(lapply(cb_list, attr, \"name\")))\n}\n\n.add_cb <- function(cb_list, cb) {\n\n # Combine two elements\n cb_list <- c(cb_list, cb)\n\n # Set names of elements\n names(cb_list) <- .callback_names(cb_list = cb_list)\n\n if (\"cb_early_stop\" %in% names(cb_list)) {\n\n # Concatenate existing elements\n cb_list <- c(cb_list, cb_list[\"cb_early_stop\"])\n\n # Remove only the first one\n cb_list[\"cb_early_stop\"] <- NULL\n\n }\n\n return(cb_list)\n\n}\n\n.categorize_callbacks <- function(cb_list) {\n\n # Check for pre-iteration or post-iteration\n return(\n list(\n pre_iter = Filter(function(x) {\n pre <- attr(x, \"is_pre_iteration\")\n !is.null(pre) && pre\n }, cb_list),\n post_iter = Filter(function(x) {\n pre <- attr(x, \"is_pre_iteration\")\n is.null(pre) || !pre\n }, cb_list)\n )\n )\n\n}\n" }, { "path": "R-package/R/lgb.Booster.R", "content": "#' @importFrom R6 R6Class\n#' @importFrom utils modifyList\nBooster <- R6::R6Class(\n classname = \"lgb.Booster\",\n cloneable = FALSE,\n public = list(\n\n best_iter = -1L,\n best_score = NA_real_,\n params = list(),\n record_evals = list(),\n data_processor = NULL,\n\n # Initialize will create a starter booster\n initialize = function(params = list(),\n train_set = NULL,\n modelfile = NULL,\n model_str = NULL) {\n\n handle <- NULL\n\n if (!is.null(train_set)) {\n\n if (!.is_Dataset(train_set)) {\n stop(\"lgb.Booster: Can only use lgb.Dataset as training data\")\n }\n train_set_handle <- train_set$.__enclos_env__$private$get_handle()\n params <- utils::modifyList(params, train_set$get_params())\n params_str <- .params2str(params = params)\n # Store booster handle\n handle <- .Call(\n LGBM_BoosterCreate_R\n , train_set_handle\n , params_str\n )\n\n # Create private booster information\n private$train_set <- train_set\n private$train_set_version <- train_set$.__enclos_env__$private$version\n private$num_dataset <- 1L\n private$init_predictor <- train_set$.__enclos_env__$private$predictor\n\n if (!is.null(private$init_predictor)) {\n\n # Merge booster\n .Call(\n LGBM_BoosterMerge_R\n , handle\n , private$init_predictor$.__enclos_env__$private$handle\n )\n\n }\n\n # Check current iteration\n private$is_predicted_cur_iter <- c(private$is_predicted_cur_iter, FALSE)\n\n } else if (!is.null(modelfile)) {\n\n # Do we have a model file as character?\n if (!is.character(modelfile)) {\n stop(\"lgb.Booster: Can only use a string as model file path\")\n }\n\n modelfile <- path.expand(modelfile)\n\n # Create booster from model\n handle <- .Call(\n LGBM_BoosterCreateFromModelfile_R\n , modelfile\n )\n params <- private$get_loaded_param(handle)\n\n } else if (!is.null(model_str)) {\n\n # Do we have a model_str as character/raw?\n if (!is.raw(model_str) && !is.character(model_str)) {\n stop(\"lgb.Booster: Can only use a character/raw vector as model_str\")\n }\n\n # Create booster from model\n handle <- .Call(\n LGBM_BoosterLoadModelFromString_R\n , model_str\n )\n\n } else {\n\n # Booster non existent\n stop(\n \"lgb.Booster: Need at least either training dataset, \"\n , \"model file, or model_str to create booster instance\"\n )\n\n }\n\n class(handle) <- \"lgb.Booster.handle\"\n private$handle <- handle\n private$num_class <- 1L\n .Call(\n LGBM_BoosterGetNumClasses_R\n , private$handle\n , private$num_class\n )\n\n self$params <- params\n\n return(invisible(NULL))\n\n },\n\n # Set training data name\n set_train_data_name = function(name) {\n\n # Set name\n private$name_train_set <- name\n return(invisible(self))\n\n },\n\n # Add validation data\n add_valid = function(data, name) {\n\n if (!.is_Dataset(data)) {\n stop(\"lgb.Booster.add_valid: Can only use lgb.Dataset as validation data\")\n }\n\n if (!identical(data$.__enclos_env__$private$predictor, private$init_predictor)) {\n stop(\n \"lgb.Booster.add_valid: Failed to add validation data; \"\n , \"you should use the same predictor for these data\"\n )\n }\n\n if (!is.character(name)) {\n stop(\"lgb.Booster.add_valid: Can only use characters as data name\")\n }\n\n # Add validation data to booster\n .Call(\n LGBM_BoosterAddValidData_R\n , private$handle\n , data$.__enclos_env__$private$get_handle()\n )\n\n private$valid_sets <- c(private$valid_sets, data)\n private$name_valid_sets <- c(private$name_valid_sets, name)\n private$num_dataset <- private$num_dataset + 1L\n private$is_predicted_cur_iter <- c(private$is_predicted_cur_iter, FALSE)\n\n return(invisible(self))\n\n },\n\n reset_parameter = function(params) {\n\n if (methods::is(self$params, \"list\")) {\n params <- utils::modifyList(self$params, params)\n }\n\n params_str <- .params2str(params = params)\n\n self$restore_handle()\n\n .Call(\n LGBM_BoosterResetParameter_R\n , private$handle\n , params_str\n )\n self$params <- params\n\n return(invisible(self))\n\n },\n\n # Perform boosting update iteration\n update = function(train_set = NULL, fobj = NULL) {\n\n if (is.null(train_set)) {\n if (private$train_set$.__enclos_env__$private$version != private$train_set_version) {\n train_set <- private$train_set\n }\n }\n\n if (!is.null(train_set)) {\n\n if (!.is_Dataset(train_set)) {\n stop(\"lgb.Booster.update: Only can use lgb.Dataset as training data\")\n }\n\n if (!identical(train_set$predictor, private$init_predictor)) {\n stop(\"lgb.Booster.update: Change train_set failed, you should use the same predictor for these data\")\n }\n\n .Call(\n LGBM_BoosterResetTrainingData_R\n , private$handle\n , train_set$.__enclos_env__$private$get_handle()\n )\n\n private$train_set <- train_set\n private$train_set_version <- train_set$.__enclos_env__$private$version\n\n }\n\n # Check if objective is empty\n if (is.null(fobj)) {\n if (private$set_objective_to_none) {\n stop(\"lgb.Booster.update: cannot update due to null objective function\")\n }\n # Boost iteration from known objective\n .Call(\n LGBM_BoosterUpdateOneIter_R\n , private$handle\n )\n\n } else {\n\n if (!is.function(fobj)) {\n stop(\"lgb.Booster.update: fobj should be a function\")\n }\n if (!private$set_objective_to_none) {\n self$reset_parameter(params = list(objective = \"none\"))\n private$set_objective_to_none <- TRUE\n }\n # Perform objective calculation\n preds <- private$inner_predict(1L)\n gpair <- fobj(preds, private$train_set)\n\n # Check for gradient and hessian as list\n if (is.null(gpair$grad) || is.null(gpair$hess)) {\n stop(\"lgb.Booster.update: custom objective should\n return a list with attributes (hess, grad)\")\n }\n\n # Check grad and hess have the right shape\n n_grad <- length(gpair$grad)\n n_hess <- length(gpair$hess)\n n_preds <- length(preds)\n if (n_grad != n_preds) {\n stop(sprintf(\"Expected custom objective function to return grad with length %d, got %d.\", n_preds, n_grad))\n }\n if (n_hess != n_preds) {\n stop(sprintf(\"Expected custom objective function to return hess with length %d, got %d.\", n_preds, n_hess))\n }\n\n # Return custom boosting gradient/hessian\n .Call(\n LGBM_BoosterUpdateOneIterCustom_R\n , private$handle\n , gpair$grad\n , gpair$hess\n , n_preds\n )\n\n }\n\n # Loop through each iteration\n for (i in seq_along(private$is_predicted_cur_iter)) {\n private$is_predicted_cur_iter[[i]] <- FALSE\n }\n\n return(invisible(self))\n\n },\n\n # Return one iteration behind\n rollback_one_iter = function() {\n\n self$restore_handle()\n\n .Call(\n LGBM_BoosterRollbackOneIter_R\n , private$handle\n )\n\n # Loop through each iteration\n for (i in seq_along(private$is_predicted_cur_iter)) {\n private$is_predicted_cur_iter[[i]] <- FALSE\n }\n\n return(invisible(self))\n\n },\n\n # Get current iteration\n current_iter = function() {\n\n self$restore_handle()\n\n cur_iter <- 0L\n .Call(\n LGBM_BoosterGetCurrentIteration_R\n , private$handle\n , cur_iter\n )\n return(cur_iter)\n\n },\n\n # Number of trees per iteration\n num_trees_per_iter = function() {\n\n self$restore_handle()\n\n trees_per_iter <- 1L\n .Call(\n LGBM_BoosterNumModelPerIteration_R\n , private$handle\n , trees_per_iter\n )\n return(trees_per_iter)\n\n },\n\n # Total number of trees\n num_trees = function() {\n\n self$restore_handle()\n\n ntrees <- 0L\n .Call(\n LGBM_BoosterNumberOfTotalModel_R\n , private$handle\n , ntrees\n )\n return(ntrees)\n\n },\n\n # Number of iterations (= rounds)\n num_iter = function() {\n\n ntrees <- self$num_trees()\n trees_per_iter <- self$num_trees_per_iter()\n\n return(ntrees / trees_per_iter)\n\n },\n\n # Get upper bound\n upper_bound = function() {\n\n self$restore_handle()\n\n upper_bound <- 0.0\n .Call(\n LGBM_BoosterGetUpperBoundValue_R\n , private$handle\n , upper_bound\n )\n return(upper_bound)\n\n },\n\n # Get lower bound\n lower_bound = function() {\n\n self$restore_handle()\n\n lower_bound <- 0.0\n .Call(\n LGBM_BoosterGetLowerBoundValue_R\n , private$handle\n , lower_bound\n )\n return(lower_bound)\n\n },\n\n # Evaluate data on metrics\n eval = function(data, name, feval = NULL) {\n\n if (!.is_Dataset(data)) {\n stop(\"lgb.Booster.eval: Can only use lgb.Dataset to eval\")\n }\n\n # Check for identical data\n data_idx <- 0L\n if (identical(data, private$train_set)) {\n data_idx <- 1L\n } else {\n\n # Check for validation data\n if (length(private$valid_sets) > 0L) {\n\n for (i in seq_along(private$valid_sets)) {\n\n # Check for identical validation data with training data\n if (identical(data, private$valid_sets[[i]])) {\n\n # Found identical data, skip\n data_idx <- i + 1L\n break\n\n }\n\n }\n\n }\n\n }\n\n # Check if evaluation was not done\n if (data_idx == 0L) {\n\n # Add validation data by name\n self$add_valid(data, name)\n data_idx <- private$num_dataset\n\n }\n\n # Evaluate data\n return(\n private$inner_eval(\n data_name = name\n , data_idx = data_idx\n , feval = feval\n )\n )\n\n },\n\n # Evaluation training data\n eval_train = function(feval = NULL) {\n return(private$inner_eval(private$name_train_set, 1L, feval))\n },\n\n # Evaluation validation data\n eval_valid = function(feval = NULL) {\n\n ret <- list()\n\n if (length(private$valid_sets) <= 0L) {\n return(ret)\n }\n\n for (i in seq_along(private$valid_sets)) {\n ret <- append(\n x = ret\n , values = private$inner_eval(private$name_valid_sets[[i]], i + 1L, feval)\n )\n }\n\n return(ret)\n\n },\n\n # Save model\n save_model = function(\n filename\n , num_iteration = NULL\n , feature_importance_type = 0L\n , start_iteration = 1L\n ) {\n\n self$restore_handle()\n\n if (is.null(num_iteration)) {\n num_iteration <- self$best_iter\n }\n\n filename <- path.expand(filename)\n\n .Call(\n LGBM_BoosterSaveModel_R\n , private$handle\n , as.integer(num_iteration)\n , as.integer(feature_importance_type)\n , filename\n , as.integer(start_iteration) - 1L # Turn to 0-based\n )\n\n return(invisible(self))\n },\n\n save_model_to_string = function(\n num_iteration = NULL\n , feature_importance_type = 0L\n , as_char = TRUE\n , start_iteration = 1L\n ) {\n\n self$restore_handle()\n\n if (is.null(num_iteration)) {\n num_iteration <- self$best_iter\n }\n\n model_str <- .Call(\n LGBM_BoosterSaveModelToString_R\n , private$handle\n , as.integer(num_iteration)\n , as.integer(feature_importance_type)\n , as.integer(start_iteration) - 1L # Turn to 0-based\n )\n\n if (as_char) {\n model_str <- rawToChar(model_str)\n }\n\n return(model_str)\n\n },\n\n # Dump model in memory\n dump_model = function(\n num_iteration = NULL, feature_importance_type = 0L, start_iteration = 1L\n ) {\n\n self$restore_handle()\n\n if (is.null(num_iteration)) {\n num_iteration <- self$best_iter\n }\n\n model_str <- .Call(\n LGBM_BoosterDumpModel_R\n , private$handle\n , as.integer(num_iteration)\n , as.integer(feature_importance_type)\n , as.integer(start_iteration) - 1L # Turn to 0-based\n )\n\n return(model_str)\n\n },\n\n # Predict on new data\n predict = function(data,\n start_iteration = NULL,\n num_iteration = NULL,\n rawscore = FALSE,\n predleaf = FALSE,\n predcontrib = FALSE,\n header = FALSE,\n params = list()) {\n\n self$restore_handle()\n\n if (is.null(num_iteration)) {\n num_iteration <- self$best_iter\n }\n\n if (is.null(start_iteration)) {\n start_iteration <- 0L\n }\n\n # possibly override keyword arguments with parameters\n #\n # NOTE: this length() check minimizes the latency introduced by these checks,\n # for the common case where params is empty\n #\n # NOTE: doing this here instead of in Predictor$predict() to keep\n # Predictor$predict() as fast as possible\n if (length(params) > 0L) {\n params <- .check_wrapper_param(\n main_param_name = \"predict_raw_score\"\n , params = params\n , alternative_kwarg_value = rawscore\n )\n params <- .check_wrapper_param(\n main_param_name = \"predict_leaf_index\"\n , params = params\n , alternative_kwarg_value = predleaf\n )\n params <- .check_wrapper_param(\n main_param_name = \"predict_contrib\"\n , params = params\n , alternative_kwarg_value = predcontrib\n )\n rawscore <- params[[\"predict_raw_score\"]]\n predleaf <- params[[\"predict_leaf_index\"]]\n predcontrib <- params[[\"predict_contrib\"]]\n }\n\n # Predict on new data\n predictor <- Predictor$new(\n modelfile = private$handle\n , params = params\n , fast_predict_config = private$fast_predict_config\n )\n return(\n predictor$predict(\n data = data\n , start_iteration = start_iteration\n , num_iteration = num_iteration\n , rawscore = rawscore\n , predleaf = predleaf\n , predcontrib = predcontrib\n , header = header\n )\n )\n\n },\n\n # Transform into predictor\n to_predictor = function() {\n return(Predictor$new(modelfile = private$handle))\n },\n\n configure_fast_predict = function(csr = FALSE,\n start_iteration = NULL,\n num_iteration = NULL,\n rawscore = FALSE,\n predleaf = FALSE,\n predcontrib = FALSE,\n params = list()) {\n\n self$restore_handle()\n ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle)\n\n if (is.null(num_iteration)) {\n num_iteration <- -1L\n }\n if (is.null(start_iteration)) {\n start_iteration <- 0L\n }\n\n if (!csr) {\n fun <- LGBM_BoosterPredictForMatSingleRowFastInit_R\n } else {\n fun <- LGBM_BoosterPredictForCSRSingleRowFastInit_R\n }\n\n fast_handle <- .Call(\n fun\n , private$handle\n , ncols\n , rawscore\n , predleaf\n , predcontrib\n , start_iteration\n , num_iteration\n , .params2str(params = params)\n )\n\n private$fast_predict_config <- list(\n handle = fast_handle\n , csr = as.logical(csr)\n , ncols = ncols\n , start_iteration = start_iteration\n , num_iteration = num_iteration\n , rawscore = as.logical(rawscore)\n , predleaf = as.logical(predleaf)\n , predcontrib = as.logical(predcontrib)\n , params = params\n )\n\n return(invisible(NULL))\n },\n\n # Used for serialization\n raw = NULL,\n\n # Store serialized raw bytes in model object\n save_raw = function() {\n if (is.null(self$raw)) {\n self$raw <- self$save_model_to_string(NULL, as_char = FALSE)\n }\n return(invisible(NULL))\n\n },\n\n drop_raw = function() {\n self$raw <- NULL\n return(invisible(NULL))\n },\n\n check_null_handle = function() {\n return(.is_null_handle(private$handle))\n },\n\n restore_handle = function() {\n if (self$check_null_handle()) {\n if (is.null(self$raw)) {\n .Call(LGBM_NullBoosterHandleError_R)\n }\n private$handle <- .Call(LGBM_BoosterLoadModelFromString_R, self$raw)\n }\n return(invisible(NULL))\n },\n\n get_handle = function() {\n return(private$handle)\n }\n\n ),\n private = list(\n handle = NULL,\n train_set = NULL,\n name_train_set = \"training\",\n valid_sets = list(),\n name_valid_sets = list(),\n predict_buffer = list(),\n is_predicted_cur_iter = list(),\n num_class = 1L,\n num_dataset = 0L,\n init_predictor = NULL,\n eval_names = NULL,\n higher_better_inner_eval = NULL,\n set_objective_to_none = FALSE,\n train_set_version = 0L,\n fast_predict_config = list(),\n\n # finalize() will free up the handles\n finalize = function() {\n .Call(\n LGBM_BoosterFree_R\n , private$handle\n )\n private$handle <- NULL\n return(invisible(NULL))\n },\n\n # Predict data\n inner_predict = function(idx) {\n\n # Store data name\n data_name <- private$name_train_set\n\n if (idx > 1L) {\n data_name <- private$name_valid_sets[[idx - 1L]]\n }\n\n # Check for unknown dataset (over the maximum provided range)\n if (idx > private$num_dataset) {\n stop(\"data_idx should not be greater than num_dataset\")\n }\n\n # Check for prediction buffer\n if (is.null(private$predict_buffer[[data_name]])) {\n\n # Store predictions\n npred <- 0L\n .Call(\n LGBM_BoosterGetNumPredict_R\n , private$handle\n , as.integer(idx - 1L)\n , npred\n )\n private$predict_buffer[[data_name]] <- numeric(npred)\n\n }\n\n # Check if current iteration was already predicted\n if (!private$is_predicted_cur_iter[[idx]]) {\n\n # Use buffer\n .Call(\n LGBM_BoosterGetPredict_R\n , private$handle\n , as.integer(idx - 1L)\n , private$predict_buffer[[data_name]]\n )\n private$is_predicted_cur_iter[[idx]] <- TRUE\n }\n\n return(private$predict_buffer[[data_name]])\n },\n\n # Get evaluation information\n get_eval_info = function() {\n\n if (is.null(private$eval_names)) {\n eval_names <- .Call(\n LGBM_BoosterGetEvalNames_R\n , private$handle\n )\n\n if (length(eval_names) > 0L) {\n\n # Parse and store privately names\n private$eval_names <- eval_names\n\n # some metrics don't map cleanly to metric names, for example \"ndcg@1\" is just the\n # ndcg metric evaluated at the first \"query result\" in learning-to-rank\n metric_names <- gsub(\"@.*\", \"\", eval_names)\n private$higher_better_inner_eval <- .METRICS_HIGHER_BETTER()[metric_names]\n\n }\n\n }\n\n return(private$eval_names)\n\n },\n\n get_loaded_param = function(handle) {\n params_str <- .Call(\n LGBM_BoosterGetLoadedParam_R\n , handle\n )\n params <- jsonlite::fromJSON(params_str)\n if (\"interaction_constraints\" %in% names(params)) {\n params[[\"interaction_constraints\"]] <- lapply(params[[\"interaction_constraints\"]], function(x) x + 1L)\n }\n\n return(params)\n\n },\n\n inner_eval = function(data_name, data_idx, feval = NULL) {\n\n # Check for unknown dataset (over the maximum provided range)\n if (data_idx > private$num_dataset) {\n stop(\"data_idx should not be greater than num_dataset\")\n }\n\n self$restore_handle()\n\n private$get_eval_info()\n\n ret <- list()\n\n if (length(private$eval_names) > 0L) {\n\n # Create evaluation values\n tmp_vals <- numeric(length(private$eval_names))\n .Call(\n LGBM_BoosterGetEval_R\n , private$handle\n , as.integer(data_idx - 1L)\n , tmp_vals\n )\n\n for (i in seq_along(private$eval_names)) {\n\n # Store evaluation and append to return\n res <- list()\n res$data_name <- data_name\n res$name <- private$eval_names[i]\n res$value <- tmp_vals[i]\n res$higher_better <- private$higher_better_inner_eval[i]\n ret <- append(ret, list(res))\n\n }\n\n }\n\n # Check if there are evaluation metrics\n if (!is.null(feval)) {\n\n # Check if evaluation metric is a function\n if (!is.function(feval)) {\n stop(\"lgb.Booster.eval: feval should be a function\")\n }\n\n data <- private$train_set\n\n # Check if data to assess is existing differently\n if (data_idx > 1L) {\n data <- private$valid_sets[[data_idx - 1L]]\n }\n\n # Perform function evaluation\n res <- feval(private$inner_predict(data_idx), data)\n\n if (is.null(res$name) || is.null(res$value) || is.null(res$higher_better)) {\n stop(\n \"lgb.Booster.eval: custom eval function should return a list with attribute (name, value, higher_better)\"\n )\n }\n\n # Append names and evaluation\n res$data_name <- data_name\n ret <- append(ret, list(res))\n }\n\n return(ret)\n\n }\n\n )\n)\n\n#' @name lgb_predict_shared_params\n#' @title Shared prediction parameter docs\n#' @param type Type of prediction to output. Allowed types are:\\itemize{\n#' \\item \\code{\"response\"}: will output the predicted score according to the objective function being\n#' optimized (depending on the link function that the objective uses), after applying any necessary\n#' transformations - for example, for \\code{objective=\"binary\"}, it will output class probabilities.\n#' \\item \\code{\"class\"}: for classification objectives, will output the class with the highest predicted\n#' probability. For other objectives, will output the same as \"response\". Note that \\code{\"class\"} is\n#' not a supported type for \\link{lgb.configure_fast_predict} (see the documentation of that function\n#' for more details).\n#' \\item \\code{\"raw\"}: will output the non-transformed numbers (sum of predictions from boosting iterations'\n#' results) from which the \"response\" number is produced for a given objective function - for example,\n#' for \\code{objective=\"binary\"}, this corresponds to log-odds. For many objectives such as\n#' \"regression\", since no transformation is applied, the output will be the same as for \"response\".\n#' \\item \\code{\"leaf\"}: will output the index of the terminal node / leaf at which each observations falls\n#' in each tree in the model, outputted as integers, with one column per tree.\n#' \\item \\code{\"contrib\"}: will return the per-feature contributions for each prediction, including an\n#' intercept (each feature will produce one column).\n#' }\n#'\n#' Note that, if using custom objectives, types \"class\" and \"response\" will not be available and will\n#' default towards using \"raw\" instead.\n#'\n#' If the model was fit through function \\link{lightgbm} and it was passed a factor as labels,\n#' passing the prediction type through \\code{params} instead of through this argument might\n#' result in factor levels for classification objectives not being applied correctly to the\n#' resulting output.\n#'\n#' \\emph{New in version 4.0.0}\n#'\n#' @param start_iteration int or None, optional (default=None)\n#' Start index of the iteration to predict.\n#' If None or <= 0, starts from the first iteration.\n#' @param num_iteration int or None, optional (default=None)\n#' Limit number of iterations in the prediction.\n#' If None, if the best iteration exists and start_iteration is None or <= 0, the\n#' best iteration is used; otherwise, all iterations from start_iteration are used.\n#' If <= 0, all iterations from start_iteration are used (no limits).\n#' @param params a list of additional named parameters. See\n#' \\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{\n#' the \"Predict Parameters\" section of the documentation} for a list of parameters and\n#' valid values. Where these conflict with the values of keyword arguments to this function,\n#' the values in \\code{params} take precedence.\n#' @details This page contains shared documentation for prediction-related parameters used throughout the package.\n#' @keywords internal\nNULL\n\n#' @name predict.lgb.Booster\n#' @title Predict method for LightGBM model\n#' @description Predicted values based on class \\code{lgb.Booster}\n#'\n#' \\emph{New in version 4.0.0}\n#'\n#' @details If the model object has been configured for fast single-row predictions through\n#' \\link{lgb.configure_fast_predict}, this function will use the prediction parameters\n#' that were configured for it - as such, extra prediction parameters should not be passed\n#' here, otherwise the configuration will be ignored and the slow route will be taken.\n#' @inheritParams lgb_predict_shared_params\n#' @param object Object of class \\code{lgb.Booster}\n#' @param newdata a \\code{matrix} object, a \\code{dgCMatrix}, a \\code{dgRMatrix} object, a \\code{dsparseVector} object,\n#' or a character representing a path to a text file (CSV, TSV, or LibSVM).\n#'\n#' For sparse inputs, if predictions are only going to be made for a single row, it will be faster to\n#' use CSR format, in which case the data may be passed as either a single-row CSR matrix (class\n#' \\code{dgRMatrix} from package \\code{Matrix}) or as a sparse numeric vector (class\n#' \\code{dsparseVector} from package \\code{Matrix}).\n#'\n#' If single-row predictions are going to be performed frequently, it is recommended to\n#' pre-configure the model object for fast single-row sparse predictions through function\n#' \\link{lgb.configure_fast_predict}.\n#'\n#' \\emph{Changed from 'data', in version 4.0.0}\n#'\n#' @param header only used for prediction for text file. True if text file has header\n#' @param ... ignored\n#' @return For prediction types that are meant to always return one output per observation (e.g. when predicting\n#' \\code{type=\"response\"} or \\code{type=\"raw\"} on a binary classification or regression objective), will\n#' return a vector with one element per row in \\code{newdata}.\n#'\n#' For prediction types that are meant to return more than one output per observation (e.g. when predicting\n#' \\code{type=\"response\"} or \\code{type=\"raw\"} on a multi-class objective, or when predicting\n#' \\code{type=\"leaf\"}, regardless of objective), will return a matrix with one row per observation in\n#' \\code{newdata} and one column per output.\n#'\n#' For \\code{type=\"leaf\"} predictions, will return a matrix with one row per observation in \\code{newdata}\n#' and one column per tree. Note that for multiclass objectives, LightGBM trains one tree per class at each\n#' boosting iteration. That means that, for example, for a multiclass model with 3 classes, the leaf\n#' predictions for the first class can be found in columns 1, 4, 7, 10, etc.\n#'\n#' For \\code{type=\"contrib\"}, will return a matrix of SHAP values with one row per observation in\n#' \\code{newdata} and columns corresponding to features. For regression, ranking, cross-entropy, and binary\n#' classification objectives, this matrix contains one column per feature plus a final column containing the\n#' Shapley base value. For multiclass objectives, this matrix will represent \\code{num_classes} such matrices,\n#' in the order \"feature contributions for first class, feature contributions for second class, feature\n#' contributions for third class, etc.\".\n#'\n#' If the model was fit through function \\link{lightgbm} and it was passed a factor as labels, predictions\n#' returned from this function will retain the factor levels (either as values for \\code{type=\"class\"}, or\n#' as column names for \\code{type=\"response\"} and \\code{type=\"raw\"} for multi-class objectives). Note that\n#' passing the requested prediction type under \\code{params} instead of through \\code{type} might result in\n#' the factor levels not being present in the output.\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' data(agaricus.test, package = \"lightgbm\")\n#' test <- agaricus.test\n#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\n#' params <- list(\n#' objective = \"regression\"\n#' , metric = \"l2\"\n#' , min_data = 1L\n#' , learning_rate = 1.0\n#' , num_threads = 2L\n#' )\n#' valids <- list(test = dtest)\n#' model <- lgb.train(\n#' params = params\n#' , data = dtrain\n#' , nrounds = 5L\n#' , valids = valids\n#' )\n#' preds <- predict(model, test$data)\n#'\n#' # pass other prediction parameters\n#' preds <- predict(\n#' model,\n#' test$data,\n#' params = list(\n#' predict_disable_shape_check = TRUE\n#' )\n#' )\n#' }\n#' @importFrom utils modifyList\n#' @export\npredict.lgb.Booster <- function(object,\n newdata,\n type = \"response\",\n start_iteration = NULL,\n num_iteration = NULL,\n header = FALSE,\n params = list(),\n ...) {\n\n if (!.is_Booster(x = object)) {\n stop(\"predict.lgb.Booster: object should be an \", sQuote(\"lgb.Booster\", q = FALSE))\n }\n\n additional_params <- list(...)\n if (length(additional_params) > 0L) {\n additional_params_names <- names(additional_params)\n if (\"reshape\" %in% additional_params_names) {\n stop(\"'reshape' argument is no longer supported.\")\n }\n\n old_args_for_type <- list(\n \"rawscore\" = \"raw\"\n , \"predleaf\" = \"leaf\"\n , \"predcontrib\" = \"contrib\"\n )\n for (arg in names(old_args_for_type)) {\n if (arg %in% additional_params_names) {\n stop(sprintf(\"Argument '%s' is no longer supported. Use type='%s' instead.\"\n , arg\n , old_args_for_type[[arg]]))\n }\n }\n\n warning(paste0(\n \"predict.lgb.Booster: Found the following passed through '...': \"\n , toString(names(additional_params))\n , \". These are ignored. Use argument 'params' instead.\"\n ))\n }\n\n if (!is.null(object$params$objective) && object$params$objective == \"none\" && type %in% c(\"class\", \"response\")) {\n warning(\"Prediction types 'class' and 'response' are not supported for custom objectives.\")\n type <- \"raw\"\n }\n\n rawscore <- FALSE\n predleaf <- FALSE\n predcontrib <- FALSE\n if (type == \"raw\") {\n rawscore <- TRUE\n } else if (type == \"leaf\") {\n predleaf <- TRUE\n } else if (type == \"contrib\") {\n predcontrib <- TRUE\n }\n\n pred <- object$predict(\n data = newdata\n , start_iteration = start_iteration\n , num_iteration = num_iteration\n , rawscore = rawscore\n , predleaf = predleaf\n , predcontrib = predcontrib\n , header = header\n , params = params\n )\n if (type == \"class\") {\n if (object$params$objective %in% .BINARY_OBJECTIVES()) {\n pred <- as.integer(pred >= 0.5)\n } else if (object$params$objective %in% .MULTICLASS_OBJECTIVES()) {\n pred <- max.col(pred) - 1L\n }\n }\n if (!is.null(object$data_processor)) {\n pred <- object$data_processor$process_predictions(\n pred = pred\n , type = type\n )\n }\n return(pred)\n}\n\n#' @title Configure Fast Single-Row Predictions\n#' @description Pre-configures a LightGBM model object to produce fast single-row predictions\n#' for a given input data type, prediction type, and parameters.\n#' @details Calling this function multiple times with different parameters might not override\n#' the previous configuration and might trigger undefined behavior.\n#'\n#' Any saved configuration for fast predictions might be lost after making a single-row\n#' prediction of a different type than what was configured (except for types \"response\" and\n#' \"class\", which can be switched between each other at any time without losing the configuration).\n#'\n#' In some situations, setting a fast prediction configuration for one type of prediction\n#' might cause the prediction function to keep using that configuration for single-row\n#' predictions even if the requested type of prediction is different from what was configured.\n#'\n#' Note that this function will not accept argument \\code{type=\"class\"} - for such cases, one\n#' can pass \\code{type=\"response\"} to this function and then \\code{type=\"class\"} to the\n#' \\code{predict} function - the fast configuration will not be lost or altered if the switch\n#' is between \"response\" and \"class\".\n#'\n#' The configuration does not survive de-serializations, so it has to be generated\n#' anew in every R process that is going to use it (e.g. if loading a model object\n#' through \\code{readRDS}, whatever configuration was there previously will be lost).\n#'\n#' Requesting a different prediction type or passing parameters to \\link{predict.lgb.Booster}\n#' will cause it to ignore the fast-predict configuration and take the slow route instead\n#' (but be aware that an existing configuration might not always be overridden by supplying\n#' different parameters or prediction type, so make sure to check that the output is what\n#' was expected when a prediction is to be made on a single row for something different than\n#' what is configured).\n#'\n#' Note that, if configuring a non-default prediction type (such as leaf indices),\n#' then that type must also be passed in the call to \\link{predict.lgb.Booster} in\n#' order for it to use the configuration. This also applies for \\code{start_iteration}\n#' and \\code{num_iteration}, but \\bold{the \\code{params} list must be empty} in the call to \\code{predict}.\n#'\n#' Predictions about feature contributions do not allow a fast route for CSR inputs,\n#' and as such, this function will produce an error if passing \\code{csr=TRUE} and\n#' \\code{type = \"contrib\"} together.\n#' @inheritParams lgb_predict_shared_params\n#' @param model LightGBM model object (class \\code{lgb.Booster}).\n#'\n#' \\bold{The object will be modified in-place}.\n#' @param csr Whether the prediction function is going to be called on sparse CSR inputs.\n#' If \\code{FALSE}, will be assumed that predictions are going to be called on single-row\n#' regular R matrices.\n#' @return The same \\code{model} that was passed as input, invisibly, with the desired\n#' configuration stored inside it and available to be used in future calls to\n#' \\link{predict.lgb.Booster}.\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' library(lightgbm)\n#' data(mtcars)\n#' X <- as.matrix(mtcars[, -1L])\n#' y <- mtcars[, 1L]\n#' dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L))\n#' params <- list(\n#' min_data_in_leaf = 2L\n#' , num_threads = 2L\n#' )\n#' model <- lgb.train(\n#' params = params\n#' , data = dtrain\n#' , obj = \"regression\"\n#' , nrounds = 5L\n#' , verbose = -1L\n#' )\n#' lgb.configure_fast_predict(model)\n#'\n#' x_single <- X[11L, , drop = FALSE]\n#' predict(model, x_single)\n#'\n#' # Will not use it if the prediction to be made\n#' # is different from what was configured\n#' predict(model, x_single, type = \"leaf\")\n#' }\n#' @export\nlgb.configure_fast_predict <- function(model,\n csr = FALSE,\n start_iteration = NULL,\n num_iteration = NULL,\n type = \"response\",\n params = list()) {\n if (!.is_Booster(x = model)) {\n stop(\"lgb.configure_fast_predict: model should be an \", sQuote(\"lgb.Booster\", q = FALSE))\n }\n if (type == \"class\") {\n stop(\"type='class' is not supported for 'lgb.configure_fast_predict'. Use 'response' instead.\")\n }\n\n rawscore <- FALSE\n predleaf <- FALSE\n predcontrib <- FALSE\n if (type == \"raw\") {\n rawscore <- TRUE\n } else if (type == \"leaf\") {\n predleaf <- TRUE\n } else if (type == \"contrib\") {\n predcontrib <- TRUE\n }\n\n if (csr && predcontrib) {\n stop(\"'lgb.configure_fast_predict' does not support feature contributions for CSR data.\")\n }\n model$configure_fast_predict(\n csr = csr\n , start_iteration = start_iteration\n , num_iteration = num_iteration\n , rawscore = rawscore\n , predleaf = predleaf\n , predcontrib = predcontrib\n , params = params\n )\n return(invisible(model))\n}\n\n#' @name print.lgb.Booster\n#' @title Print method for LightGBM model\n#' @description Show summary information about a LightGBM model object (same as \\code{summary}).\n#'\n#' \\emph{New in version 4.0.0}\n#'\n#' @param x Object of class \\code{lgb.Booster}\n#' @param ... Not used\n#' @return The same input \\code{x}, returned as invisible.\n#' @export\nprint.lgb.Booster <- function(x, ...) {\n # nolint start\n handle <- x$.__enclos_env__$private$handle\n handle_is_null <- .is_null_handle(handle)\n\n if (!handle_is_null) {\n ntrees <- x$current_iter()\n if (ntrees == 1L) {\n cat(\"LightGBM Model (1 tree)\\n\")\n } else {\n cat(sprintf(\"LightGBM Model (%d trees)\\n\", ntrees))\n }\n } else {\n cat(\"LightGBM Model\\n\")\n }\n\n if (!handle_is_null) {\n obj <- x$params$objective\n if (is.null(obj)) {\n obj <- \"(default)\"\n }\n if (obj == \"none\") {\n obj <- \"custom\"\n }\n num_class <- x$.__enclos_env__$private$num_class\n if (num_class == 1L) {\n cat(sprintf(\"Objective: %s\\n\", obj))\n } else {\n cat(sprintf(\"Objective: %s (%d classes)\\n\"\n , obj\n , num_class))\n }\n } else {\n cat(\"(Booster handle is invalid)\\n\")\n }\n\n if (!handle_is_null) {\n ncols <- .Call(LGBM_BoosterGetNumFeature_R, handle)\n cat(sprintf(\"Fitted to dataset with %d columns\\n\", ncols))\n }\n # nolint end\n\n return(invisible(x))\n}\n\n#' @name summary.lgb.Booster\n#' @title Summary method for LightGBM model\n#' @description Show summary information about a LightGBM model object (same as \\code{print}).\n#'\n#' \\emph{New in version 4.0.0}\n#'\n#' @param object Object of class \\code{lgb.Booster}\n#' @param ... Not used\n#' @return The same input \\code{object}, returned as invisible.\n#' @export\nsummary.lgb.Booster <- function(object, ...) {\n print(object)\n}\n\n#' @name lgb.load\n#' @title Load LightGBM model\n#' @description Load LightGBM takes in either a file path or model string.\n#' If both are provided, Load will default to loading from file\n#' @param filename path of model file\n#' @param model_str a str containing the model (as a \\code{character} or \\code{raw} vector)\n#'\n#' @return lgb.Booster\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' data(agaricus.test, package = \"lightgbm\")\n#' test <- agaricus.test\n#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\n#' params <- list(\n#' objective = \"regression\"\n#' , metric = \"l2\"\n#' , min_data = 1L\n#' , learning_rate = 1.0\n#' , num_threads = 2L\n#' )\n#' valids <- list(test = dtest)\n#' model <- lgb.train(\n#' params = params\n#' , data = dtrain\n#' , nrounds = 5L\n#' , valids = valids\n#' , early_stopping_rounds = 3L\n#' )\n#' model_file <- tempfile(fileext = \".txt\")\n#' lgb.save(model, model_file)\n#' load_booster <- lgb.load(filename = model_file)\n#' model_string <- model$save_model_to_string(NULL) # saves best iteration\n#' load_booster_from_str <- lgb.load(model_str = model_string)\n#' }\n#' @export\nlgb.load <- function(filename = NULL, model_str = NULL) {\n\n filename_provided <- !is.null(filename)\n model_str_provided <- !is.null(model_str)\n\n if (filename_provided) {\n if (!is.character(filename)) {\n stop(\"lgb.load: filename should be character\")\n }\n filename <- path.expand(filename)\n if (!file.exists(filename)) {\n stop(sprintf(\"lgb.load: file '%s' passed to filename does not exist\", filename))\n }\n return(invisible(Booster$new(modelfile = filename)))\n }\n\n if (model_str_provided) {\n if (!is.raw(model_str) && !is.character(model_str)) {\n stop(\"lgb.load: model_str should be a character/raw vector\")\n }\n return(invisible(Booster$new(model_str = model_str)))\n }\n\n stop(\"lgb.load: either filename or model_str must be given\")\n}\n\n#' @name lgb.save\n#' @title Save LightGBM model\n#' @description Save LightGBM model\n#' @param booster Object of class \\code{lgb.Booster}\n#' @param filename Saved filename\n#' @param num_iteration Number of iterations to save, NULL or <= 0 means use best iteration\n#' @param start_iteration Index (1-based) of the first boosting round to save.\n#' For example, passing \\code{start_iteration=5, num_iteration=3} for a regression model\n#' means \"save the fifth, sixth, and seventh tree\"\n#'\n#' \\emph{New in version 4.4.0}\n#'\n#' @return lgb.Booster\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' library(lightgbm)\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' data(agaricus.test, package = \"lightgbm\")\n#' test <- agaricus.test\n#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\n#' params <- list(\n#' objective = \"regression\"\n#' , metric = \"l2\"\n#' , min_data = 1L\n#' , learning_rate = 1.0\n#' , num_threads = 2L\n#' )\n#' valids <- list(test = dtest)\n#' model <- lgb.train(\n#' params = params\n#' , data = dtrain\n#' , nrounds = 10L\n#' , valids = valids\n#' , early_stopping_rounds = 5L\n#' )\n#' lgb.save(model, tempfile(fileext = \".txt\"))\n#' }\n#' @export\nlgb.save <- function(\n booster, filename, num_iteration = NULL, start_iteration = 1L\n ) {\n\n if (!.is_Booster(x = booster)) {\n stop(\"lgb.save: booster should be an \", sQuote(\"lgb.Booster\", q = FALSE))\n }\n\n if (!(is.character(filename) && length(filename) == 1L)) {\n stop(\"lgb.save: filename should be a string\")\n }\n filename <- path.expand(filename)\n\n # Store booster\n return(\n invisible(booster$save_model(\n filename = filename\n , num_iteration = num_iteration\n , start_iteration = start_iteration\n ))\n )\n\n}\n\n#' @name lgb.dump\n#' @title Dump LightGBM model to json\n#' @description Dump LightGBM model to json\n#' @param booster Object of class \\code{lgb.Booster}\n#' @param num_iteration Number of iterations to be dumped. NULL or <= 0 means use best iteration\n#' @param start_iteration Index (1-based) of the first boosting round to dump.\n#' For example, passing \\code{start_iteration=5, num_iteration=3} for a regression model\n#' means \"dump the fifth, sixth, and seventh tree\"\n#'\n#' \\emph{New in version 4.4.0}\n#'\n#' @return json format of model\n#'\n#' @examples\n#' \\donttest{\n#' library(lightgbm)\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' data(agaricus.test, package = \"lightgbm\")\n#' test <- agaricus.test\n#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\n#' params <- list(\n#' objective = \"regression\"\n#' , metric = \"l2\"\n#' , min_data = 1L\n#' , learning_rate = 1.0\n#' , num_threads = 2L\n#' )\n#' valids <- list(test = dtest)\n#' model <- lgb.train(\n#' params = params\n#' , data = dtrain\n#' , nrounds = 10L\n#' , valids = valids\n#' , early_stopping_rounds = 5L\n#' )\n#' json_model <- lgb.dump(model)\n#' }\n#' @export\nlgb.dump <- function(booster, num_iteration = NULL, start_iteration = 1L) {\n\n if (!.is_Booster(x = booster)) {\n stop(\"lgb.dump: booster should be an \", sQuote(\"lgb.Booster\", q = FALSE))\n }\n\n # Return booster at requested iteration\n return(\n booster$dump_model(\n num_iteration = num_iteration, start_iteration = start_iteration\n )\n )\n\n}\n\n#' @name lgb.get.eval.result\n#' @title Get record evaluation result from booster\n#' @description Given a \\code{lgb.Booster}, return evaluation results for a\n#' particular metric on a particular dataset.\n#' @param booster Object of class \\code{lgb.Booster}\n#' @param data_name Name of the dataset to return evaluation results for.\n#' @param eval_name Name of the evaluation metric to return results for.\n#' @param iters An integer vector of iterations you want to get evaluation results for. If NULL\n#' (the default), evaluation results for all iterations will be returned.\n#' @param is_err TRUE will return evaluation error instead\n#'\n#' @return numeric vector of evaluation result\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' # train a regression model\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' data(agaricus.test, package = \"lightgbm\")\n#' test <- agaricus.test\n#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\n#' params <- list(\n#' objective = \"regression\"\n#' , metric = \"l2\"\n#' , min_data = 1L\n#' , learning_rate = 1.0\n#' , num_threads = 2L\n#' )\n#' valids <- list(test = dtest)\n#' model <- lgb.train(\n#' params = params\n#' , data = dtrain\n#' , nrounds = 5L\n#' , valids = valids\n#' )\n#'\n#' # Examine valid data_name values\n#' print(setdiff(names(model$record_evals), \"start_iter\"))\n#'\n#' # Examine valid eval_name values for dataset \"test\"\n#' print(names(model$record_evals[[\"test\"]]))\n#'\n#' # Get L2 values for \"test\" dataset\n#' lgb.get.eval.result(model, \"test\", \"l2\")\n#' }\n#' @export\nlgb.get.eval.result <- function(booster, data_name, eval_name, iters = NULL, is_err = FALSE) {\n\n if (!.is_Booster(x = booster)) {\n stop(\"lgb.get.eval.result: Can only use \", sQuote(\"lgb.Booster\", q = FALSE), \" to get eval result\")\n }\n\n if (!is.character(data_name) || !is.character(eval_name)) {\n stop(\"lgb.get.eval.result: data_name and eval_name should be characters\")\n }\n\n # NOTE: \"start_iter\" exists in booster$record_evals but is not a valid data_name\n data_names <- setdiff(names(booster$record_evals), \"start_iter\")\n if (!(data_name %in% data_names)) {\n stop(paste0(\n \"lgb.get.eval.result: data_name \"\n , shQuote(data_name)\n , \" not found. Only the following datasets exist in record evals: [\"\n , toString(data_names)\n , \"]\"\n ))\n }\n\n # Check if evaluation result is existing\n eval_names <- names(booster$record_evals[[data_name]])\n if (!(eval_name %in% eval_names)) {\n stop(paste0(\n \"lgb.get.eval.result: eval_name \"\n , shQuote(eval_name)\n , \" not found. Only the following eval_names exist for dataset \"\n , shQuote(data_name)\n , \": [\"\n , toString(eval_names)\n , \"]\"\n ))\n }\n\n result <- booster$record_evals[[data_name]][[eval_name]][[.EVAL_KEY()]]\n\n # Check if error is requested\n if (is_err) {\n result <- booster$record_evals[[data_name]][[eval_name]][[.EVAL_ERR_KEY()]]\n }\n\n if (is.null(iters)) {\n return(as.numeric(result))\n }\n\n # Parse iteration and booster delta\n iters <- as.integer(iters)\n delta <- booster$record_evals$start_iter - 1.0\n iters <- iters - delta\n\n return(as.numeric(result[iters]))\n}\n" }, { "path": "R-package/R/lgb.DataProcessor.R", "content": "DataProcessor <- R6::R6Class(\n classname = \"lgb.DataProcessor\",\n public = list(\n factor_levels = NULL,\n\n process_label = function(label, objective, params) {\n\n if (is.character(label)) {\n label <- factor(label)\n }\n\n if (is.factor(label)) {\n\n self$factor_levels <- levels(label)\n if (length(self$factor_levels) <= 1L) {\n stop(\"Labels to predict is a factor with <2 possible values.\")\n }\n\n label <- as.numeric(label) - 1.0\n out <- list(label = label)\n if (length(self$factor_levels) == 2L) {\n if (objective == \"auto\") {\n objective <- \"binary\"\n }\n if (!(objective %in% .BINARY_OBJECTIVES())) {\n stop(\"Two-level factors as labels only allowed for objective='binary' or objective='auto'.\")\n }\n } else {\n if (objective == \"auto\") {\n objective <- \"multiclass\"\n }\n if (!(objective %in% .MULTICLASS_OBJECTIVES())) {\n stop(\n sprintf(\n \"Factors with >2 levels as labels only allowed for multi-class objectives. Got: %s (allowed: %s)\"\n , objective\n , toString(.MULTICLASS_OBJECTIVES())\n )\n )\n }\n data_num_class <- length(self$factor_levels)\n params <- .check_wrapper_param(\n main_param_name = \"num_class\"\n , params = params\n , alternative_kwarg_value = data_num_class\n )\n if (params[[\"num_class\"]] != data_num_class) {\n warning(\n sprintf(\n \"Found num_class=%d in params, but 'label' is a factor with %d levels. 'num_class' will be ignored.\"\n , params[[\"num_class\"]]\n , data_num_class\n )\n )\n params$num_class <- data_num_class\n }\n }\n out$objective <- objective\n out$params <- params\n return(out)\n\n } else {\n\n label <- as.numeric(label)\n if (objective == \"auto\") {\n objective <- \"regression\"\n }\n out <- list(\n label = label\n , objective = objective\n , params = params\n )\n return(out)\n\n }\n },\n\n process_predictions = function(pred, type) {\n if (NROW(self$factor_levels)) {\n if (type == \"class\") {\n pred <- as.integer(pred) + 1L\n attributes(pred)$levels <- self$factor_levels\n attributes(pred)$class <- \"factor\"\n } else if (type %in% c(\"response\", \"raw\")) {\n if (is.matrix(pred) && ncol(pred) == length(self$factor_levels)) {\n colnames(pred) <- self$factor_levels\n }\n }\n }\n\n return(pred)\n }\n )\n)\n" }, { "path": "R-package/R/lgb.Dataset.R", "content": "#' @name lgb_shared_dataset_params\n#' @title Shared Dataset parameter docs\n#' @description Parameter docs for fields used in \\code{lgb.Dataset} construction\n#' @param label vector of labels to use as the target variable\n#' @param weight numeric vector of sample weights\n#' @param init_score initial score is the base prediction lightgbm will boost from\n#' @param group used for learning-to-rank tasks. An integer vector describing how to\n#' group rows together as ordered results from the same set of candidate results\n#' to be ranked. For example, if you have a 100-document dataset with\n#' \\code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups,\n#' where the first 10 records are in the first group, records 11-30 are in the\n#' second group, etc.\n#' @details This page contains shared documentation for dataset-related parameters used throughout the package.\n#' @keywords internal\nNULL\n\n# [description] List of valid keys for \"info\" arguments in lgb.Dataset.\n# Wrapped in a function to take advantage of lazy evaluation\n# (so it doesn't matter what order R sources files during installation).\n# [return] A character vector of names.\n.INFO_KEYS <- function() {\n return(c(\"label\", \"weight\", \"init_score\", \"group\"))\n}\n\n#' @importFrom methods is\n#' @importFrom R6 R6Class\n#' @importFrom utils modifyList\nDataset <- R6::R6Class(\n\n classname = \"lgb.Dataset\",\n cloneable = FALSE,\n public = list(\n\n # Initialize will create a starter dataset\n initialize = function(data,\n params = list(),\n reference = NULL,\n colnames = NULL,\n categorical_feature = NULL,\n predictor = NULL,\n free_raw_data = TRUE,\n used_indices = NULL,\n label = NULL,\n weight = NULL,\n group = NULL,\n init_score = NULL) {\n\n # validate inputs early to avoid unnecessary computation\n if (!(is.null(reference) || .is_Dataset(reference))) {\n stop(\"lgb.Dataset: If provided, reference must be a \", sQuote(\"lgb.Dataset\", q = FALSE))\n }\n if (!(is.null(predictor) || .is_Predictor(predictor))) {\n stop(\"lgb.Dataset: If provided, predictor must be a \", sQuote(\"lgb.Predictor\", q = FALSE))\n }\n\n info <- list()\n if (!is.null(label)) {\n info[[\"label\"]] <- label\n }\n if (!is.null(weight)) {\n info[[\"weight\"]] <- weight\n }\n if (!is.null(group)) {\n info[[\"group\"]] <- group\n }\n if (!is.null(init_score)) {\n info[[\"init_score\"]] <- init_score\n }\n\n # Check for matrix format\n if (is.matrix(data)) {\n # Check whether matrix is the correct type first (\"double\")\n if (storage.mode(data) != \"double\") {\n storage.mode(data) <- \"double\"\n }\n }\n\n # Setup private attributes\n private$raw_data <- data\n private$params <- params\n private$reference <- reference\n private$colnames <- colnames\n\n private$categorical_feature <- categorical_feature\n private$predictor <- predictor\n private$free_raw_data <- free_raw_data\n private$used_indices <- sort(used_indices, decreasing = FALSE)\n private$info <- info\n private$version <- 0L\n\n return(invisible(NULL))\n\n },\n\n create_valid = function(data,\n label = NULL,\n weight = NULL,\n group = NULL,\n init_score = NULL,\n params = list()) {\n\n # the Dataset's existing parameters should be overwritten by any passed in to this call\n params <- modifyList(private$params, params)\n\n # Create new dataset\n ret <- Dataset$new(\n data = data\n , params = params\n , reference = self\n , colnames = private$colnames\n , categorical_feature = private$categorical_feature\n , predictor = private$predictor\n , free_raw_data = private$free_raw_data\n , used_indices = NULL\n , label = label\n , weight = weight\n , group = group\n , init_score = init_score\n )\n\n return(invisible(ret))\n\n },\n\n # Dataset constructor\n construct = function() {\n\n # Check for handle null\n if (!.is_null_handle(x = private$handle)) {\n return(invisible(self))\n }\n\n # Get feature names\n cnames <- NULL\n if (is.matrix(private$raw_data) || methods::is(private$raw_data, \"dgCMatrix\")) {\n cnames <- colnames(private$raw_data)\n }\n\n # set feature names if they do not exist\n if (is.null(private$colnames) && !is.null(cnames)) {\n private$colnames <- as.character(cnames)\n }\n\n # Get categorical feature index\n if (!is.null(private$categorical_feature)) {\n\n # Check for character name\n if (is.character(private$categorical_feature)) {\n\n cate_indices <- as.list(match(private$categorical_feature, private$colnames) - 1L)\n\n # Provided indices, but some indices are missing?\n if (anyNA(cate_indices)) {\n stop(\n \"lgb.Dataset.construct: supplied an unknown feature in categorical_feature: \"\n , sQuote(private$categorical_feature[is.na(cate_indices)], q = FALSE)\n )\n }\n\n } else {\n\n # Check if more categorical features were output over the feature space\n data_is_not_filename <- !is.character(private$raw_data)\n if (\n data_is_not_filename\n && !is.null(private$raw_data)\n && is.null(private$used_indices)\n && max(private$categorical_feature) > ncol(private$raw_data)\n ) {\n stop(\n \"lgb.Dataset.construct: supplied a too large value in categorical_feature: \"\n , max(private$categorical_feature)\n , \" but only \"\n , ncol(private$raw_data)\n , \" features\"\n )\n }\n\n # Store indices as [0, n-1] indexed instead of [1, n] indexed\n cate_indices <- as.list(private$categorical_feature - 1L)\n\n }\n\n # Store indices for categorical features\n private$params$categorical_feature <- cate_indices\n\n }\n\n # Generate parameter str\n params_str <- .params2str(params = private$params)\n\n # Get handle of reference dataset\n ref_handle <- NULL\n if (!is.null(private$reference)) {\n ref_handle <- private$reference$.__enclos_env__$private$get_handle()\n }\n\n # not subsetting, constructing from raw data\n if (is.null(private$used_indices)) {\n\n if (is.null(private$raw_data)) {\n stop(paste0(\n \"Attempting to create a Dataset without any raw data. \"\n , \"This can happen if the Dataset's finalizer was called or if this Dataset was saved with saveRDS(). \"\n , \"To avoid this error in the future, use lgb.Dataset.save() or \"\n , \"Dataset$save_binary() to save lightgbm Datasets.\"\n ))\n }\n\n # Are we using a data file?\n if (is.character(private$raw_data)) {\n\n handle <- .Call(\n LGBM_DatasetCreateFromFile_R\n , path.expand(private$raw_data)\n , params_str\n , ref_handle\n )\n\n } else if (is.matrix(private$raw_data)) {\n\n # Are we using a matrix?\n handle <- .Call(\n LGBM_DatasetCreateFromMat_R\n , private$raw_data\n , nrow(private$raw_data)\n , ncol(private$raw_data)\n , params_str\n , ref_handle\n )\n\n } else if (methods::is(private$raw_data, \"dgCMatrix\")) {\n if (length(private$raw_data@p) > 2147483647L) {\n stop(\"Cannot support large CSC matrix\")\n }\n # Are we using a dgCMatrix (sparse matrix column compressed)\n handle <- .Call(\n LGBM_DatasetCreateFromCSC_R\n , private$raw_data@p\n , private$raw_data@i\n , private$raw_data@x\n , length(private$raw_data@p)\n , length(private$raw_data@x)\n , nrow(private$raw_data)\n , params_str\n , ref_handle\n )\n\n } else {\n\n # Unknown data type\n stop(\n \"lgb.Dataset.construct: does not support constructing from \"\n , sQuote(class(private$raw_data), q = FALSE)\n )\n\n }\n\n } else {\n\n # Reference is empty\n if (is.null(private$reference)) {\n stop(\"lgb.Dataset.construct: reference cannot be NULL for constructing data subset\")\n }\n\n # Construct subset\n handle <- .Call(\n LGBM_DatasetGetSubset_R\n , ref_handle\n , c(private$used_indices)\n , length(private$used_indices)\n , params_str\n )\n\n }\n if (.is_null_handle(x = handle)) {\n stop(\"lgb.Dataset.construct: cannot create Dataset handle\")\n }\n # Setup class and private type\n class(handle) <- \"lgb.Dataset.handle\"\n private$handle <- handle\n\n # Set feature names\n if (!is.null(private$colnames)) {\n self$set_colnames(colnames = private$colnames)\n }\n\n # Ensure that private$colnames matches the feature names on the C++ side. This line is necessary\n # in cases like constructing from a file or from a matrix with no column names.\n private$colnames <- .Call(\n LGBM_DatasetGetFeatureNames_R\n , private$handle\n )\n\n # Load init score if requested\n if (!is.null(private$predictor) && is.null(private$used_indices)) {\n\n # Setup initial scores\n init_score <- private$predictor$predict(\n data = private$raw_data\n , rawscore = TRUE\n )\n\n # Not needed to transpose, for is col_marjor\n init_score <- as.vector(init_score)\n private$info$init_score <- init_score\n\n }\n\n # Should we free raw data?\n if (isTRUE(private$free_raw_data)) {\n private$raw_data <- NULL\n }\n\n # Get private information\n if (length(private$info) > 0L) {\n\n # Set infos\n for (i in seq_along(private$info)) {\n\n p <- private$info[i]\n self$set_field(\n field_name = names(p)\n , data = p[[1L]]\n )\n\n }\n\n }\n\n # Get label information existence\n if (is.null(self$get_field(field_name = \"label\"))) {\n stop(\"lgb.Dataset.construct: label should be set\")\n }\n\n return(invisible(self))\n\n },\n\n # Dimension function\n dim = function() {\n\n # Check for handle\n if (!.is_null_handle(x = private$handle)) {\n\n num_row <- 0L\n num_col <- 0L\n\n # Get numeric data and numeric features\n .Call(\n LGBM_DatasetGetNumData_R\n , private$handle\n , num_row\n )\n .Call(\n LGBM_DatasetGetNumFeature_R\n , private$handle\n , num_col\n )\n return(\n c(num_row, num_col)\n )\n\n } else if (is.matrix(private$raw_data) || methods::is(private$raw_data, \"dgCMatrix\")) {\n\n # Check if dgCMatrix (sparse matrix column compressed)\n # NOTE: requires Matrix package\n return(dim(private$raw_data))\n\n } else {\n\n # Trying to work with unknown dimensions is not possible\n stop(\n \"dim: cannot get dimensions before dataset has been constructed, \"\n , \"please call lgb.Dataset.construct explicitly\"\n )\n\n }\n\n },\n\n # Get number of bins for feature\n get_feature_num_bin = function(feature) {\n if (.is_null_handle(x = private$handle)) {\n stop(\"Cannot get number of bins in feature before constructing Dataset.\")\n }\n if (is.character(feature)) {\n feature_name <- feature\n feature <- which(private$colnames == feature_name)\n if (length(feature) == 0L) {\n stop(sprintf(\"feature '%s' not found\", feature_name))\n }\n }\n num_bin <- integer(1L)\n .Call(\n LGBM_DatasetGetFeatureNumBin_R\n , private$handle\n , feature - 1L\n , num_bin\n )\n return(num_bin)\n },\n\n # Get column names\n get_colnames = function() {\n\n # Check for handle\n if (!.is_null_handle(x = private$handle)) {\n private$colnames <- .Call(\n LGBM_DatasetGetFeatureNames_R\n , private$handle\n )\n return(private$colnames)\n\n } else if (is.matrix(private$raw_data) || methods::is(private$raw_data, \"dgCMatrix\")) {\n\n # Check if dgCMatrix (sparse matrix column compressed)\n return(colnames(private$raw_data))\n\n } else {\n\n # Trying to work with unknown formats is not possible\n stop(\n \"Dataset$get_colnames(): cannot get column names before dataset has been constructed, please call \"\n , \"lgb.Dataset.construct() explicitly\"\n )\n\n }\n\n },\n\n # Set column names\n set_colnames = function(colnames) {\n\n # Check column names non-existence\n if (is.null(colnames)) {\n return(invisible(self))\n }\n\n # Check empty column names\n colnames <- as.character(colnames)\n if (length(colnames) == 0L) {\n return(invisible(self))\n }\n\n # Write column names\n private$colnames <- colnames\n if (!.is_null_handle(x = private$handle)) {\n\n # Merge names with tab separation\n merged_name <- paste(as.list(private$colnames), collapse = \"\\t\")\n .Call(\n LGBM_DatasetSetFeatureNames_R\n , private$handle\n , merged_name\n )\n\n }\n\n return(invisible(self))\n\n },\n\n get_field = function(field_name) {\n\n # Check if attribute key is in the known attribute list\n if (!is.character(field_name) || length(field_name) != 1L || !field_name %in% .INFO_KEYS()) {\n stop(\n \"Dataset$get_field(): field_name must be one of the following: \"\n , toString(sQuote(.INFO_KEYS(), q = FALSE))\n )\n }\n\n # Check for info name and handle\n if (is.null(private$info[[field_name]])) {\n\n if (.is_null_handle(x = private$handle)) {\n stop(\"Cannot perform Dataset$get_field() before constructing Dataset.\")\n }\n\n # Get field size of info\n info_len <- 0L\n .Call(\n LGBM_DatasetGetFieldSize_R\n , private$handle\n , field_name\n , info_len\n )\n\n if (info_len > 0L) {\n\n # Get back fields\n if (field_name == \"group\") {\n ret <- integer(info_len)\n } else {\n ret <- numeric(info_len)\n }\n\n .Call(\n LGBM_DatasetGetField_R\n , private$handle\n , field_name\n , ret\n )\n\n private$info[[field_name]] <- ret\n\n }\n }\n\n return(private$info[[field_name]])\n\n },\n\n set_field = function(field_name, data) {\n\n # Check if attribute key is in the known attribute list\n if (!is.character(field_name) || length(field_name) != 1L || !field_name %in% .INFO_KEYS()) {\n stop(\n \"Dataset$set_field(): field_name must be one of the following: \"\n , toString(sQuote(.INFO_KEYS(), q = FALSE))\n )\n }\n\n # Check for type of information\n data <- if (field_name == \"group\") {\n as.integer(data)\n } else {\n as.numeric(data)\n }\n\n # Store information privately\n private$info[[field_name]] <- data\n\n if (!.is_null_handle(x = private$handle) && !is.null(data)) {\n\n if (length(data) > 0L) {\n\n .Call(\n LGBM_DatasetSetField_R\n , private$handle\n , field_name\n , data\n , length(data)\n )\n\n private$version <- private$version + 1L\n\n }\n\n }\n\n return(invisible(self))\n\n },\n\n slice = function(idxset) {\n\n return(\n Dataset$new(\n data = NULL\n , params = private$params\n , reference = self\n , colnames = private$colnames\n , categorical_feature = private$categorical_feature\n , predictor = private$predictor\n , free_raw_data = private$free_raw_data\n , used_indices = sort(idxset, decreasing = FALSE)\n )\n )\n\n },\n\n # [description] Update Dataset parameters. If it has not been constructed yet,\n # this operation just happens on the R side (updating private$params).\n # If it has been constructed, parameters will be updated on the C++ side.\n update_params = function(params) {\n if (length(params) == 0L) {\n return(invisible(self))\n }\n new_params <- utils::modifyList(private$params, params)\n if (.is_null_handle(x = private$handle)) {\n private$params <- new_params\n } else {\n tryCatch({\n .Call(\n LGBM_DatasetUpdateParamChecking_R\n , .params2str(params = private$params)\n , .params2str(params = new_params)\n )\n private$params <- new_params\n }, error = function(e) {\n # If updating failed but raw data is not available, raise an error because\n # achieving what the user asked for is not possible\n if (is.null(private$raw_data)) {\n stop(e)\n }\n\n # If updating failed but raw data is available, modify the params\n # on the R side and re-set (\"deconstruct\") the Dataset\n private$params <- new_params\n private$finalize()\n })\n }\n return(invisible(self))\n\n },\n\n # [description] Get only Dataset-specific parameters. This is primarily used by\n # Booster to update its parameters based on the characteristics of\n # a Dataset. It should not be used by other methods in this class,\n # since \"verbose\" is not a Dataset parameter and needs to be passed\n # through to avoid globally re-setting verbosity.\n get_params = function() {\n dataset_params <- unname(unlist(.DATASET_PARAMETERS()))\n ret <- list()\n for (param_key in names(private$params)) {\n if (param_key %in% dataset_params) {\n ret[[param_key]] <- private$params[[param_key]]\n }\n }\n return(ret)\n },\n\n # Set categorical feature parameter\n set_categorical_feature = function(categorical_feature) {\n\n # Check for identical input\n if (identical(private$categorical_feature, categorical_feature)) {\n return(invisible(self))\n }\n\n # Check for empty data\n if (is.null(private$raw_data)) {\n stop(\"set_categorical_feature: cannot set categorical feature after freeing raw data,\n please set \", sQuote(\"free_raw_data = FALSE\"), \" when you construct lgb.Dataset\")\n }\n\n # Overwrite categorical features\n private$categorical_feature <- categorical_feature\n\n # Finalize and return self\n private$finalize()\n return(invisible(self))\n\n },\n\n set_reference = function(reference) {\n\n # setting reference to this same Dataset object doesn't require any changes\n if (identical(private$reference, reference)) {\n return(invisible(self))\n }\n\n # changing the reference removes the Dataset object on the C++ side, so it should only\n # be done if you still have the raw_data available, so that the new Dataset can be reconstructed\n if (is.null(private$raw_data)) {\n stop(\"set_reference: cannot set reference after freeing raw data,\n please set \", sQuote(\"free_raw_data = FALSE\"), \" when you construct lgb.Dataset\")\n }\n\n if (!.is_Dataset(reference)) {\n stop(\"set_reference: Can only use lgb.Dataset as a reference\")\n }\n\n # Set known references\n self$set_categorical_feature(categorical_feature = reference$.__enclos_env__$private$categorical_feature)\n self$set_colnames(colnames = reference$get_colnames())\n private$set_predictor(predictor = reference$.__enclos_env__$private$predictor)\n\n # Store reference\n private$reference <- reference\n\n # Finalize and return self\n private$finalize()\n return(invisible(self))\n\n },\n\n # Save binary model\n save_binary = function(fname) {\n\n # Store binary data\n self$construct()\n .Call(\n LGBM_DatasetSaveBinary_R\n , private$handle\n , path.expand(fname)\n )\n return(invisible(self))\n }\n\n ),\n private = list(\n handle = NULL,\n raw_data = NULL,\n params = list(),\n reference = NULL,\n colnames = NULL,\n categorical_feature = NULL,\n predictor = NULL,\n free_raw_data = TRUE,\n used_indices = NULL,\n info = NULL,\n version = 0L,\n\n # finalize() will free up the handles\n finalize = function() {\n .Call(\n LGBM_DatasetFree_R\n , private$handle\n )\n private$handle <- NULL\n return(invisible(NULL))\n },\n\n get_handle = function() {\n\n # Get handle and construct if needed\n if (.is_null_handle(x = private$handle)) {\n self$construct()\n }\n return(private$handle)\n\n },\n\n set_predictor = function(predictor) {\n\n if (identical(private$predictor, predictor)) {\n return(invisible(self))\n }\n\n # Check for empty data\n if (is.null(private$raw_data)) {\n stop(\"set_predictor: cannot set predictor after free raw data,\n please set \", sQuote(\"free_raw_data = FALSE\"), \" when you construct lgb.Dataset\")\n }\n\n # Check for empty predictor\n if (!is.null(predictor)) {\n\n # Predictor is unknown\n if (!.is_Predictor(predictor)) {\n stop(\"set_predictor: Can only use lgb.Predictor as predictor\")\n }\n\n }\n\n # Store predictor\n private$predictor <- predictor\n\n # Finalize and return self\n private$finalize()\n return(invisible(self))\n\n }\n\n )\n)\n\n#' @title Construct \\code{lgb.Dataset} object\n#' @description LightGBM does not train on raw data.\n#' It discretizes continuous features into histogram bins, tries to\n#' combine categorical features, and automatically handles missing and\n# infinite values.\n#'\n#' The \\code{Dataset} class handles that preprocessing, and holds that\n#' alternative representation of the input data.\n#' @inheritParams lgb_shared_dataset_params\n#' @param data a \\code{matrix} object, a \\code{dgCMatrix} object,\n#' a character representing a path to a text file (CSV, TSV, or LibSVM),\n#' or a character representing a path to a binary \\code{lgb.Dataset} file\n#' @param params a list of parameters. See\n#' \\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{\n#' The \"Dataset Parameters\" section of the documentation} for a list of parameters\n#' and valid values.\n#' @param reference reference dataset. When LightGBM creates a Dataset, it does some preprocessing like binning\n#' continuous features into histograms. If you want to apply the same bin boundaries from an existing\n#' dataset to new \\code{data}, pass that existing Dataset to this argument.\n#' @param colnames names of columns\n#' @param categorical_feature categorical features. This can either be a character vector of feature\n#' names or an integer vector with the indices of the features (e.g.\n#' \\code{c(1L, 10L)} to say \"the first and tenth columns\").\n#' @param free_raw_data LightGBM constructs its data format, called a \"Dataset\", from tabular data.\n#' By default, that Dataset object on the R side does not keep a copy of the raw data.\n#' This reduces LightGBM's memory consumption, but it means that the Dataset object\n#' cannot be changed after it has been constructed. If you'd prefer to be able to\n#' change the Dataset object after construction, set \\code{free_raw_data = FALSE}.\n#'\n#' @return constructed dataset\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' data_file <- tempfile(fileext = \".data\")\n#' lgb.Dataset.save(dtrain, data_file)\n#' dtrain <- lgb.Dataset(data_file)\n#' lgb.Dataset.construct(dtrain)\n#' }\n#' @export\nlgb.Dataset <- function(data,\n params = list(),\n reference = NULL,\n colnames = NULL,\n categorical_feature = NULL,\n free_raw_data = TRUE,\n label = NULL,\n weight = NULL,\n group = NULL,\n init_score = NULL) {\n\n return(\n invisible(Dataset$new(\n data = data\n , params = params\n , reference = reference\n , colnames = colnames\n , categorical_feature = categorical_feature\n , predictor = NULL\n , free_raw_data = free_raw_data\n , used_indices = NULL\n , label = label\n , weight = weight\n , group = group\n , init_score = init_score\n ))\n )\n\n}\n\n#' @name lgb.Dataset.create.valid\n#' @title Construct validation data\n#' @description Construct validation data according to training data\n#' @inheritParams lgb_shared_dataset_params\n#' @param dataset \\code{lgb.Dataset} object, training data\n#' @param data a \\code{matrix} object, a \\code{dgCMatrix} object,\n#' a character representing a path to a text file (CSV, TSV, or LibSVM),\n#' or a character representing a path to a binary \\code{Dataset} file\n#' @param params a list of parameters. See\n#' \\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{\n#' The \"Dataset Parameters\" section of the documentation} for a list of parameters\n#' and valid values. If this is an empty list (the default), the validation Dataset\n#' will have the same parameters as the Dataset passed to argument \\code{dataset}.\n#'\n#' @return constructed dataset\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' data(agaricus.test, package = \"lightgbm\")\n#' test <- agaricus.test\n#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\n#'\n#' # parameters can be changed between the training data and validation set,\n#' # for example to account for training data in a text file with a header row\n#' # and validation data in a text file without it\n#' train_file <- tempfile(pattern = \"train_\", fileext = \".csv\")\n#' write.table(\n#' data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L))\n#' , file = train_file\n#' , sep = \",\"\n#' , col.names = TRUE\n#' , row.names = FALSE\n#' , quote = FALSE\n#' )\n#'\n#' valid_file <- tempfile(pattern = \"valid_\", fileext = \".csv\")\n#' write.table(\n#' data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L))\n#' , file = valid_file\n#' , sep = \",\"\n#' , col.names = FALSE\n#' , row.names = FALSE\n#' , quote = FALSE\n#' )\n#'\n#' dtrain <- lgb.Dataset(\n#' data = train_file\n#' , params = list(has_header = TRUE)\n#' )\n#' dtrain$construct()\n#'\n#' dvalid <- lgb.Dataset(\n#' data = valid_file\n#' , params = list(has_header = FALSE)\n#' )\n#' dvalid$construct()\n#' }\n#' @export\nlgb.Dataset.create.valid <- function(dataset,\n data,\n label = NULL,\n weight = NULL,\n group = NULL,\n init_score = NULL,\n params = list()) {\n\n if (!.is_Dataset(x = dataset)) {\n stop(\"lgb.Dataset.create.valid: input data should be an lgb.Dataset object\")\n }\n\n # Create validation dataset\n return(invisible(\n dataset$create_valid(\n data = data\n , label = label\n , weight = weight\n , group = group\n , init_score = init_score\n , params = params\n )\n ))\n\n}\n\n#' @name lgb.Dataset.construct\n#' @title Construct Dataset explicitly\n#' @description Construct Dataset explicitly\n#' @param dataset Object of class \\code{lgb.Dataset}\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' lgb.Dataset.construct(dtrain)\n#' }\n#' @return constructed dataset\n#' @export\nlgb.Dataset.construct <- function(dataset) {\n\n if (!.is_Dataset(x = dataset)) {\n stop(\"lgb.Dataset.construct: input data should be an lgb.Dataset object\")\n }\n\n return(invisible(dataset$construct()))\n\n}\n\n#' @title Dimensions of an \\code{lgb.Dataset}\n#' @description Returns a vector of numbers of rows and of columns in an \\code{lgb.Dataset}.\n#' @param x Object of class \\code{lgb.Dataset}\n#'\n#' @return a vector of numbers of rows and of columns\n#'\n#' @details\n#' Note: since \\code{nrow} and \\code{ncol} internally use \\code{dim}, they can also\n#' be directly used with an \\code{lgb.Dataset} object.\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#'\n#' stopifnot(nrow(dtrain) == nrow(train$data))\n#' stopifnot(ncol(dtrain) == ncol(train$data))\n#' stopifnot(all(dim(dtrain) == dim(train$data)))\n#' }\n#' @rdname dim\n#' @export\ndim.lgb.Dataset <- function(x) {\n\n if (!.is_Dataset(x = x)) {\n stop(\"dim.lgb.Dataset: input data should be an lgb.Dataset object\")\n }\n\n return(x$dim())\n\n}\n\n#' @title Handling of column names of \\code{lgb.Dataset}\n#' @description Only column names are supported for \\code{lgb.Dataset}, thus setting of\n#' row names would have no effect and returned row names would be NULL.\n#' @param x object of class \\code{lgb.Dataset}\n#' @param value a list of two elements: the first one is ignored\n#' and the second one is column names\n#'\n#' @details\n#' Generic \\code{dimnames} methods are used by \\code{colnames}.\n#' Since row names are irrelevant, it is recommended to use \\code{colnames} directly.\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' lgb.Dataset.construct(dtrain)\n#' dimnames(dtrain)\n#' colnames(dtrain)\n#' colnames(dtrain) <- make.names(seq_len(ncol(train$data)))\n#' print(dtrain, verbose = TRUE)\n#' }\n#' @rdname dimnames.lgb.Dataset\n#' @return A list with the dimension names of the dataset\n#' @export\ndimnames.lgb.Dataset <- function(x) {\n\n if (!.is_Dataset(x = x)) {\n stop(\"dimnames.lgb.Dataset: input data should be an lgb.Dataset object\")\n }\n\n # Return dimension names\n return(list(NULL, x$get_colnames()))\n\n}\n\n#' @rdname dimnames.lgb.Dataset\n#' @export\n`dimnames<-.lgb.Dataset` <- function(x, value) {\n\n # Check if invalid element list\n if (!identical(class(value), \"list\") || length(value) != 2L) {\n stop(\"invalid \", sQuote(\"value\", q = FALSE), \" given: must be a list of two elements\")\n }\n\n # Check for unknown row names\n if (!is.null(value[[1L]])) {\n stop(\"lgb.Dataset does not have rownames\")\n }\n\n if (is.null(value[[2L]])) {\n\n x$set_colnames(colnames = NULL)\n return(x)\n\n }\n\n # Check for unmatching column size\n if (ncol(x) != length(value[[2L]])) {\n stop(\n \"can't assign \"\n , sQuote(length(value[[2L]]), q = FALSE)\n , \" colnames to an lgb.Dataset with \"\n , sQuote(ncol(x), q = FALSE)\n , \" columns\"\n )\n }\n\n # Set column names properly, and return\n x$set_colnames(colnames = value[[2L]])\n return(x)\n\n}\n\n#' @title Slice a dataset\n#' @description Get a new \\code{lgb.Dataset} containing the specified rows of\n#' original \\code{lgb.Dataset} object\n#'\n#' \\emph{Renamed from} \\code{slice()} \\emph{in 4.4.0}\n#'\n#' @param dataset Object of class \\code{lgb.Dataset}\n#' @param idxset an integer vector of indices of rows needed\n#' @return constructed sub dataset\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#'\n#' dsub <- lgb.slice.Dataset(dtrain, seq_len(42L))\n#' lgb.Dataset.construct(dsub)\n#' labels <- lightgbm::get_field(dsub, \"label\")\n#' }\n#' @export\nlgb.slice.Dataset <- function(dataset, idxset) {\n\n if (!.is_Dataset(x = dataset)) {\n stop(\"lgb.slice.Dataset: input dataset should be an lgb.Dataset object\")\n }\n\n return(invisible(dataset$slice(idxset = idxset)))\n\n}\n\n#' @name get_field\n#' @title Get one attribute of a \\code{lgb.Dataset}\n#' @description Get one attribute of a \\code{lgb.Dataset}\n#' @param dataset Object of class \\code{lgb.Dataset}\n#' @param field_name String with the name of the attribute to get. One of the following.\n#' \\itemize{\n#' \\item \\code{label}: label lightgbm learns from ;\n#' \\item \\code{weight}: to do a weight rescale ;\n#' \\item{\\code{group}: used for learning-to-rank tasks. An integer vector describing how to\n#' group rows together as ordered results from the same set of candidate results to be ranked.\n#' For example, if you have a 100-document dataset with \\code{group = c(10, 20, 40, 10, 10, 10)},\n#' that means that you have 6 groups, where the first 10 records are in the first group,\n#' records 11-30 are in the second group, etc.}\n#' \\item \\code{init_score}: initial score is the base prediction lightgbm will boost from.\n#' }\n#' @return requested attribute\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' lgb.Dataset.construct(dtrain)\n#'\n#' labels <- lightgbm::get_field(dtrain, \"label\")\n#' lightgbm::set_field(dtrain, \"label\", 1 - labels)\n#'\n#' labels2 <- lightgbm::get_field(dtrain, \"label\")\n#' stopifnot(all(labels2 == 1 - labels))\n#' }\n#' @export\nget_field <- function(dataset, field_name) {\n UseMethod(\"get_field\")\n}\n\n#' @rdname get_field\n#' @export\nget_field.lgb.Dataset <- function(dataset, field_name) {\n\n # Check if dataset is not a dataset\n if (!.is_Dataset(x = dataset)) {\n stop(\"get_field.lgb.Dataset(): input dataset should be an lgb.Dataset object\")\n }\n\n return(dataset$get_field(field_name = field_name))\n\n}\n\n#' @name set_field\n#' @title Set one attribute of a \\code{lgb.Dataset} object\n#' @description Set one attribute of a \\code{lgb.Dataset}\n#' @param dataset Object of class \\code{lgb.Dataset}\n#' @param field_name String with the name of the attribute to set. One of the following.\n#' \\itemize{\n#' \\item \\code{label}: label lightgbm learns from ;\n#' \\item \\code{weight}: to do a weight rescale ;\n#' \\item{\\code{group}: used for learning-to-rank tasks. An integer vector describing how to\n#' group rows together as ordered results from the same set of candidate results to be ranked.\n#' For example, if you have a 100-document dataset with \\code{group = c(10, 20, 40, 10, 10, 10)},\n#' that means that you have 6 groups, where the first 10 records are in the first group,\n#' records 11-30 are in the second group, etc.}\n#' \\item \\code{init_score}: initial score is the base prediction lightgbm will boost from.\n#' }\n#' @param data The data for the field. See examples.\n#' @return The \\code{lgb.Dataset} you passed in.\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' lgb.Dataset.construct(dtrain)\n#'\n#' labels <- lightgbm::get_field(dtrain, \"label\")\n#' lightgbm::set_field(dtrain, \"label\", 1 - labels)\n#'\n#' labels2 <- lightgbm::get_field(dtrain, \"label\")\n#' stopifnot(all.equal(labels2, 1 - labels))\n#' }\n#' @export\nset_field <- function(dataset, field_name, data) {\n UseMethod(\"set_field\")\n}\n\n#' @rdname set_field\n#' @export\nset_field.lgb.Dataset <- function(dataset, field_name, data) {\n\n if (!.is_Dataset(x = dataset)) {\n stop(\"set_field.lgb.Dataset: input dataset should be an lgb.Dataset object\")\n }\n\n return(invisible(dataset$set_field(field_name = field_name, data = data)))\n}\n\n#' @name lgb.Dataset.set.categorical\n#' @title Set categorical feature of \\code{lgb.Dataset}\n#' @description Set the categorical features of an \\code{lgb.Dataset} object. Use this function\n#' to tell LightGBM which features should be treated as categorical.\n#' @param dataset object of class \\code{lgb.Dataset}\n#' @param categorical_feature categorical features. This can either be a character vector of feature\n#' names or an integer vector with the indices of the features (e.g.\n#' \\code{c(1L, 10L)} to say \"the first and tenth columns\").\n#' @return the dataset you passed in\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' data_file <- tempfile(fileext = \".data\")\n#' lgb.Dataset.save(dtrain, data_file)\n#' dtrain <- lgb.Dataset(data_file)\n#' lgb.Dataset.set.categorical(dtrain, 1L:2L)\n#' }\n#' @rdname lgb.Dataset.set.categorical\n#' @export\nlgb.Dataset.set.categorical <- function(dataset, categorical_feature) {\n\n if (!.is_Dataset(x = dataset)) {\n stop(\"lgb.Dataset.set.categorical: input dataset should be an lgb.Dataset object\")\n }\n\n return(invisible(dataset$set_categorical_feature(categorical_feature = categorical_feature)))\n\n}\n\n#' @name lgb.Dataset.set.reference\n#' @title Set reference of \\code{lgb.Dataset}\n#' @description If you want to use validation data, you should set reference to training data\n#' @param dataset object of class \\code{lgb.Dataset}\n#' @param reference object of class \\code{lgb.Dataset}\n#'\n#' @return the dataset you passed in\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' # create training Dataset\n#' data(agaricus.train, package =\"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#'\n#' # create a validation Dataset, using dtrain as a reference\n#' data(agaricus.test, package = \"lightgbm\")\n#' test <- agaricus.test\n#' dtest <- lgb.Dataset(test$data, label = test$label)\n#' lgb.Dataset.set.reference(dtest, dtrain)\n#' }\n#' @rdname lgb.Dataset.set.reference\n#' @export\nlgb.Dataset.set.reference <- function(dataset, reference) {\n\n if (!.is_Dataset(x = dataset)) {\n stop(\"lgb.Dataset.set.reference: input dataset should be an lgb.Dataset object\")\n }\n\n return(invisible(dataset$set_reference(reference = reference)))\n}\n\n#' @name lgb.Dataset.save\n#' @title Save \\code{lgb.Dataset} to a binary file\n#' @description Please note that \\code{init_score} is not saved in binary file.\n#' If you need it, please set it again after loading Dataset.\n#' @param dataset object of class \\code{lgb.Dataset}\n#' @param fname object filename of output file\n#'\n#' @return the dataset you passed in\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' lgb.Dataset.save(dtrain, tempfile(fileext = \".bin\"))\n#' }\n#' @export\nlgb.Dataset.save <- function(dataset, fname) {\n\n if (!.is_Dataset(x = dataset)) {\n stop(\"lgb.Dataset.save: input dataset should be an lgb.Dataset object\")\n }\n\n if (!is.character(fname)) {\n stop(\"lgb.Dataset.save: fname should be a character or a file connection\")\n }\n\n return(invisible(dataset$save_binary(fname = fname)))\n}\n" }, { "path": "R-package/R/lgb.Predictor.R", "content": "#' @importFrom methods is new\n#' @importFrom R6 R6Class\n#' @importFrom utils read.delim\n#' @importClassesFrom Matrix dsparseMatrix dsparseVector dgCMatrix dgRMatrix CsparseMatrix RsparseMatrix\nPredictor <- R6::R6Class(\n\n classname = \"lgb.Predictor\",\n cloneable = FALSE,\n public = list(\n\n # Initialize will create a starter model\n initialize = function(modelfile, params = list(), fast_predict_config = list()) {\n private$params <- .params2str(params = params)\n handle <- NULL\n\n if (is.character(modelfile)) {\n\n # Create handle on it\n handle <- .Call(\n LGBM_BoosterCreateFromModelfile_R\n , path.expand(modelfile)\n )\n private$need_free_handle <- TRUE\n\n } else if (methods::is(modelfile, \"lgb.Booster.handle\") || inherits(modelfile, \"externalptr\")) {\n\n # Check if model file is a booster handle already\n handle <- modelfile\n private$need_free_handle <- FALSE\n\n } else if (.is_Booster(modelfile)) {\n\n handle <- modelfile$get_handle()\n private$need_free_handle <- FALSE\n\n } else {\n\n stop(\"lgb.Predictor: modelfile must be either a character filename or an lgb.Booster.handle\")\n\n }\n\n private$fast_predict_config <- fast_predict_config\n\n # Override class and store it\n class(handle) <- \"lgb.Booster.handle\"\n private$handle <- handle\n\n return(invisible(NULL))\n\n },\n\n # Get current iteration\n current_iter = function() {\n\n cur_iter <- 0L\n .Call(\n LGBM_BoosterGetCurrentIteration_R\n , private$handle\n , cur_iter\n )\n return(cur_iter)\n\n },\n\n # Predict from data\n predict = function(data,\n start_iteration = NULL,\n num_iteration = NULL,\n rawscore = FALSE,\n predleaf = FALSE,\n predcontrib = FALSE,\n header = FALSE) {\n\n # Check if number of iterations is existing - if not, then set it to -1 (use all)\n if (is.null(num_iteration)) {\n num_iteration <- -1L\n }\n # Check if start iterations is existing - if not, then set it to 0 (start from the first iteration)\n if (is.null(start_iteration)) {\n start_iteration <- 0L\n }\n\n # Check if data is a file name and not a matrix\n if (identical(class(data), \"character\") && length(data) == 1L) {\n\n data <- path.expand(data)\n\n # Data is a filename, create a temporary file with a \"lightgbm_\" pattern in it\n tmp_filename <- tempfile(pattern = \"lightgbm_\")\n on.exit(unlink(tmp_filename), add = TRUE)\n\n # Predict from temporary file\n .Call(\n LGBM_BoosterPredictForFile_R\n , private$handle\n , data\n , as.integer(header)\n , as.integer(rawscore)\n , as.integer(predleaf)\n , as.integer(predcontrib)\n , as.integer(start_iteration)\n , as.integer(num_iteration)\n , private$params\n , tmp_filename\n )\n\n # Get predictions from file\n preds <- utils::read.delim(tmp_filename, header = FALSE, sep = \"\\t\")\n num_row <- nrow(preds)\n preds <- as.vector(t(preds))\n\n } else if (predcontrib && inherits(data, c(\"dsparseMatrix\", \"dsparseVector\"))) {\n\n ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle)\n ncols_out <- integer(1L)\n .Call(LGBM_BoosterGetNumClasses_R, private$handle, ncols_out)\n ncols_out <- (ncols + 1L) * max(ncols_out, 1L)\n if (is.na(ncols_out)) {\n ncols_out <- as.numeric(ncols + 1L) * as.numeric(max(ncols_out, 1L))\n }\n if (!inherits(data, \"dsparseVector\") && ncols_out > .Machine$integer.max) {\n stop(\"Resulting matrix of feature contributions is too large for R to handle.\")\n }\n\n if (inherits(data, \"dsparseVector\")) {\n\n if (length(data) > ncols) {\n stop(sprintf(\"Model was fitted to data with %d columns, input data has %.0f columns.\"\n , ncols\n , length(data)))\n }\n res <- .Call(\n LGBM_BoosterPredictSparseOutput_R\n , private$handle\n , c(0L, as.integer(length(data@x)))\n , data@i - 1L\n , data@x\n , TRUE\n , 1L\n , ncols\n , start_iteration\n , num_iteration\n , private$params\n )\n out <- methods::new(\"dsparseVector\")\n out@i <- res$indices + 1L\n out@x <- res$data\n out@length <- ncols_out\n return(out)\n\n } else if (inherits(data, \"dgRMatrix\")) {\n\n if (ncol(data) > ncols) {\n stop(sprintf(\"Model was fitted to data with %d columns, input data has %.0f columns.\"\n , ncols\n , ncol(data)))\n }\n res <- .Call(\n LGBM_BoosterPredictSparseOutput_R\n , private$handle\n , data@p\n , data@j\n , data@x\n , TRUE\n , nrow(data)\n , ncols\n , start_iteration\n , num_iteration\n , private$params\n )\n out <- methods::new(\"dgRMatrix\")\n out@p <- res$indptr\n out@j <- res$indices\n out@x <- res$data\n out@Dim <- as.integer(c(nrow(data), ncols_out))\n\n } else if (inherits(data, \"dgCMatrix\")) {\n\n if (ncol(data) != ncols) {\n stop(sprintf(\"Model was fitted to data with %d columns, input data has %.0f columns.\"\n , ncols\n , ncol(data)))\n }\n res <- .Call(\n LGBM_BoosterPredictSparseOutput_R\n , private$handle\n , data@p\n , data@i\n , data@x\n , FALSE\n , nrow(data)\n , ncols\n , start_iteration\n , num_iteration\n , private$params\n )\n out <- methods::new(\"dgCMatrix\")\n out@p <- res$indptr\n out@i <- res$indices\n out@x <- res$data\n out@Dim <- as.integer(c(nrow(data), length(res$indptr) - 1L))\n\n } else {\n\n stop(sprintf(\"Predictions on sparse inputs are only allowed for '%s', '%s', '%s' - got: %s\"\n , \"dsparseVector\"\n , \"dgRMatrix\"\n , \"dgCMatrix\"\n , toString(class(data))))\n }\n\n if (NROW(row.names(data))) {\n out@Dimnames[[1L]] <- row.names(data)\n }\n return(out)\n\n } else {\n\n # Not a file, we need to predict from R object\n num_row <- nrow(data)\n if (is.null(num_row)) {\n num_row <- 1L\n }\n\n npred <- 0L\n\n # Check number of predictions to do\n .Call(\n LGBM_BoosterCalcNumPredict_R\n , private$handle\n , as.integer(num_row)\n , as.integer(rawscore)\n , as.integer(predleaf)\n , as.integer(predcontrib)\n , as.integer(start_iteration)\n , as.integer(num_iteration)\n , npred\n )\n\n # Pre-allocate empty vector\n preds <- numeric(npred)\n\n # Check if data is a matrix\n if (is.matrix(data)) {\n # this if() prevents the memory and computational costs\n # of converting something that is already \"double\" to \"double\"\n if (storage.mode(data) != \"double\") {\n storage.mode(data) <- \"double\"\n }\n\n if (nrow(data) == 1L) {\n\n use_fast_config <- private$check_can_use_fast_predict_config(\n csr = FALSE\n , rawscore = rawscore\n , predleaf = predleaf\n , predcontrib = predcontrib\n , start_iteration = start_iteration\n , num_iteration = num_iteration\n )\n\n if (use_fast_config) {\n .Call(\n LGBM_BoosterPredictForMatSingleRowFast_R\n , private$fast_predict_config$handle\n , data\n , preds\n )\n } else {\n .Call(\n LGBM_BoosterPredictForMatSingleRow_R\n , private$handle\n , data\n , rawscore\n , predleaf\n , predcontrib\n , start_iteration\n , num_iteration\n , private$params\n , preds\n )\n }\n\n } else {\n .Call(\n LGBM_BoosterPredictForMat_R\n , private$handle\n , data\n , as.integer(nrow(data))\n , as.integer(ncol(data))\n , as.integer(rawscore)\n , as.integer(predleaf)\n , as.integer(predcontrib)\n , as.integer(start_iteration)\n , as.integer(num_iteration)\n , private$params\n , preds\n )\n }\n\n } else if (inherits(data, \"dsparseVector\")) {\n\n if (length(self$fast_predict_config)) {\n ncols <- self$fast_predict_config$ncols\n use_fast_config <- private$check_can_use_fast_predict_config(\n csr = TRUE\n , rawscore = rawscore\n , predleaf = predleaf\n , predcontrib = predcontrib\n , start_iteration = start_iteration\n , num_iteration = num_iteration\n )\n } else {\n ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle)\n use_fast_config <- FALSE\n }\n\n if (length(data) > ncols) {\n stop(sprintf(\"Model was fitted to data with %d columns, input data has %.0f columns.\"\n , ncols\n , length(data)))\n }\n\n if (use_fast_config) {\n .Call(\n LGBM_BoosterPredictForCSRSingleRowFast_R\n , self$fast_predict_config$handle\n , data@i - 1L\n , data@x\n , preds\n )\n } else {\n .Call(\n LGBM_BoosterPredictForCSRSingleRow_R\n , private$handle\n , data@i - 1L\n , data@x\n , ncols\n , as.integer(rawscore)\n , as.integer(predleaf)\n , as.integer(predcontrib)\n , start_iteration\n , num_iteration\n , private$params\n , preds\n )\n }\n\n } else if (inherits(data, \"dgRMatrix\")) {\n\n ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle)\n if (ncol(data) > ncols) {\n stop(sprintf(\"Model was fitted to data with %d columns, input data has %.0f columns.\"\n , ncols\n , ncol(data)))\n }\n\n if (nrow(data) == 1L) {\n\n if (length(self$fast_predict_config)) {\n ncols <- self$fast_predict_config$ncols\n use_fast_config <- private$check_can_use_fast_predict_config(\n csr = TRUE\n , rawscore = rawscore\n , predleaf = predleaf\n , predcontrib = predcontrib\n , start_iteration = start_iteration\n , num_iteration = num_iteration\n )\n } else {\n ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle)\n use_fast_config <- FALSE\n }\n\n if (use_fast_config) {\n .Call(\n LGBM_BoosterPredictForCSRSingleRowFast_R\n , self$fast_predict_config$handle\n , data@j\n , data@x\n , preds\n )\n } else {\n .Call(\n LGBM_BoosterPredictForCSRSingleRow_R\n , private$handle\n , data@j\n , data@x\n , ncols\n , as.integer(rawscore)\n , as.integer(predleaf)\n , as.integer(predcontrib)\n , start_iteration\n , num_iteration\n , private$params\n , preds\n )\n }\n\n } else {\n\n .Call(\n LGBM_BoosterPredictForCSR_R\n , private$handle\n , data@p\n , data@j\n , data@x\n , ncols\n , as.integer(rawscore)\n , as.integer(predleaf)\n , as.integer(predcontrib)\n , start_iteration\n , num_iteration\n , private$params\n , preds\n )\n\n }\n\n } else if (methods::is(data, \"dgCMatrix\")) {\n if (length(data@p) > 2147483647L) {\n stop(\"Cannot support large CSC matrix\")\n }\n # Check if data is a dgCMatrix (sparse matrix, column compressed format)\n .Call(\n LGBM_BoosterPredictForCSC_R\n , private$handle\n , data@p\n , data@i\n , data@x\n , length(data@p)\n , length(data@x)\n , nrow(data)\n , as.integer(rawscore)\n , as.integer(predleaf)\n , as.integer(predcontrib)\n , as.integer(start_iteration)\n , as.integer(num_iteration)\n , private$params\n , preds\n )\n\n } else {\n\n stop(\"predict: cannot predict on data of class \", sQuote(class(data), q = FALSE))\n\n }\n }\n\n # Check if number of rows is strange (not a multiple of the dataset rows)\n if (length(preds) %% num_row != 0L) {\n stop(\n \"predict: prediction length \"\n , sQuote(length(preds), q = FALSE)\n , \" is not a multiple of nrows(data): \"\n , sQuote(num_row, q = FALSE)\n )\n }\n\n # Get number of cases per row\n npred_per_case <- length(preds) / num_row\n\n # Data reshaping\n if (npred_per_case > 1L || predleaf || predcontrib) {\n preds <- matrix(preds, ncol = npred_per_case, byrow = TRUE)\n }\n\n # Keep row names if possible\n if (NROW(row.names(data)) && NROW(data) == NROW(preds)) {\n if (is.null(dim(preds))) {\n names(preds) <- row.names(data)\n } else {\n row.names(preds) <- row.names(data)\n }\n }\n\n return(preds)\n }\n\n ),\n private = list(\n handle = NULL\n , need_free_handle = FALSE\n , params = \"\"\n , fast_predict_config = list()\n , check_can_use_fast_predict_config = function(csr,\n rawscore,\n predleaf,\n predcontrib,\n start_iteration,\n num_iteration) {\n\n if (!NROW(private$fast_predict_config)) {\n return(FALSE)\n }\n\n if (.is_null_handle(private$fast_predict_config$handle)) {\n warning(paste0(\"Model had fast CSR predict configuration, but it is inactive.\"\n , \" Try re-generating it through 'lgb.configure_fast_predict'.\"))\n return(FALSE)\n }\n\n if (isTRUE(csr) != private$fast_predict_config$csr) {\n return(FALSE)\n }\n\n return(\n private$params == \"\" &&\n private$fast_predict_config$rawscore == rawscore &&\n private$fast_predict_config$predleaf == predleaf &&\n private$fast_predict_config$predcontrib == predcontrib &&\n .equal_or_both_null(private$fast_predict_config$start_iteration, start_iteration) &&\n .equal_or_both_null(private$fast_predict_config$num_iteration, num_iteration)\n )\n }\n\n # finalize() will free up the handles\n , finalize = function() {\n if (private$need_free_handle) {\n .Call(\n LGBM_BoosterFree_R\n , private$handle\n )\n private$handle <- NULL\n }\n return(invisible(NULL))\n }\n )\n)\n" }, { "path": "R-package/R/lgb.convert_with_rules.R", "content": "# [description] get all column classes of a data.table or data.frame.\n# This function collapses the result of class() into a single string\n.get_column_classes <- function(df) {\n return(\n vapply(\n X = df\n , FUN = function(x) {\n paste(class(x), collapse = \",\")\n }\n , FUN.VALUE = character(1L)\n )\n )\n}\n\n# [description] check a data frame or data table for columns that are any\n# type other than numeric and integer. This is used by lgb.convert_with_rules()\n# to warn if more action is needed by users\n# before a dataset can be converted to a lgb.Dataset.\n.warn_for_unconverted_columns <- function(df, function_name) {\n column_classes <- .get_column_classes(df = df)\n unconverted_columns <- column_classes[!(column_classes %in% c(\"numeric\", \"integer\"))]\n if (length(unconverted_columns) > 0L) {\n col_detail_string <- toString(\n paste0(\n names(unconverted_columns)\n , \" (\"\n , unconverted_columns\n , \")\"\n )\n )\n msg <- paste0(\n function_name\n , \": \"\n , length(unconverted_columns)\n , \" columns are not numeric or integer. These need to be dropped or converted to \"\n , \"be used in an lgb.Dataset object. \"\n , col_detail_string\n )\n warning(msg)\n }\n return(invisible(NULL))\n}\n\n.LGB_CONVERT_DEFAULT_FOR_LOGICAL_NA <- function() {\n return(-1L)\n}\n.LGB_CONVERT_DEFAULT_FOR_NON_LOGICAL_NA <- function() {\n return(0L)\n}\n\n\n#' @name lgb.convert_with_rules\n#' @title Data preparator for LightGBM datasets with rules (integer)\n#' @description Attempts to prepare a clean dataset to prepare to put in a \\code{lgb.Dataset}.\n#' Factor, character, and logical columns are converted to integer. Missing values\n#' in factors and characters will be filled with 0L. Missing values in logicals\n#' will be filled with -1L.\n#'\n#' This function returns and optionally takes in \"rules\" the describe exactly\n#' how to convert values in columns.\n#'\n#' Columns that contain only NA values will be converted by this function but will\n#' not show up in the returned \\code{rules}.\n#'\n#' NOTE: In previous releases of LightGBM, this function was called \\code{lgb.prepare_rules2}.\n#' @param data A data.frame or data.table to prepare.\n#' @param rules A set of rules from the data preparator, if already used. This should be an R list,\n#' where names are column names in \\code{data} and values are named character\n#' vectors whose names are column values and whose values are new values to\n#' replace them with.\n#' @return A list with the cleaned dataset (\\code{data}) and the rules (\\code{rules}).\n#' Note that the data must be converted to a matrix format (\\code{as.matrix}) for input in\n#' \\code{lgb.Dataset}.\n#'\n#' @examples\n#' \\donttest{\n#' data(iris)\n#'\n#' str(iris)\n#'\n#' new_iris <- lgb.convert_with_rules(data = iris)\n#' str(new_iris$data)\n#'\n#' data(iris) # Erase iris dataset\n#' iris$Species[1L] <- \"NEW FACTOR\" # Introduce junk factor (NA)\n#'\n#' # Use conversion using known rules\n#' # Unknown factors become 0, excellent for sparse datasets\n#' newer_iris <- lgb.convert_with_rules(data = iris, rules = new_iris$rules)\n#'\n#' # Unknown factor is now zero, perfect for sparse datasets\n#' newer_iris$data[1L, ] # Species became 0 as it is an unknown factor\n#'\n#' newer_iris$data[1L, 5L] <- 1.0 # Put back real initial value\n#'\n#' # Is the newly created dataset equal? YES!\n#' all.equal(new_iris$data, newer_iris$data)\n#'\n#' # Can we test our own rules?\n#' data(iris) # Erase iris dataset\n#'\n#' # We remapped values differently\n#' personal_rules <- list(\n#' Species = c(\n#' \"setosa\" = 3L\n#' , \"versicolor\" = 2L\n#' , \"virginica\" = 1L\n#' )\n#' )\n#' newest_iris <- lgb.convert_with_rules(data = iris, rules = personal_rules)\n#' str(newest_iris$data) # SUCCESS!\n#' }\n#' @importFrom data.table set\n#' @export\nlgb.convert_with_rules <- function(data, rules = NULL) {\n\n column_classes <- .get_column_classes(df = data)\n\n is_data_table <- data.table::is.data.table(x = data)\n is_data_frame <- is.data.frame(data)\n\n if (!(is_data_table || is_data_frame)) {\n stop(\n \"lgb.convert_with_rules: you provided \"\n , paste(class(data), collapse = \" & \")\n , \" but data should have class data.frame or data.table\"\n )\n }\n\n # if user didn't provide rules, create them\n if (is.null(rules)) {\n rules <- list()\n columns_to_fix <- which(column_classes %in% c(\"character\", \"factor\", \"logical\"))\n\n for (i in columns_to_fix) {\n\n col_values <- data[[i]]\n\n # Get unique values\n if (is.factor(col_values)) {\n unique_vals <- levels(col_values)\n unique_vals <- unique_vals[!is.na(unique_vals)]\n mini_numeric <- seq_along(unique_vals) # respect ordinal\n } else if (is.character(col_values)) {\n unique_vals <- as.factor(unique(col_values))\n unique_vals <- unique_vals[!is.na(unique_vals)]\n mini_numeric <- as.integer(unique_vals) # no respect for ordinal\n } else if (is.logical(col_values)) {\n unique_vals <- c(FALSE, TRUE)\n mini_numeric <- c(0L, 1L)\n }\n\n # don't add rules for all-NA columns\n if (length(unique_vals) > 0L) {\n col_name <- names(data)[i]\n rules[[col_name]] <- mini_numeric\n names(rules[[col_name]]) <- unique_vals\n }\n }\n }\n\n for (col_name in names(rules)) {\n if (column_classes[[col_name]] == \"logical\") {\n default_value_for_na <- .LGB_CONVERT_DEFAULT_FOR_LOGICAL_NA()\n } else {\n default_value_for_na <- .LGB_CONVERT_DEFAULT_FOR_NON_LOGICAL_NA()\n }\n if (is_data_table) {\n data.table::set(\n x = data\n , j = col_name\n , value = unname(rules[[col_name]][data[[col_name]]])\n )\n data[is.na(get(col_name)), (col_name) := default_value_for_na]\n } else {\n data[[col_name]] <- unname(rules[[col_name]][data[[col_name]]])\n data[is.na(data[col_name]), col_name] <- default_value_for_na\n }\n }\n\n # if any all-NA columns exist, they won't be in rules. Convert them\n all_na_cols <- which(\n sapply(\n X = data\n , FUN = function(x) {\n (is.factor(x) || is.character(x) || is.logical(x)) && all(is.na(unique(x)))\n }\n )\n )\n for (col_name in all_na_cols) {\n if (column_classes[[col_name]] == \"logical\") {\n default_value_for_na <- .LGB_CONVERT_DEFAULT_FOR_LOGICAL_NA()\n } else {\n default_value_for_na <- .LGB_CONVERT_DEFAULT_FOR_NON_LOGICAL_NA()\n }\n if (is_data_table) {\n data[, (col_name) := rep(default_value_for_na, .N)]\n } else {\n data[[col_name]] <- default_value_for_na\n }\n }\n\n .warn_for_unconverted_columns(df = data, function_name = \"lgb.convert_with_rules\")\n\n return(list(data = data, rules = rules))\n\n}\n" }, { "path": "R-package/R/lgb.cv.R", "content": "#' @importFrom R6 R6Class\nCVBooster <- R6::R6Class(\n classname = \"lgb.CVBooster\",\n cloneable = FALSE,\n public = list(\n best_iter = -1L,\n best_score = NA,\n record_evals = list(),\n boosters = list(),\n initialize = function(x) {\n self$boosters <- x\n return(invisible(NULL))\n },\n reset_parameter = function(new_params) {\n for (x in self$boosters) {\n x[[\"booster\"]]$reset_parameter(params = new_params)\n }\n return(invisible(self))\n }\n )\n)\n\n#' @name lgb.cv\n#' @title Main CV logic for LightGBM\n#' @description Cross validation logic used by LightGBM\n#' @inheritParams lgb_shared_params\n#' @param nfold the original dataset is randomly partitioned into \\code{nfold} equal size subsamples.\n#' @param record Boolean, TRUE will record iteration message to \\code{booster$record_evals}\n#' @param showsd \\code{boolean}, whether to show standard deviation of cross validation.\n#' This parameter defaults to \\code{TRUE}. Setting it to \\code{FALSE} can lead to a\n#' slight speedup by avoiding unnecessary computation.\n#' @param stratified a \\code{boolean} indicating whether sampling of folds should be stratified\n#' by the values of outcome labels.\n#' @param folds \\code{list} provides a possibility to use a list of pre-defined CV folds\n#' (each element must be a vector of test fold's indices). When folds are supplied,\n#' the \\code{nfold} and \\code{stratified} parameters are ignored.\n#' @param callbacks List of callback functions that are applied at each iteration.\n#' @param reset_data Boolean, setting it to TRUE (not the default value) will transform the booster model\n#' into a predictor model which frees up memory and the original datasets\n#' @param eval_train_metric \\code{boolean}, whether to add the cross validation results on the\n#' training data. This parameter defaults to \\code{FALSE}. Setting it to \\code{TRUE}\n#' will increase run time.\n#' @inheritSection lgb_shared_params Early Stopping\n#' @return a trained model \\code{lgb.CVBooster}.\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' params <- list(\n#' objective = \"regression\"\n#' , metric = \"l2\"\n#' , min_data = 1L\n#' , learning_rate = 1.0\n#' , num_threads = 2L\n#' )\n#' model <- lgb.cv(\n#' params = params\n#' , data = dtrain\n#' , nrounds = 5L\n#' , nfold = 3L\n#' )\n#' }\n#'\n#' @importFrom data.table data.table setorderv\n#' @export\nlgb.cv <- function(params = list()\n , data\n , nrounds = 100L\n , nfold = 3L\n , obj = NULL\n , eval = NULL\n , verbose = 1L\n , record = TRUE\n , eval_freq = 1L\n , showsd = TRUE\n , stratified = TRUE\n , folds = NULL\n , init_model = NULL\n , early_stopping_rounds = NULL\n , callbacks = list()\n , reset_data = FALSE\n , serializable = TRUE\n , eval_train_metric = FALSE\n ) {\n\n if (nrounds <= 0L) {\n stop(\"nrounds should be greater than zero\")\n }\n if (!.is_Dataset(x = data)) {\n stop(\"lgb.cv: data must be an lgb.Dataset instance\")\n }\n\n # set some parameters, resolving the way they were passed in with other parameters\n # in `params`.\n # this ensures that the model stored with Booster$save() correctly represents\n # what was passed in\n params <- .check_wrapper_param(\n main_param_name = \"verbosity\"\n , params = params\n , alternative_kwarg_value = verbose\n )\n params <- .check_wrapper_param(\n main_param_name = \"num_iterations\"\n , params = params\n , alternative_kwarg_value = nrounds\n )\n params <- .check_wrapper_param(\n main_param_name = \"metric\"\n , params = params\n , alternative_kwarg_value = NULL\n )\n params <- .check_wrapper_param(\n main_param_name = \"objective\"\n , params = params\n , alternative_kwarg_value = obj\n )\n params <- .check_wrapper_param(\n main_param_name = \"early_stopping_round\"\n , params = params\n , alternative_kwarg_value = early_stopping_rounds\n )\n early_stopping_rounds <- params[[\"early_stopping_round\"]]\n\n # extract any function objects passed for objective or metric\n fobj <- NULL\n if (is.function(params$objective)) {\n fobj <- params$objective\n params$objective <- \"none\"\n }\n\n # If eval is a single function, store it as a 1-element list\n # (for backwards compatibility). If it is a list of functions, store\n # all of them. This makes it possible to pass any mix of strings like \"auc\"\n # and custom functions to eval\n params <- .check_eval(params = params, eval = eval)\n eval_functions <- list(NULL)\n if (is.function(eval)) {\n eval_functions <- list(eval)\n }\n if (methods::is(eval, \"list\")) {\n eval_functions <- Filter(\n f = is.function\n , x = eval\n )\n }\n\n # Init predictor to empty\n predictor <- NULL\n\n # Check for boosting from a trained model\n if (is.character(init_model)) {\n predictor <- Predictor$new(modelfile = init_model)\n } else if (.is_Booster(x = init_model)) {\n predictor <- init_model$to_predictor()\n }\n\n # Set the iteration to start from / end to (and check for boosting from a trained model, again)\n begin_iteration <- 1L\n if (!is.null(predictor)) {\n begin_iteration <- predictor$current_iter() + 1L\n }\n end_iteration <- begin_iteration + params[[\"num_iterations\"]] - 1L\n\n # pop interaction_constraints off of params. It needs some preprocessing on the\n # R side before being passed into the Dataset object\n interaction_constraints <- params[[\"interaction_constraints\"]]\n params[\"interaction_constraints\"] <- NULL\n\n # Construct datasets, if needed\n data$update_params(params = params)\n data$construct()\n\n # Check interaction constraints\n params[[\"interaction_constraints\"]] <- .check_interaction_constraints(\n interaction_constraints = interaction_constraints\n , column_names = data$get_colnames()\n )\n\n # Update parameters with parsed parameters\n data$update_params(params = params)\n\n # Create the predictor set\n data$.__enclos_env__$private$set_predictor(predictor = predictor)\n\n if (!is.null(folds)) {\n\n # Check for list of folds or for single value\n if (!identical(class(folds), \"list\") || length(folds) < 2L) {\n stop(sQuote(\"folds\"), \" must be a list with 2 or more elements that are vectors of indices for each CV-fold\")\n }\n\n } else {\n\n if (nfold <= 1L) {\n stop(sQuote(\"nfold\"), \" must be > 1\")\n }\n\n # Create folds\n folds <- .generate_cv_folds(\n nfold = nfold\n , nrows = nrow(data)\n , stratified = stratified\n , label = get_field(dataset = data, field_name = \"label\")\n , group = get_field(dataset = data, field_name = \"group\")\n , params = params\n )\n\n }\n\n # Add printing log callback\n if (params[[\"verbosity\"]] > 0L && eval_freq > 0L) {\n callbacks <- .add_cb(cb_list = callbacks, cb = cb_print_evaluation(period = eval_freq))\n }\n\n # Add evaluation log callback\n if (record) {\n callbacks <- .add_cb(cb_list = callbacks, cb = cb_record_evaluation())\n }\n\n # Did user pass parameters that indicate they want to use early stopping?\n using_early_stopping <- !is.null(early_stopping_rounds) && early_stopping_rounds > 0L\n\n boosting_param_names <- .PARAMETER_ALIASES()[[\"boosting\"]]\n using_dart <- any(\n sapply(\n X = boosting_param_names\n , FUN = function(param) {\n identical(params[[param]], \"dart\")\n }\n )\n )\n\n # Cannot use early stopping with 'dart' boosting\n if (using_dart) {\n if (using_early_stopping) {\n warning(\"Early stopping is not available in 'dart' mode.\")\n }\n using_early_stopping <- FALSE\n\n # Remove the cb_early_stop() function if it was passed in to callbacks\n callbacks <- Filter(\n f = function(cb_func) {\n !identical(attr(cb_func, \"name\"), \"cb_early_stop\")\n }\n , x = callbacks\n )\n }\n\n # If user supplied early_stopping_rounds, add the early stopping callback\n if (using_early_stopping) {\n callbacks <- .add_cb(\n cb_list = callbacks\n , cb = cb_early_stop(\n stopping_rounds = early_stopping_rounds\n , first_metric_only = isTRUE(params[[\"first_metric_only\"]])\n , verbose = params[[\"verbosity\"]] > 0L\n )\n )\n }\n\n cb <- .categorize_callbacks(cb_list = callbacks)\n\n # Construct booster for each fold. The data.table() code below is used to\n # guarantee that indices are sorted while keeping init_score and weight together\n # with the correct indices. Note that it takes advantage of the fact that\n # someDT$some_column returns NULL is 'some_column' does not exist in the data.table\n bst_folds <- lapply(\n X = seq_along(folds)\n , FUN = function(k) {\n\n # For learning-to-rank, each fold is a named list with two elements:\n # * `fold` = an integer vector of row indices\n # * `group` = an integer vector describing which groups are in the fold\n # For classification or regression tasks, it will just be an integer\n # vector of row indices\n folds_have_group <- \"group\" %in% names(folds[[k]])\n if (folds_have_group) {\n test_indices <- folds[[k]]$fold\n test_group_indices <- folds[[k]]$group\n test_groups <- get_field(dataset = data, field_name = \"group\")[test_group_indices]\n train_groups <- get_field(dataset = data, field_name = \"group\")[-test_group_indices]\n } else {\n test_indices <- folds[[k]]\n }\n train_indices <- seq_len(nrow(data))[-test_indices]\n\n # set up test set\n indexDT <- data.table::data.table(\n indices = test_indices\n , weight = get_field(dataset = data, field_name = \"weight\")[test_indices]\n , init_score = get_field(dataset = data, field_name = \"init_score\")[test_indices]\n )\n data.table::setorderv(x = indexDT, cols = \"indices\", order = 1L)\n dtest <- lgb.slice.Dataset(data, indexDT$indices)\n set_field(dataset = dtest, field_name = \"weight\", data = indexDT$weight)\n set_field(dataset = dtest, field_name = \"init_score\", data = indexDT$init_score)\n\n # set up training set\n indexDT <- data.table::data.table(\n indices = train_indices\n , weight = get_field(dataset = data, field_name = \"weight\")[train_indices]\n , init_score = get_field(dataset = data, field_name = \"init_score\")[train_indices]\n )\n data.table::setorderv(x = indexDT, cols = \"indices\", order = 1L)\n dtrain <- lgb.slice.Dataset(data, indexDT$indices)\n set_field(dataset = dtrain, field_name = \"weight\", data = indexDT$weight)\n set_field(dataset = dtrain, field_name = \"init_score\", data = indexDT$init_score)\n\n if (folds_have_group) {\n set_field(dataset = dtest, field_name = \"group\", data = test_groups)\n set_field(dataset = dtrain, field_name = \"group\", data = train_groups)\n }\n\n booster <- Booster$new(params = params, train_set = dtrain)\n if (isTRUE(eval_train_metric)) {\n booster$add_valid(data = dtrain, name = \"train\")\n }\n booster$add_valid(data = dtest, name = \"valid\")\n return(\n list(booster = booster)\n )\n }\n )\n\n # Create new booster\n cv_booster <- CVBooster$new(x = bst_folds)\n\n # Callback env\n env <- CB_ENV$new()\n env$model <- cv_booster\n env$begin_iteration <- begin_iteration\n env$end_iteration <- end_iteration\n\n # Start training model using number of iterations to start and end with\n for (i in seq.int(from = begin_iteration, to = end_iteration)) {\n\n # Overwrite iteration in environment\n env$iteration <- i\n env$eval_list <- list()\n\n for (f in cb$pre_iter) {\n f(env)\n }\n\n # Update one boosting iteration\n msg <- lapply(cv_booster$boosters, function(fd) {\n fd$booster$update(fobj = fobj)\n out <- list()\n for (eval_function in eval_functions) {\n out <- append(out, fd$booster$eval_valid(feval = eval_function))\n }\n return(out)\n })\n\n # Prepare collection of evaluation results\n merged_msg <- .merge_cv_result(\n msg = msg\n , showsd = showsd\n )\n\n # Write evaluation result in environment\n env$eval_list <- merged_msg$eval_list\n\n # Check for standard deviation requirement\n if (showsd) {\n env$eval_err_list <- merged_msg$eval_err_list\n }\n\n # Loop through env\n for (f in cb$post_iter) {\n f(env)\n }\n\n # Check for early stopping and break if needed\n if (env$met_early_stop) break\n\n }\n\n # When early stopping is not activated, we compute the best iteration / score ourselves\n # based on the first first metric\n if (record && is.na(env$best_score)) {\n # when using a custom eval function, the metric name is returned from the\n # function, so figure it out from record_evals\n if (!is.null(eval_functions[1L])) {\n first_metric <- names(cv_booster$record_evals[[\"valid\"]])[1L]\n } else {\n first_metric <- cv_booster$.__enclos_env__$private$eval_names[1L]\n }\n .find_best <- which.min\n if (isTRUE(env$eval_list[[1L]]$higher_better[1L])) {\n .find_best <- which.max\n }\n cv_booster$best_iter <- unname(\n .find_best(\n unlist(\n cv_booster$record_evals[[\"valid\"]][[first_metric]][[.EVAL_KEY()]]\n )\n )\n )\n cv_booster$best_score <- cv_booster$record_evals[[\"valid\"]][[first_metric]][[.EVAL_KEY()]][[cv_booster$best_iter]]\n }\n # Propagate the best_iter attribute from the cv_booster to the individual boosters\n for (bst in cv_booster$boosters) {\n bst$booster$best_iter <- cv_booster$best_iter\n }\n\n if (reset_data) {\n lapply(cv_booster$boosters, function(fd) {\n # Store temporarily model data elsewhere\n booster_old <- list(\n best_iter = fd$booster$best_iter\n , best_score = fd$booster$best_score\n , record_evals = fd$booster$record_evals\n )\n # Reload model\n fd$booster <- lgb.load(model_str = fd$booster$save_model_to_string())\n fd$booster$best_iter <- booster_old$best_iter\n fd$booster$best_score <- booster_old$best_score\n fd$booster$record_evals <- booster_old$record_evals\n })\n }\n\n if (serializable) {\n lapply(cv_booster$boosters, function(model) model$booster$save_raw())\n }\n\n return(cv_booster)\n\n}\n\n# Generates random (stratified if needed) CV folds\n.generate_cv_folds <- function(nfold, nrows, stratified, label, group, params) {\n\n # Check for group existence\n if (is.null(group)) {\n\n # Shuffle\n rnd_idx <- sample.int(nrows)\n\n # Request stratified folds\n if (isTRUE(stratified) && params$objective %in% c(\"binary\", \"multiclass\") && length(label) == length(rnd_idx)) {\n\n y <- label[rnd_idx]\n y <- as.factor(y)\n folds <- .stratified_folds(y = y, k = nfold)\n\n } else {\n\n # Make simple non-stratified folds\n folds <- list()\n\n # Loop through each fold\n for (i in seq_len(nfold)) {\n kstep <- length(rnd_idx) %/% (nfold - i + 1L)\n folds[[i]] <- rnd_idx[seq_len(kstep)]\n rnd_idx <- rnd_idx[-seq_len(kstep)]\n }\n\n }\n\n } else {\n\n # When doing group, stratified is not possible (only random selection)\n if (nfold > length(group)) {\n stop(\"\\nYou requested too many folds for the number of available groups.\\n\")\n }\n\n # Degroup the groups\n ungrouped <- inverse.rle(list(lengths = group, values = seq_along(group)))\n\n # Can't stratify, shuffle\n rnd_idx <- sample.int(length(group))\n\n # Make simple non-stratified folds\n folds <- list()\n\n # Loop through each fold\n for (i in seq_len(nfold)) {\n kstep <- length(rnd_idx) %/% (nfold - i + 1L)\n folds[[i]] <- list(\n fold = which(ungrouped %in% rnd_idx[seq_len(kstep)])\n , group = rnd_idx[seq_len(kstep)]\n )\n rnd_idx <- rnd_idx[-seq_len(kstep)]\n }\n\n }\n\n return(folds)\n\n}\n\n# Creates CV folds stratified by the values of y.\n# It was borrowed from caret::createFolds and simplified\n# by always returning an unnamed list of fold indices.\n#' @importFrom stats quantile\n.stratified_folds <- function(y, k) {\n\n # Group the numeric data based on their magnitudes\n # and sample within those groups.\n # When the number of samples is low, we may have\n # issues further slicing the numeric data into\n # groups. The number of groups will depend on the\n # ratio of the number of folds to the sample size.\n # At most, we will use quantiles. If the sample\n # is too small, we just do regular unstratified CV\n if (is.numeric(y)) {\n\n cuts <- length(y) %/% k\n if (cuts < 2L) {\n cuts <- 2L\n }\n if (cuts > 5L) {\n cuts <- 5L\n }\n y <- cut(\n y\n , unique(stats::quantile(y, probs = seq.int(0.0, 1.0, length.out = cuts)))\n , include.lowest = TRUE\n )\n\n }\n\n if (k < length(y)) {\n\n # Reset levels so that the possible levels and\n # the levels in the vector are the same\n y <- as.factor(as.character(y))\n numInClass <- table(y)\n foldVector <- vector(mode = \"integer\", length(y))\n\n # For each class, balance the fold allocation as far\n # as possible, then resample the remainder.\n # The final assignment of folds is also randomized.\n for (i in seq_along(numInClass)) {\n\n # Create a vector of integers from 1:k as many times as possible without\n # going over the number of samples in the class. Note that if the number\n # of samples in a class is less than k, nothing is produced here.\n seqVector <- rep(seq_len(k), numInClass[i] %/% k)\n\n # Add enough random integers to get length(seqVector) == numInClass[i]\n if (numInClass[i] %% k > 0L) {\n seqVector <- c(seqVector, sample.int(k, numInClass[i] %% k))\n }\n\n # Shuffle the integers for fold assignment and assign to this classes's data\n foldVector[y == dimnames(numInClass)$y[i]] <- sample(seqVector)\n\n }\n\n } else {\n\n foldVector <- seq(along = y)\n\n }\n\n out <- split(seq(along = y), foldVector)\n names(out) <- NULL\n return(out)\n}\n\n.merge_cv_result <- function(msg, showsd) {\n\n if (length(msg) == 0L) {\n stop(\"lgb.cv: size of cv result error\")\n }\n\n eval_len <- length(msg[[1L]])\n\n if (eval_len == 0L) {\n stop(\"lgb.cv: should provide at least one metric for CV\")\n }\n\n # Get evaluation results using a list apply\n eval_result <- lapply(seq_len(eval_len), function(j) {\n as.numeric(lapply(seq_along(msg), function(i) {\n msg[[i]][[j]]$value }))\n })\n\n # Get evaluation. Just taking the first element here to\n # get structure (name, higher_better, data_name)\n ret_eval <- msg[[1L]]\n\n for (j in seq_len(eval_len)) {\n ret_eval[[j]]$value <- mean(eval_result[[j]])\n }\n\n ret_eval_err <- NULL\n\n # Check for standard deviation\n if (showsd) {\n\n # Parse standard deviation\n for (j in seq_len(eval_len)) {\n ret_eval_err <- c(\n ret_eval_err\n , sqrt(mean(eval_result[[j]] ^ 2L) - mean(eval_result[[j]]) ^ 2L)\n )\n }\n\n ret_eval_err <- as.list(ret_eval_err)\n\n }\n\n return(\n list(\n eval_list = ret_eval\n , eval_err_list = ret_eval_err\n )\n )\n\n}\n" }, { "path": "R-package/R/lgb.drop_serialized.R", "content": "#' @name lgb.drop_serialized\n#' @title Drop serialized raw bytes in a LightGBM model object\n#' @description If a LightGBM model object was produced with argument `serializable=TRUE`, the R object will keep\n#' a copy of the underlying C++ object as raw bytes, which can be used to reconstruct such object after getting\n#' serialized and de-serialized, but at the cost of extra memory usage. If these raw bytes are not needed anymore,\n#' they can be dropped through this function in order to save memory. Note that the object will be modified in-place.\n#'\n#' \\emph{New in version 4.0.0}\n#'\n#' @param model \\code{lgb.Booster} object which was produced with `serializable=TRUE`.\n#'\n#' @return \\code{lgb.Booster} (the same `model` object that was passed as input, as invisible).\n#' @seealso \\link{lgb.restore_handle}, \\link{lgb.make_serializable}.\n#' @export\nlgb.drop_serialized <- function(model) {\n if (!.is_Booster(x = model)) {\n stop(\"lgb.drop_serialized: model should be an \", sQuote(\"lgb.Booster\", q = FALSE))\n }\n model$drop_raw()\n return(invisible(model))\n}\n" }, { "path": "R-package/R/lgb.importance.R", "content": "#' @name lgb.importance\n#' @title Compute feature importance in a model\n#' @description Creates a \\code{data.table} of feature importances in a model.\n#' @param model object of class \\code{lgb.Booster}.\n#' @param percentage whether to show importance in relative percentage.\n#'\n#' @return For a tree model, a \\code{data.table} with the following columns:\n#' \\itemize{\n#' \\item{\\code{Feature}: Feature names in the model.}\n#' \\item{\\code{Gain}: The total gain of this feature's splits.}\n#' \\item{\\code{Cover}: The number of observation related to this feature.}\n#' \\item{\\code{Frequency}: The number of times a feature split in trees.}\n#' }\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#'\n#' params <- list(\n#' objective = \"binary\"\n#' , learning_rate = 0.1\n#' , max_depth = -1L\n#' , min_data_in_leaf = 1L\n#' , min_sum_hessian_in_leaf = 1.0\n#' , num_threads = 2L\n#' )\n#' model <- lgb.train(\n#' params = params\n#' , data = dtrain\n#' , nrounds = 5L\n#' )\n#'\n#' tree_imp1 <- lgb.importance(model, percentage = TRUE)\n#' tree_imp2 <- lgb.importance(model, percentage = FALSE)\n#' }\n#' @importFrom data.table := setnames setorderv\n#' @export\nlgb.importance <- function(model, percentage = TRUE) {\n\n if (!.is_Booster(x = model)) {\n stop(\"'model' has to be an object of class lgb.Booster\")\n }\n\n # Setup importance\n tree_dt <- lgb.model.dt.tree(model = model)\n\n # Extract elements\n tree_imp_dt <- tree_dt[\n !is.na(split_index)\n , .(Gain = sum(split_gain), Cover = sum(internal_count), Frequency = .N)\n , by = \"split_feature\"\n ]\n\n data.table::setnames(\n x = tree_imp_dt\n , old = \"split_feature\"\n , new = \"Feature\"\n )\n\n # Sort features by Gain\n data.table::setorderv(\n x = tree_imp_dt\n , cols = \"Gain\"\n , order = -1L\n )\n\n # Check if relative values are requested\n if (percentage) {\n tree_imp_dt[, `:=`(\n Gain = Gain / sum(Gain)\n , Cover = Cover / sum(Cover)\n , Frequency = Frequency / sum(Frequency)\n )]\n }\n\n # adding an empty [] to ensure the table is printed the first time print.data.table() is called\n return(tree_imp_dt[])\n\n}\n" }, { "path": "R-package/R/lgb.interprete.R", "content": "#' @name lgb.interprete\n#' @title Compute feature contribution of prediction\n#' @description Computes feature contribution components of rawscore prediction.\n#' @param model object of class \\code{lgb.Booster}.\n#' @param data a matrix object or a dgCMatrix object.\n#' @param idxset an integer vector of indices of rows needed.\n#' @param num_iteration number of iteration want to predict with, NULL or <= 0 means use best iteration.\n#'\n#' @return For regression, binary classification and lambdarank model, a \\code{list} of \\code{data.table}\n#' with the following columns:\n#' \\itemize{\n#' \\item{\\code{Feature}: Feature names in the model.}\n#' \\item{\\code{Contribution}: The total contribution of this feature's splits.}\n#' }\n#' For multiclass classification, a \\code{list} of \\code{data.table} with the Feature column and\n#' Contribution columns to each class.\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' Logit <- function(x) log(x / (1.0 - x))\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' set_field(\n#' dataset = dtrain\n#' , field_name = \"init_score\"\n#' , data = rep(Logit(mean(train$label)), length(train$label))\n#' )\n#' data(agaricus.test, package = \"lightgbm\")\n#' test <- agaricus.test\n#'\n#' params <- list(\n#' objective = \"binary\"\n#' , learning_rate = 0.1\n#' , max_depth = -1L\n#' , min_data_in_leaf = 1L\n#' , min_sum_hessian_in_leaf = 1.0\n#' , num_threads = 2L\n#' )\n#' model <- lgb.train(\n#' params = params\n#' , data = dtrain\n#' , nrounds = 3L\n#' )\n#'\n#' tree_interpretation <- lgb.interprete(model, test$data, 1L:5L)\n#' }\n#' @importFrom data.table as.data.table\n#' @export\nlgb.interprete <- function(model,\n data,\n idxset,\n num_iteration = NULL) {\n\n # Get tree model\n tree_dt <- lgb.model.dt.tree(model = model, num_iteration = num_iteration)\n\n # Check number of classes\n num_class <- model$.__enclos_env__$private$num_class\n\n # Get vector list\n tree_interpretation_dt_list <- vector(mode = \"list\", length = length(idxset))\n\n # Get parsed predictions of data\n pred_mat <- t(\n model$predict(\n data = data[idxset, , drop = FALSE]\n , num_iteration = num_iteration\n , predleaf = TRUE\n )\n )\n leaf_index_dt <- data.table::as.data.table(x = pred_mat)\n leaf_index_mat_list <- lapply(\n X = leaf_index_dt\n , FUN = matrix\n , ncol = num_class\n , byrow = TRUE\n )\n\n # Get list of trees\n tree_index_mat_list <- lapply(\n X = leaf_index_mat_list\n , FUN = function(x) {\n matrix(seq_along(x) - 1L, ncol = num_class, byrow = TRUE)\n }\n )\n\n for (i in seq_along(idxset)) {\n tree_interpretation_dt_list[[i]] <- .single_row_interprete(\n tree_dt = tree_dt\n , num_class = num_class\n , tree_index_mat = tree_index_mat_list[[i]]\n , leaf_index_mat = leaf_index_mat_list[[i]]\n )\n }\n\n return(tree_interpretation_dt_list)\n\n}\n\n#' @importFrom data.table data.table\nsingle.tree.interprete <- function(tree_dt,\n tree_id,\n leaf_id) {\n\n # Match tree id\n single_tree_dt <- tree_dt[tree_index == tree_id, ]\n\n # Get leaves\n leaf_dt <- single_tree_dt[leaf_index == leaf_id, .(leaf_index, leaf_parent, leaf_value)]\n\n # Get nodes\n node_dt <- single_tree_dt[!is.na(split_index), .(split_index, split_feature, node_parent, internal_value)]\n\n # Prepare sequences\n feature_seq <- character(0L)\n value_seq <- numeric(0L)\n\n # Get to root from leaf\n leaf_to_root <- function(parent_id, current_value) {\n\n value_seq <<- c(current_value, value_seq)\n\n if (!is.na(parent_id)) {\n\n # Not null means existing node\n this_node <- node_dt[split_index == parent_id, ]\n feature_seq <<- c(this_node[[\"split_feature\"]], feature_seq)\n leaf_to_root(\n parent_id = this_node[[\"node_parent\"]]\n , current_value = this_node[[\"internal_value\"]]\n )\n\n }\n\n }\n\n # Perform leaf to root conversion\n leaf_to_root(\n parent_id = leaf_dt[[\"leaf_parent\"]]\n , current_value = leaf_dt[[\"leaf_value\"]]\n )\n\n return(\n data.table::data.table(\n Feature = feature_seq\n , Contribution = diff.default(value_seq)\n )\n )\n\n}\n\n#' @importFrom data.table := rbindlist setorder\n.multiple_tree_interprete <- function(tree_dt,\n tree_index,\n leaf_index) {\n\n interp_dt <- data.table::rbindlist(\n l = mapply(\n FUN = single.tree.interprete\n , tree_id = tree_index\n , leaf_id = leaf_index\n , MoreArgs = list(\n tree_dt = tree_dt\n )\n , SIMPLIFY = FALSE\n , USE.NAMES = TRUE\n )\n , use.names = TRUE\n )\n\n interp_dt <- interp_dt[, .(Contribution = sum(Contribution)), by = \"Feature\"]\n\n # Sort features in descending order by contribution\n interp_dt[, abs_contribution := abs(Contribution)]\n data.table::setorder(\n x = interp_dt\n , -abs_contribution\n )\n\n # Drop absolute value of contribution (only needed for sorting)\n interp_dt[, abs_contribution := NULL]\n\n return(interp_dt)\n\n}\n\n#' @importFrom data.table set setnames\n.single_row_interprete <- function(tree_dt, num_class, tree_index_mat, leaf_index_mat) {\n\n # Prepare vector list\n tree_interpretation <- vector(mode = \"list\", length = num_class)\n\n # Loop throughout each class\n for (i in seq_len(num_class)) {\n\n next_interp_dt <- .multiple_tree_interprete(\n tree_dt = tree_dt\n , tree_index = tree_index_mat[, i]\n , leaf_index = leaf_index_mat[, i]\n )\n\n if (num_class > 1L) {\n data.table::setnames(\n x = next_interp_dt\n , old = \"Contribution\"\n , new = paste(\"Class\", i - 1L)\n )\n }\n\n tree_interpretation[[i]] <- next_interp_dt\n\n }\n\n if (num_class == 1L) {\n\n tree_interpretation_dt <- tree_interpretation[[1L]]\n\n } else {\n\n # Full interpretation elements\n tree_interpretation_dt <- Reduce(\n f = function(x, y) {\n merge(x, y, by = \"Feature\", all = TRUE)\n }\n , x = tree_interpretation\n )\n\n # Loop throughout each tree\n for (j in 2L:ncol(tree_interpretation_dt)) {\n\n data.table::set(\n x = tree_interpretation_dt\n , i = which(is.na(tree_interpretation_dt[[j]]))\n , j = j\n , value = 0.0\n )\n\n }\n\n }\n\n return(tree_interpretation_dt)\n}\n" }, { "path": "R-package/R/lgb.make_serializable.R", "content": "#' @name lgb.make_serializable\n#' @title Make a LightGBM object serializable by keeping raw bytes\n#' @description If a LightGBM model object was produced with argument `serializable=FALSE`, the R object will not\n#' be serializable (e.g. cannot save and load with \\code{saveRDS} and \\code{readRDS}) as it will lack the raw bytes\n#' needed to reconstruct its underlying C++ object. This function can be used to forcibly produce those serialized\n#' raw bytes and make the object serializable. Note that the object will be modified in-place.\n#'\n#' \\emph{New in version 4.0.0}\n#'\n#' @param model \\code{lgb.Booster} object which was produced with `serializable=FALSE`.\n#'\n#' @return \\code{lgb.Booster} (the same `model` object that was passed as input, as invisible).\n#' @seealso \\link{lgb.restore_handle}, \\link{lgb.drop_serialized}.\n#' @export\nlgb.make_serializable <- function(model) {\n if (!.is_Booster(x = model)) {\n stop(\"lgb.make_serializable: model should be an \", sQuote(\"lgb.Booster\", q = FALSE))\n }\n model$save_raw()\n return(invisible(model))\n}\n" }, { "path": "R-package/R/lgb.model.dt.tree.R", "content": "#' @name lgb.model.dt.tree\n#' @title Parse a LightGBM model json dump\n#' @description Parse a LightGBM model json dump into a \\code{data.table} structure.\n#' @param model object of class \\code{lgb.Booster}.\n#' @param num_iteration Number of iterations to include. NULL or <= 0 means use best iteration.\n#' @param start_iteration Index (1-based) of the first boosting round to include in the output.\n#' For example, passing \\code{start_iteration=5, num_iteration=3} for a regression model\n#' means \"return information about the fifth, sixth, and seventh trees\".\n#'\n#' \\emph{New in version 4.4.0}\n#'\n#' @return\n#' A \\code{data.table} with detailed information about model trees' nodes and leaves.\n#'\n#' The columns of the \\code{data.table} are:\n#'\n#' \\itemize{\n#' \\item{\\code{tree_index}: ID of a tree in a model (integer)}\n#' \\item{\\code{split_index}: ID of a node in a tree (integer)}\n#' \\item{\\code{split_feature}: for a node, it's a feature name (character);\n#' for a leaf, it simply labels it as \\code{\"NA\"}}\n#' \\item{\\code{node_parent}: ID of the parent node for current node (integer)}\n#' \\item{\\code{leaf_index}: ID of a leaf in a tree (integer)}\n#' \\item{\\code{leaf_parent}: ID of the parent node for current leaf (integer)}\n#' \\item{\\code{split_gain}: Split gain of a node}\n#' \\item{\\code{threshold}: Splitting threshold value of a node}\n#' \\item{\\code{decision_type}: Decision type of a node}\n#' \\item{\\code{default_left}: Determine how to handle NA value, TRUE -> Left, FALSE -> Right}\n#' \\item{\\code{internal_value}: Node value}\n#' \\item{\\code{internal_count}: The number of observation collected by a node}\n#' \\item{\\code{leaf_value}: Leaf value}\n#' \\item{\\code{leaf_count}: The number of observation collected by a leaf}\n#' }\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#'\n#' params <- list(\n#' objective = \"binary\"\n#' , learning_rate = 0.01\n#' , num_leaves = 63L\n#' , max_depth = -1L\n#' , min_data_in_leaf = 1L\n#' , min_sum_hessian_in_leaf = 1.0\n#' , num_threads = 2L\n#' )\n#' model <- lgb.train(params, dtrain, 10L)\n#'\n#' tree_dt <- lgb.model.dt.tree(model)\n#' }\n#' @importFrom data.table := rbindlist\n#' @importFrom jsonlite fromJSON\n#' @export\nlgb.model.dt.tree <- function(\n model, num_iteration = NULL, start_iteration = 1L\n ) {\n\n json_model <- lgb.dump(\n booster = model\n , num_iteration = num_iteration\n , start_iteration = start_iteration\n )\n\n parsed_json_model <- jsonlite::fromJSON(\n txt = json_model\n , simplifyVector = TRUE\n , simplifyDataFrame = FALSE\n , simplifyMatrix = FALSE\n , flatten = FALSE\n )\n\n # Parse tree model\n tree_list <- lapply(\n X = parsed_json_model$tree_info\n , FUN = .single_tree_parse\n )\n\n # Combine into single data.table\n tree_dt <- data.table::rbindlist(l = tree_list, use.names = TRUE)\n\n # Substitute feature index with the actual feature name\n\n # Since the index comes from C++ (which is 0-indexed), be sure\n # to add 1 (e.g. index 28 means the 29th feature in feature_names)\n split_feature_indx <- tree_dt[, split_feature] + 1L\n\n # Get corresponding feature names. Positions in split_feature_indx\n # which are NA will result in an NA feature name\n feature_names <- parsed_json_model$feature_names[split_feature_indx]\n tree_dt[, split_feature := feature_names]\n\n return(tree_dt)\n}\n\n\n#' @importFrom data.table := data.table rbindlist\n.single_tree_parse <- function(lgb_tree) {\n tree_info_cols <- c(\n \"split_index\"\n , \"split_feature\"\n , \"split_gain\"\n , \"threshold\"\n , \"decision_type\"\n , \"default_left\"\n , \"internal_value\"\n , \"internal_count\"\n )\n\n # Traverse tree function\n pre_order_traversal <- function(env = NULL, tree_node_leaf, current_depth = 0L, parent_index = NA_integer_) {\n\n if (is.null(env)) {\n # Setup initial default data.table with default types\n env <- new.env(parent = emptyenv())\n env$single_tree_dt <- list()\n env$single_tree_dt[[1L]] <- data.table::data.table(\n tree_index = integer(0L)\n , depth = integer(0L)\n , split_index = integer(0L)\n , split_feature = integer(0L)\n , node_parent = integer(0L)\n , leaf_index = integer(0L)\n , leaf_parent = integer(0L)\n , split_gain = numeric(0L)\n , threshold = numeric(0L)\n , decision_type = character(0L)\n , default_left = character(0L)\n , internal_value = integer(0L)\n , internal_count = integer(0L)\n , leaf_value = integer(0L)\n , leaf_count = integer(0L)\n )\n # start tree traversal\n pre_order_traversal(\n env = env\n , tree_node_leaf = tree_node_leaf\n , current_depth = current_depth\n , parent_index = parent_index\n )\n } else {\n\n # Check if split index is not null in leaf\n if (!is.null(tree_node_leaf$split_index)) {\n\n # update data.table\n env$single_tree_dt[[length(env$single_tree_dt) + 1L]] <- c(\n tree_node_leaf[tree_info_cols]\n , list(\"depth\" = current_depth, \"node_parent\" = parent_index)\n )\n\n # Traverse tree again both left and right\n pre_order_traversal(\n env = env\n , tree_node_leaf = tree_node_leaf$left_child\n , current_depth = current_depth + 1L\n , parent_index = tree_node_leaf$split_index\n )\n pre_order_traversal(\n env = env\n , tree_node_leaf = tree_node_leaf$right_child\n , current_depth = current_depth + 1L\n , parent_index = tree_node_leaf$split_index\n )\n } else if (!is.null(tree_node_leaf$leaf_index)) {\n\n # update list\n env$single_tree_dt[[length(env$single_tree_dt) + 1L]] <- c(\n tree_node_leaf[c(\"leaf_index\", \"leaf_value\", \"leaf_count\")]\n , list(\"depth\" = current_depth, \"leaf_parent\" = parent_index)\n )\n }\n }\n return(env$single_tree_dt)\n }\n\n # Traverse structure and rowbind everything\n single_tree_dt <- data.table::rbindlist(\n pre_order_traversal(tree_node_leaf = lgb_tree$tree_structure)\n , use.names = TRUE\n , fill = TRUE\n )\n\n # Store index\n single_tree_dt[, tree_index := lgb_tree$tree_index]\n\n return(single_tree_dt)\n}\n" }, { "path": "R-package/R/lgb.plot.importance.R", "content": "#' @name lgb.plot.importance\n#' @title Plot feature importance as a bar graph\n#' @description Plot previously calculated feature importance: Gain, Cover and Frequency, as a bar graph.\n#' @param tree_imp a \\code{data.table} returned by \\code{\\link{lgb.importance}}.\n#' @param top_n maximal number of top features to include into the plot.\n#' @param measure the name of importance measure to plot, can be \"Gain\", \"Cover\" or \"Frequency\".\n#' @param left_margin (base R barplot) allows to adjust the left margin size to fit feature names.\n#' @param cex (base R barplot) passed as \\code{cex.names} parameter to \\code{\\link[graphics]{barplot}}.\n#' Set a number smaller than 1.0 to make the bar labels smaller than R's default and values\n#' greater than 1.0 to make them larger.\n#'\n#' @details\n#' The graph represents each feature as a horizontal bar of length proportional to the defined importance of a feature.\n#' Features are shown ranked in a decreasing importance order.\n#'\n#' @return\n#' The \\code{lgb.plot.importance} function creates a \\code{barplot}\n#' and silently returns a processed data.table with \\code{top_n} features sorted by defined importance.\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#'\n#' params <- list(\n#' objective = \"binary\"\n#' , learning_rate = 0.1\n#' , min_data_in_leaf = 1L\n#' , min_sum_hessian_in_leaf = 1.0\n#' , num_threads = 2L\n#' )\n#'\n#' model <- lgb.train(\n#' params = params\n#' , data = dtrain\n#' , nrounds = 5L\n#' )\n#'\n#' tree_imp <- lgb.importance(model, percentage = TRUE)\n#' lgb.plot.importance(tree_imp, top_n = 5L, measure = \"Gain\")\n#' }\n#' @importFrom graphics barplot par\n#' @export\nlgb.plot.importance <- function(tree_imp,\n top_n = 10L,\n measure = \"Gain\",\n left_margin = 10L,\n cex = NULL\n ) {\n\n # Check for measurement (column names) correctness\n measure <- match.arg(\n measure\n , choices = c(\"Gain\", \"Cover\", \"Frequency\")\n , several.ok = FALSE\n )\n\n # Get top N importance (defaults to 10)\n top_n <- min(top_n, nrow(tree_imp))\n\n # Parse importance\n tree_imp <- tree_imp[order(abs(get(measure)), decreasing = TRUE), ][seq_len(top_n), ]\n\n # Attempt to setup a correct cex\n if (is.null(cex)) {\n cex <- 2.5 / log2(1.0 + top_n)\n }\n\n # Refresh plot\n op <- graphics::par(no.readonly = TRUE)\n on.exit(graphics::par(op))\n\n graphics::par(\n mar = c(\n op$mar[1L]\n , left_margin\n , op$mar[3L]\n , op$mar[4L]\n )\n )\n\n tree_imp[rev(seq_len(.N)),\n graphics::barplot(\n height = get(measure)\n , names.arg = Feature\n , horiz = TRUE\n , border = NA\n , main = \"Feature Importance\"\n , xlab = measure\n , cex.names = cex\n , las = 1L\n )]\n\n return(invisible(tree_imp))\n\n}\n" }, { "path": "R-package/R/lgb.plot.interpretation.R", "content": "#' @name lgb.plot.interpretation\n#' @title Plot feature contribution as a bar graph\n#' @description Plot previously calculated feature contribution as a bar graph.\n#' @param tree_interpretation_dt a \\code{data.table} returned by \\code{\\link{lgb.interprete}}.\n#' @param top_n maximal number of top features to include into the plot.\n#' @param cols the column numbers of layout, will be used only for multiclass classification feature contribution.\n#' @param left_margin (base R barplot) allows to adjust the left margin size to fit feature names.\n#' @param cex (base R barplot) passed as \\code{cex.names} parameter to \\code{barplot}.\n#'\n#' @details\n#' The graph represents each feature as a horizontal bar of length proportional to the defined\n#' contribution of a feature. Features are shown ranked in a decreasing contribution order.\n#'\n#' @return\n#' The \\code{lgb.plot.interpretation} function creates a \\code{barplot}.\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' Logit <- function(x) {\n#' log(x / (1.0 - x))\n#' }\n#' data(agaricus.train, package = \"lightgbm\")\n#' labels <- agaricus.train$label\n#' dtrain <- lgb.Dataset(\n#' agaricus.train$data\n#' , label = labels\n#' )\n#' set_field(\n#' dataset = dtrain\n#' , field_name = \"init_score\"\n#' , data = rep(Logit(mean(labels)), length(labels))\n#' )\n#'\n#' data(agaricus.test, package = \"lightgbm\")\n#'\n#' params <- list(\n#' objective = \"binary\"\n#' , learning_rate = 0.1\n#' , max_depth = -1L\n#' , min_data_in_leaf = 1L\n#' , min_sum_hessian_in_leaf = 1.0\n#' , num_threads = 2L\n#' )\n#' model <- lgb.train(\n#' params = params\n#' , data = dtrain\n#' , nrounds = 5L\n#' )\n#'\n#' tree_interpretation <- lgb.interprete(\n#' model = model\n#' , data = agaricus.test$data\n#' , idxset = 1L:5L\n#' )\n#' lgb.plot.interpretation(\n#' tree_interpretation_dt = tree_interpretation[[1L]]\n#' , top_n = 3L\n#' )\n#' }\n#' @importFrom data.table setnames\n#' @importFrom graphics barplot par\n#' @export\nlgb.plot.interpretation <- function(tree_interpretation_dt,\n top_n = 10L,\n cols = 1L,\n left_margin = 10L,\n cex = NULL) {\n\n num_class <- ncol(tree_interpretation_dt) - 1L\n\n # Refresh plot\n op <- graphics::par(no.readonly = TRUE)\n on.exit(graphics::par(op))\n\n # Do some magic plotting\n bottom_margin <- 3.0\n top_margin <- 2.0\n right_margin <- op$mar[4L]\n\n graphics::par(\n mar = c(\n bottom_margin\n , left_margin\n , top_margin\n , right_margin\n )\n )\n\n if (num_class == 1L) {\n\n # Only one class, plot straight away\n .multiple_tree_plot_interpretation(\n tree_interpretation = tree_interpretation_dt\n , top_n = top_n\n , title = NULL\n , cex = cex\n )\n\n } else {\n\n # More than one class, shape data first\n layout_mat <- matrix(\n seq.int(to = cols * ceiling(num_class / cols))\n , ncol = cols\n , nrow = ceiling(num_class / cols)\n )\n\n # Shape output\n graphics::par(mfcol = c(nrow(layout_mat), ncol(layout_mat)))\n\n # Loop throughout all classes\n for (i in seq_len(num_class)) {\n\n # Prepare interpretation, perform T, get the names, and plot straight away\n plot_dt <- tree_interpretation_dt[, c(1L, i + 1L), with = FALSE]\n data.table::setnames(\n x = plot_dt\n , old = names(plot_dt)\n , new = c(\"Feature\", \"Contribution\")\n )\n .multiple_tree_plot_interpretation(\n tree_interpretation = plot_dt\n , top_n = top_n\n , title = paste(\"Class\", i - 1L)\n , cex = cex\n )\n\n }\n }\n return(invisible(NULL))\n}\n\n#' @importFrom graphics barplot\n.multiple_tree_plot_interpretation <- function(tree_interpretation,\n top_n,\n title,\n cex) {\n\n # Parse tree\n tree_interpretation <- tree_interpretation[order(abs(Contribution), decreasing = TRUE), ][seq_len(min(top_n, .N)), ]\n\n # Attempt to setup a correct cex\n if (is.null(cex)) {\n cex <- 2.5 / log2(1.0 + top_n)\n }\n\n # create plot\n tree_interpretation[abs(Contribution) > 0.0, bar_color := \"firebrick\"]\n tree_interpretation[Contribution == 0.0, bar_color := \"steelblue\"]\n tree_interpretation[rev(seq_len(.N)),\n graphics::barplot(\n height = Contribution\n , names.arg = Feature\n , horiz = TRUE\n , col = bar_color\n , border = NA\n , main = title\n , cex.names = cex\n , las = 1L\n )]\n\n return(invisible(NULL))\n\n}\n" }, { "path": "R-package/R/lgb.restore_handle.R", "content": "#' @name lgb.restore_handle\n#' @title Restore the C++ component of a de-serialized LightGBM model\n#' @description After a LightGBM model object is de-serialized through functions such as \\code{save} or\n#' \\code{saveRDS}, its underlying C++ object will be blank and needs to be restored to able to use it. Such\n#' object is restored automatically when calling functions such as \\code{predict}, but this function can be\n#' used to forcibly restore it beforehand. Note that the object will be modified in-place.\n#'\n#' \\emph{New in version 4.0.0}\n#'\n#' @details Be aware that fast single-row prediction configurations are not restored through this\n#' function. If you wish to make fast single-row predictions using a \\code{lgb.Booster} loaded this way,\n#' call \\link{lgb.configure_fast_predict} on the loaded \\code{lgb.Booster} object.\n#' @param model \\code{lgb.Booster} object which was de-serialized and whose underlying C++ object and R handle\n#' need to be restored.\n#'\n#' @return \\code{lgb.Booster} (the same `model` object that was passed as input, invisibly).\n#' @seealso \\link{lgb.make_serializable}, \\link{lgb.drop_serialized}.\n#' @examples\n#' \\donttest{\n#' library(lightgbm)\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(\"agaricus.train\")\n#' model <- lightgbm(\n#' agaricus.train$data\n#' , agaricus.train$label\n#' , params = list(objective = \"binary\")\n#' , nrounds = 5L\n#' , verbose = 0\n#' , num_threads = 2L\n#' )\n#' fname <- tempfile(fileext=\"rds\")\n#' saveRDS(model, fname)\n#'\n#' model_new <- readRDS(fname)\n#' model_new$check_null_handle()\n#' lgb.restore_handle(model_new)\n#' model_new$check_null_handle()\n#' }\n#' @export\nlgb.restore_handle <- function(model) {\n if (!.is_Booster(x = model)) {\n stop(\"lgb.restore_handle: model should be an \", sQuote(\"lgb.Booster\", q = FALSE))\n }\n model$restore_handle()\n return(invisible(model))\n}\n" }, { "path": "R-package/R/lgb.train.R", "content": "#' @name lgb.train\n#' @title Main training logic for LightGBM\n#' @description Low-level R interface to train a LightGBM model. Unlike \\code{\\link{lightgbm}},\n#' this function is focused on performance (e.g. speed, memory efficiency). It is also\n#' less likely to have breaking API changes in new releases than \\code{\\link{lightgbm}}.\n#' @inheritParams lgb_shared_params\n#' @param valids a list of \\code{lgb.Dataset} objects, used for validation\n#' @param record Boolean, TRUE will record iteration message to \\code{booster$record_evals}\n#' @param callbacks List of callback functions that are applied at each iteration.\n#' @param reset_data Boolean, setting it to TRUE (not the default value) will transform the\n#' booster model into a predictor model which frees up memory and the\n#' original datasets\n#' @inheritSection lgb_shared_params Early Stopping\n#' @return a trained booster model \\code{lgb.Booster}.\n#'\n#' @examples\n#' \\donttest{\n#' \\dontshow{setLGBMthreads(2L)}\n#' \\dontshow{data.table::setDTthreads(1L)}\n#' data(agaricus.train, package = \"lightgbm\")\n#' train <- agaricus.train\n#' dtrain <- lgb.Dataset(train$data, label = train$label)\n#' data(agaricus.test, package = \"lightgbm\")\n#' test <- agaricus.test\n#' dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\n#' params <- list(\n#' objective = \"regression\"\n#' , metric = \"l2\"\n#' , min_data = 1L\n#' , learning_rate = 1.0\n#' , num_threads = 2L\n#' )\n#' valids <- list(test = dtest)\n#' model <- lgb.train(\n#' params = params\n#' , data = dtrain\n#' , nrounds = 5L\n#' , valids = valids\n#' , early_stopping_rounds = 3L\n#' )\n#' }\n#'\n#' @export\nlgb.train <- function(params = list(),\n data,\n nrounds = 100L,\n valids = list(),\n obj = NULL,\n eval = NULL,\n verbose = 1L,\n record = TRUE,\n eval_freq = 1L,\n init_model = NULL,\n early_stopping_rounds = NULL,\n callbacks = list(),\n reset_data = FALSE,\n serializable = TRUE) {\n\n # validate inputs early to avoid unnecessary computation\n if (nrounds <= 0L) {\n stop(\"nrounds should be greater than zero\")\n }\n if (!.is_Dataset(x = data)) {\n stop(\"lgb.train: data must be an lgb.Dataset instance\")\n }\n if (length(valids) > 0L) {\n if (!identical(class(valids), \"list\") || !all(vapply(valids, .is_Dataset, logical(1L)))) {\n stop(\"lgb.train: valids must be a list of lgb.Dataset elements\")\n }\n evnames <- names(valids)\n if (is.null(evnames) || !all(nzchar(evnames))) {\n stop(\"lgb.train: each element of valids must have a name\")\n }\n }\n\n # set some parameters, resolving the way they were passed in with other parameters\n # in `params`.\n # this ensures that the model stored with Booster$save() correctly represents\n # what was passed in\n params <- .check_wrapper_param(\n main_param_name = \"verbosity\"\n , params = params\n , alternative_kwarg_value = verbose\n )\n params <- .check_wrapper_param(\n main_param_name = \"num_iterations\"\n , params = params\n , alternative_kwarg_value = nrounds\n )\n params <- .check_wrapper_param(\n main_param_name = \"metric\"\n , params = params\n , alternative_kwarg_value = NULL\n )\n params <- .check_wrapper_param(\n main_param_name = \"objective\"\n , params = params\n , alternative_kwarg_value = obj\n )\n params <- .check_wrapper_param(\n main_param_name = \"early_stopping_round\"\n , params = params\n , alternative_kwarg_value = early_stopping_rounds\n )\n early_stopping_rounds <- params[[\"early_stopping_round\"]]\n\n # extract any function objects passed for objective or metric\n fobj <- NULL\n if (is.function(params$objective)) {\n fobj <- params$objective\n params$objective <- \"none\"\n }\n\n # If eval is a single function, store it as a 1-element list\n # (for backwards compatibility). If it is a list of functions, store\n # all of them. This makes it possible to pass any mix of strings like \"auc\"\n # and custom functions to eval\n params <- .check_eval(params = params, eval = eval)\n eval_functions <- list(NULL)\n if (is.function(eval)) {\n eval_functions <- list(eval)\n }\n if (methods::is(eval, \"list\")) {\n eval_functions <- Filter(\n f = is.function\n , x = eval\n )\n }\n\n # Init predictor to empty\n predictor <- NULL\n\n # Check for boosting from a trained model\n if (is.character(init_model)) {\n predictor <- Predictor$new(modelfile = init_model)\n } else if (.is_Booster(x = init_model)) {\n predictor <- init_model$to_predictor()\n }\n\n # Set the iteration to start from / end to (and check for boosting from a trained model, again)\n begin_iteration <- 1L\n if (!is.null(predictor)) {\n begin_iteration <- predictor$current_iter() + 1L\n }\n end_iteration <- begin_iteration + params[[\"num_iterations\"]] - 1L\n\n # pop interaction_constraints off of params. It needs some preprocessing on the\n # R side before being passed into the Dataset object\n interaction_constraints <- params[[\"interaction_constraints\"]]\n params[\"interaction_constraints\"] <- NULL\n\n # Construct datasets, if needed\n data$update_params(params = params)\n data$construct()\n\n # Check interaction constraints\n params[[\"interaction_constraints\"]] <- .check_interaction_constraints(\n interaction_constraints = interaction_constraints\n , column_names = data$get_colnames()\n )\n\n # Update parameters with parsed parameters\n data$update_params(params)\n\n # Create the predictor set\n data$.__enclos_env__$private$set_predictor(predictor)\n\n valid_contain_train <- FALSE\n train_data_name <- \"train\"\n reduced_valid_sets <- list()\n\n # Parse validation datasets\n if (length(valids) > 0L) {\n\n for (key in names(valids)) {\n\n # Use names to get validation datasets\n valid_data <- valids[[key]]\n\n # Check for duplicate train/validation dataset\n if (identical(data, valid_data)) {\n valid_contain_train <- TRUE\n train_data_name <- key\n next\n }\n\n # Update parameters, data\n valid_data$update_params(params)\n valid_data$set_reference(data)\n reduced_valid_sets[[key]] <- valid_data\n\n }\n\n }\n\n # Add printing log callback\n if (params[[\"verbosity\"]] > 0L && eval_freq > 0L) {\n callbacks <- .add_cb(\n cb_list = callbacks\n , cb = cb_print_evaluation(period = eval_freq)\n )\n }\n\n # Add evaluation log callback\n if (record && length(valids) > 0L) {\n callbacks <- .add_cb(\n cb_list = callbacks\n , cb = cb_record_evaluation()\n )\n }\n\n # Did user pass parameters that indicate they want to use early stopping?\n using_early_stopping <- !is.null(early_stopping_rounds) && early_stopping_rounds > 0L\n\n boosting_param_names <- .PARAMETER_ALIASES()[[\"boosting\"]]\n using_dart <- any(\n sapply(\n X = boosting_param_names\n , FUN = function(param) {\n identical(params[[param]], \"dart\")\n }\n )\n )\n\n # Cannot use early stopping with 'dart' boosting\n if (using_dart) {\n if (using_early_stopping) {\n warning(\"Early stopping is not available in 'dart' mode.\")\n }\n using_early_stopping <- FALSE\n\n # Remove the cb_early_stop() function if it was passed in to callbacks\n callbacks <- Filter(\n f = function(cb_func) {\n !identical(attr(cb_func, \"name\"), \"cb_early_stop\")\n }\n , x = callbacks\n )\n }\n\n # If user supplied early_stopping_rounds, add the early stopping callback\n if (using_early_stopping) {\n callbacks <- .add_cb(\n cb_list = callbacks\n , cb = cb_early_stop(\n stopping_rounds = early_stopping_rounds\n , first_metric_only = isTRUE(params[[\"first_metric_only\"]])\n , verbose = params[[\"verbosity\"]] > 0L\n )\n )\n }\n\n cb <- .categorize_callbacks(cb_list = callbacks)\n\n # Construct booster with datasets\n booster <- Booster$new(params = params, train_set = data)\n if (valid_contain_train) {\n booster$set_train_data_name(name = train_data_name)\n }\n\n for (key in names(reduced_valid_sets)) {\n booster$add_valid(data = reduced_valid_sets[[key]], name = key)\n }\n\n # Callback env\n env <- CB_ENV$new()\n env$model <- booster\n env$begin_iteration <- begin_iteration\n env$end_iteration <- end_iteration\n\n # Start training model using number of iterations to start and end with\n for (i in seq.int(from = begin_iteration, to = end_iteration)) {\n\n # Overwrite iteration in environment\n env$iteration <- i\n env$eval_list <- list()\n\n # Loop through \"pre_iter\" element\n for (f in cb$pre_iter) {\n f(env)\n }\n\n # Update one boosting iteration\n booster$update(fobj = fobj)\n\n # Prepare collection of evaluation results\n eval_list <- list()\n\n # Collection: Has validation dataset?\n if (length(valids) > 0L) {\n\n # Get evaluation results with passed-in functions\n for (eval_function in eval_functions) {\n\n # Validation has training dataset?\n if (valid_contain_train) {\n eval_list <- append(eval_list, booster$eval_train(feval = eval_function))\n }\n\n eval_list <- append(eval_list, booster$eval_valid(feval = eval_function))\n }\n\n # Calling booster$eval_valid() will get\n # evaluation results with the metrics in params$metric by calling LGBM_BoosterGetEval_R\",\n # so need to be sure that gets called, which it wouldn't be above if no functions\n # were passed in\n if (length(eval_functions) == 0L) {\n if (valid_contain_train) {\n eval_list <- append(eval_list, booster$eval_train(feval = eval_function))\n }\n eval_list <- append(eval_list, booster$eval_valid(feval = eval_function))\n }\n\n }\n\n # Write evaluation result in environment\n env$eval_list <- eval_list\n\n # Loop through env\n for (f in cb$post_iter) {\n f(env)\n }\n\n # Check for early stopping and break if needed\n if (env$met_early_stop) break\n\n }\n\n # check if any valids were given other than the training data\n non_train_valid_names <- names(valids)[!(names(valids) == train_data_name)]\n first_valid_name <- non_train_valid_names[1L]\n\n # When early stopping is not activated, we compute the best iteration / score ourselves by\n # selecting the first metric and the first dataset\n if (record && length(non_train_valid_names) > 0L && is.na(env$best_score)) {\n\n # when using a custom eval function, the metric name is returned from the\n # function, so figure it out from record_evals\n if (!is.null(eval_functions[1L])) {\n first_metric <- names(booster$record_evals[[first_valid_name]])[1L]\n } else {\n first_metric <- booster$.__enclos_env__$private$eval_names[1L]\n }\n\n .find_best <- which.min\n if (isTRUE(env$eval_list[[1L]]$higher_better[1L])) {\n .find_best <- which.max\n }\n booster$best_iter <- unname(\n .find_best(\n unlist(\n booster$record_evals[[first_valid_name]][[first_metric]][[.EVAL_KEY()]]\n )\n )\n )\n booster$best_score <- booster$record_evals[[first_valid_name]][[first_metric]][[.EVAL_KEY()]][[booster$best_iter]]\n }\n\n # Check for booster model conversion to predictor model\n if (reset_data) {\n\n # Store temporarily model data elsewhere\n booster_old <- list(\n best_iter = booster$best_iter\n , best_score = booster$best_score\n , record_evals = booster$record_evals\n )\n\n # Reload model\n booster <- lgb.load(model_str = booster$save_model_to_string())\n booster$best_iter <- booster_old$best_iter\n booster$best_score <- booster_old$best_score\n booster$record_evals <- booster_old$record_evals\n\n }\n\n if (serializable) {\n booster$save_raw()\n }\n\n return(booster)\n\n}\n" }, { "path": "R-package/R/lightgbm.R", "content": "#' @name lgb_shared_params\n#' @title Shared parameter docs\n#' @description Parameter docs shared by \\code{lgb.train}, \\code{lgb.cv}, and \\code{lightgbm}\n#' @param callbacks List of callback functions that are applied at each iteration.\n#' @param data a \\code{lgb.Dataset} object, used for training. Some functions, such as \\code{\\link{lgb.cv}},\n#' may allow you to pass other types of data like \\code{matrix} and then separately supply\n#' \\code{label} as a keyword argument.\n#' @param early_stopping_rounds int. Activates early stopping. When this parameter is non-null,\n#' training will stop if the evaluation of any metric on any validation set\n#' fails to improve for \\code{early_stopping_rounds} consecutive boosting rounds.\n#' If training stops early, the returned model will have attribute \\code{best_iter}\n#' set to the iteration number of the best iteration.\n#' @param eval evaluation function(s). This can be a character vector, function, or list with a mixture of\n#' strings and functions.\n#'\n#' \\itemize{\n#' \\item{\\bold{a. character vector}:\n#' If you provide a character vector to this argument, it should contain strings with valid\n#' evaluation metrics.\n#' See \\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric}{\n#' The \"metric\" section of the documentation}\n#' for a list of valid metrics.\n#' }\n#' \\item{\\bold{b. function}:\n#' You can provide a custom evaluation function. This\n#' should accept the keyword arguments \\code{preds} and \\code{dtrain} and should return a named\n#' list with three elements:\n#' \\itemize{\n#' \\item{\\code{name}: A string with the name of the metric, used for printing\n#' and storing results.\n#' }\n#' \\item{\\code{value}: A single number indicating the value of the metric for the\n#' given predictions and true values\n#' }\n#' \\item{\n#' \\code{higher_better}: A boolean indicating whether higher values indicate a better fit.\n#' For example, this would be \\code{FALSE} for metrics like MAE or RMSE.\n#' }\n#' }\n#' }\n#' \\item{\\bold{c. list}:\n#' If a list is given, it should only contain character vectors and functions.\n#' These should follow the requirements from the descriptions above.\n#' }\n#' }\n#' @param eval_freq evaluation output frequency, only effective when verbose > 0 and \\code{valids} has been provided\n#' @param init_model path of model file or \\code{lgb.Booster} object, will continue training from this model\n#' @param nrounds number of training rounds\n#' @param obj objective function, can be character or custom objective function. Examples include\n#' \\code{regression}, \\code{regression_l1}, \\code{huber},\n#' \\code{binary}, \\code{lambdarank}, \\code{multiclass}, \\code{multiclass}\n#' @param params a list of parameters. See \\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{\n#' the \"Parameters\" section of the documentation} for a list of parameters and valid values.\n#' @param verbose verbosity for output, if <= 0 and \\code{valids} has been provided, also will disable the\n#' printing of evaluation during training\n#' @param serializable whether to make the resulting objects serializable through functions such as\n#' \\code{save} or \\code{saveRDS} (see section \"Model serialization\").\n#' @section Early Stopping:\n#'\n#' \"early stopping\" refers to stopping the training process if the model's performance on a given\n#' validation set does not improve for several consecutive iterations.\n#'\n#' If multiple arguments are given to \\code{eval}, their order will be preserved. If you enable\n#' early stopping by setting \\code{early_stopping_rounds} in \\code{params}, by default all\n#' metrics will be considered for early stopping.\n#'\n#' If you want to only consider the first metric for early stopping, pass\n#' \\code{first_metric_only = TRUE} in \\code{params}. Note that if you also specify \\code{metric}\n#' in \\code{params}, that metric will be considered the \"first\" one. If you omit \\code{metric},\n#' a default metric will be used based on your choice for the parameter \\code{obj} (keyword argument)\n#' or \\code{objective} (passed into \\code{params}).\n#'\n#' \\bold{NOTE:} if using \\code{boosting_type=\"dart\"}, any early stopping configuration will be ignored\n#' and early stopping will not be performed.\n#' @section Model serialization:\n#'\n#' LightGBM model objects can be serialized and de-serialized through functions such as \\code{save}\n#' or \\code{saveRDS}, but similarly to libraries such as 'xgboost', serialization works a bit differently\n#' from typical R objects. In order to make models serializable in R, a copy of the underlying C++ object\n#' as serialized raw bytes is produced and stored in the R model object, and when this R object is\n#' de-serialized, the underlying C++ model object gets reconstructed from these raw bytes, but will only\n#' do so once some function that uses it is called, such as \\code{predict}. In order to forcibly\n#' reconstruct the C++ object after deserialization (e.g. after calling \\code{readRDS} or similar), one\n#' can use the function \\link{lgb.restore_handle} (for example, if one makes predictions in parallel or in\n#' forked processes, it will be faster to restore the handle beforehand).\n#'\n#' Producing and keeping these raw bytes however uses extra memory, and if they are not required,\n#' it is possible to avoid producing them by passing `serializable=FALSE`. In such cases, these raw\n#' bytes can be added to the model on demand through function \\link{lgb.make_serializable}.\n#'\n#' \\emph{New in version 4.0.0}\n#'\n#' @keywords internal\nNULL\n\n#' @name lightgbm\n#' @title Train a LightGBM model\n#' @description High-level R interface to train a LightGBM model. Unlike \\code{\\link{lgb.train}}, this function\n#' is focused on compatibility with other statistics and machine learning interfaces in R.\n#' This focus on compatibility means that this interface may experience more frequent breaking API changes\n#' than \\code{\\link{lgb.train}}.\n#' For efficiency-sensitive applications, or for applications where breaking API changes across releases\n#' is very expensive, use \\code{\\link{lgb.train}}.\n#' @inheritParams lgb_shared_params\n#' @param label Vector of labels, used if \\code{data} is not an \\code{\\link{lgb.Dataset}}\n#' @param weights Sample / observation weights for rows in the input data. If \\code{NULL}, will assume that all\n#' observations / rows have the same importance / weight.\n#'\n#' \\emph{Changed from 'weight', in version 4.0.0}\n#'\n#' @param objective Optimization objective (e.g. `\"regression\"`, `\"binary\"`, etc.).\n#' For a list of accepted objectives, see\n#' \\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#objective}{\n#' the \"objective\" item of the \"Parameters\" section of the documentation}.\n#'\n#' If passing \\code{\"auto\"} and \\code{data} is not of type \\code{lgb.Dataset}, the objective will\n#' be determined according to what is passed for \\code{label}:\\itemize{\n#' \\item If passing a factor with two variables, will use objective \\code{\"binary\"}.\n#' \\item If passing a factor with more than two variables, will use objective \\code{\"multiclass\"}\n#' (note that parameter \\code{num_class} in this case will also be determined automatically from\n#' \\code{label}).\n#' \\item Otherwise (or if passing \\code{lgb.Dataset} as input), will use objective \\code{\"regression\"}.\n#' }\n#'\n#' \\emph{New in version 4.0.0}\n#'\n#' @param init_score initial score is the base prediction lightgbm will boost from\n#'\n#' \\emph{New in version 4.0.0}\n#'\n#' @param num_threads Number of parallel threads to use. For best speed, this should be set to the number of\n#' physical cores in the CPU - in a typical x86-64 machine, this corresponds to half the\n#' number of maximum threads.\n#'\n#' Be aware that using too many threads can result in speed degradation in smaller datasets\n#' (see the parameters documentation for more details).\n#'\n#' If passing zero, will use the default number of threads configured for OpenMP\n#' (typically controlled through an environment variable \\code{OMP_NUM_THREADS}).\n#'\n#' If passing \\code{NULL} (the default), will try to use the number of physical cores in the\n#' system, but be aware that getting the number of cores detected correctly requires package\n#' \\code{RhpcBLASctl} to be installed.\n#'\n#' This parameter gets overridden by \\code{num_threads} and its aliases under \\code{params}\n#' if passed there.\n#'\n#' \\emph{New in version 4.0.0}\n#'\n#' @param colnames Character vector of features. Only used if \\code{data} is not an \\code{\\link{lgb.Dataset}}.\n#' @param categorical_feature categorical features. This can either be a character vector of feature\n#' names or an integer vector with the indices of the features (e.g.\n#' \\code{c(1L, 10L)} to say \"the first and tenth columns\").\n#' Only used if \\code{data} is not an \\code{\\link{lgb.Dataset}}.\n#'\n#' @param ... Additional arguments passed to \\code{\\link{lgb.train}}. For example\n#' \\itemize{\n#' \\item{\\code{valids}: a list of \\code{lgb.Dataset} objects, used for validation}\n#' \\item{\\code{obj}: objective function, can be character or custom objective function. Examples include\n#' \\code{regression}, \\code{regression_l1}, \\code{huber},\n#' \\code{binary}, \\code{lambdarank}, \\code{multiclass}, \\code{multiclass}}\n#' \\item{\\code{eval}: evaluation function, can be (a list of) character or custom eval function}\n#' \\item{\\code{record}: Boolean, TRUE will record iteration message to \\code{booster$record_evals}}\n#' \\item{\\code{reset_data}: Boolean, setting it to TRUE (not the default value) will transform the booster model\n#' into a predictor model which frees up memory and the original datasets}\n#' }\n#' @inheritSection lgb_shared_params Early Stopping\n#' @return a trained \\code{lgb.Booster}\n#' @export\nlightgbm <- function(data,\n label = NULL,\n weights = NULL,\n params = list(),\n nrounds = 100L,\n verbose = 1L,\n eval_freq = 1L,\n early_stopping_rounds = NULL,\n init_model = NULL,\n callbacks = list(),\n serializable = TRUE,\n objective = \"auto\",\n init_score = NULL,\n num_threads = NULL,\n colnames = NULL,\n categorical_feature = NULL,\n ...) {\n\n # validate inputs early to avoid unnecessary computation\n if (nrounds <= 0L) {\n stop(\"nrounds should be greater than zero\")\n }\n\n if (is.null(num_threads)) {\n num_threads <- .get_default_num_threads()\n }\n params <- .check_wrapper_param(\n main_param_name = \"num_threads\"\n , params = params\n , alternative_kwarg_value = num_threads\n )\n params <- .check_wrapper_param(\n main_param_name = \"verbosity\"\n , params = params\n , alternative_kwarg_value = verbose\n )\n\n # Process factors as labels and auto-determine objective\n if (!.is_Dataset(data)) {\n data_processor <- DataProcessor$new()\n temp <- data_processor$process_label(\n label = label\n , objective = objective\n , params = params\n )\n label <- temp$label\n objective <- temp$objective\n params <- temp$params\n rm(temp)\n } else {\n data_processor <- NULL\n if (objective == \"auto\") {\n objective <- \"regression\"\n }\n }\n\n # Set data to a temporary variable\n dtrain <- data\n\n # Check whether data is lgb.Dataset, if not then create lgb.Dataset manually\n if (!.is_Dataset(x = dtrain)) {\n dtrain <- lgb.Dataset(\n data = data\n , label = label\n , weight = weights\n , init_score = init_score\n , categorical_feature = categorical_feature\n , colnames = colnames\n )\n }\n\n train_args <- list(\n \"params\" = params\n , \"data\" = dtrain\n , \"nrounds\" = nrounds\n , \"obj\" = objective\n , \"verbose\" = params[[\"verbosity\"]]\n , \"eval_freq\" = eval_freq\n , \"early_stopping_rounds\" = early_stopping_rounds\n , \"init_model\" = init_model\n , \"callbacks\" = callbacks\n , \"serializable\" = serializable\n )\n train_args <- append(train_args, list(...))\n\n if (! \"valids\" %in% names(train_args)) {\n train_args[[\"valids\"]] <- list()\n }\n\n # Train a model using the regular way\n bst <- do.call(\n what = lgb.train\n , args = train_args\n )\n bst$data_processor <- data_processor\n\n return(bst)\n}\n\n#' @name agaricus.train\n#' @title Training part from Mushroom Data Set\n#' @description This data set is originally from the Mushroom data set,\n#' UCI Machine Learning Repository.\n#' This data set includes the following fields:\n#'\n#' \\itemize{\n#' \\item{\\code{label}: the label for each record}\n#' \\item{\\code{data}: a sparse Matrix of \\code{dgCMatrix} class, with 126 columns.}\n#' }\n#'\n#' @references\n#' https://archive.ics.uci.edu/ml/datasets/Mushroom\n#'\n#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository\n#' [https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n#' School of Information and Computer Science.\n#'\n#' @docType data\n#' @keywords datasets\n#' @usage data(agaricus.train)\n#' @format A list containing a label vector, and a dgCMatrix object with 6513\n#' rows and 127 variables\nNULL\n\n#' @name agaricus.test\n#' @title Test part from Mushroom Data Set\n#' @description This data set is originally from the Mushroom data set,\n#' UCI Machine Learning Repository.\n#' This data set includes the following fields:\n#'\n#' \\itemize{\n#' \\item{\\code{label}: the label for each record}\n#' \\item{\\code{data}: a sparse Matrix of \\code{dgCMatrix} class, with 126 columns.}\n#' }\n#' @references\n#' https://archive.ics.uci.edu/ml/datasets/Mushroom\n#'\n#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository\n#' [https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\n#' School of Information and Computer Science.\n#'\n#' @docType data\n#' @keywords datasets\n#' @usage data(agaricus.test)\n#' @format A list containing a label vector, and a dgCMatrix object with 1611\n#' rows and 126 variables\nNULL\n\n#' @name bank\n#' @title Bank Marketing Data Set\n#' @description This data set is originally from the Bank Marketing data set,\n#' UCI Machine Learning Repository.\n#'\n#' It contains only the following: bank.csv with 10% of the examples and 17 inputs,\n#' randomly selected from 3 (older version of this dataset with less inputs).\n#'\n#' @references\n#' https://archive.ics.uci.edu/ml/datasets/Bank+Marketing\n#'\n#' S. Moro, P. Cortez and P. Rita. (2014)\n#' A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems\n#'\n#' @docType data\n#' @keywords datasets\n#' @usage data(bank)\n#' @format A data.table with 4521 rows and 17 variables\nNULL\n\n# Various imports\n#' @import methods\n#' @importFrom Matrix Matrix\n#' @importFrom R6 R6Class\n#' @useDynLib lightgbm , .registration = TRUE\nNULL\n\n# Suppress false positive warnings from R CMD CHECK about\n# \"unrecognized global variable\"\nglobalVariables(c(\n \".\"\n , \".N\"\n , \".SD\"\n , \"abs_contribution\"\n , \"bar_color\"\n , \"Contribution\"\n , \"Cover\"\n , \"Feature\"\n , \"Frequency\"\n , \"Gain\"\n , \"internal_count\"\n , \"internal_value\"\n , \"leaf_index\"\n , \"leaf_parent\"\n , \"leaf_value\"\n , \"node_parent\"\n , \"split_feature\"\n , \"split_gain\"\n , \"split_index\"\n , \"tree_index\"\n))\n" }, { "path": "R-package/R/metrics.R", "content": "# [description] List of metrics known to LightGBM. The most up to date list can be found\n# at https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric-parameters\n#\n# [return] A named logical vector, where each key is a metric name and each value is a boolean.\n# TRUE if higher values of the metric are desirable, FALSE if lower values are desirable.\n# Note that only the 'main' metrics are stored here, not aliases, since only the 'main' metrics\n# are returned from the C++ side. For example, if you use `metric = \"mse\"` in your code,\n# the metric name `\"l2\"` will be returned.\n.METRICS_HIGHER_BETTER <- function() {\n return(\n c(\n \"l1\" = FALSE\n , \"l2\" = FALSE\n , \"mape\" = FALSE\n , \"rmse\" = FALSE\n , \"quantile\" = FALSE\n , \"huber\" = FALSE\n , \"fair\" = FALSE\n , \"poisson\" = FALSE\n , \"gamma\" = FALSE\n , \"gamma_deviance\" = FALSE\n , \"tweedie\" = FALSE\n , \"ndcg\" = TRUE\n , \"map\" = TRUE\n , \"auc\" = TRUE\n , \"average_precision\" = TRUE\n , \"r2\" = TRUE\n , \"binary_logloss\" = FALSE\n , \"binary_error\" = FALSE\n , \"auc_mu\" = TRUE\n , \"multi_logloss\" = FALSE\n , \"multi_error\" = FALSE\n , \"cross_entropy\" = FALSE\n , \"cross_entropy_lambda\" = FALSE\n , \"kullback_leibler\" = FALSE\n )\n )\n}\n" }, { "path": "R-package/R/multithreading.R", "content": "#' @name setLGBMThreads\n#' @title Set maximum number of threads used by LightGBM\n#' @description LightGBM attempts to speed up many operations by using multi-threading.\n#' The number of threads used in those operations can be controlled via the\n#' \\code{num_threads} parameter passed through \\code{params} to functions like\n#' \\link{lgb.train} and \\link{lgb.Dataset}. However, some operations (like materializing\n#' a model from a text file) are done via code paths that don't explicitly accept thread-control\n#' configuration.\n#'\n#' Use this function to set the maximum number of threads LightGBM will use for such operations.\n#'\n#' This function affects all LightGBM operations in the same process.\n#'\n#' So, for example, if you call \\code{setLGBMthreads(4)}, no other multi-threaded LightGBM\n#' operation in the same process will use more than 4 threads.\n#'\n#' Call \\code{setLGBMthreads(-1)} to remove this limitation.\n#' @param num_threads maximum number of threads to be used by LightGBM in multi-threaded operations\n#' @return NULL\n#' @seealso \\link{getLGBMthreads}\n#' @export\nsetLGBMthreads <- function(num_threads) {\n .Call(\n LGBM_SetMaxThreads_R,\n num_threads\n )\n return(invisible(NULL))\n}\n\n#' @name getLGBMThreads\n#' @title Get default number of threads used by LightGBM\n#' @description LightGBM attempts to speed up many operations by using multi-threading.\n#' The number of threads used in those operations can be controlled via the\n#' \\code{num_threads} parameter passed through \\code{params} to functions like\n#' \\link{lgb.train} and \\link{lgb.Dataset}. However, some operations (like materializing\n#' a model from a text file) are done via code paths that don't explicitly accept thread-control\n#' configuration.\n#'\n#' Use this function to see the default number of threads LightGBM will use for such operations.\n#' @return number of threads as an integer. \\code{-1} means that in situations where parameter \\code{num_threads} is\n#' not explicitly supplied, LightGBM will choose a number of threads to use automatically.\n#' @seealso \\link{setLGBMthreads}\n#' @export\ngetLGBMthreads <- function() {\n out <- 0L\n .Call(\n LGBM_GetMaxThreads_R,\n out\n )\n return(out)\n}\n" }, { "path": "R-package/R/utils.R", "content": ".is_Booster <- function(x) {\n return(all(c(\"R6\", \"lgb.Booster\") %in% class(x))) # nolint: class_equals.\n}\n\n.is_Dataset <- function(x) {\n return(all(c(\"R6\", \"lgb.Dataset\") %in% class(x))) # nolint: class_equals.\n}\n\n.is_Predictor <- function(x) {\n return(all(c(\"R6\", \"lgb.Predictor\") %in% class(x))) # nolint: class_equals.\n}\n\n.is_null_handle <- function(x) {\n if (is.null(x)) {\n return(TRUE)\n }\n return(\n isTRUE(.Call(LGBM_HandleIsNull_R, x))\n )\n}\n\n.params2str <- function(params) {\n\n if (!identical(class(params), \"list\")) {\n stop(\"params must be a list\")\n }\n\n names(params) <- gsub(\".\", \"_\", names(params), fixed = TRUE)\n param_names <- names(params)\n ret <- list()\n\n # Perform key value join\n for (i in seq_along(params)) {\n\n # If a parameter has multiple values, join those values together with commas.\n # trimws() is necessary because format() will pad to make strings the same width\n val <- paste(\n trimws(\n format(\n x = unname(params[[i]])\n , scientific = FALSE\n )\n )\n , collapse = \",\"\n )\n if (nchar(val) <= 0L) next # Skip join\n\n # Join key value\n pair <- paste(c(param_names[[i]], val), collapse = \"=\")\n ret <- c(ret, pair)\n\n }\n\n if (length(ret) == 0L) {\n return(\"\")\n }\n\n return(paste(ret, collapse = \" \"))\n\n}\n\n# [description]\n#\n# Besides applying checks, this function\n#\n# 1. turns feature *names* into 1-based integer positions, then\n# 2. adds an extra list element with skipped features, then\n# 3. turns 1-based integer positions into 0-based positions, and finally\n# 4. collapses the values of each list element into a string like \"[0, 1]\".\n#\n.check_interaction_constraints <- function(interaction_constraints, column_names) {\n if (is.null(interaction_constraints)) {\n return(list())\n }\n if (!identical(class(interaction_constraints), \"list\")) {\n stop(\"interaction_constraints must be a list\")\n }\n\n column_indices <- seq_along(column_names)\n\n # Convert feature names to 1-based integer positions and apply checks\n for (j in seq_along(interaction_constraints)) {\n constraint <- interaction_constraints[[j]]\n\n if (is.character(constraint)) {\n constraint_indices <- match(constraint, column_names)\n } else if (is.numeric(constraint)) {\n constraint_indices <- as.integer(constraint)\n } else {\n stop(\"every element in interaction_constraints must be a character vector or numeric vector\")\n }\n\n # Features outside range?\n bad <- !(constraint_indices %in% column_indices)\n if (any(bad)) {\n stop(\n \"unknown feature(s) in interaction_constraints: \"\n , toString(sQuote(constraint[bad], q = FALSE))\n )\n }\n\n interaction_constraints[[j]] <- constraint_indices\n }\n\n # Add missing features as new interaction set\n remaining_indices <- setdiff(\n column_indices, sort(unique(unlist(interaction_constraints)))\n )\n if (length(remaining_indices) > 0L) {\n interaction_constraints <- c(\n interaction_constraints, list(remaining_indices)\n )\n }\n\n # Turn indices 0-based and convert to string\n for (j in seq_along(interaction_constraints)) {\n interaction_constraints[[j]] <- paste0(\n \"[\", paste(interaction_constraints[[j]] - 1L, collapse = \",\"), \"]\"\n )\n }\n return(interaction_constraints)\n}\n\n\n# [description]\n# Take any character values from eval and store them in params$metric.\n# This has to account for the fact that `eval` could be a character vector,\n# a function, a list of functions, or a list with a mix of strings and\n# functions\n.check_eval <- function(params, eval) {\n\n if (is.null(params$metric)) {\n params$metric <- list()\n } else if (is.character(params$metric)) {\n params$metric <- as.list(params$metric)\n }\n\n # if 'eval' is a character vector or list, find the character\n # elements and add them to 'metric'\n if (!is.function(eval)) {\n for (i in seq_along(eval)) {\n element <- eval[[i]]\n if (is.character(element)) {\n params$metric <- append(params$metric, element)\n }\n }\n }\n\n # If more than one character metric was given, then \"None\" should\n # not be included\n if (length(params$metric) > 1L) {\n params$metric <- Filter(\n f = function(metric) {\n !(metric %in% .NO_METRIC_STRINGS())\n }\n , x = params$metric\n )\n }\n\n # duplicate metrics should be filtered out\n params$metric <- as.list(unique(unlist(params$metric)))\n\n return(params)\n}\n\n\n# [description]\n#\n# Resolve differences between passed-in keyword arguments, parameters,\n# and parameter aliases. This function exists because some functions in the\n# package take in parameters through their own keyword arguments other than\n# the `params` list.\n#\n# If the same underlying parameter is provided multiple\n# ways, the first item in this list is used:\n#\n# 1. the main (non-alias) parameter found in `params`\n# 2. the alias with the highest priority found in `params`\n# 3. the keyword argument passed in\n#\n# For example, \"num_iterations\" can also be provided to lgb.train()\n# via keyword \"nrounds\". lgb.train() will choose one value for this parameter\n# based on the first match in this list:\n#\n# 1. params[[\"num_iterations]]\n# 2. the highest priority alias of \"num_iterations\" found in params\n# 3. the nrounds keyword argument\n#\n# If multiple aliases are found in `params` for the same parameter, they are\n# all removed before returning `params`.\n#\n# [return]\n# params with num_iterations set to the chosen value, and other aliases\n# of num_iterations removed\n.check_wrapper_param <- function(main_param_name, params, alternative_kwarg_value) {\n\n aliases <- .PARAMETER_ALIASES()[[main_param_name]]\n aliases_provided <- aliases[aliases %in% names(params)]\n aliases_provided <- aliases_provided[aliases_provided != main_param_name]\n\n # prefer the main parameter\n if (!is.null(params[[main_param_name]])) {\n for (param in aliases_provided) {\n params[[param]] <- NULL\n }\n return(params)\n }\n\n # if the main parameter wasn't provided, prefer the first alias\n if (length(aliases_provided) > 0L) {\n first_param <- aliases_provided[1L]\n params[[main_param_name]] <- params[[first_param]]\n for (param in aliases_provided) {\n params[[param]] <- NULL\n }\n return(params)\n }\n\n # if not provided in params at all, use the alternative value provided\n # through a keyword argument from lgb.train(), lgb.cv(), etc.\n params[[main_param_name]] <- alternative_kwarg_value\n return(params)\n}\n\n#' @importFrom parallel detectCores\n.get_default_num_threads <- function() {\n if (requireNamespace(\"RhpcBLASctl\", quietly = TRUE)) { # nolint: undesirable_function.\n return(RhpcBLASctl::get_num_cores())\n } else {\n msg <- \"Optional package 'RhpcBLASctl' not found.\"\n cores <- 0L\n if (Sys.info()[\"sysname\"] != \"Linux\") {\n cores <- parallel::detectCores(logical = FALSE)\n if (is.na(cores) || cores < 0L) {\n cores <- 0L\n }\n }\n if (cores == 0L) {\n msg <- paste(msg, \"Will use default number of OpenMP threads.\", sep = \" \")\n } else {\n msg <- paste(msg, \"Detection of CPU cores might not be accurate.\", sep = \" \")\n }\n warning(msg)\n return(cores)\n }\n}\n\n.equal_or_both_null <- function(a, b) {\n if (is.null(a)) {\n if (!is.null(b)) {\n return(FALSE)\n }\n return(TRUE)\n } else {\n if (is.null(b)) {\n return(FALSE)\n }\n return(a == b)\n }\n}\n" }, { "path": "R-package/README.md", "content": "# LightGBM R-package\n\n[![CRAN Version](https://www.r-pkg.org/badges/version/lightgbm)](https://cran.r-project.org/package=lightgbm)\n[![Downloads](https://cranlogs.r-pkg.org/badges/grand-total/lightgbm)](https://cran.r-project.org/package=lightgbm)\n[![API Docs](https://readthedocs.org/projects/lightgbm/badge/?version=latest)](https://lightgbm.readthedocs.io/en/latest/R/reference/)\n\n\"\"\n\n### Contents\n\n* [Installation](#installation)\n - [Installing the CRAN Package](#installing-the-cran-package)\n - [Installing from Source with CMake](#install)\n - [Installing a GPU-enabled Build](#installing-a-gpu-enabled-build)\n - [Installing Precompiled Binaries](#installing-precompiled-binaries)\n - [Installing from a Pre-compiled lib_lightgbm](#lib_lightgbm)\n* [Examples](#examples)\n* [Testing](#testing)\n - [Running the Tests](#running-the-tests)\n - [Code Coverage](#code-coverage)\n* [Updating Documentation](#updating-documentation)\n* [Preparing a CRAN Package](#preparing-a-cran-package)\n* [Known Issues](#known-issues)\n\nInstallation\n------------\n\nFor the easiest installation, go to [\"Installing the CRAN package\"](#installing-the-cran-package).\n\nIf you experience any issues with that, try [\"Installing from Source with CMake\"](#install). This can produce a more efficient version of the library on Windows systems with Visual Studio.\n\nTo build a GPU-enabled version of the package, follow the steps in [\"Installing a GPU-enabled Build\"](#installing-a-gpu-enabled-build).\n\nIf any of the above options do not work for you or do not meet your needs, please let the maintainers know by [opening an issue](https://github.com/lightgbm-org/LightGBM/issues).\n\nWhen your package installation is done, you can check quickly if your LightGBM R-package is working by running the following:\n\n```r\nlibrary(lightgbm)\ndata(agaricus.train, package='lightgbm')\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\nmodel <- lgb.cv(\n params = list(\n objective = \"regression\"\n , metric = \"l2\"\n )\n , data = dtrain\n)\n```\n\n### Installing the CRAN package\n\n`{lightgbm}` is [available on CRAN](https://cran.r-project.org/package=lightgbm), and can be installed with the following R code.\n\n```r\ninstall.packages(\"lightgbm\", repos = \"https://cran.r-project.org\")\n```\n\nThis is the easiest way to install `{lightgbm}`. It does not require `CMake` or `Visual Studio`, and should work well on many different operating systems and compilers.\n\nEach CRAN package is also available on [LightGBM releases](https://github.com/lightgbm-org/LightGBM/releases), with a name like `lightgbm-{VERSION}-r-cran.tar.gz`.\n\n#### Custom Installation (Linux, Mac)\n\nThe steps above should work on most systems, but users with highly-customized environments might want to change how R builds packages from source.\n\nTo change the compiler used when installing the CRAN package, you can create a file `~/.R/Makevars` which overrides `CC` (`C` compiler) and `CXX` (`C++` compiler).\n\nFor example, to use `gcc-14` instead of `clang` on macOS, you could use something like the following:\n\n```make\n# ~/.R/Makevars\nCC=gcc-14\nCC17=gcc-14\nCXX=g++-14\nCXX17=g++-14\n```\n\nTo check the values R is using, run the following:\n\n```shell\nR CMD config --all\n```\n\n### Installing from Source with CMake \n\nYou need to install git and [CMake](https://cmake.org/) first.\n\nNote: this method is only supported on 64-bit systems. If you need to run LightGBM on 32-bit Windows (i386), follow the instructions in [\"Installing the CRAN Package\"](#installing-the-cran-package).\n\n#### Windows Preparation\n\nNOTE: Windows users may need to run with administrator rights (either R or the command prompt, depending on the way you are installing this package).\n\nInstalling a 64-bit version of [Rtools](https://cran.r-project.org/bin/windows/Rtools/) is mandatory.\n\nAfter installing `Rtools` and `CMake`, be sure the following paths are added to the environment variable `PATH`. These may have been automatically added when installing other software.\n\n* `Rtools`\n - If you have `Rtools` 4.0, example:\n - `C:\\rtools40\\mingw64\\bin`\n - `C:\\rtools40\\usr\\bin`\n - If you have `Rtools` 4.2+, example:\n - `C:\\rtools42\\x86_64-w64-mingw32.static.posix\\bin`\n - `C:\\rtools42\\usr\\bin`\n - **NOTE**: this is e.g. `rtools43\\` for R 4.3\n* `CMake`\n - example: `C:\\Program Files\\CMake\\bin`\n* `R`\n - example: `C:\\Program Files\\R\\R-4.5.1\\bin`\n\nNOTE: Two `Rtools` paths are required from `Rtools` 4.0 onwards because paths and the list of included software was changed in `Rtools` 4.0.\n\nNOTE: `Rtools42` and later take a very different approach to the compiler toolchain than previous releases, and how you install it changes what is required to build packages. See [\"Howto: Building R 4.2 and packages on Windows\"](https://cran.r-project.org/bin/windows/base/howto-R-4.2.html).\n\n#### Windows Toolchain Options\n\nA \"toolchain\" refers to the collection of software used to build the library. The R-package can be built with three different toolchains.\n\n**Warning for Windows users**: it is recommended to use *Visual Studio* for its better multi-threading efficiency in Windows for many core systems. For very simple systems (dual core computers or worse), MinGW64 is recommended for maximum performance. If you do not know what to choose, it is recommended to use [Visual Studio](https://visualstudio.microsoft.com/downloads/), the default compiler. **Do not try using MinGW in Windows on many core systems. It may result in 10x slower results than Visual Studio.**\n\n**Visual Studio (default)**\n\nBy default, the package will be built with [Visual Studio Build Tools](https://visualstudio.microsoft.com/downloads/).\n\n**MSYS2 (R 4.x)**\n\nIf you are using R 4.x and installation fails with Visual Studio, `LightGBM` will fall back to using [MSYS2](https://www.msys2.org/). This should work with the tools already bundled in `Rtools` 4.0.\n\nIf you want to force `LightGBM` to use MSYS2 (for any R version), pass `--use-msys2` to the installation script.\n\n```shell\nRscript build_r.R --use-msys2\n```\n\n**MinGW**\n\nIf you want to force `LightGBM` to use [MinGW](https://www.mingw-w64.org/) (for any R version), pass `--use-mingw` to the installation script.\n\n```shell\nRscript build_r.R --use-mingw\n```\n\n#### Mac OS Preparation\n\nYou can perform installation either with **Apple Clang** or **gcc**. In case you prefer **Apple Clang**, you should install **OpenMP** (details for installation can be found in [Installation Guide](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Installation-Guide.rst#apple-clang)) first. In case you prefer **gcc**, you need to install it (details for installation can be found in [Installation Guide](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Installation-Guide.rst#gcc)) and set some environment variables to tell R to use `gcc` and `g++`. If you install these from Homebrew, your versions of `g++` and `gcc` are most likely in `/usr/local/bin`, as shown below.\n\n```\n# replace 8 with version of gcc installed on your machine\nexport CXX=/usr/local/bin/g++-8 CC=/usr/local/bin/gcc-8\n```\n\n#### Install with CMake\n\nAfter following the \"preparation\" steps above for your operating system, build and install the R-package with the following commands:\n\n```sh\ngit clone --recursive https://github.com/lightgbm-org/LightGBM\ncd LightGBM\nRscript build_r.R\n```\n\nThe `build_r.R` script builds the package in a temporary directory called `lightgbm_r`. It will destroy and recreate that directory each time you run the script. That script supports the following command-line options:\n\n- `--no-build-vignettes`: Skip building vignettes.\n- `-j[jobs]`: Number of threads to use when compiling LightGBM. E.g., `-j4` will try to compile 4 objects at a time.\n - by default, this script uses single-thread compilation\n - for best results, set `-j` to the number of physical CPUs\n- `--skip-install`: Build the package tarball, but do not install it.\n- `--use-gpu`: Build a GPU-enabled version of the library.\n- `--use-mingw`: Force the use of MinGW toolchain, regardless of R version.\n- `--use-msys2`: Force the use of MSYS2 toolchain, regardless of R version.\n\nNote: for the build with Visual Studio/VS Build Tools in Windows, you should use the Windows CMD or PowerShell.\n\n### Installing a GPU-enabled Build\n\nYou will need to install Boost and OpenCL first: details for installation can be found in [Installation-Guide](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Installation-Guide.rst#build-gpu-version).\n\nAfter installing these other libraries, follow the steps in [\"Installing from Source with CMake\"](#install). When you reach the step that mentions `build_r.R`, pass the flag `--use-gpu`.\n\n```shell\nRscript build_r.R --use-gpu\n```\n\nYou may also need or want to provide additional configuration, depending on your setup. For example, you may need to provide locations for Boost and OpenCL.\n\n```shell\nRscript build_r.R \\\n --use-gpu \\\n --opencl-library=/usr/lib/x86_64-linux-gnu/libOpenCL.so \\\n --boost-librarydir=/usr/lib/x86_64-linux-gnu\n```\n\nThe following options correspond to the [CMake FindBoost options](https://cmake.org/cmake/help/latest/module/FindBoost.html) by the same names.\n\n* `--boost-root`\n* `--boost-dir`\n* `--boost-include-dir`\n* `--boost-librarydir`\n\nThe following options correspond to the [CMake FindOpenCL options](https://cmake.org/cmake/help/latest/module/FindOpenCL.html) by the same names.\n\n* `--opencl-include-dir`\n* `--opencl-library`\n\n### Installing Precompiled Binaries\n\nPrecompiled binaries for Mac and Windows are prepared by CRAN a few days after each release to CRAN. They can be installed with the following R code.\n\n```r\ninstall.packages(\n \"lightgbm\"\n , type = \"both\"\n , repos = \"https://cran.r-project.org\"\n)\n```\n\nThese packages do not require compilation, so they will be faster and easier to install than packages that are built from source.\n\nCRAN does not prepare precompiled binaries for Linux, and as of this writing neither does this project.\n\n### Installing from a Pre-compiled lib_lightgbm \n\nPrevious versions of LightGBM offered the ability to first compile the C++ library (`lib_lightgbm.{dll,dylib,so}`) and then build an R-package that wraps it.\n\nAs of version 3.0.0, this is no longer supported. If building from source is difficult for you, please [open an issue](https://github.com/lightgbm-org/LightGBM/issues).\n\nExamples\n--------\n\nPlease visit [demo](https://github.com/lightgbm-org/LightGBM/tree/master/R-package/demo):\n\n* [Basic walkthrough of wrappers](https://github.com/lightgbm-org/LightGBM/blob/master/R-package/demo/basic_walkthrough.R)\n* [Boosting from existing prediction](https://github.com/lightgbm-org/LightGBM/blob/master/R-package/demo/boost_from_prediction.R)\n* [Early Stopping](https://github.com/lightgbm-org/LightGBM/blob/master/R-package/demo/early_stopping.R)\n* [Cross Validation](https://github.com/lightgbm-org/LightGBM/blob/master/R-package/demo/cross_validation.R)\n* [Multiclass Training/Prediction](https://github.com/lightgbm-org/LightGBM/blob/master/R-package/demo/multiclass.R)\n* [Leaf (in)Stability](https://github.com/lightgbm-org/LightGBM/blob/master/R-package/demo/leaf_stability.R)\n* [Weight-Parameter Adjustment Relationship](https://github.com/lightgbm-org/LightGBM/blob/master/R-package/demo/weight_param.R)\n\nTesting\n-------\n\nThe R-package's unit tests are run automatically on every commit, via integrations like [GitHub Actions](https://github.com/lightgbm-org/LightGBM/actions). Adding new tests in `R-package/tests/testthat` is a valuable way to improve the reliability of the R-package.\n\n### Running the Tests\n\nWhile developing the R-package, run the code below to run the unit tests.\n\n```shell\nsh build-cran-package.sh \\\n --no-build-vignettes\n\nR CMD INSTALL --with-keep.source lightgbm*.tar.gz\ncd R-package/tests\nRscript testthat.R\n```\n\nTo run the tests with more verbose logs, set environment variable `LIGHTGBM_TEST_VERBOSITY` to a valid value for parameter [`verbosity`](https://lightgbm.readthedocs.io/en/latest/Parameters.html#verbosity).\n\n```shell\nexport LIGHTGBM_TEST_VERBOSITY=1\ncd R-package/tests\nRscript testthat.R\n```\n\n### Code Coverage\n\nWhen adding tests, you may want to use test coverage to identify untested areas and to check if the tests you've added are covering all branches of the intended code.\n\nThe example below shows how to generate code coverage for the R-package on a macOS or Linux setup. To adjust for your environment, refer to [the customization step described above](#custom-installation-linux-mac).\n\n```shell\n# Install\nsh build-cran-package.sh \\\n --no-build-vignettes\n\n# Get coverage\nRscript -e \" \\\n library(covr);\n coverage <- covr::package_coverage('./lightgbm_r', type = 'tests', quiet = FALSE);\n print(coverage);\n covr::report(coverage, file = file.path(getwd(), 'coverage.html'), browse = TRUE);\n \"\n```\n\nUpdating Documentation\n----------------------\n\nThe R-package uses [`{roxygen2}`](https://CRAN.R-project.org/package=roxygen2) to generate its documentation.\nThe generated `DESCRIPTION`, `NAMESPACE`, and `man/` files are checked into source control.\nTo regenerate those files, run the following.\n\n```shell\nRscript \\\n --vanilla \\\n -e \"install.packages('roxygen2', repos = 'https://cran.rstudio.com')\"\n\nsh build-cran-package.sh --no-build-vignettes\nR CMD INSTALL \\\n --with-keep.source \\\n ./lightgbm_*.tar.gz\n\ncd R-package\nRscript \\\n --vanilla \\\n -e \"roxygen2::roxygenize(load = 'installed')\"\n```\n\nPreparing a CRAN Package\n------------------------\n\nThis section is primarily for maintainers, but may help users and contributors to understand the structure of the R-package.\n\nMost of `LightGBM` uses `CMake` to handle tasks like setting compiler and linker flags, including header file locations, and linking to other libraries. Because CRAN packages typically do not assume the presence of `CMake`, the R-package uses an alternative method that is in the CRAN-supported toolchain for building R packages with C++ code: `Autoconf`.\n\nFor more information on this approach, see [\"Writing R Extensions\"](https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Configure-and-cleanup).\n\n### Build a CRAN Package\n\nFrom the root of the repository, run the following.\n\n```shell\ngit submodule update --init --recursive\nsh build-cran-package.sh\n```\n\nThis will create a file `lightgbm_${VERSION}.tar.gz`, where `VERSION` is the version of `LightGBM`.\n\nThat script supports the following command-line options:\n\n- `--no-build-vignettes`: Skip building vignettes.\n- `--r-executable=[path-to-executable]`: Use an alternative build of R.\n\n### Standard Installation from CRAN Package\n\nAfter building the package, install it with a command like the following:\n\n```shell\nR CMD install lightgbm_*.tar.gz\n```\n\n### Changing the CRAN Package\n\nA lot of details are handled automatically by `R CMD build` and `R CMD install`, so it can be difficult to understand how the files in the R-package are related to each other. An extensive treatment of those details is available in [\"Writing R Extensions\"](https://cran.r-project.org/doc/manuals/r-release/R-exts.html).\n\nThis section briefly explains the key files for building a CRAN package. To update the package, edit the files relevant to your change and re-run the steps in [Build a CRAN Package](#build-a-cran-package).\n\n**Linux or Mac**\n\nAt build time, `configure` will be run and used to create a file `Makevars`, using `Makevars.in` as a template.\n\n1. Edit `configure.ac`.\n2. Create `configure` with `autoconf`. Do not edit it by hand. This file must be generated on Ubuntu 22.04.\n\n If you have an Ubuntu 22.04 environment available, run the provided script from the root of the `LightGBM` repository.\n\n ```shell\n ./R-package/recreate-configure.sh\n ```\n\n If you do not have easy access to an Ubuntu 22.04 environment, the `configure` script can be generated using Docker by running the code below from the root of this repo.\n\n ```shell\n docker run \\\n --rm \\\n -v $(pwd):/opt/LightGBM \\\n -w /opt/LightGBM \\\n ubuntu:22.04 \\\n ./R-package/recreate-configure.sh\n ```\n\n The version of `autoconf` used by this project is stored in `R-package/AUTOCONF_UBUNTU_VERSION`. To update that version, update that file and run the commands above. To see available versions, see https://packages.ubuntu.com/search?keywords=autoconf.\n\n3. Edit `src/Makevars.in`.\n\nAlternatively, GitHub Actions can re-generate this file for you.\n\n1. navigate to https://github.com/lightgbm-org/LightGBM/actions/workflows/r_configure.yml\n2. click \"Run workflow\" (drop-down)\n3. enter the branch from the pull request for the `pr-branch` input\n4. click \"Run workflow\" (button)\n\n**Configuring for Windows**\n\nAt build time, `configure.win` will be run and used to create a file `Makevars.win`, using `Makevars.win.in` as a template.\n\n1. Edit `configure.win` directly.\n2. Edit `src/Makevars.win.in`.\n\n### Testing the CRAN Package\n\n`{lightgbm}` is tested automatically on every commit, across many combinations of operating system, R version, and compiler. This section describes how to test the package locally while you are developing.\n\n#### Windows, Mac, and Linux\n\n```shell\nsh build-cran-package.sh\nR CMD check --as-cran lightgbm_*.tar.gz\n```\n\n#### ASAN and UBSAN\n\nAll packages uploaded to CRAN must pass builds using `gcc` and `clang`, instrumented with two sanitizers: the Address Sanitizer (ASAN) and the Undefined Behavior Sanitizer (UBSAN).\n\nFor more background, see\n\n* [this blog post](https://dirk.eddelbuettel.com/code/sanitizers.html)\n* [top-level CRAN documentation on these checks](https://cran.r-project.org/web/checks/check_issue_kinds.html)\n* [CRAN's configuration of these checks](https://www.stats.ox.ac.uk/pub/bdr/memtests/README.txt)\n\nYou can replicate these checks locally using Docker.\nFor more information on the image used for testing, see https://github.com/wch/r-debug.\n\nIn the code below, environment variable `R_CUSTOMIZATION` should be set to one of two values.\n\n* `\"san\"` = replicates CRAN's `gcc-ASAN` and `gcc-UBSAN` checks\n* `\"csan\"` = replicates CRAN's `clang-ASAN` and `clang-UBSAN` checks\n\n```shell\ndocker run \\\n --rm \\\n -it \\\n -v $(pwd):/opt/LightGBM \\\n -w /opt/LightGBM \\\n --env R_CUSTOMIZATION=san \\\n wch1/r-debug:latest \\\n /bin/bash\n\n# install dependencies\nRDscript${R_CUSTOMIZATION} \\\n -e \"install.packages(c('R6', 'data.table', 'jsonlite', 'knitr', 'markdown', 'Matrix', 'RhpcBLASctl', 'testthat'), repos = 'https://cran.r-project.org', Ncpus = parallel::detectCores())\"\n\n# install lightgbm\nsh build-cran-package.sh --r-executable=RD${R_CUSTOMIZATION}\nRD${R_CUSTOMIZATION} \\\n CMD INSTALL lightgbm_*.tar.gz\n\n# run tests\ncd R-package/tests\nrm -f ./tests.log\nRDscript${R_CUSTOMIZATION} testthat.R >> tests.log 2>&1\n\n# check that tests passed\necho \"test exit code: $?\"\ntail -300 ./tests.log\n```\n\n#### Valgrind\n\nAll packages uploaded to CRAN must be built and tested without raising any issues from `valgrind`. `valgrind` is a profiler that can catch serious issues like memory leaks and illegal writes. For more information, see [this blog post](https://reside-ic.github.io/blog/debugging-and-fixing-crans-additional-checks-errors/).\n\nYou can replicate these checks locally using Docker. Note that instrumented versions of R built to use `valgrind` run much slower, and these tests may take as long as 20 minutes to run.\n\n```shell\ndocker run \\\n --rm \\\n -v $(pwd):/opt/LightGBM \\\n -w /opt/LightGBM \\\n -it \\\n wch1/r-debug\n\nRDscriptvalgrind -e \"install.packages(c('R6', 'data.table', 'jsonlite', 'knitr', 'markdown', 'Matrix', 'RhpcBLASctl', 'testthat'), repos = 'https://cran.rstudio.com', Ncpus = parallel::detectCores())\"\n\nsh build-cran-package.sh \\\n --r-executable=RDvalgrind\n\nRDvalgrind CMD INSTALL \\\n --preclean \\\n --install-tests \\\n lightgbm_*.tar.gz\n\ncd R-package/tests\n\nRDvalgrind \\\n --no-readline \\\n --vanilla \\\n -d \"valgrind --tool=memcheck --leak-check=full --track-origins=yes\" \\\n -f testthat.R \\\n2>&1 \\\n| tee out.log \\\n| cat\n```\n\nThese tests can also be triggered on a pull request branch, using GitHub Actions.\n\n1. navigate to https://github.com/lightgbm-org/LightGBM/actions/workflows/r_valgrind.yml\n2. click \"Run workflow\" (drop-down)\n3. enter the branch from the pull request for the `pr-branch` input\n4. enter the pull request ID for the `pr-number` input\n5. click \"Run workflow\" (button)\n\nOr by using the GitHub CLI, using a command similar to this:\n\n```shell\ngh workflow run \\\n --repo lightgbm-org/LightGBM \\\n r_valgrind.yml \\\n -f pr-branch=ci/fix-rerun-workflow \\\n -f pr-number=7072\n```\n\nKnown Issues\n------------\n\nFor information about known issues with the R-package, see the [R-package section of LightGBM's main FAQ page](https://lightgbm.readthedocs.io/en/latest/FAQ.html#r-package).\n" }, { "path": "R-package/cleanup", "content": "#!/bin/sh\nrm -f src/Makevars\n" }, { "path": "R-package/configure", "content": "#! /bin/sh\n# Guess values for system-dependent variables and create Makefiles.\n# Generated by GNU Autoconf 2.71 for lightgbm 4.6.0.99.\n#\n#\n# Copyright (C) 1992-1996, 1998-2017, 2020-2021 Free Software Foundation,\n# Inc.\n#\n#\n# This configure script is free software; the Free Software Foundation\n# gives unlimited permission to copy, distribute and modify it.\n## -------------------- ##\n## M4sh Initialization. ##\n## -------------------- ##\n\n# Be more Bourne compatible\nDUALCASE=1; export DUALCASE # for MKS sh\nas_nop=:\nif test ${ZSH_VERSION+y} && (emulate sh) >/dev/null 2>&1\nthen :\n emulate sh\n NULLCMD=:\n # Pre-4.2 versions of Zsh do word splitting on ${1+\"$@\"}, which\n # is contrary to our usage. Disable this feature.\n alias -g '${1+\"$@\"}'='\"$@\"'\n setopt NO_GLOB_SUBST\nelse $as_nop\n case `(set -o) 2>/dev/null` in #(\n *posix*) :\n set -o posix ;; #(\n *) :\n ;;\nesac\nfi\n\n\n\n# Reset variables that may have inherited troublesome values from\n# the environment.\n\n# IFS needs to be set, to space, tab, and newline, in precisely that order.\n# (If _AS_PATH_WALK were called with IFS unset, it would have the\n# side effect of setting IFS to empty, thus disabling word splitting.)\n# Quoting is to prevent editors from complaining about space-tab.\nas_nl='\n'\nexport as_nl\nIFS=\" \"\"\t$as_nl\"\n\nPS1='$ '\nPS2='> '\nPS4='+ '\n\n# Ensure predictable behavior from utilities with locale-dependent output.\nLC_ALL=C\nexport LC_ALL\nLANGUAGE=C\nexport LANGUAGE\n\n# We cannot yet rely on \"unset\" to work, but we need these variables\n# to be unset--not just set to an empty or harmless value--now, to\n# avoid bugs in old shells (e.g. pre-3.0 UWIN ksh). This construct\n# also avoids known problems related to \"unset\" and subshell syntax\n# in other old shells (e.g. bash 2.01 and pdksh 5.2.14).\nfor as_var in BASH_ENV ENV MAIL MAILPATH CDPATH\ndo eval test \\${$as_var+y} \\\n && ( (unset $as_var) || exit 1) >/dev/null 2>&1 && unset $as_var || :\ndone\n\n# Ensure that fds 0, 1, and 2 are open.\nif (exec 3>&0) 2>/dev/null; then :; else exec 0&1) 2>/dev/null; then :; else exec 1>/dev/null; fi\nif (exec 3>&2) ; then :; else exec 2>/dev/null; fi\n\n# The user is always right.\nif ${PATH_SEPARATOR+false} :; then\n PATH_SEPARATOR=:\n (PATH='/bin;/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 && {\n (PATH='/bin:/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 ||\n PATH_SEPARATOR=';'\n }\nfi\n\n\n# Find who we are. Look in the path if we contain no directory separator.\nas_myself=\ncase $0 in #((\n *[\\\\/]* ) as_myself=$0 ;;\n *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR\nfor as_dir in $PATH\ndo\n IFS=$as_save_IFS\n case $as_dir in #(((\n '') as_dir=./ ;;\n */) ;;\n *) as_dir=$as_dir/ ;;\n esac\n test -r \"$as_dir$0\" && as_myself=$as_dir$0 && break\n done\nIFS=$as_save_IFS\n\n ;;\nesac\n# We did not find ourselves, most probably we were run as `sh COMMAND'\n# in which case we are not to be found in the path.\nif test \"x$as_myself\" = x; then\n as_myself=$0\nfi\nif test ! -f \"$as_myself\"; then\n printf \"%s\\n\" \"$as_myself: error: cannot find myself; rerun with an absolute file name\" >&2\n exit 1\nfi\n\n\n# Use a proper internal environment variable to ensure we don't fall\n # into an infinite loop, continuously re-executing ourselves.\n if test x\"${_as_can_reexec}\" != xno && test \"x$CONFIG_SHELL\" != x; then\n _as_can_reexec=no; export _as_can_reexec;\n # We cannot yet assume a decent shell, so we have to provide a\n# neutralization value for shells without unset; and this also\n# works around shells that cannot unset nonexistent variables.\n# Preserve -v and -x to the replacement shell.\nBASH_ENV=/dev/null\nENV=/dev/null\n(unset BASH_ENV) >/dev/null 2>&1 && unset BASH_ENV ENV\ncase $- in # ((((\n *v*x* | *x*v* ) as_opts=-vx ;;\n *v* ) as_opts=-v ;;\n *x* ) as_opts=-x ;;\n * ) as_opts= ;;\nesac\nexec $CONFIG_SHELL $as_opts \"$as_myself\" ${1+\"$@\"}\n# Admittedly, this is quite paranoid, since all the known shells bail\n# out after a failed `exec'.\nprintf \"%s\\n\" \"$0: could not re-execute with $CONFIG_SHELL\" >&2\nexit 255\n fi\n # We don't want this to propagate to other subprocesses.\n { _as_can_reexec=; unset _as_can_reexec;}\nif test \"x$CONFIG_SHELL\" = x; then\n as_bourne_compatible=\"as_nop=:\nif test \\${ZSH_VERSION+y} && (emulate sh) >/dev/null 2>&1\nthen :\n emulate sh\n NULLCMD=:\n # Pre-4.2 versions of Zsh do word splitting on \\${1+\\\"\\$@\\\"}, which\n # is contrary to our usage. Disable this feature.\n alias -g '\\${1+\\\"\\$@\\\"}'='\\\"\\$@\\\"'\n setopt NO_GLOB_SUBST\nelse \\$as_nop\n case \\`(set -o) 2>/dev/null\\` in #(\n *posix*) :\n set -o posix ;; #(\n *) :\n ;;\nesac\nfi\n\"\n as_required=\"as_fn_return () { (exit \\$1); }\nas_fn_success () { as_fn_return 0; }\nas_fn_failure () { as_fn_return 1; }\nas_fn_ret_success () { return 0; }\nas_fn_ret_failure () { return 1; }\n\nexitcode=0\nas_fn_success || { exitcode=1; echo as_fn_success failed.; }\nas_fn_failure && { exitcode=1; echo as_fn_failure succeeded.; }\nas_fn_ret_success || { exitcode=1; echo as_fn_ret_success failed.; }\nas_fn_ret_failure && { exitcode=1; echo as_fn_ret_failure succeeded.; }\nif ( set x; as_fn_ret_success y && test x = \\\"\\$1\\\" )\nthen :\n\nelse \\$as_nop\n exitcode=1; echo positional parameters were not saved.\nfi\ntest x\\$exitcode = x0 || exit 1\nblah=\\$(echo \\$(echo blah))\ntest x\\\"\\$blah\\\" = xblah || exit 1\ntest -x / || exit 1\"\n as_suggested=\" as_lineno_1=\";as_suggested=$as_suggested$LINENO;as_suggested=$as_suggested\" as_lineno_1a=\\$LINENO\n as_lineno_2=\";as_suggested=$as_suggested$LINENO;as_suggested=$as_suggested\" as_lineno_2a=\\$LINENO\n eval 'test \\\"x\\$as_lineno_1'\\$as_run'\\\" != \\\"x\\$as_lineno_2'\\$as_run'\\\" &&\n test \\\"x\\`expr \\$as_lineno_1'\\$as_run' + 1\\`\\\" = \\\"x\\$as_lineno_2'\\$as_run'\\\"' || exit 1\"\n if (eval \"$as_required\") 2>/dev/null\nthen :\n as_have_required=yes\nelse $as_nop\n as_have_required=no\nfi\n if test x$as_have_required = xyes && (eval \"$as_suggested\") 2>/dev/null\nthen :\n\nelse $as_nop\n as_save_IFS=$IFS; IFS=$PATH_SEPARATOR\nas_found=false\nfor as_dir in /bin$PATH_SEPARATOR/usr/bin$PATH_SEPARATOR$PATH\ndo\n IFS=$as_save_IFS\n case $as_dir in #(((\n '') as_dir=./ ;;\n */) ;;\n *) as_dir=$as_dir/ ;;\n esac\n as_found=:\n case $as_dir in #(\n\t /*)\n\t for as_base in sh bash ksh sh5; do\n\t # Try only shells that exist, to save several forks.\n\t as_shell=$as_dir$as_base\n\t if { test -f \"$as_shell\" || test -f \"$as_shell.exe\"; } &&\n\t\t as_run=a \"$as_shell\" -c \"$as_bourne_compatible\"\"$as_required\" 2>/dev/null\nthen :\n CONFIG_SHELL=$as_shell as_have_required=yes\n\t\t if as_run=a \"$as_shell\" -c \"$as_bourne_compatible\"\"$as_suggested\" 2>/dev/null\nthen :\n break 2\nfi\nfi\n\t done;;\n esac\n as_found=false\ndone\nIFS=$as_save_IFS\nif $as_found\nthen :\n\nelse $as_nop\n if { test -f \"$SHELL\" || test -f \"$SHELL.exe\"; } &&\n\t as_run=a \"$SHELL\" -c \"$as_bourne_compatible\"\"$as_required\" 2>/dev/null\nthen :\n CONFIG_SHELL=$SHELL as_have_required=yes\nfi\nfi\n\n\n if test \"x$CONFIG_SHELL\" != x\nthen :\n export CONFIG_SHELL\n # We cannot yet assume a decent shell, so we have to provide a\n# neutralization value for shells without unset; and this also\n# works around shells that cannot unset nonexistent variables.\n# Preserve -v and -x to the replacement shell.\nBASH_ENV=/dev/null\nENV=/dev/null\n(unset BASH_ENV) >/dev/null 2>&1 && unset BASH_ENV ENV\ncase $- in # ((((\n *v*x* | *x*v* ) as_opts=-vx ;;\n *v* ) as_opts=-v ;;\n *x* ) as_opts=-x ;;\n * ) as_opts= ;;\nesac\nexec $CONFIG_SHELL $as_opts \"$as_myself\" ${1+\"$@\"}\n# Admittedly, this is quite paranoid, since all the known shells bail\n# out after a failed `exec'.\nprintf \"%s\\n\" \"$0: could not re-execute with $CONFIG_SHELL\" >&2\nexit 255\nfi\n\n if test x$as_have_required = xno\nthen :\n printf \"%s\\n\" \"$0: This script requires a shell more modern than all\"\n printf \"%s\\n\" \"$0: the shells that I found on your system.\"\n if test ${ZSH_VERSION+y} ; then\n printf \"%s\\n\" \"$0: In particular, zsh $ZSH_VERSION has bugs and should\"\n printf \"%s\\n\" \"$0: be upgraded to zsh 4.3.4 or later.\"\n else\n printf \"%s\\n\" \"$0: Please tell bug-autoconf@gnu.org about your system,\n$0: including any error possibly output before this\n$0: message. Then install a modern shell, or manually run\n$0: the script under such a shell if you do have one.\"\n fi\n exit 1\nfi\nfi\nfi\nSHELL=${CONFIG_SHELL-/bin/sh}\nexport SHELL\n# Unset more variables known to interfere with behavior of common tools.\nCLICOLOR_FORCE= GREP_OPTIONS=\nunset CLICOLOR_FORCE GREP_OPTIONS\n\n## --------------------- ##\n## M4sh Shell Functions. ##\n## --------------------- ##\n# as_fn_unset VAR\n# ---------------\n# Portably unset VAR.\nas_fn_unset ()\n{\n { eval $1=; unset $1;}\n}\nas_unset=as_fn_unset\n\n\n# as_fn_set_status STATUS\n# -----------------------\n# Set $? to STATUS, without forking.\nas_fn_set_status ()\n{\n return $1\n} # as_fn_set_status\n\n# as_fn_exit STATUS\n# -----------------\n# Exit the shell with STATUS, even in a \"trap 0\" or \"set -e\" context.\nas_fn_exit ()\n{\n set +e\n as_fn_set_status $1\n exit $1\n} # as_fn_exit\n# as_fn_nop\n# ---------\n# Do nothing but, unlike \":\", preserve the value of $?.\nas_fn_nop ()\n{\n return $?\n}\nas_nop=as_fn_nop\n\n# as_fn_mkdir_p\n# -------------\n# Create \"$as_dir\" as a directory, including parents if necessary.\nas_fn_mkdir_p ()\n{\n\n case $as_dir in #(\n -*) as_dir=./$as_dir;;\n esac\n test -d \"$as_dir\" || eval $as_mkdir_p || {\n as_dirs=\n while :; do\n case $as_dir in #(\n *\\'*) as_qdir=`printf \"%s\\n\" \"$as_dir\" | sed \"s/'/'\\\\\\\\\\\\\\\\''/g\"`;; #'(\n *) as_qdir=$as_dir;;\n esac\n as_dirs=\"'$as_qdir' $as_dirs\"\n as_dir=`$as_dirname -- \"$as_dir\" ||\n$as_expr X\"$as_dir\" : 'X\\(.*[^/]\\)//*[^/][^/]*/*$' \\| \\\n\t X\"$as_dir\" : 'X\\(//\\)[^/]' \\| \\\n\t X\"$as_dir\" : 'X\\(//\\)$' \\| \\\n\t X\"$as_dir\" : 'X\\(/\\)' \\| . 2>/dev/null ||\nprintf \"%s\\n\" X\"$as_dir\" |\n sed '/^X\\(.*[^/]\\)\\/\\/*[^/][^/]*\\/*$/{\n\t s//\\1/\n\t q\n\t }\n\t /^X\\(\\/\\/\\)[^/].*/{\n\t s//\\1/\n\t q\n\t }\n\t /^X\\(\\/\\/\\)$/{\n\t s//\\1/\n\t q\n\t }\n\t /^X\\(\\/\\).*/{\n\t s//\\1/\n\t q\n\t }\n\t s/.*/./; q'`\n test -d \"$as_dir\" && break\n done\n test -z \"$as_dirs\" || eval \"mkdir $as_dirs\"\n } || test -d \"$as_dir\" || as_fn_error $? \"cannot create directory $as_dir\"\n\n\n} # as_fn_mkdir_p\n\n# as_fn_executable_p FILE\n# -----------------------\n# Test if FILE is an executable regular file.\nas_fn_executable_p ()\n{\n test -f \"$1\" && test -x \"$1\"\n} # as_fn_executable_p\n# as_fn_append VAR VALUE\n# ----------------------\n# Append the text in VALUE to the end of the definition contained in VAR. Take\n# advantage of any shell optimizations that allow amortized linear growth over\n# repeated appends, instead of the typical quadratic growth present in naive\n# implementations.\nif (eval \"as_var=1; as_var+=2; test x\\$as_var = x12\") 2>/dev/null\nthen :\n eval 'as_fn_append ()\n {\n eval $1+=\\$2\n }'\nelse $as_nop\n as_fn_append ()\n {\n eval $1=\\$$1\\$2\n }\nfi # as_fn_append\n\n# as_fn_arith ARG...\n# ------------------\n# Perform arithmetic evaluation on the ARGs, and store the result in the\n# global $as_val. Take advantage of shells that can avoid forks. The arguments\n# must be portable across $(()) and expr.\nif (eval \"test \\$(( 1 + 1 )) = 2\") 2>/dev/null\nthen :\n eval 'as_fn_arith ()\n {\n as_val=$(( $* ))\n }'\nelse $as_nop\n as_fn_arith ()\n {\n as_val=`expr \"$@\" || test $? -eq 1`\n }\nfi # as_fn_arith\n\n# as_fn_nop\n# ---------\n# Do nothing but, unlike \":\", preserve the value of $?.\nas_fn_nop ()\n{\n return $?\n}\nas_nop=as_fn_nop\n\n# as_fn_error STATUS ERROR [LINENO LOG_FD]\n# ----------------------------------------\n# Output \"`basename $0`: error: ERROR\" to stderr. If LINENO and LOG_FD are\n# provided, also output the error to LOG_FD, referencing LINENO. Then exit the\n# script with STATUS, using 1 if that was 0.\nas_fn_error ()\n{\n as_status=$1; test $as_status -eq 0 && as_status=1\n if test \"$4\"; then\n as_lineno=${as_lineno-\"$3\"} as_lineno_stack=as_lineno_stack=$as_lineno_stack\n printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: error: $2\" >&$4\n fi\n printf \"%s\\n\" \"$as_me: error: $2\" >&2\n as_fn_exit $as_status\n} # as_fn_error\n\nif expr a : '\\(a\\)' >/dev/null 2>&1 &&\n test \"X`expr 00001 : '.*\\(...\\)'`\" = X001; then\n as_expr=expr\nelse\n as_expr=false\nfi\n\nif (basename -- /) >/dev/null 2>&1 && test \"X`basename -- / 2>&1`\" = \"X/\"; then\n as_basename=basename\nelse\n as_basename=false\nfi\n\nif (as_dir=`dirname -- /` && test \"X$as_dir\" = X/) >/dev/null 2>&1; then\n as_dirname=dirname\nelse\n as_dirname=false\nfi\n\nas_me=`$as_basename -- \"$0\" ||\n$as_expr X/\"$0\" : '.*/\\([^/][^/]*\\)/*$' \\| \\\n\t X\"$0\" : 'X\\(//\\)$' \\| \\\n\t X\"$0\" : 'X\\(/\\)' \\| . 2>/dev/null ||\nprintf \"%s\\n\" X/\"$0\" |\n sed '/^.*\\/\\([^/][^/]*\\)\\/*$/{\n\t s//\\1/\n\t q\n\t }\n\t /^X\\/\\(\\/\\/\\)$/{\n\t s//\\1/\n\t q\n\t }\n\t /^X\\/\\(\\/\\).*/{\n\t s//\\1/\n\t q\n\t }\n\t s/.*/./; q'`\n\n# Avoid depending upon Character Ranges.\nas_cr_letters='abcdefghijklmnopqrstuvwxyz'\nas_cr_LETTERS='ABCDEFGHIJKLMNOPQRSTUVWXYZ'\nas_cr_Letters=$as_cr_letters$as_cr_LETTERS\nas_cr_digits='0123456789'\nas_cr_alnum=$as_cr_Letters$as_cr_digits\n\n\n as_lineno_1=$LINENO as_lineno_1a=$LINENO\n as_lineno_2=$LINENO as_lineno_2a=$LINENO\n eval 'test \"x$as_lineno_1'$as_run'\" != \"x$as_lineno_2'$as_run'\" &&\n test \"x`expr $as_lineno_1'$as_run' + 1`\" = \"x$as_lineno_2'$as_run'\"' || {\n # Blame Lee E. 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Invocation command line was\n\n $ $0$ac_configure_args_raw\n\n_ACEOF\nexec 5>>config.log\n{\ncat <<_ASUNAME\n## --------- ##\n## Platform. ##\n## --------- ##\n\nhostname = `(hostname || uname -n) 2>/dev/null | sed 1q`\nuname -m = `(uname -m) 2>/dev/null || echo unknown`\nuname -r = `(uname -r) 2>/dev/null || echo unknown`\nuname -s = `(uname -s) 2>/dev/null || echo unknown`\nuname -v = `(uname -v) 2>/dev/null || echo unknown`\n\n/usr/bin/uname -p = `(/usr/bin/uname -p) 2>/dev/null || echo unknown`\n/bin/uname -X = `(/bin/uname -X) 2>/dev/null || echo unknown`\n\n/bin/arch = `(/bin/arch) 2>/dev/null || echo unknown`\n/usr/bin/arch -k = `(/usr/bin/arch -k) 2>/dev/null || echo unknown`\n/usr/convex/getsysinfo = `(/usr/convex/getsysinfo) 2>/dev/null || echo unknown`\n/usr/bin/hostinfo = `(/usr/bin/hostinfo) 2>/dev/null || echo unknown`\n/bin/machine = `(/bin/machine) 2>/dev/null || echo unknown`\n/usr/bin/oslevel = `(/usr/bin/oslevel) 2>/dev/null || echo unknown`\n/bin/universe = `(/bin/universe) 2>/dev/null || echo unknown`\n\n_ASUNAME\n\nas_save_IFS=$IFS; IFS=$PATH_SEPARATOR\nfor as_dir in $PATH\ndo\n IFS=$as_save_IFS\n case $as_dir in #(((\n '') as_dir=./ ;;\n */) ;;\n *) as_dir=$as_dir/ ;;\n esac\n printf \"%s\\n\" \"PATH: $as_dir\"\n done\nIFS=$as_save_IFS\n\n} >&5\n\ncat >&5 <<_ACEOF\n\n\n## ----------- ##\n## Core tests. ##\n## ----------- ##\n\n_ACEOF\n\n\n# Keep a trace of the command line.\n# Strip out --no-create and --no-recursion so they do not pile up.\n# Strip out --silent because we don't want to record it for future runs.\n# Also quote any args containing shell meta-characters.\n# Make two passes to allow for proper duplicate-argument suppression.\nac_configure_args=\nac_configure_args0=\nac_configure_args1=\nac_must_keep_next=false\nfor ac_pass in 1 2\ndo\n for ac_arg\n do\n case $ac_arg in\n -no-create | --no-c* | -n | -no-recursion | --no-r*) continue ;;\n -q | -quiet | --quiet | --quie | --qui | --qu | --q \\\n | -silent | --silent | --silen | --sile | --sil)\n continue ;;\n *\\'*)\n ac_arg=`printf \"%s\\n\" \"$ac_arg\" | sed \"s/'/'\\\\\\\\\\\\\\\\''/g\"` ;;\n esac\n case $ac_pass in\n 1) as_fn_append ac_configure_args0 \" '$ac_arg'\" ;;\n 2)\n as_fn_append ac_configure_args1 \" '$ac_arg'\"\n if test $ac_must_keep_next = true; then\n\tac_must_keep_next=false # Got value, back to normal.\n else\n\tcase $ac_arg in\n\t *=* | --config-cache | -C | -disable-* | --disable-* \\\n\t | -enable-* | --enable-* | -gas | --g* | -nfp | --nf* \\\n\t | -q | -quiet | --q* | -silent | --sil* | -v | -verb* \\\n\t | -with-* | --with-* | -without-* | --without-* | --x)\n\t case \"$ac_configure_args0 \" in\n\t \"$ac_configure_args1\"*\" '$ac_arg' \"* ) continue ;;\n\t esac\n\t ;;\n\t -* ) ac_must_keep_next=true ;;\n\tesac\n fi\n as_fn_append ac_configure_args \" '$ac_arg'\"\n ;;\n esac\n done\ndone\n{ ac_configure_args0=; unset ac_configure_args0;}\n{ ac_configure_args1=; unset ac_configure_args1;}\n\n# When interrupted or exit'd, cleanup temporary files, and complete\n# config.log. We remove comments because anyway the quotes in there\n# would cause problems or look ugly.\n# WARNING: Use '\\'' to represent an apostrophe within the trap.\n# WARNING: Do not start the trap code with a newline, due to a FreeBSD 4.0 bug.\ntrap 'exit_status=$?\n # Sanitize IFS.\n IFS=\" \"\"\t$as_nl\"\n # Save into config.log some information that might help in debugging.\n {\n echo\n\n printf \"%s\\n\" \"## ---------------- ##\n## Cache variables. ##\n## ---------------- ##\"\n echo\n # The following way of writing the cache mishandles newlines in values,\n(\n for ac_var in `(set) 2>&1 | sed -n '\\''s/^\\([a-zA-Z_][a-zA-Z0-9_]*\\)=.*/\\1/p'\\''`; do\n eval ac_val=\\$$ac_var\n case $ac_val in #(\n *${as_nl}*)\n case $ac_var in #(\n *_cv_*) { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: WARNING: cache variable $ac_var contains a newline\" >&5\nprintf \"%s\\n\" \"$as_me: WARNING: cache variable $ac_var contains a newline\" >&2;} ;;\n esac\n case $ac_var in #(\n _ | IFS | as_nl) ;; #(\n BASH_ARGV | BASH_SOURCE) eval $ac_var= ;; #(\n *) { eval $ac_var=; unset $ac_var;} ;;\n esac ;;\n esac\n done\n (set) 2>&1 |\n case $as_nl`(ac_space='\\'' '\\''; set) 2>&1` in #(\n *${as_nl}ac_space=\\ *)\n sed -n \\\n\t\"s/'\\''/'\\''\\\\\\\\'\\'''\\''/g;\n\t s/^\\\\([_$as_cr_alnum]*_cv_[_$as_cr_alnum]*\\\\)=\\\\(.*\\\\)/\\\\1='\\''\\\\2'\\''/p\"\n ;; #(\n *)\n sed -n \"/^[_$as_cr_alnum]*_cv_[_$as_cr_alnum]*=/p\"\n ;;\n esac |\n sort\n)\n echo\n\n printf \"%s\\n\" \"## ----------------- ##\n## Output variables. ##\n## ----------------- ##\"\n echo\n for ac_var in $ac_subst_vars\n do\n eval ac_val=\\$$ac_var\n case $ac_val in\n *\\'\\''*) ac_val=`printf \"%s\\n\" \"$ac_val\" | sed \"s/'\\''/'\\''\\\\\\\\\\\\\\\\'\\'''\\''/g\"`;;\n esac\n printf \"%s\\n\" \"$ac_var='\\''$ac_val'\\''\"\n done | sort\n echo\n\n if test -n \"$ac_subst_files\"; then\n printf \"%s\\n\" \"## ------------------- ##\n## File substitutions. ##\n## ------------------- ##\"\n echo\n for ac_var in $ac_subst_files\n do\n\teval ac_val=\\$$ac_var\n\tcase $ac_val in\n\t*\\'\\''*) ac_val=`printf \"%s\\n\" \"$ac_val\" | sed \"s/'\\''/'\\''\\\\\\\\\\\\\\\\'\\'''\\''/g\"`;;\n\tesac\n\tprintf \"%s\\n\" \"$ac_var='\\''$ac_val'\\''\"\n done | sort\n echo\n fi\n\n if test -s confdefs.h; then\n printf \"%s\\n\" \"## ----------- ##\n## confdefs.h. ##\n## ----------- ##\"\n echo\n cat confdefs.h\n echo\n fi\n test \"$ac_signal\" != 0 &&\n printf \"%s\\n\" \"$as_me: caught signal $ac_signal\"\n printf \"%s\\n\" \"$as_me: exit $exit_status\"\n } >&5\n rm -f core *.core core.conftest.* &&\n rm -f -r conftest* confdefs* conf$$* $ac_clean_files &&\n exit $exit_status\n' 0\nfor ac_signal in 1 2 13 15; do\n trap 'ac_signal='$ac_signal'; as_fn_exit 1' $ac_signal\ndone\nac_signal=0\n\n# confdefs.h avoids OS command line length limits that DEFS can exceed.\nrm -f -r conftest* confdefs.h\n\nprintf \"%s\\n\" \"/* confdefs.h */\" > confdefs.h\n\n# Predefined preprocessor variables.\n\nprintf \"%s\\n\" \"#define PACKAGE_NAME \\\"$PACKAGE_NAME\\\"\" >>confdefs.h\n\nprintf \"%s\\n\" \"#define PACKAGE_TARNAME \\\"$PACKAGE_TARNAME\\\"\" >>confdefs.h\n\nprintf \"%s\\n\" \"#define PACKAGE_VERSION \\\"$PACKAGE_VERSION\\\"\" >>confdefs.h\n\nprintf \"%s\\n\" \"#define PACKAGE_STRING \\\"$PACKAGE_STRING\\\"\" >>confdefs.h\n\nprintf \"%s\\n\" \"#define PACKAGE_BUGREPORT \\\"$PACKAGE_BUGREPORT\\\"\" >>confdefs.h\n\nprintf \"%s\\n\" \"#define PACKAGE_URL \\\"$PACKAGE_URL\\\"\" >>confdefs.h\n\n\n# Let the site file select an alternate cache file if it wants to.\n# Prefer an explicitly selected file to automatically selected ones.\nif test -n \"$CONFIG_SITE\"; then\n ac_site_files=\"$CONFIG_SITE\"\nelif test \"x$prefix\" != xNONE; then\n ac_site_files=\"$prefix/share/config.site $prefix/etc/config.site\"\nelse\n ac_site_files=\"$ac_default_prefix/share/config.site $ac_default_prefix/etc/config.site\"\nfi\n\nfor ac_site_file in $ac_site_files\ndo\n case $ac_site_file in #(\n */*) :\n ;; #(\n *) :\n ac_site_file=./$ac_site_file ;;\nesac\n if test -f \"$ac_site_file\" && test -r \"$ac_site_file\"; then\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: loading site script $ac_site_file\" >&5\nprintf \"%s\\n\" \"$as_me: loading site script $ac_site_file\" >&6;}\n sed 's/^/| /' \"$ac_site_file\" >&5\n . \"$ac_site_file\" \\\n || { { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: error: in \\`$ac_pwd':\" >&5\nprintf \"%s\\n\" \"$as_me: error: in \\`$ac_pwd':\" >&2;}\nas_fn_error $? \"failed to load site script $ac_site_file\nSee \\`config.log' for more details\" \"$LINENO\" 5; }\n fi\ndone\n\nif test -r \"$cache_file\"; then\n # Some versions of bash will fail to source /dev/null (special files\n # actually), so we avoid doing that. DJGPP emulates it as a regular file.\n if test /dev/null != \"$cache_file\" && test -f \"$cache_file\"; then\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: loading cache $cache_file\" >&5\nprintf \"%s\\n\" \"$as_me: loading cache $cache_file\" >&6;}\n case $cache_file in\n [\\\\/]* | ?:[\\\\/]* ) . \"$cache_file\";;\n *) . \"./$cache_file\";;\n esac\n fi\nelse\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: creating cache $cache_file\" >&5\nprintf \"%s\\n\" \"$as_me: creating cache $cache_file\" >&6;}\n >$cache_file\nfi\n\n# Check that the precious variables saved in the cache have kept the same\n# value.\nac_cache_corrupted=false\nfor ac_var in $ac_precious_vars; do\n eval ac_old_set=\\$ac_cv_env_${ac_var}_set\n eval ac_new_set=\\$ac_env_${ac_var}_set\n eval ac_old_val=\\$ac_cv_env_${ac_var}_value\n eval ac_new_val=\\$ac_env_${ac_var}_value\n case $ac_old_set,$ac_new_set in\n set,)\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: error: \\`$ac_var' was set to \\`$ac_old_val' in the previous run\" >&5\nprintf \"%s\\n\" \"$as_me: error: \\`$ac_var' was set to \\`$ac_old_val' in the previous run\" >&2;}\n ac_cache_corrupted=: ;;\n ,set)\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: error: \\`$ac_var' was not set in the previous run\" >&5\nprintf \"%s\\n\" \"$as_me: error: \\`$ac_var' was not set in the previous run\" >&2;}\n ac_cache_corrupted=: ;;\n ,);;\n *)\n if test \"x$ac_old_val\" != \"x$ac_new_val\"; then\n\t# differences in whitespace do not lead to failure.\n\tac_old_val_w=`echo x $ac_old_val`\n\tac_new_val_w=`echo x $ac_new_val`\n\tif test \"$ac_old_val_w\" != \"$ac_new_val_w\"; then\n\t { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: error: \\`$ac_var' has changed since the previous run:\" >&5\nprintf \"%s\\n\" \"$as_me: error: \\`$ac_var' has changed since the previous run:\" >&2;}\n\t ac_cache_corrupted=:\n\telse\n\t { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: warning: ignoring whitespace changes in \\`$ac_var' since the previous run:\" >&5\nprintf \"%s\\n\" \"$as_me: warning: ignoring whitespace changes in \\`$ac_var' since the previous run:\" >&2;}\n\t eval $ac_var=\\$ac_old_val\n\tfi\n\t{ printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: former value: \\`$ac_old_val'\" >&5\nprintf \"%s\\n\" \"$as_me: former value: \\`$ac_old_val'\" >&2;}\n\t{ printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: current value: \\`$ac_new_val'\" >&5\nprintf \"%s\\n\" \"$as_me: current value: \\`$ac_new_val'\" >&2;}\n fi;;\n esac\n # Pass precious variables to config.status.\n if test \"$ac_new_set\" = set; then\n case $ac_new_val in\n *\\'*) ac_arg=$ac_var=`printf \"%s\\n\" \"$ac_new_val\" | sed \"s/'/'\\\\\\\\\\\\\\\\''/g\"` ;;\n *) ac_arg=$ac_var=$ac_new_val ;;\n esac\n case \" $ac_configure_args \" in\n *\" '$ac_arg' \"*) ;; # Avoid dups. Use of quotes ensures accuracy.\n *) as_fn_append ac_configure_args \" '$ac_arg'\" ;;\n esac\n fi\ndone\nif $ac_cache_corrupted; then\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: error: in \\`$ac_pwd':\" >&5\nprintf \"%s\\n\" \"$as_me: error: in \\`$ac_pwd':\" >&2;}\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: error: changes in the environment can compromise the build\" >&5\nprintf \"%s\\n\" \"$as_me: error: changes in the environment can compromise the build\" >&2;}\n as_fn_error $? \"run \\`${MAKE-make} distclean' and/or \\`rm $cache_file'\n\t and start over\" \"$LINENO\" 5\nfi\n## -------------------- ##\n## Main body of script. ##\n## -------------------- ##\n\nac_ext=c\nac_cpp='$CPP $CPPFLAGS'\nac_compile='$CC -c $CFLAGS $CPPFLAGS conftest.$ac_ext >&5'\nac_link='$CC -o conftest$ac_exeext $CFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5'\nac_compiler_gnu=$ac_cv_c_compiler_gnu\n\n\n\n###########################\n# find compiler and flags #\n###########################\n\n{ printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: checking location of R\" >&5\nprintf %s \"checking location of R... \" >&6; }\n{ printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: result: ${R_HOME}\" >&5\nprintf \"%s\\n\" \"${R_HOME}\" >&6; }\n\n# set up CPP flags\n# find the compiler and compiler flags used by R.\n: ${R_HOME=`R HOME`}\nif test -z \"${R_HOME}\"; then\n echo \"could not determine R_HOME\"\n exit 1\nfi\nCXX17=`\"${R_HOME}/bin/R\" CMD config CXX17`\nCXX17STD=`\"${R_HOME}/bin/R\" CMD config CXX17STD`\nCXX=\"${CXX17} ${CXX17STD}\"\nCPPFLAGS=`\"${R_HOME}/bin/R\" CMD config CPPFLAGS`\nCXXFLAGS=`\"${R_HOME}/bin/R\" CMD config CXX17FLAGS`\nLDFLAGS=`\"${R_HOME}/bin/R\" CMD config LDFLAGS`\nac_ext=cpp\nac_cpp='$CXXCPP $CPPFLAGS'\nac_compile='$CXX -c $CXXFLAGS $CPPFLAGS conftest.$ac_ext >&5'\nac_link='$CXX -o conftest$ac_exeext $CXXFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5'\nac_compiler_gnu=$ac_cv_cxx_compiler_gnu\n\n\n# LightGBM-specific flags\nLGB_CPPFLAGS=\"\"\n\n#########\n# Eigen #\n#########\n\nLGB_CPPFLAGS=\"${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY -DEIGEN_DONT_PARALLELIZE\"\n\n###############\n# MM_PREFETCH #\n###############\n\n{ printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: checking whether MM_PREFETCH works\" >&5\nprintf %s \"checking whether MM_PREFETCH works... \" >&6; }\nac_mmprefetch=no\ncat confdefs.h - <<_ACEOF >conftest.$ac_ext\n/* end confdefs.h. */\n\n\n #include \n\nint\nmain (void)\n{\n\n int a = 0;\n _mm_prefetch(&a, _MM_HINT_NTA);\n return 0;\n\n\n ;\n return 0;\n}\n\n\n_ACEOF\n${CXX} ${CPPFLAGS} ${CXXFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mmprefetch=yes\n{ printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: result: ${ac_mmprefetch}\" >&5\nprintf \"%s\\n\" \"${ac_mmprefetch}\" >&6; }\nif test \"${ac_mmprefetch}\" = yes; then\n LGB_CPPFLAGS=\"${LGB_CPPFLAGS} -DMM_PREFETCH=1\"\nfi\n\n############\n# MM_ALLOC #\n############\n\n{ printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: checking whether MM_MALLOC works\" >&5\nprintf %s \"checking whether MM_MALLOC works... \" >&6; }\nac_mm_malloc=no\ncat confdefs.h - <<_ACEOF >conftest.$ac_ext\n/* end confdefs.h. */\n\n\n #include \n\nint\nmain (void)\n{\n\n char *a = (char*)_mm_malloc(8, 16);\n _mm_free(a);\n return 0;\n\n\n ;\n return 0;\n}\n\n\n_ACEOF\n${CXX} ${CPPFLAGS} ${CXXFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mm_malloc=yes\n{ printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: result: ${ac_mm_malloc}\" >&5\nprintf \"%s\\n\" \"${ac_mm_malloc}\" >&6; }\nif test \"${ac_mm_malloc}\" = yes; then\n LGB_CPPFLAGS=\"${LGB_CPPFLAGS} -DMM_MALLOC=1\"\nfi\n\n##########\n# OpenMP #\n##########\n\nOPENMP_CXXFLAGS=\"\"\n\nif test `uname -s` = \"Linux\"\nthen\n OPENMP_CXXFLAGS=\"\\$(SHLIB_OPENMP_CXXFLAGS)\"\nfi\n\nif test `uname -s` = \"Darwin\"\nthen\n OPENMP_CXXFLAGS='-Xclang -fopenmp'\n OPENMP_LIB='-lomp'\n\n # libomp 15.0+ from brew is keg-only (i.e. not symlinked into the standard paths search by the linker),\n # so need to search in other locations.\n # See https://github.com/Homebrew/homebrew-core/issues/112107#issuecomment-1278042927.\n #\n # If Homebrew is found and libomp was installed with it, this code adds the necessary\n # flags for the compiler to find libomp headers and for the linker to find libomp.dylib.\n HOMEBREW_LIBOMP_PREFIX=\"\"\n if command -v brew >/dev/null 2>&1; then\n ac_brew_openmp=no\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: checking whether OpenMP was installed via Homebrew\" >&5\nprintf %s \"checking whether OpenMP was installed via Homebrew... \" >&6; }\n brew --prefix libomp >/dev/null 2>&1 && ac_brew_openmp=yes\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: result: ${ac_brew_openmp}\" >&5\nprintf \"%s\\n\" \"${ac_brew_openmp}\" >&6; }\n if test \"${ac_brew_openmp}\" = yes; then\n HOMEBREW_LIBOMP_PREFIX=`brew --prefix libomp`\n OPENMP_CXXFLAGS=\"${OPENMP_CXXFLAGS} -I${HOMEBREW_LIBOMP_PREFIX}/include\"\n OPENMP_LIB=\"${OPENMP_LIB} -L${HOMEBREW_LIBOMP_PREFIX}/lib\"\n fi\n fi\n ac_pkg_openmp=no\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: checking whether OpenMP will work in a package\" >&5\nprintf %s \"checking whether OpenMP will work in a package... \" >&6; }\n cat confdefs.h - <<_ACEOF >conftest.$ac_ext\n/* end confdefs.h. */\n\n\n #include \n\nint\nmain (void)\n{\n\n return (omp_get_max_threads() <= 1);\n\n\n ;\n return 0;\n}\n\n\n_ACEOF\n ${CXX} ${CPPFLAGS} ${CXXFLAGS} ${LDFLAGS} ${OPENMP_CXXFLAGS} ${OPENMP_LIB} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_pkg_openmp=yes\n\n # -Xclang is not portable (it is clang-specific)\n # if compilation above failed, try without that flag\n if test \"${ac_pkg_openmp}\" = no; then\n if test -f \"./conftest\"; then\n rm ./conftest\n fi\n OPENMP_CXXFLAGS=\"-fopenmp\"\n ${CXX} ${CPPFLAGS} ${CXXFLAGS} ${LDFLAGS} ${OPENMP_CXXFLAGS} ${OPENMP_LIB} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_pkg_openmp=yes\n fi\n\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: result: ${ac_pkg_openmp}\" >&5\nprintf \"%s\\n\" \"${ac_pkg_openmp}\" >&6; }\n if test \"${ac_pkg_openmp}\" = no; then\n OPENMP_CXXFLAGS=''\n OPENMP_LIB=''\n echo '***********************************************************************************************'\n echo ' OpenMP is unavailable on this macOS system. LightGBM code will run single-threaded as a result.'\n echo ' To use all CPU cores for training jobs, you should install OpenMP by running'\n echo ''\n echo ' brew install libomp'\n echo '***********************************************************************************************'\n fi\nfi\n\n# substitute variables from this script into Makevars.in\n\n\n\nac_config_files=\"$ac_config_files src/Makevars\"\n\n\n# write out Autoconf output\ncat >confcache <<\\_ACEOF\n# This file is a shell script that caches the results of configure\n# tests run on this system so they can be shared between configure\n# scripts and configure runs, see configure's option --config-cache.\n# It is not useful on other systems. 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New code should use\n# AS_ECHO([\"message\"]) and AS_ECHO_N([\"message\"]), respectively.\nas_echo='printf %s\\n'\nas_echo_n='printf %s'\n\nrm -f conf$$ conf$$.exe conf$$.file\nif test -d conf$$.dir; then\n rm -f conf$$.dir/conf$$.file\nelse\n rm -f conf$$.dir\n mkdir conf$$.dir 2>/dev/null\nfi\nif (echo >conf$$.file) 2>/dev/null; then\n if ln -s conf$$.file conf$$ 2>/dev/null; then\n as_ln_s='ln -s'\n # ... but there are two gotchas:\n # 1) On MSYS, both `ln -s file dir' and `ln file dir' fail.\n # 2) DJGPP < 2.04 has no symlinks; `ln -s' creates a wrapper executable.\n # In both cases, we have to default to `cp -pR'.\n ln -s conf$$.file conf$$.dir 2>/dev/null && test ! -f conf$$.exe ||\n as_ln_s='cp -pR'\n elif ln conf$$.file conf$$ 2>/dev/null; then\n as_ln_s=ln\n else\n as_ln_s='cp -pR'\n fi\nelse\n as_ln_s='cp -pR'\nfi\nrm -f conf$$ conf$$.exe conf$$.dir/conf$$.file conf$$.file\nrmdir conf$$.dir 2>/dev/null\n\n\n# as_fn_mkdir_p\n# -------------\n# Create \"$as_dir\" as a directory, including parents if necessary.\nas_fn_mkdir_p ()\n{\n\n case $as_dir in #(\n -*) as_dir=./$as_dir;;\n esac\n test -d \"$as_dir\" || eval $as_mkdir_p || {\n as_dirs=\n while :; do\n case $as_dir in #(\n *\\'*) as_qdir=`printf \"%s\\n\" \"$as_dir\" | sed \"s/'/'\\\\\\\\\\\\\\\\''/g\"`;; #'(\n *) as_qdir=$as_dir;;\n esac\n as_dirs=\"'$as_qdir' $as_dirs\"\n as_dir=`$as_dirname -- \"$as_dir\" ||\n$as_expr X\"$as_dir\" : 'X\\(.*[^/]\\)//*[^/][^/]*/*$' \\| \\\n\t X\"$as_dir\" : 'X\\(//\\)[^/]' \\| \\\n\t X\"$as_dir\" : 'X\\(//\\)$' \\| \\\n\t X\"$as_dir\" : 'X\\(/\\)' \\| . 2>/dev/null ||\nprintf \"%s\\n\" X\"$as_dir\" |\n sed '/^X\\(.*[^/]\\)\\/\\/*[^/][^/]*\\/*$/{\n\t s//\\1/\n\t q\n\t }\n\t /^X\\(\\/\\/\\)[^/].*/{\n\t s//\\1/\n\t q\n\t }\n\t /^X\\(\\/\\/\\)$/{\n\t s//\\1/\n\t q\n\t }\n\t /^X\\(\\/\\).*/{\n\t s//\\1/\n\t q\n\t }\n\t s/.*/./; q'`\n test -d \"$as_dir\" && break\n done\n test -z \"$as_dirs\" || eval \"mkdir $as_dirs\"\n } || test -d \"$as_dir\" || as_fn_error $? \"cannot create directory $as_dir\"\n\n\n} # as_fn_mkdir_p\nif mkdir -p . 2>/dev/null; then\n as_mkdir_p='mkdir -p \"$as_dir\"'\nelse\n test -d ./-p && rmdir ./-p\n as_mkdir_p=false\nfi\n\n\n# as_fn_executable_p FILE\n# -----------------------\n# Test if FILE is an executable regular file.\nas_fn_executable_p ()\n{\n test -f \"$1\" && test -x \"$1\"\n} # as_fn_executable_p\nas_test_x='test -x'\nas_executable_p=as_fn_executable_p\n\n# Sed expression to map a string onto a valid CPP name.\nas_tr_cpp=\"eval sed 'y%*$as_cr_letters%P$as_cr_LETTERS%;s%[^_$as_cr_alnum]%_%g'\"\n\n# Sed expression to map a string onto a valid variable name.\nas_tr_sh=\"eval sed 'y%*+%pp%;s%[^_$as_cr_alnum]%_%g'\"\n\n\nexec 6>&1\n## ----------------------------------- ##\n## Main body of $CONFIG_STATUS script. ##\n## ----------------------------------- ##\n_ASEOF\ntest $as_write_fail = 0 && chmod +x $CONFIG_STATUS || ac_write_fail=1\n\ncat >>$CONFIG_STATUS <<\\_ACEOF || ac_write_fail=1\n# Save the log message, to keep $0 and so on meaningful, and to\n# report actual input values of CONFIG_FILES etc. instead of their\n# values after options handling.\nac_log=\"\nThis file was extended by lightgbm $as_me 4.6.0.99, which was\ngenerated by GNU Autoconf 2.71. 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[TAG]...\n\n -h, --help print this help, then exit\n -V, --version print version number and configuration settings, then exit\n --config print configuration, then exit\n -q, --quiet, --silent\n do not print progress messages\n -d, --debug don't remove temporary files\n --recheck update $as_me by reconfiguring in the same conditions\n --file=FILE[:TEMPLATE]\n instantiate the configuration file FILE\n\nConfiguration files:\n$config_files\n\nReport bugs to the package provider.\"\n\n_ACEOF\nac_cs_config=`printf \"%s\\n\" \"$ac_configure_args\" | sed \"$ac_safe_unquote\"`\nac_cs_config_escaped=`printf \"%s\\n\" \"$ac_cs_config\" | sed \"s/^ //; s/'/'\\\\\\\\\\\\\\\\''/g\"`\ncat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1\nac_cs_config='$ac_cs_config_escaped'\nac_cs_version=\"\\\\\nlightgbm config.status 4.6.0.99\nconfigured by $0, generated by GNU Autoconf 2.71,\n with options \\\\\"\\$ac_cs_config\\\\\"\n\nCopyright (C) 2021 Free Software Foundation, Inc.\nThis config.status script is free software; the Free Software Foundation\ngives unlimited permission to copy, distribute and modify it.\"\n\nac_pwd='$ac_pwd'\nsrcdir='$srcdir'\ntest -n \"\\$AWK\" || AWK=awk\n_ACEOF\n\ncat >>$CONFIG_STATUS <<\\_ACEOF || ac_write_fail=1\n# The default lists apply if the user does not specify any file.\nac_need_defaults=:\nwhile test $# != 0\ndo\n case $1 in\n --*=?*)\n ac_option=`expr \"X$1\" : 'X\\([^=]*\\)='`\n ac_optarg=`expr \"X$1\" : 'X[^=]*=\\(.*\\)'`\n ac_shift=:\n ;;\n --*=)\n ac_option=`expr \"X$1\" : 'X\\([^=]*\\)='`\n ac_optarg=\n ac_shift=:\n ;;\n *)\n ac_option=$1\n ac_optarg=$2\n ac_shift=shift\n ;;\n esac\n\n case $ac_option in\n # Handling of the options.\n -recheck | --recheck | --rechec | --reche | --rech | --rec | --re | --r)\n ac_cs_recheck=: ;;\n --version | --versio | --versi | --vers | --ver | --ve | --v | -V )\n printf \"%s\\n\" \"$ac_cs_version\"; exit ;;\n --config | --confi | --conf | --con | --co | --c )\n printf \"%s\\n\" \"$ac_cs_config\"; exit ;;\n --debug | --debu | --deb | --de | --d | -d )\n debug=: ;;\n --file | --fil | --fi | --f )\n $ac_shift\n case $ac_optarg in\n *\\'*) ac_optarg=`printf \"%s\\n\" \"$ac_optarg\" | sed \"s/'/'\\\\\\\\\\\\\\\\''/g\"` ;;\n '') as_fn_error $? \"missing file argument\" ;;\n esac\n as_fn_append CONFIG_FILES \" '$ac_optarg'\"\n ac_need_defaults=false;;\n --he | --h | --help | --hel | -h )\n printf \"%s\\n\" \"$ac_cs_usage\"; exit ;;\n -q | -quiet | --quiet | --quie | --qui | --qu | --q \\\n | -silent | --silent | --silen | --sile | --sil | --si | --s)\n ac_cs_silent=: ;;\n\n # This is an error.\n -*) as_fn_error $? \"unrecognized option: \\`$1'\nTry \\`$0 --help' for more information.\" ;;\n\n *) as_fn_append ac_config_targets \" $1\"\n ac_need_defaults=false ;;\n\n esac\n shift\ndone\n\nac_configure_extra_args=\n\nif $ac_cs_silent; then\n exec 6>/dev/null\n ac_configure_extra_args=\"$ac_configure_extra_args --silent\"\nfi\n\n_ACEOF\ncat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1\nif \\$ac_cs_recheck; then\n set X $SHELL '$0' $ac_configure_args \\$ac_configure_extra_args --no-create --no-recursion\n shift\n \\printf \"%s\\n\" \"running CONFIG_SHELL=$SHELL \\$*\" >&6\n CONFIG_SHELL='$SHELL'\n export CONFIG_SHELL\n exec \"\\$@\"\nfi\n\n_ACEOF\ncat >>$CONFIG_STATUS <<\\_ACEOF || ac_write_fail=1\nexec 5>>config.log\n{\n echo\n sed 'h;s/./-/g;s/^.../## /;s/...$/ ##/;p;x;p;x' <<_ASBOX\n## Running $as_me. ##\n_ASBOX\n printf \"%s\\n\" \"$ac_log\"\n} >&5\n\n_ACEOF\ncat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1\n_ACEOF\n\ncat >>$CONFIG_STATUS <<\\_ACEOF || ac_write_fail=1\n\n# Handling of arguments.\nfor ac_config_target in $ac_config_targets\ndo\n case $ac_config_target in\n \"src/Makevars\") CONFIG_FILES=\"$CONFIG_FILES src/Makevars\" ;;\n\n *) as_fn_error $? \"invalid argument: \\`$ac_config_target'\" \"$LINENO\" 5;;\n esac\ndone\n\n\n# If the user did not use the arguments to specify the items to instantiate,\n# then the envvar interface is used. Set only those that are not.\n# We use the long form for the default assignment because of an extremely\n# bizarre bug on SunOS 4.1.3.\nif $ac_need_defaults; then\n test ${CONFIG_FILES+y} || CONFIG_FILES=$config_files\nfi\n\n# Have a temporary directory for convenience. Make it in the build tree\n# simply because there is no reason against having it here, and in addition,\n# creating and moving files from /tmp can sometimes cause problems.\n# Hook for its removal unless debugging.\n# Note that there is a small window in which the directory will not be cleaned:\n# after its creation but before its name has been assigned to `$tmp'.\n$debug ||\n{\n tmp= ac_tmp=\n trap 'exit_status=$?\n : \"${ac_tmp:=$tmp}\"\n { test ! -d \"$ac_tmp\" || rm -fr \"$ac_tmp\"; } && exit $exit_status\n' 0\n trap 'as_fn_exit 1' 1 2 13 15\n}\n# Create a (secure) tmp directory for tmp files.\n\n{\n tmp=`(umask 077 && mktemp -d \"./confXXXXXX\") 2>/dev/null` &&\n test -d \"$tmp\"\n} ||\n{\n tmp=./conf$$-$RANDOM\n (umask 077 && mkdir \"$tmp\")\n} || as_fn_error $? \"cannot create a temporary directory in .\" \"$LINENO\" 5\nac_tmp=$tmp\n\n# Set up the scripts for CONFIG_FILES section.\n# No need to generate them if there are no CONFIG_FILES.\n# This happens for instance with `./config.status config.h'.\nif test -n \"$CONFIG_FILES\"; then\n\n\nac_cr=`echo X | tr X '\\015'`\n# On cygwin, bash can eat \\r inside `` if the user requested igncr.\n# But we know of no other shell where ac_cr would be empty at this\n# point, so we can use a bashism as a fallback.\nif test \"x$ac_cr\" = x; then\n eval ac_cr=\\$\\'\\\\r\\'\nfi\nac_cs_awk_cr=`$AWK 'BEGIN { print \"a\\rb\" }' /dev/null`\nif test \"$ac_cs_awk_cr\" = \"a${ac_cr}b\"; then\n ac_cs_awk_cr='\\\\r'\nelse\n ac_cs_awk_cr=$ac_cr\nfi\n\necho 'BEGIN {' >\"$ac_tmp/subs1.awk\" &&\n_ACEOF\n\n\n{\n echo \"cat >conf$$subs.awk <<_ACEOF\" &&\n echo \"$ac_subst_vars\" | sed 's/.*/&!$&$ac_delim/' &&\n echo \"_ACEOF\"\n} >conf$$subs.sh ||\n as_fn_error $? \"could not make $CONFIG_STATUS\" \"$LINENO\" 5\nac_delim_num=`echo \"$ac_subst_vars\" | grep -c '^'`\nac_delim='%!_!# '\nfor ac_last_try in false false false false false :; do\n . ./conf$$subs.sh ||\n as_fn_error $? \"could not make $CONFIG_STATUS\" \"$LINENO\" 5\n\n ac_delim_n=`sed -n \"s/.*$ac_delim\\$/X/p\" conf$$subs.awk | grep -c X`\n if test $ac_delim_n = $ac_delim_num; then\n break\n elif $ac_last_try; then\n as_fn_error $? \"could not make $CONFIG_STATUS\" \"$LINENO\" 5\n else\n ac_delim=\"$ac_delim!$ac_delim _$ac_delim!! \"\n fi\ndone\nrm -f conf$$subs.sh\n\ncat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1\ncat >>\"\\$ac_tmp/subs1.awk\" <<\\\\_ACAWK &&\n_ACEOF\nsed -n '\nh\ns/^/S[\"/; s/!.*/\"]=/\np\ng\ns/^[^!]*!//\n:repl\nt repl\ns/'\"$ac_delim\"'$//\nt delim\n:nl\nh\ns/\\(.\\{148\\}\\)..*/\\1/\nt more1\ns/[\"\\\\]/\\\\&/g; s/^/\"/; s/$/\\\\n\"\\\\/\np\nn\nb repl\n:more1\ns/[\"\\\\]/\\\\&/g; s/^/\"/; s/$/\"\\\\/\np\ng\ns/.\\{148\\}//\nt nl\n:delim\nh\ns/\\(.\\{148\\}\\)..*/\\1/\nt more2\ns/[\"\\\\]/\\\\&/g; s/^/\"/; s/$/\"/\np\nb\n:more2\ns/[\"\\\\]/\\\\&/g; s/^/\"/; s/$/\"\\\\/\np\ng\ns/.\\{148\\}//\nt delim\n' >$CONFIG_STATUS || ac_write_fail=1\nrm -f conf$$subs.awk\ncat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1\n_ACAWK\ncat >>\"\\$ac_tmp/subs1.awk\" <<_ACAWK &&\n for (key in S) S_is_set[key] = 1\n FS = \"\u0007\"\n\n}\n{\n line = $ 0\n nfields = split(line, field, \"@\")\n substed = 0\n len = length(field[1])\n for (i = 2; i < nfields; i++) {\n key = field[i]\n keylen = length(key)\n if (S_is_set[key]) {\n value = S[key]\n line = substr(line, 1, len) \"\" value \"\" substr(line, len + keylen + 3)\n len += length(value) + length(field[++i])\n substed = 1\n } else\n len += 1 + keylen\n }\n\n print line\n}\n\n_ACAWK\n_ACEOF\ncat >>$CONFIG_STATUS <<\\_ACEOF || ac_write_fail=1\nif sed \"s/$ac_cr//\" < /dev/null > /dev/null 2>&1; then\n sed \"s/$ac_cr\\$//; s/$ac_cr/$ac_cs_awk_cr/g\"\nelse\n cat\nfi < \"$ac_tmp/subs1.awk\" > \"$ac_tmp/subs.awk\" \\\n || as_fn_error $? \"could not setup config files machinery\" \"$LINENO\" 5\n_ACEOF\n\n# VPATH may cause trouble with some makes, so we remove sole $(srcdir),\n# ${srcdir} and @srcdir@ entries from VPATH if srcdir is \".\", strip leading and\n# trailing colons and then remove the whole line if VPATH becomes empty\n# (actually we leave an empty line to preserve line numbers).\nif test \"x$srcdir\" = x.; then\n ac_vpsub='/^[\t ]*VPATH[\t ]*=[\t ]*/{\nh\ns///\ns/^/:/\ns/[\t ]*$/:/\ns/:\\$(srcdir):/:/g\ns/:\\${srcdir}:/:/g\ns/:@srcdir@:/:/g\ns/^:*//\ns/:*$//\nx\ns/\\(=[\t ]*\\).*/\\1/\nG\ns/\\n//\ns/^[^=]*=[\t ]*$//\n}'\nfi\n\ncat >>$CONFIG_STATUS <<\\_ACEOF || ac_write_fail=1\nfi # test -n \"$CONFIG_FILES\"\n\n\neval set X \" :F $CONFIG_FILES \"\nshift\nfor ac_tag\ndo\n case $ac_tag in\n :[FHLC]) ac_mode=$ac_tag; continue;;\n esac\n case $ac_mode$ac_tag in\n :[FHL]*:*);;\n :L* | :C*:*) as_fn_error $? \"invalid tag \\`$ac_tag'\" \"$LINENO\" 5;;\n :[FH]-) ac_tag=-:-;;\n :[FH]*) ac_tag=$ac_tag:$ac_tag.in;;\n esac\n ac_save_IFS=$IFS\n IFS=:\n set x $ac_tag\n IFS=$ac_save_IFS\n shift\n ac_file=$1\n shift\n\n case $ac_mode in\n :L) ac_source=$1;;\n :[FH])\n ac_file_inputs=\n for ac_f\n do\n case $ac_f in\n -) ac_f=\"$ac_tmp/stdin\";;\n *) # Look for the file first in the build tree, then in the source tree\n\t # (if the path is not absolute). The absolute path cannot be DOS-style,\n\t # because $ac_f cannot contain `:'.\n\t test -f \"$ac_f\" ||\n\t case $ac_f in\n\t [\\\\/$]*) false;;\n\t *) test -f \"$srcdir/$ac_f\" && ac_f=\"$srcdir/$ac_f\";;\n\t esac ||\n\t as_fn_error 1 \"cannot find input file: \\`$ac_f'\" \"$LINENO\" 5;;\n esac\n case $ac_f in *\\'*) ac_f=`printf \"%s\\n\" \"$ac_f\" | sed \"s/'/'\\\\\\\\\\\\\\\\''/g\"`;; esac\n as_fn_append ac_file_inputs \" '$ac_f'\"\n done\n\n # Let's still pretend it is `configure' which instantiates (i.e., don't\n # use $as_me), people would be surprised to read:\n # /* config.h. Generated by config.status. */\n configure_input='Generated from '`\n\t printf \"%s\\n\" \"$*\" | sed 's|^[^:]*/||;s|:[^:]*/|, |g'\n\t`' by configure.'\n if test x\"$ac_file\" != x-; then\n configure_input=\"$ac_file. $configure_input\"\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: creating $ac_file\" >&5\nprintf \"%s\\n\" \"$as_me: creating $ac_file\" >&6;}\n fi\n # Neutralize special characters interpreted by sed in replacement strings.\n case $configure_input in #(\n *\\&* | *\\|* | *\\\\* )\n ac_sed_conf_input=`printf \"%s\\n\" \"$configure_input\" |\n sed 's/[\\\\\\\\&|]/\\\\\\\\&/g'`;; #(\n *) ac_sed_conf_input=$configure_input;;\n esac\n\n case $ac_tag in\n *:-:* | *:-) cat >\"$ac_tmp/stdin\" \\\n || as_fn_error $? \"could not create $ac_file\" \"$LINENO\" 5 ;;\n esac\n ;;\n esac\n\n ac_dir=`$as_dirname -- \"$ac_file\" ||\n$as_expr X\"$ac_file\" : 'X\\(.*[^/]\\)//*[^/][^/]*/*$' \\| \\\n\t X\"$ac_file\" : 'X\\(//\\)[^/]' \\| \\\n\t X\"$ac_file\" : 'X\\(//\\)$' \\| \\\n\t X\"$ac_file\" : 'X\\(/\\)' \\| . 2>/dev/null ||\nprintf \"%s\\n\" X\"$ac_file\" |\n sed '/^X\\(.*[^/]\\)\\/\\/*[^/][^/]*\\/*$/{\n\t s//\\1/\n\t q\n\t }\n\t /^X\\(\\/\\/\\)[^/].*/{\n\t s//\\1/\n\t q\n\t }\n\t /^X\\(\\/\\/\\)$/{\n\t s//\\1/\n\t q\n\t }\n\t /^X\\(\\/\\).*/{\n\t s//\\1/\n\t q\n\t }\n\t s/.*/./; q'`\n as_dir=\"$ac_dir\"; as_fn_mkdir_p\n ac_builddir=.\n\ncase \"$ac_dir\" in\n.) ac_dir_suffix= ac_top_builddir_sub=. ac_top_build_prefix= ;;\n*)\n ac_dir_suffix=/`printf \"%s\\n\" \"$ac_dir\" | sed 's|^\\.[\\\\/]||'`\n # A \"..\" for each directory in $ac_dir_suffix.\n ac_top_builddir_sub=`printf \"%s\\n\" \"$ac_dir_suffix\" | sed 's|/[^\\\\/]*|/..|g;s|/||'`\n case $ac_top_builddir_sub in\n \"\") ac_top_builddir_sub=. ac_top_build_prefix= ;;\n *) ac_top_build_prefix=$ac_top_builddir_sub/ ;;\n esac ;;\nesac\nac_abs_top_builddir=$ac_pwd\nac_abs_builddir=$ac_pwd$ac_dir_suffix\n# for backward compatibility:\nac_top_builddir=$ac_top_build_prefix\n\ncase $srcdir in\n .) # We are building in place.\n ac_srcdir=.\n ac_top_srcdir=$ac_top_builddir_sub\n ac_abs_top_srcdir=$ac_pwd ;;\n [\\\\/]* | ?:[\\\\/]* ) # Absolute name.\n ac_srcdir=$srcdir$ac_dir_suffix;\n ac_top_srcdir=$srcdir\n ac_abs_top_srcdir=$srcdir ;;\n *) # Relative name.\n ac_srcdir=$ac_top_build_prefix$srcdir$ac_dir_suffix\n ac_top_srcdir=$ac_top_build_prefix$srcdir\n ac_abs_top_srcdir=$ac_pwd/$srcdir ;;\nesac\nac_abs_srcdir=$ac_abs_top_srcdir$ac_dir_suffix\n\n\n case $ac_mode in\n :F)\n #\n # CONFIG_FILE\n #\n\n_ACEOF\n\ncat >>$CONFIG_STATUS <<\\_ACEOF || ac_write_fail=1\n# If the template does not know about datarootdir, expand it.\n# FIXME: This hack should be removed a few years after 2.60.\nac_datarootdir_hack=; ac_datarootdir_seen=\nac_sed_dataroot='\n/datarootdir/ {\n p\n q\n}\n/@datadir@/p\n/@docdir@/p\n/@infodir@/p\n/@localedir@/p\n/@mandir@/p'\ncase `eval \"sed -n \\\"\\$ac_sed_dataroot\\\" $ac_file_inputs\"` in\n*datarootdir*) ac_datarootdir_seen=yes;;\n*@datadir@*|*@docdir@*|*@infodir@*|*@localedir@*|*@mandir@*)\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: WARNING: $ac_file_inputs seems to ignore the --datarootdir setting\" >&5\nprintf \"%s\\n\" \"$as_me: WARNING: $ac_file_inputs seems to ignore the --datarootdir setting\" >&2;}\n_ACEOF\ncat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1\n ac_datarootdir_hack='\n s&@datadir@&$datadir&g\n s&@docdir@&$docdir&g\n s&@infodir@&$infodir&g\n s&@localedir@&$localedir&g\n s&@mandir@&$mandir&g\n s&\\\\\\${datarootdir}&$datarootdir&g' ;;\nesac\n_ACEOF\n\n# Neutralize VPATH when `$srcdir' = `.'.\n# Shell code in configure.ac might set extrasub.\n# FIXME: do we really want to maintain this feature?\ncat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1\nac_sed_extra=\"$ac_vpsub\n$extrasub\n_ACEOF\ncat >>$CONFIG_STATUS <<\\_ACEOF || ac_write_fail=1\n:t\n/@[a-zA-Z_][a-zA-Z_0-9]*@/!b\ns|@configure_input@|$ac_sed_conf_input|;t t\ns&@top_builddir@&$ac_top_builddir_sub&;t t\ns&@top_build_prefix@&$ac_top_build_prefix&;t t\ns&@srcdir@&$ac_srcdir&;t t\ns&@abs_srcdir@&$ac_abs_srcdir&;t t\ns&@top_srcdir@&$ac_top_srcdir&;t t\ns&@abs_top_srcdir@&$ac_abs_top_srcdir&;t t\ns&@builddir@&$ac_builddir&;t t\ns&@abs_builddir@&$ac_abs_builddir&;t t\ns&@abs_top_builddir@&$ac_abs_top_builddir&;t t\n$ac_datarootdir_hack\n\"\neval sed \\\"\\$ac_sed_extra\\\" \"$ac_file_inputs\" | $AWK -f \"$ac_tmp/subs.awk\" \\\n >$ac_tmp/out || as_fn_error $? \"could not create $ac_file\" \"$LINENO\" 5\n\ntest -z \"$ac_datarootdir_hack$ac_datarootdir_seen\" &&\n { ac_out=`sed -n '/\\${datarootdir}/p' \"$ac_tmp/out\"`; test -n \"$ac_out\"; } &&\n { ac_out=`sed -n '/^[\t ]*datarootdir[\t ]*:*=/p' \\\n \"$ac_tmp/out\"`; test -z \"$ac_out\"; } &&\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: WARNING: $ac_file contains a reference to the variable \\`datarootdir'\nwhich seems to be undefined. Please make sure it is defined\" >&5\nprintf \"%s\\n\" \"$as_me: WARNING: $ac_file contains a reference to the variable \\`datarootdir'\nwhich seems to be undefined. Please make sure it is defined\" >&2;}\n\n rm -f \"$ac_tmp/stdin\"\n case $ac_file in\n -) cat \"$ac_tmp/out\" && rm -f \"$ac_tmp/out\";;\n *) rm -f \"$ac_file\" && mv \"$ac_tmp/out\" \"$ac_file\";;\n esac \\\n || as_fn_error $? \"could not create $ac_file\" \"$LINENO\" 5\n ;;\n\n\n\n esac\n\ndone # for ac_tag\n\n\nas_fn_exit 0\n_ACEOF\nac_clean_files=$ac_clean_files_save\n\ntest $ac_write_fail = 0 ||\n as_fn_error $? \"write failure creating $CONFIG_STATUS\" \"$LINENO\" 5\n\n\n# configure is writing to config.log, and then calls config.status.\n# config.status does its own redirection, appending to config.log.\n# Unfortunately, on DOS this fails, as config.log is still kept open\n# by configure, so config.status won't be able to write to it; its\n# output is simply discarded. So we exec the FD to /dev/null,\n# effectively closing config.log, so it can be properly (re)opened and\n# appended to by config.status. When coming back to configure, we\n# need to make the FD available again.\nif test \"$no_create\" != yes; then\n ac_cs_success=:\n ac_config_status_args=\n test \"$silent\" = yes &&\n ac_config_status_args=\"$ac_config_status_args --quiet\"\n exec 5>/dev/null\n $SHELL $CONFIG_STATUS $ac_config_status_args || ac_cs_success=false\n exec 5>>config.log\n # Use ||, not &&, to avoid exiting from the if with $? = 1, which\n # would make configure fail if this is the last instruction.\n $ac_cs_success || as_fn_exit 1\nfi\nif test -n \"$ac_unrecognized_opts\" && test \"$enable_option_checking\" != no; then\n { printf \"%s\\n\" \"$as_me:${as_lineno-$LINENO}: WARNING: unrecognized options: $ac_unrecognized_opts\" >&5\nprintf \"%s\\n\" \"$as_me: WARNING: unrecognized options: $ac_unrecognized_opts\" >&2;}\nfi\n\n\n" }, { "path": "R-package/configure.ac", "content": "### configure.ac -*- Autoconf -*-\n# Template used by Autoconf to generate 'configure' script. For more see:\n# * https://unconj.ca/blog/an-autoconf-primer-for-r-package-authors.html\n# * https://cran.r-project.org/doc/manuals/r-release/R-exts.html#Configure-and-cleanup\n\nAC_PREREQ(2.69)\nAC_INIT([lightgbm], [~~VERSION~~], [], [lightgbm], [])\n\n###########################\n# find compiler and flags #\n###########################\n\nAC_MSG_CHECKING([location of R])\nAC_MSG_RESULT([${R_HOME}])\n\n# set up CPP flags\n# find the compiler and compiler flags used by R.\n: ${R_HOME=`R HOME`}\nif test -z \"${R_HOME}\"; then\n echo \"could not determine R_HOME\"\n exit 1\nfi\nCXX17=`\"${R_HOME}/bin/R\" CMD config CXX17`\nCXX17STD=`\"${R_HOME}/bin/R\" CMD config CXX17STD`\nCXX=\"${CXX17} ${CXX17STD}\"\nCPPFLAGS=`\"${R_HOME}/bin/R\" CMD config CPPFLAGS`\nCXXFLAGS=`\"${R_HOME}/bin/R\" CMD config CXX17FLAGS`\nLDFLAGS=`\"${R_HOME}/bin/R\" CMD config LDFLAGS`\nAC_LANG(C++)\n\n# LightGBM-specific flags\nLGB_CPPFLAGS=\"\"\n\n#########\n# Eigen #\n#########\n\nLGB_CPPFLAGS=\"${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY -DEIGEN_DONT_PARALLELIZE\"\n\n###############\n# MM_PREFETCH #\n###############\n\nAC_MSG_CHECKING([whether MM_PREFETCH works])\nac_mmprefetch=no\nAC_LANG_CONFTEST(\n [\n AC_LANG_PROGRAM(\n [[\n #include \n ]],\n [[\n int a = 0;\n _mm_prefetch(&a, _MM_HINT_NTA);\n return 0;\n ]]\n )\n ]\n)\n${CXX} ${CPPFLAGS} ${CXXFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mmprefetch=yes\nAC_MSG_RESULT([${ac_mmprefetch}])\nif test \"${ac_mmprefetch}\" = yes; then\n LGB_CPPFLAGS=\"${LGB_CPPFLAGS} -DMM_PREFETCH=1\"\nfi\n\n############\n# MM_ALLOC #\n############\n\nAC_MSG_CHECKING([whether MM_MALLOC works])\nac_mm_malloc=no\nAC_LANG_CONFTEST(\n [\n AC_LANG_PROGRAM(\n [[\n #include \n ]],\n [[\n char *a = (char*)_mm_malloc(8, 16);\n _mm_free(a);\n return 0;\n ]]\n )\n ]\n)\n${CXX} ${CPPFLAGS} ${CXXFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mm_malloc=yes\nAC_MSG_RESULT([${ac_mm_malloc}])\nif test \"${ac_mm_malloc}\" = yes; then\n LGB_CPPFLAGS=\"${LGB_CPPFLAGS} -DMM_MALLOC=1\"\nfi\n\n##########\n# OpenMP #\n##########\n\nOPENMP_CXXFLAGS=\"\"\n\nif test `uname -s` = \"Linux\"\nthen\n OPENMP_CXXFLAGS=\"\\$(SHLIB_OPENMP_CXXFLAGS)\"\nfi\n\nif test `uname -s` = \"Darwin\"\nthen\n OPENMP_CXXFLAGS='-Xclang -fopenmp'\n OPENMP_LIB='-lomp'\n\n # libomp 15.0+ from brew is keg-only (i.e. not symlinked into the standard paths search by the linker),\n # so need to search in other locations.\n # See https://github.com/Homebrew/homebrew-core/issues/112107#issuecomment-1278042927.\n #\n # If Homebrew is found and libomp was installed with it, this code adds the necessary\n # flags for the compiler to find libomp headers and for the linker to find libomp.dylib.\n HOMEBREW_LIBOMP_PREFIX=\"\"\n if command -v brew >/dev/null 2>&1; then\n ac_brew_openmp=no\n AC_MSG_CHECKING([whether OpenMP was installed via Homebrew])\n brew --prefix libomp >/dev/null 2>&1 && ac_brew_openmp=yes\n AC_MSG_RESULT([${ac_brew_openmp}])\n if test \"${ac_brew_openmp}\" = yes; then\n HOMEBREW_LIBOMP_PREFIX=`brew --prefix libomp`\n OPENMP_CXXFLAGS=\"${OPENMP_CXXFLAGS} -I${HOMEBREW_LIBOMP_PREFIX}/include\"\n OPENMP_LIB=\"${OPENMP_LIB} -L${HOMEBREW_LIBOMP_PREFIX}/lib\"\n fi\n fi\n ac_pkg_openmp=no\n AC_MSG_CHECKING([whether OpenMP will work in a package])\n AC_LANG_CONFTEST(\n [\n AC_LANG_PROGRAM(\n [[\n #include \n ]],\n [[\n return (omp_get_max_threads() <= 1);\n ]]\n )\n ]\n )\n ${CXX} ${CPPFLAGS} ${CXXFLAGS} ${LDFLAGS} ${OPENMP_CXXFLAGS} ${OPENMP_LIB} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_pkg_openmp=yes\n\n # -Xclang is not portable (it is clang-specific)\n # if compilation above failed, try without that flag\n if test \"${ac_pkg_openmp}\" = no; then\n if test -f \"./conftest\"; then\n rm ./conftest\n fi\n OPENMP_CXXFLAGS=\"-fopenmp\"\n ${CXX} ${CPPFLAGS} ${CXXFLAGS} ${LDFLAGS} ${OPENMP_CXXFLAGS} ${OPENMP_LIB} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_pkg_openmp=yes\n fi\n\n AC_MSG_RESULT([${ac_pkg_openmp}])\n if test \"${ac_pkg_openmp}\" = no; then\n OPENMP_CXXFLAGS=''\n OPENMP_LIB=''\n echo '***********************************************************************************************'\n echo ' OpenMP is unavailable on this macOS system. LightGBM code will run single-threaded as a result.'\n echo ' To use all CPU cores for training jobs, you should install OpenMP by running'\n echo ''\n echo ' brew install libomp'\n echo '***********************************************************************************************'\n fi\nfi\n\n# substitute variables from this script into Makevars.in\nAC_SUBST(OPENMP_CXXFLAGS)\nAC_SUBST(OPENMP_LIB)\nAC_SUBST(LGB_CPPFLAGS)\nAC_CONFIG_FILES([src/Makevars])\n\n# write out Autoconf output\nAC_OUTPUT\n" }, { "path": "R-package/configure.win", "content": "# Script used to generate `Makevars.win` from `Makevars.win.in`\n# on Windows\n\n###########################\n# find compiler and flags #\n###########################\n\nR_EXE=\"${R_HOME}/bin${R_ARCH_BIN}/R\"\n\nCXX17=`\"${R_EXE}\" CMD config CXX17`\nCXX17STD=`\"${R_EXE}\" CMD config CXX17STD`\nCXX=\"${CXX17} ${CXX17STD}\"\nCXXFLAGS=`\"${R_EXE}\" CMD config CXX17FLAGS`\nCPPFLAGS=`\"${R_EXE}\" CMD config CPPFLAGS`\n\n# LightGBM-specific flags\nLGB_CPPFLAGS=\"\"\n\n#########\n# Eigen #\n#########\n\nLGB_CPPFLAGS=\"${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY -DEIGEN_DONT_PARALLELIZE\"\n\n###############\n# MM_PREFETCH #\n###############\n\nac_mm_prefetch=\"no\"\n\ncat > conftest.cpp <\nint main() {\n int a = 0;\n _mm_prefetch(&a, _MM_HINT_NTA);\n return 0;\n}\nEOL\n\n${CXX} ${CXXFLAGS} ${CPPFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mm_prefetch=\"yes\"\nrm -f ./conftest\nrm -f ./conftest.cpp\necho \"checking whether MM_PREFETCH works...${ac_mm_prefetch}\"\n\nif test \"${ac_mm_prefetch}\" = \"yes\";\nthen\n LGB_CPPFLAGS=\"${LGB_CPPFLAGS} -DMM_PREFETCH=1\"\nfi\n\n############\n# MM_ALLOC #\n############\nac_mm_malloc=\"no\"\n\ncat > conftest.cpp <\nint main() {\n char *a = (char*)_mm_malloc(8, 16);\n _mm_free(a);\n return 0;\n}\nEOL\n\n${CXX} ${CXXFLAGS} ${CPPFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_mm_malloc=\"yes\"\nrm -f ./conftest\nrm -f ./conftest.cpp\necho \"checking whether MM_MALLOC works...${ac_mm_malloc}\"\n\nif test \"${ac_mm_malloc}\" = \"yes\";\nthen\n LGB_CPPFLAGS=\"${LGB_CPPFLAGS} -DMM_MALLOC=1\"\nfi\n\n#############\n# INET_PTON #\n#############\n\nac_inet_pton=\"no\"\n\ncat > conftest.cpp <\nint main() {\n int (*fptr)(int, const char*, void*);\n fptr = &inet_pton;\n return 0;\n}\nEOL\n\n${CXX} ${CXXFLAGS} ${CPPFLAGS} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_inet_pton=\"yes\"\nrm -f ./conftest\nrm -f ./conftest.cpp\necho \"checking whether INET_PTON works...${ac_inet_pton}\"\n\nif test \"${ac_inet_pton}\" = \"yes\";\nthen\n LGB_CPPFLAGS=\"${LGB_CPPFLAGS} -DWIN_HAS_INET_PTON=1\"\nfi\n\n# Generate Makevars.win from Makevars.win.in\nsed -e \\\n \"s/@LGB_CPPFLAGS@/$LGB_CPPFLAGS/\" \\\n < src/Makevars.win.in > src/Makevars.win\n" }, { "path": "R-package/cran-comments.md", "content": "# CRAN Submission History\n\n## v4.6.0 - Submission 1 - (February 13, 2025)\n\n### CRAN response\n\nAccepted to CRAN\n\n### Maintainer Notes\n\nThis release fixed several issues reported by CRAN.\nBashisms in `configure`\n\n```text\npossible bashism in configure.ac line 63 (should be VAR=\"${VAR}foo\"):\n LGB_CPPFLAGS+=\" -DMM_PREFETCH=1\"\npossible bashism in configure.ac line 89 (should be VAR=\"${VAR}foo\"):\n LGB_CPPFLAGS+=\" -DMM_MALLOC=1\"\n```\n\nCompilation errors on GCC 15.\n\n```text\nio/json11.cpp:97:28: error: 'uint8_t' does not name a type\n 97 | } else if (static_cast(ch) == 0xe2 &&\n | ^~~~~~~\nio/json11.cpp:97:28: note: 'uint8_t' is defined in header ''; this is probably fixable by adding '#include '\nio/json11.cpp:98:28: error: 'uint8_t' does not name a type\n 98 | static_cast(value[i + 1]) == 0x80 &&\n```\n\nThis release contains fixes for those issues.\n\n## v4.5.0 - Submission 1 - (July 25, 2024)\n\n### CRAN response\n\nAccepted to CRAN\n\n### Maintainer Notes\n\nThis release was a response to a request from CRAN.\nOn July 4, 2024, CRAN notified us that the following compiler warnings raised by `gcc` 14 needed to be fixed by August 3, 2024.\n\n```text\nResult: WARN\n Found the following significant warnings:\n io/dense_bin.hpp:617:27: warning: template-id not allowed for constructor in C++20 [-Wtemplate-id-cdtor]\n io/multi_val_dense_bin.hpp:346:26: warning: template-id not allowed for constructor in C++20 [-Wtemplate-id-cdtor]\n io/multi_val_sparse_bin.hpp:433:36: warning: template-id not allowed for constructor in C++20 [-Wtemplate-id-cdtor]\n io/sparse_bin.hpp:785:19: warning: template-id not allowed for constructor in C++20 [-Wtemplate-id-cdtor]\n See ‘/data/gannet/ripley/R/packages/tests-devel/lightgbm.Rcheck/00install.out’ for details.\n```\n\nThis release contains fixes for those issues.\n\n## v4.4.0 - Submission 1 - (June 14, 2024)\n\n### CRAN response\n\nAccepted to CRAN\n\n### Maintainer Notes\n\nThis was a standard release of `{lightgbm}`, not intended to fix any particular R-specific issues.\n\n## v4.3.0 - Submission 1 - (January 18, 2024)\n\n### CRAN response\n\nAccepted to CRAN\n\n### Maintainer Notes\n\nThis submission was put up in response to CRAN saying the package would be archived if the following\nwarning was not fixed within 14 days.\n\n```text\n/usr/local/clang-trunk/bin/../include/c++/v1/__fwd/string_view.h:22:41:\nwarning: 'char_traits' is deprecated:\nchar_traits for T not equal to char, wchar_t, char8_t, char16_t or char32_t is non-standard and is provided for a temporary period.\nIt will be removed in LLVM 19, so please migrate off of it. [-Wdeprecated-declarations]\n```\n\nSee https://github.com/lightgbm-org/LightGBM/issues/6264.\n\n## v4.2.0 - Submission 1 - (December 7, 2023)\n\n### CRAN response\n\nAccepted to CRAN\n\n### Maintainer Notes\n\nThis submission included many changes from the last 2 years, as well as fixes for a warning\nCRAN said could cause the package to be archived: https://github.com/lightgbm-org/LightGBM/issues/6221.\n\n## v4.1.0 - not submitted\n\nv4.1.0 was not submitted to CRAN, because https://github.com/lightgbm-org/LightGBM/issues/5987 had not been resolved.\n\n## v4.0.0 - Submission 2 - (July 19, 2023)\n\n### CRAN response\n\n> Dear maintainer,\n> package lightgbm_4.0.0.tar.gz does not pass the incoming checks automatically.\n\nThe logs linked from those messagges showed one issue remaining on Debian (0 on Windows).\n\n```text\n* checking examples ... [7s/4s] NOTE\nExamples with CPU time > 2.5 times elapsed time\n user system elapsed ratio\nlgb.restore_handle 1.206 0.085 0.128 10.08\n```\n\n### Maintainer Notes\n\nChose to document the issue and need for a fix in https://github.com/lightgbm-org/LightGBM/issues/5987, but not resubmit,\nto avoid annoying CRAN maintainers.\n\n## v4.0.0 - Submission 1 - (July 16, 2023)\n\n### CRAN response\n\n> Dear maintainer,\n> package lightgbm_4.0.0.tar.gz does not pass the incoming checks automatically.\n\nThe logs linked from those messages showed the following issues from `R CMD check`.\n\n```text\n* checking S3 generic/method consistency ... NOTE\nMismatches for apparent methods not registered:\nmerge:\n function(x, y, ...)\nmerge.eval.string:\n function(env)\n\nformat:\n function(x, ...)\nformat.eval.string:\n function(eval_res, eval_err)\nSee section 'Registering S3 methods' in the 'Writing R Extensions'\nmanual.\n```\n\n```text\n* checking examples ... [8s/4s] NOTE\nExamples with CPU time > 2.5 times elapsed time\n user system elapsed ratio\nlgb.restore_handle 1.819 0.128 0.165 11.8\n```\n\n### Maintainer Notes\n\nAttempted to fix these with https://github.com/lightgbm-org/LightGBM/pull/5988 and resubmitted.\n\n## v3.3.5 - Submission 2 - (January 16, 2023)\n\n### CRAN response\n\n> Reason was\n>\n> Flavor: r-devel-windows-x86_64\n> Check: OOverall checktime, Result: NOTE\n> Overall checktime 14 min > 10 min\n>\n> but the maintainer cannot do much to reduce this, so I triggered revdep checks now.\n> Please reply to the archival message in case the issue is not fixable easily.\n>\n> Best,\n> Uwe Ligges\n\n### Maintainer Notes\n\nThis was technically not a \"resubmission\".\nWe asked CRAN why the first v3.3.5 submission had been archived, and they responded with the response above... and then v3.3.5 passed all checks with no further work from LightGBM maintainers.\n\n## v3.3.5 - Submission 1 - (January 11, 2023)\n\n### CRAN response\n\nArchived without a response.\n\n### Maintainer Notes\n\nSubmitted with the following comment.\n\n> This submission contains {lightgbm} 3.3.5\n\n> Per CRAN's policies, I am submitting it on behalf of the project's maintainer (Yu Shi), with his permission.\n\n> This submission includes patches to address the following warnings observed on the fedora and debian CRAN checks.\n\n> Found the following significant warnings:\n> io/json11.cpp:207:47: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call]\n> io/json11.cpp:216:51: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call]\n> io/json11.cpp:225:53: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call]\n> io/json11.cpp:268:60: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call]\n> io/json11.cpp:272:36: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call]\n> io/json11.cpp:276:37: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call]\n> io/json11.cpp:381:41: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call]\n> io/json11.cpp:150:39: warning: unqualified call to 'std::move' [-Wunqualified-std-cast-call]\n\nThank you very much for your time and consideration.\n\n## v3.3.4 - Submission 1 - (December 15, 2022)\n\n### CRAN response\n\nAccepted to CRAN\n\n### Maintainer Notes\n\nSubmitted with the following comment:\n\n> This submission contains {lightgbm} 3.3.4\n\n> Per CRAN's policies, I am submitting it on behalf of the project's maintainer (Yu Shi), with his permission.\n\n> This submission includes patches to address the following warnings observed on the fedora and debian CRAN checks.\n>\n> Compiled code should not call entry points which might terminate R nor write to stdout/stderr instead of to the console, nor use Fortran I/O nor system RNGs nor [v]sprintf.\n\n> Thank you very much for your time and consideration.\n\n## v3.3.3 - Submission 1 - (October 10, 2022)\n\n### CRAN response\n\nAccepted to CRAN\n\n### Maintainer Notes\n\nSubmitted with the following comment:\n\n> This submission contains {lightgbm} 3.3.3.\n\n> Per CRAN's policies, I am submitting on it on behalf of the project's maintainer (Yu Shi), with his permission (https://github.com/lightgbm-org/LightGBM/pull/5525).\n\n> This submission includes two patches:\n> * a change to testing to avoid a failed test related to non-ASCII strings on the `r-devel-linux-x86_64-debian-clang` check flavor (https://github.com/lightgbm-org/LightGBM/pull/5526)\n> * modifications to allow compatibility with the RTools42 build toolchain (https://github.com/lightgbm-org/LightGBM/pull/5503)\n\n> Thank you very much for your time and consideration.\n\n## v3.3.2 - Submission 1 - (January 7, 2022)\n\n### CRAN response\n\nAccepted to CRAN on January 14, 2022.\n\n### Maintainer Notes\n\nIn this submission, we uploaded a patch that CRAN stuff provided us via e-mail. The full text of the e-mail from CRAN:\n\n```text\nDear maintainers,\n\nThis concerns the CRAN packages\n\nCairo cepreader gpboost httpuv ipaddress lightgbm proj4 prophet\nRcppCWB RcppParallel RDieHarder re2 redux rgeolocate RGtk2 tth\nudunits2 unrtf\n\nmaintained by one of you:\n\nAndreas Blaette andreas.blaette@uni-due.de: RcppCWB\nDavid Hall david.hall.physics@gmail.com: ipaddress\nDirk Eddelbuettel edd@debian.org: RDieHarder\nFabio Sigrist fabiosigrist@gmail.com: gpboost\nFriedrich Leisch Friedrich.Leisch@R-project.org: tth\nGirish Palya girishji@gmail.com: re2\nJames Hiebert hiebert@uvic.ca: udunits2\nJari Oksanen jhoksane@gmail.com: cepreader\nKevin Ushey kevin@rstudio.com: RcppParallel\nORPHANED: RGtk2\nOs Keyes ironholds@gmail.com: rgeolocate\nRich FitzJohn rich.fitzjohn@gmail.com: redux\nSean Taylor sjtz@pm.me: prophet\nSimon Urbanek simon.urbanek@r-project.org: proj4\nSimon Urbanek Simon.Urbanek@r-project.org: Cairo\nWinston Chang winston@rstudio.com: httpuv\nYu Shi yushi2@microsoft.com: lightgbm\n\nyour packages need to be updated for R-devel/R 4.2 to work on Windows,\nfollowing the recent switch to UCRT and Rtools42.\n\nSorry for the group message, please feel free to respond individually\nregarding your package or ask specifically about what needs to be fixed.\n\nI've created patches for you, so please review them and fix your packages:\n\nhttps://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsvn.r-project.org%2FR-dev-web%2Ftrunk%2FWindowsBuilds%2Fwinutf8%2Fucrt3%2Fr_packages%2Fpatches%2FCRAN%2F&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=rFGf7Y4Dvo6g1kzV%2BeAJDLGm1TUtzQsLsavElTw6H1U%3D&reserved=0\n\nYou can apply them as follows\n\ntar xfz package_1.0.0.tar.gz\n\nwget\nhttps://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsvn.r-project.org%2FR-dev-web%2Ftrunk%2FWindowsBuilds%2Fwinutf8%2Fucrt3%2Fr_packages%2Fpatches%2FCRAN%2Fpackage.diff&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=iyTjhoqvzj3IbQ8HGCZeh1IQl34FAGpIdVyZWkzNvO0%3D&reserved=0\n\npatch --binary < package.diff\n\nThese patches are currently automatically applied by R-devel on Windows\nat installation time, which makes most of your packages pass their\nchecks (as OK or NOTE), but please check your results carefully and\ncarefully review the patches. Usually these changes were because of\nnewer GCC or newer MinGW in the toolchain, but some for other reasons,\nand some of them will definitely have to be improved so that the package\nkeeps building also for older versions of R using Rtools40. We have only\nbeen testing the patches with UCRT (and Rtools42) on Windows.\n\nFor more information, please see\n\nhttps://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdeveloper.r-project.org%2FBlog%2Fpublic%2F2021%2F12%2F07%2Fupcoming-changes-in-r-4.2-on-windows%2F&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=SY77zgtbDbHvTxTgPLOoe%2Fw5OZDhXvJoxpVOoEaKoYo%3D&reserved=0\nhttps://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdeveloper.r-project.org%2FWindowsBuilds%2Fwinutf8%2Fucrt3%2Fhowto.html&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=dlVJ4nhQlmDPd56bHoVsWZuRfrUUorvOWxoUTmVDM%2Bg%3D&reserved=0\n\nOnce you add your patches/fix the issues, your package will probably\nshow a warning during R CMD check (as patching would be attempted to be\napplied again). That's ok, at that point please let me know and I will\nremove my patch from the repository of automatically applied patches.\n\nIf you end up just applying the patch as is, there is probably no need\ntesting on your end, but you can do so using Winbuilder, r-hub, github\nactions (e.g. https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fkalibera%2Fucrt3&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=msqoPzqDStlAUn%2Bb6gGevwFPD%2FaNL5dTxiNud2Sqzy8%3D&reserved=0).\n\nIf you wanted to test locally on your Windows machine and do not have a\nUCRT version of R-devel yet, please uninstall your old version of\nR-devel, delete the old library used with that, install a new UCRT\nversion of R-devel , and install Rtools42. You can keep Rtools40\ninstalled if you need it with R 4.1 or earlier.\n\nCurrently, the new R-devel can be downloaded from\nhttps://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.r-project.org%2Fnosvn%2Fwinutf8%2Fucrt3%2Fweb%2Frdevel.html&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=0hCwONzLmcW0GIXNqiOZQEIuhNA%2BjHhQvXsofs8J98o%3D&reserved=0\n\nAnd Rtools42 from\nhttps://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.r-project.org%2Fnosvn%2Fwinutf8%2Fucrt3%2Fweb%2Frtools.html&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=WLWLbOyQKbaYz8gkfKz2sqoGknjIOtl1aGAhUF%2Bpylg%3D&reserved=0\n\nIf you end up testing locally, you can use R_INSTALL_TIME_PATCHES\nenvironment variable to disable the automated patching, see the \"howto\"\ndocument above. That way you could also see what the original issue was\ncausing.\n\nIf you wanted to find libraries to link for yourself, e.g. in a newer\nversion of your package, please look for \"Using findLinkingOrder with\nRtools42 (tiff package example)\" in the \"howto\" document above. I\ncreated the patches for you manually before we finished this script, so\nyou may be able to create a shorter version using it, but - it's\nprobably not worth the effort.\n\nIf you wanted to try in a virtual machine, but did not have a license,\nyou can use also an automated setup of a free trial VM from\nhttps://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdeveloper.r-project.org%2FBlog%2Fpublic%2F2021%2F03%2F18%2Fvirtual-windows-machine-for-checking-r-packages&data=04%7C01%7Cyushi2%40microsoft.com%7C8e6c353d1a8842c81eeb08d9bef5d835%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637750786169848244%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=aFFQYuC9CoBwBiLgZHi8N3yUnSiHu5Xtdqb2YBiMIHQ%3D&reserved=0\n\n(but that needs a very good and un-metered network connection to install)\n\nPlease let us know if you have any questions.\n\nThanks,\nTomas & Uwe\n```\n\n## v3.3.1 - Submission 1 - (October 27, 2021)\n\n### CRAN response\n\nAccepted to CRAN on October 30, 2021.\n\nCRAN completed its checks and preparation of binaries on November 6, 2021.\n\n### Maintainer Notes\n\nSubmitted v3.3.1 to CRAN, with the following fixes for the issues that caused CRAN to reject v3.3.0 and archive the package:\n\n* https://github.com/lightgbm-org/LightGBM/pull/4673\n* https://github.com/lightgbm-org/LightGBM/pull/4714\n\nSubmitted with the following comment:\n\n> This submission contains {lightgbm} 3.3.1.\n> Per CRAN's policies, I am submitting on it on behalf of the project's maintainer (Yu Shi), with his permission (https://github.com/lightgbm-org/LightGBM/pull/4715#issuecomment-952537783).\n\n> {lightgbm} was removed from CRAN on October 25, 2021 due to issues detected in the gcc-ASAN and clang-ASAN checks. To the best of our knowledge, we believe this release fixes those issues. We have introduced automated testing that we believe faithfully reproduces CRAN's tests with sanitizers (https://github.com/lightgbm-org/LightGBM/pull/4678).\n\n> Thank you very much for your time and consideration.\n\nProgress on the submission was tracked in https://github.com/lightgbm-org/LightGBM/issues/4713.\n\n## v3.3.0 - Submission 1 - (October 8, 2021)\n\n### CRAN response\n\n`{lightgbm}` was removed from CRAN entirely on October 25, 2021.\n\nOn October 12, 2021, maintainers received the following message from CRAN (ripley@stats.ox.ac.uk):\n\n> Dear maintainer,\n\n> Please see the problems shown on https://cran.r-project.org/web/checks/check_results_lightgbm.html\n\n> Please correct before 2021-10-25 to safely retain your package on CRAN.\n\n> Do remember to look at the 'Additional issues'.\n\n> The CRAN Team\n\nWe failed to produce a new submission prior to that date, so the package was removed entirely.\n\nSee https://github.com/lightgbm-org/LightGBM/issues/4713 for additional background and links explaining the specific failed CRAN checks.\n\n### Maintainer Notes\n\nIn this submission, we attempted to switch the maintainer of the package (in the CRAN official sense) from Guolin Ke to Yu Shi.\nDid this by adding a note in the CRAN submission web form explaining Guolin's departure from Microsoft.\n\n## v3.2.1 - Submission 1 - (April 12, 2021)\n\n### CRAN response\n\nAccepted to CRAN.\n\n### Maintainer Notes\n\n## v3.2.0 - Submission 1 - (March 22, 2021)\n\n### CRAN response\n\nPackage is failing checks in the `r-devel-linux-x86_64-debian-clang` environment (described [here](https://cran.r-project.org/web/checks/check_flavors.html#r-devel-linux-x86_64-debian-clang)). Specifically, one unit test on the use of non-ASCII feature names in `Booster$dump_model()` fails.\n\n> Apparently your package fails its checks in a strict Latin-1* locale,\ne.g. under Linux using LANG=en_US.iso88591 (see the debian-clang\nresults).\n\n> Please correct before 2021-04-21 to safely retain your package on CRAN.\n\n### Maintainer Notes\n\nSubmitted a version 3.2.1 to correct the errors noted.\n\n## v3.1.1 - Submission 1 - (December 7, 2020)\n\n### CRAN response\n\nAccepted to CRAN, December 8.\n\n### Maintainer Notes\n\nSubmitted a fix to 3.1.0 that skips some learning-to-rank tests on 32-bit Windows.\n\n## v3.1.0 - Submission 1 - (November 15, 2020)\n\n### CRAN response\n\nAccepted to CRAN, November 18.\n\nOn November 21, found out that the CRAN's `r-oldrel-windows-ix86+x86_64` check was failing, with an issue similar to the one faced on Solaris and fixed in https://github.com/lightgbm-org/LightGBM/pull/3534.\n\nCRAN did not ask for a re-submission, but this was fixed in 3.1.1.\n\n### Maintainer Notes\n\nThis package was submitted with the following information in the \"optional comments\" box.\n\n```text\nHello,\n\nI'm submitting {lightgbm} 3.1.0 on behalf of the maintainer, Guolin Ke. I am a co-author on the package, and he has asked me to handle this submission. We saw in https://cran.r-project.org/web/packages/policies.html#Submission that this is permitted.\n\n{lightgbm} was removed from CRAN in October for issues found by valgrind checks. We have invested significant effort in addressing those issues and creating an automatic test that tries to replicate CRAN's valgrind checks: https://github.com/lightgbm-org/LightGBM/blob/742d72f8bb051105484fd5cca11620493ffb0b2b/.github/workflows/r_valgrind.yml.\n\nWe see two warnings from valgrind that we believe are not problematic.\n\n==2063== Conditional jump or move depends on uninitialised value(s)\n==2063== at 0x49CF138: gregexpr_Regexc (grep.c:2439)\n==2063== by 0x49D1F13: do_regexpr (grep.c:3100)\n==2063== by 0x49A0058: bcEval (eval.c:7121)\n==2063== by 0x498B67F: Rf_eval (eval.c:727)\n==2063== by 0x498E414: R_execClosure (eval.c:1895)\n==2063== by 0x498E0C7: Rf_applyClosure (eval.c:1821)\n==2063== by 0x499FC8C: bcEval (eval.c:7089)\n==2063== by 0x498B67F: Rf_eval (eval.c:727)\n==2063== by 0x498B1CB: forcePromise (eval.c:555)\n==2063== by 0x49963AB: FORCE_PROMISE (eval.c:5142)\n==2063== by 0x4996566: getvar (eval.c:5183)\n==2063== by 0x499D1A5: bcEval (eval.c:6873)\n==2063== Uninitialised value was created by a stack allocation\n==2063== at 0x49CEC37: gregexpr_Regexc (grep.c:2369)\n\nThis seems to be related to R itself and not any code in {lightgbm}.\n\n==2063== 336 bytes in 1 blocks are possibly lost in loss record 153 of 2,709\n==2063== at 0x483DD99: calloc (in /usr/lib/x86_64-linux-gnu/valgrind/vgpreload_memcheck-amd64-linux.so)\n==2063== by 0x40149CA: allocate_dtv (dl-tls.c:286)\n==2063== by 0x40149CA: _dl_allocate_tls (dl-tls.c:532)\n==2063== by 0x5702322: allocate_stack (allocatestack.c:622)\n==2063== by 0x5702322: pthread_create@@GLIBC_2.2.5 (pthread_create.c:660)\n==2063== by 0x56D0DDA: ??? (in /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0)\n==2063== by 0x56C88E0: GOMP_parallel (in /usr/lib/x86_64-linux-gnu/libgomp.so.1.0.0)\n==2063== by 0x1544D29C: LGBM_DatasetCreateFromCSC (c_api.cpp:1286)\n==2063== by 0x1546F980: LGBM_DatasetCreateFromCSC_R (lightgbm_R.cpp:91)\n==2063== by 0x4941E2F: R_doDotCall (dotcode.c:634)\n==2063== by 0x494CCC6: do_dotcall (dotcode.c:1281)\n==2063== by 0x499FB01: bcEval (eval.c:7078)\n==2063== by 0x498B67F: Rf_eval (eval.c:727)\n==2063== by 0x498E414: R_execClosure (eval.c:1895)\n\nWe believe this is a false positive, and related to a misunderstanding between valgrind and openmp (https://gcc.gnu.org/bugzilla/show_bug.cgi?id=36298).\n\nWe have also added automated tests with ASAN/UBSAN to our testing setup, and have checked the package on Solaris 10 and found no issues.\n\nThanks for your time and consideration.\n```\n\n## v3.0.0.2 - Submission 1 - (September 29, 2020)\n\n### CRAN response\n\nFirst response was a message talking about failing checks on 3.0.0.\n\n```text\npackage lightgbm_3.0.0.2.tar.gz has been auto-processed.\nThe auto-check found additional issues for the last version released on CRAN:\ngcc-UBSAN \nvalgrind \nCRAN incoming checks do not test for these additional issues and you will need an appropriately instrumented build of R to reproduce these.\nHence please reply-all and explain: Have these been fixed?\n\nPlease correct before 2020-10-05 to safely retain your package on CRAN.\n\nThere is still a valgrind error. This did not happen when tested on\nsubmission, but the tests did run until timeout at 4 hours. When you\nwrite illegally, corruption is common.\n\nIllegal writes are serious errors.\n```\n\nThen in later responses to email correspondence with CRAN, CRAN expressed frustration with the number of failed submission and banned this package from new submissions for a month.\n\nThe content of that frustrated message was regrettable and it does not need to be preserved forever in this file.\n\n### Maintainer Notes\n\nThe 3.0.0.x series is officially not making it to CRAN. We will wait until November, and try again.\n\nDetailed plan about what will be tried before November 2020 to increase the likelihood of success for that package: https://github.com/lightgbm-org/LightGBM/pull/3338#issuecomment-702756840.\n\n## v3.0.0.1 - Submission 1 - (September 24, 2020)\n\n### CRAN response\n\n```text\nThanks, we see:\n\nStill lots of alignment errors, such as\n\nlightgbm.Rcheck/tests/testthat.Rout:io/dataset_loader.cpp:340:59:\nruntime error: reference binding to misaligned address 0x7f51fefad81e for type 'const value_type', which requires 4 byte alignment\nlightgbm.Rcheck/tests/testthat.Rout:/usr/include/c++/10/bits/stl_vector.h:1198:21:\nruntime error: reference binding to misaligned address 0x7f51fefad81e for type 'const int', which requires 4 byte alignment lightgbm.Rcheck/tests/testthat.Rout:/usr/include/c++/10/bits/vector.tcc:449:28:runtime\nerror: reference binding to misaligned address 0x7f51fefad81e for type 'const type', which requires 4 byte alignment\nlightgbm.Rcheck/tests/testthat.Rout:/usr/include/c++/10/bits/move.h:77:36:\nruntime error: reference binding to misaligned address 0x7f51fefad81e for type 'const int', which requires 4 byte alignment\nlightgbm.Rcheck/tests/testthat.Rout:/usr/include/c++/10/bits/alloc_traits.h:512:17:\nruntime error: reference binding to misaligned address 0x7f51fefad81e for type 'const type', which requires 4 byte alignment\n\nPlease fix and resubmit.\n```\n\n### Maintainer Notes\n\nOk, these are the notes from the UBSAN tests. Was able to reproduce them with https://github.com/lightgbm-org/LightGBM/pull/3338#issuecomment-700399862, and they were fixed in https://github.com/lightgbm-org/LightGBM/pull/3415.\n\nStruggling to replicate the valgrind result (running `R CMD check --use-valgrind` returns no issues), so trying submission again. Hoping that the fixes for mis-alignment fix the other errors too.\n\n## v3.0.0 - Submission 6 - (September 24, 2020)\n\n### CRAN response\n\nFailing pre-checks.\n\n### `R CMD check` results\n\n```text\n* checking CRAN incoming feasibility ... WARNING\nMaintainer: ‘Guolin Ke ’\n\nInsufficient package version (submitted: 3.0.0, existing: 3.0.0)\n\nDays since last update: 4\n```\n\n### Maintainer Notes\n\nDid not think the version needed to be incremented if submitting a package in response to CRAN saying \"you are failing checks and will be kicked off if you don't fix it\", but I guess you do!\n\nThis can be fixed by just re-submitting but with the version changed from `3.0.0` to `3.0.0.1`.\n\n## v3.0.0 - Submission 5 - (September 11, 2020)\n\n### CRAN Response\n\nAccepted to CRAN!\n\nPlease correct the problems below before 2020-10-05 to safely retain your package on CRAN:\n\n```text\nchecking installed package size ... NOTE\n installed size is 49.7Mb\n sub-directories of 1Mb or more:\n libs 49.1Mb\n\n\"network/socket_wrapper.hpp\", line 30: Error: Could not open include file.\n\"network/socket_wrapper.hpp\", line 216: Error: The type \"ifaddrs\" is incomplete.\n\"network/socket_wrapper.hpp\", line 217: Error: The type \"ifaddrs\" is incomplete.\n\"network/socket_wrapper.hpp\", line 220: Error: The type \"ifaddrs\" is incomplete.\n\"network/socket_wrapper.hpp\", line 222: Error: The type \"ifaddrs\" is incomplete.\n\"network/socket_wrapper.hpp\", line 214: Error: The function \"getifaddrs\" must have a prototype.\n\"network/socket_wrapper.hpp\", line 228: Error: The function \"freeifaddrs\" must have a prototype.\n\"network/linkers_socket.cpp\", line 76: Warning: A non-POD object of type \"std::chrono::duration>\" passed as a variable argument to function \"static LightGBM::Log::Info(const char*, ...)\".\n7 Error(s) and 1 Warning(s) detected.\n*** Error code 2\nmake: Fatal error: Command failed for target `network/linkers_socket.o'\nCurrent working directory /tmp/RtmpNfaavG/R.INSTALL40a84f70130a/lightgbm/src\nERROR: compilation failed for package ‘lightgbm’\n* removing ‘/home/ripley/R/Lib32/lightgbm’\n```\n\n### Maintainer Notes\n\nAdded a patch that `psutil` has used to fix missing `ifaddrs.h` on Solaris 10: https://github.com/lightgbm-org/LightGBM/issues/629#issuecomment-665091451.\n\n## v3.0.0 - Submission 4 - (September 4, 2020)\n\n### CRAN Response\n\n> Thanks, if the running time is the only reason to wrap the examples in\n\\donttest, please replace \\donttest by \\donttest (\\donttest examples are\nnot executed in the CRAN checks).\n\n> Please replace cat() by message() or warning() in your functions (except\nfor print() and summary() functions). Messages and warnings can be\nsuppressed if needed.\n\n> Missing Rd-tags:\n lightgbm/man/dimnames.lgb.Dataset.Rd: \\value\n lightgbm/man/lgb.Dataset.construct.Rd: \\value\n lightgbm/man/lgb.prepare.Rd: \\value\n ...\n\n> Please add the tag and explain in detail the returned objects.\n\n### Maintainer Notes\n\nResponded to CRAN with the following:\n\nAll examples have been wrapped with `\\donttest` as requested. We have replied to Swetlana Herbrandt asking for clarification on the donttest news item in the R 4.0.2 changelog (https://cran.r-project.org/doc/manuals/r-devel/NEWS.html).\n\nAll uses of `cat()` have been replaced with `print()`. We chose `print()` over `message()` because it's important that they be written to stdout alongside all the other logs coming from the library's C++ code. `message()` and `warning()` write to stderr.\n\nAll exported objects now have `\\value{}` statements in their documentation files in `man/`.\n\n**We also replied directly to CRAN's feedback email**\n\n> Swetlana,\n\n> Thank you for your comments. I've just created a new submission that I believe addresses them.\n\n> Can you help us understand something? In your message you said \"\\donttest examples are\nnot executed in the CRAN checks)\", but in https://cran.r-project.org/doc/manuals/r-devel/NEWS.html we see the following:\n\n> > \"`R CMD check --as-cran` now runs \\donttest examples (which are run by example()) instead of instructing the tester to do so. This can be temporarily circumvented during development by setting environment variable `_R_CHECK_DONTTEST_EXAMPLES_` to a false value.\"\n\n> Could you help us understand how both of those statements can be true?\n\n## v3.0.0 - Submission 3 - (August 29, 2020)\n\n### CRAN response\n\n* Please write references in the description of the DESCRIPTION file in\nthe form\n - authors (year) doi:...\n - authors (year) arXiv:...\n - authors (year, ISBN:...)\n* if those are not available: authors (year) https:... with no space after 'doi:', 'arXiv:', 'https:' and angle brackets for auto-linking.\n* (If you want to add a title as well please put it in quotes: \"Title\")\n\n* \\donttest{} should only be used if the example really cannot be executed (e.g. because of missing additional software, missing API keys, ...) by the user. That's why wrapping examples in \\donttest{} adds the comment (\"# Not run:\") as a warning for the user. Does not seem necessary. Please unwrap the examples if they are executable in < 5 sec, or replace\n\\donttest{} with \\donttest{}.\n\n* Please do not modify the global environment (e.g. by using <<-) in your\nfunctions. This is not allowed by the CRAN policies.\n\n* Please always add all authors, contributors and copyright holders in the Authors@R field with the appropriate roles. From CRAN policies you agreed to: \"The ownership of copyright and intellectual property rights of all components of the package must be clear and unambiguous (including from the authors specification in the DESCRIPTION file). Where code is copied (or derived) from the work of others (including from R itself), care must be taken that any copyright/license statements are preserved and authorship is not misrepresented.\" e.g.: Microsoft Corporation, Dropbox Inc. Please explain in the submission comments what you did about this issue.\n\nPlease fix and resubmit\n\n### Maintainer Notes\n\nResponded to CRAN with the following:\n\nThe paper citation has been adjusted as requested. We were using 'glmnet' as a guide on how to include the URL but maybe they are no longer in compliance with CRAN policies: https://github.com/cran/glmnet/blob/b1a4b50de01e0cd24343959d7cf86452bac17b26/DESCRIPTION\n\nAll authors from the original LightGBM paper have been added to Authors@R as `\"aut\"`. We have also added Microsoft and DropBox, Inc. as `\"cph\"` (copyright holders). These roles were chosen based on the guidance in https://journal.r-project.org/archive/2012/RJ-2012-009/index.html.\n\nlightgbm's code does use `<<-`, but it does not modify the global environment. The uses of `<<-` in R/lgb.interprete.R and R/callback.R are in functions which are called in an environment created by the lightgbm functions that call them, and this operator is used to reach one level up into the calling function's environment.\n\nWe chose to wrap our examples in `\\donttest{}` because we found, through testing on https://r-hub.github.io/rhub/ and in our own continuous integration environments, that their run time varies a lot between platforms, and we cannot guarantee that all examples will run in under 5 seconds. We intentionally chose `\\donttest{}` over `\\donttest{}` because this item in the R 4.0.0 changelog (https://cran.r-project.org/doc/manuals/r-devel/NEWS.html) seems to indicate that \\donttest will be ignored by CRAN's automated checks:\n\n> \"`R CMD check --as-cran` now runs \\donttest examples (which are run by example()) instead of instructing the tester to do so. This can be temporarily circumvented during development by setting environment variable `_R_CHECK_DONTTEST_EXAMPLES_` to a false value.\"\n\nWe run all examples with `R CMD check --as-cran --run-dontrun` in our continuous integration tests on every commit to the package, so we have high confidence that they are working correctly.\n\n## v3.0.0 - Submission 2 - (August 28, 2020)\n\n### CRAN response\n\nFailing pre-checks.\n\n### `R CMD check` results\n\n* Debian: 2 NOTEs\n\n ```text\n * checking CRAN incoming feasibility ... NOTE\n Maintainer: 'Guolin Ke '\n\n New submission\n\n Possibly mis-spelled words in DESCRIPTION:\n Guolin (13:52)\n Ke (13:48)\n LightGBM (14:20)\n al (13:62)\n et (13:59)\n\n * checking top-level files ... NOTE\n Non-standard files/directories found at top level:\n 'docs' 'lightgbm-hex-logo.png' 'lightgbm-hex-logo.svg'\n ```\n\n* Windows: 2 NOTEs\n\n ```text\n * checking CRAN incoming feasibility ... NOTE\n Maintainer: 'Guolin Ke '\n\n New submission\n\n Possibly mis-spelled words in DESCRIPTION:\n Guolin (13:52)\n Ke (13:48)\n LightGBM (14:20)\n al (13:62)\n et (13:59)\n\n * checking top-level files ... NOTE\n Non-standard files/directories found at top level:\n 'docs' 'lightgbm-hex-logo.png' 'lightgbm-hex-logo.svg'\n ```\n\n### Maintainer Notes\n\nWe should tell them the misspellings note is a false positive.\n\nFor the note about included files, that is my fault. I had extra files laying around when I generated the package. I'm surprised to see `docs/` in that list, since it is ignored in `.Rbuildignore`. I even tested that with [the exact code Rbuildignore uses](https://github.com/wch/r-source/blob/9d13622f41cfa0f36db2595bd6a5bf93e2010e21/src/library/tools/R/build.R#L85). For now, I added `rm -r docs/` to `build-cran-package.sh`. We can figure out what is happening with `.Rbuildignore` in the future, but it shouldn't block a release.\n\n## v3.0.0 - Submission 1 - (August 24, 2020)\n\nNOTE: 3.0.0-1 was never released to CRAN. CRAN was on vacation August 14-24, 2020, and in that time version 3.0.0-1 (a release candidate) became 3.0.0.\n\n### CRAN response\n\n> Please only ship the CRAN template for the MIT license.\n\n> Is there some reference about the method you can add in the Description field in the form Authors (year) doi:.....?\n\n> Please fix and resubmit.\n\n### `R CMD check` results\n\n* Debian: 1 NOTE\n\n ```text\n * checking CRAN incoming feasibility ... NOTE\n Maintainer: ‘Guolin Ke ’\n\n New submission\n\n License components with restrictions and base license permitting such:\n MIT + file LICENSE\n ```\n\n* Windows: 1 NOTE\n\n ```text\n * checking CRAN incoming feasibility ... NOTE\n Maintainer: 'Guolin Ke '\n\n New submission\n\n License components with restrictions and base license permitting such:\n MIT + file LICENSE\n ```\n\n### Maintainer Notes\n\nTried updating `LICENSE` file to this template:\n\n```yaml\nYEAR: 2016\nCOPYRIGHT HOLDER: Microsoft Corporation\n```\n\nAdded a citation and link for [the main paper](https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html) in `DESCRIPTION`.\n\n## v3.0.0-1 - Submission 3 - (August 12, 2020)\n\n### CRAN response\n\nFailing pre-checks.\n\n### `R CMD check` results\n\n* Debian: 1 NOTE\n\n ```text\n * checking CRAN incoming feasibility ... NOTE\n Maintainer: ‘Guolin Ke ’\n\n New submission\n\n License components with restrictions and base license permitting such:\n MIT + file LICENSE\n ```\n\n* Windows: 1 ERROR, 1 NOTE\n\n ```text\n * checking CRAN incoming feasibility ... NOTE\n Maintainer: ‘Guolin Ke ’\n\n New submission\n\n License components with restrictions and base license permitting such:\n MIT + file LICENSE\n\n ** running tests for arch 'i386' ... [9s] ERROR\n Running 'testthat.R' [8s]\n Running the tests in 'tests/testthat.R' failed.\n Complete output:\n > library(testthat)\n > library(lightgbm)\n Loading required package: R6\n >\n > test_check(\n + package = \"lightgbm\"\n + , stop_on_failure = TRUE\n + , stop_on_warning = FALSE\n + )\n -- 1. Error: predictions do not fail for integer input (@test_Predictor.R#7) --\n lgb.Dataset.construct: cannot create Dataset handle\n Backtrace:\n 1. lightgbm::lgb.train(...)\n 2. data$construct()\n ```\n\n### Maintainer Notes\n\nThe \"checking CRAN incoming feasibility\" NOTE can be safely ignored. It only shows up the first time you submit a package to CRAN.\n\nSo the only thing I see broken right now is the test error on 32-bit Windows. This is documented in https://github.com/lightgbm-org/LightGBM/issues/3187.\n\n## v3.0.0-1 - Submission 2 - (August 10, 2020)\n\n### CRAN response\n\nFailing pre-checks.\n\n### `R CMD check` results\n\n* Debian: 2 NOTEs\n\n ```text\n * checking CRAN incoming feasibility ... NOTE\n Maintainer: ‘Guolin Ke ’\n\n New submission\n\n License components with restrictions and base license permitting such:\n MIT + file LICENSE\n\n Non-standard files/directories found at top level:\n ‘cran-comments.md’ ‘docs’\n ```\n\n* Windows: 1 ERROR, 2 NOTEs\n\n ```text\n * checking CRAN incoming feasibility ... NOTE\n Maintainer: 'Guolin Ke '\n\n New submission\n\n License components with restrictions and base license permitting such:\n MIT + file LICENSE\n\n * checking top-level files ... NOTE\n Non-standard files/directories found at top level:\n 'cran-comments.md' 'docs'\n\n ** checking whether the package can be loaded ... ERROR\n Loading this package had a fatal error status code 1\n Loading log:\n Error: package 'lightgbm' is not installed for 'arch = i386'\n Execution halted\n ```\n\n### Maintainer Notes\n\nSeems removing `Biarch` field didn't work. Noticed this in the install logs:\n\n> Warning: this package has a non-empty 'configure.win' file, so building only the main architecture\n\nTried adding `Biarch: true` to `DESCRIPTION` to overcome this.\n\nNOTE about non-standard files was the result of a mistake in `.Rbuildignore` syntax, and something strange with how `cran-comments.md` line in `.Rbuildignore` was treated. Updated `.Rbuildignore` and added an `rm cran-comments.md` to `build-cran-package.sh`.\n\n## v3.0.0-1 - Submission 1 - (August 9, 2020)\n\n### CRAN response\n\nFailing pre-checks.\n\n### `R CMD check` results\n\n* Debian: 1 NOTE\n\n ```text\n Possibly mis-spelled words in DESCRIPTION:\n LightGBM (12:88, 19:41, 20:60, 20:264)\n ```\n\n* Windows: 1 ERROR, 1 NOTE\n\n ```text\n Possibly mis-spelled words in DESCRIPTION:\n LightGBM (12:88, 19:41, 20:60, 20:264)\n\n ** checking whether the package can be loaded ... ERROR\n Loading this package had a fatal error status code 1\n Loading log:\n Error: package 'lightgbm' is not installed for 'arch = i386'\n Execution halted\n ```\n\n### Maintainer Notes\n\nThought the issue on Windows was caused by `Biarch: false` in `DESCRIPTION`. Removed `Biarch` field.\n\nThought the \"misspellings\" issue could be resolved by adding single quotes around LightGBM, like `'LightGBM'`.\n" }, { "path": "R-package/demo/00Index", "content": "basic_walkthrough Basic feature walkthrough\nboost_from_prediction Boosting from existing prediction\ncategorical_features_rules Categorical Feature Preparation with Rules\ncross_validation Cross Validation\nearly_stopping Early Stop in training\nefficient_many_training Efficiency for Many Model Trainings\nmulticlass Multiclass training/prediction\nmulticlass_custom_objective Multiclass with Custom Objective Function\nleaf_stability Leaf (in)Stability example\nweight_param Weight-Parameter adjustment relationship\n" }, { "path": "R-package/demo/basic_walkthrough.R", "content": "library(lightgbm)\n\n# We load in the agaricus dataset\n# In this example, we are aiming to predict whether a mushroom is edible\ndata(agaricus.train, package = \"lightgbm\")\ndata(agaricus.test, package = \"lightgbm\")\ntrain <- agaricus.train\ntest <- agaricus.test\n\n# The loaded data is stored in sparseMatrix, and label is a numeric vector in {0,1}\nclass(train$label)\nclass(train$data)\n\n# Set parameters for model training\ntrain_params <- list(\n num_leaves = 4L\n , learning_rate = 1.0\n , objective = \"binary\"\n , nthread = 2L\n)\n\n#--------------------Basic Training using lightgbm----------------\n# This is the basic usage of lightgbm you can put matrix in data field\n# Note: we are putting in sparse matrix here, lightgbm naturally handles sparse input\n# Use sparse matrix when your feature is sparse (e.g. when you are using one-hot encoding vector)\nprint(\"Training lightgbm with sparseMatrix\")\nbst <- lightgbm(\n data = train$data\n , params = train_params\n , label = train$label\n , nrounds = 2L\n)\n\n# Alternatively, you can put in dense matrix, i.e. basic R-matrix\nprint(\"Training lightgbm with Matrix\")\nbst <- lightgbm(\n data = as.matrix(train$data)\n , params = train_params\n , label = train$label\n , nrounds = 2L\n)\n\n# You can also put in lgb.Dataset object, which stores label, data and other meta datas needed for advanced features\nprint(\"Training lightgbm with lgb.Dataset\")\ndtrain <- lgb.Dataset(\n data = train$data\n , label = train$label\n)\nbst <- lightgbm(\n data = dtrain\n , params = train_params\n , nrounds = 2L\n)\n\n# Verbose = 0,1,2\nprint(\"Train lightgbm with verbose 0, no message\")\nbst <- lightgbm(\n data = dtrain\n , params = train_params\n , nrounds = 2L\n , verbose = 0L\n)\n\nprint(\"Train lightgbm with verbose 1, print evaluation metric\")\nbst <- lightgbm(\n data = dtrain\n , params = train_params\n , nrounds = 2L\n , verbose = 1L\n)\n\nprint(\"Train lightgbm with verbose 2, also print information about tree\")\nbst <- lightgbm(\n data = dtrain\n , params = train_params\n , nrounds = 2L\n , verbose = 2L\n)\n\n# You can also specify data as file path to a LibSVM/TCV/CSV format input\n# Since we do not have this file with us, the following line is just for illustration\n# bst <- lightgbm(\n# data = \"agaricus.train.svm\"\n# , num_leaves = 4L\n# , learning_rate = 1.0\n# , nrounds = 2L\n# , objective = \"binary\"\n# )\n\n#--------------------Basic prediction using lightgbm--------------\n# You can do prediction using the following line\n# You can put in Matrix, sparseMatrix, or lgb.Dataset\npred <- predict(bst, test$data)\nerr <- mean(as.numeric(pred > 0.5) != test$label)\nprint(paste(\"test-error=\", err))\n\n#--------------------Save and load models-------------------------\n# Save model to binary local file\nlgb.save(bst, \"lightgbm.model\")\n\n# Load binary model to R\nbst2 <- lgb.load(\"lightgbm.model\")\npred2 <- predict(bst2, test$data)\n\n# pred2 should be identical to pred\nprint(paste(\"sum(abs(pred2-pred))=\", sum(abs(pred2 - pred))))\n\n#--------------------Advanced features ---------------------------\n# To use advanced features, we need to put data in lgb.Dataset\ndtrain <- lgb.Dataset(data = train$data, label = train$label, free_raw_data = FALSE)\ndtest <- lgb.Dataset.create.valid(dtrain, data = test$data, label = test$label)\n\n#--------------------Using validation set-------------------------\n# valids is a list of lgb.Dataset, each of them is tagged with name\nvalids <- list(train = dtrain, test = dtest)\n\n# To train with valids, use lgb.train, which contains more advanced features\n# valids allows us to monitor the evaluation result on all data in the list\nprint(\"Train lightgbm using lgb.train with valids\")\nbst <- lgb.train(\n data = dtrain\n , params = train_params\n , nrounds = 2L\n , valids = valids\n)\n\n# We can change evaluation metrics, or use multiple evaluation metrics\nprint(\"Train lightgbm using lgb.train with valids, watch logloss and error\")\nbst <- lgb.train(\n data = dtrain\n , params = train_params\n , nrounds = 2L\n , valids = valids\n , eval = c(\"binary_error\", \"binary_logloss\")\n)\n\n# lgb.Dataset can also be saved using lgb.Dataset.save\nlgb.Dataset.save(dtrain, \"dtrain.buffer\")\n\n# To load it in, simply call lgb.Dataset\ndtrain2 <- lgb.Dataset(\"dtrain.buffer\")\nbst <- lgb.train(\n data = dtrain2\n , params = train_params\n , nrounds = 2L\n , valids = valids\n)\n\n# information can be extracted from lgb.Dataset using get_field()\nlabel <- get_field(dtest, \"label\")\npred <- predict(bst, test$data)\nerr <- as.numeric(sum(as.integer(pred > 0.5) != label)) / length(label)\nprint(paste(\"test-error=\", err))\n" }, { "path": "R-package/demo/boost_from_prediction.R", "content": "library(lightgbm)\n\n# Load in the agaricus dataset\ndata(agaricus.train, package = \"lightgbm\")\ndata(agaricus.test, package = \"lightgbm\")\ndtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label)\ndtest <- lgb.Dataset.create.valid(dtrain, data = agaricus.test$data, label = agaricus.test$label)\n\nvalids <- list(eval = dtest, train = dtrain)\n#--------------------Advanced features ---------------------------\n# advanced: start from an initial base prediction\nprint(\"Start running example to start from an initial prediction\")\n\n# Train lightgbm for 1 round\nparam <- list(\n num_leaves = 4L\n , learning_rate = 1.0\n , nthread = 2L\n , objective = \"binary\"\n)\nbst <- lgb.train(param, dtrain, 1L, valids = valids)\n\n# Note: we need the margin value instead of transformed prediction in set_init_score\nptrain <- predict(bst, agaricus.train$data, type = \"raw\")\nptest <- predict(bst, agaricus.test$data, type = \"raw\")\n\n# set the init_score property of dtrain and dtest\n# base margin is the base prediction we will boost from\nset_field(dtrain, \"init_score\", ptrain)\nset_field(dtest, \"init_score\", ptest)\n\nprint(\"This is result of boost from initial prediction\")\nbst <- lgb.train(\n params = param\n , data = dtrain\n , nrounds = 5L\n , valids = valids\n)\n" }, { "path": "R-package/demo/categorical_features_rules.R", "content": "# Here we are going to try training a model with categorical features\n\n# Load libraries\nlibrary(data.table)\nlibrary(lightgbm)\n\n# Load data and look at the structure\n#\n# Classes 'data.table' and 'data.frame':\t4521 obs. of 17 variables:\n# $ age : int 30 33 35 30 59 35 36 39 41 43 ...\n# $ job : chr \"unemployed\" \"services\" \"management\" \"management\" ...\n# $ marital : chr \"married\" \"married\" \"single\" \"married\" ...\n# $ education: chr \"primary\" \"secondary\" \"tertiary\" \"tertiary\" ...\n# $ default : chr \"no\" \"no\" \"no\" \"no\" ...\n# $ balance : int 1787 4789 1350 1476 0 747 307 147 221 -88 ...\n# $ housing : chr \"no\" \"yes\" \"yes\" \"yes\" ...\n# $ loan : chr \"no\" \"yes\" \"no\" \"yes\" ...\n# $ contact : chr \"cellular\" \"cellular\" \"cellular\" \"unknown\" ...\n# $ day : int 19 11 16 3 5 23 14 6 14 17 ...\n# $ month : chr \"oct\" \"may\" \"apr\" \"jun\" ...\n# $ duration : int 79 220 185 199 226 141 341 151 57 313 ...\n# $ campaign : int 1 1 1 4 1 2 1 2 2 1 ...\n# $ pdays : int -1 339 330 -1 -1 176 330 -1 -1 147 ...\n# $ previous : int 0 4 1 0 0 3 2 0 0 2 ...\n# $ poutcome : chr \"unknown\" \"failure\" \"failure\" \"unknown\" ...\n# $ y : chr \"no\" \"no\" \"no\" \"no\" ...\ndata(bank, package = \"lightgbm\")\nstr(bank)\n\n# We are dividing the dataset into two: one train, one validation\nbank_train <- bank[1L:4000L, ]\nbank_test <- bank[4001L:4521L, ]\n\n# We must now transform the data to fit in LightGBM\n# For this task, we use lgb.convert_with_rules\n# The function transforms the data into a fittable data\n#\n# Classes 'data.table' and 'data.frame':\t521 obs. of 17 variables:\n# $ age : int 53 36 58 26 34 55 55 34 41 38 ...\n# $ job : num 1 10 10 9 10 2 2 3 3 4 ...\n# $ marital : num 1 2 1 3 3 2 2 2 1 1 ...\n# $ education: num 2 2 2 2 2 1 2 3 2 2 ...\n# $ default : num 1 1 1 1 1 1 1 1 1 1 ...\n# $ balance : int 26 191 -123 -147 179 1086 471 105 1588 70 ...\n# $ housing : num 2 1 1 1 1 2 2 2 2 1 ...\n# $ loan : num 1 1 1 1 1 1 1 1 2 1 ...\n# $ contact : num 1 1 1 3 1 1 3 3 3 1 ...\n# $ day : int 7 31 5 4 19 6 30 28 20 27 ...\n# $ month : num 9 2 2 7 2 9 9 9 7 11 ...\n# $ duration : int 56 69 131 95 294 146 58 249 10 255 ...\n# $ campaign : int 1 1 2 2 3 1 2 2 8 3 ...\n# $ pdays : int 359 -1 -1 -1 -1 272 -1 -1 -1 148 ...\n# $ previous : int 1 0 0 0 0 2 0 0 0 1 ...\n# $ poutcome : num 1 4 4 4 4 1 4 4 4 3 ...\n# $ y : num 1 1 1 1 1 1 1 1 1 2 ...\nbank_rules <- lgb.convert_with_rules(data = bank_train)\nbank_train <- bank_rules$data\nbank_test <- lgb.convert_with_rules(data = bank_test, rules = bank_rules$rules)$data\nstr(bank_test)\n\n# Remove 1 to label because it must be between 0 and 1\nbank_train$y <- bank_train$y - 1L\nbank_test$y <- bank_test$y - 1L\n\n# Data input to LightGBM must be a matrix, without the label\nmy_data_train <- as.matrix(bank_train[, 1L:16L, with = FALSE])\nmy_data_test <- as.matrix(bank_test[, 1L:16L, with = FALSE])\n\n# Creating the LightGBM dataset with categorical features\n# The categorical features can be passed to lgb.train to not copy and paste a lot\ndtrain <- lgb.Dataset(\n data = my_data_train\n , label = bank_train$y\n , categorical_feature = c(2L, 3L, 4L, 5L, 7L, 8L, 9L, 11L, 16L)\n)\ndtest <- lgb.Dataset.create.valid(\n dtrain\n , data = my_data_test\n , label = bank_test$y\n)\n\n# We can now train a model\nparams <- list(\n objective = \"binary\"\n , metric = \"l2\"\n , min_data = 1L\n , learning_rate = 0.1\n , min_hessian = 1.0\n , max_depth = 2L\n)\nmodel <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 100L\n , valids = list(train = dtrain, valid = dtest)\n)\n\n# Try to find split_feature: 11\n# If you find it, it means it used a categorical feature in the first tree\nlgb.dump(model, num_iteration = 1L)\n" }, { "path": "R-package/demo/cross_validation.R", "content": "library(lightgbm)\n\n# load in the agaricus dataset\ndata(agaricus.train, package = \"lightgbm\")\ndata(agaricus.test, package = \"lightgbm\")\ndtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label)\ndtest <- lgb.Dataset.create.valid(dtrain, data = agaricus.test$data, label = agaricus.test$label)\n\nnrounds <- 2L\nparam <- list(\n num_leaves = 4L\n , learning_rate = 1.0\n , objective = \"binary\"\n)\n\nprint(\"Running cross validation\")\n# Do cross validation, this will print result out as\n# [iteration] metric_name:mean_value+std_value\n# std_value is standard deviation of the metric\nlgb.cv(\n param\n , dtrain\n , nrounds\n , nfold = 5L\n , eval = \"binary_error\"\n)\n\nprint(\"Running cross validation, disable standard deviation display\")\n# do cross validation, this will print result out as\n# [iteration] metric_name:mean_value+std_value\n# std_value is standard deviation of the metric\nlgb.cv(\n param\n , dtrain\n , nrounds\n , nfold = 5L\n , eval = \"binary_error\"\n , showsd = FALSE\n)\n\n# You can also do cross validation with customized loss function\nprint(\"Running cross validation, with cutomsized loss function\")\n\nlogregobj <- function(preds, dtrain) {\n labels <- get_field(dtrain, \"label\")\n preds <- 1.0 / (1.0 + exp(-preds))\n grad <- preds - labels\n hess <- preds * (1.0 - preds)\n return(list(grad = grad, hess = hess))\n}\n\n# User-defined evaluation function returns a pair (metric_name, result, higher_better)\n# NOTE: when you do customized loss function, the default prediction value is margin\n# This may make built-in evaluation metric calculate wrong results\n# For example, we are doing logistic loss, the prediction is score before logistic transformation\n# Keep this in mind when you use the customization, and maybe you need write customized evaluation function\nevalerror <- function(preds, dtrain) {\n labels <- get_field(dtrain, \"label\")\n preds <- 1.0 / (1.0 + exp(-preds))\n err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels)\n return(list(name = \"error\", value = err, higher_better = FALSE))\n}\n\n# train with customized objective\nlgb.cv(\n params = param\n , data = dtrain\n , nrounds = nrounds\n , obj = logregobj\n , eval = evalerror\n , nfold = 5L\n)\n" }, { "path": "R-package/demo/early_stopping.R", "content": "library(lightgbm)\n\n# Load in the agaricus dataset\ndata(agaricus.train, package = \"lightgbm\")\ndata(agaricus.test, package = \"lightgbm\")\n\ndtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label)\ndtest <- lgb.Dataset.create.valid(dtrain, data = agaricus.test$data, label = agaricus.test$label)\n\n# Note: for customized objective function, we leave objective as default\n# Note: what we are getting is margin value in prediction\n# You must know what you are doing\nparam <- list(\n num_leaves = 4L\n , learning_rate = 1.0\n)\nvalids <- list(eval = dtest)\nnum_round <- 20L\n\n# User define objective function, given prediction, return gradient and second order gradient\n# This is loglikelihood loss\nlogregobj <- function(preds, dtrain) {\n labels <- get_field(dtrain, \"label\")\n preds <- 1.0 / (1.0 + exp(-preds))\n grad <- preds - labels\n hess <- preds * (1.0 - preds)\n return(list(grad = grad, hess = hess))\n}\n\n# User-defined evaluation function returns a pair (metric_name, result, higher_better)\n# NOTE: when you do customized loss function, the default prediction value is margin\n# This may make built-in evaluation metric calculate wrong results\n# For example, we are doing logistic loss, the prediction is score before logistic transformation\n# The built-in evaluation error assumes input is after logistic transformation\n# Keep this in mind when you use the customization, and maybe you need write customized evaluation function\nevalerror <- function(preds, dtrain) {\n labels <- get_field(dtrain, \"label\")\n err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels)\n return(list(name = \"error\", value = err, higher_better = FALSE))\n}\nprint(\"Start training with early Stopping setting\")\n\nbst <- lgb.train(\n param\n , dtrain\n , num_round\n , valids\n , obj = logregobj\n , eval = evalerror\n , early_stopping_round = 3L\n)\n" }, { "path": "R-package/demo/efficient_many_training.R", "content": "# Efficient training means training without giving up too much RAM\n# In the case of many trainings (like 100+ models), RAM will be eaten very quickly\n# Therefore, it is essential to know a strategy to deal with such issue\n\n# More results can be found here: https://github.com/lightgbm-org/LightGBM/issues/879#issuecomment-326656580\n# Quote: \"@Laurae2 Thanks for nice easily reproducible example (unlike mine).\n# With reset=FALSE you get after 500 iterations (not 1000): OS reports 27GB usage, while R gc() reports 1.5GB.\n# Just doing reset=TRUE will already improve things: OS reports 4.6GB.\n# Doing reset=TRUE and calling gc() in the loop will have OS 1.3GB. Thanks for the latest tip.\"\n\n# Load library\nlibrary(lightgbm)\n\n# Generate fictive data of size 1M x 100\nset.seed(11111L)\nx_data <- matrix(rnorm(n = 100000000L, mean = 0.0, sd = 100.0), nrow = 1000000L, ncol = 100L)\ny_data <- rnorm(n = 1000000L, mean = 0.0, sd = 5.0)\n\n# Create lgb.Dataset for training\ndata <- lgb.Dataset(x_data, label = y_data)\ndata$construct()\n\n# Loop through a training of 1000 models, please check your RAM on your task manager\n# It MUST remain constant (if not increasing very slightly)\ngbm <- list()\n\nfor (i in 1L:1000L) {\n print(i)\n gbm[[i]] <- lgb.train(\n params = list(objective = \"regression\")\n , data = data\n , 1L\n , reset_data = TRUE\n )\n gc(verbose = FALSE)\n}\n" }, { "path": "R-package/demo/leaf_stability.R", "content": "# We are going to look at how iterating too much might generate observation instability.\n# Obviously, we are in a controlled environment, without issues (real rules).\n# Do not do this in a real scenario.\n\nlibrary(lightgbm)\n\n# define helper functions for creating plots\n\n# output of `RColorBrewer::brewer.pal(10, \"RdYlGn\")`, hardcooded here to avoid a dependency\n.diverging_palette <- c(\n \"#A50026\", \"#D73027\", \"#F46D43\", \"#FDAE61\", \"#FEE08B\"\n , \"#D9EF8B\", \"#A6D96A\", \"#66BD63\", \"#1A9850\", \"#006837\"\n)\n\n.prediction_depth_plot <- function(df) {\n plot(\n x = df$X\n , y = df$Y\n , type = \"p\"\n , main = \"Prediction Depth\"\n , xlab = \"Leaf Bin\"\n , ylab = \"Prediction Probability\"\n , pch = 19L\n , col = .diverging_palette[df$binned + 1L]\n )\n legend(\n \"topright\"\n , title = \"bin\"\n , legend = sort(unique(df$binned))\n , pch = 19L\n , col = .diverging_palette[sort(unique(df$binned + 1L))]\n , cex = 0.7\n )\n}\n\n.prediction_depth_spread_plot <- function(df) {\n plot(\n x = df$binned\n , xlim = c(0L, 9L)\n , y = df$Z\n , type = \"p\"\n , main = \"Prediction Depth Spread\"\n , xlab = \"Leaf Bin\"\n , ylab = \"Logloss\"\n , pch = 19L\n , col = .diverging_palette[df$binned + 1L]\n )\n legend(\n \"topright\"\n , title = \"bin\"\n , legend = sort(unique(df$binned))\n , pch = 19L\n , col = .diverging_palette[sort(unique(df$binned + 1L))]\n , cex = 0.7\n )\n}\n\n.depth_density_plot <- function(df) {\n plot(\n x = density(df$Y)\n , xlim = c(min(df$Y), max(df$Y))\n , type = \"p\"\n , main = \"Depth Density\"\n , xlab = \"Prediction Probability\"\n , ylab = \"Bin Density\"\n , pch = 19L\n , col = .diverging_palette[df$binned + 1L]\n )\n legend(\n \"topright\"\n , title = \"bin\"\n , legend = sort(unique(df$binned))\n , pch = 19L\n , col = .diverging_palette[sort(unique(df$binned + 1L))]\n , cex = 0.7\n )\n}\n\n# load some data\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\ndata(agaricus.test, package = \"lightgbm\")\ntest <- agaricus.test\ndtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\n\n# setup parameters and we train a model\nparams <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data = 1L\n , learning_rate = 0.1\n , bagging_fraction = 0.1\n , bagging_freq = 1L\n , bagging_seed = 1L\n)\nvalids <- list(test = dtest)\nmodel <- lgb.train(\n params\n , dtrain\n , 50L\n , valids\n)\n\n# We create a data.frame with the following structure:\n# X = average leaf of the observation throughout all trees\n# Y = prediction probability (clamped to [1e-15, 1-1e-15])\n# Z = logloss\n# binned = binned quantile of average leaf\nnew_data <- data.frame(\n X = rowMeans(predict(\n model\n , agaricus.test$data\n , type = \"leaf\"\n ))\n , Y = pmin(\n pmax(\n predict(model, agaricus.test$data)\n , 1e-15\n )\n , 1.0 - 1e-15\n )\n)\nnew_data$Z <- -1.0 * (agaricus.test$label * log(new_data$Y) + (1L - agaricus.test$label) * log(1L - new_data$Y))\nnew_data$binned <- .bincode(\n x = new_data$X\n , breaks = quantile(\n x = new_data$X\n , probs = seq_len(9L) / 10.0\n )\n , right = TRUE\n , include.lowest = TRUE\n)\nnew_data$binned[is.na(new_data$binned)] <- 0L\n\n# We can check the binned content\ntable(new_data$binned)\n\n# We can plot the binned content\n# On the second plot, we clearly notice the lower the bin (the lower the leaf value), the higher the loss\n# On the third plot, it is smooth!\n.prediction_depth_plot(df = new_data)\n.prediction_depth_spread_plot(df = new_data)\n.depth_density_plot(df = new_data)\n\n# Now, let's show with other parameters\nparams <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data = 1L\n , learning_rate = 1.0\n)\nmodel2 <- lgb.train(\n params\n , dtrain\n , 100L\n , valids\n)\n\n# We create the data structure, but for model2\nnew_data2 <- data.frame(\n X = rowMeans(predict(\n model2\n , agaricus.test$data\n , type = \"leaf\"\n ))\n , Y = pmin(\n pmax(\n predict(\n model2\n , agaricus.test$data\n )\n , 1e-15\n )\n , 1.0 - 1e-15\n )\n)\nnew_data2$Z <- -1.0 * (agaricus.test$label * log(new_data2$Y) + (1L - agaricus.test$label) * log(1L - new_data2$Y))\nnew_data2$binned <- .bincode(\n x = new_data2$X\n , breaks = quantile(\n x = new_data2$X\n , probs = seq_len(9L) / 10.0\n )\n , right = TRUE\n , include.lowest = TRUE\n)\nnew_data2$binned[is.na(new_data2$binned)] <- 0L\n\n# We can check the binned content\ntable(new_data2$binned)\n\n# We can plot the binned content\n# On the second plot, we clearly notice the lower the bin (the lower the leaf value), the higher the loss\n# On the third plot, it is clearly not smooth! We are severely overfitting the data, but the rules are\n# real thus it is not an issue\n# However, if the rules were not true, the loss would explode.\n.prediction_depth_plot(df = new_data2)\n.prediction_depth_spread_plot(df = new_data2)\n.depth_density_plot(df = new_data2)\n\n# Now, try with very severe overfitting\nparams <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data = 1L\n , learning_rate = 1.0\n)\nmodel3 <- lgb.train(\n params\n , dtrain\n , 1000L\n , valids\n)\n\n# We create the data structure, but for model3\nnew_data3 <- data.frame(\n X = rowMeans(predict(\n model3\n , agaricus.test$data\n , type = \"leaf\"\n ))\n , Y = pmin(\n pmax(\n predict(\n model3\n , agaricus.test$data\n )\n , 1e-15\n )\n , 1.0 - 1e-15\n )\n)\nnew_data3$Z <- -1.0 * (agaricus.test$label * log(new_data3$Y) + (1L - agaricus.test$label) * log(1L - new_data3$Y))\nnew_data3$binned <- .bincode(\n x = new_data3$X\n , breaks = quantile(\n x = new_data3$X\n , probs = seq_len(9L) / 10.0\n )\n , right = TRUE\n , include.lowest = TRUE\n)\nnew_data3$binned[is.na(new_data3$binned)] <- 0L\n\n# We can check the binned content\ntable(new_data3$binned)\n\n# We can plot the binned content\n# On the third plot, it is clearly not smooth! We are severely overfitting the data, but the rules\n# are real thus it is not an issue.\n# However, if the rules were not true, the loss would explode. See the sudden spikes?\n.depth_density_plot(df = new_data3)\n\n# Compare with our second model, the difference is severe. This is smooth.\n.depth_density_plot(df = new_data2)\n" }, { "path": "R-package/demo/multiclass.R", "content": "library(lightgbm)\n\n# We load the default iris dataset shipped with R\ndata(iris)\n\n# We must convert factors to numeric\n# They must be starting from number 0 to use multiclass\n# For instance: 0, 1, 2, 3, 4, 5...\niris$Species <- as.numeric(as.factor(iris$Species)) - 1L\n\n# We cut the data set into 80% train and 20% validation\n# The 10 last samples of each class are for validation\n\ntrain <- as.matrix(iris[c(1L:40L, 51L:90L, 101L:140L), ])\ntest <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ])\ndtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L])\ndtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L])\nvalids <- list(test = dtest)\n\n# Method 1 of training\nparams <- list(\n objective = \"multiclass\"\n , metric = \"multi_error\"\n , num_class = 3L\n , min_data = 1L\n , learning_rate = 1.0\n)\nmodel <- lgb.train(\n params\n , dtrain\n , 100L\n , valids\n , early_stopping_rounds = 10L\n)\n\n# We can predict on test data, outputs a 90-length vector\n# Order: obs1 class1, obs1 class2, obs1 class3, obs2 class1, obs2 class2, obs2 class3...\nmy_preds <- predict(model, test[, 1L:4L])\n\n# Method 2 of training, identical\nparams <- list(\n min_data = 1L\n , learning_rate = 1.0\n , objective = \"multiclass\"\n , metric = \"multi_error\"\n , num_class = 3L\n)\nmodel <- lgb.train(\n params\n , dtrain\n , 100L\n , valids\n , early_stopping_rounds = 10L\n)\n\n# We can predict on test data, identical\nmy_preds <- predict(model, test[, 1L:4L])\n\n# A (30x3) matrix with the predictions\n# class1 class2 class3\n# obs1 obs1 obs1\n# obs2 obs2 obs2\n# .... .... ....\nmy_preds <- predict(model, test[, 1L:4L])\n\n# We can also get the predicted scores before the Sigmoid/Softmax application\nmy_preds <- predict(model, test[, 1L:4L], type = \"raw\")\n\n# We can also get the leaf index\nmy_preds <- predict(model, test[, 1L:4L], type = \"leaf\")\n" }, { "path": "R-package/demo/multiclass_custom_objective.R", "content": "library(lightgbm)\n\n# We load the default iris dataset shipped with R\ndata(iris)\n\n# We must convert factors to numeric\n# They must be starting from number 0 to use multiclass\n# For instance: 0, 1, 2, 3, 4, 5...\niris$Species <- as.numeric(as.factor(iris$Species)) - 1L\n\n# Create imbalanced training data (20, 30, 40 examples for classes 0, 1, 2)\ntrain <- as.matrix(iris[c(1L:20L, 51L:80L, 101L:140L), ])\n# The 10 last samples of each class are for validation\ntest <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ])\n\ndtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L])\ndtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L])\nvalids <- list(train = dtrain, test = dtest)\n\n# Method 1 of training with built-in multiclass objective\n# Note: need to turn off boost from average to match custom objective\n# (https://github.com/lightgbm-org/LightGBM/issues/1846)\nparams <- list(\n min_data = 1L\n , learning_rate = 1.0\n , num_class = 3L\n , boost_from_average = FALSE\n , metric = \"multi_logloss\"\n)\nmodel_builtin <- lgb.train(\n params\n , dtrain\n , 100L\n , valids\n , early_stopping_rounds = 10L\n , obj = \"multiclass\"\n)\n\npreds_builtin <- predict(model_builtin, test[, 1L:4L], type = \"raw\")\nprobs_builtin <- exp(preds_builtin) / rowSums(exp(preds_builtin))\n\n# Method 2 of training with custom objective function\n\n# User defined objective function, given prediction, return gradient and second order gradient\ncustom_multiclass_obj <- function(preds, dtrain) {\n labels <- get_field(dtrain, \"label\")\n\n # preds is a matrix with rows corresponding to samples and columns corresponding to choices\n preds <- matrix(preds, nrow = length(labels))\n\n # to prevent overflow, normalize preds by row\n preds <- preds - apply(preds, MARGIN = 1L, max)\n prob <- exp(preds) / rowSums(exp(preds))\n\n # compute gradient\n grad <- prob\n subset_index <- as.matrix(\n data.frame(\n seq_along(labels)\n , labels + 1L\n , fix.empty.names = FALSE\n )\n , nrow = length(labels)\n , dimnames = NULL\n )\n grad[subset_index] <- grad[subset_index] - 1L\n\n # compute hessian (approximation)\n hess <- 2.0 * prob * (1.0 - prob)\n\n return(list(grad = grad, hess = hess))\n}\n\n# define custom metric\ncustom_multiclass_metric <- function(preds, dtrain) {\n labels <- get_field(dtrain, \"label\")\n preds <- matrix(preds, nrow = length(labels))\n preds <- preds - apply(preds, 1L, max)\n prob <- exp(preds) / rowSums(exp(preds))\n\n subset_index <- as.matrix(\n data.frame(\n seq_along(labels)\n , labels + 1L\n , fix.empty.names = FALSE\n )\n , nrow = length(labels)\n , dimnames = NULL\n )\n return(list(\n name = \"error\"\n , value = -mean(log(prob[subset_index]))\n , higher_better = FALSE\n ))\n}\n\nparams <- list(\n min_data = 1L\n , learning_rate = 1.0\n , num_class = 3L\n)\nmodel_custom <- lgb.train(\n params\n , dtrain\n , 100L\n , valids\n , early_stopping_rounds = 10L\n , obj = custom_multiclass_obj\n , eval = custom_multiclass_metric\n)\n\npreds_custom <- predict(model_custom, test[, 1L:4L], type = \"raw\")\nprobs_custom <- exp(preds_custom) / rowSums(exp(preds_custom))\n\n# compare predictions\nstopifnot(identical(probs_builtin, probs_custom))\nstopifnot(identical(preds_builtin, preds_custom))\n" }, { "path": "R-package/demo/weight_param.R", "content": "# This demo R code is to provide a demonstration of hyperparameter adjustment\n# when scaling weights for appropriate learning\n# As with any optimizers, bad parameters can impair performance\n\n# Load library\nlibrary(lightgbm)\n\n# We will train a model with the following scenarii:\n# - Run 1: sum of weights equal to 6513 (x 1e-5) without adjusted regularization (not learning)\n# - Run 2: sum of weights equal to 6513 (x 1e-5) adjusted regularization (learning)\n# - Run 3: sum of weights equal to 6513 with adjusted regularization (learning)\n\n# Setup small weights\nweights1 <- rep(1e-5, 6513L)\nweights2 <- rep(1e-5, 1611L)\n\n# Load data and create datasets\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label, weight = weights1)\ndata(agaricus.test, package = \"lightgbm\")\ntest <- agaricus.test\ndtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label, weight = weights2)\nvalids <- list(test = dtest)\n\n# Run 1: sum of weights equal to 6513 (x 1e-5) without adjusted regularization (not learning)\n# It cannot learn because regularization is too large!\n# min_sum_hessian alone is bigger than the sum of weights, thus you will never learn anything\nparams <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , device = \"cpu\"\n , min_sum_hessian = 10.0\n , num_leaves = 7L\n , max_depth = 3L\n , nthread = 1L\n , min_data = 1L\n , learning_rate = 1.0\n)\nmodel <- lgb.train(\n params\n , dtrain\n , 50L\n , valids\n , early_stopping_rounds = 10L\n)\nweight_loss <- as.numeric(model$record_evals$test$l2$eval)\nplot(weight_loss) # Shows how poor the learning was: a straight line!\n\n# Run 2: sum of weights equal to 6513 (x 1e-5) with adjusted regularization (learning)\n# Adjusted regularization just consisting in multiplicating results by 1e4 (x10000)\n# Notice how it learns, there is no issue as we adjusted regularization ourselves\nparams <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , device = \"cpu\"\n , min_sum_hessian = 1e-4\n , num_leaves = 7L\n , max_depth = 3L\n , nthread = 1L\n , min_data = 1L\n , learning_rate = 1.0\n)\nmodel <- lgb.train(\n params\n , dtrain\n , 50L\n , valids\n , early_stopping_rounds = 10L\n)\nsmall_weight_loss <- as.numeric(model$record_evals$test$l2$eval)\nplot(small_weight_loss) # It learns!\n\n# Run 3: sum of weights equal to 6513 with adjusted regularization (learning)\ndtrain <- lgb.Dataset(train$data, label = train$label)\ndtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\nvalids <- list(test = dtest)\n\n# Setup parameters and run model...\nparams <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , device = \"cpu\"\n , min_sum_hessian = 10.0\n , num_leaves = 7L\n , max_depth = 3L\n , nthread = 1L\n , min_data = 1L\n , learning_rate = 1.0\n)\nmodel <- lgb.train(\n params\n , dtrain\n , 50L\n , valids\n , early_stopping_rounds = 10L\n)\nlarge_weight_loss <- as.numeric(model$record_evals$test$l2$eval)\nplot(large_weight_loss) # It learns!\n\n\n# Do you want to compare the learning? They both converge.\nplot(small_weight_loss, large_weight_loss)\ncurve(1.0 * x, from = 0L, to = 0.02, add = TRUE)\n" }, { "path": "R-package/inst/Makevars", "content": "\n" }, { "path": "R-package/inst/make-r-def.R", "content": "# [description]\n# Create a definition file (.def) from a .dll file, using objdump.\n#\n# [usage]\n#\n# Rscript make-r-def.R something.dll something.def\n#\n# [references]\n# * https://www.cs.colorado.edu/~main/cs1300/doc/mingwfaq.html\n\nargs <- commandArgs(trailingOnly = TRUE)\n\nIN_DLL_FILE <- args[[1L]]\nOUT_DEF_FILE <- args[[2L]]\nDLL_BASE_NAME <- basename(IN_DLL_FILE)\n\nmessage(sprintf(\"Creating '%s' from '%s'\", OUT_DEF_FILE, IN_DLL_FILE))\n\n# system() will not raise an R exception if the process called\n# fails. Wrapping it here to get that behavior.\n#\n# system() introduces a lot of overhead, at least on Windows,\n# so trying processx if it is available\n.pipe_shell_command_to_stdout <- function(command, args, out_file) {\n has_processx <- suppressMessages({\n suppressWarnings({\n require(\"processx\") # nolint: undesirable_function.\n })\n })\n if (has_processx) {\n p <- processx::process$new(\n command = command\n , args = args\n , stdout = out_file\n , windows_verbatim_args = FALSE\n )\n invisible(p$wait())\n } else {\n message(paste0(\n \"Using system2() to run shell commands. Installing \"\n , \"'processx' with install.packages('processx') might \"\n , \"make this faster.\"\n ))\n # shQuote() is necessary here since one of the arguments\n # is a file-path to R.dll, which may have spaces. processx\n # does such quoting but system2() does not\n exit_code <- system2(\n command = command\n , args = shoQuote(args)\n , stdout = out_file\n )\n if (exit_code != 0L) {\n stop(paste0(\"Command failed with exit code: \", exit_code))\n }\n }\n return(invisible(NULL))\n}\n\n# use objdump to dump all the symbols\nOBJDUMP_FILE <- \"objdump-out.txt\"\n.pipe_shell_command_to_stdout(\n command = \"objdump\"\n , args = c(\"-p\", IN_DLL_FILE)\n , out_file = OBJDUMP_FILE\n)\n\nobjdump_results <- readLines(OBJDUMP_FILE)\ninvisible(file.remove(OBJDUMP_FILE))\n\n# Only one table in the objdump results matters for our purposes,\n# see https://www.cs.colorado.edu/~main/cs1300/doc/mingwfaq.html\nstart_index <- which(\n grepl(\n pattern = \"[Ordinal/Name Pointer] Table\" # nolint: non_portable_path.\n , x = objdump_results\n , fixed = TRUE\n )\n)\nempty_lines <- which(objdump_results == \"\")\nend_of_table <- empty_lines[empty_lines > start_index][1L]\n\n# Read the contents of the table\nexported_symbols <- objdump_results[(start_index + 1L):end_of_table]\nexported_symbols <- gsub(\"\\t\", \"\", exported_symbols, fixed = TRUE)\nexported_symbols <- gsub(\".*\\\\] \", \"\", exported_symbols)\nexported_symbols <- gsub(\" \", \"\", exported_symbols, fixed = TRUE)\n\n# Write R.def file\nwriteLines(\n text = c(\n paste0(\"LIBRARY \\\"\", DLL_BASE_NAME, \"\\\"\")\n , \"EXPORTS\"\n , exported_symbols\n )\n , con = OUT_DEF_FILE\n , sep = \"\\n\"\n)\nmessage(sprintf(\"Successfully created '%s'\", OUT_DEF_FILE))\n" }, { "path": "R-package/man/agaricus.test.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lightgbm.R\n\\docType{data}\n\\name{agaricus.test}\n\\alias{agaricus.test}\n\\title{Test part from Mushroom Data Set}\n\\format{\nA list containing a label vector, and a dgCMatrix object with 1611\nrows and 126 variables\n}\n\\usage{\ndata(agaricus.test)\n}\n\\description{\nThis data set is originally from the Mushroom data set,\n UCI Machine Learning Repository.\n This data set includes the following fields:\n\n \\itemize{\n \\item{\\code{label}: the label for each record}\n \\item{\\code{data}: a sparse Matrix of \\code{dgCMatrix} class, with 126 columns.}\n }\n}\n\\references{\nhttps://archive.ics.uci.edu/ml/datasets/Mushroom\n\nBache, K. & Lichman, M. (2013). UCI Machine Learning Repository\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\nSchool of Information and Computer Science.\n}\n\\keyword{datasets}\n" }, { "path": "R-package/man/agaricus.train.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lightgbm.R\n\\docType{data}\n\\name{agaricus.train}\n\\alias{agaricus.train}\n\\title{Training part from Mushroom Data Set}\n\\format{\nA list containing a label vector, and a dgCMatrix object with 6513\nrows and 127 variables\n}\n\\usage{\ndata(agaricus.train)\n}\n\\description{\nThis data set is originally from the Mushroom data set,\n UCI Machine Learning Repository.\n This data set includes the following fields:\n\n \\itemize{\n \\item{\\code{label}: the label for each record}\n \\item{\\code{data}: a sparse Matrix of \\code{dgCMatrix} class, with 126 columns.}\n }\n}\n\\references{\nhttps://archive.ics.uci.edu/ml/datasets/Mushroom\n\nBache, K. & Lichman, M. (2013). UCI Machine Learning Repository\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\nSchool of Information and Computer Science.\n}\n\\keyword{datasets}\n" }, { "path": "R-package/man/bank.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lightgbm.R\n\\docType{data}\n\\name{bank}\n\\alias{bank}\n\\title{Bank Marketing Data Set}\n\\format{\nA data.table with 4521 rows and 17 variables\n}\n\\usage{\ndata(bank)\n}\n\\description{\nThis data set is originally from the Bank Marketing data set,\n UCI Machine Learning Repository.\n\n It contains only the following: bank.csv with 10% of the examples and 17 inputs,\n randomly selected from 3 (older version of this dataset with less inputs).\n}\n\\references{\nhttps://archive.ics.uci.edu/ml/datasets/Bank+Marketing\n\nS. Moro, P. Cortez and P. Rita. (2014)\nA Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems\n}\n\\keyword{datasets}\n" }, { "path": "R-package/man/dim.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Dataset.R\n\\name{dim.lgb.Dataset}\n\\alias{dim.lgb.Dataset}\n\\title{Dimensions of an \\code{lgb.Dataset}}\n\\usage{\n\\method{dim}{lgb.Dataset}(x)\n}\n\\arguments{\n\\item{x}{Object of class \\code{lgb.Dataset}}\n}\n\\value{\na vector of numbers of rows and of columns\n}\n\\description{\nReturns a vector of numbers of rows and of columns in an \\code{lgb.Dataset}.\n}\n\\details{\nNote: since \\code{nrow} and \\code{ncol} internally use \\code{dim}, they can also\nbe directly used with an \\code{lgb.Dataset} object.\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\n\nstopifnot(nrow(dtrain) == nrow(train$data))\nstopifnot(ncol(dtrain) == ncol(train$data))\nstopifnot(all(dim(dtrain) == dim(train$data)))\n}\n}\n" }, { "path": "R-package/man/dimnames.lgb.Dataset.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Dataset.R\n\\name{dimnames.lgb.Dataset}\n\\alias{dimnames.lgb.Dataset}\n\\alias{dimnames<-.lgb.Dataset}\n\\title{Handling of column names of \\code{lgb.Dataset}}\n\\usage{\n\\method{dimnames}{lgb.Dataset}(x)\n\n\\method{dimnames}{lgb.Dataset}(x) <- value\n}\n\\arguments{\n\\item{x}{object of class \\code{lgb.Dataset}}\n\n\\item{value}{a list of two elements: the first one is ignored\nand the second one is column names}\n}\n\\value{\nA list with the dimension names of the dataset\n}\n\\description{\nOnly column names are supported for \\code{lgb.Dataset}, thus setting of\n row names would have no effect and returned row names would be NULL.\n}\n\\details{\nGeneric \\code{dimnames} methods are used by \\code{colnames}.\nSince row names are irrelevant, it is recommended to use \\code{colnames} directly.\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\nlgb.Dataset.construct(dtrain)\ndimnames(dtrain)\ncolnames(dtrain)\ncolnames(dtrain) <- make.names(seq_len(ncol(train$data)))\nprint(dtrain, verbose = TRUE)\n}\n}\n" }, { "path": "R-package/man/getLGBMThreads.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/multithreading.R\n\\name{getLGBMThreads}\n\\alias{getLGBMThreads}\n\\alias{getLGBMthreads}\n\\title{Get default number of threads used by LightGBM}\n\\usage{\ngetLGBMthreads()\n}\n\\value{\nnumber of threads as an integer. \\code{-1} means that in situations where parameter \\code{num_threads} is\n not explicitly supplied, LightGBM will choose a number of threads to use automatically.\n}\n\\description{\nLightGBM attempts to speed up many operations by using multi-threading.\n The number of threads used in those operations can be controlled via the\n \\code{num_threads} parameter passed through \\code{params} to functions like\n \\link{lgb.train} and \\link{lgb.Dataset}. However, some operations (like materializing\n a model from a text file) are done via code paths that don't explicitly accept thread-control\n configuration.\n\n Use this function to see the default number of threads LightGBM will use for such operations.\n}\n\\seealso{\n\\link{setLGBMthreads}\n}\n" }, { "path": "R-package/man/get_field.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Dataset.R\n\\name{get_field}\n\\alias{get_field}\n\\alias{get_field.lgb.Dataset}\n\\title{Get one attribute of a \\code{lgb.Dataset}}\n\\usage{\nget_field(dataset, field_name)\n\n\\method{get_field}{lgb.Dataset}(dataset, field_name)\n}\n\\arguments{\n\\item{dataset}{Object of class \\code{lgb.Dataset}}\n\n\\item{field_name}{String with the name of the attribute to get. One of the following.\n\\itemize{\n \\item \\code{label}: label lightgbm learns from ;\n \\item \\code{weight}: to do a weight rescale ;\n \\item{\\code{group}: used for learning-to-rank tasks. An integer vector describing how to\n group rows together as ordered results from the same set of candidate results to be ranked.\n For example, if you have a 100-document dataset with \\code{group = c(10, 20, 40, 10, 10, 10)},\n that means that you have 6 groups, where the first 10 records are in the first group,\n records 11-30 are in the second group, etc.}\n \\item \\code{init_score}: initial score is the base prediction lightgbm will boost from.\n}}\n}\n\\value{\nrequested attribute\n}\n\\description{\nGet one attribute of a \\code{lgb.Dataset}\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\nlgb.Dataset.construct(dtrain)\n\nlabels <- lightgbm::get_field(dtrain, \"label\")\nlightgbm::set_field(dtrain, \"label\", 1 - labels)\n\nlabels2 <- lightgbm::get_field(dtrain, \"label\")\nstopifnot(all(labels2 == 1 - labels))\n}\n}\n" }, { "path": "R-package/man/lgb.Dataset.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Dataset.R\n\\name{lgb.Dataset}\n\\alias{lgb.Dataset}\n\\title{Construct \\code{lgb.Dataset} object}\n\\usage{\nlgb.Dataset(\n data,\n params = list(),\n reference = NULL,\n colnames = NULL,\n categorical_feature = NULL,\n free_raw_data = TRUE,\n label = NULL,\n weight = NULL,\n group = NULL,\n init_score = NULL\n)\n}\n\\arguments{\n\\item{data}{a \\code{matrix} object, a \\code{dgCMatrix} object,\na character representing a path to a text file (CSV, TSV, or LibSVM),\nor a character representing a path to a binary \\code{lgb.Dataset} file}\n\n\\item{params}{a list of parameters. See\n\\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{\nThe \"Dataset Parameters\" section of the documentation} for a list of parameters\nand valid values.}\n\n\\item{reference}{reference dataset. When LightGBM creates a Dataset, it does some preprocessing like binning\ncontinuous features into histograms. If you want to apply the same bin boundaries from an existing\ndataset to new \\code{data}, pass that existing Dataset to this argument.}\n\n\\item{colnames}{names of columns}\n\n\\item{categorical_feature}{categorical features. This can either be a character vector of feature\nnames or an integer vector with the indices of the features (e.g.\n\\code{c(1L, 10L)} to say \"the first and tenth columns\").}\n\n\\item{free_raw_data}{LightGBM constructs its data format, called a \"Dataset\", from tabular data.\nBy default, that Dataset object on the R side does not keep a copy of the raw data.\nThis reduces LightGBM's memory consumption, but it means that the Dataset object\ncannot be changed after it has been constructed. If you'd prefer to be able to\nchange the Dataset object after construction, set \\code{free_raw_data = FALSE}.}\n\n\\item{label}{vector of labels to use as the target variable}\n\n\\item{weight}{numeric vector of sample weights}\n\n\\item{group}{used for learning-to-rank tasks. An integer vector describing how to\ngroup rows together as ordered results from the same set of candidate results\nto be ranked. For example, if you have a 100-document dataset with\n\\code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups,\nwhere the first 10 records are in the first group, records 11-30 are in the\nsecond group, etc.}\n\n\\item{init_score}{initial score is the base prediction lightgbm will boost from}\n}\n\\value{\nconstructed dataset\n}\n\\description{\nLightGBM does not train on raw data.\n It discretizes continuous features into histogram bins, tries to\n combine categorical features, and automatically handles missing and\n\n The \\code{Dataset} class handles that preprocessing, and holds that\n alternative representation of the input data.\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\ndata_file <- tempfile(fileext = \".data\")\nlgb.Dataset.save(dtrain, data_file)\ndtrain <- lgb.Dataset(data_file)\nlgb.Dataset.construct(dtrain)\n}\n}\n" }, { "path": "R-package/man/lgb.Dataset.construct.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Dataset.R\n\\name{lgb.Dataset.construct}\n\\alias{lgb.Dataset.construct}\n\\title{Construct Dataset explicitly}\n\\usage{\nlgb.Dataset.construct(dataset)\n}\n\\arguments{\n\\item{dataset}{Object of class \\code{lgb.Dataset}}\n}\n\\value{\nconstructed dataset\n}\n\\description{\nConstruct Dataset explicitly\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\nlgb.Dataset.construct(dtrain)\n}\n}\n" }, { "path": "R-package/man/lgb.Dataset.create.valid.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Dataset.R\n\\name{lgb.Dataset.create.valid}\n\\alias{lgb.Dataset.create.valid}\n\\title{Construct validation data}\n\\usage{\nlgb.Dataset.create.valid(\n dataset,\n data,\n label = NULL,\n weight = NULL,\n group = NULL,\n init_score = NULL,\n params = list()\n)\n}\n\\arguments{\n\\item{dataset}{\\code{lgb.Dataset} object, training data}\n\n\\item{data}{a \\code{matrix} object, a \\code{dgCMatrix} object,\na character representing a path to a text file (CSV, TSV, or LibSVM),\nor a character representing a path to a binary \\code{Dataset} file}\n\n\\item{label}{vector of labels to use as the target variable}\n\n\\item{weight}{numeric vector of sample weights}\n\n\\item{group}{used for learning-to-rank tasks. An integer vector describing how to\ngroup rows together as ordered results from the same set of candidate results\nto be ranked. For example, if you have a 100-document dataset with\n\\code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups,\nwhere the first 10 records are in the first group, records 11-30 are in the\nsecond group, etc.}\n\n\\item{init_score}{initial score is the base prediction lightgbm will boost from}\n\n\\item{params}{a list of parameters. See\n\\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{\nThe \"Dataset Parameters\" section of the documentation} for a list of parameters\nand valid values. If this is an empty list (the default), the validation Dataset\nwill have the same parameters as the Dataset passed to argument \\code{dataset}.}\n}\n\\value{\nconstructed dataset\n}\n\\description{\nConstruct validation data according to training data\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\ndata(agaricus.test, package = \"lightgbm\")\ntest <- agaricus.test\ndtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\n\n# parameters can be changed between the training data and validation set,\n# for example to account for training data in a text file with a header row\n# and validation data in a text file without it\ntrain_file <- tempfile(pattern = \"train_\", fileext = \".csv\")\nwrite.table(\n data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L))\n , file = train_file\n , sep = \",\"\n , col.names = TRUE\n , row.names = FALSE\n , quote = FALSE\n)\n\nvalid_file <- tempfile(pattern = \"valid_\", fileext = \".csv\")\nwrite.table(\n data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L))\n , file = valid_file\n , sep = \",\"\n , col.names = FALSE\n , row.names = FALSE\n , quote = FALSE\n)\n\ndtrain <- lgb.Dataset(\n data = train_file\n , params = list(has_header = TRUE)\n)\ndtrain$construct()\n\ndvalid <- lgb.Dataset(\n data = valid_file\n , params = list(has_header = FALSE)\n)\ndvalid$construct()\n}\n}\n" }, { "path": "R-package/man/lgb.Dataset.save.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Dataset.R\n\\name{lgb.Dataset.save}\n\\alias{lgb.Dataset.save}\n\\title{Save \\code{lgb.Dataset} to a binary file}\n\\usage{\nlgb.Dataset.save(dataset, fname)\n}\n\\arguments{\n\\item{dataset}{object of class \\code{lgb.Dataset}}\n\n\\item{fname}{object filename of output file}\n}\n\\value{\nthe dataset you passed in\n}\n\\description{\nPlease note that \\code{init_score} is not saved in binary file.\n If you need it, please set it again after loading Dataset.\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\nlgb.Dataset.save(dtrain, tempfile(fileext = \".bin\"))\n}\n}\n" }, { "path": "R-package/man/lgb.Dataset.set.categorical.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Dataset.R\n\\name{lgb.Dataset.set.categorical}\n\\alias{lgb.Dataset.set.categorical}\n\\title{Set categorical feature of \\code{lgb.Dataset}}\n\\usage{\nlgb.Dataset.set.categorical(dataset, categorical_feature)\n}\n\\arguments{\n\\item{dataset}{object of class \\code{lgb.Dataset}}\n\n\\item{categorical_feature}{categorical features. This can either be a character vector of feature\nnames or an integer vector with the indices of the features (e.g.\n\\code{c(1L, 10L)} to say \"the first and tenth columns\").}\n}\n\\value{\nthe dataset you passed in\n}\n\\description{\nSet the categorical features of an \\code{lgb.Dataset} object. Use this function\n to tell LightGBM which features should be treated as categorical.\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\ndata_file <- tempfile(fileext = \".data\")\nlgb.Dataset.save(dtrain, data_file)\ndtrain <- lgb.Dataset(data_file)\nlgb.Dataset.set.categorical(dtrain, 1L:2L)\n}\n}\n" }, { "path": "R-package/man/lgb.Dataset.set.reference.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Dataset.R\n\\name{lgb.Dataset.set.reference}\n\\alias{lgb.Dataset.set.reference}\n\\title{Set reference of \\code{lgb.Dataset}}\n\\usage{\nlgb.Dataset.set.reference(dataset, reference)\n}\n\\arguments{\n\\item{dataset}{object of class \\code{lgb.Dataset}}\n\n\\item{reference}{object of class \\code{lgb.Dataset}}\n}\n\\value{\nthe dataset you passed in\n}\n\\description{\nIf you want to use validation data, you should set reference to training data\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\n# create training Dataset\ndata(agaricus.train, package =\"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\n\n# create a validation Dataset, using dtrain as a reference\ndata(agaricus.test, package = \"lightgbm\")\ntest <- agaricus.test\ndtest <- lgb.Dataset(test$data, label = test$label)\nlgb.Dataset.set.reference(dtest, dtrain)\n}\n}\n" }, { "path": "R-package/man/lgb.configure_fast_predict.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Booster.R\n\\name{lgb.configure_fast_predict}\n\\alias{lgb.configure_fast_predict}\n\\title{Configure Fast Single-Row Predictions}\n\\usage{\nlgb.configure_fast_predict(\n model,\n csr = FALSE,\n start_iteration = NULL,\n num_iteration = NULL,\n type = \"response\",\n params = list()\n)\n}\n\\arguments{\n\\item{model}{LightGBM model object (class \\code{lgb.Booster}).\n\n \\bold{The object will be modified in-place}.}\n\n\\item{csr}{Whether the prediction function is going to be called on sparse CSR inputs.\nIf \\code{FALSE}, will be assumed that predictions are going to be called on single-row\nregular R matrices.}\n\n\\item{start_iteration}{int or None, optional (default=None)\nStart index of the iteration to predict.\nIf None or <= 0, starts from the first iteration.}\n\n\\item{num_iteration}{int or None, optional (default=None)\nLimit number of iterations in the prediction.\nIf None, if the best iteration exists and start_iteration is None or <= 0, the\nbest iteration is used; otherwise, all iterations from start_iteration are used.\nIf <= 0, all iterations from start_iteration are used (no limits).}\n\n\\item{type}{Type of prediction to output. Allowed types are:\\itemize{\n \\item \\code{\"response\"}: will output the predicted score according to the objective function being\n optimized (depending on the link function that the objective uses), after applying any necessary\n transformations - for example, for \\code{objective=\"binary\"}, it will output class probabilities.\n \\item \\code{\"class\"}: for classification objectives, will output the class with the highest predicted\n probability. For other objectives, will output the same as \"response\". Note that \\code{\"class\"} is\n not a supported type for \\link{lgb.configure_fast_predict} (see the documentation of that function\n for more details).\n \\item \\code{\"raw\"}: will output the non-transformed numbers (sum of predictions from boosting iterations'\n results) from which the \"response\" number is produced for a given objective function - for example,\n for \\code{objective=\"binary\"}, this corresponds to log-odds. For many objectives such as\n \"regression\", since no transformation is applied, the output will be the same as for \"response\".\n \\item \\code{\"leaf\"}: will output the index of the terminal node / leaf at which each observations falls\n in each tree in the model, outputted as integers, with one column per tree.\n \\item \\code{\"contrib\"}: will return the per-feature contributions for each prediction, including an\n intercept (each feature will produce one column).\n }\n\n Note that, if using custom objectives, types \"class\" and \"response\" will not be available and will\n default towards using \"raw\" instead.\n\n If the model was fit through function \\link{lightgbm} and it was passed a factor as labels,\n passing the prediction type through \\code{params} instead of through this argument might\n result in factor levels for classification objectives not being applied correctly to the\n resulting output.\n\n \\emph{New in version 4.0.0}}\n\n\\item{params}{a list of additional named parameters. See\n\\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{\nthe \"Predict Parameters\" section of the documentation} for a list of parameters and\nvalid values. Where these conflict with the values of keyword arguments to this function,\nthe values in \\code{params} take precedence.}\n}\n\\value{\nThe same \\code{model} that was passed as input, invisibly, with the desired\n configuration stored inside it and available to be used in future calls to\n \\link{predict.lgb.Booster}.\n}\n\\description{\nPre-configures a LightGBM model object to produce fast single-row predictions\n for a given input data type, prediction type, and parameters.\n}\n\\details{\nCalling this function multiple times with different parameters might not override\n the previous configuration and might trigger undefined behavior.\n\n Any saved configuration for fast predictions might be lost after making a single-row\n prediction of a different type than what was configured (except for types \"response\" and\n \"class\", which can be switched between each other at any time without losing the configuration).\n\n In some situations, setting a fast prediction configuration for one type of prediction\n might cause the prediction function to keep using that configuration for single-row\n predictions even if the requested type of prediction is different from what was configured.\n\n Note that this function will not accept argument \\code{type=\"class\"} - for such cases, one\n can pass \\code{type=\"response\"} to this function and then \\code{type=\"class\"} to the\n \\code{predict} function - the fast configuration will not be lost or altered if the switch\n is between \"response\" and \"class\".\n\n The configuration does not survive de-serializations, so it has to be generated\n anew in every R process that is going to use it (e.g. if loading a model object\n through \\code{readRDS}, whatever configuration was there previously will be lost).\n\n Requesting a different prediction type or passing parameters to \\link{predict.lgb.Booster}\n will cause it to ignore the fast-predict configuration and take the slow route instead\n (but be aware that an existing configuration might not always be overridden by supplying\n different parameters or prediction type, so make sure to check that the output is what\n was expected when a prediction is to be made on a single row for something different than\n what is configured).\n\n Note that, if configuring a non-default prediction type (such as leaf indices),\n then that type must also be passed in the call to \\link{predict.lgb.Booster} in\n order for it to use the configuration. This also applies for \\code{start_iteration}\n and \\code{num_iteration}, but \\bold{the \\code{params} list must be empty} in the call to \\code{predict}.\n\n Predictions about feature contributions do not allow a fast route for CSR inputs,\n and as such, this function will produce an error if passing \\code{csr=TRUE} and\n \\code{type = \"contrib\"} together.\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\nlibrary(lightgbm)\ndata(mtcars)\nX <- as.matrix(mtcars[, -1L])\ny <- mtcars[, 1L]\ndtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L))\nparams <- list(\n min_data_in_leaf = 2L\n , num_threads = 2L\n)\nmodel <- lgb.train(\n params = params\n , data = dtrain\n , obj = \"regression\"\n , nrounds = 5L\n , verbose = -1L\n)\nlgb.configure_fast_predict(model)\n\nx_single <- X[11L, , drop = FALSE]\npredict(model, x_single)\n\n# Will not use it if the prediction to be made\n# is different from what was configured\npredict(model, x_single, type = \"leaf\")\n}\n}\n" }, { "path": "R-package/man/lgb.convert_with_rules.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.convert_with_rules.R\n\\name{lgb.convert_with_rules}\n\\alias{lgb.convert_with_rules}\n\\title{Data preparator for LightGBM datasets with rules (integer)}\n\\usage{\nlgb.convert_with_rules(data, rules = NULL)\n}\n\\arguments{\n\\item{data}{A data.frame or data.table to prepare.}\n\n\\item{rules}{A set of rules from the data preparator, if already used. This should be an R list,\nwhere names are column names in \\code{data} and values are named character\nvectors whose names are column values and whose values are new values to\nreplace them with.}\n}\n\\value{\nA list with the cleaned dataset (\\code{data}) and the rules (\\code{rules}).\n Note that the data must be converted to a matrix format (\\code{as.matrix}) for input in\n \\code{lgb.Dataset}.\n}\n\\description{\nAttempts to prepare a clean dataset to prepare to put in a \\code{lgb.Dataset}.\n Factor, character, and logical columns are converted to integer. Missing values\n in factors and characters will be filled with 0L. Missing values in logicals\n will be filled with -1L.\n\n This function returns and optionally takes in \"rules\" the describe exactly\n how to convert values in columns.\n\n Columns that contain only NA values will be converted by this function but will\n not show up in the returned \\code{rules}.\n\n NOTE: In previous releases of LightGBM, this function was called \\code{lgb.prepare_rules2}.\n}\n\\examples{\n\\donttest{\ndata(iris)\n\nstr(iris)\n\nnew_iris <- lgb.convert_with_rules(data = iris)\nstr(new_iris$data)\n\ndata(iris) # Erase iris dataset\niris$Species[1L] <- \"NEW FACTOR\" # Introduce junk factor (NA)\n\n# Use conversion using known rules\n# Unknown factors become 0, excellent for sparse datasets\nnewer_iris <- lgb.convert_with_rules(data = iris, rules = new_iris$rules)\n\n# Unknown factor is now zero, perfect for sparse datasets\nnewer_iris$data[1L, ] # Species became 0 as it is an unknown factor\n\nnewer_iris$data[1L, 5L] <- 1.0 # Put back real initial value\n\n# Is the newly created dataset equal? YES!\nall.equal(new_iris$data, newer_iris$data)\n\n# Can we test our own rules?\ndata(iris) # Erase iris dataset\n\n# We remapped values differently\npersonal_rules <- list(\n Species = c(\n \"setosa\" = 3L\n , \"versicolor\" = 2L\n , \"virginica\" = 1L\n )\n)\nnewest_iris <- lgb.convert_with_rules(data = iris, rules = personal_rules)\nstr(newest_iris$data) # SUCCESS!\n}\n}\n" }, { "path": "R-package/man/lgb.cv.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.cv.R\n\\name{lgb.cv}\n\\alias{lgb.cv}\n\\title{Main CV logic for LightGBM}\n\\usage{\nlgb.cv(\n params = list(),\n data,\n nrounds = 100L,\n nfold = 3L,\n obj = NULL,\n eval = NULL,\n verbose = 1L,\n record = TRUE,\n eval_freq = 1L,\n showsd = TRUE,\n stratified = TRUE,\n folds = NULL,\n init_model = NULL,\n early_stopping_rounds = NULL,\n callbacks = list(),\n reset_data = FALSE,\n serializable = TRUE,\n eval_train_metric = FALSE\n)\n}\n\\arguments{\n\\item{params}{a list of parameters. See \\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{\nthe \"Parameters\" section of the documentation} for a list of parameters and valid values.}\n\n\\item{data}{a \\code{lgb.Dataset} object, used for training. Some functions, such as \\code{\\link{lgb.cv}},\nmay allow you to pass other types of data like \\code{matrix} and then separately supply\n\\code{label} as a keyword argument.}\n\n\\item{nrounds}{number of training rounds}\n\n\\item{nfold}{the original dataset is randomly partitioned into \\code{nfold} equal size subsamples.}\n\n\\item{obj}{objective function, can be character or custom objective function. Examples include\n\\code{regression}, \\code{regression_l1}, \\code{huber},\n\\code{binary}, \\code{lambdarank}, \\code{multiclass}, \\code{multiclass}}\n\n\\item{eval}{evaluation function(s). This can be a character vector, function, or list with a mixture of\n strings and functions.\n\n \\itemize{\n \\item{\\bold{a. character vector}:\n If you provide a character vector to this argument, it should contain strings with valid\n evaluation metrics.\n See \\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric}{\n The \"metric\" section of the documentation}\n for a list of valid metrics.\n }\n \\item{\\bold{b. function}:\n You can provide a custom evaluation function. This\n should accept the keyword arguments \\code{preds} and \\code{dtrain} and should return a named\n list with three elements:\n \\itemize{\n \\item{\\code{name}: A string with the name of the metric, used for printing\n and storing results.\n }\n \\item{\\code{value}: A single number indicating the value of the metric for the\n given predictions and true values\n }\n \\item{\n \\code{higher_better}: A boolean indicating whether higher values indicate a better fit.\n For example, this would be \\code{FALSE} for metrics like MAE or RMSE.\n }\n }\n }\n \\item{\\bold{c. list}:\n If a list is given, it should only contain character vectors and functions.\n These should follow the requirements from the descriptions above.\n }\n }}\n\n\\item{verbose}{verbosity for output, if <= 0 and \\code{valids} has been provided, also will disable the\nprinting of evaluation during training}\n\n\\item{record}{Boolean, TRUE will record iteration message to \\code{booster$record_evals}}\n\n\\item{eval_freq}{evaluation output frequency, only effective when verbose > 0 and \\code{valids} has been provided}\n\n\\item{showsd}{\\code{boolean}, whether to show standard deviation of cross validation.\nThis parameter defaults to \\code{TRUE}. Setting it to \\code{FALSE} can lead to a\nslight speedup by avoiding unnecessary computation.}\n\n\\item{stratified}{a \\code{boolean} indicating whether sampling of folds should be stratified\nby the values of outcome labels.}\n\n\\item{folds}{\\code{list} provides a possibility to use a list of pre-defined CV folds\n(each element must be a vector of test fold's indices). When folds are supplied,\nthe \\code{nfold} and \\code{stratified} parameters are ignored.}\n\n\\item{init_model}{path of model file or \\code{lgb.Booster} object, will continue training from this model}\n\n\\item{early_stopping_rounds}{int. Activates early stopping. When this parameter is non-null,\ntraining will stop if the evaluation of any metric on any validation set\nfails to improve for \\code{early_stopping_rounds} consecutive boosting rounds.\nIf training stops early, the returned model will have attribute \\code{best_iter}\nset to the iteration number of the best iteration.}\n\n\\item{callbacks}{List of callback functions that are applied at each iteration.}\n\n\\item{reset_data}{Boolean, setting it to TRUE (not the default value) will transform the booster model\ninto a predictor model which frees up memory and the original datasets}\n\n\\item{serializable}{whether to make the resulting objects serializable through functions such as\n\\code{save} or \\code{saveRDS} (see section \"Model serialization\").}\n\n\\item{eval_train_metric}{\\code{boolean}, whether to add the cross validation results on the\ntraining data. This parameter defaults to \\code{FALSE}. Setting it to \\code{TRUE}\nwill increase run time.}\n}\n\\value{\na trained model \\code{lgb.CVBooster}.\n}\n\\description{\nCross validation logic used by LightGBM\n}\n\\section{Early Stopping}{\n\n\n \"early stopping\" refers to stopping the training process if the model's performance on a given\n validation set does not improve for several consecutive iterations.\n\n If multiple arguments are given to \\code{eval}, their order will be preserved. If you enable\n early stopping by setting \\code{early_stopping_rounds} in \\code{params}, by default all\n metrics will be considered for early stopping.\n\n If you want to only consider the first metric for early stopping, pass\n \\code{first_metric_only = TRUE} in \\code{params}. Note that if you also specify \\code{metric}\n in \\code{params}, that metric will be considered the \"first\" one. If you omit \\code{metric},\n a default metric will be used based on your choice for the parameter \\code{obj} (keyword argument)\n or \\code{objective} (passed into \\code{params}).\n\n \\bold{NOTE:} if using \\code{boosting_type=\"dart\"}, any early stopping configuration will be ignored\n and early stopping will not be performed.\n}\n\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\nparams <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data = 1L\n , learning_rate = 1.0\n , num_threads = 2L\n)\nmodel <- lgb.cv(\n params = params\n , data = dtrain\n , nrounds = 5L\n , nfold = 3L\n)\n}\n\n}\n" }, { "path": "R-package/man/lgb.drop_serialized.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.drop_serialized.R\n\\name{lgb.drop_serialized}\n\\alias{lgb.drop_serialized}\n\\title{Drop serialized raw bytes in a LightGBM model object}\n\\usage{\nlgb.drop_serialized(model)\n}\n\\arguments{\n\\item{model}{\\code{lgb.Booster} object which was produced with `serializable=TRUE`.}\n}\n\\value{\n\\code{lgb.Booster} (the same `model` object that was passed as input, as invisible).\n}\n\\description{\nIf a LightGBM model object was produced with argument `serializable=TRUE`, the R object will keep\na copy of the underlying C++ object as raw bytes, which can be used to reconstruct such object after getting\nserialized and de-serialized, but at the cost of extra memory usage. If these raw bytes are not needed anymore,\nthey can be dropped through this function in order to save memory. Note that the object will be modified in-place.\n\n \\emph{New in version 4.0.0}\n}\n\\seealso{\n\\link{lgb.restore_handle}, \\link{lgb.make_serializable}.\n}\n" }, { "path": "R-package/man/lgb.dump.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Booster.R\n\\name{lgb.dump}\n\\alias{lgb.dump}\n\\title{Dump LightGBM model to json}\n\\usage{\nlgb.dump(booster, num_iteration = NULL, start_iteration = 1L)\n}\n\\arguments{\n\\item{booster}{Object of class \\code{lgb.Booster}}\n\n\\item{num_iteration}{Number of iterations to be dumped. NULL or <= 0 means use best iteration}\n\n\\item{start_iteration}{Index (1-based) of the first boosting round to dump.\n For example, passing \\code{start_iteration=5, num_iteration=3} for a regression model\n means \"dump the fifth, sixth, and seventh tree\"\n\n \\emph{New in version 4.4.0}}\n}\n\\value{\njson format of model\n}\n\\description{\nDump LightGBM model to json\n}\n\\examples{\n\\donttest{\nlibrary(lightgbm)\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\ndata(agaricus.test, package = \"lightgbm\")\ntest <- agaricus.test\ndtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\nparams <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data = 1L\n , learning_rate = 1.0\n , num_threads = 2L\n)\nvalids <- list(test = dtest)\nmodel <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 10L\n , valids = valids\n , early_stopping_rounds = 5L\n)\njson_model <- lgb.dump(model)\n}\n}\n" }, { "path": "R-package/man/lgb.get.eval.result.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Booster.R\n\\name{lgb.get.eval.result}\n\\alias{lgb.get.eval.result}\n\\title{Get record evaluation result from booster}\n\\usage{\nlgb.get.eval.result(\n booster,\n data_name,\n eval_name,\n iters = NULL,\n is_err = FALSE\n)\n}\n\\arguments{\n\\item{booster}{Object of class \\code{lgb.Booster}}\n\n\\item{data_name}{Name of the dataset to return evaluation results for.}\n\n\\item{eval_name}{Name of the evaluation metric to return results for.}\n\n\\item{iters}{An integer vector of iterations you want to get evaluation results for. If NULL\n(the default), evaluation results for all iterations will be returned.}\n\n\\item{is_err}{TRUE will return evaluation error instead}\n}\n\\value{\nnumeric vector of evaluation result\n}\n\\description{\nGiven a \\code{lgb.Booster}, return evaluation results for a\n particular metric on a particular dataset.\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\n# train a regression model\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\ndata(agaricus.test, package = \"lightgbm\")\ntest <- agaricus.test\ndtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\nparams <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data = 1L\n , learning_rate = 1.0\n , num_threads = 2L\n)\nvalids <- list(test = dtest)\nmodel <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 5L\n , valids = valids\n)\n\n# Examine valid data_name values\nprint(setdiff(names(model$record_evals), \"start_iter\"))\n\n# Examine valid eval_name values for dataset \"test\"\nprint(names(model$record_evals[[\"test\"]]))\n\n# Get L2 values for \"test\" dataset\nlgb.get.eval.result(model, \"test\", \"l2\")\n}\n}\n" }, { "path": "R-package/man/lgb.importance.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.importance.R\n\\name{lgb.importance}\n\\alias{lgb.importance}\n\\title{Compute feature importance in a model}\n\\usage{\nlgb.importance(model, percentage = TRUE)\n}\n\\arguments{\n\\item{model}{object of class \\code{lgb.Booster}.}\n\n\\item{percentage}{whether to show importance in relative percentage.}\n}\n\\value{\nFor a tree model, a \\code{data.table} with the following columns:\n\\itemize{\n \\item{\\code{Feature}: Feature names in the model.}\n \\item{\\code{Gain}: The total gain of this feature's splits.}\n \\item{\\code{Cover}: The number of observation related to this feature.}\n \\item{\\code{Frequency}: The number of times a feature split in trees.}\n}\n}\n\\description{\nCreates a \\code{data.table} of feature importances in a model.\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\n\nparams <- list(\n objective = \"binary\"\n , learning_rate = 0.1\n , max_depth = -1L\n , min_data_in_leaf = 1L\n , min_sum_hessian_in_leaf = 1.0\n , num_threads = 2L\n)\nmodel <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 5L\n)\n\ntree_imp1 <- lgb.importance(model, percentage = TRUE)\ntree_imp2 <- lgb.importance(model, percentage = FALSE)\n}\n}\n" }, { "path": "R-package/man/lgb.interprete.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.interprete.R\n\\name{lgb.interprete}\n\\alias{lgb.interprete}\n\\title{Compute feature contribution of prediction}\n\\usage{\nlgb.interprete(model, data, idxset, num_iteration = NULL)\n}\n\\arguments{\n\\item{model}{object of class \\code{lgb.Booster}.}\n\n\\item{data}{a matrix object or a dgCMatrix object.}\n\n\\item{idxset}{an integer vector of indices of rows needed.}\n\n\\item{num_iteration}{number of iteration want to predict with, NULL or <= 0 means use best iteration.}\n}\n\\value{\nFor regression, binary classification and lambdarank model, a \\code{list} of \\code{data.table}\n with the following columns:\n \\itemize{\n \\item{\\code{Feature}: Feature names in the model.}\n \\item{\\code{Contribution}: The total contribution of this feature's splits.}\n }\n For multiclass classification, a \\code{list} of \\code{data.table} with the Feature column and\n Contribution columns to each class.\n}\n\\description{\nComputes feature contribution components of rawscore prediction.\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\nLogit <- function(x) log(x / (1.0 - x))\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\nset_field(\n dataset = dtrain\n , field_name = \"init_score\"\n , data = rep(Logit(mean(train$label)), length(train$label))\n)\ndata(agaricus.test, package = \"lightgbm\")\ntest <- agaricus.test\n\nparams <- list(\n objective = \"binary\"\n , learning_rate = 0.1\n , max_depth = -1L\n , min_data_in_leaf = 1L\n , min_sum_hessian_in_leaf = 1.0\n , num_threads = 2L\n)\nmodel <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 3L\n)\n\ntree_interpretation <- lgb.interprete(model, test$data, 1L:5L)\n}\n}\n" }, { "path": "R-package/man/lgb.load.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Booster.R\n\\name{lgb.load}\n\\alias{lgb.load}\n\\title{Load LightGBM model}\n\\usage{\nlgb.load(filename = NULL, model_str = NULL)\n}\n\\arguments{\n\\item{filename}{path of model file}\n\n\\item{model_str}{a str containing the model (as a \\code{character} or \\code{raw} vector)}\n}\n\\value{\nlgb.Booster\n}\n\\description{\nLoad LightGBM takes in either a file path or model string.\n If both are provided, Load will default to loading from file\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\ndata(agaricus.test, package = \"lightgbm\")\ntest <- agaricus.test\ndtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\nparams <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data = 1L\n , learning_rate = 1.0\n , num_threads = 2L\n)\nvalids <- list(test = dtest)\nmodel <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 5L\n , valids = valids\n , early_stopping_rounds = 3L\n)\nmodel_file <- tempfile(fileext = \".txt\")\nlgb.save(model, model_file)\nload_booster <- lgb.load(filename = model_file)\nmodel_string <- model$save_model_to_string(NULL) # saves best iteration\nload_booster_from_str <- lgb.load(model_str = model_string)\n}\n}\n" }, { "path": "R-package/man/lgb.make_serializable.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.make_serializable.R\n\\name{lgb.make_serializable}\n\\alias{lgb.make_serializable}\n\\title{Make a LightGBM object serializable by keeping raw bytes}\n\\usage{\nlgb.make_serializable(model)\n}\n\\arguments{\n\\item{model}{\\code{lgb.Booster} object which was produced with `serializable=FALSE`.}\n}\n\\value{\n\\code{lgb.Booster} (the same `model` object that was passed as input, as invisible).\n}\n\\description{\nIf a LightGBM model object was produced with argument `serializable=FALSE`, the R object will not\nbe serializable (e.g. cannot save and load with \\code{saveRDS} and \\code{readRDS}) as it will lack the raw bytes\nneeded to reconstruct its underlying C++ object. This function can be used to forcibly produce those serialized\nraw bytes and make the object serializable. Note that the object will be modified in-place.\n\n \\emph{New in version 4.0.0}\n}\n\\seealso{\n\\link{lgb.restore_handle}, \\link{lgb.drop_serialized}.\n}\n" }, { "path": "R-package/man/lgb.model.dt.tree.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.model.dt.tree.R\n\\name{lgb.model.dt.tree}\n\\alias{lgb.model.dt.tree}\n\\title{Parse a LightGBM model json dump}\n\\usage{\nlgb.model.dt.tree(model, num_iteration = NULL, start_iteration = 1L)\n}\n\\arguments{\n\\item{model}{object of class \\code{lgb.Booster}.}\n\n\\item{num_iteration}{Number of iterations to include. NULL or <= 0 means use best iteration.}\n\n\\item{start_iteration}{Index (1-based) of the first boosting round to include in the output.\n For example, passing \\code{start_iteration=5, num_iteration=3} for a regression model\n means \"return information about the fifth, sixth, and seventh trees\".\n\n \\emph{New in version 4.4.0}}\n}\n\\value{\nA \\code{data.table} with detailed information about model trees' nodes and leaves.\n\nThe columns of the \\code{data.table} are:\n\n\\itemize{\n \\item{\\code{tree_index}: ID of a tree in a model (integer)}\n \\item{\\code{split_index}: ID of a node in a tree (integer)}\n \\item{\\code{split_feature}: for a node, it's a feature name (character);\n for a leaf, it simply labels it as \\code{\"NA\"}}\n \\item{\\code{node_parent}: ID of the parent node for current node (integer)}\n \\item{\\code{leaf_index}: ID of a leaf in a tree (integer)}\n \\item{\\code{leaf_parent}: ID of the parent node for current leaf (integer)}\n \\item{\\code{split_gain}: Split gain of a node}\n \\item{\\code{threshold}: Splitting threshold value of a node}\n \\item{\\code{decision_type}: Decision type of a node}\n \\item{\\code{default_left}: Determine how to handle NA value, TRUE -> Left, FALSE -> Right}\n \\item{\\code{internal_value}: Node value}\n \\item{\\code{internal_count}: The number of observation collected by a node}\n \\item{\\code{leaf_value}: Leaf value}\n \\item{\\code{leaf_count}: The number of observation collected by a leaf}\n}\n}\n\\description{\nParse a LightGBM model json dump into a \\code{data.table} structure.\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\n\nparams <- list(\n objective = \"binary\"\n , learning_rate = 0.01\n , num_leaves = 63L\n , max_depth = -1L\n , min_data_in_leaf = 1L\n , min_sum_hessian_in_leaf = 1.0\n , num_threads = 2L\n)\nmodel <- lgb.train(params, dtrain, 10L)\n\ntree_dt <- lgb.model.dt.tree(model)\n}\n}\n" }, { "path": "R-package/man/lgb.plot.importance.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.plot.importance.R\n\\name{lgb.plot.importance}\n\\alias{lgb.plot.importance}\n\\title{Plot feature importance as a bar graph}\n\\usage{\nlgb.plot.importance(\n tree_imp,\n top_n = 10L,\n measure = \"Gain\",\n left_margin = 10L,\n cex = NULL\n)\n}\n\\arguments{\n\\item{tree_imp}{a \\code{data.table} returned by \\code{\\link{lgb.importance}}.}\n\n\\item{top_n}{maximal number of top features to include into the plot.}\n\n\\item{measure}{the name of importance measure to plot, can be \"Gain\", \"Cover\" or \"Frequency\".}\n\n\\item{left_margin}{(base R barplot) allows to adjust the left margin size to fit feature names.}\n\n\\item{cex}{(base R barplot) passed as \\code{cex.names} parameter to \\code{\\link[graphics]{barplot}}.\nSet a number smaller than 1.0 to make the bar labels smaller than R's default and values\ngreater than 1.0 to make them larger.}\n}\n\\value{\nThe \\code{lgb.plot.importance} function creates a \\code{barplot}\nand silently returns a processed data.table with \\code{top_n} features sorted by defined importance.\n}\n\\description{\nPlot previously calculated feature importance: Gain, Cover and Frequency, as a bar graph.\n}\n\\details{\nThe graph represents each feature as a horizontal bar of length proportional to the defined importance of a feature.\nFeatures are shown ranked in a decreasing importance order.\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\n\nparams <- list(\n objective = \"binary\"\n , learning_rate = 0.1\n , min_data_in_leaf = 1L\n , min_sum_hessian_in_leaf = 1.0\n , num_threads = 2L\n)\n\nmodel <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 5L\n)\n\ntree_imp <- lgb.importance(model, percentage = TRUE)\nlgb.plot.importance(tree_imp, top_n = 5L, measure = \"Gain\")\n}\n}\n" }, { "path": "R-package/man/lgb.plot.interpretation.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.plot.interpretation.R\n\\name{lgb.plot.interpretation}\n\\alias{lgb.plot.interpretation}\n\\title{Plot feature contribution as a bar graph}\n\\usage{\nlgb.plot.interpretation(\n tree_interpretation_dt,\n top_n = 10L,\n cols = 1L,\n left_margin = 10L,\n cex = NULL\n)\n}\n\\arguments{\n\\item{tree_interpretation_dt}{a \\code{data.table} returned by \\code{\\link{lgb.interprete}}.}\n\n\\item{top_n}{maximal number of top features to include into the plot.}\n\n\\item{cols}{the column numbers of layout, will be used only for multiclass classification feature contribution.}\n\n\\item{left_margin}{(base R barplot) allows to adjust the left margin size to fit feature names.}\n\n\\item{cex}{(base R barplot) passed as \\code{cex.names} parameter to \\code{barplot}.}\n}\n\\value{\nThe \\code{lgb.plot.interpretation} function creates a \\code{barplot}.\n}\n\\description{\nPlot previously calculated feature contribution as a bar graph.\n}\n\\details{\nThe graph represents each feature as a horizontal bar of length proportional to the defined\ncontribution of a feature. Features are shown ranked in a decreasing contribution order.\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\nLogit <- function(x) {\n log(x / (1.0 - x))\n}\ndata(agaricus.train, package = \"lightgbm\")\nlabels <- agaricus.train$label\ndtrain <- lgb.Dataset(\n agaricus.train$data\n , label = labels\n)\nset_field(\n dataset = dtrain\n , field_name = \"init_score\"\n , data = rep(Logit(mean(labels)), length(labels))\n)\n\ndata(agaricus.test, package = \"lightgbm\")\n\nparams <- list(\n objective = \"binary\"\n , learning_rate = 0.1\n , max_depth = -1L\n , min_data_in_leaf = 1L\n , min_sum_hessian_in_leaf = 1.0\n , num_threads = 2L\n)\nmodel <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 5L\n)\n\ntree_interpretation <- lgb.interprete(\n model = model\n , data = agaricus.test$data\n , idxset = 1L:5L\n)\nlgb.plot.interpretation(\n tree_interpretation_dt = tree_interpretation[[1L]]\n , top_n = 3L\n)\n}\n}\n" }, { "path": "R-package/man/lgb.restore_handle.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.restore_handle.R\n\\name{lgb.restore_handle}\n\\alias{lgb.restore_handle}\n\\title{Restore the C++ component of a de-serialized LightGBM model}\n\\usage{\nlgb.restore_handle(model)\n}\n\\arguments{\n\\item{model}{\\code{lgb.Booster} object which was de-serialized and whose underlying C++ object and R handle\nneed to be restored.}\n}\n\\value{\n\\code{lgb.Booster} (the same `model` object that was passed as input, invisibly).\n}\n\\description{\nAfter a LightGBM model object is de-serialized through functions such as \\code{save} or\n\\code{saveRDS}, its underlying C++ object will be blank and needs to be restored to able to use it. Such\nobject is restored automatically when calling functions such as \\code{predict}, but this function can be\nused to forcibly restore it beforehand. Note that the object will be modified in-place.\n\n \\emph{New in version 4.0.0}\n}\n\\details{\nBe aware that fast single-row prediction configurations are not restored through this\nfunction. If you wish to make fast single-row predictions using a \\code{lgb.Booster} loaded this way,\ncall \\link{lgb.configure_fast_predict} on the loaded \\code{lgb.Booster} object.\n}\n\\examples{\n\\donttest{\nlibrary(lightgbm)\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(\"agaricus.train\")\nmodel <- lightgbm(\n agaricus.train$data\n , agaricus.train$label\n , params = list(objective = \"binary\")\n , nrounds = 5L\n , verbose = 0\n , num_threads = 2L\n)\nfname <- tempfile(fileext=\"rds\")\nsaveRDS(model, fname)\n\nmodel_new <- readRDS(fname)\nmodel_new$check_null_handle()\nlgb.restore_handle(model_new)\nmodel_new$check_null_handle()\n}\n}\n\\seealso{\n\\link{lgb.make_serializable}, \\link{lgb.drop_serialized}.\n}\n" }, { "path": "R-package/man/lgb.save.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Booster.R\n\\name{lgb.save}\n\\alias{lgb.save}\n\\title{Save LightGBM model}\n\\usage{\nlgb.save(booster, filename, num_iteration = NULL, start_iteration = 1L)\n}\n\\arguments{\n\\item{booster}{Object of class \\code{lgb.Booster}}\n\n\\item{filename}{Saved filename}\n\n\\item{num_iteration}{Number of iterations to save, NULL or <= 0 means use best iteration}\n\n\\item{start_iteration}{Index (1-based) of the first boosting round to save.\n For example, passing \\code{start_iteration=5, num_iteration=3} for a regression model\n means \"save the fifth, sixth, and seventh tree\"\n\n \\emph{New in version 4.4.0}}\n}\n\\value{\nlgb.Booster\n}\n\\description{\nSave LightGBM model\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\nlibrary(lightgbm)\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\ndata(agaricus.test, package = \"lightgbm\")\ntest <- agaricus.test\ndtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\nparams <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data = 1L\n , learning_rate = 1.0\n , num_threads = 2L\n)\nvalids <- list(test = dtest)\nmodel <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 10L\n , valids = valids\n , early_stopping_rounds = 5L\n)\nlgb.save(model, tempfile(fileext = \".txt\"))\n}\n}\n" }, { "path": "R-package/man/lgb.slice.Dataset.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Dataset.R\n\\name{lgb.slice.Dataset}\n\\alias{lgb.slice.Dataset}\n\\title{Slice a dataset}\n\\usage{\nlgb.slice.Dataset(dataset, idxset)\n}\n\\arguments{\n\\item{dataset}{Object of class \\code{lgb.Dataset}}\n\n\\item{idxset}{an integer vector of indices of rows needed}\n}\n\\value{\nconstructed sub dataset\n}\n\\description{\nGet a new \\code{lgb.Dataset} containing the specified rows of\n original \\code{lgb.Dataset} object\n\n \\emph{Renamed from} \\code{slice()} \\emph{in 4.4.0}\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\n\ndsub <- lgb.slice.Dataset(dtrain, seq_len(42L))\nlgb.Dataset.construct(dsub)\nlabels <- lightgbm::get_field(dsub, \"label\")\n}\n}\n" }, { "path": "R-package/man/lgb.train.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.train.R\n\\name{lgb.train}\n\\alias{lgb.train}\n\\title{Main training logic for LightGBM}\n\\usage{\nlgb.train(\n params = list(),\n data,\n nrounds = 100L,\n valids = list(),\n obj = NULL,\n eval = NULL,\n verbose = 1L,\n record = TRUE,\n eval_freq = 1L,\n init_model = NULL,\n early_stopping_rounds = NULL,\n callbacks = list(),\n reset_data = FALSE,\n serializable = TRUE\n)\n}\n\\arguments{\n\\item{params}{a list of parameters. See \\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{\nthe \"Parameters\" section of the documentation} for a list of parameters and valid values.}\n\n\\item{data}{a \\code{lgb.Dataset} object, used for training. Some functions, such as \\code{\\link{lgb.cv}},\nmay allow you to pass other types of data like \\code{matrix} and then separately supply\n\\code{label} as a keyword argument.}\n\n\\item{nrounds}{number of training rounds}\n\n\\item{valids}{a list of \\code{lgb.Dataset} objects, used for validation}\n\n\\item{obj}{objective function, can be character or custom objective function. Examples include\n\\code{regression}, \\code{regression_l1}, \\code{huber},\n\\code{binary}, \\code{lambdarank}, \\code{multiclass}, \\code{multiclass}}\n\n\\item{eval}{evaluation function(s). This can be a character vector, function, or list with a mixture of\n strings and functions.\n\n \\itemize{\n \\item{\\bold{a. character vector}:\n If you provide a character vector to this argument, it should contain strings with valid\n evaluation metrics.\n See \\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric}{\n The \"metric\" section of the documentation}\n for a list of valid metrics.\n }\n \\item{\\bold{b. function}:\n You can provide a custom evaluation function. This\n should accept the keyword arguments \\code{preds} and \\code{dtrain} and should return a named\n list with three elements:\n \\itemize{\n \\item{\\code{name}: A string with the name of the metric, used for printing\n and storing results.\n }\n \\item{\\code{value}: A single number indicating the value of the metric for the\n given predictions and true values\n }\n \\item{\n \\code{higher_better}: A boolean indicating whether higher values indicate a better fit.\n For example, this would be \\code{FALSE} for metrics like MAE or RMSE.\n }\n }\n }\n \\item{\\bold{c. list}:\n If a list is given, it should only contain character vectors and functions.\n These should follow the requirements from the descriptions above.\n }\n }}\n\n\\item{verbose}{verbosity for output, if <= 0 and \\code{valids} has been provided, also will disable the\nprinting of evaluation during training}\n\n\\item{record}{Boolean, TRUE will record iteration message to \\code{booster$record_evals}}\n\n\\item{eval_freq}{evaluation output frequency, only effective when verbose > 0 and \\code{valids} has been provided}\n\n\\item{init_model}{path of model file or \\code{lgb.Booster} object, will continue training from this model}\n\n\\item{early_stopping_rounds}{int. Activates early stopping. When this parameter is non-null,\ntraining will stop if the evaluation of any metric on any validation set\nfails to improve for \\code{early_stopping_rounds} consecutive boosting rounds.\nIf training stops early, the returned model will have attribute \\code{best_iter}\nset to the iteration number of the best iteration.}\n\n\\item{callbacks}{List of callback functions that are applied at each iteration.}\n\n\\item{reset_data}{Boolean, setting it to TRUE (not the default value) will transform the\nbooster model into a predictor model which frees up memory and the\noriginal datasets}\n\n\\item{serializable}{whether to make the resulting objects serializable through functions such as\n\\code{save} or \\code{saveRDS} (see section \"Model serialization\").}\n}\n\\value{\na trained booster model \\code{lgb.Booster}.\n}\n\\description{\nLow-level R interface to train a LightGBM model. Unlike \\code{\\link{lightgbm}},\n this function is focused on performance (e.g. speed, memory efficiency). It is also\n less likely to have breaking API changes in new releases than \\code{\\link{lightgbm}}.\n}\n\\section{Early Stopping}{\n\n\n \"early stopping\" refers to stopping the training process if the model's performance on a given\n validation set does not improve for several consecutive iterations.\n\n If multiple arguments are given to \\code{eval}, their order will be preserved. If you enable\n early stopping by setting \\code{early_stopping_rounds} in \\code{params}, by default all\n metrics will be considered for early stopping.\n\n If you want to only consider the first metric for early stopping, pass\n \\code{first_metric_only = TRUE} in \\code{params}. Note that if you also specify \\code{metric}\n in \\code{params}, that metric will be considered the \"first\" one. If you omit \\code{metric},\n a default metric will be used based on your choice for the parameter \\code{obj} (keyword argument)\n or \\code{objective} (passed into \\code{params}).\n\n \\bold{NOTE:} if using \\code{boosting_type=\"dart\"}, any early stopping configuration will be ignored\n and early stopping will not be performed.\n}\n\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\ndata(agaricus.test, package = \"lightgbm\")\ntest <- agaricus.test\ndtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\nparams <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data = 1L\n , learning_rate = 1.0\n , num_threads = 2L\n)\nvalids <- list(test = dtest)\nmodel <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 5L\n , valids = valids\n , early_stopping_rounds = 3L\n)\n}\n\n}\n" }, { "path": "R-package/man/lgb_predict_shared_params.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Booster.R\n\\name{lgb_predict_shared_params}\n\\alias{lgb_predict_shared_params}\n\\title{Shared prediction parameter docs}\n\\arguments{\n\\item{type}{Type of prediction to output. Allowed types are:\\itemize{\n \\item \\code{\"response\"}: will output the predicted score according to the objective function being\n optimized (depending on the link function that the objective uses), after applying any necessary\n transformations - for example, for \\code{objective=\"binary\"}, it will output class probabilities.\n \\item \\code{\"class\"}: for classification objectives, will output the class with the highest predicted\n probability. For other objectives, will output the same as \"response\". Note that \\code{\"class\"} is\n not a supported type for \\link{lgb.configure_fast_predict} (see the documentation of that function\n for more details).\n \\item \\code{\"raw\"}: will output the non-transformed numbers (sum of predictions from boosting iterations'\n results) from which the \"response\" number is produced for a given objective function - for example,\n for \\code{objective=\"binary\"}, this corresponds to log-odds. For many objectives such as\n \"regression\", since no transformation is applied, the output will be the same as for \"response\".\n \\item \\code{\"leaf\"}: will output the index of the terminal node / leaf at which each observations falls\n in each tree in the model, outputted as integers, with one column per tree.\n \\item \\code{\"contrib\"}: will return the per-feature contributions for each prediction, including an\n intercept (each feature will produce one column).\n }\n\n Note that, if using custom objectives, types \"class\" and \"response\" will not be available and will\n default towards using \"raw\" instead.\n\n If the model was fit through function \\link{lightgbm} and it was passed a factor as labels,\n passing the prediction type through \\code{params} instead of through this argument might\n result in factor levels for classification objectives not being applied correctly to the\n resulting output.\n\n \\emph{New in version 4.0.0}}\n\n\\item{start_iteration}{int or None, optional (default=None)\nStart index of the iteration to predict.\nIf None or <= 0, starts from the first iteration.}\n\n\\item{num_iteration}{int or None, optional (default=None)\nLimit number of iterations in the prediction.\nIf None, if the best iteration exists and start_iteration is None or <= 0, the\nbest iteration is used; otherwise, all iterations from start_iteration are used.\nIf <= 0, all iterations from start_iteration are used (no limits).}\n\n\\item{params}{a list of additional named parameters. See\n\\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{\nthe \"Predict Parameters\" section of the documentation} for a list of parameters and\nvalid values. Where these conflict with the values of keyword arguments to this function,\nthe values in \\code{params} take precedence.}\n}\n\\description{\nShared prediction parameter docs\n}\n\\details{\nThis page contains shared documentation for prediction-related parameters used throughout the package.\n}\n\\keyword{internal}\n" }, { "path": "R-package/man/lgb_shared_dataset_params.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Dataset.R\n\\name{lgb_shared_dataset_params}\n\\alias{lgb_shared_dataset_params}\n\\title{Shared Dataset parameter docs}\n\\arguments{\n\\item{label}{vector of labels to use as the target variable}\n\n\\item{weight}{numeric vector of sample weights}\n\n\\item{init_score}{initial score is the base prediction lightgbm will boost from}\n\n\\item{group}{used for learning-to-rank tasks. An integer vector describing how to\ngroup rows together as ordered results from the same set of candidate results\nto be ranked. For example, if you have a 100-document dataset with\n\\code{group = c(10, 20, 40, 10, 10, 10)}, that means that you have 6 groups,\nwhere the first 10 records are in the first group, records 11-30 are in the\nsecond group, etc.}\n}\n\\description{\nParameter docs for fields used in \\code{lgb.Dataset} construction\n}\n\\details{\nThis page contains shared documentation for dataset-related parameters used throughout the package.\n}\n\\keyword{internal}\n" }, { "path": "R-package/man/lgb_shared_params.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lightgbm.R\n\\name{lgb_shared_params}\n\\alias{lgb_shared_params}\n\\title{Shared parameter docs}\n\\arguments{\n\\item{callbacks}{List of callback functions that are applied at each iteration.}\n\n\\item{data}{a \\code{lgb.Dataset} object, used for training. Some functions, such as \\code{\\link{lgb.cv}},\nmay allow you to pass other types of data like \\code{matrix} and then separately supply\n\\code{label} as a keyword argument.}\n\n\\item{early_stopping_rounds}{int. Activates early stopping. When this parameter is non-null,\ntraining will stop if the evaluation of any metric on any validation set\nfails to improve for \\code{early_stopping_rounds} consecutive boosting rounds.\nIf training stops early, the returned model will have attribute \\code{best_iter}\nset to the iteration number of the best iteration.}\n\n\\item{eval}{evaluation function(s). This can be a character vector, function, or list with a mixture of\n strings and functions.\n\n \\itemize{\n \\item{\\bold{a. character vector}:\n If you provide a character vector to this argument, it should contain strings with valid\n evaluation metrics.\n See \\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#metric}{\n The \"metric\" section of the documentation}\n for a list of valid metrics.\n }\n \\item{\\bold{b. function}:\n You can provide a custom evaluation function. This\n should accept the keyword arguments \\code{preds} and \\code{dtrain} and should return a named\n list with three elements:\n \\itemize{\n \\item{\\code{name}: A string with the name of the metric, used for printing\n and storing results.\n }\n \\item{\\code{value}: A single number indicating the value of the metric for the\n given predictions and true values\n }\n \\item{\n \\code{higher_better}: A boolean indicating whether higher values indicate a better fit.\n For example, this would be \\code{FALSE} for metrics like MAE or RMSE.\n }\n }\n }\n \\item{\\bold{c. list}:\n If a list is given, it should only contain character vectors and functions.\n These should follow the requirements from the descriptions above.\n }\n }}\n\n\\item{eval_freq}{evaluation output frequency, only effective when verbose > 0 and \\code{valids} has been provided}\n\n\\item{init_model}{path of model file or \\code{lgb.Booster} object, will continue training from this model}\n\n\\item{nrounds}{number of training rounds}\n\n\\item{obj}{objective function, can be character or custom objective function. Examples include\n\\code{regression}, \\code{regression_l1}, \\code{huber},\n\\code{binary}, \\code{lambdarank}, \\code{multiclass}, \\code{multiclass}}\n\n\\item{params}{a list of parameters. See \\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{\nthe \"Parameters\" section of the documentation} for a list of parameters and valid values.}\n\n\\item{verbose}{verbosity for output, if <= 0 and \\code{valids} has been provided, also will disable the\nprinting of evaluation during training}\n\n\\item{serializable}{whether to make the resulting objects serializable through functions such as\n\\code{save} or \\code{saveRDS} (see section \"Model serialization\").}\n}\n\\description{\nParameter docs shared by \\code{lgb.train}, \\code{lgb.cv}, and \\code{lightgbm}\n}\n\\section{Early Stopping}{\n\n\n \"early stopping\" refers to stopping the training process if the model's performance on a given\n validation set does not improve for several consecutive iterations.\n\n If multiple arguments are given to \\code{eval}, their order will be preserved. If you enable\n early stopping by setting \\code{early_stopping_rounds} in \\code{params}, by default all\n metrics will be considered for early stopping.\n\n If you want to only consider the first metric for early stopping, pass\n \\code{first_metric_only = TRUE} in \\code{params}. Note that if you also specify \\code{metric}\n in \\code{params}, that metric will be considered the \"first\" one. If you omit \\code{metric},\n a default metric will be used based on your choice for the parameter \\code{obj} (keyword argument)\n or \\code{objective} (passed into \\code{params}).\n\n \\bold{NOTE:} if using \\code{boosting_type=\"dart\"}, any early stopping configuration will be ignored\n and early stopping will not be performed.\n}\n\n\\section{Model serialization}{\n\n\n LightGBM model objects can be serialized and de-serialized through functions such as \\code{save}\n or \\code{saveRDS}, but similarly to libraries such as 'xgboost', serialization works a bit differently\n from typical R objects. In order to make models serializable in R, a copy of the underlying C++ object\n as serialized raw bytes is produced and stored in the R model object, and when this R object is\n de-serialized, the underlying C++ model object gets reconstructed from these raw bytes, but will only\n do so once some function that uses it is called, such as \\code{predict}. In order to forcibly\n reconstruct the C++ object after deserialization (e.g. after calling \\code{readRDS} or similar), one\n can use the function \\link{lgb.restore_handle} (for example, if one makes predictions in parallel or in\n forked processes, it will be faster to restore the handle beforehand).\n\n Producing and keeping these raw bytes however uses extra memory, and if they are not required,\n it is possible to avoid producing them by passing `serializable=FALSE`. In such cases, these raw\n bytes can be added to the model on demand through function \\link{lgb.make_serializable}.\n\n \\emph{New in version 4.0.0}\n}\n\n\\keyword{internal}\n" }, { "path": "R-package/man/lightgbm.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lightgbm.R\n\\name{lightgbm}\n\\alias{lightgbm}\n\\title{Train a LightGBM model}\n\\usage{\nlightgbm(\n data,\n label = NULL,\n weights = NULL,\n params = list(),\n nrounds = 100L,\n verbose = 1L,\n eval_freq = 1L,\n early_stopping_rounds = NULL,\n init_model = NULL,\n callbacks = list(),\n serializable = TRUE,\n objective = \"auto\",\n init_score = NULL,\n num_threads = NULL,\n colnames = NULL,\n categorical_feature = NULL,\n ...\n)\n}\n\\arguments{\n\\item{data}{a \\code{lgb.Dataset} object, used for training. Some functions, such as \\code{\\link{lgb.cv}},\nmay allow you to pass other types of data like \\code{matrix} and then separately supply\n\\code{label} as a keyword argument.}\n\n\\item{label}{Vector of labels, used if \\code{data} is not an \\code{\\link{lgb.Dataset}}}\n\n\\item{weights}{Sample / observation weights for rows in the input data. If \\code{NULL}, will assume that all\n observations / rows have the same importance / weight.\n\n \\emph{Changed from 'weight', in version 4.0.0}}\n\n\\item{params}{a list of parameters. See \\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html}{\nthe \"Parameters\" section of the documentation} for a list of parameters and valid values.}\n\n\\item{nrounds}{number of training rounds}\n\n\\item{verbose}{verbosity for output, if <= 0 and \\code{valids} has been provided, also will disable the\nprinting of evaluation during training}\n\n\\item{eval_freq}{evaluation output frequency, only effective when verbose > 0 and \\code{valids} has been provided}\n\n\\item{early_stopping_rounds}{int. Activates early stopping. When this parameter is non-null,\ntraining will stop if the evaluation of any metric on any validation set\nfails to improve for \\code{early_stopping_rounds} consecutive boosting rounds.\nIf training stops early, the returned model will have attribute \\code{best_iter}\nset to the iteration number of the best iteration.}\n\n\\item{init_model}{path of model file or \\code{lgb.Booster} object, will continue training from this model}\n\n\\item{callbacks}{List of callback functions that are applied at each iteration.}\n\n\\item{serializable}{whether to make the resulting objects serializable through functions such as\n\\code{save} or \\code{saveRDS} (see section \"Model serialization\").}\n\n\\item{objective}{Optimization objective (e.g. `\"regression\"`, `\"binary\"`, etc.).\n For a list of accepted objectives, see\n \\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#objective}{\n the \"objective\" item of the \"Parameters\" section of the documentation}.\n\n If passing \\code{\"auto\"} and \\code{data} is not of type \\code{lgb.Dataset}, the objective will\n be determined according to what is passed for \\code{label}:\\itemize{\n \\item If passing a factor with two variables, will use objective \\code{\"binary\"}.\n \\item If passing a factor with more than two variables, will use objective \\code{\"multiclass\"}\n (note that parameter \\code{num_class} in this case will also be determined automatically from\n \\code{label}).\n \\item Otherwise (or if passing \\code{lgb.Dataset} as input), will use objective \\code{\"regression\"}.\n }\n\n \\emph{New in version 4.0.0}}\n\n\\item{init_score}{initial score is the base prediction lightgbm will boost from\n\n \\emph{New in version 4.0.0}}\n\n\\item{num_threads}{Number of parallel threads to use. For best speed, this should be set to the number of\n physical cores in the CPU - in a typical x86-64 machine, this corresponds to half the\n number of maximum threads.\n\n Be aware that using too many threads can result in speed degradation in smaller datasets\n (see the parameters documentation for more details).\n\n If passing zero, will use the default number of threads configured for OpenMP\n (typically controlled through an environment variable \\code{OMP_NUM_THREADS}).\n\n If passing \\code{NULL} (the default), will try to use the number of physical cores in the\n system, but be aware that getting the number of cores detected correctly requires package\n \\code{RhpcBLASctl} to be installed.\n\n This parameter gets overridden by \\code{num_threads} and its aliases under \\code{params}\n if passed there.\n\n \\emph{New in version 4.0.0}}\n\n\\item{colnames}{Character vector of features. Only used if \\code{data} is not an \\code{\\link{lgb.Dataset}}.}\n\n\\item{categorical_feature}{categorical features. This can either be a character vector of feature\nnames or an integer vector with the indices of the features (e.g.\n\\code{c(1L, 10L)} to say \"the first and tenth columns\").\nOnly used if \\code{data} is not an \\code{\\link{lgb.Dataset}}.}\n\n\\item{...}{Additional arguments passed to \\code{\\link{lgb.train}}. For example\n\\itemize{\n \\item{\\code{valids}: a list of \\code{lgb.Dataset} objects, used for validation}\n \\item{\\code{obj}: objective function, can be character or custom objective function. Examples include\n \\code{regression}, \\code{regression_l1}, \\code{huber},\n \\code{binary}, \\code{lambdarank}, \\code{multiclass}, \\code{multiclass}}\n \\item{\\code{eval}: evaluation function, can be (a list of) character or custom eval function}\n \\item{\\code{record}: Boolean, TRUE will record iteration message to \\code{booster$record_evals}}\n \\item{\\code{reset_data}: Boolean, setting it to TRUE (not the default value) will transform the booster model\n into a predictor model which frees up memory and the original datasets}\n}}\n}\n\\value{\na trained \\code{lgb.Booster}\n}\n\\description{\nHigh-level R interface to train a LightGBM model. Unlike \\code{\\link{lgb.train}}, this function\n is focused on compatibility with other statistics and machine learning interfaces in R.\n This focus on compatibility means that this interface may experience more frequent breaking API changes\n than \\code{\\link{lgb.train}}.\n For efficiency-sensitive applications, or for applications where breaking API changes across releases\n is very expensive, use \\code{\\link{lgb.train}}.\n}\n\\section{Early Stopping}{\n\n\n \"early stopping\" refers to stopping the training process if the model's performance on a given\n validation set does not improve for several consecutive iterations.\n\n If multiple arguments are given to \\code{eval}, their order will be preserved. If you enable\n early stopping by setting \\code{early_stopping_rounds} in \\code{params}, by default all\n metrics will be considered for early stopping.\n\n If you want to only consider the first metric for early stopping, pass\n \\code{first_metric_only = TRUE} in \\code{params}. Note that if you also specify \\code{metric}\n in \\code{params}, that metric will be considered the \"first\" one. If you omit \\code{metric},\n a default metric will be used based on your choice for the parameter \\code{obj} (keyword argument)\n or \\code{objective} (passed into \\code{params}).\n\n \\bold{NOTE:} if using \\code{boosting_type=\"dart\"}, any early stopping configuration will be ignored\n and early stopping will not be performed.\n}\n\n" }, { "path": "R-package/man/predict.lgb.Booster.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Booster.R\n\\name{predict.lgb.Booster}\n\\alias{predict.lgb.Booster}\n\\title{Predict method for LightGBM model}\n\\usage{\n\\method{predict}{lgb.Booster}(\n object,\n newdata,\n type = \"response\",\n start_iteration = NULL,\n num_iteration = NULL,\n header = FALSE,\n params = list(),\n ...\n)\n}\n\\arguments{\n\\item{object}{Object of class \\code{lgb.Booster}}\n\n\\item{newdata}{a \\code{matrix} object, a \\code{dgCMatrix}, a \\code{dgRMatrix} object, a \\code{dsparseVector} object,\n or a character representing a path to a text file (CSV, TSV, or LibSVM).\n\n For sparse inputs, if predictions are only going to be made for a single row, it will be faster to\n use CSR format, in which case the data may be passed as either a single-row CSR matrix (class\n \\code{dgRMatrix} from package \\code{Matrix}) or as a sparse numeric vector (class\n \\code{dsparseVector} from package \\code{Matrix}).\n\n If single-row predictions are going to be performed frequently, it is recommended to\n pre-configure the model object for fast single-row sparse predictions through function\n \\link{lgb.configure_fast_predict}.\n\n \\emph{Changed from 'data', in version 4.0.0}}\n\n\\item{type}{Type of prediction to output. Allowed types are:\\itemize{\n \\item \\code{\"response\"}: will output the predicted score according to the objective function being\n optimized (depending on the link function that the objective uses), after applying any necessary\n transformations - for example, for \\code{objective=\"binary\"}, it will output class probabilities.\n \\item \\code{\"class\"}: for classification objectives, will output the class with the highest predicted\n probability. For other objectives, will output the same as \"response\". Note that \\code{\"class\"} is\n not a supported type for \\link{lgb.configure_fast_predict} (see the documentation of that function\n for more details).\n \\item \\code{\"raw\"}: will output the non-transformed numbers (sum of predictions from boosting iterations'\n results) from which the \"response\" number is produced for a given objective function - for example,\n for \\code{objective=\"binary\"}, this corresponds to log-odds. For many objectives such as\n \"regression\", since no transformation is applied, the output will be the same as for \"response\".\n \\item \\code{\"leaf\"}: will output the index of the terminal node / leaf at which each observations falls\n in each tree in the model, outputted as integers, with one column per tree.\n \\item \\code{\"contrib\"}: will return the per-feature contributions for each prediction, including an\n intercept (each feature will produce one column).\n }\n\n Note that, if using custom objectives, types \"class\" and \"response\" will not be available and will\n default towards using \"raw\" instead.\n\n If the model was fit through function \\link{lightgbm} and it was passed a factor as labels,\n passing the prediction type through \\code{params} instead of through this argument might\n result in factor levels for classification objectives not being applied correctly to the\n resulting output.\n\n \\emph{New in version 4.0.0}}\n\n\\item{start_iteration}{int or None, optional (default=None)\nStart index of the iteration to predict.\nIf None or <= 0, starts from the first iteration.}\n\n\\item{num_iteration}{int or None, optional (default=None)\nLimit number of iterations in the prediction.\nIf None, if the best iteration exists and start_iteration is None or <= 0, the\nbest iteration is used; otherwise, all iterations from start_iteration are used.\nIf <= 0, all iterations from start_iteration are used (no limits).}\n\n\\item{header}{only used for prediction for text file. True if text file has header}\n\n\\item{params}{a list of additional named parameters. See\n\\href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#predict-parameters}{\nthe \"Predict Parameters\" section of the documentation} for a list of parameters and\nvalid values. Where these conflict with the values of keyword arguments to this function,\nthe values in \\code{params} take precedence.}\n\n\\item{...}{ignored}\n}\n\\value{\nFor prediction types that are meant to always return one output per observation (e.g. when predicting\n \\code{type=\"response\"} or \\code{type=\"raw\"} on a binary classification or regression objective), will\n return a vector with one element per row in \\code{newdata}.\n\n For prediction types that are meant to return more than one output per observation (e.g. when predicting\n \\code{type=\"response\"} or \\code{type=\"raw\"} on a multi-class objective, or when predicting\n \\code{type=\"leaf\"}, regardless of objective), will return a matrix with one row per observation in\n \\code{newdata} and one column per output.\n\n For \\code{type=\"leaf\"} predictions, will return a matrix with one row per observation in \\code{newdata}\n and one column per tree. Note that for multiclass objectives, LightGBM trains one tree per class at each\n boosting iteration. That means that, for example, for a multiclass model with 3 classes, the leaf\n predictions for the first class can be found in columns 1, 4, 7, 10, etc.\n\n For \\code{type=\"contrib\"}, will return a matrix of SHAP values with one row per observation in\n \\code{newdata} and columns corresponding to features. For regression, ranking, cross-entropy, and binary\n classification objectives, this matrix contains one column per feature plus a final column containing the\n Shapley base value. For multiclass objectives, this matrix will represent \\code{num_classes} such matrices,\n in the order \"feature contributions for first class, feature contributions for second class, feature\n contributions for third class, etc.\".\n\n If the model was fit through function \\link{lightgbm} and it was passed a factor as labels, predictions\n returned from this function will retain the factor levels (either as values for \\code{type=\"class\"}, or\n as column names for \\code{type=\"response\"} and \\code{type=\"raw\"} for multi-class objectives). Note that\n passing the requested prediction type under \\code{params} instead of through \\code{type} might result in\n the factor levels not being present in the output.\n}\n\\description{\nPredicted values based on class \\code{lgb.Booster}\n\n \\emph{New in version 4.0.0}\n}\n\\details{\nIf the model object has been configured for fast single-row predictions through\n \\link{lgb.configure_fast_predict}, this function will use the prediction parameters\n that were configured for it - as such, extra prediction parameters should not be passed\n here, otherwise the configuration will be ignored and the slow route will be taken.\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\ndata(agaricus.test, package = \"lightgbm\")\ntest <- agaricus.test\ndtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\nparams <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data = 1L\n , learning_rate = 1.0\n , num_threads = 2L\n)\nvalids <- list(test = dtest)\nmodel <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 5L\n , valids = valids\n)\npreds <- predict(model, test$data)\n\n# pass other prediction parameters\npreds <- predict(\n model,\n test$data,\n params = list(\n predict_disable_shape_check = TRUE\n )\n)\n}\n}\n" }, { "path": "R-package/man/print.lgb.Booster.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Booster.R\n\\name{print.lgb.Booster}\n\\alias{print.lgb.Booster}\n\\title{Print method for LightGBM model}\n\\usage{\n\\method{print}{lgb.Booster}(x, ...)\n}\n\\arguments{\n\\item{x}{Object of class \\code{lgb.Booster}}\n\n\\item{...}{Not used}\n}\n\\value{\nThe same input \\code{x}, returned as invisible.\n}\n\\description{\nShow summary information about a LightGBM model object (same as \\code{summary}).\n\n \\emph{New in version 4.0.0}\n}\n" }, { "path": "R-package/man/setLGBMThreads.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/multithreading.R\n\\name{setLGBMThreads}\n\\alias{setLGBMThreads}\n\\alias{setLGBMthreads}\n\\title{Set maximum number of threads used by LightGBM}\n\\usage{\nsetLGBMthreads(num_threads)\n}\n\\arguments{\n\\item{num_threads}{maximum number of threads to be used by LightGBM in multi-threaded operations}\n}\n\\description{\nLightGBM attempts to speed up many operations by using multi-threading.\n The number of threads used in those operations can be controlled via the\n \\code{num_threads} parameter passed through \\code{params} to functions like\n \\link{lgb.train} and \\link{lgb.Dataset}. However, some operations (like materializing\n a model from a text file) are done via code paths that don't explicitly accept thread-control\n configuration.\n\n Use this function to set the maximum number of threads LightGBM will use for such operations.\n\n This function affects all LightGBM operations in the same process.\n\n So, for example, if you call \\code{setLGBMthreads(4)}, no other multi-threaded LightGBM\n operation in the same process will use more than 4 threads.\n\n Call \\code{setLGBMthreads(-1)} to remove this limitation.\n}\n\\seealso{\n\\link{getLGBMthreads}\n}\n" }, { "path": "R-package/man/set_field.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Dataset.R\n\\name{set_field}\n\\alias{set_field}\n\\alias{set_field.lgb.Dataset}\n\\title{Set one attribute of a \\code{lgb.Dataset} object}\n\\usage{\nset_field(dataset, field_name, data)\n\n\\method{set_field}{lgb.Dataset}(dataset, field_name, data)\n}\n\\arguments{\n\\item{dataset}{Object of class \\code{lgb.Dataset}}\n\n\\item{field_name}{String with the name of the attribute to set. One of the following.\n\\itemize{\n \\item \\code{label}: label lightgbm learns from ;\n \\item \\code{weight}: to do a weight rescale ;\n \\item{\\code{group}: used for learning-to-rank tasks. An integer vector describing how to\n group rows together as ordered results from the same set of candidate results to be ranked.\n For example, if you have a 100-document dataset with \\code{group = c(10, 20, 40, 10, 10, 10)},\n that means that you have 6 groups, where the first 10 records are in the first group,\n records 11-30 are in the second group, etc.}\n \\item \\code{init_score}: initial score is the base prediction lightgbm will boost from.\n}}\n\n\\item{data}{The data for the field. See examples.}\n}\n\\value{\nThe \\code{lgb.Dataset} you passed in.\n}\n\\description{\nSet one attribute of a \\code{lgb.Dataset}\n}\n\\examples{\n\\donttest{\n\\dontshow{setLGBMthreads(2L)}\n\\dontshow{data.table::setDTthreads(1L)}\ndata(agaricus.train, package = \"lightgbm\")\ntrain <- agaricus.train\ndtrain <- lgb.Dataset(train$data, label = train$label)\nlgb.Dataset.construct(dtrain)\n\nlabels <- lightgbm::get_field(dtrain, \"label\")\nlightgbm::set_field(dtrain, \"label\", 1 - labels)\n\nlabels2 <- lightgbm::get_field(dtrain, \"label\")\nstopifnot(all.equal(labels2, 1 - labels))\n}\n}\n" }, { "path": "R-package/man/summary.lgb.Booster.Rd", "content": "% Generated by roxygen2: do not edit by hand\n% Please edit documentation in R/lgb.Booster.R\n\\name{summary.lgb.Booster}\n\\alias{summary.lgb.Booster}\n\\title{Summary method for LightGBM model}\n\\usage{\n\\method{summary}{lgb.Booster}(object, ...)\n}\n\\arguments{\n\\item{object}{Object of class \\code{lgb.Booster}}\n\n\\item{...}{Not used}\n}\n\\value{\nThe same input \\code{object}, returned as invisible.\n}\n\\description{\nShow summary information about a LightGBM model object (same as \\code{print}).\n\n \\emph{New in version 4.0.0}\n}\n" }, { "path": "R-package/pkgdown/_pkgdown.yml", "content": "template:\n params:\n bootswatch: cerulean\n\nsite:\n root: ''\n title: LightGBM, Light Gradient Boosting Machine\n\nrepo:\n url:\n home: https://github.com/lightgbm-org/LightGBM/\n source: https://github.com/lightgbm-org/LightGBM/tree/master/R-package/\n issue: https://github.com/lightgbm-org/LightGBM/issues/\n user: https://github.com/\n\ndevelopment:\n mode: unreleased\n\nauthors:\n Yu Shi:\n href: https://github.com/shiyu1994\n html: Yu Shi\n Guolin Ke:\n href: https://github.com/guolinke\n html: Guolin Ke\n Damien Soukhavong:\n href: https://github.com/Laurae2\n html: Damien Soukhavong\n Yachen Yan:\n href: https://github.com/yanyachen\n html: Yachen Yan\n James Lamb:\n href: https://github.com/jameslamb\n html: James Lamb\n\nnavbar:\n title: LightGBM\n type: default\n left:\n - icon: fa-reply fa-lg\n href: ../\n - icon: fa-home fa-lg\n href: index.html\n - text: Articles\n href: articles/index.html\n - text: Reference\n href: reference/index.html\n right:\n - icon: fa-github fa-lg\n href: https://github.com/lightgbm-org/LightGBM/tree/master/R-package\n\nreference:\n - title: Datasets\n desc: Datasets included with the R-package\n contents:\n - '`agaricus.train`'\n - '`agaricus.test`'\n - '`bank`'\n - title: Data Input / Output\n desc: Data I/O required for LightGBM\n contents:\n - '`dim.lgb.Dataset`'\n - '`dimnames.lgb.Dataset`'\n - '`get_field`'\n - '`set_field`'\n - '`lgb.Dataset`'\n - '`lgb.Dataset.construct`'\n - '`lgb.Dataset.create.valid`'\n - '`lgb.Dataset.save`'\n - '`lgb.Dataset.set.categorical`'\n - '`lgb.Dataset.set.reference`'\n - '`lgb.convert_with_rules`'\n - '`lgb.slice.Dataset`'\n - title: Machine Learning\n desc: Train models with LightGBM and then use them to make predictions on new data\n contents:\n - '`lightgbm`'\n - '`lgb.train`'\n - '`predict.lgb.Booster`'\n - '`lgb.cv`'\n - '`lgb.configure_fast_predict`'\n - title: Saving / Loading Models\n desc: Save and load LightGBM models\n contents:\n - '`lgb.dump`'\n - '`lgb.save`'\n - '`lgb.load`'\n - '`lgb.model.dt.tree`'\n - '`lgb.drop_serialized`'\n - '`lgb.make_serializable`'\n - '`lgb.restore_handle`'\n - title: Model Interpretation\n desc: Analyze your models\n contents:\n - '`lgb.get.eval.result`'\n - '`lgb.importance`'\n - '`lgb.interprete`'\n - '`lgb.plot.importance`'\n - '`lgb.plot.interpretation`'\n - '`print.lgb.Booster`'\n - '`summary.lgb.Booster`'\n - title: Multithreading Control\n desc: Manage degree of parallelism used by LightGBM\n contents:\n - '`getLGBMThreads`'\n - '`setLGBMThreads`'\n" }, { "path": "R-package/recreate-configure.sh", "content": "#!/bin/bash\n\nset -e -E -u -o pipefail\n\n# recreates 'configure' from 'configure.ac'\n# this script should run on Ubuntu 22.04\nAUTOCONF_VERSION=$(cat R-package/AUTOCONF_UBUNTU_VERSION)\n\n# R packages cannot have versions like 3.0.0rc1, but\n# 3.0.0-1 is acceptable\nLGB_VERSION=$(sed \"s/rc/-/g\" < VERSION.txt)\n\n# this script changes configure.ac. Copying to a temporary file\n# so changes to configure.ac don't get committed in git\nTMP_CONFIGURE_AC=\".configure.ac\"\n\necho \"Creating 'configure' script with Autoconf ${AUTOCONF_VERSION}\"\n\napt update\napt-get install \\\n --no-install-recommends \\\n -y \\\n autoconf=\"${AUTOCONF_VERSION}\"\n\ncd R-package\n\ncp configure.ac ${TMP_CONFIGURE_AC}\nsed -i.bak -e \"s/~~VERSION~~/${LGB_VERSION}/\" ${TMP_CONFIGURE_AC}\n\nautoconf \\\n --output configure \\\n ${TMP_CONFIGURE_AC} \\\n || exit 1\n\nrm ${TMP_CONFIGURE_AC}\n\nrm -r autom4te.cache || echo \"no autoconf cache found\"\n\necho \"done creating 'configure' script\"\n" }, { "path": "R-package/src/Makevars.in", "content": "CXX_STD = CXX17\n\nPKGROOT=.\n\nLGB_CPPFLAGS = \\\n @LGB_CPPFLAGS@ \\\n -DUSE_SOCKET \\\n -DLGB_R_BUILD\n\nPKG_CPPFLAGS = \\\n -I$(PKGROOT)/include \\\n $(LGB_CPPFLAGS)\n\nPKG_CXXFLAGS = \\\n @OPENMP_CXXFLAGS@ \\\n -pthread\n\nPKG_LIBS = \\\n @OPENMP_CXXFLAGS@ \\\n @OPENMP_LIB@ \\\n -pthread\n\nOBJECTS = \\\n boosting/boosting.o \\\n boosting/gbdt.o \\\n boosting/gbdt_model_text.o \\\n boosting/gbdt_prediction.o \\\n boosting/prediction_early_stop.o \\\n boosting/sample_strategy.o \\\n io/bin.o \\\n io/config.o \\\n io/config_auto.o \\\n io/dataset.o \\\n io/dataset_loader.o \\\n io/file_io.o \\\n io/json11.o \\\n io/metadata.o \\\n io/parser.o \\\n io/train_share_states.o \\\n io/tree.o \\\n metric/dcg_calculator.o \\\n metric/metric.o \\\n objective/objective_function.o \\\n network/linker_topo.o \\\n network/linkers_mpi.o \\\n network/linkers_socket.o \\\n network/network.o \\\n treelearner/data_parallel_tree_learner.o \\\n treelearner/feature_histogram.o \\\n treelearner/feature_parallel_tree_learner.o \\\n treelearner/gpu_tree_learner.o \\\n treelearner/gradient_discretizer.o \\\n treelearner/linear_tree_learner.o \\\n treelearner/serial_tree_learner.o \\\n treelearner/tree_learner.o \\\n treelearner/voting_parallel_tree_learner.o \\\n utils/openmp_wrapper.o \\\n c_api.o \\\n lightgbm_R.o\n" }, { "path": "R-package/src/Makevars.win.in", "content": "CXX_STD = CXX17\n\nPKGROOT=.\n\nLGB_CPPFLAGS = \\\n @LGB_CPPFLAGS@ \\\n -DUSE_SOCKET \\\n -DLGB_R_BUILD\n\nPKG_CPPFLAGS = \\\n -I$(PKGROOT)/include \\\n $(LGB_CPPFLAGS)\n\nPKG_CXXFLAGS = \\\n ${SHLIB_OPENMP_CXXFLAGS} \\\n ${SHLIB_PTHREAD_FLAGS}\n\nPKG_LIBS = \\\n ${SHLIB_OPENMP_CXXFLAGS} \\\n ${SHLIB_PTHREAD_FLAGS} \\\n -lws2_32 \\\n -liphlpapi\n\nOBJECTS = \\\n boosting/boosting.o \\\n boosting/gbdt.o \\\n boosting/gbdt_model_text.o \\\n boosting/gbdt_prediction.o \\\n boosting/prediction_early_stop.o \\\n boosting/sample_strategy.o \\\n io/bin.o \\\n io/config.o \\\n io/config_auto.o \\\n io/dataset.o \\\n io/dataset_loader.o \\\n io/file_io.o \\\n io/json11.o \\\n io/metadata.o \\\n io/parser.o \\\n io/train_share_states.o \\\n io/tree.o \\\n metric/dcg_calculator.o \\\n metric/metric.o \\\n objective/objective_function.o \\\n network/linker_topo.o \\\n network/linkers_mpi.o \\\n network/linkers_socket.o \\\n network/network.o \\\n treelearner/data_parallel_tree_learner.o \\\n treelearner/feature_histogram.o \\\n treelearner/feature_parallel_tree_learner.o \\\n treelearner/gpu_tree_learner.o \\\n treelearner/gradient_discretizer.o \\\n treelearner/linear_tree_learner.o \\\n treelearner/serial_tree_learner.o \\\n treelearner/tree_learner.o \\\n treelearner/voting_parallel_tree_learner.o \\\n utils/openmp_wrapper.o \\\n c_api.o \\\n lightgbm_R.o\n" }, { "path": "R-package/src/install.libs.R", "content": "# User options\nuse_gpu <- FALSE\nmake_args_from_build_script <- character(0L)\n\n# For Windows, the package will be built with Visual Studio\n# unless you set one of these to TRUE\nuse_mingw <- FALSE\nuse_msys2 <- FALSE\n\nif (use_mingw && use_msys2) {\n stop(\"Cannot use both MinGW and MSYS2. Please choose only one.\")\n}\n\nif (.Machine$sizeof.pointer != 8L) {\n stop(\"LightGBM only supports 64-bit R, please check the version of R and Rtools.\")\n}\n\n# Get some paths\nsource_dir <- file.path(R_PACKAGE_SOURCE, \"src\", fsep = \"/\")\nbuild_dir <- file.path(source_dir, \"build\", fsep = \"/\")\ninst_dir <- file.path(R_PACKAGE_SOURCE, \"inst\", fsep = \"/\")\n\n# system() will not raise an R exception if the process called\n# fails. Wrapping it here to get that behavior.\n#\n# system() introduces a lot of overhead, at least on Windows,\n# so trying processx if it is available\n.run_shell_command <- function(cmd, args, strict = TRUE) {\n on_windows <- .Platform$OS.type == \"windows\"\n has_processx <- suppressMessages({\n suppressWarnings({\n require(\"processx\") # nolint: undesirable_function, unused_import.\n })\n })\n if (has_processx && on_windows) {\n result <- processx::run(\n command = cmd\n , args = args\n , windows_verbatim_args = TRUE\n , error_on_status = FALSE\n , echo = TRUE\n )\n exit_code <- result$status\n } else {\n if (on_windows) {\n message(paste0(\n \"Using system() to run shell commands. Installing \"\n , \"'processx' with install.packages('processx') might \"\n , \"make this faster.\"\n ))\n }\n cmd <- paste0(cmd, \" \", paste(args, collapse = \" \"))\n exit_code <- system(cmd)\n }\n\n if (exit_code != 0L && isTRUE(strict)) {\n stop(paste0(\"Command failed with exit code: \", exit_code))\n }\n return(invisible(exit_code))\n}\n\n# try to generate Visual Studio build files\n.generate_vs_makefiles <- function(cmake_args) {\n vs_versions <- c(\n \"Visual Studio 17 2022\"\n , \"Visual Studio 16 2019\"\n , \"Visual Studio 15 2017\"\n )\n working_vs_version <- NULL\n for (vs_version in vs_versions) {\n message(sprintf(\"Trying '%s'\", vs_version))\n # if the build directory is not empty, clean it\n if (file.exists(\"CMakeCache.txt\")) {\n file.remove(\"CMakeCache.txt\")\n }\n vs_cmake_args <- c(\n cmake_args\n , \"-G\"\n , shQuote(vs_version)\n , \"-A\"\n , \"x64\"\n )\n exit_code <- .run_shell_command(\"cmake\", c(vs_cmake_args, \"..\"), strict = FALSE)\n if (exit_code == 0L) {\n message(sprintf(\"Successfully created build files for '%s'\", vs_version))\n return(invisible(TRUE))\n }\n\n }\n return(invisible(FALSE))\n}\n\n# Move in CMakeLists.txt\nwrite_succeeded <- file.copy(\n file.path(inst_dir, \"bin\", \"CMakeLists.txt\")\n , \"CMakeLists.txt\"\n , overwrite = TRUE\n)\nif (!write_succeeded) {\n stop(\"Copying CMakeLists.txt failed\")\n}\n\n# Prepare building package\ndir.create(\n build_dir\n , recursive = TRUE\n , showWarnings = FALSE\n)\nsetwd(build_dir)\n\nuse_visual_studio <- !(use_mingw || use_msys2)\n\n# If using MSVC to build, pull in the script used\n# to create R.def from R.dll\nif (WINDOWS && use_visual_studio) {\n write_succeeded <- file.copy(\n file.path(inst_dir, \"make-r-def.R\")\n , file.path(build_dir, \"make-r-def.R\")\n , overwrite = TRUE\n )\n if (!write_succeeded) {\n stop(\"Copying make-r-def.R failed\")\n }\n}\n\n# Prepare installation steps\ncmake_args <- c(\n \"-D__BUILD_FOR_R=ON\"\n # pass in R version, to help FindLibR find the R library\n , sprintf(\"-DCMAKE_R_VERSION='%s.%s'\", R.Version()[[\"major\"]], R.Version()[[\"minor\"]])\n # ensure CMake build respects how R is configured (`R CMD config SHLIB_EXT`)\n , sprintf(\"-DCMAKE_SHARED_LIBRARY_SUFFIX_CXX='%s'\", SHLIB_EXT)\n)\nbuild_cmd <- \"make\"\nbuild_args <- c(\"_lightgbm\", make_args_from_build_script)\nlib_folder <- file.path(source_dir, fsep = \"/\")\n\n# add in command-line arguments\n# NOTE: build_r.R replaces the line below\ncommand_line_args <- NULL\ncmake_args <- c(cmake_args, command_line_args)\n\nWINDOWS_BUILD_TOOLS <- list(\n \"MinGW\" = c(\n build_tool = \"mingw32-make.exe\"\n , makefile_generator = \"MinGW Makefiles\"\n )\n , \"MSYS2\" = c(\n build_tool = \"make.exe\"\n , makefile_generator = \"MSYS Makefiles\"\n )\n)\n\nif (use_mingw) {\n windows_toolchain <- \"MinGW\"\n} else if (use_msys2) {\n windows_toolchain <- \"MSYS2\"\n} else {\n # Rtools 4.0 moved from MinGW to MSYS toolchain. If user tries\n # Visual Studio install but that fails, fall back to the toolchain\n # supported in Rtools\n windows_toolchain <- \"MSYS2\"\n}\nwindows_build_tool <- WINDOWS_BUILD_TOOLS[[windows_toolchain]][[\"build_tool\"]]\nwindows_makefile_generator <- WINDOWS_BUILD_TOOLS[[windows_toolchain]][[\"makefile_generator\"]]\n\nif (use_gpu) {\n cmake_args <- c(cmake_args, \"-DUSE_GPU=ON\")\n}\n\n# the checks below might already run `cmake -G`. If they do, set this flag\n# to TRUE to avoid re-running it later\nmakefiles_already_generated <- FALSE\n\n# Check if Windows installation (for gcc vs Visual Studio)\nif (WINDOWS) {\n if (!use_visual_studio) {\n message(sprintf(\"Trying to build with %s\", windows_toolchain))\n # Must build twice for Windows due sh.exe in Rtools\n cmake_args <- c(cmake_args, \"-G\", shQuote(windows_makefile_generator))\n .run_shell_command(\"cmake\", c(cmake_args, \"..\"), strict = FALSE)\n build_cmd <- windows_build_tool\n build_args <- c(\"_lightgbm\", make_args_from_build_script)\n } else {\n visual_studio_succeeded <- .generate_vs_makefiles(cmake_args)\n if (!isTRUE(visual_studio_succeeded)) {\n warning(sprintf(\"Building with Visual Studio failed. Attempting with %s\", windows_toolchain))\n # Must build twice for Windows due sh.exe in Rtools\n cmake_args <- c(cmake_args, \"-G\", shQuote(windows_makefile_generator))\n .run_shell_command(\"cmake\", c(cmake_args, \"..\"), strict = FALSE)\n build_cmd <- windows_build_tool\n build_args <- c(\"_lightgbm\", make_args_from_build_script)\n } else {\n build_cmd <- \"cmake\"\n build_args <- c(\"--build\", \".\", \"--target\", \"_lightgbm\", \"--config\", \"Release\")\n lib_folder <- file.path(source_dir, \"Release\", fsep = \"/\")\n makefiles_already_generated <- TRUE\n }\n }\n} else {\n .run_shell_command(\"cmake\", c(cmake_args, \"..\"))\n makefiles_already_generated <- TRUE\n}\n\n# generate build files\nif (!makefiles_already_generated) {\n .run_shell_command(\"cmake\", c(cmake_args, \"..\"))\n}\n\n# build the library\nmessage(paste0(\"Building lightgbm\", SHLIB_EXT))\n.run_shell_command(build_cmd, build_args)\nsrc <- file.path(lib_folder, paste0(\"lightgbm\", SHLIB_EXT), fsep = \"/\")\n\n# Packages with install.libs.R need to copy some artifacts into the\n# expected places in the package structure.\n# see https://cran.r-project.org/doc/manuals/r-devel/R-exts.html#Package-subdirectories,\n# especially the paragraph on install.libs.R\ndest <- file.path(R_PACKAGE_DIR, paste0(\"libs\", R_ARCH), fsep = \"/\")\ndir.create(dest, recursive = TRUE, showWarnings = FALSE)\nif (file.exists(src)) {\n message(paste0(\"Found library file: \", src, \" to move to \", dest))\n file.copy(src, dest, overwrite = TRUE)\n\n symbols_file <- file.path(source_dir, \"symbols.rds\")\n if (file.exists(symbols_file)) {\n file.copy(symbols_file, dest, overwrite = TRUE)\n }\n\n} else {\n stop(paste0(\"Cannot find lightgbm\", SHLIB_EXT))\n}\n\n# clean up the \"build\" directory\nif (dir.exists(build_dir)) {\n message(\"Removing 'build/' directory\")\n unlink(\n x = build_dir\n , recursive = TRUE\n , force = TRUE\n )\n}\n" }, { "path": "R-package/src/lightgbm_R.cpp", "content": "/*!\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#include \"lightgbm_R.h\"\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#ifndef R_NO_REMAP\n#define R_NO_REMAP\n#endif\n\n#ifndef R_USE_C99_IN_CXX\n#define R_USE_C99_IN_CXX\n#endif\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nR_altrep_class_t lgb_altrepped_char_vec;\nR_altrep_class_t lgb_altrepped_int_arr;\nR_altrep_class_t lgb_altrepped_dbl_arr;\n\ntemplate \nvoid delete_cpp_array(SEXP R_ptr) {\n T *ptr_to_cpp_obj = static_cast(R_ExternalPtrAddr(R_ptr));\n delete[] ptr_to_cpp_obj;\n R_ClearExternalPtr(R_ptr);\n}\n\nvoid delete_cpp_char_vec(SEXP R_ptr) {\n std::vector *ptr_to_cpp_obj = static_cast*>(R_ExternalPtrAddr(R_ptr));\n delete ptr_to_cpp_obj;\n R_ClearExternalPtr(R_ptr);\n}\n\n// Note: MSVC has issues with Altrep classes, so they are disabled for it.\n// See: https://github.com/lightgbm-org/LightGBM/pull/6213#issuecomment-2111025768\n#ifdef _MSC_VER\n# define LGB_NO_ALTREP\n#endif\n\n#ifndef LGB_NO_ALTREP\nSEXP make_altrepped_raw_vec(void *void_ptr) {\n std::unique_ptr> *ptr_to_cpp_vec = static_cast>*>(void_ptr);\n SEXP R_ptr = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue));\n SEXP R_raw = Rf_protect(R_new_altrep(lgb_altrepped_char_vec, R_NilValue, R_NilValue));\n\n R_SetExternalPtrAddr(R_ptr, ptr_to_cpp_vec->get());\n R_RegisterCFinalizerEx(R_ptr, delete_cpp_char_vec, TRUE);\n ptr_to_cpp_vec->release();\n\n R_set_altrep_data1(R_raw, R_ptr);\n Rf_unprotect(2);\n return R_raw;\n}\n#else\nSEXP make_r_raw_vec(void *void_ptr) {\n std::unique_ptr> *ptr_to_cpp_vec = static_cast>*>(void_ptr);\n R_xlen_t len = ptr_to_cpp_vec->get()->size();\n SEXP out = Rf_protect(Rf_allocVector(RAWSXP, len));\n std::copy(ptr_to_cpp_vec->get()->begin(), ptr_to_cpp_vec->get()->end(), reinterpret_cast(RAW(out)));\n Rf_unprotect(1);\n return out;\n}\n#define make_altrepped_raw_vec make_r_raw_vec\n#endif\n\nstd::vector* get_ptr_from_altrepped_raw(SEXP R_raw) {\n return static_cast*>(R_ExternalPtrAddr(R_altrep_data1(R_raw)));\n}\n\nR_xlen_t get_altrepped_raw_len(SEXP R_raw) {\n return get_ptr_from_altrepped_raw(R_raw)->size();\n}\n\nconst void* get_altrepped_raw_dataptr_or_null(SEXP R_raw) {\n return get_ptr_from_altrepped_raw(R_raw)->data();\n}\n\nvoid* get_altrepped_raw_dataptr(SEXP R_raw, Rboolean writeable) {\n return get_ptr_from_altrepped_raw(R_raw)->data();\n}\n\n#ifndef LGB_NO_ALTREP\ntemplate \nR_altrep_class_t get_altrep_class_for_type() {\n if (std::is_same::value) {\n return lgb_altrepped_dbl_arr;\n } else {\n return lgb_altrepped_int_arr;\n }\n}\n#else\ntemplate \nSEXPTYPE get_sexptype_class_for_type() {\n if (std::is_same::value) {\n return REALSXP;\n } else {\n return INTSXP;\n }\n}\n\ntemplate \nT* get_r_vec_ptr(SEXP x) {\n if (std::is_same::value) {\n return static_cast(static_cast(REAL(x)));\n } else {\n return static_cast(static_cast(INTEGER(x)));\n }\n}\n#endif\n\ntemplate \nstruct arr_and_len {\n T *arr;\n int64_t len;\n};\n\n#ifndef LGB_NO_ALTREP\ntemplate \nSEXP make_altrepped_vec_from_arr(void *void_ptr) {\n T *arr = static_cast*>(void_ptr)->arr;\n uint64_t len = static_cast*>(void_ptr)->len;\n SEXP R_ptr = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue));\n SEXP R_len = Rf_protect(Rf_allocVector(REALSXP, 1));\n SEXP R_vec = Rf_protect(R_new_altrep(get_altrep_class_for_type(), R_NilValue, R_NilValue));\n\n REAL(R_len)[0] = static_cast(len);\n R_SetExternalPtrAddr(R_ptr, arr);\n R_RegisterCFinalizerEx(R_ptr, delete_cpp_array, TRUE);\n\n R_set_altrep_data1(R_vec, R_ptr);\n R_set_altrep_data2(R_vec, R_len);\n Rf_unprotect(3);\n return R_vec;\n}\n#else\ntemplate \nSEXP make_R_vec_from_arr(void *void_ptr) {\n T *arr = static_cast*>(void_ptr)->arr;\n uint64_t len = static_cast*>(void_ptr)->len;\n SEXP out = Rf_protect(Rf_allocVector(get_sexptype_class_for_type(), len));\n std::copy(arr, arr + len, get_r_vec_ptr(out));\n Rf_unprotect(1);\n return out;\n}\n#define make_altrepped_vec_from_arr make_R_vec_from_arr\n#endif\n\nR_xlen_t get_altrepped_vec_len(SEXP R_vec) {\n return static_cast(Rf_asReal(R_altrep_data2(R_vec)));\n}\n\nconst void* get_altrepped_vec_dataptr_or_null(SEXP R_vec) {\n return R_ExternalPtrAddr(R_altrep_data1(R_vec));\n}\n\nvoid* get_altrepped_vec_dataptr(SEXP R_vec, Rboolean writeable) {\n return R_ExternalPtrAddr(R_altrep_data1(R_vec));\n}\n\n#define COL_MAJOR (0)\n\n#define MAX_LENGTH_ERR_MSG 1024\nchar R_errmsg_buffer[MAX_LENGTH_ERR_MSG];\nstruct LGBM_R_ErrorClass { SEXP cont_token; };\nvoid LGBM_R_save_exception_msg(const std::exception &err);\nvoid LGBM_R_save_exception_msg(const std::string &err);\n\n#define R_API_BEGIN() \\\n try {\n#define R_API_END() } \\\n catch(LGBM_R_ErrorClass &cont) { R_ContinueUnwind(cont.cont_token); } \\\n catch(std::exception& ex) { LGBM_R_save_exception_msg(ex); } \\\n catch(std::string& ex) { LGBM_R_save_exception_msg(ex); } \\\n catch(...) { Rf_error(\"unknown exception\"); } \\\n Rf_error(\"%s\", R_errmsg_buffer); \\\n return R_NilValue; /* <- won't be reached */\n\n#define CHECK_CALL(x) \\\n if ((x) != 0) { \\\n throw std::runtime_error(LGBM_GetLastError()); \\\n }\n\n// These are helper functions to allow doing a stack unwind\n// after an R allocation error, which would trigger a long jump.\nvoid LGBM_R_save_exception_msg(const std::exception &err) {\n std::snprintf(R_errmsg_buffer, MAX_LENGTH_ERR_MSG, \"%s\\n\", err.what());\n}\n\nvoid LGBM_R_save_exception_msg(const std::string &err) {\n std::snprintf(R_errmsg_buffer, MAX_LENGTH_ERR_MSG, \"%s\\n\", err.c_str());\n}\n\nSEXP wrapped_R_string(void *len) {\n return Rf_allocVector(STRSXP, *(reinterpret_cast(len)));\n}\n\nSEXP wrapped_R_raw(void *len) {\n return Rf_allocVector(RAWSXP, *(reinterpret_cast(len)));\n}\n\nSEXP wrapped_R_int(void *len) {\n return Rf_allocVector(INTSXP, *(reinterpret_cast(len)));\n}\n\nSEXP wrapped_R_real(void *len) {\n return Rf_allocVector(REALSXP, *(reinterpret_cast(len)));\n}\n\nSEXP wrapped_Rf_mkChar(void *txt) {\n return Rf_mkChar(reinterpret_cast(txt));\n}\n\nvoid throw_R_memerr(void *ptr_cont_token, Rboolean jump) {\n if (jump) {\n LGBM_R_ErrorClass err{*(reinterpret_cast(ptr_cont_token))};\n throw err;\n }\n}\n\nSEXP safe_R_string(R_xlen_t len, SEXP *cont_token) {\n return R_UnwindProtect(wrapped_R_string, reinterpret_cast(&len), throw_R_memerr, cont_token, *cont_token);\n}\n\nSEXP safe_R_raw(R_xlen_t len, SEXP *cont_token) {\n return R_UnwindProtect(wrapped_R_raw, reinterpret_cast(&len), throw_R_memerr, cont_token, *cont_token);\n}\n\nSEXP safe_R_int(R_xlen_t len, SEXP *cont_token) {\n return R_UnwindProtect(wrapped_R_int, reinterpret_cast(&len), throw_R_memerr, cont_token, *cont_token);\n}\n\nSEXP safe_R_real(R_xlen_t len, SEXP *cont_token) {\n return R_UnwindProtect(wrapped_R_real, reinterpret_cast(&len), throw_R_memerr, cont_token, *cont_token);\n}\n\nSEXP safe_R_mkChar(char *txt, SEXP *cont_token) {\n return R_UnwindProtect(wrapped_Rf_mkChar, reinterpret_cast(txt), throw_R_memerr, cont_token, *cont_token);\n}\n\nusing LightGBM::Common::Split;\nusing LightGBM::Log;\n\nSEXP LGBM_HandleIsNull_R(SEXP handle) {\n return Rf_ScalarLogical(R_ExternalPtrAddr(handle) == NULL);\n}\n\nvoid _DatasetFinalizer(SEXP handle) {\n LGBM_DatasetFree_R(handle);\n}\n\nSEXP LGBM_NullBoosterHandleError_R() {\n Rf_error(\n \"Attempting to use a Booster which no longer exists and/or cannot be restored. \"\n \"This can happen if the Booster's finalizer was called \"\n \"or if this Booster was saved through saveRDS() using 'serializable=FALSE'.\");\n return R_NilValue;\n}\n\nvoid _AssertBoosterHandleNotNull(SEXP handle) {\n if (Rf_isNull(handle) || !R_ExternalPtrAddr(handle)) {\n LGBM_NullBoosterHandleError_R();\n }\n}\n\nvoid _AssertDatasetHandleNotNull(SEXP handle) {\n if (Rf_isNull(handle) || !R_ExternalPtrAddr(handle)) {\n Rf_error(\n \"Attempting to use a Dataset which no longer exists. \"\n \"This can happen if the Dataset's finalizer was called or if this Dataset was saved with saveRDS(). \"\n \"To avoid this error in the future, use lgb.Dataset.save() or Dataset$save_binary() to save lightgbm Datasets.\");\n }\n}\n\nSEXP LGBM_DatasetCreateFromFile_R(SEXP filename,\n SEXP parameters,\n SEXP reference) {\n R_API_BEGIN();\n SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue));\n DatasetHandle handle = nullptr;\n DatasetHandle ref = nullptr;\n if (!Rf_isNull(reference)) {\n ref = R_ExternalPtrAddr(reference);\n }\n const char* filename_ptr = CHAR(Rf_protect(Rf_asChar(filename)));\n const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters)));\n CHECK_CALL(LGBM_DatasetCreateFromFile(filename_ptr, parameters_ptr, ref, &handle));\n R_SetExternalPtrAddr(ret, handle);\n R_RegisterCFinalizerEx(ret, _DatasetFinalizer, TRUE);\n Rf_unprotect(3);\n return ret;\n R_API_END();\n}\n\nSEXP LGBM_DatasetCreateFromCSC_R(SEXP indptr,\n SEXP indices,\n SEXP data,\n SEXP num_indptr,\n SEXP nelem,\n SEXP num_row,\n SEXP parameters,\n SEXP reference) {\n R_API_BEGIN();\n SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue));\n const int* p_indptr = INTEGER(indptr);\n const int* p_indices = INTEGER(indices);\n const double* p_data = REAL(data);\n int64_t nindptr = static_cast(Rf_asInteger(num_indptr));\n int64_t ndata = static_cast(Rf_asInteger(nelem));\n int64_t nrow = static_cast(Rf_asInteger(num_row));\n const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters)));\n DatasetHandle handle = nullptr;\n DatasetHandle ref = nullptr;\n if (!Rf_isNull(reference)) {\n ref = R_ExternalPtrAddr(reference);\n }\n CHECK_CALL(LGBM_DatasetCreateFromCSC(p_indptr, C_API_DTYPE_INT32, p_indices,\n p_data, C_API_DTYPE_FLOAT64, nindptr, ndata,\n nrow, parameters_ptr, ref, &handle));\n R_SetExternalPtrAddr(ret, handle);\n R_RegisterCFinalizerEx(ret, _DatasetFinalizer, TRUE);\n Rf_unprotect(2);\n return ret;\n R_API_END();\n}\n\nSEXP LGBM_DatasetCreateFromMat_R(SEXP data,\n SEXP num_row,\n SEXP num_col,\n SEXP parameters,\n SEXP reference) {\n R_API_BEGIN();\n SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue));\n int32_t nrow = static_cast(Rf_asInteger(num_row));\n int32_t ncol = static_cast(Rf_asInteger(num_col));\n double* p_mat = REAL(data);\n const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters)));\n DatasetHandle handle = nullptr;\n DatasetHandle ref = nullptr;\n if (!Rf_isNull(reference)) {\n ref = R_ExternalPtrAddr(reference);\n }\n CHECK_CALL(LGBM_DatasetCreateFromMat(p_mat, C_API_DTYPE_FLOAT64, nrow, ncol, COL_MAJOR,\n parameters_ptr, ref, &handle));\n R_SetExternalPtrAddr(ret, handle);\n R_RegisterCFinalizerEx(ret, _DatasetFinalizer, TRUE);\n Rf_unprotect(2);\n return ret;\n R_API_END();\n}\n\nSEXP LGBM_DatasetGetSubset_R(SEXP handle,\n SEXP used_row_indices,\n SEXP len_used_row_indices,\n SEXP parameters) {\n R_API_BEGIN();\n _AssertDatasetHandleNotNull(handle);\n SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue));\n int32_t len = static_cast(Rf_asInteger(len_used_row_indices));\n std::unique_ptr idxvec(new int32_t[len]);\n // convert from one-based to zero-based index\n const int *used_row_indices_ = INTEGER(used_row_indices);\n#ifndef _MSC_VER\n#pragma omp simd\n#endif\n for (int32_t i = 0; i < len; ++i) {\n idxvec[i] = static_cast(used_row_indices_[i] - 1);\n }\n const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters)));\n DatasetHandle res = nullptr;\n CHECK_CALL(LGBM_DatasetGetSubset(R_ExternalPtrAddr(handle),\n idxvec.get(), len, parameters_ptr,\n &res));\n R_SetExternalPtrAddr(ret, res);\n R_RegisterCFinalizerEx(ret, _DatasetFinalizer, TRUE);\n Rf_unprotect(2);\n return ret;\n R_API_END();\n}\n\nSEXP LGBM_DatasetSetFeatureNames_R(SEXP handle,\n SEXP feature_names) {\n R_API_BEGIN();\n _AssertDatasetHandleNotNull(handle);\n auto vec_names = Split(CHAR(Rf_protect(Rf_asChar(feature_names))), '\\t');\n int len = static_cast(vec_names.size());\n std::unique_ptr vec_sptr(new const char*[len]);\n for (int i = 0; i < len; ++i) {\n vec_sptr[i] = vec_names[i].c_str();\n }\n CHECK_CALL(LGBM_DatasetSetFeatureNames(R_ExternalPtrAddr(handle),\n vec_sptr.get(), len));\n Rf_unprotect(1);\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_DatasetGetFeatureNames_R(SEXP handle) {\n SEXP cont_token = Rf_protect(R_MakeUnwindCont());\n R_API_BEGIN();\n _AssertDatasetHandleNotNull(handle);\n SEXP feature_names;\n int len = 0;\n CHECK_CALL(LGBM_DatasetGetNumFeature(R_ExternalPtrAddr(handle), &len));\n const size_t reserved_string_size = 256;\n std::vector> names(len);\n std::vector ptr_names(len);\n for (int i = 0; i < len; ++i) {\n names[i].resize(reserved_string_size);\n ptr_names[i] = names[i].data();\n }\n int out_len;\n size_t required_string_size;\n CHECK_CALL(\n LGBM_DatasetGetFeatureNames(\n R_ExternalPtrAddr(handle),\n len, &out_len,\n reserved_string_size, &required_string_size,\n ptr_names.data()));\n // if any feature names were larger than allocated size,\n // allow for a larger size and try again\n if (required_string_size > reserved_string_size) {\n for (int i = 0; i < len; ++i) {\n names[i].resize(required_string_size);\n ptr_names[i] = names[i].data();\n }\n CHECK_CALL(\n LGBM_DatasetGetFeatureNames(\n R_ExternalPtrAddr(handle),\n len,\n &out_len,\n required_string_size,\n &required_string_size,\n ptr_names.data()));\n }\n CHECK_EQ(len, out_len);\n feature_names = Rf_protect(safe_R_string(static_cast(len), &cont_token));\n for (int i = 0; i < len; ++i) {\n SET_STRING_ELT(feature_names, i, safe_R_mkChar(ptr_names[i], &cont_token));\n }\n Rf_unprotect(2);\n return feature_names;\n R_API_END();\n}\n\nSEXP LGBM_DatasetSaveBinary_R(SEXP handle,\n SEXP filename) {\n R_API_BEGIN();\n _AssertDatasetHandleNotNull(handle);\n const char* filename_ptr = CHAR(Rf_protect(Rf_asChar(filename)));\n CHECK_CALL(LGBM_DatasetSaveBinary(R_ExternalPtrAddr(handle),\n filename_ptr));\n Rf_unprotect(1);\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_DatasetFree_R(SEXP handle) {\n R_API_BEGIN();\n if (!Rf_isNull(handle) && R_ExternalPtrAddr(handle)) {\n CHECK_CALL(LGBM_DatasetFree(R_ExternalPtrAddr(handle)));\n R_ClearExternalPtr(handle);\n }\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_DatasetSetField_R(SEXP handle,\n SEXP field_name,\n SEXP field_data,\n SEXP num_element) {\n R_API_BEGIN();\n _AssertDatasetHandleNotNull(handle);\n int len = Rf_asInteger(num_element);\n const char* name = CHAR(Rf_protect(Rf_asChar(field_name)));\n if (!strcmp(\"group\", name) || !strcmp(\"query\", name)) {\n CHECK_CALL(LGBM_DatasetSetField(R_ExternalPtrAddr(handle), name, INTEGER(field_data), len, C_API_DTYPE_INT32));\n } else if (!strcmp(\"init_score\", name)) {\n CHECK_CALL(LGBM_DatasetSetField(R_ExternalPtrAddr(handle), name, REAL(field_data), len, C_API_DTYPE_FLOAT64));\n } else {\n std::unique_ptr vec(new float[len]);\n std::copy(REAL(field_data), REAL(field_data) + len, vec.get());\n CHECK_CALL(LGBM_DatasetSetField(R_ExternalPtrAddr(handle), name, vec.get(), len, C_API_DTYPE_FLOAT32));\n }\n Rf_unprotect(1);\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_DatasetGetField_R(SEXP handle,\n SEXP field_name,\n SEXP field_data) {\n R_API_BEGIN();\n _AssertDatasetHandleNotNull(handle);\n const char* name = CHAR(Rf_protect(Rf_asChar(field_name)));\n int out_len = 0;\n int out_type = 0;\n const void* res;\n CHECK_CALL(LGBM_DatasetGetField(R_ExternalPtrAddr(handle), name, &out_len, &res, &out_type));\n if (!strcmp(\"group\", name) || !strcmp(\"query\", name)) {\n auto p_data = reinterpret_cast(res);\n // convert from boundaries to size\n int *field_data_ = INTEGER(field_data);\n#ifndef _MSC_VER\n#pragma omp simd\n#endif\n for (int i = 0; i < out_len - 1; ++i) {\n field_data_[i] = p_data[i + 1] - p_data[i];\n }\n } else if (!strcmp(\"init_score\", name)) {\n auto p_data = reinterpret_cast(res);\n std::copy(p_data, p_data + out_len, REAL(field_data));\n } else {\n auto p_data = reinterpret_cast(res);\n std::copy(p_data, p_data + out_len, REAL(field_data));\n }\n Rf_unprotect(1);\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_DatasetGetFieldSize_R(SEXP handle,\n SEXP field_name,\n SEXP out) {\n R_API_BEGIN();\n _AssertDatasetHandleNotNull(handle);\n const char* name = CHAR(Rf_protect(Rf_asChar(field_name)));\n int out_len = 0;\n int out_type = 0;\n const void* res;\n CHECK_CALL(LGBM_DatasetGetField(R_ExternalPtrAddr(handle), name, &out_len, &res, &out_type));\n if (!strcmp(\"group\", name) || !strcmp(\"query\", name)) {\n out_len -= 1;\n }\n INTEGER(out)[0] = out_len;\n Rf_unprotect(1);\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_DatasetUpdateParamChecking_R(SEXP old_params,\n SEXP new_params) {\n R_API_BEGIN();\n const char* old_params_ptr = CHAR(Rf_protect(Rf_asChar(old_params)));\n const char* new_params_ptr = CHAR(Rf_protect(Rf_asChar(new_params)));\n CHECK_CALL(LGBM_DatasetUpdateParamChecking(old_params_ptr, new_params_ptr));\n Rf_unprotect(2);\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_DatasetGetNumData_R(SEXP handle, SEXP out) {\n R_API_BEGIN();\n _AssertDatasetHandleNotNull(handle);\n int nrow;\n CHECK_CALL(LGBM_DatasetGetNumData(R_ExternalPtrAddr(handle), &nrow));\n INTEGER(out)[0] = nrow;\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_DatasetGetNumFeature_R(SEXP handle,\n SEXP out) {\n R_API_BEGIN();\n _AssertDatasetHandleNotNull(handle);\n int nfeature;\n CHECK_CALL(LGBM_DatasetGetNumFeature(R_ExternalPtrAddr(handle), &nfeature));\n INTEGER(out)[0] = nfeature;\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_DatasetGetFeatureNumBin_R(SEXP handle, SEXP feature_idx, SEXP out) {\n R_API_BEGIN();\n _AssertDatasetHandleNotNull(handle);\n int feature = Rf_asInteger(feature_idx);\n int nbins;\n CHECK_CALL(LGBM_DatasetGetFeatureNumBin(R_ExternalPtrAddr(handle), feature, &nbins));\n INTEGER(out)[0] = nbins;\n return R_NilValue;\n R_API_END();\n}\n\n// --- start Booster interfaces\n\nvoid _BoosterFinalizer(SEXP handle) {\n LGBM_BoosterFree_R(handle);\n}\n\nSEXP LGBM_BoosterFree_R(SEXP handle) {\n R_API_BEGIN();\n if (!Rf_isNull(handle) && R_ExternalPtrAddr(handle)) {\n CHECK_CALL(LGBM_BoosterFree(R_ExternalPtrAddr(handle)));\n R_ClearExternalPtr(handle);\n }\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterCreate_R(SEXP train_data,\n SEXP parameters) {\n R_API_BEGIN();\n _AssertDatasetHandleNotNull(train_data);\n SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue));\n const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters)));\n BoosterHandle handle = nullptr;\n CHECK_CALL(LGBM_BoosterCreate(R_ExternalPtrAddr(train_data), parameters_ptr, &handle));\n R_SetExternalPtrAddr(ret, handle);\n R_RegisterCFinalizerEx(ret, _BoosterFinalizer, TRUE);\n Rf_unprotect(2);\n return ret;\n R_API_END();\n}\n\nSEXP LGBM_BoosterCreateFromModelfile_R(SEXP filename) {\n R_API_BEGIN();\n SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue));\n int out_num_iterations = 0;\n const char* filename_ptr = CHAR(Rf_protect(Rf_asChar(filename)));\n BoosterHandle handle = nullptr;\n CHECK_CALL(LGBM_BoosterCreateFromModelfile(filename_ptr, &out_num_iterations, &handle));\n R_SetExternalPtrAddr(ret, handle);\n R_RegisterCFinalizerEx(ret, _BoosterFinalizer, TRUE);\n Rf_unprotect(2);\n return ret;\n R_API_END();\n}\n\nSEXP LGBM_BoosterLoadModelFromString_R(SEXP model_str) {\n R_API_BEGIN();\n SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue));\n SEXP temp = NULL;\n int n_protected = 1;\n int out_num_iterations = 0;\n const char* model_str_ptr = nullptr;\n switch (TYPEOF(model_str)) {\n case RAWSXP: {\n model_str_ptr = reinterpret_cast(RAW(model_str));\n break;\n }\n case CHARSXP: {\n model_str_ptr = reinterpret_cast(CHAR(model_str));\n break;\n }\n case STRSXP: {\n temp = Rf_protect(STRING_ELT(model_str, 0));\n n_protected++;\n model_str_ptr = reinterpret_cast(CHAR(temp));\n }\n }\n BoosterHandle handle = nullptr;\n CHECK_CALL(LGBM_BoosterLoadModelFromString(model_str_ptr, &out_num_iterations, &handle));\n R_SetExternalPtrAddr(ret, handle);\n R_RegisterCFinalizerEx(ret, _BoosterFinalizer, TRUE);\n Rf_unprotect(n_protected);\n return ret;\n R_API_END();\n}\n\nSEXP LGBM_BoosterMerge_R(SEXP handle,\n SEXP other_handle) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n _AssertBoosterHandleNotNull(other_handle);\n CHECK_CALL(LGBM_BoosterMerge(R_ExternalPtrAddr(handle), R_ExternalPtrAddr(other_handle)));\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterAddValidData_R(SEXP handle,\n SEXP valid_data) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n _AssertDatasetHandleNotNull(valid_data);\n CHECK_CALL(LGBM_BoosterAddValidData(R_ExternalPtrAddr(handle), R_ExternalPtrAddr(valid_data)));\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterResetTrainingData_R(SEXP handle,\n SEXP train_data) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n _AssertDatasetHandleNotNull(train_data);\n CHECK_CALL(LGBM_BoosterResetTrainingData(R_ExternalPtrAddr(handle), R_ExternalPtrAddr(train_data)));\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterResetParameter_R(SEXP handle,\n SEXP parameters) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n const char* parameters_ptr = CHAR(Rf_protect(Rf_asChar(parameters)));\n CHECK_CALL(LGBM_BoosterResetParameter(R_ExternalPtrAddr(handle), parameters_ptr));\n Rf_unprotect(1);\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterGetNumClasses_R(SEXP handle,\n SEXP out) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int num_class;\n CHECK_CALL(LGBM_BoosterGetNumClasses(R_ExternalPtrAddr(handle), &num_class));\n INTEGER(out)[0] = num_class;\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterGetNumFeature_R(SEXP handle) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int out = 0;\n CHECK_CALL(LGBM_BoosterGetNumFeature(R_ExternalPtrAddr(handle), &out));\n return Rf_ScalarInteger(out);\n R_API_END();\n}\n\nSEXP LGBM_BoosterUpdateOneIter_R(SEXP handle) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int produced_empty_tree = 0;\n CHECK_CALL(LGBM_BoosterUpdateOneIter(R_ExternalPtrAddr(handle), &produced_empty_tree));\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterUpdateOneIterCustom_R(SEXP handle,\n SEXP grad,\n SEXP hess,\n SEXP len) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int produced_empty_tree = 0;\n int int_len = Rf_asInteger(len);\n std::unique_ptr tgrad(new float[int_len]), thess(new float[int_len]);\n std::copy(REAL(grad), REAL(grad) + int_len, tgrad.get());\n std::copy(REAL(hess), REAL(hess) + int_len, thess.get());\n CHECK_CALL(LGBM_BoosterUpdateOneIterCustom(R_ExternalPtrAddr(handle), tgrad.get(), thess.get(),\n &produced_empty_tree));\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterRollbackOneIter_R(SEXP handle) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n CHECK_CALL(LGBM_BoosterRollbackOneIter(R_ExternalPtrAddr(handle)));\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterGetCurrentIteration_R(SEXP handle, SEXP out) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int out_iteration;\n CHECK_CALL(LGBM_BoosterGetCurrentIteration(R_ExternalPtrAddr(handle), &out_iteration));\n INTEGER(out)[0] = out_iteration;\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterNumModelPerIteration_R(SEXP handle, SEXP out) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int models_per_iter;\n CHECK_CALL(LGBM_BoosterNumModelPerIteration(R_ExternalPtrAddr(handle), &models_per_iter));\n INTEGER(out)[0] = models_per_iter;\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterNumberOfTotalModel_R(SEXP handle, SEXP out) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int total_models;\n CHECK_CALL(LGBM_BoosterNumberOfTotalModel(R_ExternalPtrAddr(handle), &total_models));\n INTEGER(out)[0] = total_models;\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterGetUpperBoundValue_R(SEXP handle,\n SEXP out_result) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n double* ptr_ret = REAL(out_result);\n CHECK_CALL(LGBM_BoosterGetUpperBoundValue(R_ExternalPtrAddr(handle), ptr_ret));\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterGetLowerBoundValue_R(SEXP handle,\n SEXP out_result) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n double* ptr_ret = REAL(out_result);\n CHECK_CALL(LGBM_BoosterGetLowerBoundValue(R_ExternalPtrAddr(handle), ptr_ret));\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterGetEvalNames_R(SEXP handle) {\n SEXP cont_token = Rf_protect(R_MakeUnwindCont());\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n SEXP eval_names;\n int len;\n CHECK_CALL(LGBM_BoosterGetEvalCounts(R_ExternalPtrAddr(handle), &len));\n const size_t reserved_string_size = 128;\n std::vector> names(len);\n std::vector ptr_names(len);\n for (int i = 0; i < len; ++i) {\n names[i].resize(reserved_string_size);\n ptr_names[i] = names[i].data();\n }\n\n int out_len;\n size_t required_string_size;\n CHECK_CALL(\n LGBM_BoosterGetEvalNames(\n R_ExternalPtrAddr(handle),\n len, &out_len,\n reserved_string_size, &required_string_size,\n ptr_names.data()));\n // if any eval names were larger than allocated size,\n // allow for a larger size and try again\n if (required_string_size > reserved_string_size) {\n for (int i = 0; i < len; ++i) {\n names[i].resize(required_string_size);\n ptr_names[i] = names[i].data();\n }\n CHECK_CALL(\n LGBM_BoosterGetEvalNames(\n R_ExternalPtrAddr(handle),\n len,\n &out_len,\n required_string_size,\n &required_string_size,\n ptr_names.data()));\n }\n CHECK_EQ(out_len, len);\n eval_names = Rf_protect(safe_R_string(static_cast(len), &cont_token));\n for (int i = 0; i < len; ++i) {\n SET_STRING_ELT(eval_names, i, safe_R_mkChar(ptr_names[i], &cont_token));\n }\n Rf_unprotect(2);\n return eval_names;\n R_API_END();\n}\n\nSEXP LGBM_BoosterGetEval_R(SEXP handle,\n SEXP data_idx,\n SEXP out_result) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int len;\n CHECK_CALL(LGBM_BoosterGetEvalCounts(R_ExternalPtrAddr(handle), &len));\n double* ptr_ret = REAL(out_result);\n int out_len;\n CHECK_CALL(LGBM_BoosterGetEval(R_ExternalPtrAddr(handle), Rf_asInteger(data_idx), &out_len, ptr_ret));\n CHECK_EQ(out_len, len);\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterGetNumPredict_R(SEXP handle,\n SEXP data_idx,\n SEXP out) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int64_t len;\n CHECK_CALL(LGBM_BoosterGetNumPredict(R_ExternalPtrAddr(handle), Rf_asInteger(data_idx), &len));\n INTEGER(out)[0] = static_cast(len);\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterGetPredict_R(SEXP handle,\n SEXP data_idx,\n SEXP out_result) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n double* ptr_ret = REAL(out_result);\n int64_t out_len;\n CHECK_CALL(LGBM_BoosterGetPredict(R_ExternalPtrAddr(handle), Rf_asInteger(data_idx), &out_len, ptr_ret));\n return R_NilValue;\n R_API_END();\n}\n\nint GetPredictType(SEXP is_rawscore, SEXP is_leafidx, SEXP is_predcontrib) {\n int pred_type = C_API_PREDICT_NORMAL;\n if (Rf_asInteger(is_rawscore)) {\n pred_type = C_API_PREDICT_RAW_SCORE;\n }\n if (Rf_asInteger(is_leafidx)) {\n pred_type = C_API_PREDICT_LEAF_INDEX;\n }\n if (Rf_asInteger(is_predcontrib)) {\n pred_type = C_API_PREDICT_CONTRIB;\n }\n return pred_type;\n}\n\nSEXP LGBM_BoosterPredictForFile_R(SEXP handle,\n SEXP data_filename,\n SEXP data_has_header,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter,\n SEXP result_filename) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n const char* data_filename_ptr = CHAR(Rf_protect(Rf_asChar(data_filename)));\n const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter)));\n const char* result_filename_ptr = CHAR(Rf_protect(Rf_asChar(result_filename)));\n int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib);\n CHECK_CALL(LGBM_BoosterPredictForFile(R_ExternalPtrAddr(handle), data_filename_ptr,\n Rf_asInteger(data_has_header), pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), parameter_ptr,\n result_filename_ptr));\n Rf_unprotect(3);\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterCalcNumPredict_R(SEXP handle,\n SEXP num_row,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP out_len) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib);\n int64_t len = 0;\n CHECK_CALL(LGBM_BoosterCalcNumPredict(R_ExternalPtrAddr(handle), Rf_asInteger(num_row),\n pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), &len));\n INTEGER(out_len)[0] = static_cast(len);\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterPredictForCSC_R(SEXP handle,\n SEXP indptr,\n SEXP indices,\n SEXP data,\n SEXP num_indptr,\n SEXP nelem,\n SEXP num_row,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter,\n SEXP out_result) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib);\n const int* p_indptr = INTEGER(indptr);\n const int32_t* p_indices = reinterpret_cast(INTEGER(indices));\n const double* p_data = REAL(data);\n int64_t nindptr = static_cast(Rf_asInteger(num_indptr));\n int64_t ndata = static_cast(Rf_asInteger(nelem));\n int64_t nrow = static_cast(Rf_asInteger(num_row));\n double* ptr_ret = REAL(out_result);\n int64_t out_len;\n const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter)));\n CHECK_CALL(LGBM_BoosterPredictForCSC(R_ExternalPtrAddr(handle),\n p_indptr, C_API_DTYPE_INT32, p_indices,\n p_data, C_API_DTYPE_FLOAT64, nindptr, ndata,\n nrow, pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), parameter_ptr, &out_len, ptr_ret));\n Rf_unprotect(1);\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterPredictForCSR_R(SEXP handle,\n SEXP indptr,\n SEXP indices,\n SEXP data,\n SEXP ncols,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter,\n SEXP out_result) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib);\n const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter)));\n int64_t out_len;\n CHECK_CALL(LGBM_BoosterPredictForCSR(R_ExternalPtrAddr(handle),\n INTEGER(indptr), C_API_DTYPE_INT32, INTEGER(indices),\n REAL(data), C_API_DTYPE_FLOAT64,\n Rf_xlength(indptr), Rf_xlength(data), Rf_asInteger(ncols),\n pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration),\n parameter_ptr, &out_len, REAL(out_result)));\n Rf_unprotect(1);\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterPredictForCSRSingleRow_R(SEXP handle,\n SEXP indices,\n SEXP data,\n SEXP ncols,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter,\n SEXP out_result) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib);\n const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter)));\n int nnz = static_cast(Rf_xlength(data));\n const int indptr[] = {0, nnz};\n int64_t out_len;\n CHECK_CALL(LGBM_BoosterPredictForCSRSingleRow(R_ExternalPtrAddr(handle),\n indptr, C_API_DTYPE_INT32, INTEGER(indices),\n REAL(data), C_API_DTYPE_FLOAT64,\n 2, nnz, Rf_asInteger(ncols),\n pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration),\n parameter_ptr, &out_len, REAL(out_result)));\n Rf_unprotect(1);\n return R_NilValue;\n R_API_END();\n}\n\nvoid LGBM_FastConfigFree_wrapped(SEXP handle) {\n LGBM_FastConfigFree(static_cast(R_ExternalPtrAddr(handle)));\n}\n\nSEXP LGBM_BoosterPredictForCSRSingleRowFastInit_R(SEXP handle,\n SEXP ncols,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib);\n SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue));\n const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter)));\n FastConfigHandle out_fastConfig;\n CHECK_CALL(LGBM_BoosterPredictForCSRSingleRowFastInit(R_ExternalPtrAddr(handle),\n pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration),\n C_API_DTYPE_FLOAT64, Rf_asInteger(ncols),\n parameter_ptr, &out_fastConfig));\n R_SetExternalPtrAddr(ret, out_fastConfig);\n R_RegisterCFinalizerEx(ret, LGBM_FastConfigFree_wrapped, TRUE);\n Rf_unprotect(2);\n return ret;\n R_API_END();\n}\n\nSEXP LGBM_BoosterPredictForCSRSingleRowFast_R(SEXP handle_fastConfig,\n SEXP indices,\n SEXP data,\n SEXP out_result) {\n R_API_BEGIN();\n int nnz = static_cast(Rf_xlength(data));\n const int indptr[] = {0, nnz};\n int64_t out_len;\n CHECK_CALL(LGBM_BoosterPredictForCSRSingleRowFast(R_ExternalPtrAddr(handle_fastConfig),\n indptr, C_API_DTYPE_INT32, INTEGER(indices),\n REAL(data),\n 2, nnz,\n &out_len, REAL(out_result)));\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterPredictForMat_R(SEXP handle,\n SEXP data,\n SEXP num_row,\n SEXP num_col,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter,\n SEXP out_result) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib);\n int32_t nrow = static_cast(Rf_asInteger(num_row));\n int32_t ncol = static_cast(Rf_asInteger(num_col));\n const double* p_mat = REAL(data);\n double* ptr_ret = REAL(out_result);\n const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter)));\n int64_t out_len;\n CHECK_CALL(LGBM_BoosterPredictForMat(R_ExternalPtrAddr(handle),\n p_mat, C_API_DTYPE_FLOAT64, nrow, ncol, COL_MAJOR,\n pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), parameter_ptr, &out_len, ptr_ret));\n Rf_unprotect(1);\n return R_NilValue;\n R_API_END();\n}\n\nstruct SparseOutputPointers {\n void* indptr;\n int32_t* indices;\n void* data;\n SparseOutputPointers(void* indptr, int32_t* indices, void* data)\n : indptr(indptr), indices(indices), data(data) {}\n};\n\nvoid delete_SparseOutputPointers(SparseOutputPointers *ptr) {\n LGBM_BoosterFreePredictSparse(ptr->indptr, ptr->indices, ptr->data, C_API_DTYPE_INT32, C_API_DTYPE_FLOAT64);\n delete ptr;\n}\n\nSEXP LGBM_BoosterPredictSparseOutput_R(SEXP handle,\n SEXP indptr,\n SEXP indices,\n SEXP data,\n SEXP is_csr,\n SEXP nrows,\n SEXP ncols,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter) {\n SEXP cont_token = Rf_protect(R_MakeUnwindCont());\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n const char* out_names[] = {\"indptr\", \"indices\", \"data\", \"\"};\n SEXP out = Rf_protect(Rf_mkNamed(VECSXP, out_names));\n const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter)));\n\n int64_t out_len[2];\n void *out_indptr;\n int32_t *out_indices;\n void *out_data;\n\n CHECK_CALL(LGBM_BoosterPredictSparseOutput(R_ExternalPtrAddr(handle),\n INTEGER(indptr), C_API_DTYPE_INT32, INTEGER(indices),\n REAL(data), C_API_DTYPE_FLOAT64,\n Rf_xlength(indptr), Rf_xlength(data),\n Rf_asLogical(is_csr)? Rf_asInteger(ncols) : Rf_asInteger(nrows),\n C_API_PREDICT_CONTRIB, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration),\n parameter_ptr,\n Rf_asLogical(is_csr)? C_API_MATRIX_TYPE_CSR : C_API_MATRIX_TYPE_CSC,\n out_len, &out_indptr, &out_indices, &out_data));\n\n std::unique_ptr pointers_struct = {\n new SparseOutputPointers(\n out_indptr,\n out_indices,\n out_data),\n &delete_SparseOutputPointers\n };\n\n arr_and_len indptr_str{static_cast(out_indptr), out_len[1]};\n SET_VECTOR_ELT(\n out, 0,\n R_UnwindProtect(make_altrepped_vec_from_arr,\n static_cast(&indptr_str), throw_R_memerr, &cont_token, cont_token));\n pointers_struct->indptr = nullptr;\n\n arr_and_len indices_str{static_cast(out_indices), out_len[0]};\n SET_VECTOR_ELT(\n out, 1,\n R_UnwindProtect(make_altrepped_vec_from_arr,\n static_cast(&indices_str), throw_R_memerr, &cont_token, cont_token));\n pointers_struct->indices = nullptr;\n\n arr_and_len data_str{static_cast(out_data), out_len[0]};\n SET_VECTOR_ELT(\n out, 2,\n R_UnwindProtect(make_altrepped_vec_from_arr,\n static_cast(&data_str), throw_R_memerr, &cont_token, cont_token));\n pointers_struct->data = nullptr;\n\n Rf_unprotect(3);\n return out;\n R_API_END();\n}\n\nSEXP LGBM_BoosterPredictForMatSingleRow_R(SEXP handle,\n SEXP data,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter,\n SEXP out_result) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib);\n const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter)));\n double* ptr_ret = REAL(out_result);\n int64_t out_len;\n CHECK_CALL(LGBM_BoosterPredictForMatSingleRow(R_ExternalPtrAddr(handle),\n REAL(data), C_API_DTYPE_FLOAT64, Rf_xlength(data), 1,\n pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration),\n parameter_ptr, &out_len, ptr_ret));\n Rf_unprotect(1);\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterPredictForMatSingleRowFastInit_R(SEXP handle,\n SEXP ncols,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int pred_type = GetPredictType(is_rawscore, is_leafidx, is_predcontrib);\n SEXP ret = Rf_protect(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue));\n const char* parameter_ptr = CHAR(Rf_protect(Rf_asChar(parameter)));\n FastConfigHandle out_fastConfig;\n CHECK_CALL(LGBM_BoosterPredictForMatSingleRowFastInit(R_ExternalPtrAddr(handle),\n pred_type, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration),\n C_API_DTYPE_FLOAT64, Rf_asInteger(ncols),\n parameter_ptr, &out_fastConfig));\n R_SetExternalPtrAddr(ret, out_fastConfig);\n R_RegisterCFinalizerEx(ret, LGBM_FastConfigFree_wrapped, TRUE);\n Rf_unprotect(2);\n return ret;\n R_API_END();\n}\n\nSEXP LGBM_BoosterPredictForMatSingleRowFast_R(SEXP handle_fastConfig,\n SEXP data,\n SEXP out_result) {\n R_API_BEGIN();\n int64_t out_len;\n CHECK_CALL(LGBM_BoosterPredictForMatSingleRowFast(R_ExternalPtrAddr(handle_fastConfig),\n REAL(data), &out_len, REAL(out_result)));\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_BoosterSaveModel_R(SEXP handle,\n SEXP num_iteration,\n SEXP feature_importance_type,\n SEXP filename,\n SEXP start_iteration) {\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n const char* filename_ptr = CHAR(Rf_protect(Rf_asChar(filename)));\n CHECK_CALL(LGBM_BoosterSaveModel(R_ExternalPtrAddr(handle), Rf_asInteger(start_iteration), Rf_asInteger(num_iteration), Rf_asInteger(feature_importance_type), filename_ptr));\n Rf_unprotect(1);\n return R_NilValue;\n R_API_END();\n}\n\n// Note: for some reason, MSVC crashes when an error is thrown here\n// if the buffer variable is defined as 'std::unique_ptr>',\n// but not if it is defined as ''.\n#ifndef _MSC_VER\nSEXP LGBM_BoosterSaveModelToString_R(SEXP handle,\n SEXP num_iteration,\n SEXP feature_importance_type,\n SEXP start_iteration) {\n SEXP cont_token = Rf_protect(R_MakeUnwindCont());\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int64_t out_len = 0;\n int64_t buf_len = 1024 * 1024;\n int num_iter = Rf_asInteger(num_iteration);\n int start_iter = Rf_asInteger(start_iteration);\n int importance_type = Rf_asInteger(feature_importance_type);\n std::unique_ptr> inner_char_buf(new std::vector(buf_len));\n CHECK_CALL(LGBM_BoosterSaveModelToString(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, buf_len, &out_len, inner_char_buf->data()));\n inner_char_buf->resize(out_len);\n if (out_len > buf_len) {\n CHECK_CALL(LGBM_BoosterSaveModelToString(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, out_len, &out_len, inner_char_buf->data()));\n }\n SEXP out = R_UnwindProtect(make_altrepped_raw_vec, &inner_char_buf, throw_R_memerr, &cont_token, cont_token);\n Rf_unprotect(1);\n return out;\n R_API_END();\n}\n#else\nSEXP LGBM_BoosterSaveModelToString_R(SEXP handle,\n SEXP num_iteration,\n SEXP feature_importance_type,\n SEXP start_iteration) {\n SEXP cont_token = Rf_protect(R_MakeUnwindCont());\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n int64_t out_len = 0;\n int64_t buf_len = 1024 * 1024;\n int num_iter = Rf_asInteger(num_iteration);\n int start_iter = Rf_asInteger(start_iteration);\n int importance_type = Rf_asInteger(feature_importance_type);\n std::vector inner_char_buf(buf_len);\n CHECK_CALL(LGBM_BoosterSaveModelToString(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, buf_len, &out_len, inner_char_buf.data()));\n SEXP model_str = Rf_protect(safe_R_raw(out_len, &cont_token));\n // if the model string was larger than the initial buffer, call the function again, writing directly to the R object\n if (out_len > buf_len) {\n CHECK_CALL(LGBM_BoosterSaveModelToString(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, out_len, &out_len, reinterpret_cast(RAW(model_str))));\n } else {\n std::copy(inner_char_buf.begin(), inner_char_buf.begin() + out_len, reinterpret_cast(RAW(model_str)));\n }\n Rf_unprotect(2);\n return model_str;\n R_API_END();\n}\n#endif\n\nSEXP LGBM_BoosterDumpModel_R(SEXP handle,\n SEXP num_iteration,\n SEXP feature_importance_type,\n SEXP start_iteration) {\n SEXP cont_token = Rf_protect(R_MakeUnwindCont());\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n SEXP model_str;\n int64_t out_len = 0;\n int64_t buf_len = 1024 * 1024;\n int num_iter = Rf_asInteger(num_iteration);\n int start_iter = Rf_asInteger(start_iteration);\n int importance_type = Rf_asInteger(feature_importance_type);\n std::vector inner_char_buf(buf_len);\n CHECK_CALL(LGBM_BoosterDumpModel(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, buf_len, &out_len, inner_char_buf.data()));\n // if the model string was larger than the initial buffer, allocate a bigger buffer and try again\n if (out_len > buf_len) {\n inner_char_buf.resize(out_len);\n CHECK_CALL(LGBM_BoosterDumpModel(R_ExternalPtrAddr(handle), start_iter, num_iter, importance_type, out_len, &out_len, inner_char_buf.data()));\n }\n model_str = Rf_protect(safe_R_string(static_cast(1), &cont_token));\n SET_STRING_ELT(model_str, 0, safe_R_mkChar(inner_char_buf.data(), &cont_token));\n Rf_unprotect(2);\n return model_str;\n R_API_END();\n}\n\nSEXP LGBM_DumpParamAliases_R() {\n SEXP cont_token = Rf_protect(R_MakeUnwindCont());\n R_API_BEGIN();\n SEXP aliases_str;\n int64_t out_len = 0;\n int64_t buf_len = 1024 * 1024;\n std::vector inner_char_buf(buf_len);\n CHECK_CALL(LGBM_DumpParamAliases(buf_len, &out_len, inner_char_buf.data()));\n // if aliases string was larger than the initial buffer, allocate a bigger buffer and try again\n if (out_len > buf_len) {\n inner_char_buf.resize(out_len);\n CHECK_CALL(LGBM_DumpParamAliases(out_len, &out_len, inner_char_buf.data()));\n }\n aliases_str = Rf_protect(safe_R_string(static_cast(1), &cont_token));\n SET_STRING_ELT(aliases_str, 0, safe_R_mkChar(inner_char_buf.data(), &cont_token));\n Rf_unprotect(2);\n return aliases_str;\n R_API_END();\n}\n\nSEXP LGBM_BoosterGetLoadedParam_R(SEXP handle) {\n SEXP cont_token = Rf_protect(R_MakeUnwindCont());\n R_API_BEGIN();\n _AssertBoosterHandleNotNull(handle);\n SEXP params_str;\n int64_t out_len = 0;\n int64_t buf_len = 1024 * 1024;\n std::vector inner_char_buf(buf_len);\n CHECK_CALL(LGBM_BoosterGetLoadedParam(R_ExternalPtrAddr(handle), buf_len, &out_len, inner_char_buf.data()));\n // if aliases string was larger than the initial buffer, allocate a bigger buffer and try again\n if (out_len > buf_len) {\n inner_char_buf.resize(out_len);\n CHECK_CALL(LGBM_BoosterGetLoadedParam(R_ExternalPtrAddr(handle), out_len, &out_len, inner_char_buf.data()));\n }\n params_str = Rf_protect(safe_R_string(static_cast(1), &cont_token));\n SET_STRING_ELT(params_str, 0, safe_R_mkChar(inner_char_buf.data(), &cont_token));\n Rf_unprotect(2);\n return params_str;\n R_API_END();\n}\n\nSEXP LGBM_GetMaxThreads_R(SEXP out) {\n R_API_BEGIN();\n int num_threads;\n CHECK_CALL(LGBM_GetMaxThreads(&num_threads));\n INTEGER(out)[0] = num_threads;\n return R_NilValue;\n R_API_END();\n}\n\nSEXP LGBM_SetMaxThreads_R(SEXP num_threads) {\n R_API_BEGIN();\n int new_num_threads = Rf_asInteger(num_threads);\n CHECK_CALL(LGBM_SetMaxThreads(new_num_threads));\n return R_NilValue;\n R_API_END();\n}\n\n// .Call() calls\nstatic const R_CallMethodDef CallEntries[] = {\n {\"LGBM_HandleIsNull_R\" , (DL_FUNC) &LGBM_HandleIsNull_R , 1},\n {\"LGBM_DatasetCreateFromFile_R\" , (DL_FUNC) &LGBM_DatasetCreateFromFile_R , 3},\n {\"LGBM_DatasetCreateFromCSC_R\" , (DL_FUNC) &LGBM_DatasetCreateFromCSC_R , 8},\n {\"LGBM_DatasetCreateFromMat_R\" , (DL_FUNC) &LGBM_DatasetCreateFromMat_R , 5},\n {\"LGBM_DatasetGetSubset_R\" , (DL_FUNC) &LGBM_DatasetGetSubset_R , 4},\n {\"LGBM_DatasetSetFeatureNames_R\" , (DL_FUNC) &LGBM_DatasetSetFeatureNames_R , 2},\n {\"LGBM_DatasetGetFeatureNames_R\" , (DL_FUNC) &LGBM_DatasetGetFeatureNames_R , 1},\n {\"LGBM_DatasetSaveBinary_R\" , (DL_FUNC) &LGBM_DatasetSaveBinary_R , 2},\n {\"LGBM_DatasetFree_R\" , (DL_FUNC) &LGBM_DatasetFree_R , 1},\n {\"LGBM_DatasetSetField_R\" , (DL_FUNC) &LGBM_DatasetSetField_R , 4},\n {\"LGBM_DatasetGetFieldSize_R\" , (DL_FUNC) &LGBM_DatasetGetFieldSize_R , 3},\n {\"LGBM_DatasetGetField_R\" , (DL_FUNC) &LGBM_DatasetGetField_R , 3},\n {\"LGBM_DatasetUpdateParamChecking_R\" , (DL_FUNC) &LGBM_DatasetUpdateParamChecking_R , 2},\n {\"LGBM_DatasetGetNumData_R\" , (DL_FUNC) &LGBM_DatasetGetNumData_R , 2},\n {\"LGBM_DatasetGetNumFeature_R\" , (DL_FUNC) &LGBM_DatasetGetNumFeature_R , 2},\n {\"LGBM_DatasetGetFeatureNumBin_R\" , (DL_FUNC) &LGBM_DatasetGetFeatureNumBin_R , 3},\n {\"LGBM_BoosterCreate_R\" , (DL_FUNC) &LGBM_BoosterCreate_R , 2},\n {\"LGBM_BoosterFree_R\" , (DL_FUNC) &LGBM_BoosterFree_R , 1},\n {\"LGBM_BoosterCreateFromModelfile_R\" , (DL_FUNC) &LGBM_BoosterCreateFromModelfile_R , 1},\n {\"LGBM_BoosterLoadModelFromString_R\" , (DL_FUNC) &LGBM_BoosterLoadModelFromString_R , 1},\n {\"LGBM_BoosterMerge_R\" , (DL_FUNC) &LGBM_BoosterMerge_R , 2},\n {\"LGBM_BoosterAddValidData_R\" , (DL_FUNC) &LGBM_BoosterAddValidData_R , 2},\n {\"LGBM_BoosterResetTrainingData_R\" , (DL_FUNC) &LGBM_BoosterResetTrainingData_R , 2},\n {\"LGBM_BoosterResetParameter_R\" , (DL_FUNC) &LGBM_BoosterResetParameter_R , 2},\n {\"LGBM_BoosterGetNumClasses_R\" , (DL_FUNC) &LGBM_BoosterGetNumClasses_R , 2},\n {\"LGBM_BoosterGetNumFeature_R\" , (DL_FUNC) &LGBM_BoosterGetNumFeature_R , 1},\n {\"LGBM_BoosterGetLoadedParam_R\" , (DL_FUNC) &LGBM_BoosterGetLoadedParam_R , 1},\n {\"LGBM_BoosterUpdateOneIter_R\" , (DL_FUNC) &LGBM_BoosterUpdateOneIter_R , 1},\n {\"LGBM_BoosterUpdateOneIterCustom_R\" , (DL_FUNC) &LGBM_BoosterUpdateOneIterCustom_R , 4},\n {\"LGBM_BoosterRollbackOneIter_R\" , (DL_FUNC) &LGBM_BoosterRollbackOneIter_R , 1},\n {\"LGBM_BoosterGetCurrentIteration_R\" , (DL_FUNC) &LGBM_BoosterGetCurrentIteration_R , 2},\n {\"LGBM_BoosterNumModelPerIteration_R\" , (DL_FUNC) &LGBM_BoosterNumModelPerIteration_R , 2},\n {\"LGBM_BoosterNumberOfTotalModel_R\" , (DL_FUNC) &LGBM_BoosterNumberOfTotalModel_R , 2},\n {\"LGBM_BoosterGetUpperBoundValue_R\" , (DL_FUNC) &LGBM_BoosterGetUpperBoundValue_R , 2},\n {\"LGBM_BoosterGetLowerBoundValue_R\" , (DL_FUNC) &LGBM_BoosterGetLowerBoundValue_R , 2},\n {\"LGBM_BoosterGetEvalNames_R\" , (DL_FUNC) &LGBM_BoosterGetEvalNames_R , 1},\n {\"LGBM_BoosterGetEval_R\" , (DL_FUNC) &LGBM_BoosterGetEval_R , 3},\n {\"LGBM_BoosterGetNumPredict_R\" , (DL_FUNC) &LGBM_BoosterGetNumPredict_R , 3},\n {\"LGBM_BoosterGetPredict_R\" , (DL_FUNC) &LGBM_BoosterGetPredict_R , 3},\n {\"LGBM_BoosterPredictForFile_R\" , (DL_FUNC) &LGBM_BoosterPredictForFile_R , 10},\n {\"LGBM_BoosterCalcNumPredict_R\" , (DL_FUNC) &LGBM_BoosterCalcNumPredict_R , 8},\n {\"LGBM_BoosterPredictForCSC_R\" , (DL_FUNC) &LGBM_BoosterPredictForCSC_R , 14},\n {\"LGBM_BoosterPredictForCSR_R\" , (DL_FUNC) &LGBM_BoosterPredictForCSR_R , 12},\n {\"LGBM_BoosterPredictForCSRSingleRow_R\" , (DL_FUNC) &LGBM_BoosterPredictForCSRSingleRow_R , 11},\n {\"LGBM_BoosterPredictForCSRSingleRowFastInit_R\", (DL_FUNC) &LGBM_BoosterPredictForCSRSingleRowFastInit_R, 8},\n {\"LGBM_BoosterPredictForCSRSingleRowFast_R\" , (DL_FUNC) &LGBM_BoosterPredictForCSRSingleRowFast_R , 4},\n {\"LGBM_BoosterPredictSparseOutput_R\" , (DL_FUNC) &LGBM_BoosterPredictSparseOutput_R , 10},\n {\"LGBM_BoosterPredictForMat_R\" , (DL_FUNC) &LGBM_BoosterPredictForMat_R , 11},\n {\"LGBM_BoosterPredictForMatSingleRow_R\" , (DL_FUNC) &LGBM_BoosterPredictForMatSingleRow_R , 9},\n {\"LGBM_BoosterPredictForMatSingleRowFastInit_R\", (DL_FUNC) &LGBM_BoosterPredictForMatSingleRowFastInit_R, 8},\n {\"LGBM_BoosterPredictForMatSingleRowFast_R\" , (DL_FUNC) &LGBM_BoosterPredictForMatSingleRowFast_R , 3},\n {\"LGBM_BoosterSaveModel_R\" , (DL_FUNC) &LGBM_BoosterSaveModel_R , 5},\n {\"LGBM_BoosterSaveModelToString_R\" , (DL_FUNC) &LGBM_BoosterSaveModelToString_R , 4},\n {\"LGBM_BoosterDumpModel_R\" , (DL_FUNC) &LGBM_BoosterDumpModel_R , 4},\n {\"LGBM_NullBoosterHandleError_R\" , (DL_FUNC) &LGBM_NullBoosterHandleError_R , 0},\n {\"LGBM_DumpParamAliases_R\" , (DL_FUNC) &LGBM_DumpParamAliases_R , 0},\n {\"LGBM_GetMaxThreads_R\" , (DL_FUNC) &LGBM_GetMaxThreads_R , 1},\n {\"LGBM_SetMaxThreads_R\" , (DL_FUNC) &LGBM_SetMaxThreads_R , 1},\n {NULL, NULL, 0}\n};\n\nLIGHTGBM_C_EXPORT void R_init_lightgbm(DllInfo *dll);\n\nvoid R_init_lightgbm(DllInfo *dll) {\n R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);\n R_useDynamicSymbols(dll, FALSE);\n\n#ifndef LGB_NO_ALTREP\n lgb_altrepped_char_vec = R_make_altraw_class(\"lgb_altrepped_char_vec\", \"lightgbm\", dll);\n R_set_altrep_Length_method(lgb_altrepped_char_vec, get_altrepped_raw_len);\n R_set_altvec_Dataptr_method(lgb_altrepped_char_vec, get_altrepped_raw_dataptr);\n R_set_altvec_Dataptr_or_null_method(lgb_altrepped_char_vec, get_altrepped_raw_dataptr_or_null);\n\n lgb_altrepped_int_arr = R_make_altinteger_class(\"lgb_altrepped_int_arr\", \"lightgbm\", dll);\n R_set_altrep_Length_method(lgb_altrepped_int_arr, get_altrepped_vec_len);\n R_set_altvec_Dataptr_method(lgb_altrepped_int_arr, get_altrepped_vec_dataptr);\n R_set_altvec_Dataptr_or_null_method(lgb_altrepped_int_arr, get_altrepped_vec_dataptr_or_null);\n\n lgb_altrepped_dbl_arr = R_make_altreal_class(\"lgb_altrepped_dbl_arr\", \"lightgbm\", dll);\n R_set_altrep_Length_method(lgb_altrepped_dbl_arr, get_altrepped_vec_len);\n R_set_altvec_Dataptr_method(lgb_altrepped_dbl_arr, get_altrepped_vec_dataptr);\n R_set_altvec_Dataptr_or_null_method(lgb_altrepped_dbl_arr, get_altrepped_vec_dataptr_or_null);\n#endif\n}\n" }, { "path": "R-package/src/lightgbm_R.h", "content": "/*!\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_R_PACKAGE_SRC_LIGHTGBM_R_H_\n#define LIGHTGBM_R_PACKAGE_SRC_LIGHTGBM_R_H_\n\n#include \n\n#ifndef R_NO_REMAP\n#define R_NO_REMAP\n#endif\n\n#ifndef R_USE_C99_IN_CXX\n#define R_USE_C99_IN_CXX\n#endif\n\n#include \n\n/*!\n* \\brief check if an R external pointer (like a Booster or Dataset handle) is a null pointer\n* \\param handle handle for a Booster, Dataset, or Predictor\n* \\return R logical, TRUE if the handle is a null pointer\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_HandleIsNull_R(\n SEXP handle\n);\n\n/*!\n* \\brief Throw a standardized error message when encountering a null Booster handle\n* \\return No return, will throw an error\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_NullBoosterHandleError_R();\n\n// --- start Dataset interface\n\n/*!\n* \\brief load Dataset from file like the command_line LightGBM does\n* \\param filename the name of the file\n* \\param parameters additional parameters\n* \\param reference used to align bin mapper with other Dataset, nullptr means not used\n* \\return Dataset handle\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetCreateFromFile_R(\n SEXP filename,\n SEXP parameters,\n SEXP reference\n);\n\n/*!\n* \\brief create a Dataset from Compressed Sparse Column (CSC) format\n* \\param indptr pointer to row headers\n* \\param indices findex\n* \\param data fvalue\n* \\param num_indptr number of cols in the matrix + 1\n* \\param nelem number of nonzero elements in the matrix\n* \\param num_row number of rows\n* \\param parameters additional parameters\n* \\param reference used to align bin mapper with other Dataset, nullptr means not used\n* \\return Dataset handle\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetCreateFromCSC_R(\n SEXP indptr,\n SEXP indices,\n SEXP data,\n SEXP num_indptr,\n SEXP nelem,\n SEXP num_row,\n SEXP parameters,\n SEXP reference\n);\n\n/*!\n* \\brief create Dataset from dense matrix\n* \\param data matrix data\n* \\param num_row number of rows\n* \\param num_col number columns\n* \\param parameters additional parameters\n* \\param reference used to align bin mapper with other Dataset, nullptr means not used\n* \\return Dataset handle\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetCreateFromMat_R(\n SEXP data,\n SEXP num_row,\n SEXP num_col,\n SEXP parameters,\n SEXP reference\n);\n\n/*!\n* \\brief Create subset of a Dataset\n* \\param handle handle of full Dataset\n* \\param used_row_indices Indices used in subset\n* \\param len_used_row_indices length of Indices used in subset\n* \\param parameters additional parameters\n* \\return Dataset handle\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetSubset_R(\n SEXP handle,\n SEXP used_row_indices,\n SEXP len_used_row_indices,\n SEXP parameters\n);\n\n/*!\n* \\brief save feature names to Dataset\n* \\param handle handle\n* \\param feature_names feature names\n* \\return R character vector of feature names\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetSetFeatureNames_R(\n SEXP handle,\n SEXP feature_names\n);\n\n/*!\n* \\brief get feature names from Dataset\n* \\param handle Dataset handle\n* \\return an R character vector with feature names from the Dataset or NULL if no feature names\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetFeatureNames_R(\n SEXP handle\n);\n\n/*!\n* \\brief save Dataset to binary file\n* \\param handle an instance of Dataset\n* \\param filename file name\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetSaveBinary_R(\n SEXP handle,\n SEXP filename\n);\n\n/*!\n* \\brief free Dataset\n* \\param handle an instance of Dataset\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetFree_R(\n SEXP handle\n);\n\n/*!\n* \\brief set vector to a content in info\n* Note: group and group_id only work for C_API_DTYPE_INT32\n* label and weight only work for C_API_DTYPE_FLOAT32\n* \\param handle an instance of Dataset\n* \\param field_name field name, can be label, weight, group, group_id\n* \\param field_data pointer to vector\n* \\param num_element number of element in field_data\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetSetField_R(\n SEXP handle,\n SEXP field_name,\n SEXP field_data,\n SEXP num_element\n);\n\n/*!\n* \\brief get size of info vector from Dataset\n* \\param handle an instance of Dataset\n* \\param field_name field name\n* \\param out size of info vector from Dataset\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetFieldSize_R(\n SEXP handle,\n SEXP field_name,\n SEXP out\n);\n\n/*!\n* \\brief get info vector from Dataset\n* \\param handle an instance of Dataset\n* \\param field_name field name\n* \\param field_data pointer to vector\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetField_R(\n SEXP handle,\n SEXP field_name,\n SEXP field_data\n);\n\n/*!\n * \\brief Raise errors for attempts to update Dataset parameters.\n * Some parameters cannot be updated after construction.\n * \\param old_params Current Dataset parameters\n * \\param new_params New Dataset parameters\n * \\return R NULL value\n */\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetUpdateParamChecking_R(\n SEXP old_params,\n SEXP new_params\n);\n\n/*!\n* \\brief get number of data.\n* \\param handle the handle to the Dataset\n* \\param out The address to hold number of data\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetNumData_R(\n SEXP handle,\n SEXP out\n);\n\n/*!\n* \\brief get number of features\n* \\param handle the handle to the Dataset\n* \\param out The output of number of features\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetNumFeature_R(\n SEXP handle,\n SEXP out\n);\n\n/*!\n* \\brief get number of bins for feature\n* \\param handle the handle to the Dataset\n* \\param feature the index of the feature\n* \\param out The output of number of bins\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DatasetGetFeatureNumBin_R(\n SEXP handle,\n SEXP feature,\n SEXP out\n);\n\n// --- start Booster interfaces\n\n/*!\n* \\brief create a new boosting learner\n* \\param train_data training Dataset\n* \\param parameters format: 'key1=value1 key2=value2'\n* \\return Booster handle\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterCreate_R(\n SEXP train_data,\n SEXP parameters\n);\n\n/*!\n* \\brief free Booster\n* \\param handle handle to be freed\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterFree_R(\n SEXP handle\n);\n\n/*!\n* \\brief load an existing Booster from model file\n* \\param filename filename of model\n* \\return Booster handle\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterCreateFromModelfile_R(\n SEXP filename\n);\n\n/*!\n* \\brief load an existing Booster from a string\n* \\param model_str string containing the model\n* \\return Booster handle\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterLoadModelFromString_R(\n SEXP model_str\n);\n\n/*!\n* \\brief Get parameters as JSON string.\n* \\param handle Booster handle\n* \\return R character vector (length=1) with parameters in JSON format\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetLoadedParam_R(\n SEXP handle\n);\n\n/*!\n* \\brief Merge model in two Boosters to first handle\n* \\param handle handle primary Booster handle, will merge other handle to this\n* \\param other_handle secondary Booster handle\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterMerge_R(\n SEXP handle,\n SEXP other_handle\n);\n\n/*!\n* \\brief Add new validation to Booster\n* \\param handle Booster handle\n* \\param valid_data validation Dataset\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterAddValidData_R(\n SEXP handle,\n SEXP valid_data\n);\n\n/*!\n* \\brief Reset training data for Booster\n* \\param handle Booster handle\n* \\param train_data training Dataset\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterResetTrainingData_R(\n SEXP handle,\n SEXP train_data\n);\n\n/*!\n* \\brief Reset config for current Booster\n* \\param handle Booster handle\n* \\param parameters format: 'key1=value1 key2=value2'\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterResetParameter_R(\n SEXP handle,\n SEXP parameters\n);\n\n/*!\n* \\brief Get number of classes\n* \\param handle Booster handle\n* \\param out number of classes\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetNumClasses_R(\n SEXP handle,\n SEXP out\n);\n\n/*!\n* \\brief Get number of features.\n* \\param handle Booster handle\n* \\return Total number of features, as R integer\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetNumFeature_R(\n SEXP handle\n);\n\n/*!\n* \\brief update the model in one round\n* \\param handle Booster handle\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterUpdateOneIter_R(\n SEXP handle\n);\n\n/*!\n* \\brief update the model, by directly specifying gradient and second order gradient,\n* this can be used to support customized loss function\n* \\param handle Booster handle\n* \\param grad gradient statistics\n* \\param hess second order gradient statistics\n* \\param len length of grad/hess\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterUpdateOneIterCustom_R(\n SEXP handle,\n SEXP grad,\n SEXP hess,\n SEXP len\n);\n\n/*!\n* \\brief Rollback one iteration\n* \\param handle Booster handle\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterRollbackOneIter_R(\n SEXP handle\n);\n\n/*!\n* \\brief Get iteration of current boosting rounds\n* \\param handle Booster handle\n* \\param out iteration of boosting rounds\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetCurrentIteration_R(\n SEXP handle,\n SEXP out\n);\n\n/*!\n * \\brief Get number of trees per iteration\n * \\param handle Booster handle\n * \\param out Number of trees per iteration\n * \\return R NULL value\n */\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterNumModelPerIteration_R(\n SEXP handle,\n SEXP out\n);\n\n/*!\n * \\brief Get total number of trees\n * \\param handle Booster handle\n * \\param out Total number of trees of Booster\n * \\return R NULL value\n */\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterNumberOfTotalModel_R(\n SEXP handle,\n SEXP out\n);\n\n/*!\n* \\brief Get model upper bound value.\n* \\param handle Handle of Booster\n* \\param[out] out_results Result pointing to max value\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetUpperBoundValue_R(\n SEXP handle,\n SEXP out_result\n);\n\n/*!\n* \\brief Get model lower bound value.\n* \\param handle Handle of Booster\n* \\param[out] out_results Result pointing to min value\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetLowerBoundValue_R(\n SEXP handle,\n SEXP out_result\n);\n\n/*!\n* \\brief Get names of eval metrics\n* \\param handle Handle of booster\n* \\return R character vector with names of eval metrics\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetEvalNames_R(\n SEXP handle\n);\n\n/*!\n* \\brief get evaluation for training data and validation data\n* \\param handle Booster handle\n* \\param data_idx 0:training data, 1: 1st valid data, 2:2nd valid data ...\n* \\param out_result float array containing result\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetEval_R(\n SEXP handle,\n SEXP data_idx,\n SEXP out_result\n);\n\n/*!\n* \\brief Get number of prediction for training data and validation data\n* \\param handle Booster handle\n* \\param data_idx 0:training data, 1: 1st valid data, 2:2nd valid data ...\n* \\param out size of predict\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetNumPredict_R(\n SEXP handle,\n SEXP data_idx,\n SEXP out\n);\n\n/*!\n* \\brief Get prediction for training data and validation data.\n* This can be used to support customized eval function\n* \\param handle Booster handle\n* \\param data_idx 0:training data, 1: 1st valid data, 2:2nd valid data ...\n* \\param out_result, used to store predict result, should pre-allocate memory\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetPredict_R(\n SEXP handle,\n SEXP data_idx,\n SEXP out_result\n);\n\n/*!\n* \\brief make prediction for file\n* \\param handle Booster handle\n* \\param data_filename filename of data file\n* \\param data_has_header data file has header or not\n* \\param is_rawscore 1 to get raw predictions, before transformations like\n* converting to probabilities, 0 otherwise\n* \\param is_leafidx 1 to get record of which leaf in each tree\n* observations fell into, 0 otherwise\n* \\param is_predcontrib 1 to get feature contributions, 0 otherwise\n* \\param start_iteration Start index of the iteration to predict\n* \\param num_iteration number of iteration for prediction, <= 0 means no limit\n* \\param parameter additional parameters\n* \\param result_filename filename of file to write predictions to\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForFile_R(\n SEXP handle,\n SEXP data_filename,\n SEXP data_has_header,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter,\n SEXP result_filename\n);\n\n/*!\n* \\brief Get number of prediction\n* \\param handle Booster handle\n* \\param num_row number of rows in input\n* \\param is_rawscore 1 to get raw predictions, before transformations like\n* converting to probabilities, 0 otherwise\n* \\param is_leafidx 1 to get record of which leaf in each tree\n* observations fell into, 0 otherwise\n* \\param is_predcontrib 1 to get feature contributions, 0 otherwise\n* \\param start_iteration Start index of the iteration to predict\n* \\param num_iteration number of iteration for prediction, <= 0 means no limit\n* \\param out_len length of prediction\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterCalcNumPredict_R(\n SEXP handle,\n SEXP num_row,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP out_len\n);\n\n/*!\n* \\brief make prediction for a new Dataset\n* Note: should pre-allocate memory for out_result,\n* for normal and raw score: its length is equal to num_class * num_data\n* for leaf index, its length is equal to num_class * num_data * num_iteration\n* for feature contributions, its length is equal to num_class * num_data * (num_features + 1)\n* \\param handle Booster handle\n* \\param indptr pointer to row headers\n* \\param indices findex\n* \\param data fvalue\n* \\param num_indptr number of cols in the matrix + 1\n* \\param nelem number of non-zero elements in the matrix\n* \\param num_row number of rows\n* \\param is_rawscore 1 to get raw predictions, before transformations like\n* converting to probabilities, 0 otherwise\n* \\param is_leafidx 1 to get record of which leaf in each tree\n* observations fell into, 0 otherwise\n* \\param is_predcontrib 1 to get feature contributions, 0 otherwise\n* \\param start_iteration start index of the iteration to predict\n* \\param num_iteration number of iteration for prediction, <= 0 means no limit\n* \\param parameter additional parameters\n* \\param out_result prediction result\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSC_R(\n SEXP handle,\n SEXP indptr,\n SEXP indices,\n SEXP data,\n SEXP num_indptr,\n SEXP nelem,\n SEXP num_row,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter,\n SEXP out_result\n);\n\n/*!\n* \\brief make prediction for a new Dataset\n* Note: should pre-allocate memory for out_result,\n* for normal and raw score: its length is equal to num_class * num_data\n* for leaf index, its length is equal to num_class * num_data * num_iteration\n* for feature contributions, its length is equal to num_class * num_data * (num_features + 1)\n* \\param handle Booster handle\n* \\param indptr array with the index pointer of the data in CSR format\n* \\param indices array with the non-zero indices of the data in CSR format\n* \\param data array with the non-zero values of the data in CSR format\n* \\param ncols number of columns in the data\n* \\param is_rawscore 1 to get raw predictions, before transformations like\n* converting to probabilities, 0 otherwise\n* \\param is_leafidx 1 to get record of which leaf in each tree\n* observations fell into, 0 otherwise\n* \\param is_predcontrib 1 to get feature contributions, 0 otherwise\n* \\param start_iteration start index of the iteration to predict\n* \\param num_iteration number of iteration for prediction, <= 0 means no limit\n* \\param parameter additional parameters\n* \\param out_result prediction result\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSR_R(\n SEXP handle,\n SEXP indptr,\n SEXP indices,\n SEXP data,\n SEXP ncols,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter,\n SEXP out_result\n);\n\n/*!\n* \\brief make prediction for a single row of data\n* Note: should pre-allocate memory for out_result,\n* for normal and raw score: its length is equal to num_class\n* for leaf index, its length is equal to num_class * num_iteration\n* for feature contributions, its length is equal to num_class * (num_features + 1)\n* \\param handle Booster handle\n* \\param indices array corresponding to the indices of the columns with non-zero values of the row to predict on\n* \\param data array corresponding to the non-zero values of row to predict on\n* \\param ncols number of columns in the data\n* \\param is_rawscore 1 to get raw predictions, before transformations like\n* converting to probabilities, 0 otherwise\n* \\param is_leafidx 1 to get record of which leaf in each tree\n* observations fell into, 0 otherwise\n* \\param is_predcontrib 1 to get feature contributions, 0 otherwise\n* \\param start_iteration start index of the iteration to predict\n* \\param num_iteration number of iteration for prediction, <= 0 means no limit\n* \\param parameter additional parameters\n* \\param out_result prediction result\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSRSingleRow_R(\n SEXP handle,\n SEXP indices,\n SEXP data,\n SEXP ncols,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter,\n SEXP out_result\n);\n\n/*!\n* \\brief Initialize and return a fast configuration handle to use with ``LGBM_BoosterPredictForCSRSingleRowFast_R``.\n* \\param handle Booster handle\n* \\param ncols number columns in the data\n* \\param is_rawscore 1 to get raw predictions, before transformations like\n* converting to probabilities, 0 otherwise\n* \\param is_leafidx 1 to get record of which leaf in each tree\n* observations fell into, 0 otherwise\n* \\param is_predcontrib 1 to get feature contributions, 0 otherwise\n* \\param start_iteration start index of the iteration to predict\n* \\param num_iteration number of iteration for prediction, <= 0 means no limit\n* \\param parameter additional parameters\n* \\return Fast configuration handle\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSRSingleRowFastInit_R(\n SEXP handle,\n SEXP ncols,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter\n);\n\n/*!\n* \\brief make prediction for a single row of data\n* Note: should pre-allocate memory for out_result,\n* for normal and raw score: its length is equal to num_class\n* for leaf index, its length is equal to num_class * num_iteration\n* for feature contributions, its length is equal to num_class * (num_features + 1)\n* \\param handle_fastConfig Fast configuration handle\n* \\param indices array corresponding to the indices of the columns with non-zero values of the row to predict on\n* \\param data array corresponding to the non-zero values of row to predict on\n* \\param out_result prediction result\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForCSRSingleRowFast_R(\n SEXP handle_fastConfig,\n SEXP indices,\n SEXP data,\n SEXP out_result\n);\n\n/*!\n* \\brief make feature contribution prediction for a new Dataset\n* \\param handle Booster handle\n* \\param indptr array with the index pointer of the data in CSR or CSC format\n* \\param indices array with the non-zero indices of the data in CSR or CSC format\n* \\param data array with the non-zero values of the data in CSR or CSC format\n* \\param is_csr whether the input data is in CSR format or not (pass FALSE for CSC)\n* \\param nrows number of rows in the data\n* \\param ncols number of columns in the data\n* \\param start_iteration start index of the iteration to predict\n* \\param num_iteration number of iteration for prediction, <= 0 means no limit\n* \\param parameter additional parameters\n* \\return An R list with entries \"indptr\", \"indices\", \"data\", constituting the\n* feature contributions in sparse format, in the same storage order as\n* the input data.\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictSparseOutput_R(\n SEXP handle,\n SEXP indptr,\n SEXP indices,\n SEXP data,\n SEXP is_csr,\n SEXP nrows,\n SEXP ncols,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter\n);\n\n/*!\n* \\brief make prediction for a new Dataset\n* Note: should pre-allocate memory for out_result,\n* for normal and raw score: its length is equal to num_class * num_data\n* for leaf index, its length is equal to num_class * num_data * num_iteration\n* for feature contributions, its length is equal to num_class * num_data * (num_features + 1)\n* \\param handle Booster handle\n* \\param data pointer to the data space\n* \\param num_row number of rows\n* \\param num_col number columns\n* \\param is_rawscore 1 to get raw predictions, before transformations like\n* converting to probabilities, 0 otherwise\n* \\param is_leafidx 1 to get record of which leaf in each tree\n* observations fell into, 0 otherwise\n* \\param is_predcontrib 1 to get feature contributions, 0 otherwise\n* \\param start_iteration start index of the iteration to predict\n* \\param num_iteration number of iteration for prediction, <= 0 means no limit\n* \\param parameter additional parameters\n* \\param out_result prediction result\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForMat_R(\n SEXP handle,\n SEXP data,\n SEXP num_row,\n SEXP num_col,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter,\n SEXP out_result\n);\n\n/*!\n* \\brief make prediction for a single row of data\n* Note: should pre-allocate memory for out_result,\n* for normal and raw score: its length is equal to num_class\n* for leaf index, its length is equal to num_class * num_iteration\n* for feature contributions, its length is equal to num_class * (num_features + 1)\n* \\param handle Booster handle\n* \\param data array corresponding to the row to predict on\n* \\param is_rawscore 1 to get raw predictions, before transformations like\n* converting to probabilities, 0 otherwise\n* \\param is_leafidx 1 to get record of which leaf in each tree\n* observations fell into, 0 otherwise\n* \\param is_predcontrib 1 to get feature contributions, 0 otherwise\n* \\param start_iteration start index of the iteration to predict\n* \\param num_iteration number of iteration for prediction, <= 0 means no limit\n* \\param parameter additional parameters\n* \\param out_result prediction result\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForMatSingleRow_R(\n SEXP handle,\n SEXP data,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter,\n SEXP out_result\n);\n\n/*!\n* \\brief Initialize and return a fast configuration handle to use with ``LGBM_BoosterPredictForMatSingleRowFast_R``.\n* \\param handle Booster handle\n* \\param ncols number columns in the data\n* \\param is_rawscore 1 to get raw predictions, before transformations like\n* converting to probabilities, 0 otherwise\n* \\param is_leafidx 1 to get record of which leaf in each tree\n* observations fell into, 0 otherwise\n* \\param is_predcontrib 1 to get feature contributions, 0 otherwise\n* \\param start_iteration start index of the iteration to predict\n* \\param num_iteration number of iteration for prediction, <= 0 means no limit\n* \\param parameter additional parameters\n* \\return Fast configuration handle\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForMatSingleRowFastInit_R(\n SEXP handle,\n SEXP ncols,\n SEXP is_rawscore,\n SEXP is_leafidx,\n SEXP is_predcontrib,\n SEXP start_iteration,\n SEXP num_iteration,\n SEXP parameter\n);\n\n/*!\n* \\brief make prediction for a single row of data\n* Note: should pre-allocate memory for out_result,\n* for normal and raw score: its length is equal to num_class\n* for leaf index, its length is equal to num_class * num_iteration\n* for feature contributions, its length is equal to num_class * (num_features + 1)\n* \\param handle_fastConfig Fast configuration handle\n* \\param data array corresponding to the row to predict on\n* \\param out_result prediction result\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForMatSingleRowFast_R(\n SEXP handle_fastConfig,\n SEXP data,\n SEXP out_result\n);\n\n/*!\n* \\brief save model into file\n* \\param handle Booster handle\n* \\param num_iteration, <= 0 means save all\n* \\param feature_importance_type type of feature importance, 0: split, 1: gain\n* \\param filename file name\n* \\param start_iteration Starting iteration (0 based)\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterSaveModel_R(\n SEXP handle,\n SEXP num_iteration,\n SEXP feature_importance_type,\n SEXP filename,\n SEXP start_iteration\n);\n\n/*!\n* \\brief create string containing model\n* \\param handle Booster handle\n* \\param num_iteration, <= 0 means save all\n* \\param feature_importance_type type of feature importance, 0: split, 1: gain\n* \\param start_iteration Starting iteration (0 based)\n* \\return R character vector (length=1) with model string\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterSaveModelToString_R(\n SEXP handle,\n SEXP num_iteration,\n SEXP feature_importance_type,\n SEXP start_iteration\n);\n\n/*!\n* \\brief dump model to JSON\n* \\param handle Booster handle\n* \\param num_iteration, <= 0 means save all\n* \\param feature_importance_type type of feature importance, 0: split, 1: gain\n* \\param start_iteration Index of starting iteration (0 based)\n* \\return R character vector (length=1) with model JSON\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_BoosterDumpModel_R(\n SEXP handle,\n SEXP num_iteration,\n SEXP feature_importance_type,\n SEXP start_iteration\n);\n\n/*!\n* \\brief Dump parameter aliases to JSON\n* \\return R character vector (length=1) with aliases JSON\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_DumpParamAliases_R();\n\n/*!\n* \\brief Get current maximum number of threads used by LightGBM routines in this process.\n* \\param[out] out current maximum number of threads used by LightGBM. -1 means defaulting to omp_get_num_threads().\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_GetMaxThreads_R(\n SEXP out\n);\n\n\n/*!\n* \\brief Set maximum number of threads used by LightGBM routines in this process.\n* \\param num_threads maximum number of threads used by LightGBM. -1 means defaulting to omp_get_num_threads().\n* \\return R NULL value\n*/\nLIGHTGBM_C_EXPORT SEXP LGBM_SetMaxThreads_R(\n SEXP num_threads\n);\n\n#endif // LIGHTGBM_R_PACKAGE_SRC_LIGHTGBM_R_H_\n" }, { "path": "R-package/tests/testthat/helper.R", "content": "# ref for this file:\n#\n# * https://r-pkgs.org/testing-design.html#testthat-helper-files\n# * https://r-pkgs.org/testing-design.html#testthat-setup-files\n\n# LightGBM-internal fix to comply with CRAN policy of only using up to 2 threads in tests and example.\n#\n# per https://cran.r-project.org/web/packages/policies.html\n#\n# > If running a package uses multiple threads/cores it must never use more than two simultaneously:\n# the check farm is a shared resource and will typically be running many checks simultaneously.\n#\n.LGB_MAX_THREADS <- 2L\nsetLGBMthreads(.LGB_MAX_THREADS)\n\n# control data.table parallelism\n# ref: https://github.com/Rdatatable/data.table/issues/5658\ndata.table::setDTthreads(1L)\n\n# by default, how much should results in tests be allowed to differ from hard-coded expected numbers?\n.LGB_NUMERIC_TOLERANCE <- 1e-6\n\n# are the tests running on Windows?\n.LGB_ON_WINDOWS <- .Platform$OS.type == \"windows\"\n.LGB_ON_32_BIT_WINDOWS <- .LGB_ON_WINDOWS && .Machine$sizeof.pointer != 8L\n\n# are the tests running in a UTF-8 locale?\n.LGB_UTF8_LOCALE <- all(endsWith(\n Sys.getlocale(category = \"LC_CTYPE\")\n , \"UTF-8\"\n))\n\n# control how many loud LightGBM's logger is in tests\n.LGB_VERBOSITY <- as.integer(\n Sys.getenv(\"LIGHTGBM_TEST_VERBOSITY\", \"-1\")\n)\n\n\n# [description]\n# test that every element of 'x' is in 'y'\n#\n# testthat::expect_in() was added in {testthat} v3.1.19.\n# This is here to support a similar interface on older {testthat} versions.\n.expect_in <- function(x, y) {\n if (exists(\"expect_in\")) {\n expect_in(x, y)\n } else {\n missing_items <- x[!(x %in% y)]\n if (length(missing_items) != 0L) {\n error_msg <- paste0(\"Some expected items not found: \", toString(missing_items))\n stop(error_msg)\n }\n }\n}\n" }, { "path": "R-package/tests/testthat/test_Predictor.R", "content": "library(Matrix)\n\ntest_that(\"Predictor's finalizer should not fail\", {\n X <- as.matrix(as.integer(iris[, \"Species\"]), ncol = 1L)\n y <- iris[[\"Sepal.Length\"]]\n dtrain <- lgb.Dataset(X, label = y)\n bst <- lgb.train(\n data = dtrain\n , params = list(\n objective = \"regression\"\n , num_threads = .LGB_MAX_THREADS\n )\n , verbose = .LGB_VERBOSITY\n , nrounds = 3L\n )\n model_file <- tempfile(fileext = \".model\")\n bst$save_model(filename = model_file)\n predictor <- Predictor$new(modelfile = model_file)\n\n expect_true(.is_Predictor(predictor))\n\n expect_false(.is_null_handle(predictor$.__enclos_env__$private$handle))\n\n predictor$.__enclos_env__$private$finalize()\n expect_true(.is_null_handle(predictor$.__enclos_env__$private$handle))\n\n # calling finalize() a second time shouldn't cause any issues\n predictor$.__enclos_env__$private$finalize()\n expect_true(.is_null_handle(predictor$.__enclos_env__$private$handle))\n})\n\ntest_that(\"predictions do not fail for integer input\", {\n X <- as.matrix(as.integer(iris[, \"Species\"]), ncol = 1L)\n y <- iris[[\"Sepal.Length\"]]\n dtrain <- lgb.Dataset(X, label = y)\n fit <- lgb.train(\n data = dtrain\n , params = list(\n objective = \"regression\"\n , num_threads = .LGB_MAX_THREADS\n )\n , verbose = .LGB_VERBOSITY\n , nrounds = 3L\n )\n X_double <- X[c(1L, 51L, 101L), , drop = FALSE]\n X_integer <- X_double\n storage.mode(X_double) <- \"double\"\n pred_integer <- predict(fit, X_integer)\n pred_double <- predict(fit, X_double)\n expect_equal(pred_integer, pred_double)\n})\n\ntest_that(\"start_iteration works correctly\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n data(agaricus.test, package = \"lightgbm\")\n train <- agaricus.train\n test <- agaricus.test\n dtrain <- lgb.Dataset(\n agaricus.train$data\n , label = agaricus.train$label\n )\n dtest <- lgb.Dataset.create.valid(\n dtrain\n , agaricus.test$data\n , label = agaricus.test$label\n )\n bst <- lightgbm(\n data = as.matrix(train$data)\n , label = train$label\n , params = list(\n num_leaves = 4L\n , learning_rate = 0.6\n , objective = \"binary\"\n , verbosity = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 50L\n , valids = list(\"test\" = dtest)\n , early_stopping_rounds = 2L\n )\n expect_true(.is_Booster(bst))\n pred1 <- predict(bst, newdata = test$data, type = \"raw\")\n pred_contrib1 <- predict(bst, test$data, type = \"contrib\")\n pred2 <- rep(0.0, length(pred1))\n pred_contrib2 <- rep(0.0, length(pred2))\n step <- 11L\n end_iter <- 49L\n if (bst$best_iter != -1L) {\n end_iter <- bst$best_iter - 1L\n }\n start_iters <- seq(0L, end_iter, by = step)\n for (start_iter in start_iters) {\n n_iter <- min(c(end_iter - start_iter + 1L, step))\n inc_pred <- predict(bst, test$data\n , start_iteration = start_iter\n , num_iteration = n_iter\n , type = \"raw\"\n )\n inc_pred_contrib <- bst$predict(test$data\n , start_iteration = start_iter\n , num_iteration = n_iter\n , predcontrib = TRUE\n )\n pred2 <- pred2 + inc_pred\n pred_contrib2 <- pred_contrib2 + inc_pred_contrib\n }\n expect_equal(pred2, pred1)\n expect_equal(pred_contrib2, pred_contrib1)\n\n pred_leaf1 <- predict(bst, test$data, type = \"leaf\")\n pred_leaf2 <- predict(bst, test$data, start_iteration = 0L, num_iteration = end_iter + 1L, type = \"leaf\")\n expect_equal(pred_leaf1, pred_leaf2)\n})\n\ntest_that(\"Feature contributions from sparse inputs produce sparse outputs\", {\n data(mtcars)\n X <- as.matrix(mtcars[, -1L])\n y <- as.numeric(mtcars[, 1L])\n dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L))\n bst <- lgb.train(\n data = dtrain\n , obj = \"regression\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS)\n )\n\n pred_dense <- predict(bst, X, type = \"contrib\")\n\n Xcsc <- as(X, \"CsparseMatrix\")\n pred_csc <- predict(bst, Xcsc, type = \"contrib\")\n expect_s4_class(pred_csc, \"dgCMatrix\")\n expect_equal(unname(pred_dense), unname(as.matrix(pred_csc)))\n\n Xcsr <- as(X, \"RsparseMatrix\")\n pred_csr <- predict(bst, Xcsr, type = \"contrib\")\n expect_s4_class(pred_csr, \"dgRMatrix\")\n expect_equal(as(pred_csr, \"CsparseMatrix\"), pred_csc)\n\n Xspv <- as(X[1L, , drop = FALSE], \"sparseVector\")\n pred_spv <- predict(bst, Xspv, type = \"contrib\")\n expect_s4_class(pred_spv, \"dsparseVector\")\n expect_equal(Matrix::t(as(pred_spv, \"CsparseMatrix\")), unname(pred_csc[1L, , drop = FALSE]))\n})\n\ntest_that(\"Sparse feature contribution predictions do not take inputs with wrong number of columns\", {\n data(mtcars)\n X <- as.matrix(mtcars[, -1L])\n y <- as.numeric(mtcars[, 1L])\n dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L))\n bst <- lgb.train(\n data = dtrain\n , obj = \"regression\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS)\n )\n\n X_wrong <- X[, c(1L:10L, 1L:10L)]\n X_wrong <- as(X_wrong, \"CsparseMatrix\")\n expect_error(predict(bst, X_wrong, type = \"contrib\"), regexp = \"input data has 20 columns\")\n\n X_wrong <- as(X_wrong, \"RsparseMatrix\")\n expect_error(predict(bst, X_wrong, type = \"contrib\"), regexp = \"input data has 20 columns\")\n\n X_wrong <- as(X_wrong, \"CsparseMatrix\")\n X_wrong <- X_wrong[, 1L:3L]\n expect_error(predict(bst, X_wrong, type = \"contrib\"), regexp = \"input data has 3 columns\")\n})\n\ntest_that(\"Feature contribution predictions do not take non-general CSR or CSC inputs\", {\n set.seed(123L)\n y <- runif(25L)\n Dmat <- matrix(runif(625L), nrow = 25L, ncol = 25L)\n Dmat <- crossprod(Dmat)\n Dmat <- as(Dmat, \"symmetricMatrix\")\n SmatC <- as(Dmat, \"sparseMatrix\")\n SmatR <- as(SmatC, \"RsparseMatrix\")\n\n dtrain <- lgb.Dataset(as.matrix(Dmat), label = y, params = list(max_bins = 5L))\n bst <- lgb.train(\n data = dtrain\n , obj = \"regression\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(min_data_in_leaf = 5L, num_threads = .LGB_MAX_THREADS)\n )\n\n expect_error(\n predict(bst, SmatC, type = \"contrib\")\n , regexp = \"Predictions on sparse inputs are only allowed for 'dsparseVector', 'dgRMatrix', 'dgCMatrix' - got: dsCMatrix\" # nolint: line_length.\n )\n expect_error(\n predict(bst, SmatR, type = \"contrib\")\n , regexp = \"Predictions on sparse inputs are only allowed for 'dsparseVector', 'dgRMatrix', 'dgCMatrix' - got: dsRMatrix\" # nolint: line_length.\n )\n})\n\ntest_that(\"predict() params should override keyword argument for raw-score predictions\", {\n data(agaricus.train, package = \"lightgbm\")\n X <- agaricus.train$data\n y <- agaricus.train$label\n bst <- lgb.train(\n data = lgb.Dataset(\n data = X\n , label = y\n , params = list(\n data_seed = 708L\n , min_data_in_bin = 5L\n )\n )\n , params = list(\n objective = \"binary\"\n , min_data_in_leaf = 1L\n , seed = 708L\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 10L\n , verbose = .LGB_VERBOSITY\n )\n\n # check that the predictions from predict.lgb.Booster() really look like raw score predictions\n preds_prob <- predict(bst, X)\n preds_raw_s3_keyword <- predict(bst, X, type = \"raw\")\n preds_prob_from_raw <- 1.0 / (1.0 + exp(-preds_raw_s3_keyword))\n expect_equal(preds_prob, preds_prob_from_raw, tolerance = .LGB_NUMERIC_TOLERANCE)\n accuracy <- sum(as.integer(preds_prob_from_raw > 0.5) == y) / length(y)\n expect_equal(accuracy, 1.0)\n\n # should get the same results from Booster$predict() method\n preds_raw_r6_keyword <- bst$predict(X, rawscore = TRUE)\n expect_equal(preds_raw_s3_keyword, preds_raw_r6_keyword)\n\n # using a parameter alias of predict_raw_score should result in raw scores being returned\n aliases <- .PARAMETER_ALIASES()[[\"predict_raw_score\"]]\n expect_true(length(aliases) > 1L)\n for (rawscore_alias in aliases) {\n params <- as.list(\n stats::setNames(\n object = TRUE\n , nm = rawscore_alias\n )\n )\n preds_raw_s3_param <- predict(bst, X, params = params)\n preds_raw_r6_param <- bst$predict(X, params = params)\n expect_equal(preds_raw_s3_keyword, preds_raw_s3_param)\n expect_equal(preds_raw_s3_keyword, preds_raw_r6_param)\n }\n})\n\ntest_that(\"predict() params should override keyword argument for leaf-index predictions\", {\n data(mtcars)\n X <- as.matrix(mtcars[, which(names(mtcars) != \"mpg\")])\n y <- as.numeric(mtcars[, \"mpg\"])\n bst <- lgb.train(\n data = lgb.Dataset(\n data = X\n , label = y\n , params = list(\n min_data_in_bin = 1L\n , data_seed = 708L\n )\n )\n , params = list(\n objective = \"regression\"\n , min_data_in_leaf = 1L\n , seed = 708L\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 10L\n , verbose = .LGB_VERBOSITY\n )\n\n # check that predictions really look like leaf index predictions\n preds_leaf_s3_keyword <- predict(bst, X, type = \"leaf\")\n expect_true(is.matrix(preds_leaf_s3_keyword))\n expect_equal(dim(preds_leaf_s3_keyword), c(nrow(X), bst$current_iter()))\n expect_true(min(preds_leaf_s3_keyword) >= 0L)\n trees_dt <- lgb.model.dt.tree(bst)\n max_leaf_by_tree_from_dt <- trees_dt[, .(idx = max(leaf_index, na.rm = TRUE)), by = tree_index]$idx\n max_leaf_by_tree_from_preds <- apply(preds_leaf_s3_keyword, 2L, max, na.rm = TRUE)\n expect_equal(max_leaf_by_tree_from_dt, max_leaf_by_tree_from_preds)\n\n # should get the same results from Booster$predict() method\n preds_leaf_r6_keyword <- bst$predict(X, predleaf = TRUE)\n expect_equal(preds_leaf_s3_keyword, preds_leaf_r6_keyword)\n\n # using a parameter alias of predict_leaf_index should result in leaf indices being returned\n aliases <- .PARAMETER_ALIASES()[[\"predict_leaf_index\"]]\n expect_true(length(aliases) > 1L)\n for (predleaf_alias in aliases) {\n params <- as.list(\n stats::setNames(\n object = TRUE\n , nm = predleaf_alias\n )\n )\n preds_leaf_s3_param <- predict(bst, X, params = params)\n preds_leaf_r6_param <- bst$predict(X, params = params)\n expect_equal(preds_leaf_s3_keyword, preds_leaf_s3_param)\n expect_equal(preds_leaf_s3_keyword, preds_leaf_r6_param)\n }\n})\n\ntest_that(\"predict() params should override keyword argument for feature contributions\", {\n data(mtcars)\n X <- as.matrix(mtcars[, which(names(mtcars) != \"mpg\")])\n y <- as.numeric(mtcars[, \"mpg\"])\n bst <- lgb.train(\n data = lgb.Dataset(\n data = X\n , label = y\n , params = list(\n min_data_in_bin = 1L\n , data_seed = 708L\n )\n )\n , params = list(\n objective = \"regression\"\n , min_data_in_leaf = 1L\n , seed = 708L\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 10L\n , verbose = .LGB_VERBOSITY\n )\n\n # check that predictions really look like feature contributions\n preds_contrib_s3_keyword <- predict(bst, X, type = \"contrib\")\n num_features <- ncol(X)\n shap_base_value <- unname(preds_contrib_s3_keyword[, ncol(preds_contrib_s3_keyword)])\n expect_true(is.matrix(preds_contrib_s3_keyword))\n expect_equal(dim(preds_contrib_s3_keyword), c(nrow(X), num_features + 1L))\n expect_equal(length(unique(shap_base_value)), 1L)\n expect_equal(mean(y), shap_base_value[1L])\n expect_equal(predict(bst, X), rowSums(preds_contrib_s3_keyword))\n\n # should get the same results from Booster$predict() method\n preds_contrib_r6_keyword <- bst$predict(X, predcontrib = TRUE)\n expect_equal(preds_contrib_s3_keyword, preds_contrib_r6_keyword)\n\n # using a parameter alias of predict_contrib should result in feature contributions being returned\n aliases <- .PARAMETER_ALIASES()[[\"predict_contrib\"]]\n expect_true(length(aliases) > 1L)\n for (predcontrib_alias in aliases) {\n params <- as.list(\n stats::setNames(\n object = TRUE\n , nm = predcontrib_alias\n )\n )\n preds_contrib_s3_param <- predict(bst, X, params = params)\n preds_contrib_r6_param <- bst$predict(X, params = params)\n expect_equal(preds_contrib_s3_keyword, preds_contrib_s3_param)\n expect_equal(preds_contrib_s3_keyword, preds_contrib_r6_param)\n }\n})\n\n.expect_has_row_names <- function(pred, X) {\n if (is.vector(pred)) {\n rnames <- names(pred)\n } else {\n rnames <- row.names(pred)\n }\n expect_false(is.null(rnames))\n expect_true(is.vector(rnames))\n expect_true(length(rnames) > 0L)\n expect_equal(row.names(X), rnames)\n}\n\n.expect_doesnt_have_row_names <- function(pred) {\n if (is.vector(pred)) {\n expect_null(names(pred))\n } else {\n expect_null(row.names(pred))\n }\n}\n\n.check_all_row_name_expectations <- function(bst, X) {\n\n # dense matrix with row names\n pred <- predict(bst, X)\n .expect_has_row_names(pred, X)\n pred <- predict(bst, X, type = \"raw\")\n .expect_has_row_names(pred, X)\n pred <- predict(bst, X, type = \"leaf\")\n .expect_has_row_names(pred, X)\n pred <- predict(bst, X, type = \"contrib\")\n .expect_has_row_names(pred, X)\n\n # dense matrix without row names\n Xcopy <- X\n row.names(Xcopy) <- NULL\n pred <- predict(bst, Xcopy)\n .expect_doesnt_have_row_names(pred)\n\n # sparse matrix with row names\n Xcsc <- as(X, \"CsparseMatrix\")\n pred <- predict(bst, Xcsc)\n .expect_has_row_names(pred, Xcsc)\n pred <- predict(bst, Xcsc, type = \"raw\")\n .expect_has_row_names(pred, Xcsc)\n pred <- predict(bst, Xcsc, type = \"leaf\")\n .expect_has_row_names(pred, Xcsc)\n pred <- predict(bst, Xcsc, type = \"contrib\")\n .expect_has_row_names(pred, Xcsc)\n pred <- predict(bst, as(Xcsc, \"RsparseMatrix\"), type = \"contrib\")\n .expect_has_row_names(pred, Xcsc)\n\n # sparse matrix without row names\n Xcopy <- Xcsc\n row.names(Xcopy) <- NULL\n pred <- predict(bst, Xcopy)\n .expect_doesnt_have_row_names(pred)\n}\n\ntest_that(\"predict() keeps row names from data (regression)\", {\n data(\"mtcars\")\n X <- as.matrix(mtcars[, -1L])\n y <- as.numeric(mtcars[, 1L])\n dtrain <- lgb.Dataset(\n X\n , label = y\n , params = list(\n max_bins = 5L\n , min_data_in_bin = 1L\n )\n )\n bst <- lgb.train(\n data = dtrain\n , obj = \"regression\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(min_data_in_leaf = 1L, num_threads = .LGB_MAX_THREADS)\n )\n .check_all_row_name_expectations(bst, X)\n})\n\ntest_that(\"predict() keeps row names from data (binary classification)\", {\n data(agaricus.train, package = \"lightgbm\")\n X <- as.matrix(agaricus.train$data)\n y <- agaricus.train$label\n row.names(X) <- paste0(\"rname\", seq(1L, nrow(X)))\n dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L))\n bst <- lgb.train(\n data = dtrain\n , obj = \"binary\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n .check_all_row_name_expectations(bst, X)\n})\n\ntest_that(\"predict() keeps row names from data (multi-class classification)\", {\n data(iris)\n y <- as.numeric(iris$Species) - 1.0\n X <- as.matrix(iris[, names(iris) != \"Species\"])\n row.names(X) <- paste0(\"rname\", seq(1L, nrow(X)))\n dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L))\n bst <- lgb.train(\n data = dtrain\n , obj = \"multiclass\"\n , params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS)\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n )\n .check_all_row_name_expectations(bst, X)\n})\n\ntest_that(\"predictions for regression and binary classification are returned as vectors\", {\n data(mtcars)\n X <- as.matrix(mtcars[, -1L])\n y <- as.numeric(mtcars[, 1L])\n dtrain <- lgb.Dataset(\n X\n , label = y\n , params = list(\n max_bins = 5L\n , min_data_in_bin = 1L\n )\n )\n model <- lgb.train(\n data = dtrain\n , obj = \"regression\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(min_data_in_leaf = 1L, num_threads = .LGB_MAX_THREADS)\n )\n pred <- predict(model, X)\n expect_true(is.vector(pred))\n expect_equal(length(pred), nrow(X))\n pred <- predict(model, X, type = \"raw\")\n expect_true(is.vector(pred))\n expect_equal(length(pred), nrow(X))\n\n data(agaricus.train, package = \"lightgbm\")\n X <- agaricus.train$data\n y <- agaricus.train$label\n dtrain <- lgb.Dataset(X, label = y)\n model <- lgb.train(\n data = dtrain\n , obj = \"binary\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n pred <- predict(model, X)\n expect_true(is.vector(pred))\n expect_equal(length(pred), nrow(X))\n pred <- predict(model, X, type = \"raw\")\n expect_true(is.vector(pred))\n expect_equal(length(pred), nrow(X))\n})\n\ntest_that(\"predictions for multiclass classification are returned as matrix\", {\n data(iris)\n X <- as.matrix(iris[, -5L])\n y <- as.numeric(iris$Species) - 1.0\n dtrain <- lgb.Dataset(X, label = y)\n model <- lgb.train(\n data = dtrain\n , obj = \"multiclass\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS)\n )\n pred <- predict(model, X)\n expect_true(is.matrix(pred))\n expect_equal(nrow(pred), nrow(X))\n expect_equal(ncol(pred), 3L)\n pred <- predict(model, X, type = \"raw\")\n expect_true(is.matrix(pred))\n expect_equal(nrow(pred), nrow(X))\n expect_equal(ncol(pred), 3L)\n})\n\ntest_that(\"Single-row predictions are identical to multi-row ones\", {\n data(mtcars)\n X <- as.matrix(mtcars[, -1L])\n y <- mtcars[, 1L]\n dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L))\n params <- list(min_data_in_leaf = 2L, num_threads = .LGB_MAX_THREADS)\n model <- lgb.train(\n params = params\n , data = dtrain\n , obj = \"regression\"\n , nrounds = 5L\n , verbose = -1L\n )\n\n x1 <- X[1L, , drop = FALSE]\n x11 <- X[11L, , drop = FALSE]\n x1_spv <- as(x1, \"sparseVector\")\n x11_spv <- as(x11, \"sparseVector\")\n x1_csr <- as(x1, \"RsparseMatrix\")\n x11_csr <- as(x11, \"RsparseMatrix\")\n\n pred_all <- predict(model, X)\n pred1_wo_config <- predict(model, x1)\n pred11_wo_config <- predict(model, x11)\n pred1_spv_wo_config <- predict(model, x1_spv)\n pred11_spv_wo_config <- predict(model, x11_spv)\n pred1_csr_wo_config <- predict(model, x1_csr)\n pred11_csr_wo_config <- predict(model, x11_csr)\n\n lgb.configure_fast_predict(model)\n pred1_w_config <- predict(model, x1)\n pred11_w_config <- predict(model, x11)\n\n model <- lgb.train(\n params = params\n , data = dtrain\n , obj = \"regression\"\n , nrounds = 5L\n , verbose = -1L\n )\n lgb.configure_fast_predict(model, csr = TRUE)\n pred1_spv_w_config <- predict(model, x1_spv)\n pred11_spv_w_config <- predict(model, x11_spv)\n pred1_csr_w_config <- predict(model, x1_csr)\n pred11_csr_w_config <- predict(model, x11_csr)\n\n expect_equal(pred1_wo_config, pred_all[1L])\n expect_equal(pred11_wo_config, pred_all[11L])\n expect_equal(pred1_spv_wo_config, unname(pred_all[1L]))\n expect_equal(pred11_spv_wo_config, unname(pred_all[11L]))\n expect_equal(pred1_csr_wo_config, pred_all[1L])\n expect_equal(pred11_csr_wo_config, pred_all[11L])\n\n expect_equal(pred1_w_config, pred_all[1L])\n expect_equal(pred11_w_config, pred_all[11L])\n expect_equal(pred1_spv_w_config, unname(pred_all[1L]))\n expect_equal(pred11_spv_w_config, unname(pred_all[11L]))\n expect_equal(pred1_csr_w_config, pred_all[1L])\n expect_equal(pred11_csr_w_config, pred_all[11L])\n})\n\ntest_that(\"Fast-predict configuration accepts non-default prediction types\", {\n data(mtcars)\n X <- as.matrix(mtcars[, -1L])\n y <- mtcars[, 1L]\n dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L))\n params <- list(min_data_in_leaf = 2L, num_threads = .LGB_MAX_THREADS)\n model <- lgb.train(\n params = params\n , data = dtrain\n , obj = \"regression\"\n , nrounds = 5L\n , verbose = -1L\n )\n\n x1 <- X[1L, , drop = FALSE]\n x11 <- X[11L, , drop = FALSE]\n\n pred_all <- predict(model, X, type = \"leaf\")\n pred1_wo_config <- predict(model, x1, type = \"leaf\")\n pred11_wo_config <- predict(model, x11, type = \"leaf\")\n expect_equal(pred1_wo_config, pred_all[1L, , drop = FALSE])\n expect_equal(pred11_wo_config, pred_all[11L, , drop = FALSE])\n\n lgb.configure_fast_predict(model, type = \"leaf\")\n pred1_w_config <- predict(model, x1, type = \"leaf\")\n pred11_w_config <- predict(model, x11, type = \"leaf\")\n expect_equal(pred1_w_config, pred_all[1L, , drop = FALSE])\n expect_equal(pred11_w_config, pred_all[11L, , drop = FALSE])\n})\n\ntest_that(\"Fast-predict configuration does not block other prediction types\", {\n data(mtcars)\n X <- as.matrix(mtcars[, -1L])\n y <- mtcars[, 1L]\n dtrain <- lgb.Dataset(X, label = y, params = list(max_bin = 5L))\n params <- list(min_data_in_leaf = 2L, num_threads = .LGB_MAX_THREADS)\n model <- lgb.train(\n params = params\n , data = dtrain\n , obj = \"regression\"\n , nrounds = 5L\n , verbose = -1L\n )\n\n x1 <- X[1L, , drop = FALSE]\n x11 <- X[11L, , drop = FALSE]\n\n pred_all <- predict(model, X)\n pred_all_leaf <- predict(model, X, type = \"leaf\")\n\n lgb.configure_fast_predict(model)\n pred1_w_config <- predict(model, x1)\n pred11_w_config <- predict(model, x11)\n pred1_leaf_w_config <- predict(model, x1, type = \"leaf\")\n pred11_leaf_w_config <- predict(model, x11, type = \"leaf\")\n\n expect_equal(pred1_w_config, pred_all[1L])\n expect_equal(pred11_w_config, pred_all[11L])\n expect_equal(pred1_leaf_w_config, pred_all_leaf[1L, , drop = FALSE])\n expect_equal(pred11_leaf_w_config, pred_all_leaf[11L, , drop = FALSE])\n})\n\ntest_that(\"predict type='class' returns predicted class for classification objectives\", {\n data(agaricus.train, package = \"lightgbm\")\n X <- as.matrix(agaricus.train$data)\n y <- agaricus.train$label\n dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L))\n bst <- lgb.train(\n data = dtrain\n , obj = \"binary\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n pred <- predict(bst, X, type = \"class\")\n expect_true(all(pred %in% c(0L, 1L)))\n\n data(iris)\n X <- as.matrix(iris[, -5L])\n y <- as.numeric(iris$Species) - 1.0\n dtrain <- lgb.Dataset(X, label = y)\n model <- lgb.train(\n data = dtrain\n , obj = \"multiclass\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(num_class = 3L, num_threads = .LGB_MAX_THREADS)\n )\n pred <- predict(model, X, type = \"class\")\n expect_true(all(pred %in% c(0L, 1L, 2L)))\n})\n\ntest_that(\"predict type='class' returns values in the target's range for regression objectives\", {\n data(agaricus.train, package = \"lightgbm\")\n X <- as.matrix(agaricus.train$data)\n y <- agaricus.train$label\n dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L))\n bst <- lgb.train(\n data = dtrain\n , obj = \"regression\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n pred <- predict(bst, X, type = \"class\")\n expect_true(!any(pred %in% c(0.0, 1.0)))\n})\n" }, { "path": "R-package/tests/testthat/test_basic.R", "content": "data(agaricus.train, package = \"lightgbm\")\ndata(agaricus.test, package = \"lightgbm\")\ntrain <- agaricus.train\ntest <- agaricus.test\n\nset.seed(708L)\n\n# [description] Every time this function is called, it adds 0.1\n# to an accumulator then returns the current value.\n# This is used to mock the situation where an evaluation\n# metric increases every iteration\nACCUMULATOR_NAME <- \"INCREASING_METRIC_ACCUMULATOR\"\nassign(x = ACCUMULATOR_NAME, value = 0.0, envir = .GlobalEnv)\n\n.increasing_metric <- function(preds, dtrain) {\n if (!exists(ACCUMULATOR_NAME, envir = .GlobalEnv)) {\n assign(ACCUMULATOR_NAME, 0.0, envir = .GlobalEnv)\n }\n assign(\n x = ACCUMULATOR_NAME\n , value = get(ACCUMULATOR_NAME, envir = .GlobalEnv) + 0.1\n , envir = .GlobalEnv\n )\n return(list(\n name = \"increasing_metric\"\n , value = get(ACCUMULATOR_NAME, envir = .GlobalEnv)\n , higher_better = TRUE\n ))\n}\n\n# [description] Evaluation function that always returns the\n# same value\nCONSTANT_METRIC_VALUE <- 0.2\n.constant_metric <- function(preds, dtrain) {\n return(list(\n name = \"constant_metric\"\n , value = CONSTANT_METRIC_VALUE\n , higher_better = FALSE\n ))\n}\n\n# sample datasets to test early stopping\nDTRAIN_RANDOM_REGRESSION <- lgb.Dataset(\n data = as.matrix(rnorm(100L), ncol = 1L, drop = FALSE)\n , label = rnorm(100L)\n , params = list(num_threads = .LGB_MAX_THREADS)\n)\nDVALID_RANDOM_REGRESSION <- lgb.Dataset(\n data = as.matrix(rnorm(50L), ncol = 1L, drop = FALSE)\n , label = rnorm(50L)\n , params = list(num_threads = .LGB_MAX_THREADS)\n)\nDTRAIN_RANDOM_CLASSIFICATION <- lgb.Dataset(\n data = as.matrix(rnorm(120L), ncol = 1L, drop = FALSE)\n , label = sample(c(0L, 1L), size = 120L, replace = TRUE)\n , params = list(num_threads = .LGB_MAX_THREADS)\n)\nDVALID_RANDOM_CLASSIFICATION <- lgb.Dataset(\n data = as.matrix(rnorm(37L), ncol = 1L, drop = FALSE)\n , label = sample(c(0L, 1L), size = 37L, replace = TRUE)\n , params = list(num_threads = .LGB_MAX_THREADS)\n)\n\ntest_that(\"train and predict binary classification\", {\n nrounds <- 10L\n bst <- lightgbm(\n data = train$data\n , label = train$label\n , params = list(\n num_leaves = 5L\n , objective = \"binary\"\n , metric = \"binary_error\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = nrounds\n , valids = list(\n \"train\" = lgb.Dataset(\n data = train$data\n , label = train$label\n )\n )\n )\n expect_false(is.null(bst$record_evals))\n record_results <- lgb.get.eval.result(bst, \"train\", \"binary_error\")\n expect_lt(min(record_results), 0.02)\n\n pred <- predict(bst, test$data)\n expect_equal(length(pred), 1611L)\n\n pred1 <- predict(bst, train$data, num_iteration = 1L)\n expect_equal(length(pred1), 6513L)\n err_pred1 <- sum((pred1 > 0.5) != train$label) / length(train$label)\n err_log <- record_results[1L]\n expect_lt(abs(err_pred1 - err_log), .LGB_NUMERIC_TOLERANCE)\n})\n\n\ntest_that(\"train and predict softmax\", {\n set.seed(708L)\n X_mat <- as.matrix(iris[, -5L])\n lb <- as.numeric(iris$Species) - 1L\n\n bst <- lightgbm(\n data = X_mat\n , label = lb\n , params = list(\n num_leaves = 4L\n , learning_rate = 0.05\n , min_data = 20L\n , min_hessian = 10.0\n , objective = \"multiclass\"\n , metric = \"multi_error\"\n , num_class = 3L\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 20L\n , valids = list(\n \"train\" = lgb.Dataset(\n data = X_mat\n , label = lb\n )\n )\n )\n\n expect_false(is.null(bst$record_evals))\n record_results <- lgb.get.eval.result(bst, \"train\", \"multi_error\")\n expect_lt(min(record_results), 0.06)\n\n pred <- predict(bst, as.matrix(iris[, -5L]))\n expect_equal(length(pred), nrow(iris) * 3L)\n})\n\n\ntest_that(\"use of multiple eval metrics works\", {\n metrics <- list(\"binary_error\", \"auc\", \"binary_logloss\")\n bst <- lightgbm(\n data = train$data\n , label = train$label\n , params = list(\n num_leaves = 4L\n , learning_rate = 1.0\n , objective = \"binary\"\n , metric = metrics\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 10L\n , valids = list(\n \"train\" = lgb.Dataset(\n data = train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n )\n )\n expect_false(is.null(bst$record_evals))\n expect_named(\n bst$record_evals[[\"train\"]]\n , unlist(metrics)\n , ignore.order = FALSE\n , ignore.case = FALSE\n )\n})\n\ntest_that(\"lgb.Booster.upper_bound() and lgb.Booster.lower_bound() work as expected for binary classification\", {\n set.seed(708L)\n nrounds <- 10L\n bst <- lightgbm(\n data = train$data\n , label = train$label\n , params = list(\n num_leaves = 5L\n , objective = \"binary\"\n , metric = \"binary_error\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = nrounds\n )\n expect_true(abs(bst$lower_bound() - -1.590853) < .LGB_NUMERIC_TOLERANCE)\n expect_true(abs(bst$upper_bound() - 1.871015) < .LGB_NUMERIC_TOLERANCE)\n})\n\ntest_that(\"lgb.Booster.upper_bound() and lgb.Booster.lower_bound() work as expected for regression\", {\n set.seed(708L)\n nrounds <- 10L\n bst <- lightgbm(\n data = train$data\n , label = train$label\n , params = list(\n num_leaves = 5L\n , objective = \"regression\"\n , metric = \"l2\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = nrounds\n )\n expect_true(abs(bst$lower_bound() - 0.1513859) < .LGB_NUMERIC_TOLERANCE)\n expect_true(abs(bst$upper_bound() - 0.9080349) < .LGB_NUMERIC_TOLERANCE)\n})\n\ntest_that(\"lightgbm() rejects negative or 0 value passed to nrounds\", {\n dtrain <- lgb.Dataset(train$data, label = train$label)\n params <- list(objective = \"regression\", metric = \"l2,l1\", num_threads = .LGB_MAX_THREADS)\n for (nround_value in c(-10L, 0L)) {\n expect_error({\n bst <- lightgbm(\n data = dtrain\n , params = params\n , nrounds = nround_value\n )\n }, \"nrounds should be greater than zero\")\n }\n})\n\ntest_that(\"lightgbm() accepts nrounds as either a top-level argument or parameter\", {\n nrounds <- 15L\n\n set.seed(708L)\n top_level_bst <- lightgbm(\n data = train$data\n , label = train$label\n , nrounds = nrounds\n , params = list(\n objective = \"regression\"\n , metric = \"l2\"\n , num_leaves = 5L\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n )\n\n set.seed(708L)\n param_bst <- lightgbm(\n data = train$data\n , label = train$label\n , params = list(\n objective = \"regression\"\n , metric = \"l2\"\n , num_leaves = 5L\n , nrounds = nrounds\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n )\n\n set.seed(708L)\n both_customized <- lightgbm(\n data = train$data\n , label = train$label\n , nrounds = 20L\n , params = list(\n objective = \"regression\"\n , metric = \"l2\"\n , num_leaves = 5L\n , nrounds = nrounds\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n )\n\n top_level_l2 <- top_level_bst$eval_train()[[1L]][[\"value\"]]\n params_l2 <- param_bst$eval_train()[[1L]][[\"value\"]]\n both_l2 <- both_customized$eval_train()[[1L]][[\"value\"]]\n\n # check type just to be sure the subsetting didn't return a NULL\n expect_true(is.numeric(top_level_l2))\n expect_true(is.numeric(params_l2))\n expect_true(is.numeric(both_l2))\n\n # check that model produces identical performance\n expect_identical(top_level_l2, params_l2)\n expect_identical(both_l2, params_l2)\n\n expect_identical(param_bst$current_iter(), top_level_bst$current_iter())\n expect_identical(param_bst$current_iter(), both_customized$current_iter())\n expect_identical(param_bst$current_iter(), nrounds)\n\n})\n\ntest_that(\"lightgbm() performs evaluation on validation sets if they are provided\", {\n set.seed(708L)\n dvalid1 <- lgb.Dataset(\n data = train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n dvalid2 <- lgb.Dataset(\n data = train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n nrounds <- 10L\n bst <- lightgbm(\n data = train$data\n , label = train$label\n , params = list(\n num_leaves = 5L\n , objective = \"binary\"\n , metric = c(\n \"binary_error\"\n , \"auc\"\n )\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid1\n , \"valid2\" = dvalid2\n , \"train\" = lgb.Dataset(\n data = train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n )\n )\n\n expect_named(\n bst$record_evals\n , c(\"train\", \"valid1\", \"valid2\", \"start_iter\")\n , ignore.order = TRUE\n , ignore.case = FALSE\n )\n for (valid_name in c(\"train\", \"valid1\", \"valid2\")) {\n eval_results <- bst$record_evals[[valid_name]][[\"binary_error\"]]\n expect_length(eval_results[[\"eval\"]], nrounds)\n }\n expect_true(abs(bst$record_evals[[\"train\"]][[\"binary_error\"]][[\"eval\"]][[1L]] - 0.02226317) < .LGB_NUMERIC_TOLERANCE)\n expect_true(abs(bst$record_evals[[\"valid1\"]][[\"binary_error\"]][[\"eval\"]][[1L]] - 0.02226317) < .LGB_NUMERIC_TOLERANCE)\n expect_true(abs(bst$record_evals[[\"valid2\"]][[\"binary_error\"]][[\"eval\"]][[1L]] - 0.02226317) < .LGB_NUMERIC_TOLERANCE)\n})\n\ntest_that(\"training continuation works\", {\n dtrain <- lgb.Dataset(\n train$data\n , label = train$label\n , free_raw_data = FALSE\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n watchlist <- list(train = dtrain)\n param <- list(\n objective = \"binary\"\n , metric = \"binary_logloss\"\n , num_leaves = 5L\n , learning_rate = 1.0\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n\n # train for 10 consecutive iterations\n bst <- lgb.train(param, dtrain, nrounds = 10L, watchlist)\n err_bst <- lgb.get.eval.result(bst, \"train\", \"binary_logloss\", 10L)\n\n # train for 5 iterations, save, load, train for 5 more\n bst1 <- lgb.train(param, dtrain, nrounds = 5L, watchlist)\n model_file <- tempfile(fileext = \".model\")\n lgb.save(bst1, model_file)\n bst2 <- lgb.train(param, dtrain, nrounds = 5L, watchlist, init_model = bst1)\n err_bst2 <- lgb.get.eval.result(bst2, \"train\", \"binary_logloss\", 10L)\n\n # evaluation metrics should be nearly identical for the model trained in 10 coonsecutive\n # iterations and the one trained in 5-then-5.\n expect_lt(abs(err_bst - err_bst2), 0.01)\n})\n\ntest_that(\"cv works\", {\n dtrain <- lgb.Dataset(train$data, label = train$label)\n params <- list(\n objective = \"regression\"\n , metric = \"l2,l1\"\n , min_data = 1L\n , learning_rate = 1.0\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n bst <- lgb.cv(\n params\n , dtrain\n , 10L\n , nfold = 5L\n , early_stopping_rounds = 10L\n )\n expect_false(is.null(bst$record_evals))\n})\n\ntest_that(\"CVBooster$reset_parameter() works as expected\", {\n dtrain <- lgb.Dataset(train$data, label = train$label)\n n_folds <- 2L\n cv_bst <- lgb.cv(\n params = list(\n objective = \"regression\"\n , min_data = 1L\n , num_leaves = 7L\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , nrounds = 3L\n , nfold = n_folds\n )\n expect_true(methods::is(cv_bst, \"lgb.CVBooster\"))\n expect_length(cv_bst$boosters, n_folds)\n for (bst in cv_bst$boosters) {\n expect_equal(bst[[\"booster\"]]$params[[\"num_leaves\"]], 7L)\n }\n cv_bst$reset_parameter(list(num_leaves = 11L))\n for (bst in cv_bst$boosters) {\n expect_equal(bst[[\"booster\"]]$params[[\"num_leaves\"]], 11L)\n }\n})\n\ntest_that(\"lgb.cv() rejects negative or 0 value passed to nrounds\", {\n dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = 2L))\n params <- list(\n objective = \"regression\"\n , metric = \"l2,l1\"\n , min_data = 1L\n , num_threads = .LGB_MAX_THREADS\n )\n for (nround_value in c(-10L, 0L)) {\n expect_error({\n bst <- lgb.cv(\n params\n , dtrain\n , nround_value\n , nfold = 5L\n )\n }, \"nrounds should be greater than zero\")\n }\n})\n\ntest_that(\"lgb.cv() throws an informative error if 'data' is not an lgb.Dataset\", {\n bad_values <- list(\n 4L\n , \"hello\"\n , list(a = TRUE, b = seq_len(10L))\n , data.frame(x = seq_len(5L), y = seq_len(5L))\n , data.table::data.table(x = seq_len(5L), y = seq_len(5L))\n , matrix(data = seq_len(10L), 2L, 5L)\n )\n for (val in bad_values) {\n expect_error({\n bst <- lgb.cv(\n params = list(\n objective = \"regression\"\n , metric = \"l2,l1\"\n , min_data = 1L\n )\n , data = val\n , 10L\n , nfold = 5L\n )\n }, regexp = \"lgb.cv: data must be an lgb.Dataset instance\", fixed = TRUE)\n }\n})\n\ntest_that(\"lightgbm.cv() gives the correct best_score and best_iter for a metric where higher values are better\", {\n set.seed(708L)\n dtrain <- lgb.Dataset(\n data = as.matrix(runif(n = 500L, min = 0.0, max = 15.0), drop = FALSE)\n , label = rep(c(0L, 1L), 250L)\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n nrounds <- 10L\n cv_bst <- lgb.cv(\n data = dtrain\n , nfold = 5L\n , nrounds = nrounds\n , params = list(\n objective = \"binary\"\n , metric = \"auc,binary_error\"\n , learning_rate = 1.5\n , num_leaves = 5L\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n )\n expect_true(methods::is(cv_bst, \"lgb.CVBooster\"))\n expect_named(\n cv_bst$record_evals\n , c(\"start_iter\", \"valid\")\n , ignore.order = FALSE\n , ignore.case = FALSE\n )\n auc_scores <- unlist(cv_bst$record_evals[[\"valid\"]][[\"auc\"]][[\"eval\"]])\n expect_length(auc_scores, nrounds)\n expect_identical(cv_bst$best_iter, which.max(auc_scores))\n expect_identical(cv_bst$best_score, auc_scores[which.max(auc_scores)])\n})\n\ntest_that(\"lgb.cv() fit on linearly-relatead data improves when using linear learners\", {\n set.seed(708L)\n .new_dataset <- function() {\n X <- matrix(rnorm(1000L), ncol = 1L)\n return(lgb.Dataset(\n data = X\n , label = 2L * X + runif(nrow(X), 0L, 0.1)\n , params = list(num_threads = .LGB_MAX_THREADS)\n ))\n }\n\n params <- list(\n objective = \"regression\"\n , verbose = -1L\n , metric = \"mse\"\n , seed = 0L\n , num_leaves = 2L\n , num_threads = .LGB_MAX_THREADS\n )\n\n dtrain <- .new_dataset()\n cv_bst <- lgb.cv(\n data = dtrain\n , nrounds = 10L\n , params = params\n , nfold = 5L\n )\n expect_true(methods::is(cv_bst, \"lgb.CVBooster\"))\n\n dtrain <- .new_dataset()\n cv_bst_linear <- lgb.cv(\n data = dtrain\n , nrounds = 10L\n , params = utils::modifyList(params, list(linear_tree = TRUE))\n , nfold = 5L\n )\n expect_true(methods::is(cv_bst_linear, \"lgb.CVBooster\"))\n\n expect_true(cv_bst_linear$best_score < cv_bst$best_score)\n})\n\ntest_that(\"lgb.cv() respects showsd argument\", {\n dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = .LGB_MAX_THREADS))\n params <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data = 1L\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n nrounds <- 5L\n set.seed(708L)\n bst_showsd <- lgb.cv(\n params = params\n , data = dtrain\n , nrounds = nrounds\n , nfold = 3L\n , showsd = TRUE\n )\n evals_showsd <- bst_showsd$record_evals[[\"valid\"]][[\"l2\"]]\n set.seed(708L)\n bst_no_showsd <- lgb.cv(\n params = params\n , data = dtrain\n , nrounds = nrounds\n , nfold = 3L\n , showsd = FALSE\n )\n evals_no_showsd <- bst_no_showsd$record_evals[[\"valid\"]][[\"l2\"]]\n expect_equal(\n evals_showsd[[\"eval\"]]\n , evals_no_showsd[[\"eval\"]]\n )\n expect_true(methods::is(evals_showsd[[\"eval_err\"]], \"list\"))\n expect_equal(length(evals_showsd[[\"eval_err\"]]), nrounds)\n expect_identical(evals_no_showsd[[\"eval_err\"]], list())\n})\n\ntest_that(\"lgb.cv() raises an informative error for unrecognized objectives\", {\n dtrain <- lgb.Dataset(\n data = train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n expect_error({\n capture.output({\n bst <- lgb.cv(\n data = dtrain\n , params = list(\n objective_type = \"not_a_real_objective\"\n , verbosity = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n )\n }, type = \"message\")\n }, regexp = \"Unknown objective type name: not_a_real_objective\")\n})\n\ntest_that(\"lgb.cv() respects parameter aliases for objective\", {\n nrounds <- 3L\n nfold <- 4L\n dtrain <- lgb.Dataset(\n data = train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n cv_bst <- lgb.cv(\n data = dtrain\n , params = list(\n num_leaves = 5L\n , application = \"binary\"\n , num_iterations = nrounds\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nfold = nfold\n )\n expect_equal(cv_bst$best_iter, nrounds)\n expect_named(cv_bst$record_evals[[\"valid\"]], \"binary_logloss\")\n expect_length(cv_bst$record_evals[[\"valid\"]][[\"binary_logloss\"]][[\"eval\"]], nrounds)\n expect_length(cv_bst$boosters, nfold)\n})\n\ntest_that(\"lgb.cv() prefers objective in params to keyword argument\", {\n data(\"EuStockMarkets\")\n cv_bst <- lgb.cv(\n data = lgb.Dataset(\n data = EuStockMarkets[, c(\"SMI\", \"CAC\", \"FTSE\")]\n , label = EuStockMarkets[, \"DAX\"]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n , params = list(\n application = \"regression_l1\"\n , verbosity = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 5L\n , obj = \"regression_l2\"\n )\n for (bst_list in cv_bst$boosters) {\n bst <- bst_list[[\"booster\"]]\n expect_equal(bst$params$objective, \"regression_l1\")\n # NOTE: using save_model_to_string() since that is the simplest public API in the R-package\n # allowing access to the \"objective\" attribute of the Booster object on the C++ side\n model_txt_lines <- strsplit(\n x = bst$save_model_to_string()\n , split = \"\\n\"\n , fixed = TRUE\n )[[1L]]\n expect_true(any(model_txt_lines == \"objective=regression_l1\"))\n expect_false(any(model_txt_lines == \"objective=regression_l2\"))\n }\n})\n\ntest_that(\"lgb.cv() respects parameter aliases for metric\", {\n nrounds <- 3L\n nfold <- 4L\n dtrain <- lgb.Dataset(\n data = train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n cv_bst <- lgb.cv(\n data = dtrain\n , params = list(\n num_leaves = 5L\n , objective = \"binary\"\n , num_iterations = nrounds\n , metric_types = c(\"auc\", \"binary_logloss\")\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nfold = nfold\n )\n expect_equal(cv_bst$best_iter, nrounds)\n expect_named(cv_bst$record_evals[[\"valid\"]], c(\"auc\", \"binary_logloss\"))\n expect_length(cv_bst$record_evals[[\"valid\"]][[\"binary_logloss\"]][[\"eval\"]], nrounds)\n expect_length(cv_bst$record_evals[[\"valid\"]][[\"auc\"]][[\"eval\"]], nrounds)\n expect_length(cv_bst$boosters, nfold)\n})\n\ntest_that(\"lgb.cv() respects eval_train_metric argument\", {\n dtrain <- lgb.Dataset(train$data, label = train$label)\n params <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data = 1L\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n nrounds <- 5L\n set.seed(708L)\n bst_train <- lgb.cv(\n params = params\n , data = dtrain\n , nrounds = nrounds\n , nfold = 3L\n , showsd = FALSE\n , eval_train_metric = TRUE\n )\n set.seed(708L)\n bst_no_train <- lgb.cv(\n params = params\n , data = dtrain\n , nrounds = nrounds\n , nfold = 3L\n , showsd = FALSE\n , eval_train_metric = FALSE\n )\n expect_equal(\n bst_train$record_evals[[\"valid\"]][[\"l2\"]]\n , bst_no_train$record_evals[[\"valid\"]][[\"l2\"]]\n )\n expect_true(\"train\" %in% names(bst_train$record_evals))\n expect_false(\"train\" %in% names(bst_no_train$record_evals))\n expect_true(methods::is(bst_train$record_evals[[\"train\"]][[\"l2\"]][[\"eval\"]], \"list\"))\n expect_equal(\n length(bst_train$record_evals[[\"train\"]][[\"l2\"]][[\"eval\"]])\n , nrounds\n )\n})\n\ntest_that(\"lgb.train() works as expected with multiple eval metrics\", {\n metrics <- c(\"binary_error\", \"auc\", \"binary_logloss\")\n bst <- lgb.train(\n data = lgb.Dataset(\n train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n , nrounds = 10L\n , params = list(\n objective = \"binary\"\n , metric = metrics\n , learning_rate = 1.0\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , valids = list(\n \"train\" = lgb.Dataset(\n train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n )\n )\n expect_false(is.null(bst$record_evals))\n expect_named(\n bst$record_evals[[\"train\"]]\n , unlist(metrics)\n , ignore.order = FALSE\n , ignore.case = FALSE\n )\n})\n\ntest_that(\"lgb.train() raises an informative error for unrecognized objectives\", {\n dtrain <- lgb.Dataset(\n data = train$data\n , label = train$label\n )\n expect_error({\n capture.output({\n bst <- lgb.train(\n data = dtrain\n , params = list(\n objective_type = \"not_a_real_objective\"\n , verbosity = .LGB_VERBOSITY\n )\n )\n }, type = \"message\")\n }, regexp = \"Unknown objective type name: not_a_real_objective\")\n})\n\ntest_that(\"lgb.train() respects parameter aliases for objective\", {\n nrounds <- 3L\n dtrain <- lgb.Dataset(\n data = train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n bst <- lgb.train(\n data = dtrain\n , params = list(\n num_leaves = 5L\n , application = \"binary\"\n , num_iterations = nrounds\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , valids = list(\n \"the_training_data\" = dtrain\n )\n )\n expect_named(bst$record_evals[[\"the_training_data\"]], \"binary_logloss\")\n expect_length(bst$record_evals[[\"the_training_data\"]][[\"binary_logloss\"]][[\"eval\"]], nrounds)\n expect_equal(bst$params[[\"objective\"]], \"binary\")\n})\n\ntest_that(\"lgb.train() prefers objective in params to keyword argument\", {\n data(\"EuStockMarkets\")\n bst <- lgb.train(\n data = lgb.Dataset(\n data = EuStockMarkets[, c(\"SMI\", \"CAC\", \"FTSE\")]\n , label = EuStockMarkets[, \"DAX\"]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n , params = list(\n loss = \"regression_l1\"\n , verbosity = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 5L\n , obj = \"regression_l2\"\n )\n expect_equal(bst$params$objective, \"regression_l1\")\n # NOTE: using save_model_to_string() since that is the simplest public API in the R-package\n # allowing access to the \"objective\" attribute of the Booster object on the C++ side\n model_txt_lines <- strsplit(\n x = bst$save_model_to_string()\n , split = \"\\n\"\n , fixed = TRUE\n )[[1L]]\n expect_true(any(model_txt_lines == \"objective=regression_l1\"))\n expect_false(any(model_txt_lines == \"objective=regression_l2\"))\n})\n\ntest_that(\"lgb.train() respects parameter aliases for metric\", {\n nrounds <- 3L\n dtrain <- lgb.Dataset(\n data = train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n bst <- lgb.train(\n data = dtrain\n , params = list(\n num_leaves = 5L\n , objective = \"binary\"\n , num_iterations = nrounds\n , metric_types = c(\"auc\", \"binary_logloss\")\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , valids = list(\n \"train\" = dtrain\n )\n )\n record_results <- bst$record_evals[[\"train\"]]\n expect_equal(sort(names(record_results)), c(\"auc\", \"binary_logloss\"))\n expect_length(record_results[[\"auc\"]][[\"eval\"]], nrounds)\n expect_length(record_results[[\"binary_logloss\"]][[\"eval\"]], nrounds)\n expect_equal(bst$params[[\"metric\"]], list(\"auc\", \"binary_logloss\"))\n})\n\ntest_that(\"lgb.train() rejects negative or 0 value passed to nrounds\", {\n dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = .LGB_MAX_THREADS))\n params <- list(\n objective = \"regression\"\n , metric = \"l2,l1\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n for (nround_value in c(-10L, 0L)) {\n expect_error({\n bst <- lgb.train(\n params\n , dtrain\n , nround_value\n )\n }, \"nrounds should be greater than zero\")\n }\n})\n\n\ntest_that(\"lgb.train() accepts nrounds as either a top-level argument or parameter\", {\n nrounds <- 15L\n\n set.seed(708L)\n top_level_bst <- lgb.train(\n data = lgb.Dataset(\n train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n , nrounds = nrounds\n , params = list(\n objective = \"regression\"\n , metric = \"l2\"\n , num_leaves = 5L\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n )\n\n set.seed(708L)\n param_bst <- lgb.train(\n data = lgb.Dataset(\n train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n , params = list(\n objective = \"regression\"\n , metric = \"l2\"\n , num_leaves = 5L\n , nrounds = nrounds\n , verbose = .LGB_VERBOSITY\n )\n )\n\n set.seed(708L)\n both_customized <- lgb.train(\n data = lgb.Dataset(\n train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n , nrounds = 20L\n , params = list(\n objective = \"regression\"\n , metric = \"l2\"\n , num_leaves = 5L\n , nrounds = nrounds\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n )\n\n top_level_l2 <- top_level_bst$eval_train()[[1L]][[\"value\"]]\n params_l2 <- param_bst$eval_train()[[1L]][[\"value\"]]\n both_l2 <- both_customized$eval_train()[[1L]][[\"value\"]]\n\n # check type just to be sure the subsetting didn't return a NULL\n expect_true(is.numeric(top_level_l2))\n expect_true(is.numeric(params_l2))\n expect_true(is.numeric(both_l2))\n\n # check that model produces identical performance\n expect_identical(top_level_l2, params_l2)\n expect_identical(both_l2, params_l2)\n\n expect_identical(param_bst$current_iter(), top_level_bst$current_iter())\n expect_identical(param_bst$current_iter(), both_customized$current_iter())\n expect_identical(param_bst$current_iter(), nrounds)\n\n})\n\n\ntest_that(\"lgb.train() throws an informative error if 'data' is not an lgb.Dataset\", {\n bad_values <- list(\n 4L\n , \"hello\"\n , list(a = TRUE, b = seq_len(10L))\n , data.frame(x = seq_len(5L), y = seq_len(5L))\n , data.table::data.table(x = seq_len(5L), y = seq_len(5L))\n , matrix(data = seq_len(10L), 2L, 5L)\n )\n for (val in bad_values) {\n expect_error({\n bst <- lgb.train(\n params = list(\n objective = \"regression\"\n , metric = \"l2,l1\"\n , verbose = .LGB_VERBOSITY\n )\n , data = val\n , 10L\n )\n }, regexp = \"data must be an lgb.Dataset instance\", fixed = TRUE)\n }\n})\n\ntest_that(\"lgb.train() throws an informative error if 'valids' is not a list of lgb.Dataset objects\", {\n valids <- list(\n \"valid1\" = data.frame(x = rnorm(5L), y = rnorm(5L))\n , \"valid2\" = data.frame(x = rnorm(5L), y = rnorm(5L))\n )\n expect_error({\n bst <- lgb.train(\n params = list(\n objective = \"regression\"\n , metric = \"l2,l1\"\n , verbose = .LGB_VERBOSITY\n )\n , data = lgb.Dataset(train$data, label = train$label)\n , 10L\n , valids = valids\n )\n }, regexp = \"valids must be a list of lgb.Dataset elements\")\n})\n\ntest_that(\"lgb.train() errors if 'valids' is a list of lgb.Dataset objects but some do not have names\", {\n valids <- list(\n \"valid1\" = lgb.Dataset(matrix(rnorm(10L), 5L, 2L))\n , lgb.Dataset(matrix(rnorm(10L), 2L, 5L))\n )\n expect_error({\n bst <- lgb.train(\n params = list(\n objective = \"regression\"\n , metric = \"l2,l1\"\n , verbose = .LGB_VERBOSITY\n )\n , data = lgb.Dataset(train$data, label = train$label)\n , 10L\n , valids = valids\n )\n }, regexp = \"each element of valids must have a name\")\n})\n\ntest_that(\"lgb.train() throws an informative error if 'valids' contains lgb.Dataset objects but none have names\", {\n valids <- list(\n lgb.Dataset(matrix(rnorm(10L), 5L, 2L))\n , lgb.Dataset(matrix(rnorm(10L), 2L, 5L))\n )\n expect_error({\n bst <- lgb.train(\n params = list(\n objective = \"regression\"\n , metric = \"l2,l1\"\n , verbose = .LGB_VERBOSITY\n )\n , data = lgb.Dataset(train$data, label = train$label)\n , 10L\n , valids = valids\n )\n }, regexp = \"each element of valids must have a name\")\n})\n\ntest_that(\"lgb.train() works with force_col_wise and force_row_wise\", {\n set.seed(1234L)\n nrounds <- 10L\n dtrain <- lgb.Dataset(\n train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n params <- list(\n objective = \"binary\"\n , metric = \"binary_error\"\n , force_col_wise = TRUE\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n bst_col_wise <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = nrounds\n )\n\n params <- list(\n objective = \"binary\"\n , metric = \"binary_error\"\n , force_row_wise = TRUE\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n bst_row_wise <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = nrounds\n )\n\n expected_error <- 0.003070782\n expect_equal(bst_col_wise$eval_train()[[1L]][[\"value\"]], expected_error)\n expect_equal(bst_row_wise$eval_train()[[1L]][[\"value\"]], expected_error)\n\n # check some basic details of the boosters just to be sure force_col_wise\n # and force_row_wise are not causing any weird side effects\n for (bst in list(bst_row_wise, bst_col_wise)) {\n expect_equal(bst$current_iter(), nrounds)\n parsed_model <- jsonlite::fromJSON(bst$dump_model())\n expect_equal(parsed_model$objective, \"binary sigmoid:1\")\n expect_false(parsed_model$average_output)\n }\n})\n\ntest_that(\"lgb.train() works as expected with sparse features\", {\n set.seed(708L)\n num_obs <- 70000L\n trainDF <- data.frame(\n y = sample(c(0L, 1L), size = num_obs, replace = TRUE)\n , x = sample(c(1.0:10.0, rep(NA_real_, 50L)), size = num_obs, replace = TRUE)\n )\n dtrain <- lgb.Dataset(\n data = as.matrix(trainDF[[\"x\"]], drop = FALSE)\n , label = trainDF[[\"y\"]]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n nrounds <- 1L\n bst <- lgb.train(\n params = list(\n objective = \"binary\"\n , min_data = 1L\n , min_data_in_bin = 1L\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , nrounds = nrounds\n )\n\n expect_true(.is_Booster(bst))\n expect_equal(bst$current_iter(), nrounds)\n parsed_model <- jsonlite::fromJSON(bst$dump_model())\n expect_equal(parsed_model$objective, \"binary sigmoid:1\")\n expect_false(parsed_model$average_output)\n expected_error <- 0.6931268\n expect_true(abs(bst$eval_train()[[1L]][[\"value\"]] - expected_error) < .LGB_NUMERIC_TOLERANCE)\n})\n\ntest_that(\"lgb.train() works with early stopping for classification\", {\n trainDF <- data.frame(\n \"feat1\" = rep(c(5.0, 10.0), 500L)\n , \"target\" = rep(c(0L, 1L), 500L)\n )\n validDF <- data.frame(\n \"feat1\" = rep(c(5.0, 10.0), 50L)\n , \"target\" = rep(c(0L, 1L), 50L)\n )\n dtrain <- lgb.Dataset(\n data = as.matrix(trainDF[[\"feat1\"]], drop = FALSE)\n , label = trainDF[[\"target\"]]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n dvalid <- lgb.Dataset(\n data = as.matrix(validDF[[\"feat1\"]], drop = FALSE)\n , label = validDF[[\"target\"]]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n nrounds <- 10L\n\n ################################\n # train with no early stopping #\n ################################\n bst <- lgb.train(\n params = list(\n objective = \"binary\"\n , metric = \"binary_error\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid\n )\n )\n\n # a perfect model should be trivial to obtain, but all 10 rounds\n # should happen\n expect_equal(bst$best_score, 0.0)\n expect_equal(bst$best_iter, 1L)\n expect_equal(length(bst$record_evals[[\"valid1\"]][[\"binary_error\"]][[\"eval\"]]), nrounds)\n\n #############################\n # train with early stopping #\n #############################\n early_stopping_rounds <- 5L\n bst <- lgb.train(\n params = list(\n objective = \"binary\"\n , metric = \"binary_error\"\n , early_stopping_rounds = early_stopping_rounds\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid\n )\n )\n\n # a perfect model should be trivial to obtain, and only 6 rounds\n # should have happen (1 with improvement, 5 consecutive with no improvement)\n expect_equal(bst$best_score, 0.0)\n expect_equal(bst$best_iter, 1L)\n expect_equal(\n length(bst$record_evals[[\"valid1\"]][[\"binary_error\"]][[\"eval\"]])\n , early_stopping_rounds + 1L\n )\n\n})\n\ntest_that(\"lgb.train() treats early_stopping_rounds<=0 as disabling early stopping\", {\n set.seed(708L)\n trainDF <- data.frame(\n \"feat1\" = rep(c(5.0, 10.0), 500L)\n , \"target\" = rep(c(0L, 1L), 500L)\n )\n validDF <- data.frame(\n \"feat1\" = rep(c(5.0, 10.0), 50L)\n , \"target\" = rep(c(0L, 1L), 50L)\n )\n dtrain <- lgb.Dataset(\n data = as.matrix(trainDF[[\"feat1\"]], drop = FALSE)\n , label = trainDF[[\"target\"]]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n dvalid <- lgb.Dataset(\n data = as.matrix(validDF[[\"feat1\"]], drop = FALSE)\n , label = validDF[[\"target\"]]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n nrounds <- 5L\n\n for (value in c(-5L, 0L)) {\n\n #----------------------------#\n # passed as keyword argument #\n #----------------------------#\n bst <- lgb.train(\n params = list(\n objective = \"binary\"\n , metric = \"binary_error\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid\n )\n , early_stopping_rounds = value\n )\n\n # a perfect model should be trivial to obtain, but all 10 rounds\n # should happen\n expect_equal(bst$best_score, 0.0)\n expect_equal(bst$best_iter, 1L)\n expect_equal(length(bst$record_evals[[\"valid1\"]][[\"binary_error\"]][[\"eval\"]]), nrounds)\n\n #---------------------------#\n # passed as parameter alias #\n #---------------------------#\n bst <- lgb.train(\n params = list(\n objective = \"binary\"\n , metric = \"binary_error\"\n , n_iter_no_change = value\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid\n )\n )\n\n # a perfect model should be trivial to obtain, but all 10 rounds\n # should happen\n expect_equal(bst$best_score, 0.0)\n expect_equal(bst$best_iter, 1L)\n expect_equal(length(bst$record_evals[[\"valid1\"]][[\"binary_error\"]][[\"eval\"]]), nrounds)\n }\n})\n\ntest_that(\"lgb.train() works with early stopping for classification with a metric that should be maximized\", {\n set.seed(708L)\n dtrain <- lgb.Dataset(\n data = train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n dvalid <- lgb.Dataset(\n data = test$data\n , label = test$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n nrounds <- 10L\n\n #############################\n # train with early stopping #\n #############################\n early_stopping_rounds <- 5L\n # the harsh max_depth guarantees that AUC improves over at least the first few iterations\n bst_auc <- lgb.train(\n params = list(\n objective = \"binary\"\n , metric = \"auc\"\n , max_depth = 3L\n , early_stopping_rounds = early_stopping_rounds\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid\n )\n )\n bst_binary_error <- lgb.train(\n params = list(\n objective = \"binary\"\n , metric = \"binary_error\"\n , max_depth = 3L\n , early_stopping_rounds = early_stopping_rounds\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid\n )\n )\n\n # early stopping should have been hit for binary_error (higher_better = FALSE)\n eval_info <- bst_binary_error$.__enclos_env__$private$get_eval_info()\n expect_identical(eval_info, \"binary_error\")\n expect_identical(\n unname(bst_binary_error$.__enclos_env__$private$higher_better_inner_eval)\n , FALSE\n )\n expect_identical(bst_binary_error$best_iter, 1L)\n expect_identical(bst_binary_error$current_iter(), early_stopping_rounds + 1L)\n expect_true(abs(bst_binary_error$best_score - 0.01613904) < .LGB_NUMERIC_TOLERANCE)\n\n # early stopping should not have been hit for AUC (higher_better = TRUE)\n eval_info <- bst_auc$.__enclos_env__$private$get_eval_info()\n expect_identical(eval_info, \"auc\")\n expect_identical(\n unname(bst_auc$.__enclos_env__$private$higher_better_inner_eval)\n , TRUE\n )\n expect_identical(bst_auc$best_iter, 9L)\n expect_identical(bst_auc$current_iter(), nrounds)\n expect_true(abs(bst_auc$best_score - 0.9999969) < .LGB_NUMERIC_TOLERANCE)\n})\n\ntest_that(\"lgb.train() works with early stopping for regression\", {\n set.seed(708L)\n trainDF <- data.frame(\n \"feat1\" = rep(c(10.0, 100.0), 500L)\n , \"target\" = rep(c(-50.0, 50.0), 500L)\n )\n validDF <- data.frame(\n \"feat1\" = rep(50.0, 4L)\n , \"target\" = rep(50.0, 4L)\n )\n dtrain <- lgb.Dataset(\n data = as.matrix(trainDF[[\"feat1\"]], drop = FALSE)\n , label = trainDF[[\"target\"]]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n dvalid <- lgb.Dataset(\n data = as.matrix(validDF[[\"feat1\"]], drop = FALSE)\n , label = validDF[[\"target\"]]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n nrounds <- 10L\n\n ################################\n # train with no early stopping #\n ################################\n bst <- lgb.train(\n params = list(\n objective = \"regression\"\n , metric = \"rmse\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid\n )\n )\n\n # the best possible model should come from the first iteration, but\n # all 10 training iterations should happen\n expect_equal(bst$best_score, 55.0)\n expect_equal(bst$best_iter, 1L)\n expect_equal(length(bst$record_evals[[\"valid1\"]][[\"rmse\"]][[\"eval\"]]), nrounds)\n\n #############################\n # train with early stopping #\n #############################\n early_stopping_rounds <- 5L\n bst <- lgb.train(\n params = list(\n objective = \"regression\"\n , metric = \"rmse\"\n , early_stopping_rounds = early_stopping_rounds\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid\n )\n )\n\n # the best model should be from the first iteration, and only 6 rounds\n # should have happen (1 with improvement, 5 consecutive with no improvement)\n expect_equal(bst$best_score, 55.0)\n expect_equal(bst$best_iter, 1L)\n expect_equal(\n length(bst$record_evals[[\"valid1\"]][[\"rmse\"]][[\"eval\"]])\n , early_stopping_rounds + 1L\n )\n})\n\ntest_that(\"lgb.train() does not stop early if early_stopping_rounds is not given\", {\n set.seed(708L)\n\n increasing_metric_starting_value <- get(\n ACCUMULATOR_NAME\n , envir = .GlobalEnv\n )\n nrounds <- 10L\n metrics <- list(\n .constant_metric\n , .increasing_metric\n )\n bst <- lgb.train(\n params = list(\n objective = \"regression\"\n , metric = \"None\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = DTRAIN_RANDOM_REGRESSION\n , nrounds = nrounds\n , valids = list(\"valid1\" = DVALID_RANDOM_REGRESSION)\n , eval = metrics\n )\n\n # Only the two functions provided to \"eval\" should have been evaluated\n expect_equal(length(bst$record_evals[[\"valid1\"]]), 2L)\n\n # all 10 iterations should have happen, and the best_iter should be\n # the first one (based on constant_metric)\n best_iter <- 1L\n expect_equal(bst$best_iter, best_iter)\n\n # best_score should be taken from the first metric\n expect_equal(\n bst$best_score\n , bst$record_evals[[\"valid1\"]][[\"constant_metric\"]][[\"eval\"]][[best_iter]]\n )\n\n # early stopping should not have happened. Even though constant_metric\n # had 9 consecutive iterations with no improvement, it is ignored because of\n # first_metric_only = TRUE\n expect_equal(\n length(bst$record_evals[[\"valid1\"]][[\"constant_metric\"]][[\"eval\"]])\n , nrounds\n )\n expect_equal(\n length(bst$record_evals[[\"valid1\"]][[\"increasing_metric\"]][[\"eval\"]])\n , nrounds\n )\n})\n\ntest_that(\"If first_metric_only is not given or is FALSE, lgb.train() decides to stop early based on all metrics\", {\n set.seed(708L)\n\n early_stopping_rounds <- 3L\n param_variations <- list(\n list(\n objective = \"regression\"\n , metric = \"None\"\n , early_stopping_rounds = early_stopping_rounds\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , list(\n objective = \"regression\"\n , metric = \"None\"\n , early_stopping_rounds = early_stopping_rounds\n , first_metric_only = FALSE\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n )\n\n for (params in param_variations) {\n\n nrounds <- 10L\n bst <- lgb.train(\n params = params\n , data = DTRAIN_RANDOM_REGRESSION\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = DVALID_RANDOM_REGRESSION\n )\n , eval = list(\n .increasing_metric\n , .constant_metric\n )\n )\n\n # Only the two functions provided to \"eval\" should have been evaluated\n expect_equal(length(bst$record_evals[[\"valid1\"]]), 2L)\n\n # early stopping should have happened, and should have stopped early_stopping_rounds + 1 rounds in\n # because constant_metric never improves\n #\n # the best iteration should be the last one, because increasing_metric was first\n # and gets better every iteration\n best_iter <- early_stopping_rounds + 1L\n expect_equal(bst$best_iter, best_iter)\n\n # best_score should be taken from \"increasing_metric\" because it was first\n expect_equal(\n bst$best_score\n , bst$record_evals[[\"valid1\"]][[\"increasing_metric\"]][[\"eval\"]][[best_iter]]\n )\n\n # early stopping should not have happened. even though increasing_metric kept\n # getting better, early stopping should have happened because \"constant_metric\"\n # did not improve\n expect_equal(\n length(bst$record_evals[[\"valid1\"]][[\"constant_metric\"]][[\"eval\"]])\n , early_stopping_rounds + 1L\n )\n expect_equal(\n length(bst$record_evals[[\"valid1\"]][[\"increasing_metric\"]][[\"eval\"]])\n , early_stopping_rounds + 1L\n )\n }\n\n})\n\ntest_that(\"If first_metric_only is TRUE, lgb.train() decides to stop early based on only the first metric\", {\n set.seed(708L)\n nrounds <- 10L\n early_stopping_rounds <- 3L\n increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)\n bst <- lgb.train(\n params = list(\n objective = \"regression\"\n , metric = \"None\"\n , early_stopping_rounds = early_stopping_rounds\n , first_metric_only = TRUE\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = DTRAIN_RANDOM_REGRESSION\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = DVALID_RANDOM_REGRESSION\n )\n , eval = list(\n .increasing_metric\n , .constant_metric\n )\n )\n\n # Only the two functions provided to \"eval\" should have been evaluated\n expect_equal(length(bst$record_evals[[\"valid1\"]]), 2L)\n\n # all 10 iterations should happen, and the best_iter should be the final one\n expect_equal(bst$best_iter, nrounds)\n\n # best_score should be taken from \"increasing_metric\"\n expect_equal(\n bst$best_score\n , increasing_metric_starting_value + 0.1 * nrounds\n )\n\n # early stopping should not have happened. Even though constant_metric\n # had 9 consecutive iterations with no improvement, it is ignored because of\n # first_metric_only = TRUE\n expect_equal(\n length(bst$record_evals[[\"valid1\"]][[\"constant_metric\"]][[\"eval\"]])\n , nrounds\n )\n expect_equal(\n length(bst$record_evals[[\"valid1\"]][[\"increasing_metric\"]][[\"eval\"]])\n , nrounds\n )\n})\n\ntest_that(\"lgb.train() works when a mixture of functions and strings are passed to eval\", {\n set.seed(708L)\n nrounds <- 10L\n increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)\n bst <- lgb.train(\n params = list(\n objective = \"regression\"\n , metric = \"None\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = DTRAIN_RANDOM_REGRESSION\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = DVALID_RANDOM_REGRESSION\n )\n , eval = list(\n .increasing_metric\n , \"rmse\"\n , .constant_metric\n , \"l2\"\n )\n )\n\n # all 4 metrics should have been used\n expect_named(\n bst$record_evals[[\"valid1\"]]\n , expected = c(\"rmse\", \"l2\", \"increasing_metric\", \"constant_metric\")\n , ignore.order = TRUE\n , ignore.case = FALSE\n )\n\n # the difference metrics shouldn't have been mixed up with each other\n results <- bst$record_evals[[\"valid1\"]]\n expect_true(abs(results[[\"rmse\"]][[\"eval\"]][[1L]] - 1.105012) < .LGB_NUMERIC_TOLERANCE)\n expect_true(abs(results[[\"l2\"]][[\"eval\"]][[1L]] - 1.221051) < .LGB_NUMERIC_TOLERANCE)\n expected_increasing_metric <- increasing_metric_starting_value + 0.1\n expect_true(\n abs(\n results[[\"increasing_metric\"]][[\"eval\"]][[1L]] - expected_increasing_metric\n ) < .LGB_NUMERIC_TOLERANCE\n )\n expect_true(abs(results[[\"constant_metric\"]][[\"eval\"]][[1L]] - CONSTANT_METRIC_VALUE) < .LGB_NUMERIC_TOLERANCE)\n\n})\n\ntest_that(\"lgb.train() works when a list of strings or a character vector is passed to eval\", {\n\n # testing list and character vector, as well as length-1 and length-2\n eval_variations <- list(\n c(\"binary_error\", \"binary_logloss\")\n , \"binary_logloss\"\n , list(\"binary_error\", \"binary_logloss\")\n , list(\"binary_logloss\")\n )\n\n for (eval_variation in eval_variations) {\n\n set.seed(708L)\n nrounds <- 10L\n increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)\n bst <- lgb.train(\n params = list(\n objective = \"binary\"\n , metric = \"None\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = DTRAIN_RANDOM_CLASSIFICATION\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = DVALID_RANDOM_CLASSIFICATION\n )\n , eval = eval_variation\n )\n\n # both metrics should have been used\n expect_named(\n bst$record_evals[[\"valid1\"]]\n , expected = unlist(eval_variation)\n , ignore.order = TRUE\n , ignore.case = FALSE\n )\n\n # the difference metrics shouldn't have been mixed up with each other\n results <- bst$record_evals[[\"valid1\"]]\n if (\"binary_error\" %in% unlist(eval_variation)) {\n expect_true(abs(results[[\"binary_error\"]][[\"eval\"]][[1L]] - 0.4864865) < .LGB_NUMERIC_TOLERANCE)\n }\n if (\"binary_logloss\" %in% unlist(eval_variation)) {\n expect_true(abs(results[[\"binary_logloss\"]][[\"eval\"]][[1L]] - 0.6932548) < .LGB_NUMERIC_TOLERANCE)\n }\n }\n})\n\ntest_that(\"lgb.train() works when you specify both 'metric' and 'eval' with strings\", {\n set.seed(708L)\n nrounds <- 10L\n increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)\n bst <- lgb.train(\n params = list(\n objective = \"binary\"\n , metric = \"binary_error\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = DTRAIN_RANDOM_CLASSIFICATION\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = DVALID_RANDOM_CLASSIFICATION\n )\n , eval = \"binary_logloss\"\n )\n\n # both metrics should have been used\n expect_named(\n bst$record_evals[[\"valid1\"]]\n , expected = c(\"binary_error\", \"binary_logloss\")\n , ignore.order = TRUE\n , ignore.case = FALSE\n )\n\n # the difference metrics shouldn't have been mixed up with each other\n results <- bst$record_evals[[\"valid1\"]]\n expect_true(abs(results[[\"binary_error\"]][[\"eval\"]][[1L]] - 0.4864865) < .LGB_NUMERIC_TOLERANCE)\n expect_true(abs(results[[\"binary_logloss\"]][[\"eval\"]][[1L]] - 0.6932548) < .LGB_NUMERIC_TOLERANCE)\n})\n\ntest_that(\"lgb.train() works when you give a function for eval\", {\n set.seed(708L)\n nrounds <- 10L\n increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)\n bst <- lgb.train(\n params = list(\n objective = \"binary\"\n , metric = \"None\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = DTRAIN_RANDOM_CLASSIFICATION\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = DVALID_RANDOM_CLASSIFICATION\n )\n , eval = .constant_metric\n )\n\n # the difference metrics shouldn't have been mixed up with each other\n results <- bst$record_evals[[\"valid1\"]]\n expect_true(abs(results[[\"constant_metric\"]][[\"eval\"]][[1L]] - CONSTANT_METRIC_VALUE) < .LGB_NUMERIC_TOLERANCE)\n})\n\ntest_that(\"lgb.train() works with early stopping for regression with a metric that should be minimized\", {\n set.seed(708L)\n trainDF <- data.frame(\n \"feat1\" = rep(c(10.0, 100.0), 500L)\n , \"target\" = rep(c(-50.0, 50.0), 500L)\n )\n validDF <- data.frame(\n \"feat1\" = rep(50.0, 4L)\n , \"target\" = rep(50.0, 4L)\n )\n dtrain <- lgb.Dataset(\n data = as.matrix(trainDF[[\"feat1\"]], drop = FALSE)\n , label = trainDF[[\"target\"]]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n dvalid <- lgb.Dataset(\n data = as.matrix(validDF[[\"feat1\"]], drop = FALSE)\n , label = validDF[[\"target\"]]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n nrounds <- 10L\n\n #############################\n # train with early stopping #\n #############################\n early_stopping_rounds <- 5L\n bst <- lgb.train(\n params = list(\n objective = \"regression\"\n , metric = c(\n \"mape\"\n , \"rmse\"\n , \"mae\"\n )\n , min_data_in_bin = 5L\n , early_stopping_rounds = early_stopping_rounds\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid\n )\n )\n\n # the best model should be from the first iteration, and only 6 rounds\n # should have happened (1 with improvement, 5 consecutive with no improvement)\n expect_equal(bst$best_score, 1.1)\n expect_equal(bst$best_iter, 1L)\n expect_equal(\n length(bst$record_evals[[\"valid1\"]][[\"mape\"]][[\"eval\"]])\n , early_stopping_rounds + 1L\n )\n\n # Booster should understand that all three of these metrics should be minimized\n eval_info <- bst$.__enclos_env__$private$get_eval_info()\n expect_identical(eval_info, c(\"mape\", \"rmse\", \"l1\"))\n expect_identical(\n unname(bst$.__enclos_env__$private$higher_better_inner_eval)\n , rep(FALSE, 3L)\n )\n})\n\n\ntest_that(\"lgb.train() supports non-ASCII feature names\", {\n # content below is equivalent to\n #\n # feature_names <- c(\"F_零\", \"F_一\", \"F_二\", \"F_三\")\n #\n # but using rawToChar() to avoid weird issues when {testthat}\n # sources files and converts their encodings prior to evaluating the code\n feature_names <- c(\n rawToChar(as.raw(c(0x46, 0x5f, 0xe9, 0x9b, 0xb6)))\n , rawToChar(as.raw(c(0x46, 0x5f, 0xe4, 0xb8, 0x80)))\n , rawToChar(as.raw(c(0x46, 0x5f, 0xe4, 0xba, 0x8c)))\n , rawToChar(as.raw(c(0x46, 0x5f, 0xe4, 0xb8, 0x89)))\n )\n dtrain <- lgb.Dataset(\n data = matrix(rnorm(400L), ncol = 4L)\n , label = rnorm(100L)\n , params = list(num_threads = .LGB_MAX_THREADS)\n , colnames = feature_names\n )\n bst <- lgb.train(\n data = dtrain\n , nrounds = 5L\n , obj = \"regression\"\n , params = list(\n metric = \"rmse\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n )\n expect_true(.is_Booster(bst))\n dumped_model <- jsonlite::fromJSON(bst$dump_model())\n\n # UTF-8 strings are not well-supported on Windows\n # * https://developer.r-project.org/Blog/public/2020/05/02/utf-8-support-on-windows/\n # * https://developer.r-project.org/Blog/public/2020/07/30/windows/utf-8-build-of-r-and-cran-packages/index.html\n if (.LGB_UTF8_LOCALE && !.LGB_ON_WINDOWS) {\n expect_identical(\n dumped_model[[\"feature_names\"]]\n , feature_names\n )\n } else {\n expect_identical(\n dumped_model[[\"feature_names\"]]\n , iconv(feature_names, to = \"UTF-8\")\n )\n }\n})\n\ntest_that(\"lgb.train() works with integer, double, and numeric data\", {\n data(mtcars)\n X <- as.matrix(mtcars[, -1L])\n y <- mtcars[, 1L, drop = TRUE]\n expected_mae <- 4.263667\n for (data_mode in c(\"numeric\", \"double\", \"integer\")) {\n mode(X) <- data_mode\n nrounds <- 10L\n bst <- lightgbm(\n data = X\n , label = y\n , params = list(\n objective = \"regression\"\n , min_data_in_bin = 1L\n , min_data_in_leaf = 1L\n , learning_rate = 0.01\n , seed = 708L\n , verbose = .LGB_VERBOSITY\n )\n , nrounds = nrounds\n )\n\n # should have trained for 10 iterations and found splits\n modelDT <- lgb.model.dt.tree(bst)\n expect_equal(modelDT[, max(tree_index)], nrounds - 1L)\n expect_gt(nrow(modelDT), nrounds * 3L)\n\n # should have achieved expected performance\n preds <- predict(bst, X)\n mae <- mean(abs(y - preds))\n expect_true(abs(mae - expected_mae) < .LGB_NUMERIC_TOLERANCE)\n }\n})\n\ntest_that(\"lgb.train() updates params based on keyword arguments\", {\n dtrain <- lgb.Dataset(\n data = matrix(rnorm(400L), ncol = 4L)\n , label = rnorm(100L)\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n\n # defaults from keyword arguments should be used if not specified in params\n invisible(\n capture.output({\n bst <- lgb.train(\n data = dtrain\n , obj = \"regression\"\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n })\n )\n expect_equal(bst$params[[\"verbosity\"]], 1L)\n expect_equal(bst$params[[\"num_iterations\"]], 100L)\n\n # main param names should be preferred to keyword arguments\n invisible(\n capture.output({\n bst <- lgb.train(\n data = dtrain\n , obj = \"regression\"\n , params = list(\n \"verbosity\" = 5L\n , \"num_iterations\" = 2L\n , num_threads = .LGB_MAX_THREADS\n )\n )\n })\n )\n expect_equal(bst$params[[\"verbosity\"]], 5L)\n expect_equal(bst$params[[\"num_iterations\"]], 2L)\n\n # aliases should be preferred to keyword arguments, and converted to main parameter name\n invisible(\n capture.output({\n bst <- lgb.train(\n data = dtrain\n , obj = \"regression\"\n , params = list(\n \"verbose\" = 5L\n , \"num_boost_round\" = 2L\n , num_threads = .LGB_MAX_THREADS\n )\n )\n })\n )\n expect_equal(bst$params[[\"verbosity\"]], 5L)\n expect_false(\"verbose\" %in% bst$params)\n expect_equal(bst$params[[\"num_iterations\"]], 2L)\n expect_false(\"num_boost_round\" %in% bst$params)\n})\n\ntest_that(\"when early stopping is not activated, best_iter and best_score come from valids and not training data\", {\n set.seed(708L)\n trainDF <- data.frame(\n \"feat1\" = rep(c(10.0, 100.0), 500L)\n , \"target\" = rep(c(-50.0, 50.0), 500L)\n )\n validDF <- data.frame(\n \"feat1\" = rep(50.0, 4L)\n , \"target\" = rep(50.0, 4L)\n )\n dtrain <- lgb.Dataset(\n data = as.matrix(trainDF[[\"feat1\"]], drop = FALSE)\n , label = trainDF[[\"target\"]]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n dvalid1 <- lgb.Dataset(\n data = as.matrix(validDF[[\"feat1\"]], drop = FALSE)\n , label = validDF[[\"target\"]]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n dvalid2 <- lgb.Dataset(\n data = as.matrix(validDF[1L:10L, \"feat1\"], drop = FALSE)\n , label = validDF[1L:10L, \"target\"]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n nrounds <- 10L\n train_params <- list(\n objective = \"regression\"\n , metric = \"rmse\"\n , learning_rate = 1.5\n , num_leaves = 5L\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n\n # example 1: two valids, neither are the training data\n bst <- lgb.train(\n data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid1\n , \"valid2\" = dvalid2\n )\n , params = train_params\n )\n expect_named(\n bst$record_evals\n , c(\"start_iter\", \"valid1\", \"valid2\")\n , ignore.order = FALSE\n , ignore.case = FALSE\n )\n rmse_scores <- unlist(bst$record_evals[[\"valid1\"]][[\"rmse\"]][[\"eval\"]])\n expect_length(rmse_scores, nrounds)\n expect_identical(bst$best_iter, which.min(rmse_scores))\n expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)])\n\n # example 2: train first (called \"train\") and two valids\n bst <- lgb.train(\n data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"train\" = dtrain\n , \"valid1\" = dvalid1\n , \"valid2\" = dvalid2\n )\n , params = train_params\n )\n expect_named(\n bst$record_evals\n , c(\"start_iter\", \"train\", \"valid1\", \"valid2\")\n , ignore.order = FALSE\n , ignore.case = FALSE\n )\n rmse_scores <- unlist(bst$record_evals[[\"valid1\"]][[\"rmse\"]][[\"eval\"]])\n expect_length(rmse_scores, nrounds)\n expect_identical(bst$best_iter, which.min(rmse_scores))\n expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)])\n\n # example 3: train second (called \"train\") and two valids\n bst <- lgb.train(\n data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid1\n , \"train\" = dtrain\n , \"valid2\" = dvalid2\n )\n , params = train_params\n )\n # note that \"train\" still ends up as the first one\n expect_named(\n bst$record_evals\n , c(\"start_iter\", \"train\", \"valid1\", \"valid2\")\n , ignore.order = FALSE\n , ignore.case = FALSE\n )\n rmse_scores <- unlist(bst$record_evals[[\"valid1\"]][[\"rmse\"]][[\"eval\"]])\n expect_length(rmse_scores, nrounds)\n expect_identical(bst$best_iter, which.min(rmse_scores))\n expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)])\n\n # example 4: train third (called \"train\") and two valids\n bst <- lgb.train(\n data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid1\n , \"valid2\" = dvalid2\n , \"train\" = dtrain\n )\n , params = train_params\n )\n # note that \"train\" still ends up as the first one\n expect_named(\n bst$record_evals\n , c(\"start_iter\", \"train\", \"valid1\", \"valid2\")\n , ignore.order = FALSE\n , ignore.case = FALSE\n )\n rmse_scores <- unlist(bst$record_evals[[\"valid1\"]][[\"rmse\"]][[\"eval\"]])\n expect_length(rmse_scores, nrounds)\n expect_identical(bst$best_iter, which.min(rmse_scores))\n expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)])\n\n # example 5: train second (called \"something-random-we-would-not-hardcode\") and two valids\n bst <- lgb.train(\n data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid1\n , \"something-random-we-would-not-hardcode\" = dtrain\n , \"valid2\" = dvalid2\n )\n , params = train_params\n )\n # note that \"something-random-we-would-not-hardcode\" was recognized as the training\n # data even though it isn't named \"train\"\n expect_named(\n bst$record_evals\n , c(\"start_iter\", \"something-random-we-would-not-hardcode\", \"valid1\", \"valid2\")\n , ignore.order = FALSE\n , ignore.case = FALSE\n )\n rmse_scores <- unlist(bst$record_evals[[\"valid1\"]][[\"rmse\"]][[\"eval\"]])\n expect_length(rmse_scores, nrounds)\n expect_identical(bst$best_iter, which.min(rmse_scores))\n expect_identical(bst$best_score, rmse_scores[which.min(rmse_scores)])\n\n # example 6: the only valid supplied is the training data\n bst <- lgb.train(\n data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"train\" = dtrain\n )\n , params = train_params\n )\n expect_identical(bst$best_iter, -1L)\n expect_identical(bst$best_score, NA_real_)\n})\n\ntest_that(\"lightgbm.train() gives the correct best_score and best_iter for a metric where higher values are better\", {\n set.seed(708L)\n trainDF <- data.frame(\n \"feat1\" = runif(n = 500L, min = 0.0, max = 15.0)\n , \"target\" = rep(c(0L, 1L), 500L)\n )\n validDF <- data.frame(\n \"feat1\" = runif(n = 50L, min = 0.0, max = 15.0)\n , \"target\" = rep(c(0L, 1L), 50L)\n )\n dtrain <- lgb.Dataset(\n data = as.matrix(trainDF[[\"feat1\"]], drop = FALSE)\n , label = trainDF[[\"target\"]]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n dvalid1 <- lgb.Dataset(\n data = as.matrix(validDF[1L:25L, \"feat1\"], drop = FALSE)\n , label = validDF[1L:25L, \"target\"]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n nrounds <- 10L\n bst <- lgb.train(\n data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid1\n , \"something-random-we-would-not-hardcode\" = dtrain\n )\n , params = list(\n objective = \"binary\"\n , metric = \"auc\"\n , learning_rate = 1.5\n , num_leaves = 5L\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n )\n # note that \"something-random-we-would-not-hardcode\" was recognized as the training\n # data even though it isn't named \"train\"\n expect_named(\n bst$record_evals\n , c(\"start_iter\", \"something-random-we-would-not-hardcode\", \"valid1\")\n , ignore.order = FALSE\n , ignore.case = FALSE\n )\n auc_scores <- unlist(bst$record_evals[[\"valid1\"]][[\"auc\"]][[\"eval\"]])\n expect_length(auc_scores, nrounds)\n expect_identical(bst$best_iter, which.max(auc_scores))\n expect_identical(bst$best_score, auc_scores[which.max(auc_scores)])\n})\n\ntest_that(\"using lightgbm() without early stopping, best_iter and best_score come from valids and not training data\", {\n set.seed(708L)\n # example: train second (called \"something-random-we-would-not-hardcode\"), two valids,\n # and a metric where higher values are better (\"auc\")\n trainDF <- data.frame(\n \"feat1\" = runif(n = 500L, min = 0.0, max = 15.0)\n , \"target\" = rep(c(0L, 1L), 500L)\n )\n validDF <- data.frame(\n \"feat1\" = runif(n = 50L, min = 0.0, max = 15.0)\n , \"target\" = rep(c(0L, 1L), 50L)\n )\n dtrain <- lgb.Dataset(\n data = as.matrix(trainDF[[\"feat1\"]], drop = FALSE)\n , label = trainDF[[\"target\"]]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n dvalid1 <- lgb.Dataset(\n data = as.matrix(validDF[1L:25L, \"feat1\"], drop = FALSE)\n , label = validDF[1L:25L, \"target\"]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n dvalid2 <- lgb.Dataset(\n data = as.matrix(validDF[26L:50L, \"feat1\"], drop = FALSE)\n , label = validDF[26L:50L, \"target\"]\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n nrounds <- 10L\n bst <- lightgbm(\n data = dtrain\n , nrounds = nrounds\n , valids = list(\n \"valid1\" = dvalid1\n , \"something-random-we-would-not-hardcode\" = dtrain\n , \"valid2\" = dvalid2\n )\n , params = list(\n objective = \"binary\"\n , metric = \"auc\"\n , learning_rate = 1.5\n , num_leaves = 5L\n , num_threads = .LGB_MAX_THREADS\n )\n , verbose = -7L\n )\n # when verbose <= 0 is passed to lightgbm(), 'valids' is passed through to lgb.train()\n # untouched. If you set verbose to > 0, the training data will still be first but called \"train\"\n expect_named(\n bst$record_evals\n , c(\"start_iter\", \"something-random-we-would-not-hardcode\", \"valid1\", \"valid2\")\n , ignore.order = FALSE\n , ignore.case = FALSE\n )\n auc_scores <- unlist(bst$record_evals[[\"valid1\"]][[\"auc\"]][[\"eval\"]])\n expect_length(auc_scores, nrounds)\n expect_identical(bst$best_iter, which.max(auc_scores))\n expect_identical(bst$best_score, auc_scores[which.max(auc_scores)])\n})\n\ntest_that(\"lgb.cv() works when you specify both 'metric' and 'eval' with strings\", {\n set.seed(708L)\n nrounds <- 10L\n nfolds <- 4L\n increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)\n bst <- lgb.cv(\n params = list(\n objective = \"binary\"\n , metric = \"binary_error\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = DTRAIN_RANDOM_CLASSIFICATION\n , nrounds = nrounds\n , nfold = nfolds\n , eval = \"binary_logloss\"\n )\n\n # both metrics should have been used\n expect_named(\n bst$record_evals[[\"valid\"]]\n , expected = c(\"binary_error\", \"binary_logloss\")\n , ignore.order = TRUE\n , ignore.case = FALSE\n )\n\n # the difference metrics shouldn't have been mixed up with each other\n results <- bst$record_evals[[\"valid\"]]\n expect_true(abs(results[[\"binary_error\"]][[\"eval\"]][[1L]] - 0.5005654) < .LGB_NUMERIC_TOLERANCE)\n expect_true(abs(results[[\"binary_logloss\"]][[\"eval\"]][[1L]] - 0.7011232) < .LGB_NUMERIC_TOLERANCE)\n\n # all boosters should have been created\n expect_length(bst$boosters, nfolds)\n})\n\ntest_that(\"lgb.cv() works when you give a function for eval\", {\n set.seed(708L)\n nrounds <- 10L\n nfolds <- 3L\n increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)\n bst <- lgb.cv(\n params = list(\n objective = \"binary\"\n , metric = \"None\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = DTRAIN_RANDOM_CLASSIFICATION\n , nfold = nfolds\n , nrounds = nrounds\n , eval = .constant_metric\n )\n\n # the difference metrics shouldn't have been mixed up with each other\n results <- bst$record_evals[[\"valid\"]]\n expect_true(abs(results[[\"constant_metric\"]][[\"eval\"]][[1L]] - CONSTANT_METRIC_VALUE) < .LGB_NUMERIC_TOLERANCE)\n expect_named(results, \"constant_metric\")\n})\n\ntest_that(\"If first_metric_only is TRUE, lgb.cv() decides to stop early based on only the first metric\", {\n set.seed(708L)\n nrounds <- 10L\n nfolds <- 5L\n early_stopping_rounds <- 3L\n increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)\n bst <- lgb.cv(\n params = list(\n objective = \"regression\"\n , metric = \"None\"\n , early_stopping_rounds = early_stopping_rounds\n , first_metric_only = TRUE\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = DTRAIN_RANDOM_REGRESSION\n , nfold = nfolds\n , nrounds = nrounds\n , eval = list(\n .increasing_metric\n , .constant_metric\n )\n )\n\n # Only the two functions provided to \"eval\" should have been evaluated\n expect_named(bst$record_evals[[\"valid\"]], c(\"increasing_metric\", \"constant_metric\"))\n\n # all 10 iterations should happen, and the best_iter should be the final one\n expect_equal(bst$best_iter, nrounds)\n\n # best_score should be taken from \"increasing_metric\"\n #\n # this expected value looks magical and confusing, but it's because\n # evaluation metrics are averaged over all folds.\n #\n # consider 5-fold CV with a metric that adds 0.1 to a global accumulator\n # each time it's called\n #\n # * iter 1: [0.1, 0.2, 0.3, 0.4, 0.5] (mean = 0.3)\n # * iter 2: [0.6, 0.7, 0.8, 0.9, 1.0] (mean = 1.3)\n # * iter 3: [1.1, 1.2, 1.3, 1.4, 1.5] (mean = 1.8)\n #\n cv_value <- increasing_metric_starting_value + mean(seq_len(nfolds) / 10.0) + (nrounds - 1L) * 0.1 * nfolds\n expect_equal(bst$best_score, cv_value)\n\n # early stopping should not have happened. Even though constant_metric\n # had 9 consecutive iterations with no improvement, it is ignored because of\n # first_metric_only = TRUE\n expect_equal(\n length(bst$record_evals[[\"valid\"]][[\"constant_metric\"]][[\"eval\"]])\n , nrounds\n )\n expect_equal(\n length(bst$record_evals[[\"valid\"]][[\"increasing_metric\"]][[\"eval\"]])\n , nrounds\n )\n})\n\ntest_that(\"early stopping works with lgb.cv()\", {\n set.seed(708L)\n nrounds <- 10L\n nfolds <- 5L\n early_stopping_rounds <- 3L\n increasing_metric_starting_value <- get(ACCUMULATOR_NAME, envir = .GlobalEnv)\n bst <- lgb.cv(\n params = list(\n objective = \"regression\"\n , metric = \"None\"\n , early_stopping_rounds = early_stopping_rounds\n , first_metric_only = TRUE\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = DTRAIN_RANDOM_REGRESSION\n , nfold = nfolds\n , nrounds = nrounds\n , eval = list(\n .constant_metric\n , .increasing_metric\n )\n )\n\n # only the two functions provided to \"eval\" should have been evaluated\n expect_named(bst$record_evals[[\"valid\"]], c(\"constant_metric\", \"increasing_metric\"))\n\n # best_iter should be based on the first metric. Since constant_metric\n # never changes, its first iteration was the best oone\n expect_equal(bst$best_iter, 1L)\n\n # best_score should be taken from the first metric\n expect_equal(bst$best_score, 0.2)\n\n # early stopping should have happened, since constant_metric was the first\n # one passed to eval and it will not improve over consecutive iterations\n #\n # note that this test is identical to the previous one, but with the\n # order of the eval metrics switched\n expect_equal(\n length(bst$record_evals[[\"valid\"]][[\"constant_metric\"]][[\"eval\"]])\n , early_stopping_rounds + 1L\n )\n expect_equal(\n length(bst$record_evals[[\"valid\"]][[\"increasing_metric\"]][[\"eval\"]])\n , early_stopping_rounds + 1L\n )\n\n # every booster's predict method should use best_iter as num_iteration in predict\n random_data <- as.matrix(rnorm(10L), ncol = 1L, drop = FALSE)\n for (x in bst$boosters) {\n expect_equal(x$booster$best_iter, bst$best_iter)\n expect_gt(x$booster$current_iter(), bst$best_iter)\n preds_iter <- predict(x$booster, random_data, num_iteration = bst$best_iter)\n preds_no_iter <- predict(x$booster, random_data)\n expect_equal(preds_iter, preds_no_iter)\n }\n})\n\ntest_that(\"lgb.cv() respects changes to logging verbosity\", {\n dtrain <- lgb.Dataset(\n data = train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n # (verbose = 1) should be INFO and WARNING level logs\n lgb_cv_logs <- capture.output({\n cv_bst <- lgb.cv(\n params = list(num_threads = .LGB_MAX_THREADS)\n , nfold = 2L\n , nrounds = 5L\n , data = dtrain\n , obj = \"binary\"\n , verbose = 1L\n )\n })\n expect_true(any(grepl(\"[LightGBM] [Info]\", lgb_cv_logs, fixed = TRUE)))\n expect_true(any(grepl(\"[LightGBM] [Warning]\", lgb_cv_logs, fixed = TRUE)))\n\n # (verbose = 0) should be WARNING level logs only\n lgb_cv_logs <- capture.output({\n cv_bst <- lgb.cv(\n params = list(num_threads = .LGB_MAX_THREADS)\n , nfold = 2L\n , nrounds = 5L\n , data = dtrain\n , obj = \"binary\"\n , verbose = 0L\n )\n })\n expect_false(any(grepl(\"[LightGBM] [Info]\", lgb_cv_logs, fixed = TRUE)))\n expect_true(any(grepl(\"[LightGBM] [Warning]\", lgb_cv_logs, fixed = TRUE)))\n\n # (verbose = -1) no logs\n lgb_cv_logs <- capture.output({\n cv_bst <- lgb.cv(\n params = list(num_threads = .LGB_MAX_THREADS)\n , nfold = 2L\n , nrounds = 5L\n , data = dtrain\n , obj = \"binary\"\n , verbose = -1L\n )\n })\n # NOTE: this is not length(lgb_cv_logs) == 0 because lightgbm's\n # dependencies might print other messages\n expect_false(any(grepl(\"[LightGBM] [Info]\", lgb_cv_logs, fixed = TRUE)))\n expect_false(any(grepl(\"[LightGBM] [Warning]\", lgb_cv_logs, fixed = TRUE)))\n})\n\ntest_that(\"lgb.cv() updates params based on keyword arguments\", {\n dtrain <- lgb.Dataset(\n data = matrix(rnorm(400L), ncol = 4L)\n , label = rnorm(100L)\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n\n # defaults from keyword arguments should be used if not specified in params\n invisible(\n capture.output({\n cv_bst <- lgb.cv(\n data = dtrain\n , obj = \"regression\"\n , params = list(num_threads = .LGB_MAX_THREADS)\n , nfold = 2L\n )\n })\n )\n\n for (bst in cv_bst$boosters) {\n bst_params <- bst[[\"booster\"]]$params\n expect_equal(bst_params[[\"verbosity\"]], 1L)\n expect_equal(bst_params[[\"num_iterations\"]], 100L)\n }\n\n # main param names should be preferred to keyword arguments\n invisible(\n capture.output({\n cv_bst <- lgb.cv(\n data = dtrain\n , obj = \"regression\"\n , params = list(\n \"verbosity\" = 5L\n , \"num_iterations\" = 2L\n , num_threads = .LGB_MAX_THREADS\n )\n , nfold = 2L\n )\n })\n )\n for (bst in cv_bst$boosters) {\n bst_params <- bst[[\"booster\"]]$params\n expect_equal(bst_params[[\"verbosity\"]], 5L)\n expect_equal(bst_params[[\"num_iterations\"]], 2L)\n }\n\n # aliases should be preferred to keyword arguments, and converted to main parameter name\n invisible(\n capture.output({\n cv_bst <- lgb.cv(\n data = dtrain\n , obj = \"regression\"\n , params = list(\n \"verbose\" = 5L\n , \"num_boost_round\" = 2L\n , num_threads = .LGB_MAX_THREADS\n )\n , nfold = 2L\n )\n })\n )\n for (bst in cv_bst$boosters) {\n bst_params <- bst[[\"booster\"]]$params\n expect_equal(bst_params[[\"verbosity\"]], 5L)\n expect_false(\"verbose\" %in% bst_params)\n expect_equal(bst_params[[\"num_iterations\"]], 2L)\n expect_false(\"num_boost_round\" %in% bst_params)\n }\n\n})\n\ntest_that(\"lgb.train() fit on linearly-relatead data improves when using linear learners\", {\n set.seed(708L)\n .new_dataset <- function() {\n X <- matrix(rnorm(100L), ncol = 1L)\n return(lgb.Dataset(\n data = X\n , label = 2L * X + runif(nrow(X), 0L, 0.1)\n , params = list(num_threads = .LGB_MAX_THREADS)\n ))\n }\n\n params <- list(\n objective = \"regression\"\n , verbose = .LGB_VERBOSITY\n , metric = \"mse\"\n , seed = 0L\n , num_leaves = 2L\n , num_threads = .LGB_MAX_THREADS\n )\n\n dtrain <- .new_dataset()\n bst <- lgb.train(\n data = dtrain\n , nrounds = 10L\n , params = params\n , valids = list(\"train\" = dtrain)\n )\n expect_true(.is_Booster(bst))\n\n dtrain <- .new_dataset()\n bst_linear <- lgb.train(\n data = dtrain\n , nrounds = 10L\n , params = utils::modifyList(params, list(linear_tree = TRUE))\n , valids = list(\"train\" = dtrain)\n )\n expect_true(.is_Booster(bst_linear))\n\n bst_last_mse <- bst$record_evals[[\"train\"]][[\"l2\"]][[\"eval\"]][[10L]]\n bst_lin_last_mse <- bst_linear$record_evals[[\"train\"]][[\"l2\"]][[\"eval\"]][[10L]]\n expect_true(bst_lin_last_mse < bst_last_mse)\n})\n\n\ntest_that(\"lgb.train() with linear learner fails already-constructed dataset with linear=false\", {\n set.seed(708L)\n params <- list(\n objective = \"regression\"\n , verbose = .LGB_VERBOSITY\n , metric = \"mse\"\n , seed = 0L\n , num_leaves = 2L\n , num_threads = .LGB_MAX_THREADS\n )\n\n dtrain <- lgb.Dataset(\n data = matrix(rnorm(100L), ncol = 1L)\n , label = rnorm(100L)\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n dtrain$construct()\n expect_error({\n capture.output({\n bst_linear <- lgb.train(\n data = dtrain\n , nrounds = 10L\n , params = utils::modifyList(params, list(linear_tree = TRUE))\n )\n }, type = \"message\")\n }, regexp = \"Cannot change linear_tree after constructed Dataset handle\")\n})\n\ntest_that(\"lgb.train() works with linear learners even if Dataset has missing values\", {\n set.seed(708L)\n .new_dataset <- function() {\n values <- rnorm(100L)\n values[sample(seq_along(values), size = 10L)] <- NA_real_\n X <- matrix(\n data = sample(values, size = 100L)\n , ncol = 1L\n )\n return(lgb.Dataset(\n data = X\n , label = 2L * X + runif(nrow(X), 0L, 0.1)\n , params = list(num_threads = .LGB_MAX_THREADS)\n ))\n }\n\n params <- list(\n objective = \"regression\"\n , verbose = .LGB_VERBOSITY\n , metric = \"mse\"\n , seed = 0L\n , num_leaves = 2L\n , num_threads = .LGB_MAX_THREADS\n )\n\n dtrain <- .new_dataset()\n bst <- lgb.train(\n data = dtrain\n , nrounds = 10L\n , params = params\n , valids = list(\"train\" = dtrain)\n )\n expect_true(.is_Booster(bst))\n\n dtrain <- .new_dataset()\n bst_linear <- lgb.train(\n data = dtrain\n , nrounds = 10L\n , params = utils::modifyList(params, list(linear_tree = TRUE))\n , valids = list(\"train\" = dtrain)\n )\n expect_true(.is_Booster(bst_linear))\n\n bst_last_mse <- bst$record_evals[[\"train\"]][[\"l2\"]][[\"eval\"]][[10L]]\n bst_lin_last_mse <- bst_linear$record_evals[[\"train\"]][[\"l2\"]][[\"eval\"]][[10L]]\n expect_true(bst_lin_last_mse < bst_last_mse)\n})\n\ntest_that(\"lgb.train() works with linear learners, bagging, and a Dataset that has missing values\", {\n set.seed(708L)\n .new_dataset <- function() {\n values <- rnorm(100L)\n values[sample(seq_along(values), size = 10L)] <- NA_real_\n X <- matrix(\n data = sample(values, size = 100L)\n , ncol = 1L\n )\n return(lgb.Dataset(\n data = X\n , label = 2L * X + runif(nrow(X), 0L, 0.1)\n , params = list(num_threads = .LGB_MAX_THREADS)\n ))\n }\n\n params <- list(\n objective = \"regression\"\n , verbose = .LGB_VERBOSITY\n , metric = \"mse\"\n , seed = 0L\n , num_leaves = 2L\n , bagging_freq = 1L\n , subsample = 0.8\n , num_threads = .LGB_MAX_THREADS\n )\n\n dtrain <- .new_dataset()\n bst <- lgb.train(\n data = dtrain\n , nrounds = 10L\n , params = params\n , valids = list(\"train\" = dtrain)\n )\n expect_true(.is_Booster(bst))\n\n dtrain <- .new_dataset()\n bst_linear <- lgb.train(\n data = dtrain\n , nrounds = 10L\n , params = utils::modifyList(params, list(linear_tree = TRUE))\n , valids = list(\"train\" = dtrain)\n )\n expect_true(.is_Booster(bst_linear))\n\n bst_last_mse <- bst$record_evals[[\"train\"]][[\"l2\"]][[\"eval\"]][[10L]]\n bst_lin_last_mse <- bst_linear$record_evals[[\"train\"]][[\"l2\"]][[\"eval\"]][[10L]]\n expect_true(bst_lin_last_mse < bst_last_mse)\n})\n\ntest_that(\"lgb.train() works with linear learners and data where a feature has only 1 non-NA value\", {\n set.seed(708L)\n .new_dataset <- function() {\n values <- c(rnorm(100L), rep(NA_real_, 100L))\n values[118L] <- rnorm(1L)\n X <- matrix(\n data = values\n , ncol = 2L\n )\n return(lgb.Dataset(\n data = X\n , label = 2L * X[, 1L] + runif(nrow(X), 0L, 0.1)\n , params = list(\n feature_pre_filter = FALSE\n , num_threads = .LGB_MAX_THREADS\n )\n ))\n }\n\n params <- list(\n objective = \"regression\"\n , verbose = -1L\n , metric = \"mse\"\n , seed = 0L\n , num_leaves = 2L\n , num_threads = .LGB_MAX_THREADS\n )\n\n dtrain <- .new_dataset()\n bst_linear <- lgb.train(\n data = dtrain\n , nrounds = 10L\n , params = utils::modifyList(params, list(linear_tree = TRUE))\n )\n expect_true(.is_Booster(bst_linear))\n})\n\ntest_that(\"lgb.train() works with linear learners when Dataset has categorical features\", {\n set.seed(708L)\n .new_dataset <- function() {\n X <- matrix(numeric(200L), nrow = 100L, ncol = 2L)\n X[, 1L] <- rnorm(100L)\n X[, 2L] <- sample(seq_len(4L), size = 100L, replace = TRUE)\n return(lgb.Dataset(\n data = X\n , label = 2L * X[, 1L] + runif(nrow(X), 0L, 0.1)\n , params = list(num_threads = .LGB_MAX_THREADS)\n ))\n }\n\n params <- list(\n objective = \"regression\"\n , verbose = -1L\n , metric = \"mse\"\n , seed = 0L\n , num_leaves = 2L\n , categorical_feature = 1L\n , num_threads = .LGB_MAX_THREADS\n )\n\n dtrain <- .new_dataset()\n bst <- lgb.train(\n data = dtrain\n , nrounds = 10L\n , params = params\n , valids = list(\"train\" = dtrain)\n )\n expect_true(.is_Booster(bst))\n\n dtrain <- .new_dataset()\n bst_linear <- lgb.train(\n data = dtrain\n , nrounds = 10L\n , params = utils::modifyList(params, list(linear_tree = TRUE))\n , valids = list(\"train\" = dtrain)\n )\n expect_true(.is_Booster(bst_linear))\n\n bst_last_mse <- bst$record_evals[[\"train\"]][[\"l2\"]][[\"eval\"]][[10L]]\n bst_lin_last_mse <- bst_linear$record_evals[[\"train\"]][[\"l2\"]][[\"eval\"]][[10L]]\n expect_true(bst_lin_last_mse < bst_last_mse)\n})\n\ntest_that(\"lgb.train() throws an informative error if interaction_constraints is not a list\", {\n dtrain <- lgb.Dataset(train$data, label = train$label)\n params <- list(objective = \"regression\", interaction_constraints = \"[1,2],[3]\")\n expect_error({\n bst <- lightgbm(\n data = dtrain\n , params = params\n , nrounds = 2L\n )\n }, \"interaction_constraints must be a list\")\n})\n\ntest_that(paste0(\"lgb.train() throws an informative error if the members of interaction_constraints \",\n \"are not character or numeric vectors\"), {\n dtrain <- lgb.Dataset(train$data, label = train$label)\n params <- list(objective = \"regression\", interaction_constraints = list(list(1L, 2L), list(3L)))\n expect_error({\n bst <- lightgbm(\n data = dtrain\n , params = params\n , nrounds = 2L\n )\n }, \"every element in interaction_constraints must be a character vector or numeric vector\")\n})\n\ntest_that(\"lgb.train() throws an informative error if interaction_constraints contains a too large index\", {\n dtrain <- lgb.Dataset(train$data, label = train$label)\n params <- list(objective = \"regression\",\n interaction_constraints = list(c(1L, ncol(train$data) + 1L:2L), 3L))\n expect_error(\n lightgbm(data = dtrain, params = params, nrounds = 2L)\n , \"unknown feature(s) in interaction_constraints: '127', '128'\"\n , fixed = TRUE\n )\n})\n\ntest_that(paste0(\"lgb.train() gives same result when interaction_constraints is specified as a list of \",\n \"character vectors, numeric vectors, or a combination\"), {\n set.seed(1L)\n dtrain <- lgb.Dataset(train$data, label = train$label, params = list(num_threads = .LGB_MAX_THREADS))\n\n params <- list(\n objective = \"regression\"\n , interaction_constraints = list(c(1L, 2L), 3L)\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n bst <- lightgbm(\n data = dtrain\n , params = params\n , nrounds = 2L\n )\n pred1 <- bst$predict(test$data)\n\n cnames <- colnames(train$data)\n params <- list(\n objective = \"regression\"\n , interaction_constraints = list(c(cnames[[1L]], cnames[[2L]]), cnames[[3L]])\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n bst <- lightgbm(\n data = dtrain\n , params = params\n , nrounds = 2L\n )\n pred2 <- bst$predict(test$data)\n\n params <- list(\n objective = \"regression\"\n , interaction_constraints = list(c(cnames[[1L]], cnames[[2L]]), 3L)\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n bst <- lightgbm(\n data = dtrain\n , params = params\n , nrounds = 2L\n )\n pred3 <- bst$predict(test$data)\n\n expect_equal(pred1, pred2)\n expect_equal(pred2, pred3)\n\n})\n\ntest_that(paste0(\"lgb.train() gives same results when using interaction_constraints and specifying colnames\"), {\n set.seed(1L)\n dtrain <- lgb.Dataset(\n train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n\n params <- list(\n objective = \"regression\"\n , interaction_constraints = list(c(1L, 2L), 3L)\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n bst <- lightgbm(\n data = dtrain\n , params = params\n , nrounds = 2L\n )\n pred1 <- bst$predict(test$data)\n\n new_colnames <- paste0(colnames(train$data), \"_x\")\n dtrain$set_colnames(new_colnames)\n params <- list(\n objective = \"regression\"\n , interaction_constraints = list(c(new_colnames[1L], new_colnames[2L]), new_colnames[3L])\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n bst <- lightgbm(\n data = dtrain\n , params = params\n , nrounds = 2L\n )\n pred2 <- bst$predict(test$data)\n\n expect_equal(pred1, pred2)\n\n})\n\ntest_that(\"Interaction constraints add missing features correctly as new group\", {\n dtrain <- lgb.Dataset(\n train$data[, 1L:6L] # Pick only some columns\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n\n list_of_constraints <- list(\n list(3L, 1L:2L)\n , list(\"cap-shape=convex\", c(\"cap-shape=bell\", \"cap-shape=conical\"))\n )\n\n for (constraints in list_of_constraints) {\n params <- list(\n objective = \"regression\"\n , interaction_constraints = constraints\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n bst <- lightgbm(data = dtrain, params = params, nrounds = 10L)\n\n expected_list <- list(\"[2]\", \"[0,1]\", \"[3,4,5]\")\n expect_equal(bst$params$interaction_constraints, expected_list)\n\n expected_string <- \"[interaction_constraints: [2],[0,1],[3,4,5]]\"\n expect_true(\n grepl(expected_string, bst$save_model_to_string(), fixed = TRUE)\n )\n }\n})\n\n.generate_trainset_for_monotone_constraints_tests <- function(x3_to_categorical) {\n n_samples <- 3000L\n x1_positively_correlated_with_y <- runif(n = n_samples, min = 0.0, max = 1.0)\n x2_negatively_correlated_with_y <- runif(n = n_samples, min = 0.0, max = 1.0)\n x3_negatively_correlated_with_y <- runif(n = n_samples, min = 0.0, max = 1.0)\n if (x3_to_categorical) {\n x3_negatively_correlated_with_y <- as.integer(x3_negatively_correlated_with_y / 0.01)\n categorical_features <- \"feature_3\"\n } else {\n categorical_features <- NULL\n }\n X <- matrix(\n data = c(\n x1_positively_correlated_with_y\n , x2_negatively_correlated_with_y\n , x3_negatively_correlated_with_y\n )\n , ncol = 3L\n )\n zs <- rnorm(n = n_samples, mean = 0.0, sd = 0.01)\n scales <- 10.0 * (runif(n = 6L, min = 0.0, max = 1.0) + 0.5)\n y <- (\n scales[1L] * x1_positively_correlated_with_y\n + sin(scales[2L] * pi * x1_positively_correlated_with_y)\n - scales[3L] * x2_negatively_correlated_with_y\n - cos(scales[4L] * pi * x2_negatively_correlated_with_y)\n - scales[5L] * x3_negatively_correlated_with_y\n - cos(scales[6L] * pi * x3_negatively_correlated_with_y)\n + zs\n )\n return(lgb.Dataset(\n data = X\n , label = y\n , categorical_feature = categorical_features\n , free_raw_data = FALSE\n , colnames = c(\"feature_1\", \"feature_2\", \"feature_3\")\n , params = list(num_threads = .LGB_MAX_THREADS)\n ))\n}\n\n.is_increasing <- function(y) {\n return(all(diff(y) >= 0.0))\n}\n\n.is_decreasing <- function(y) {\n return(all(diff(y) <= 0.0))\n}\n\n.is_non_monotone <- function(y) {\n return(any(diff(y) < 0.0) & any(diff(y) > 0.0))\n}\n\n# R equivalent of numpy.linspace()\n.linspace <- function(start_val, stop_val, num) {\n weights <- (seq_len(num) - 1L) / (num - 1L)\n return(start_val + weights * (stop_val - start_val))\n}\n\n.is_correctly_constrained <- function(learner, x3_to_categorical) {\n iterations <- 10L\n n <- 1000L\n variable_x <- .linspace(0L, 1L, n)\n fixed_xs_values <- .linspace(0L, 1L, n)\n for (i in seq_len(iterations)) {\n fixed_x <- fixed_xs_values[i] * rep(1.0, n)\n monotonically_increasing_x <- matrix(\n data = c(variable_x, fixed_x, fixed_x)\n , ncol = 3L\n )\n monotonically_increasing_y <- predict(\n learner\n , monotonically_increasing_x\n )\n\n monotonically_decreasing_x <- matrix(\n data = c(fixed_x, variable_x, fixed_x)\n , ncol = 3L\n )\n monotonically_decreasing_y <- predict(\n learner\n , monotonically_decreasing_x\n )\n\n if (x3_to_categorical) {\n non_monotone_data <- c(\n fixed_x\n , fixed_x\n , as.integer(variable_x / 0.01)\n )\n } else {\n non_monotone_data <- c(fixed_x, fixed_x, variable_x)\n }\n non_monotone_x <- matrix(\n data = non_monotone_data\n , ncol = 3L\n )\n non_monotone_y <- predict(\n learner\n , non_monotone_x\n )\n if (!(.is_increasing(monotonically_increasing_y) &&\n .is_decreasing(monotonically_decreasing_y) &&\n .is_non_monotone(non_monotone_y)\n )) {\n return(FALSE)\n }\n }\n return(TRUE)\n}\n\nfor (x3_to_categorical in c(TRUE, FALSE)) {\n set.seed(708L)\n dtrain <- .generate_trainset_for_monotone_constraints_tests(\n x3_to_categorical = x3_to_categorical\n )\n for (monotone_constraints_method in c(\"basic\", \"intermediate\", \"advanced\")) {\n test_msg <- paste0(\n \"lgb.train() supports monotone constraints (\"\n , \"categoricals=\"\n , x3_to_categorical\n , \", method=\"\n , monotone_constraints_method\n , \")\"\n )\n test_that(test_msg, {\n params <- list(\n min_data = 20L\n , num_leaves = 20L\n , monotone_constraints = c(1L, -1L, 0L)\n , monotone_constraints_method = monotone_constraints_method\n , use_missing = FALSE\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n constrained_model <- lgb.train(\n params = params\n , data = dtrain\n , obj = \"regression_l2\"\n , nrounds = 100L\n )\n expect_true({\n .is_correctly_constrained(\n learner = constrained_model\n , x3_to_categorical = x3_to_categorical\n )\n })\n })\n }\n}\n\ntest_that(\"lightgbm() accepts objective as function argument and under params\", {\n bst1 <- lightgbm(\n data = train$data\n , label = train$label\n , params = list(objective = \"regression_l1\", num_threads = .LGB_MAX_THREADS)\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n )\n expect_equal(bst1$params$objective, \"regression_l1\")\n model_txt_lines <- strsplit(\n x = bst1$save_model_to_string()\n , split = \"\\n\"\n , fixed = TRUE\n )[[1L]]\n expect_true(any(model_txt_lines == \"objective=regression_l1\"))\n expect_false(any(model_txt_lines == \"objective=regression_l2\"))\n\n bst2 <- lightgbm(\n data = train$data\n , label = train$label\n , objective = \"regression_l1\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n )\n expect_equal(bst2$params$objective, \"regression_l1\")\n model_txt_lines <- strsplit(\n x = bst2$save_model_to_string()\n , split = \"\\n\"\n , fixed = TRUE\n )[[1L]]\n expect_true(any(model_txt_lines == \"objective=regression_l1\"))\n expect_false(any(model_txt_lines == \"objective=regression_l2\"))\n})\n\ntest_that(\"lightgbm() prioritizes objective under params over objective as function argument\", {\n bst1 <- lightgbm(\n data = train$data\n , label = train$label\n , objective = \"regression\"\n , params = list(objective = \"regression_l1\", num_threads = .LGB_MAX_THREADS)\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n )\n expect_equal(bst1$params$objective, \"regression_l1\")\n model_txt_lines <- strsplit(\n x = bst1$save_model_to_string()\n , split = \"\\n\"\n , fixed = TRUE\n )[[1L]]\n expect_true(any(model_txt_lines == \"objective=regression_l1\"))\n expect_false(any(model_txt_lines == \"objective=regression_l2\"))\n\n bst2 <- lightgbm(\n data = train$data\n , label = train$label\n , objective = \"regression\"\n , params = list(loss = \"regression_l1\", num_threads = .LGB_MAX_THREADS)\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n )\n expect_equal(bst2$params$objective, \"regression_l1\")\n model_txt_lines <- strsplit(\n x = bst2$save_model_to_string()\n , split = \"\\n\"\n , fixed = TRUE\n )[[1L]]\n expect_true(any(model_txt_lines == \"objective=regression_l1\"))\n expect_false(any(model_txt_lines == \"objective=regression_l2\"))\n})\n\ntest_that(\"lightgbm() accepts init_score as function argument\", {\n bst1 <- lightgbm(\n data = train$data\n , label = train$label\n , objective = \"binary\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n pred1 <- predict(bst1, train$data, type = \"raw\")\n\n bst2 <- lightgbm(\n data = train$data\n , label = train$label\n , init_score = pred1\n , objective = \"binary\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n pred2 <- predict(bst2, train$data, type = \"raw\")\n\n expect_true(any(pred1 != pred2))\n})\n\ntest_that(\"lightgbm() defaults to 'regression' objective if objective not otherwise provided\", {\n bst <- lightgbm(\n data = train$data\n , label = train$label\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n expect_equal(bst$params$objective, \"regression\")\n model_txt_lines <- strsplit(\n x = bst$save_model_to_string()\n , split = \"\\n\"\n , fixed = TRUE\n )[[1L]]\n expect_true(any(model_txt_lines == \"objective=regression\"))\n expect_false(any(model_txt_lines == \"objective=regression_l1\"))\n})\n\ntest_that(\"lightgbm() accepts 'num_threads' as either top-level argument or under params\", {\n bst <- lightgbm(\n data = train$data\n , label = train$label\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , num_threads = 1L\n )\n expect_equal(bst$params$num_threads, 1L)\n model_txt_lines <- strsplit(\n x = bst$save_model_to_string()\n , split = \"\\n\"\n , fixed = TRUE\n )[[1L]]\n expect_true(any(grepl(\"[num_threads: 1]\", model_txt_lines, fixed = TRUE)))\n\n bst <- lightgbm(\n data = train$data\n , label = train$label\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(num_threads = 1L)\n )\n expect_equal(bst$params$num_threads, 1L)\n model_txt_lines <- strsplit(\n x = bst$save_model_to_string()\n , split = \"\\n\"\n , fixed = TRUE\n )[[1L]]\n expect_true(any(grepl(\"[num_threads: 1]\", model_txt_lines, fixed = TRUE)))\n\n bst <- lightgbm(\n data = train$data\n , label = train$label\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , num_threads = 10L\n , params = list(num_threads = 1L)\n )\n expect_equal(bst$params$num_threads, 1L)\n model_txt_lines <- strsplit(\n x = bst$save_model_to_string()\n , split = \"\\n\"\n , fixed = TRUE\n )[[1L]]\n expect_true(any(grepl(\"[num_threads: 1]\", model_txt_lines, fixed = TRUE)))\n})\n\ntest_that(\"lightgbm() accepts 'weight' and 'weights'\", {\n data(mtcars)\n X <- as.matrix(mtcars[, -1L])\n y <- as.numeric(mtcars[, 1L])\n w <- rep(1.0, nrow(X))\n model <- lightgbm(\n X\n , y\n , weights = w\n , obj = \"regression\"\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n , params = list(\n min_data_in_bin = 1L\n , min_data_in_leaf = 1L\n , num_threads = .LGB_MAX_THREADS\n )\n )\n expect_equal(model$.__enclos_env__$private$train_set$get_field(\"weight\"), w)\n\n # Avoid a bad CRAN check due to partial argument matches\n lgb_args <- list(\n X\n , y\n , weight = w\n , obj = \"regression\"\n , nrounds = 5L\n , verbose = -1L\n )\n model <- do.call(lightgbm, lgb_args)\n expect_equal(model$.__enclos_env__$private$train_set$get_field(\"weight\"), w)\n})\n\n.assert_has_expected_logs <- function(log_txt, lgb_info, lgb_warn, early_stopping, valid_eval_msg) {\n expect_identical(\n object = any(grepl(\"[LightGBM] [Info]\", log_txt, fixed = TRUE))\n , expected = lgb_info\n )\n expect_identical(\n object = any(grepl(\"[LightGBM] [Warning]\", log_txt, fixed = TRUE))\n , expected = lgb_warn\n )\n expect_identical(\n object = any(grepl(\"Will train until there is no improvement in 5 rounds\", log_txt, fixed = TRUE))\n , expected = early_stopping\n )\n expect_identical(\n object = any(grepl(\"Did not meet early stopping\", log_txt, fixed = TRUE))\n , expected = early_stopping\n )\n expect_identical(\n object = any(grepl(\"valid's auc\\\\:[0-9]+\", log_txt))\n , expected = valid_eval_msg\n )\n}\n\n.assert_has_expected_record_evals <- function(fitted_model) {\n record_evals <- fitted_model$record_evals\n expect_equal(record_evals$start_iter, 1L)\n if (inherits(fitted_model, \"lgb.CVBooster\")) {\n expected_valid_auc <- c(0.979056, 0.9844697, 0.9900813, 0.9908026, 0.9935588)\n } else {\n expected_valid_auc <- c(0.9805752, 0.9805752, 0.9934957, 0.9934957, 0.9949372)\n }\n expect_equal(\n object = unlist(record_evals[[\"valid\"]][[\"auc\"]][[\"eval\"]])\n , expected = expected_valid_auc\n , tolerance = .LGB_NUMERIC_TOLERANCE\n )\n expect_named(record_evals, c(\"start_iter\", \"valid\"), ignore.order = TRUE, ignore.case = FALSE)\n expect_equal(record_evals[[\"valid\"]][[\"auc\"]][[\"eval_err\"]], list())\n}\n\n.train_for_verbosity_test <- function(train_function, verbose_kwarg, verbose_param) {\n set.seed(708L)\n nrounds <- 5L\n params <- list(\n num_leaves = 5L\n , objective = \"binary\"\n , metric = \"auc\"\n , early_stopping_round = nrounds\n , num_threads = .LGB_MAX_THREADS\n # include a nonsense parameter just to trigger a WARN-level log\n , nonsense_param = 1.0\n )\n if (!is.null(verbose_param)) {\n params[[\"verbose\"]] <- verbose_param\n }\n train_kwargs <- list(\n params = params\n , nrounds = nrounds\n )\n if (!is.null(verbose_kwarg)) {\n train_kwargs[[\"verbose\"]] <- verbose_kwarg\n }\n function_name <- deparse(substitute(train_function))\n if (function_name == \"lgb.train\") {\n train_kwargs[[\"data\"]] <- lgb.Dataset(\n data = train$data\n , label = train$label\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n train_kwargs[[\"valids\"]] <- list(\n \"valid\" = lgb.Dataset(data = test$data, label = test$label)\n )\n } else if (function_name == \"lightgbm\") {\n train_kwargs[[\"data\"]] <- train$data\n train_kwargs[[\"label\"]] <- train$label\n train_kwargs[[\"valids\"]] <- list(\n \"valid\" = lgb.Dataset(data = test$data, label = test$label)\n )\n } else if (function_name == \"lgb.cv\") {\n train_kwargs[[\"data\"]] <- lgb.Dataset(\n data = train$data\n , label = train$label\n )\n train_kwargs[[\"nfold\"]] <- 3L\n train_kwargs[[\"showsd\"]] <- FALSE\n }\n log_txt <- capture.output({\n bst <- do.call(\n what = train_function\n , args = train_kwargs\n )\n })\n return(list(booster = bst, logs = log_txt))\n}\n\ntest_that(\"lgb.train() only prints eval metrics when expected to\", {\n\n # regardless of value passed to keyword argument 'verbose', value in params\n # should take precedence\n for (verbose_keyword_arg in c(-5L, -1L, 0L, 1L, 5L)) {\n\n # (verbose = -1) should not be any logs, should be record evals\n out <- .train_for_verbosity_test(\n train_function = lgb.train\n , verbose_kwarg = verbose_keyword_arg\n , verbose_param = -1L\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = FALSE\n , lgb_warn = FALSE\n , early_stopping = FALSE\n , valid_eval_msg = FALSE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n\n # (verbose = 0) should be only WARN-level LightGBM logs\n out <- .train_for_verbosity_test(\n train_function = lgb.train\n , verbose_kwarg = verbose_keyword_arg\n , verbose_param = 0L\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = FALSE\n , lgb_warn = TRUE\n , early_stopping = FALSE\n , valid_eval_msg = FALSE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n\n # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages\n out <- .train_for_verbosity_test(\n train_function = lgb.train\n , verbose_kwarg = verbose_keyword_arg\n , verbose_param = 1L\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = TRUE\n , lgb_warn = TRUE\n , early_stopping = TRUE\n , valid_eval_msg = TRUE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n }\n\n # if verbosity isn't specified in `params`, changing keyword argument `verbose` should\n # alter what messages are printed\n\n # (verbose = -1) should not be any logs, should be record evals\n out <- .train_for_verbosity_test(\n train_function = lgb.train\n , verbose_kwarg = -1L\n , verbose_param = NULL\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = FALSE\n , lgb_warn = FALSE\n , early_stopping = FALSE\n , valid_eval_msg = FALSE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n\n # (verbose = 0) should be only WARN-level LightGBM logs\n out <- .train_for_verbosity_test(\n train_function = lgb.train\n , verbose_kwarg = 0L\n , verbose_param = NULL\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = FALSE\n , lgb_warn = TRUE\n , early_stopping = FALSE\n , valid_eval_msg = FALSE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n\n # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages\n out <- .train_for_verbosity_test(\n train_function = lgb.train\n , verbose_kwarg = 1L\n , verbose_param = NULL\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = TRUE\n , lgb_warn = TRUE\n , early_stopping = TRUE\n , valid_eval_msg = TRUE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n})\n\ntest_that(\"lightgbm() only prints eval metrics when expected to\", {\n\n # regardless of value passed to keyword argument 'verbose', value in params\n # should take precedence\n for (verbose_keyword_arg in c(-5L, -1L, 0L, 1L, 5L)) {\n\n # (verbose = -1) should not be any logs, train should not be in valids\n out <- .train_for_verbosity_test(\n train_function = lightgbm\n , verbose_kwarg = verbose_keyword_arg\n , verbose_param = -1L\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = FALSE\n , lgb_warn = FALSE\n , early_stopping = FALSE\n , valid_eval_msg = FALSE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n\n # (verbose = 0) should be only WARN-level LightGBM logs, train should not be in valids\n out <- .train_for_verbosity_test(\n train_function = lightgbm\n , verbose_kwarg = verbose_keyword_arg\n , verbose_param = 0L\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = FALSE\n , lgb_warn = TRUE\n , early_stopping = FALSE\n , valid_eval_msg = FALSE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n\n # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages, and\n # train should be in valids\n out <- .train_for_verbosity_test(\n train_function = lightgbm\n , verbose_kwarg = verbose_keyword_arg\n , verbose_param = 1L\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = TRUE\n , lgb_warn = TRUE\n , early_stopping = TRUE\n , valid_eval_msg = TRUE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n }\n\n # if verbosity isn't specified in `params`, changing keyword argument `verbose` should\n # alter what messages are printed\n\n # (verbose = -1) should not be any logs, train should not be in valids\n out <- .train_for_verbosity_test(\n train_function = lightgbm\n , verbose_kwarg = -1L\n , verbose_param = NULL\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = FALSE\n , lgb_warn = FALSE\n , early_stopping = FALSE\n , valid_eval_msg = FALSE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n\n # (verbose = 0) should be only WARN-level LightGBM logs, train should not be in valids\n out <- .train_for_verbosity_test(\n train_function = lightgbm\n , verbose_kwarg = 0L\n , verbose_param = NULL\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = FALSE\n , lgb_warn = TRUE\n , early_stopping = FALSE\n , valid_eval_msg = FALSE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n\n # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages, and\n # train should be in valids\n out <- .train_for_verbosity_test(\n train_function = lightgbm\n , verbose_kwarg = 1L\n , verbose_param = NULL\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = TRUE\n , lgb_warn = TRUE\n , early_stopping = TRUE\n , valid_eval_msg = TRUE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n})\n\ntest_that(\"lgb.cv() only prints eval metrics when expected to\", {\n\n # regardless of value passed to keyword argument 'verbose', value in params\n # should take precedence\n for (verbose_keyword_arg in c(-5L, -1L, 0L, 1L, 5L)) {\n\n # (verbose = -1) should not be any logs, should be record evals\n out <- .train_for_verbosity_test(\n verbose_kwarg = verbose_keyword_arg\n , verbose_param = -1L\n , train_function = lgb.cv\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = FALSE\n , lgb_warn = FALSE\n , early_stopping = FALSE\n , valid_eval_msg = FALSE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n\n # (verbose = 0) should be only WARN-level LightGBM logs\n out <- .train_for_verbosity_test(\n verbose_kwarg = verbose_keyword_arg\n , verbose_param = 0L\n , train_function = lgb.cv\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = FALSE\n , lgb_warn = TRUE\n , early_stopping = FALSE\n , valid_eval_msg = FALSE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n\n # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages\n out <- .train_for_verbosity_test(\n verbose_kwarg = verbose_keyword_arg\n , verbose_param = 1L\n , train_function = lgb.cv\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = TRUE\n , lgb_warn = TRUE\n , early_stopping = TRUE\n , valid_eval_msg = TRUE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n }\n\n # if verbosity isn't specified in `params`, changing keyword argument `verbose` should\n # alter what messages are printed\n\n # (verbose = -1) should not be any logs, should be record evals\n out <- .train_for_verbosity_test(\n verbose_kwarg = verbose_keyword_arg\n , verbose_param = -1L\n , train_function = lgb.cv\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = FALSE\n , lgb_warn = FALSE\n , early_stopping = FALSE\n , valid_eval_msg = FALSE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n\n # (verbose = 0) should be only WARN-level LightGBM logs\n out <- .train_for_verbosity_test(\n verbose_kwarg = verbose_keyword_arg\n , verbose_param = 0L\n , train_function = lgb.cv\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = FALSE\n , lgb_warn = TRUE\n , early_stopping = FALSE\n , valid_eval_msg = FALSE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n\n # (verbose > 0) should be INFO- and WARN-level LightGBM logs, and record eval messages\n out <- .train_for_verbosity_test(\n verbose_kwarg = verbose_keyword_arg\n , verbose_param = 1L\n , train_function = lgb.cv\n )\n .assert_has_expected_logs(\n log_txt = out[[\"logs\"]]\n , lgb_info = TRUE\n , lgb_warn = TRUE\n , early_stopping = TRUE\n , valid_eval_msg = TRUE\n )\n .assert_has_expected_record_evals(\n fitted_model = out[[\"booster\"]]\n )\n})\n\ntest_that(\"lightgbm() changes objective='auto' appropriately\", {\n # Regression\n data(\"mtcars\")\n y <- mtcars$mpg\n x <- as.matrix(mtcars[, -1L])\n model <- lightgbm(x, y, objective = \"auto\", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS)\n expect_equal(model$params$objective, \"regression\")\n model_txt_lines <- strsplit(\n x = model$save_model_to_string()\n , split = \"\\n\"\n , fixed = TRUE\n )[[1L]]\n expect_true(any(grepl(\"objective=regression\", model_txt_lines, fixed = TRUE)))\n expect_false(any(grepl(\"objective=regression_l1\", model_txt_lines, fixed = TRUE)))\n\n # Binary classification\n x <- train$data\n y <- factor(train$label)\n model <- lightgbm(x, y, objective = \"auto\", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS)\n expect_equal(model$params$objective, \"binary\")\n model_txt_lines <- strsplit(\n x = model$save_model_to_string()\n , split = \"\\n\"\n , fixed = TRUE\n )[[1L]]\n expect_true(any(grepl(\"objective=binary\", model_txt_lines, fixed = TRUE)))\n\n # Multi-class classification\n data(\"iris\")\n y <- factor(iris$Species)\n x <- as.matrix(iris[, -5L])\n model <- lightgbm(x, y, objective = \"auto\", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS)\n expect_equal(model$params$objective, \"multiclass\")\n expect_equal(model$params$num_class, 3L)\n model_txt_lines <- strsplit(\n x = model$save_model_to_string()\n , split = \"\\n\"\n , fixed = TRUE\n )[[1L]]\n expect_true(any(grepl(\"objective=multiclass\", model_txt_lines, fixed = TRUE)))\n})\n\ntest_that(\"lightgbm() determines number of classes for non-default multiclass objectives\", {\n data(\"iris\")\n y <- factor(iris$Species)\n x <- as.matrix(iris[, -5L])\n model <- lightgbm(\n x\n , y\n , objective = \"multiclassova\"\n , verbose = .LGB_VERBOSITY\n , nrounds = 5L\n , num_threads = .LGB_MAX_THREADS\n )\n expect_equal(model$params$objective, \"multiclassova\")\n expect_equal(model$params$num_class, 3L)\n model_txt_lines <- strsplit(\n x = model$save_model_to_string()\n , split = \"\\n\"\n , fixed = TRUE\n )[[1L]]\n expect_true(any(grepl(\"objective=multiclassova\", model_txt_lines, fixed = TRUE)))\n})\n\ntest_that(\"lightgbm() doesn't accept binary classification with non-binary factors\", {\n data(\"iris\")\n y <- factor(iris$Species)\n x <- as.matrix(iris[, -5L])\n expect_error({\n lightgbm(x, y, objective = \"binary\", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS)\n }, regexp = \"Factors with >2 levels as labels only allowed for multi-class objectives\")\n})\n\ntest_that(\"lightgbm() doesn't accept multi-class classification with binary factors\", {\n data(\"iris\")\n y <- as.character(iris$Species)\n y[y == \"setosa\"] <- \"versicolor\"\n y <- factor(y)\n x <- as.matrix(iris[, -5L])\n expect_error({\n lightgbm(x, y, objective = \"multiclass\", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS)\n }, regexp = \"Two-level factors as labels only allowed for objective='binary'\")\n})\n\ntest_that(\"lightgbm() model predictions retain factor levels for multiclass classification\", {\n data(\"iris\")\n y <- factor(iris$Species)\n x <- as.matrix(iris[, -5L])\n model <- lightgbm(x, y, objective = \"auto\", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS)\n\n pred <- predict(model, x, type = \"class\")\n expect_true(is.factor(pred))\n expect_equal(levels(pred), levels(y))\n\n pred <- predict(model, x, type = \"response\")\n expect_equal(colnames(pred), levels(y))\n\n pred <- predict(model, x, type = \"raw\")\n expect_equal(colnames(pred), levels(y))\n})\n\ntest_that(\"lightgbm() model predictions retain factor levels for binary classification\", {\n data(\"iris\")\n y <- as.character(iris$Species)\n y[y == \"setosa\"] <- \"versicolor\"\n y <- factor(y)\n x <- as.matrix(iris[, -5L])\n model <- lightgbm(x, y, objective = \"auto\", verbose = .LGB_VERBOSITY, nrounds = 5L, num_threads = .LGB_MAX_THREADS)\n\n pred <- predict(model, x, type = \"class\")\n expect_true(is.factor(pred))\n expect_equal(levels(pred), levels(y))\n\n pred <- predict(model, x, type = \"response\")\n expect_true(is.vector(pred))\n expect_true(is.numeric(pred))\n expect_false(any(pred %in% y))\n\n pred <- predict(model, x, type = \"raw\")\n expect_true(is.vector(pred))\n expect_true(is.numeric(pred))\n expect_false(any(pred %in% y))\n})\n\ntest_that(\"lightgbm() accepts named categorical_features\", {\n data(mtcars)\n y <- mtcars$mpg\n x <- as.matrix(mtcars[, -1L])\n model <- lightgbm(\n x\n , y\n , categorical_feature = \"cyl\"\n , verbose = .LGB_VERBOSITY\n , nrounds = 5L\n , num_threads = .LGB_MAX_THREADS\n )\n expect_true(length(model$params$categorical_feature) > 0L)\n})\n\ntest_that(\"lightgbm() correctly sets objective when passing lgb.Dataset as input\", {\n data(mtcars)\n y <- mtcars$mpg\n x <- as.matrix(mtcars[, -1L])\n ds <- lgb.Dataset(x, label = y)\n model <- lightgbm(\n ds\n , objective = \"auto\"\n , verbose = .LGB_VERBOSITY\n , nrounds = 5L\n , num_threads = .LGB_MAX_THREADS\n )\n expect_equal(model$params$objective, \"regression\")\n})\n\ntest_that(\"Evaluation metrics aren't printed as a single-element vector\", {\n log_txt <- capture_output({\n data(mtcars)\n y <- mtcars$mpg\n x <- as.matrix(mtcars[, -1L])\n cv_result <- lgb.cv(\n data = lgb.Dataset(x, label = y)\n , params = list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data_in_leaf = 5L\n , max_depth = 3L\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 2L\n , nfold = 3L\n , verbose = 1L\n , eval_train_metric = TRUE\n )\n })\n expect_false(grepl(\"[1] \\\"[1]\", log_txt, fixed = TRUE))\n})\n" }, { "path": "R-package/tests/testthat/test_custom_objective.R", "content": "data(agaricus.train, package = \"lightgbm\")\ndata(agaricus.test, package = \"lightgbm\")\ndtrain <- lgb.Dataset(agaricus.train$data, label = agaricus.train$label)\ndtest <- lgb.Dataset(agaricus.test$data, label = agaricus.test$label)\nwatchlist <- list(eval = dtest, train = dtrain)\n\nlogregobj <- function(preds, dtrain) {\n labels <- get_field(dtrain, \"label\")\n preds <- 1.0 / (1.0 + exp(-preds))\n grad <- preds - labels\n hess <- preds * (1.0 - preds)\n return(list(grad = grad, hess = hess))\n}\n\n# User-defined evaluation function returns a pair (metric_name, result, higher_better)\n# NOTE: when you do customized loss function, the default prediction value is margin\n# This may make built-in evaluation metric calculate wrong results\n# Keep this in mind when you use the customization, and maybe you need write customized evaluation function\nevalerror <- function(preds, dtrain) {\n labels <- get_field(dtrain, \"label\")\n preds <- 1.0 / (1.0 + exp(-preds))\n err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels)\n return(list(\n name = \"error\"\n , value = err\n , higher_better = FALSE\n ))\n}\n\nparam <- list(\n num_leaves = 8L\n , learning_rate = 1.0\n , objective = logregobj\n , metric = \"auc\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n)\nnum_round <- 10L\n\ntest_that(\"custom objective works\", {\n bst <- lgb.train(param, dtrain, num_round, watchlist, eval = evalerror)\n expect_false(is.null(bst$record_evals))\n})\n\ntest_that(\"using a custom objective, custom eval, and no other metrics works\", {\n set.seed(708L)\n bst <- lgb.train(\n params = list(\n num_leaves = 8L\n , learning_rate = 1.0\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , nrounds = 4L\n , valids = watchlist\n , obj = logregobj\n , eval = evalerror\n )\n expect_false(is.null(bst$record_evals))\n expect_equal(bst$best_iter, 4L)\n expect_true(abs(bst$best_score - 0.000621) < .LGB_NUMERIC_TOLERANCE)\n\n eval_results <- bst$eval_valid(feval = evalerror)[[1L]]\n expect_true(eval_results[[\"data_name\"]] == \"eval\")\n expect_true(abs(eval_results[[\"value\"]] - 0.0006207325) < .LGB_NUMERIC_TOLERANCE)\n expect_true(eval_results[[\"name\"]] == \"error\")\n expect_false(eval_results[[\"higher_better\"]])\n})\n\ntest_that(\"using a custom objective that returns wrong shape grad or hess raises an informative error\", {\n bad_grad <- function(preds, dtrain) {\n return(list(grad = numeric(0L), hess = rep(1.0, length(preds))))\n }\n bad_hess <- function(preds, dtrain) {\n return(list(grad = rep(1.0, length(preds)), hess = numeric(0L)))\n }\n params <- list(num_leaves = 3L, verbose = .LGB_VERBOSITY)\n expect_error({\n lgb.train(params = params, data = dtrain, obj = bad_grad)\n }, sprintf(\"Expected custom objective function to return grad with length %d, got 0.\", nrow(dtrain)))\n expect_error({\n lgb.train(params = params, data = dtrain, obj = bad_hess)\n }, sprintf(\"Expected custom objective function to return hess with length %d, got 0.\", nrow(dtrain)))\n})\n" }, { "path": "R-package/tests/testthat/test_dataset.R", "content": "data(agaricus.train, package = \"lightgbm\")\ntrain_data <- agaricus.train$data[seq_len(1000L), ]\ntrain_label <- agaricus.train$label[seq_len(1000L)]\n\ndata(agaricus.test, package = \"lightgbm\")\ntest_data <- agaricus.test$data[1L:100L, ]\ntest_label <- agaricus.test$label[1L:100L]\n\ntest_that(\"lgb.Dataset: basic construction, saving, loading\", {\n # from sparse matrix\n dtest1 <- lgb.Dataset(\n test_data\n , label = test_label\n , params = list(\n verbose = .LGB_VERBOSITY\n )\n )\n # from dense matrix\n dtest2 <- lgb.Dataset(as.matrix(test_data), label = test_label)\n expect_equal(get_field(dtest1, \"label\"), get_field(dtest2, \"label\"))\n\n # save to a local file\n tmp_file <- tempfile(\"lgb.Dataset_\")\n lgb.Dataset.save(dtest1, tmp_file)\n # read from a local file\n dtest3 <- lgb.Dataset(\n tmp_file\n , params = list(\n verbose = .LGB_VERBOSITY\n )\n )\n lgb.Dataset.construct(dtest3)\n unlink(tmp_file)\n expect_equal(get_field(dtest1, \"label\"), get_field(dtest3, \"label\"))\n})\n\ntest_that(\"lgb.Dataset: get_field & set_field\", {\n dtest <- lgb.Dataset(test_data)\n dtest$construct()\n\n set_field(dtest, \"label\", test_label)\n labels <- get_field(dtest, \"label\")\n expect_equal(test_label, get_field(dtest, \"label\"))\n\n expect_true(length(get_field(dtest, \"weight\")) == 0L)\n expect_true(length(get_field(dtest, \"init_score\")) == 0L)\n\n # any other label should error\n expect_error(\n set_field(dtest, \"asdf\", test_label)\n , regexp = \"Dataset$set_field(): field_name must be one of the following: 'label', 'weight', 'init_score', 'group'\" # nolint: line_length.\n , fixed = TRUE\n )\n})\n\ntest_that(\"lgb.Dataset: slice, dim\", {\n dtest <- lgb.Dataset(test_data, label = test_label)\n lgb.Dataset.construct(dtest)\n expect_equal(dim(dtest), dim(test_data))\n dsub1 <- lgb.slice.Dataset(dtest, seq_len(42L))\n lgb.Dataset.construct(dsub1)\n expect_equal(nrow(dsub1), 42L)\n expect_equal(ncol(dsub1), ncol(test_data))\n})\n\ntest_that(\"Dataset$set_reference() on a constructed Dataset fails if raw data has been freed\", {\n dtrain <- lgb.Dataset(train_data, label = train_label)\n dtrain$construct()\n dtest <- lgb.Dataset(test_data, label = test_label)\n dtest$construct()\n expect_error({\n dtest$set_reference(dtrain)\n }, regexp = \"cannot set reference after freeing raw data\")\n})\n\ntest_that(\"Dataset$set_reference() fails if reference is not a Dataset\", {\n dtrain <- lgb.Dataset(\n train_data\n , label = train_label\n , free_raw_data = FALSE\n )\n expect_error({\n dtrain$set_reference(reference = data.frame(x = rnorm(10L)))\n }, regexp = \"Can only use lgb.Dataset as a reference\")\n\n # passing NULL when the Dataset already has a reference raises an error\n dtest <- lgb.Dataset(\n test_data\n , label = test_label\n , free_raw_data = FALSE\n )\n dtrain$set_reference(dtest)\n expect_error({\n dtrain$set_reference(reference = NULL)\n }, regexp = \"Can only use lgb.Dataset as a reference\")\n})\n\ntest_that(\"Dataset$set_reference() setting reference to the same Dataset has no side effects\", {\n dtrain <- lgb.Dataset(\n train_data\n , label = train_label\n , free_raw_data = FALSE\n , categorical_feature = c(2L, 3L)\n )\n dtrain$construct()\n\n cat_features_before <- dtrain$.__enclos_env__$private$categorical_feature\n colnames_before <- dtrain$get_colnames()\n predictor_before <- dtrain$.__enclos_env__$private$predictor\n\n dtrain$set_reference(dtrain)\n expect_identical(\n cat_features_before\n , dtrain$.__enclos_env__$private$categorical_feature\n )\n expect_identical(\n colnames_before\n , dtrain$get_colnames()\n )\n expect_identical(\n predictor_before\n , dtrain$.__enclos_env__$private$predictor\n )\n})\n\ntest_that(\"Dataset$set_reference() updates categorical_feature, colnames, and predictor\", {\n dtrain <- lgb.Dataset(\n train_data\n , label = train_label\n , free_raw_data = FALSE\n , categorical_feature = c(2L, 3L)\n )\n dtrain$construct()\n bst <- Booster$new(\n train_set = dtrain\n , params = list(verbose = -1L, num_threads = .LGB_MAX_THREADS)\n )\n dtrain$.__enclos_env__$private$predictor <- bst$to_predictor()\n\n test_original_feature_names <- paste0(\"feature_col_\", seq_len(ncol(test_data)))\n dtest <- lgb.Dataset(\n test_data\n , label = test_label\n , free_raw_data = FALSE\n , colnames = test_original_feature_names\n )\n dtest$construct()\n\n # at this point, dtest should not have categorical_feature\n expect_null(dtest$.__enclos_env__$private$predictor)\n expect_null(dtest$.__enclos_env__$private$categorical_feature)\n expect_identical(\n dtest$get_colnames()\n , test_original_feature_names\n )\n\n dtest$set_reference(dtrain)\n\n # after setting reference to dtrain, those attributes should have dtrain's values\n expect_true(methods::is(\n dtest$.__enclos_env__$private$predictor\n , \"lgb.Predictor\"\n ))\n expect_identical(\n dtest$.__enclos_env__$private$predictor$.__enclos_env__$private$handle\n , dtrain$.__enclos_env__$private$predictor$.__enclos_env__$private$handle\n )\n expect_identical(\n dtest$.__enclos_env__$private$categorical_feature\n , dtrain$.__enclos_env__$private$categorical_feature\n )\n expect_identical(\n dtest$get_colnames()\n , dtrain$get_colnames()\n )\n expect_false(\n identical(dtest$get_colnames(), test_original_feature_names)\n )\n})\n\ntest_that(\"lgb.Dataset: colnames\", {\n dtest <- lgb.Dataset(test_data, label = test_label)\n expect_equal(colnames(dtest), colnames(test_data))\n lgb.Dataset.construct(dtest)\n expect_equal(colnames(dtest), colnames(test_data))\n expect_error({\n colnames(dtest) <- \"asdf\"\n }, regexp = \"can't assign '1' colnames to an lgb.Dataset with '126' columns\")\n new_names <- make.names(seq_len(ncol(test_data)))\n expect_silent({\n colnames(dtest) <- new_names\n })\n expect_equal(colnames(dtest), new_names)\n})\n\ntest_that(\"lgb.Dataset: nrow is correct for a very sparse matrix\", {\n nr <- 1000L\n x <- Matrix::rsparsematrix(nr, 100L, density = 0.0005)\n # we want it very sparse, so that last rows are empty\n expect_lt(max(x@i), nr)\n dtest <- lgb.Dataset(x)\n expect_equal(dim(dtest), dim(x))\n})\n\ntest_that(\"lgb.Dataset: Dataset should be able to construct from matrix and return non-null handle\", {\n rawData <- matrix(runif(1000L), ncol = 10L)\n ref_handle <- NULL\n handle <- .Call(\n LGBM_DatasetCreateFromMat_R\n , rawData\n , nrow(rawData)\n , ncol(rawData)\n , lightgbm:::.params2str(params = list())\n , ref_handle\n )\n expect_true(methods::is(handle, \"externalptr\"))\n expect_false(is.null(handle))\n .Call(LGBM_DatasetFree_R, handle)\n handle <- NULL\n})\n\ntest_that(\"cpp errors should be raised as proper R errors\", {\n testthat::skip_if(\n Sys.getenv(\"COMPILER\", \"\") == \"MSVC\"\n , message = \"Skipping on Visual Studio\"\n )\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n dtrain <- lgb.Dataset(\n train$data\n , label = train$label\n , init_score = seq_len(10L)\n )\n expect_error({\n capture.output({\n dtrain$construct()\n }, type = \"message\")\n }, regexp = \"Initial score size doesn't match data size\")\n})\n\ntest_that(\"lgb.Dataset$set_field() should convert 'group' to integer\", {\n ds <- lgb.Dataset(\n data = matrix(rnorm(100L), nrow = 50L, ncol = 2L)\n , label = sample(c(0L, 1L), size = 50L, replace = TRUE)\n )\n ds$construct()\n current_group <- ds$get_field(\"group\")\n expect_null(current_group)\n group_as_numeric <- rep(25.0, 2L)\n ds$set_field(\"group\", group_as_numeric)\n expect_identical(ds$get_field(\"group\"), as.integer(group_as_numeric))\n})\n\ntest_that(\"lgb.Dataset should throw an error if 'reference' is provided but of the wrong format\", {\n data(agaricus.test, package = \"lightgbm\")\n test_data <- agaricus.test$data[1L:100L, ]\n test_label <- agaricus.test$label[1L:100L]\n # Try to trick lgb.Dataset() into accepting bad input\n expect_error({\n dtest <- lgb.Dataset(\n data = test_data\n , label = test_label\n , reference = data.frame(x = seq_len(10L), y = seq_len(10L))\n )\n }, regexp = \"reference must be a\")\n})\n\ntest_that(\"Dataset$new() should throw an error if 'predictor' is provided but of the wrong format\", {\n data(agaricus.test, package = \"lightgbm\")\n test_data <- agaricus.test$data[1L:100L, ]\n test_label <- agaricus.test$label[1L:100L]\n expect_error({\n dtest <- Dataset$new(\n data = test_data\n , label = test_label\n , predictor = data.frame(x = seq_len(10L), y = seq_len(10L))\n )\n }, regexp = \"predictor must be a\", fixed = TRUE)\n})\n\ntest_that(\"Dataset$get_params() successfully returns parameters if you passed them\", {\n # note that this list uses one \"main\" parameter (feature_pre_filter) and one that\n # is an alias (is_sparse), to check that aliases are handled correctly\n params <- list(\n \"feature_pre_filter\" = TRUE\n , \"is_sparse\" = FALSE\n )\n ds <- lgb.Dataset(\n test_data\n , label = test_label\n , params = params\n )\n returned_params <- ds$get_params()\n expect_identical(class(returned_params), \"list\")\n expect_identical(length(params), length(returned_params))\n expect_identical(sort(names(params)), sort(names(returned_params)))\n for (param_name in names(params)) {\n expect_identical(params[[param_name]], returned_params[[param_name]])\n }\n})\n\ntest_that(\"Dataset$get_params() ignores irrelevant parameters\", {\n params <- list(\n \"feature_pre_filter\" = TRUE\n , \"is_sparse\" = FALSE\n , \"nonsense_parameter\" = c(1.0, 2.0, 5.0)\n )\n ds <- lgb.Dataset(\n test_data\n , label = test_label\n , params = params\n )\n returned_params <- ds$get_params()\n expect_false(\"nonsense_parameter\" %in% names(returned_params))\n})\n\ntest_that(\"Dataset$update_parameters() does nothing for empty inputs\", {\n ds <- lgb.Dataset(\n test_data\n , label = test_label\n )\n initial_params <- ds$get_params()\n expect_identical(initial_params, list())\n\n # update_params() should return \"self\" so it can be chained\n res <- ds$update_params(\n params = list()\n )\n expect_true(.is_Dataset(res))\n\n new_params <- ds$get_params()\n expect_identical(new_params, initial_params)\n})\n\ntest_that(\"Dataset$update_params() works correctly for recognized Dataset parameters\", {\n ds <- lgb.Dataset(\n test_data\n , label = test_label\n )\n initial_params <- ds$get_params()\n expect_identical(initial_params, list())\n\n new_params <- list(\n \"data_random_seed\" = 708L\n , \"enable_bundle\" = FALSE\n )\n res <- ds$update_params(\n params = new_params\n )\n expect_true(.is_Dataset(res))\n\n updated_params <- ds$get_params()\n for (param_name in names(new_params)) {\n expect_identical(new_params[[param_name]], updated_params[[param_name]])\n }\n})\n\ntest_that(\"Dataset's finalizer should not fail on an already-finalized Dataset\", {\n dtest <- lgb.Dataset(\n data = test_data\n , label = test_label\n )\n expect_true(.is_null_handle(dtest$.__enclos_env__$private$handle))\n\n dtest$construct()\n expect_false(.is_null_handle(dtest$.__enclos_env__$private$handle))\n\n dtest$.__enclos_env__$private$finalize()\n expect_true(.is_null_handle(dtest$.__enclos_env__$private$handle))\n\n # calling finalize() a second time shouldn't cause any issues\n dtest$.__enclos_env__$private$finalize()\n expect_true(.is_null_handle(dtest$.__enclos_env__$private$handle))\n})\n\ntest_that(\"lgb.Dataset: should be able to run lgb.train() immediately after using lgb.Dataset() on a file\", {\n dtest <- lgb.Dataset(\n data = test_data\n , label = test_label\n , params = list(\n verbose = .LGB_VERBOSITY\n )\n )\n tmp_file <- tempfile(pattern = \"lgb.Dataset_\")\n lgb.Dataset.save(\n dataset = dtest\n , fname = tmp_file\n )\n\n # read from a local file\n dtest_read_in <- lgb.Dataset(data = tmp_file)\n\n param <- list(\n objective = \"binary\"\n , metric = \"binary_logloss\"\n , num_leaves = 5L\n , learning_rate = 1.0\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n\n # should be able to train right away\n bst <- lgb.train(\n params = param\n , data = dtest_read_in\n )\n\n expect_true(.is_Booster(x = bst))\n})\n\ntest_that(\"lgb.Dataset: should be able to run lgb.cv() immediately after using lgb.Dataset() on a file\", {\n dtest <- lgb.Dataset(\n data = test_data\n , label = test_label\n , params = list(\n verbosity = .LGB_VERBOSITY\n )\n )\n tmp_file <- tempfile(pattern = \"lgb.Dataset_\")\n lgb.Dataset.save(\n dataset = dtest\n , fname = tmp_file\n )\n\n # read from a local file\n dtest_read_in <- lgb.Dataset(data = tmp_file)\n\n param <- list(\n objective = \"binary\"\n , metric = \"binary_logloss\"\n , num_leaves = 5L\n , learning_rate = 1.0\n , num_iterations = 5L\n , verbosity = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n\n # should be able to train right away\n bst <- lgb.cv(\n params = param\n , data = dtest_read_in\n )\n\n expect_true(methods::is(bst, \"lgb.CVBooster\"))\n})\n\ntest_that(\"lgb.Dataset: should be able to be used in lgb.cv() when constructed with categorical feature indices\", {\n data(\"mtcars\")\n y <- mtcars$mpg\n x <- as.matrix(mtcars[, -1L])\n categorical_feature <- which(names(mtcars) %in% c(\"cyl\", \"vs\", \"am\", \"gear\", \"carb\")) - 1L\n dtrain <- lgb.Dataset(\n data = x\n , label = y\n , categorical_feature = categorical_feature\n , free_raw_data = TRUE\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n # constructing the Dataset frees the raw data\n dtrain$construct()\n params <- list(\n objective = \"regression\"\n , num_leaves = 2L\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n # cv should reuse the same categorical features without checking the indices\n bst <- lgb.cv(params = params, data = dtrain, stratified = FALSE, nrounds = 1L)\n expect_equal(\n unlist(bst$boosters[[1L]]$booster$params$categorical_feature)\n , categorical_feature - 1L # 0-based\n )\n})\n\n\ntest_that(\"lgb.Dataset: should be able to use and retrieve long feature names\", {\n # set one feature to a value longer than the default buffer size used\n # in LGBM_DatasetGetFeatureNames_R\n feature_names <- names(iris)\n long_name <- strrep(\"a\", 1000L)\n feature_names[1L] <- long_name\n names(iris) <- feature_names\n # check that feature name survived the trip from R to C++ and back\n dtrain <- lgb.Dataset(\n data = as.matrix(iris[, -5L])\n , label = as.numeric(iris$Species) - 1L\n )\n dtrain$construct()\n col_names <- dtrain$get_colnames()\n expect_equal(col_names[1L], long_name)\n expect_equal(nchar(col_names[1L]), 1000L)\n})\n\ntest_that(\"lgb.Dataset: should be able to create a Dataset from a text file with a header\", {\n train_file <- tempfile(pattern = \"train_\", fileext = \".csv\")\n write.table(\n data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L))\n , file = train_file\n , sep = \",\"\n , col.names = TRUE\n , row.names = FALSE\n , quote = FALSE\n )\n\n dtrain <- lgb.Dataset(\n data = train_file\n , params = list(\n header = TRUE\n , verbosity = .LGB_VERBOSITY\n )\n )\n dtrain$construct()\n expect_identical(dtrain$get_colnames(), c(\"x1\", \"x2\"))\n expect_identical(dtrain$get_params(), list(header = TRUE))\n expect_identical(dtrain$dim(), c(100L, 2L))\n})\n\ntest_that(\"lgb.Dataset: should be able to create a Dataset from a text file without a header\", {\n train_file <- tempfile(pattern = \"train_\", fileext = \".csv\")\n write.table(\n data.frame(y = rnorm(100L), x1 = rnorm(100L), x2 = rnorm(100L))\n , file = train_file\n , sep = \",\"\n , col.names = FALSE\n , row.names = FALSE\n , quote = FALSE\n )\n\n dtrain <- lgb.Dataset(\n data = train_file\n , params = list(\n header = FALSE\n , verbosity = .LGB_VERBOSITY\n )\n )\n dtrain$construct()\n expect_identical(dtrain$get_colnames(), c(\"Column_0\", \"Column_1\"))\n expect_identical(dtrain$get_params(), list(header = FALSE))\n expect_identical(dtrain$dim(), c(100L, 2L))\n})\n\ntest_that(\"Dataset: method calls on a Dataset with a null handle should raise an informative error and not segfault\", {\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n dtrain <- lgb.Dataset(train$data, label = train$label)\n dtrain$construct()\n dvalid <- dtrain$create_valid(\n data = train$data[seq_len(100L), ]\n , label = train$label[seq_len(100L)]\n )\n dvalid$construct()\n tmp_file <- tempfile(fileext = \".rds\")\n saveRDS(dtrain, tmp_file)\n rm(dtrain)\n dtrain <- readRDS(tmp_file)\n expect_error({\n dtrain$construct()\n }, regexp = \"Attempting to create a Dataset without any raw data\")\n expect_error({\n dtrain$dim()\n }, regexp = \"cannot get dimensions before dataset has been constructed\")\n expect_error({\n dtrain$get_colnames()\n }, regexp = \"cannot get column names before dataset has been constructed\")\n expect_error({\n dtrain$get_feature_num_bin(1L)\n }, regexp = \"Cannot get number of bins in feature before constructing Dataset.\")\n expect_error({\n dtrain$save_binary(fname = tempfile(fileext = \".bin\"))\n }, regexp = \"Attempting to create a Dataset without any raw data\")\n expect_error({\n dtrain$set_categorical_feature(categorical_feature = 1L)\n }, regexp = \"cannot set categorical feature after freeing raw data\")\n expect_error({\n dtrain$set_reference(reference = dvalid)\n }, regexp = \"cannot set reference after freeing raw data\")\n\n tmp_valid_file <- tempfile(fileext = \".rds\")\n saveRDS(dvalid, tmp_valid_file)\n rm(dvalid)\n dvalid <- readRDS(tmp_valid_file)\n dtrain <- lgb.Dataset(\n train$data\n , label = train$label\n , free_raw_data = FALSE\n )\n dtrain$construct()\n expect_error({\n dtrain$set_reference(reference = dvalid)\n }, regexp = \"cannot get column names before dataset has been constructed\")\n})\n\ntest_that(\"lgb.Dataset$get_feature_num_bin() works\", {\n raw_df <- data.frame(\n all_random = runif(100L)\n , two_vals = rep(c(1.0, 2.0), 50L)\n , three_vals = c(rep(c(0.0, 1.0, 2.0), 33L), 0.0)\n , two_vals_plus_missing = c(rep(c(1.0, 2.0), 49L), NA_real_, NA_real_)\n , all_zero = rep(0.0, 100L)\n , categorical = sample.int(2L, 100L, replace = TRUE)\n )\n n_features <- ncol(raw_df)\n raw_mat <- data.matrix(raw_df)\n min_data_in_bin <- 2L\n ds <- lgb.Dataset(\n raw_mat\n , params = list(min_data_in_bin = min_data_in_bin)\n , categorical_feature = n_features\n )\n ds$construct()\n expected_num_bins <- c(\n 100L %/% min_data_in_bin + 1L # extra bin for zero\n , 3L # 0, 1, 2\n , 3L # 0, 1, 2\n , 4L # 0, 1, 2 + NA\n , 0L # unused\n , 3L # 1, 2 + NA\n )\n actual_num_bins <- sapply(1L:n_features, ds$get_feature_num_bin)\n expect_identical(actual_num_bins, expected_num_bins)\n # test using defined feature names\n bins_by_name <- sapply(colnames(raw_mat), ds$get_feature_num_bin)\n expect_identical(unname(bins_by_name), expected_num_bins)\n # test using default feature names\n no_names_mat <- raw_mat\n colnames(no_names_mat) <- NULL\n ds_no_names <- lgb.Dataset(\n no_names_mat\n , params = list(min_data_in_bin = min_data_in_bin)\n , categorical_feature = n_features\n )\n ds_no_names$construct()\n default_names <- lapply(\n X = seq(1L, ncol(raw_mat))\n , FUN = function(i) {\n sprintf(\"Column_%d\", i - 1L)\n }\n )\n bins_by_default_name <- sapply(default_names, ds_no_names$get_feature_num_bin)\n expect_identical(bins_by_default_name, expected_num_bins)\n})\n\ntest_that(\"lgb.Dataset can be constructed with categorical features and without colnames\", {\n # check that dataset can be constructed\n raw_mat <- matrix(rep(c(0L, 1L), 50L), ncol = 1L)\n ds <- lgb.Dataset(raw_mat, categorical_feature = 1L)$construct()\n sparse_mat <- as(raw_mat, \"dgCMatrix\")\n ds2 <- lgb.Dataset(sparse_mat, categorical_feature = 1L)$construct()\n # check that the column names are the default ones\n expect_equal(ds$.__enclos_env__$private$colnames, \"Column_0\")\n expect_equal(ds2$.__enclos_env__$private$colnames, \"Column_0\")\n # check for error when index is greater than the number of columns\n expect_error({\n lgb.Dataset(raw_mat, categorical_feature = 2L)$construct()\n }, regexp = \"supplied a too large value in categorical_feature: 2 but only 1 features\")\n})\n\ntest_that(\"lgb.Dataset.slice fails with a categorical feature index greater than the number of features\", {\n data <- matrix(runif(100L), nrow = 50L, ncol = 2L)\n ds <- lgb.Dataset(data = data, categorical_feature = 3L)\n subset <- ds$slice(1L:20L)\n expect_error({\n subset$construct()\n }, regexp = \"supplied a too large value in categorical_feature: 3 but only 2 features\")\n})\n" }, { "path": "R-package/tests/testthat/test_learning_to_rank.R", "content": "test_that(\"learning-to-rank with lgb.train() works as expected\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n # just keep a few features,to generate an model with imperfect fit\n train <- agaricus.train\n train_data <- train$data[1L:6000L, 1L:20L]\n dtrain <- lgb.Dataset(\n train_data\n , label = train$label[1L:6000L]\n , group = rep(150L, 40L)\n )\n ndcg_at <- \"1,2,3\"\n eval_names <- paste0(\"ndcg@\", strsplit(ndcg_at, \",\", fixed = TRUE)[[1L]])\n params <- list(\n objective = \"lambdarank\"\n , metric = \"ndcg\"\n , ndcg_at = ndcg_at\n , lambdarank_truncation_level = 3L\n , learning_rate = 0.001\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n model <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 10L\n )\n expect_true(.is_Booster(model))\n\n dumped_model <- jsonlite::fromJSON(\n model$dump_model()\n )\n expect_equal(dumped_model[[\"objective\"]], \"lambdarank\")\n expect_equal(dumped_model[[\"max_feature_idx\"]], ncol(train_data) - 1L)\n\n # check that evaluation results make sense (0.0 < nDCG < 1.0)\n eval_results <- model$eval_train()\n expect_equal(length(eval_results), length(eval_names))\n for (result in eval_results) {\n expect_true(result[[\"value\"]] > 0.0)\n expect_true(result[[\"value\"]] < 1.0)\n expect_true(result[[\"higher_better\"]])\n expect_identical(result[[\"data_name\"]], \"training\")\n }\n expect_identical(\n sapply(\n X = eval_results\n , FUN = function(x) {\n x$name\n }\n )\n , eval_names\n )\n expect_equal(eval_results[[1L]][[\"value\"]], 0.775)\n if (!.LGB_ON_32_BIT_WINDOWS) {\n expect_true(abs(eval_results[[2L]][[\"value\"]] - 0.745986) < .LGB_NUMERIC_TOLERANCE)\n expect_true(abs(eval_results[[3L]][[\"value\"]] - 0.7351959) < .LGB_NUMERIC_TOLERANCE)\n }\n})\n\ntest_that(\"learning-to-rank with lgb.cv() works as expected\", {\n testthat::skip_if(\n .LGB_ON_32_BIT_WINDOWS\n , message = \"Skipping on 32-bit Windows\"\n )\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n # just keep a few features,to generate an model with imperfect fit\n train <- agaricus.train\n train_data <- train$data[1L:6000L, 1L:20L]\n dtrain <- lgb.Dataset(\n train_data\n , label = train$label[1L:6000L]\n , group = rep(150L, 40L)\n )\n ndcg_at <- \"1,2,3\"\n eval_names <- paste0(\"ndcg@\", strsplit(ndcg_at, \",\", fixed = TRUE)[[1L]])\n params <- list(\n objective = \"lambdarank\"\n , metric = \"ndcg\"\n , ndcg_at = ndcg_at\n , lambdarank_truncation_level = 3L\n , label_gain = \"0,1,3\"\n , min_data = 1L\n , learning_rate = 0.01\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n nfold <- 4L\n nrounds <- 10L\n cv_bst <- lgb.cv(\n params = params\n , data = dtrain\n , nrounds = nrounds\n , nfold = nfold\n )\n expect_true(methods::is(cv_bst, \"lgb.CVBooster\"))\n expect_equal(length(cv_bst$boosters), nfold)\n\n # \"valid\" should contain results for each metric\n eval_results <- cv_bst$record_evals[[\"valid\"]]\n eval_names <- c(\"ndcg@1\", \"ndcg@2\", \"ndcg@3\")\n expect_identical(names(eval_results), eval_names)\n\n # check that best score and iter make sense (0.0 < nDCG < 1.0)\n best_iter <- cv_bst$best_iter\n best_score <- cv_bst$best_score\n expect_true(best_iter > 0L)\n expect_true(best_iter <= nrounds)\n expect_true(best_score > 0.0)\n expect_true(best_score < 1.0)\n expect_true(abs(best_score - 0.75) < .LGB_NUMERIC_TOLERANCE)\n\n # best_score should be set for the first metric\n first_metric <- eval_names[[1L]]\n expect_equal(best_score, eval_results[[first_metric]][[\"eval\"]][[best_iter]])\n\n for (eval_name in eval_names) {\n results_for_this_metric <- eval_results[[eval_name]]\n\n # each set of metrics should have eval and eval_err\n expect_identical(names(results_for_this_metric), c(\"eval\", \"eval_err\"))\n\n # there should be one \"eval\" and \"eval_err\" per round\n expect_equal(length(results_for_this_metric[[\"eval\"]]), nrounds)\n expect_equal(length(results_for_this_metric[[\"eval_err\"]]), nrounds)\n\n # check that evaluation results make sense (0.0 < nDCG < 1.0)\n all_evals <- unlist(results_for_this_metric[[\"eval\"]])\n expect_true(all(all_evals > 0.0 & all_evals < 1.0))\n }\n\n # first and last value of each metric should be as expected\n ndcg1_values <- c(0.675, 0.725, 0.65, 0.725, 0.75, 0.725, 0.75, 0.725, 0.75, 0.75)\n expect_true(all(abs(unlist(eval_results[[\"ndcg@1\"]][[\"eval\"]]) - ndcg1_values) < .LGB_NUMERIC_TOLERANCE))\n\n ndcg2_values <- c(\n 0.6556574, 0.6669721, 0.6306574, 0.6476294, 0.6629581,\n 0.6476294, 0.6629581, 0.6379581, 0.7113147, 0.6823008\n )\n expect_true(all(abs(unlist(eval_results[[\"ndcg@2\"]][[\"eval\"]]) - ndcg2_values) < .LGB_NUMERIC_TOLERANCE))\n\n ndcg3_values <- c(\n 0.6484639, 0.6571238, 0.6469279, 0.6540516, 0.6481857,\n 0.6481857, 0.6481857, 0.6466496, 0.7027939, 0.6629898\n )\n expect_true(all(abs(unlist(eval_results[[\"ndcg@3\"]][[\"eval\"]]) - ndcg3_values) < .LGB_NUMERIC_TOLERANCE))\n\n # check details of each booster\n for (bst in cv_bst$boosters) {\n dumped_model <- jsonlite::fromJSON(\n bst$booster$dump_model()\n )\n expect_equal(dumped_model[[\"objective\"]], \"lambdarank\")\n expect_equal(dumped_model[[\"max_feature_idx\"]], ncol(train_data) - 1L)\n }\n})\n" }, { "path": "R-package/tests/testthat/test_lgb.Booster.R", "content": "test_that(\"Booster's finalizer should not fail\", {\n X <- as.matrix(as.integer(iris[, \"Species\"]), ncol = 1L)\n y <- iris[[\"Sepal.Length\"]]\n dtrain <- lgb.Dataset(X, label = y)\n bst <- lgb.train(\n data = dtrain\n , params = list(\n objective = \"regression\"\n , num_threads = .LGB_MAX_THREADS\n )\n , verbose = .LGB_VERBOSITY\n , nrounds = 3L\n )\n expect_true(.is_Booster(bst))\n\n expect_false(.is_null_handle(bst$.__enclos_env__$private$handle))\n\n bst$.__enclos_env__$private$finalize()\n expect_true(.is_null_handle(bst$.__enclos_env__$private$handle))\n\n # calling finalize() a second time shouldn't cause any issues\n bst$.__enclos_env__$private$finalize()\n expect_true(.is_null_handle(bst$.__enclos_env__$private$handle))\n})\n\ntest_that(\"lgb.get.eval.result() should throw an informative error if booster is not an lgb.Booster\", {\n bad_inputs <- list(\n matrix(1.0:10.0, 2L, 5L)\n , TRUE\n , c(\"a\", \"b\")\n , NA\n , 10L\n , lgb.Dataset(\n data = matrix(1.0:10.0, 2L, 5L)\n , params = list()\n )\n )\n for (bad_input in bad_inputs) {\n expect_error({\n lgb.get.eval.result(\n booster = bad_input\n , data_name = \"test\"\n , eval_name = \"l2\"\n )\n }, regexp = \"Can only use\", fixed = TRUE)\n }\n})\n\ntest_that(\"lgb.get.eval.result() should throw an informative error for incorrect data_name\", {\n data(agaricus.train, package = \"lightgbm\")\n data(agaricus.test, package = \"lightgbm\")\n dtrain <- lgb.Dataset(\n agaricus.train$data\n , label = agaricus.train$label\n )\n model <- lgb.train(\n params = list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data = 1L\n , learning_rate = 1.0\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , nrounds = 5L\n , valids = list(\n \"test\" = lgb.Dataset.create.valid(\n dtrain\n , agaricus.test$data\n , label = agaricus.test$label\n )\n )\n )\n expect_error({\n eval_results <- lgb.get.eval.result(\n booster = model\n , data_name = \"testing\"\n , eval_name = \"l2\"\n )\n }, regexp = \"Only the following datasets exist in record evals: [test]\", fixed = TRUE)\n})\n\ntest_that(\"lgb.get.eval.result() should throw an informative error for incorrect eval_name\", {\n data(agaricus.train, package = \"lightgbm\")\n data(agaricus.test, package = \"lightgbm\")\n dtrain <- lgb.Dataset(\n agaricus.train$data\n , label = agaricus.train$label\n )\n model <- lgb.train(\n params = list(\n objective = \"regression\"\n , metric = \"l2\"\n , min_data = 1L\n , learning_rate = 1.0\n , verbose = .LGB_VERBOSITY\n )\n , data = dtrain\n , nrounds = 5L\n , valids = list(\n \"test\" = lgb.Dataset.create.valid(\n dtrain\n , agaricus.test$data\n , label = agaricus.test$label\n )\n )\n )\n expect_error({\n eval_results <- lgb.get.eval.result(\n booster = model\n , data_name = \"test\"\n , eval_name = \"l1\"\n )\n }, regexp = \"Only the following eval_names exist for dataset.*\\\\: \\\\[l2\\\\]\", fixed = FALSE)\n})\n\ntest_that(\"lgb.load() gives the expected error messages given different incorrect inputs\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n data(agaricus.test, package = \"lightgbm\")\n train <- agaricus.train\n test <- agaricus.test\n bst <- lightgbm(\n data = as.matrix(train$data)\n , label = train$label\n , params = list(\n objective = \"binary\"\n , num_leaves = 4L\n , learning_rate = 1.0\n , verbose = .LGB_VERBOSITY\n )\n , nrounds = 2L\n )\n\n # you have to give model_str or filename\n expect_error({\n lgb.load()\n }, regexp = \"either filename or model_str must be given\")\n expect_error({\n lgb.load(filename = NULL, model_str = NULL)\n }, regexp = \"either filename or model_str must be given\")\n\n # if given, filename should be a string that points to an existing file\n model_file <- tempfile(fileext = \".model\")\n expect_error({\n lgb.load(filename = list(model_file))\n }, regexp = \"filename should be character\")\n file_to_check <- paste0(\"a.model\")\n while (file.exists(file_to_check)) {\n file_to_check <- paste0(\"a\", file_to_check)\n }\n expect_error({\n lgb.load(filename = file_to_check)\n }, regexp = \"passed to filename does not exist\")\n\n # if given, model_str should be a string\n expect_error({\n lgb.load(model_str = c(4.0, 5.0, 6.0))\n }, regexp = \"lgb.load: model_str should be a character/raw vector\")\n\n})\n\ntest_that(\"Loading a Booster from a text file works\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n data(agaricus.test, package = \"lightgbm\")\n train <- agaricus.train\n test <- agaricus.test\n params <- list(\n num_leaves = 4L\n , boosting = \"rf\"\n , bagging_fraction = 0.8\n , bagging_freq = 1L\n , boost_from_average = FALSE\n , categorical_feature = c(1L, 2L)\n , interaction_constraints = list(1L:2L, 3L, 4L:ncol(train$data))\n , feature_contri = rep(0.5, ncol(train$data))\n , metric = c(\"mape\", \"average_precision\")\n , learning_rate = 1.0\n , objective = \"binary\"\n , verbosity = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n bst <- lightgbm(\n data = as.matrix(train$data)\n , label = train$label\n , params = params\n , nrounds = 2L\n )\n expect_true(.is_Booster(bst))\n\n pred <- predict(bst, test$data)\n model_file <- tempfile(fileext = \".model\")\n lgb.save(bst, model_file)\n\n # finalize the booster and destroy it so you know we aren't cheating\n bst$.__enclos_env__$private$finalize()\n expect_null(bst$.__enclos_env__$private$handle)\n rm(bst)\n\n bst2 <- lgb.load(\n filename = model_file\n )\n pred2 <- predict(bst2, test$data)\n expect_identical(pred, pred2)\n\n # check that the parameters are loaded correctly\n expect_equal(bst2$params[names(params)], params)\n})\n\ntest_that(\"boosters with linear models at leaves can be written to text file and re-loaded successfully\", {\n X <- matrix(rnorm(100L), ncol = 1L)\n labels <- 2L * X + runif(nrow(X), 0L, 0.1)\n dtrain <- lgb.Dataset(\n data = X\n , label = labels\n )\n\n params <- list(\n objective = \"regression\"\n , verbose = -1L\n , metric = \"mse\"\n , seed = 0L\n , num_leaves = 2L\n , num_threads = .LGB_MAX_THREADS\n )\n\n bst <- lgb.train(\n data = dtrain\n , nrounds = 10L\n , params = params\n , verbose = .LGB_VERBOSITY\n )\n expect_true(.is_Booster(bst))\n\n # save predictions, then write the model to a file and destroy it in R\n preds <- predict(bst, X)\n model_file <- tempfile(fileext = \".model\")\n lgb.save(bst, model_file)\n bst$.__enclos_env__$private$finalize()\n expect_null(bst$.__enclos_env__$private$handle)\n rm(bst)\n\n # load the booster and make predictions...should be the same\n bst2 <- lgb.load(\n filename = model_file\n )\n preds2 <- predict(bst2, X)\n expect_identical(preds, preds2)\n})\n\n\ntest_that(\"Loading a Booster from a string works\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n data(agaricus.test, package = \"lightgbm\")\n train <- agaricus.train\n test <- agaricus.test\n bst <- lightgbm(\n data = as.matrix(train$data)\n , label = train$label\n , params = list(\n num_leaves = 4L\n , learning_rate = 1.0\n , objective = \"binary\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 2L\n )\n expect_true(.is_Booster(bst))\n\n pred <- predict(bst, test$data)\n model_string <- bst$save_model_to_string()\n\n # finalize the booster and destroy it so you know we aren't cheating\n bst$.__enclos_env__$private$finalize()\n expect_null(bst$.__enclos_env__$private$handle)\n rm(bst)\n\n bst2 <- lgb.load(\n model_str = model_string\n )\n pred2 <- predict(bst2, test$data)\n expect_identical(pred, pred2)\n})\n\ntest_that(\"Saving a large model to string should work\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n bst <- lightgbm(\n data = as.matrix(train$data)\n , label = train$label\n , params = list(\n num_leaves = 100L\n , learning_rate = 0.01\n , objective = \"binary\"\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 500L\n , verbose = .LGB_VERBOSITY\n )\n\n pred <- predict(bst, train$data)\n pred_leaf_indx <- predict(bst, train$data, type = \"leaf\")\n pred_raw_score <- predict(bst, train$data, type = \"raw\")\n model_string <- bst$save_model_to_string()\n\n # make sure this test is still producing a model bigger than the default\n # buffer size used in LGBM_BoosterSaveModelToString_R\n expect_gt(nchar(model_string), 1024L * 1024L)\n\n # finalize the booster and destroy it so you know we aren't cheating\n bst$.__enclos_env__$private$finalize()\n expect_null(bst$.__enclos_env__$private$handle)\n rm(bst)\n\n # make sure a new model can be created from this string, and that it\n # produces expected results\n bst2 <- lgb.load(\n model_str = model_string\n )\n pred2 <- predict(bst2, train$data)\n pred2_leaf_indx <- predict(bst2, train$data, type = \"leaf\")\n pred2_raw_score <- predict(bst2, train$data, type = \"raw\")\n expect_identical(pred, pred2)\n expect_identical(pred_leaf_indx, pred2_leaf_indx)\n expect_identical(pred_raw_score, pred2_raw_score)\n})\n\ntest_that(\"Saving a large model to JSON should work\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n bst <- lightgbm(\n data = as.matrix(train$data)\n , label = train$label\n , params = list(\n num_leaves = 100L\n , learning_rate = 0.01\n , objective = \"binary\"\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 200L\n , verbose = .LGB_VERBOSITY\n )\n\n model_json <- bst$dump_model()\n\n # make sure this test is still producing a model bigger than the default\n # buffer size used in LGBM_BoosterDumpModel_R\n expect_gt(nchar(model_json), 1024L * 1024L)\n\n # check that it is valid JSON that looks like a LightGBM model\n model_list <- jsonlite::fromJSON(model_json)\n expect_equal(model_list[[\"objective\"]], \"binary sigmoid:1\")\n})\n\ntest_that(\"If a string and a file are both passed to lgb.load() the file is used model_str is totally ignored\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n data(agaricus.test, package = \"lightgbm\")\n train <- agaricus.train\n test <- agaricus.test\n bst <- lightgbm(\n data = as.matrix(train$data)\n , label = train$label\n , params = list(\n num_leaves = 4L\n , learning_rate = 1.0\n , objective = \"binary\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 2L\n )\n expect_true(.is_Booster(bst))\n\n pred <- predict(bst, test$data)\n model_file <- tempfile(fileext = \".model\")\n lgb.save(bst, model_file)\n\n # finalize the booster and destroy it so you know we aren't cheating\n bst$.__enclos_env__$private$finalize()\n expect_null(bst$.__enclos_env__$private$handle)\n rm(bst)\n\n bst2 <- lgb.load(\n filename = model_file\n , model_str = 4.0\n )\n pred2 <- predict(bst2, test$data)\n expect_identical(pred, pred2)\n})\n\ntest_that(\"Creating a Booster from a Dataset should work\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n data(agaricus.test, package = \"lightgbm\")\n dtrain <- lgb.Dataset(\n agaricus.train$data\n , label = agaricus.train$label\n )\n bst <- Booster$new(\n params = list(\n objective = \"binary\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n ),\n train_set = dtrain\n )\n expect_true(.is_Booster(bst))\n expect_equal(bst$current_iter(), 0L)\n expect_true(is.na(bst$best_score))\n expect_true(all(bst$predict(agaricus.train$data) == 0.5))\n})\n\ntest_that(\"Creating a Booster from a Dataset with an existing predictor should work\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n nrounds <- 2L\n bst <- lightgbm(\n data = as.matrix(agaricus.train$data)\n , label = agaricus.train$label\n , params = list(\n num_leaves = 4L\n , learning_rate = 1.0\n , objective = \"binary\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = nrounds\n )\n data(agaricus.test, package = \"lightgbm\")\n dtest <- Dataset$new(\n data = agaricus.test$data\n , label = agaricus.test$label\n , predictor = bst$to_predictor()\n )\n bst_from_ds <- Booster$new(\n train_set = dtest\n , params = list(\n verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n )\n expect_true(.is_Booster(bst))\n expect_equal(bst$current_iter(), nrounds)\n expect_equal(bst$eval_train()[[1L]][[\"value\"]], 0.1115352)\n expect_true(.is_Booster(bst_from_ds))\n expect_equal(bst_from_ds$current_iter(), nrounds)\n expect_equal(bst_from_ds$eval_train()[[1L]][[\"value\"]], 5.65704892)\n dumped_model <- jsonlite::fromJSON(bst$dump_model())\n})\n\ntest_that(\"Booster$eval() should work on a Dataset stored in a binary file\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n dtrain <- lgb.Dataset(train$data, label = train$label)\n\n bst <- lgb.train(\n params = list(\n objective = \"regression\"\n , metric = \"l2\"\n , num_leaves = 4L\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , nrounds = 2L\n )\n\n data(agaricus.test, package = \"lightgbm\")\n test <- agaricus.test\n dtest <- lgb.Dataset.create.valid(\n dataset = dtrain\n , data = test$data\n , label = test$label\n )\n dtest$construct()\n\n eval_in_mem <- bst$eval(\n data = dtest\n , name = \"test\"\n )\n\n test_file <- tempfile(pattern = \"lgb.Dataset_\")\n lgb.Dataset.save(\n dataset = dtest\n , fname = test_file\n )\n rm(dtest)\n\n eval_from_file <- bst$eval(\n data = lgb.Dataset(\n data = test_file\n , params = list(verbose = .LGB_VERBOSITY, num_threads = .LGB_MAX_THREADS)\n )$construct()\n , name = \"test\"\n )\n\n expect_true(abs(eval_in_mem[[1L]][[\"value\"]] - 0.1744423) < .LGB_NUMERIC_TOLERANCE)\n # refer to https://github.com/lightgbm-org/LightGBM/issues/4680\n if (isTRUE(.LGB_ON_WINDOWS)) {\n expect_equal(eval_in_mem, eval_from_file)\n } else {\n expect_identical(eval_in_mem, eval_from_file)\n }\n})\n\ntest_that(\"Booster$rollback_one_iter() should work as expected\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n data(agaricus.test, package = \"lightgbm\")\n train <- agaricus.train\n test <- agaricus.test\n nrounds <- 5L\n bst <- lightgbm(\n data = as.matrix(train$data)\n , label = train$label\n , params = list(\n num_leaves = 4L\n , learning_rate = 1.0\n , objective = \"binary\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = nrounds\n )\n expect_equal(bst$current_iter(), nrounds)\n expect_true(.is_Booster(bst))\n logloss <- bst$eval_train()[[1L]][[\"value\"]]\n expect_equal(logloss, 0.01904786)\n\n x <- bst$rollback_one_iter()\n\n # rollback_one_iter() should return a booster and modify the original\n # booster in place\n expect_true(.is_Booster(x))\n expect_equal(bst$current_iter(), nrounds - 1L)\n\n # score should now come from the model as of 4 iterations\n logloss <- bst$eval_train()[[1L]][[\"value\"]]\n expect_equal(logloss, 0.027915146)\n})\n\ntest_that(\"Booster$update() passing a train_set works as expected\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n nrounds <- 2L\n\n # train with 2 rounds and then update\n bst <- lightgbm(\n data = as.matrix(agaricus.train$data)\n , label = agaricus.train$label\n , params = list(\n num_leaves = 4L\n , learning_rate = 1.0\n , objective = \"binary\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = nrounds\n )\n expect_true(.is_Booster(bst))\n expect_equal(bst$current_iter(), nrounds)\n bst$update(\n train_set = Dataset$new(\n data = agaricus.train$data\n , label = agaricus.train$label\n , params = list(verbose = .LGB_VERBOSITY)\n )\n )\n expect_true(.is_Booster(bst))\n expect_equal(bst$current_iter(), nrounds + 1L)\n\n # train with 3 rounds directly\n bst2 <- lightgbm(\n data = as.matrix(agaricus.train$data)\n , label = agaricus.train$label\n , params = list(\n num_leaves = 4L\n , learning_rate = 1.0\n , objective = \"binary\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = nrounds + 1L\n )\n expect_true(.is_Booster(bst2))\n expect_equal(bst2$current_iter(), nrounds + 1L)\n\n # model with 2 rounds + 1 update should be identical to 3 rounds\n expect_equal(bst2$eval_train()[[1L]][[\"value\"]], 0.04806585)\n expect_equal(bst$eval_train()[[1L]][[\"value\"]], bst2$eval_train()[[1L]][[\"value\"]])\n})\n\ntest_that(\"Booster$update() throws an informative error if you provide a non-Dataset to update()\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n nrounds <- 2L\n\n # train with 2 rounds and then update\n bst <- lightgbm(\n data = as.matrix(agaricus.train$data)\n , label = agaricus.train$label\n , params = list(\n num_leaves = 4L\n , learning_rate = 1.0\n , objective = \"binary\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = nrounds\n )\n expect_error({\n bst$update(\n train_set = data.frame(x = rnorm(10L))\n )\n }, regexp = \"lgb.Booster.update: Only can use lgb.Dataset\", fixed = TRUE)\n})\n\ntest_that(\"Booster$num_trees_per_iter() works as expected\", {\n set.seed(708L)\n\n X <- data.matrix(iris[2L:4L])\n y_reg <- iris[, 1L]\n y_binary <- as.integer(y_reg > median(y_reg))\n y_class <- as.integer(iris[, 5L]) - 1L\n num_class <- 3L\n\n nrounds <- 10L\n\n # Regression and binary probabilistic classification (1 iteration = 1 tree)\n fit_reg <- lgb.train(\n params = list(\n objective = \"mse\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = lgb.Dataset(X, label = y_reg)\n , nrounds = nrounds\n )\n\n fit_binary <- lgb.train(\n params = list(\n objective = \"binary\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = lgb.Dataset(X, label = y_binary)\n , nrounds = nrounds\n )\n\n # Multiclass probabilistic classification (1 iteration = num_class trees)\n fit_class <- lgb.train(\n params = list(\n objective = \"multiclass\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n , num_class = num_class\n )\n , data = lgb.Dataset(X, label = y_class)\n , nrounds = nrounds\n )\n\n expect_equal(fit_reg$num_trees_per_iter(), 1L)\n expect_equal(fit_binary$num_trees_per_iter(), 1L)\n expect_equal(fit_class$num_trees_per_iter(), num_class)\n})\n\ntest_that(\"Booster$num_trees() and $num_iter() works (no early stopping)\", {\n set.seed(708L)\n\n X <- data.matrix(iris[2L:4L])\n y_reg <- iris[, 1L]\n y_binary <- as.integer(y_reg > median(y_reg))\n y_class <- as.integer(iris[, 5L]) - 1L\n num_class <- 3L\n nrounds <- 10L\n\n # Regression and binary probabilistic classification (1 iteration = 1 tree)\n fit_reg <- lgb.train(\n params = list(\n objective = \"mse\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = lgb.Dataset(X, label = y_reg)\n , nrounds = nrounds\n )\n\n fit_binary <- lgb.train(\n params = list(\n objective = \"binary\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = lgb.Dataset(X, label = y_binary)\n , nrounds = nrounds\n )\n\n # Multiclass probabilistic classification (1 iteration = num_class trees)\n fit_class <- lgb.train(\n params = list(\n objective = \"multiclass\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n , num_class = num_class\n )\n , data = lgb.Dataset(X, label = y_class)\n , nrounds = nrounds\n )\n\n expect_equal(fit_reg$num_trees(), nrounds)\n expect_equal(fit_binary$num_trees(), nrounds)\n expect_equal(fit_class$num_trees(), num_class * nrounds)\n\n expect_equal(fit_reg$num_iter(), nrounds)\n expect_equal(fit_binary$num_iter(), nrounds)\n expect_equal(fit_class$num_iter(), nrounds)\n})\n\ntest_that(\"Booster$num_trees() and $num_iter() work (with early stopping)\", {\n set.seed(708L)\n\n X <- data.matrix(iris[2L:4L])\n y_reg <- iris[, 1L]\n y_binary <- as.integer(y_reg > median(y_reg))\n y_class <- as.integer(iris[, 5L]) - 1L\n train_ix <- c(1L:40L, 51L:90L, 101L:140L)\n X_train <- X[train_ix, ]\n X_valid <- X[-train_ix, ]\n\n num_class <- 3L\n nrounds <- 1000L\n early_stopping <- 2L\n\n # Regression and binary probabilistic classification (1 iteration = 1 tree)\n fit_reg <- lgb.train(\n params = list(\n objective = \"mse\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = lgb.Dataset(X_train, label = y_reg[train_ix])\n , valids = list(valid = lgb.Dataset(X_valid, label = y_reg[-train_ix]))\n , nrounds = nrounds\n , early_stopping_round = early_stopping\n )\n\n fit_binary <- lgb.train(\n params = list(\n objective = \"binary\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , data = lgb.Dataset(X_train, label = y_binary[train_ix])\n , valids = list(valid = lgb.Dataset(X_valid, label = y_binary[-train_ix]))\n , nrounds = nrounds\n , early_stopping_round = early_stopping\n )\n\n # Multiclass probabilistic classification (1 iteration = num_class trees)\n fit_class <- lgb.train(\n params = list(\n objective = \"multiclass\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n , num_class = num_class\n )\n , data = lgb.Dataset(X_train, label = y_class[train_ix])\n , valids = list(valid = lgb.Dataset(X_valid, label = y_class[-train_ix]))\n , nrounds = nrounds\n , early_stopping_round = early_stopping\n )\n\n expected_trees_reg <- fit_reg$best_iter + early_stopping\n expected_trees_binary <- fit_binary$best_iter + early_stopping\n expected_trees_class <- (fit_class$best_iter + early_stopping) * num_class\n\n expect_equal(fit_reg$num_trees(), expected_trees_reg)\n expect_equal(fit_binary$num_trees(), expected_trees_binary)\n expect_equal(fit_class$num_trees(), expected_trees_class)\n\n expect_equal(fit_reg$num_iter(), expected_trees_reg)\n expect_equal(fit_binary$num_iter(), expected_trees_binary)\n expect_equal(fit_class$num_iter(), expected_trees_class / num_class)\n})\n\ntest_that(\"Booster should store parameters and Booster$reset_parameter() should update them\", {\n data(agaricus.train, package = \"lightgbm\")\n dtrain <- lgb.Dataset(\n agaricus.train$data\n , label = agaricus.train$label\n )\n # testing that this works for some cases that could break it:\n # - multiple metrics\n # - using \"metric\", \"boosting\", \"num_class\" in params\n params <- list(\n objective = \"multiclass\"\n , max_depth = 4L\n , bagging_fraction = 0.8\n , metric = c(\"multi_logloss\", \"multi_error\")\n , boosting = \"gbdt\"\n , num_class = 5L\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n bst <- Booster$new(\n params = params\n , train_set = dtrain\n )\n expect_identical(bst$params, params)\n\n params[[\"bagging_fraction\"]] <- 0.9\n ret_bst <- bst$reset_parameter(params = params)\n expect_identical(ret_bst$params, params)\n expect_identical(bst$params, params)\n})\n\ntest_that(\"Booster$params should include dataset params, before and after Booster$reset_parameter()\", {\n data(agaricus.train, package = \"lightgbm\")\n dtrain <- lgb.Dataset(\n agaricus.train$data\n , label = agaricus.train$label\n , params = list(\n max_bin = 17L\n )\n )\n params <- list(\n objective = \"binary\"\n , max_depth = 4L\n , bagging_fraction = 0.8\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n bst <- Booster$new(\n params = params\n , train_set = dtrain\n )\n expect_identical(\n bst$params\n , list(\n objective = \"binary\"\n , max_depth = 4L\n , bagging_fraction = 0.8\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n , max_bin = 17L\n )\n )\n\n params[[\"bagging_fraction\"]] <- 0.9\n ret_bst <- bst$reset_parameter(params = params)\n expected_params <- list(\n objective = \"binary\"\n , max_depth = 4L\n , bagging_fraction = 0.9\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n , max_bin = 17L\n )\n expect_identical(ret_bst$params, expected_params)\n expect_identical(bst$params, expected_params)\n})\n\ntest_that(\"Saving a model with different feature importance types works\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n bst <- lightgbm(\n data = as.matrix(train$data)\n , label = train$label\n , params = list(\n num_leaves = 4L\n , learning_rate = 1.0\n , objective = \"binary\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 2L\n )\n expect_true(.is_Booster(bst))\n\n .feat_importance_from_string <- function(model_string) {\n file_lines <- strsplit(model_string, \"\\n\", fixed = TRUE)[[1L]]\n start_indx <- which(file_lines == \"feature_importances:\") + 1L\n blank_line_indices <- which(file_lines == \"\")\n end_indx <- blank_line_indices[blank_line_indices > start_indx][1L] - 1L\n importances <- file_lines[start_indx: end_indx]\n return(importances)\n }\n\n GAIN_IMPORTANCE <- 1L\n model_string <- bst$save_model_to_string(feature_importance_type = GAIN_IMPORTANCE)\n expect_equal(\n .feat_importance_from_string(model_string)\n , c(\n \"odor=none=4010\"\n , \"stalk-root=club=1163\"\n , \"stalk-root=rooted=573\"\n , \"stalk-surface-above-ring=silky=450\"\n , \"spore-print-color=green=397\"\n , \"gill-color=buff=281\"\n )\n )\n\n SPLIT_IMPORTANCE <- 0L\n model_string <- bst$save_model_to_string(feature_importance_type = SPLIT_IMPORTANCE)\n expect_equal(\n .feat_importance_from_string(model_string)\n , c(\n \"odor=none=1\"\n , \"gill-color=buff=1\"\n , \"stalk-root=club=1\"\n , \"stalk-root=rooted=1\"\n , \"stalk-surface-above-ring=silky=1\"\n , \"spore-print-color=green=1\"\n )\n )\n})\n\ntest_that(\"Saving a model with unknown importance type fails\", {\n set.seed(708L)\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n bst <- lightgbm(\n data = as.matrix(train$data)\n , label = train$label\n , params = list(\n num_leaves = 4L\n , learning_rate = 1.0\n , objective = \"binary\"\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 2L\n )\n expect_true(.is_Booster(bst))\n\n UNSUPPORTED_IMPORTANCE <- 2L\n expect_error({\n capture.output({\n model_string <- bst$save_model_to_string(\n feature_importance_type = UNSUPPORTED_IMPORTANCE\n )\n }, type = \"message\")\n }, \"Unknown importance type\")\n})\n\n\n.params_from_model_string <- function(model_str) {\n file_lines <- strsplit(model_str, \"\\n\", fixed = TRUE)[[1L]]\n start_indx <- which(file_lines == \"parameters:\") + 1L\n blank_line_indices <- which(file_lines == \"\")\n end_indx <- blank_line_indices[blank_line_indices > start_indx][1L] - 1L\n params <- file_lines[start_indx: end_indx]\n return(params)\n}\n\ntest_that(\"all parameters are stored correctly with save_model_to_string()\", {\n dtrain <- lgb.Dataset(\n data = matrix(rnorm(500L), nrow = 100L)\n , label = rnorm(100L)\n )\n bst <- lgb.train(\n params = list(\n objective = \"mape\"\n , metric = c(\"l2\", \"mae\")\n , num_threads = .LGB_MAX_THREADS\n , seed = 708L\n , data_sample_strategy = \"bagging\"\n , sub_row = 0.8234\n )\n , data = dtrain\n , nrounds = 3L\n , verbose = .LGB_VERBOSITY\n )\n\n # entries whose values should reflect params passed to lgb.train()\n non_default_param_entries <- c(\n \"[objective: mape]\"\n # 'l1' was passed in with alias 'mae'\n , \"[metric: l2,l1]\"\n , \"[data_sample_strategy: bagging]\"\n , \"[seed: 708]\"\n # this was passed in with alias 'sub_row'\n , \"[bagging_fraction: 0.8234]\"\n , \"[num_iterations: 3]\"\n )\n\n # entries with default values of params\n default_param_entries <- c(\n \"[boosting: gbdt]\"\n , \"[tree_learner: serial]\"\n , \"[device_type: cpu]\"\n , \"[data: ]\"\n , \"[valid: ]\"\n , \"[learning_rate: 0.1]\"\n , \"[num_leaves: 31]\"\n , sprintf(\"[num_threads: %i]\", .LGB_MAX_THREADS)\n , \"[deterministic: 0]\"\n , \"[histogram_pool_size: -1]\"\n , \"[max_depth: -1]\"\n , \"[min_data_in_leaf: 20]\"\n , \"[min_sum_hessian_in_leaf: 0.001]\"\n , \"[pos_bagging_fraction: 1]\"\n , \"[neg_bagging_fraction: 1]\"\n , \"[bagging_freq: 0]\"\n , \"[bagging_seed: 15415]\"\n , \"[feature_fraction: 1]\"\n , \"[feature_fraction_bynode: 1]\"\n , \"[feature_fraction_seed: 32671]\"\n , \"[extra_trees: 0]\"\n , \"[extra_seed: 6642]\"\n , \"[early_stopping_round: 0]\"\n , \"[early_stopping_min_delta: 0]\"\n , \"[first_metric_only: 0]\"\n , \"[max_delta_step: 0]\"\n , \"[lambda_l1: 0]\"\n , \"[lambda_l2: 0]\"\n , \"[linear_lambda: 0]\"\n , \"[min_gain_to_split: 0]\"\n , \"[drop_rate: 0.1]\"\n , \"[max_drop: 50]\"\n , \"[skip_drop: 0.5]\"\n , \"[xgboost_dart_mode: 0]\"\n , \"[uniform_drop: 0]\"\n , \"[drop_seed: 20623]\"\n , \"[top_rate: 0.2]\"\n , \"[other_rate: 0.1]\"\n , \"[min_data_per_group: 100]\"\n , \"[max_cat_threshold: 32]\"\n , \"[cat_l2: 10]\"\n , \"[cat_smooth: 10]\"\n , \"[max_cat_to_onehot: 4]\"\n , \"[top_k: 20]\"\n , \"[monotone_constraints: ]\"\n , \"[monotone_constraints_method: basic]\"\n , \"[monotone_penalty: 0]\"\n , \"[feature_contri: ]\"\n , \"[forcedsplits_filename: ]\"\n , \"[force_col_wise: 0]\"\n , \"[force_row_wise: 0]\"\n , \"[refit_decay_rate: 0.9]\"\n , \"[cegb_tradeoff: 1]\"\n , \"[cegb_penalty_split: 0]\"\n , \"[cegb_penalty_feature_lazy: ]\"\n , \"[cegb_penalty_feature_coupled: ]\"\n , \"[path_smooth: 0]\"\n , \"[interaction_constraints: ]\"\n , sprintf(\"[verbosity: %i]\", .LGB_VERBOSITY)\n , \"[saved_feature_importance_type: 0]\"\n , \"[use_quantized_grad: 0]\"\n , \"[num_grad_quant_bins: 4]\"\n , \"[quant_train_renew_leaf: 0]\"\n , \"[stochastic_rounding: 1]\"\n , \"[linear_tree: 0]\"\n , \"[max_bin: 255]\"\n , \"[max_bin_by_feature: ]\"\n , \"[min_data_in_bin: 3]\"\n , \"[bin_construct_sample_cnt: 200000]\"\n , \"[data_random_seed: 2350]\"\n , \"[is_enable_sparse: 1]\"\n , \"[enable_bundle: 1]\"\n , \"[use_missing: 1]\"\n , \"[zero_as_missing: 0]\"\n , \"[feature_pre_filter: 1]\"\n , \"[pre_partition: 0]\"\n , \"[two_round: 0]\"\n , \"[header: 0]\"\n , \"[label_column: ]\"\n , \"[weight_column: ]\"\n , \"[group_column: ]\"\n , \"[ignore_column: ]\"\n , \"[categorical_feature: ]\"\n , \"[forcedbins_filename: ]\"\n , \"[precise_float_parser: 0]\"\n , \"[parser_config_file: ]\"\n , \"[objective_seed: 4309]\"\n , \"[num_class: 1]\"\n , \"[is_unbalance: 0]\"\n , \"[scale_pos_weight: 1]\"\n , \"[sigmoid: 1]\"\n , \"[boost_from_average: 1]\"\n , \"[reg_sqrt: 0]\"\n , \"[alpha: 0.9]\"\n , \"[fair_c: 1]\"\n , \"[poisson_max_delta_step: 0.7]\"\n , \"[tweedie_variance_power: 1.5]\"\n , \"[lambdarank_truncation_level: 30]\"\n , \"[lambdarank_norm: 1]\"\n , \"[label_gain: ]\"\n , \"[lambdarank_position_bias_regularization: 0]\"\n , \"[eval_at: ]\"\n , \"[multi_error_top_k: 1]\"\n , \"[auc_mu_weights: ]\"\n , \"[num_machines: 1]\"\n , \"[local_listen_port: 12400]\"\n , \"[time_out: 120]\"\n , \"[machine_list_filename: ]\"\n , \"[machines: ]\"\n , \"[gpu_platform_id: -1]\"\n , \"[gpu_device_id: -1]\"\n , \"[gpu_use_dp: 0]\"\n , \"[num_gpu: 1]\"\n )\n all_param_entries <- c(non_default_param_entries, default_param_entries)\n\n # parameters should match what was passed from the R-package\n model_str <- bst$save_model_to_string()\n params_in_file <- .params_from_model_string(model_str = model_str)\n .expect_in(all_param_entries, params_in_file)\n\n # early stopping should be off by default\n expect_equal(sum(startsWith(params_in_file, \"[early_stopping_round:\")), 1L)\n expect_equal(sum(params_in_file == \"[early_stopping_round: 0]\"), 1L)\n\n # since save_model_to_string() is used when serializing with saveRDS(), check that parameters all\n # roundtrip saveRDS()/loadRDS() successfully\n rds_file <- tempfile()\n saveRDS(bst, rds_file)\n bst_rds <- readRDS(rds_file)\n model_str <- bst_rds$save_model_to_string()\n params_in_file <- .params_from_model_string(model_str = model_str)\n .expect_in(all_param_entries, params_in_file)\n})\n\ntest_that(\"early_stopping, num_iterations are stored correctly in model string even with aliases\", {\n dtrain <- lgb.Dataset(\n data = matrix(rnorm(500L), nrow = 100L)\n , label = rnorm(100L)\n )\n dvalid <- lgb.Dataset(\n data = matrix(rnorm(500L), nrow = 100L)\n , label = rnorm(100L)\n )\n\n # num_iterations values (all different)\n num_iterations <- 4L\n num_boost_round <- 2L\n n_iter <- 3L\n nrounds_kwarg <- 6L\n\n # early_stopping_round values (all different)\n early_stopping_round <- 2L\n early_stopping_round_kwarg <- 3L\n n_iter_no_change <- 4L\n\n params <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , num_boost_round = num_boost_round\n , num_iterations = num_iterations\n , n_iter = n_iter\n , early_stopping_round = early_stopping_round\n , n_iter_no_change = n_iter_no_change\n , num_threads = .LGB_MAX_THREADS\n )\n\n bst <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = nrounds_kwarg\n , early_stopping_rounds = early_stopping_round_kwarg\n , valids = list(\n \"random_valid\" = dvalid\n )\n , verbose = .LGB_VERBOSITY\n )\n\n model_str <- bst$save_model_to_string()\n params_in_file <- .params_from_model_string(model_str = model_str)\n\n # parameters should match what was passed from the R-package, and the \"main\" (non-alias)\n # params values in `params` should be preferred to keyword argumentts or aliases\n expect_equal(sum(startsWith(params_in_file, \"[num_iterations:\")), 1L)\n expect_equal(sum(params_in_file == sprintf(\"[num_iterations: %s]\", num_iterations)), 1L)\n expect_equal(sum(startsWith(params_in_file, \"[early_stopping_round:\")), 1L)\n expect_equal(sum(params_in_file == sprintf(\"[early_stopping_round: %s]\", early_stopping_round)), 1L)\n\n # none of the aliases shouold have been written to the model file\n expect_equal(sum(startsWith(params_in_file, \"[num_boost_round:\")), 0L)\n expect_equal(sum(startsWith(params_in_file, \"[n_iter:\")), 0L)\n expect_equal(sum(startsWith(params_in_file, \"[n_iter_no_change:\")), 0L)\n\n})\n\ntest_that(\"Booster: method calls Booster with a null handle should raise an informative error and not segfault\", {\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n dtrain <- lgb.Dataset(train$data, label = train$label)\n bst <- lgb.train(\n params = list(\n objective = \"regression\"\n , metric = \"l2\"\n , num_leaves = 8L\n , num_threads = .LGB_MAX_THREADS\n )\n , data = dtrain\n , verbose = .LGB_VERBOSITY\n , nrounds = 5L\n , valids = list(\n train = dtrain\n )\n , serializable = FALSE\n )\n tmp_file <- tempfile(fileext = \".rds\")\n saveRDS(bst, tmp_file)\n rm(bst)\n bst <- readRDS(tmp_file)\n .expect_booster_error <- function(object) {\n error_regexp <- \"Attempting to use a Booster which no longer exists\"\n expect_error(object, regexp = error_regexp)\n }\n .expect_booster_error({\n bst$current_iter()\n })\n .expect_booster_error({\n bst$dump_model()\n })\n .expect_booster_error({\n bst$eval(data = dtrain, name = \"valid\")\n })\n .expect_booster_error({\n bst$eval_train()\n })\n .expect_booster_error({\n bst$lower_bound()\n })\n .expect_booster_error({\n bst$predict(data = train$data[seq_len(5L), ])\n })\n .expect_booster_error({\n bst$reset_parameter(params = list(learning_rate = 0.123))\n })\n .expect_booster_error({\n bst$rollback_one_iter()\n })\n .expect_booster_error({\n bst$save_raw()\n })\n .expect_booster_error({\n bst$save_model(filename = tempfile(fileext = \".model\"))\n })\n .expect_booster_error({\n bst$save_model_to_string()\n })\n .expect_booster_error({\n bst$update()\n })\n .expect_booster_error({\n bst$upper_bound()\n })\n predictor <- bst$to_predictor()\n .expect_booster_error({\n predictor$current_iter()\n })\n .expect_booster_error({\n predictor$predict(data = train$data[seq_len(5L), ])\n })\n})\n\ntest_that(\"Booster$new() using a Dataset with a null handle should raise an informative error and not segfault\", {\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n dtrain <- lgb.Dataset(train$data, label = train$label)\n dtrain$construct()\n tmp_file <- tempfile(fileext = \".bin\")\n saveRDS(dtrain, tmp_file)\n rm(dtrain)\n dtrain <- readRDS(tmp_file)\n expect_error({\n bst <- Booster$new(\n train_set = dtrain\n , params = list(\n verbose = .LGB_VERBOSITY\n )\n )\n }, regexp = \"Attempting to create a Dataset without any raw data\")\n})\n\ntest_that(\"Booster$new() raises informative errors for malformed inputs\", {\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n dtrain <- lgb.Dataset(train$data, label = train$label)\n\n # no inputs\n expect_error({\n Booster$new()\n }, regexp = \"lgb.Booster: Need at least either training dataset, model file, or model_str\")\n\n # unrecognized objective\n expect_error({\n capture.output({\n Booster$new(\n params = list(objective = \"not_a_real_objective\")\n , train_set = dtrain\n )\n }, type = \"message\")\n }, regexp = \"Unknown objective type name: not_a_real_objective\")\n\n # train_set is not a Dataset\n expect_error({\n Booster$new(\n train_set = data.table::data.table(rnorm(1L:10L))\n )\n }, regexp = \"lgb.Booster: Can only use lgb.Dataset as training data\")\n\n # model file isn't a string\n expect_error({\n Booster$new(\n modelfile = list()\n )\n }, regexp = \"lgb.Booster: Can only use a string as model file path\")\n\n # model file doesn't exist\n expect_error({\n capture.output({\n Booster$new(\n params = list()\n , modelfile = \"file-that-does-not-exist.model\"\n )\n }, type = \"message\")\n }, regexp = \"Could not open file-that-does-not-exist.model\")\n\n # model file doesn't contain a valid LightGBM model\n model_file <- tempfile(fileext = \".model\")\n writeLines(\n text = c(\"make\", \"good\", \"predictions\")\n , con = model_file\n )\n expect_error({\n capture.output({\n Booster$new(\n params = list()\n , modelfile = model_file\n )\n }, type = \"message\")\n }, regexp = \"Unknown model format or submodel type in model file\")\n\n # malformed model string\n expect_error({\n capture.output({\n Booster$new(\n params = list()\n , model_str = \"a\\nb\\n\"\n )\n }, type = \"message\")\n }, regexp = \"Model file doesn't specify the number of classes\")\n\n # model string isn't character or raw\n expect_error({\n Booster$new(\n model_str = numeric()\n )\n }, regexp = \"lgb.Booster: Can only use a character/raw vector as model_str\")\n})\n\n# this is almost identical to the test above it, but for lgb.cv(). A lot of code\n# is duplicated between lgb.train() and lgb.cv(), and this will catch cases where\n# one is updated and the other isn't\ntest_that(\"lgb.cv() correctly handles passing through params to the model file\", {\n dtrain <- lgb.Dataset(\n data = matrix(rnorm(500L), nrow = 100L)\n , label = rnorm(100L)\n )\n\n # num_iterations values (all different)\n num_iterations <- 4L\n num_boost_round <- 2L\n n_iter <- 3L\n nrounds_kwarg <- 6L\n\n # early_stopping_round values (all different)\n early_stopping_round <- 2L\n early_stopping_round_kwarg <- 3L\n n_iter_no_change <- 4L\n\n params <- list(\n objective = \"regression\"\n , metric = \"l2\"\n , num_boost_round = num_boost_round\n , num_iterations = num_iterations\n , n_iter = n_iter\n , early_stopping_round = early_stopping_round\n , n_iter_no_change = n_iter_no_change\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n\n cv_bst <- lgb.cv(\n params = params\n , data = dtrain\n , nrounds = nrounds_kwarg\n , early_stopping_rounds = early_stopping_round_kwarg\n , nfold = 3L\n , verbose = .LGB_VERBOSITY\n )\n\n for (bst in cv_bst$boosters) {\n model_str <- bst[[\"booster\"]]$save_model_to_string()\n params_in_file <- .params_from_model_string(model_str = model_str)\n\n # parameters should match what was passed from the R-package, and the \"main\" (non-alias)\n # params values in `params` should be preferred to keyword argumentts or aliases\n expect_equal(sum(startsWith(params_in_file, \"[num_iterations:\")), 1L)\n expect_equal(sum(params_in_file == sprintf(\"[num_iterations: %s]\", num_iterations)), 1L)\n expect_equal(sum(startsWith(params_in_file, \"[early_stopping_round:\")), 1L)\n expect_equal(sum(params_in_file == sprintf(\"[early_stopping_round: %s]\", early_stopping_round)), 1L)\n\n # none of the aliases shouold have been written to the model file\n expect_equal(sum(startsWith(params_in_file, \"[num_boost_round:\")), 0L)\n expect_equal(sum(startsWith(params_in_file, \"[n_iter:\")), 0L)\n expect_equal(sum(startsWith(params_in_file, \"[n_iter_no_change:\")), 0L)\n }\n\n})\n\ntest_that(\"params (including dataset params) should be stored in .rds file for Booster\", {\n data(agaricus.train, package = \"lightgbm\")\n dtrain <- lgb.Dataset(\n agaricus.train$data\n , label = agaricus.train$label\n , params = list(\n max_bin = 17L\n )\n )\n params <- list(\n objective = \"binary\"\n , max_depth = 4L\n , bagging_fraction = 0.8\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n bst <- Booster$new(\n params = params\n , train_set = dtrain\n )\n bst_file <- tempfile(fileext = \".rds\")\n saveRDS(bst, file = bst_file)\n\n bst_from_file <- readRDS(file = bst_file)\n expect_identical(\n bst_from_file$params\n , list(\n objective = \"binary\"\n , max_depth = 4L\n , bagging_fraction = 0.8\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n , max_bin = 17L\n )\n )\n})\n\ntest_that(\"Handle is automatically restored when calling predict\", {\n data(agaricus.train, package = \"lightgbm\")\n bst <- lightgbm(\n agaricus.train$data\n , agaricus.train$label\n , nrounds = 5L\n , obj = \"binary\"\n , params = list(\n verbose = .LGB_VERBOSITY\n )\n , num_threads = .LGB_MAX_THREADS\n )\n bst_file <- tempfile(fileext = \".rds\")\n saveRDS(bst, file = bst_file)\n\n bst_from_file <- readRDS(file = bst_file)\n\n pred_before <- predict(bst, agaricus.train$data)\n pred_after <- predict(bst_from_file, agaricus.train$data)\n expect_equal(pred_before, pred_after)\n})\n\ntest_that(\"boosters with linear models at leaves can be written to RDS and re-loaded successfully\", {\n X <- matrix(rnorm(100L), ncol = 1L)\n labels <- 2L * X + runif(nrow(X), 0L, 0.1)\n dtrain <- lgb.Dataset(\n data = X\n , label = labels\n )\n\n params <- list(\n objective = \"regression\"\n , verbose = .LGB_VERBOSITY\n , metric = \"mse\"\n , seed = 0L\n , num_leaves = 2L\n , num_threads = .LGB_MAX_THREADS\n )\n\n bst <- lgb.train(\n data = dtrain\n , nrounds = 10L\n , params = params\n )\n expect_true(.is_Booster(bst))\n\n # save predictions, then write the model to a file and destroy it in R\n preds <- predict(bst, X)\n model_file <- tempfile(fileext = \".rds\")\n saveRDS(bst, file = model_file)\n bst$.__enclos_env__$private$finalize()\n expect_null(bst$.__enclos_env__$private$handle)\n rm(bst)\n\n # load the booster and make predictions...should be the same\n bst2 <- readRDS(file = model_file)\n preds2 <- predict(bst2, X)\n expect_identical(preds, preds2)\n})\n\n.have_same_handle <- function(model, other_model) {\n expect_equal(\n model$.__enclos_env__$private$handle\n , other_model$.__enclos_env__$private$handle\n )\n}\n\n.has_expected_content_for_fitted_model <- function(printed_txt) {\n expect_true(any(startsWith(printed_txt, \"LightGBM Model\")))\n expect_true(any(startsWith(printed_txt, \"Fitted to dataset\")))\n}\n\n.has_expected_content_for_finalized_model <- function(printed_txt) {\n expect_true(any(printed_txt == \"LightGBM Model\"))\n expect_true(any(grepl(\"Booster handle is invalid\", printed_txt, fixed = TRUE)))\n}\n\n.check_methods_work <- function(model) {\n\n #--- should work for fitted models --- #\n\n # print()\n log_txt <- capture.output({\n ret <- print(model)\n })\n .have_same_handle(ret, model)\n .has_expected_content_for_fitted_model(log_txt)\n\n # show()\n log_txt <- capture.output({\n ret <- show(model)\n })\n expect_null(ret)\n .has_expected_content_for_fitted_model(log_txt)\n\n # summary()\n log_txt <- capture.output({\n ret <- summary(model)\n })\n .have_same_handle(ret, model)\n .has_expected_content_for_fitted_model(log_txt)\n\n #--- should not fail for finalized models ---#\n model$.__enclos_env__$private$finalize()\n\n # print()\n log_txt <- capture.output({\n ret <- print(model)\n })\n .has_expected_content_for_finalized_model(log_txt)\n\n # show()\n .have_same_handle(ret, model)\n log_txt <- capture.output({\n ret <- show(model)\n })\n expect_null(ret)\n .has_expected_content_for_finalized_model(log_txt)\n\n # summary()\n log_txt <- capture.output({\n ret <- summary(model)\n })\n .have_same_handle(ret, model)\n .has_expected_content_for_finalized_model(log_txt)\n}\n\ntest_that(\"Booster's print, show, and summary work correctly for built-in objectives\", {\n data(\"mtcars\")\n model <- lgb.train(\n params = list(\n objective = \"regression\"\n , min_data_in_leaf = 1L\n , num_threads = .LGB_MAX_THREADS\n )\n , data = lgb.Dataset(\n as.matrix(mtcars[, -1L])\n , label = mtcars$mpg\n , params = list(\n min_data_in_bin = 1L\n )\n )\n , verbose = .LGB_VERBOSITY\n , nrounds = 5L\n )\n .check_methods_work(model)\n\n data(\"iris\")\n model <- lgb.train(\n params = list(objective = \"multiclass\", num_class = 3L, num_threads = .LGB_MAX_THREADS)\n , data = lgb.Dataset(\n as.matrix(iris[, -5L])\n , label = as.numeric(factor(iris$Species)) - 1.0\n )\n , verbose = .LGB_VERBOSITY\n , nrounds = 5L\n )\n .check_methods_work(model)\n})\n\ntest_that(\"Booster's print, show, and summary work correctly for custom objective\", {\n .logregobj <- function(preds, dtrain) {\n labels <- get_field(dtrain, \"label\")\n preds <- 1.0 / (1.0 + exp(-preds))\n grad <- preds - labels\n hess <- preds * (1.0 - preds)\n return(list(grad = grad, hess = hess))\n }\n\n .evalerror <- function(preds, dtrain) {\n labels <- get_field(dtrain, \"label\")\n preds <- 1.0 / (1.0 + exp(-preds))\n err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels)\n return(list(\n name = \"error\"\n , value = err\n , higher_better = FALSE\n ))\n }\n\n data(\"iris\")\n model <- lgb.train(\n data = lgb.Dataset(\n as.matrix(iris[, -5L])\n , label = as.numeric(iris$Species == \"virginica\")\n )\n , obj = .logregobj\n , eval = .evalerror\n , verbose = .LGB_VERBOSITY\n , nrounds = 5L\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n\n .check_methods_work(model)\n})\n\ntest_that(\"Booster's print, show, and summary work correctly when objective is not provided\", {\n data(\"iris\")\n model <- lgb.train(\n data = lgb.Dataset(\n as.matrix(iris[, seq_len(3L)])\n , label = iris[, 4L]\n )\n , verbose = .LGB_VERBOSITY\n , nrounds = 5L\n , params = list(num_threads = .LGB_MAX_THREADS)\n )\n\n log_txt <- capture.output(print(model))\n expect_true(any(log_txt == \"Objective: (default)\"))\n\n .check_methods_work(model)\n})\n\ntest_that(\"LGBM_BoosterGetNumFeature_R returns correct outputs\", {\n data(\"mtcars\")\n model <- lgb.train(\n params = list(\n objective = \"regression\"\n , min_data_in_leaf = 1L\n , num_threads = .LGB_MAX_THREADS\n )\n , data = lgb.Dataset(\n as.matrix(mtcars[, -1L])\n , label = mtcars$mpg\n , params = list(\n min_data_in_bin = 1L\n )\n )\n , verbose = .LGB_VERBOSITY\n , nrounds = 5L\n )\n ncols <- .Call(LGBM_BoosterGetNumFeature_R, model$.__enclos_env__$private$handle)\n expect_equal(ncols, ncol(mtcars) - 1L)\n\n data(\"iris\")\n model <- lgb.train(\n params = list(objective = \"multiclass\", num_class = 3L)\n , data = lgb.Dataset(\n as.matrix(iris[, -5L])\n , label = as.numeric(factor(iris$Species)) - 1.0\n )\n , verbose = .LGB_VERBOSITY\n , nrounds = 5L\n )\n ncols <- .Call(LGBM_BoosterGetNumFeature_R, model$.__enclos_env__$private$handle)\n expect_equal(ncols, ncol(iris) - 1L)\n})\n\n# Helper function that creates a fitted model with nrounds boosting rounds\n.get_test_model <- function(nrounds) {\n set.seed(1L)\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n bst <- lightgbm(\n data = as.matrix(train$data)\n , label = train$label\n , params = list(objective = \"binary\", num_threads = .LGB_MAX_THREADS)\n , nrounds = nrounds\n , verbose = .LGB_VERBOSITY\n )\n return(bst)\n}\n\n# Simplified version of lgb.model.dt.tree()\n.get_trees_from_dump <- function(x) {\n parsed <- jsonlite::fromJSON(\n txt = x\n , simplifyVector = TRUE\n , simplifyDataFrame = FALSE\n , simplifyMatrix = FALSE\n , flatten = FALSE\n )\n return(lapply(parsed$tree_info, FUN = .single_tree_parse))\n}\n\ntest_that(\"num_iteration and start_iteration work for lgb.dump()\", {\n bst <- .get_test_model(5L)\n\n first2 <- .get_trees_from_dump(lgb.dump(bst, num_iteration = 2L))\n last3 <- .get_trees_from_dump(\n lgb.dump(bst, num_iteration = 3L, start_iteration = 3L)\n )\n all5 <- .get_trees_from_dump(lgb.dump(bst))\n too_many <- .get_trees_from_dump(lgb.dump(bst, num_iteration = 10L))\n\n expect_equal(\n data.table::rbindlist(c(first2, last3)), data.table::rbindlist(all5)\n )\n expect_equal(too_many, all5)\n})\n\ntest_that(\"num_iteration and start_iteration work for lgb.save()\", {\n .get_n_trees <- function(x) {\n return(length(.get_trees_from_dump(lgb.dump(x))))\n }\n\n .save_and_load <- function(bst, ...) {\n model_file <- tempfile(fileext = \".model\")\n lgb.save(bst, model_file, ...)\n return(lgb.load(model_file))\n }\n\n bst <- .get_test_model(5L)\n n_first2 <- .get_n_trees(.save_and_load(bst, num_iteration = 2L))\n n_last3 <- .get_n_trees(\n .save_and_load(bst, num_iteration = 3L, start_iteration = 3L)\n )\n n_all5 <- .get_n_trees(.save_and_load(bst))\n n_too_many <- .get_n_trees(.save_and_load(bst, num_iteration = 10L))\n\n expect_equal(n_first2, 2L)\n expect_equal(n_last3, 3L)\n expect_equal(n_all5, 5L)\n expect_equal(n_too_many, 5L)\n})\n\ntest_that(\"num_iteration and start_iteration work for save_model_to_string()\", {\n .get_n_trees_from_string <- function(x) {\n return(sum(gregexpr(\"Tree=\", x, fixed = TRUE)[[1L]] > 0L))\n }\n\n bst <- .get_test_model(5L)\n\n n_first2 <- .get_n_trees_from_string(\n bst$save_model_to_string(num_iteration = 2L)\n )\n n_last3 <- .get_n_trees_from_string(\n bst$save_model_to_string(num_iteration = 3L, start_iteration = 3L)\n )\n n_all5 <- .get_n_trees_from_string(bst$save_model_to_string())\n n_too_many <- .get_n_trees_from_string(\n bst$save_model_to_string(num_iteration = 10L)\n )\n\n expect_equal(n_first2, 2L)\n expect_equal(n_last3, 3L)\n expect_equal(n_all5, 5L)\n expect_equal(n_too_many, 5L)\n})\n" }, { "path": "R-package/tests/testthat/test_lgb.convert_with_rules.R", "content": "test_that(\"lgb.convert_with_rules() rejects inputs that are not a data.table or data.frame\", {\n bad_inputs <- list(\n matrix(1.0:10.0, 2L, 5L)\n , TRUE\n , c(\"a\", \"b\")\n , NA\n , 10L\n , lgb.Dataset(\n data = matrix(1.0:10.0, 2L, 5L)\n , params = list()\n )\n )\n for (bad_input in bad_inputs) {\n expect_error({\n conversion_result <- lgb.convert_with_rules(bad_input)\n }, regexp = \"lgb.convert_with_rules: you provided\", fixed = TRUE)\n }\n})\n\ntest_that(\"lgb.convert_with_rules() should work correctly for a dataset with only character columns\", {\n testDF <- data.frame(\n col1 = c(\"a\", \"b\", \"c\")\n , col2 = c(\"green\", \"green\", \"red\")\n , stringsAsFactors = FALSE\n )\n testDT <- data.table::as.data.table(testDF)\n for (input_data in list(testDF, testDT)) {\n conversion_result <- lgb.convert_with_rules(input_data)\n # dataset should have been converted to integer\n converted_dataset <- conversion_result[[\"data\"]]\n expect_identical(class(input_data), class(converted_dataset))\n expect_identical(class(converted_dataset[[\"col1\"]]), \"integer\")\n expect_identical(class(converted_dataset[[\"col2\"]]), \"integer\")\n expect_identical(converted_dataset[[\"col1\"]], c(1L, 2L, 3L))\n expect_identical(converted_dataset[[\"col2\"]], c(1L, 1L, 2L))\n # rules should be returned and correct\n rules <- conversion_result$rules\n expect_true(methods::is(rules, \"list\"))\n expect_length(rules, ncol(input_data))\n expect_identical(rules[[\"col1\"]], c(\"a\" = 1L, \"b\" = 2L, \"c\" = 3L))\n expect_identical(rules[[\"col2\"]], c(\"green\" = 1L, \"red\" = 2L))\n }\n})\n\ntest_that(\"lgb.convert_with_rules() should work correctly for a dataset with only factor columns\", {\n testDF <- data.frame(\n col1 = as.factor(c(\"a\", \"b\", \"c\"))\n , col2 = as.factor(c(\"green\", \"green\", \"red\"))\n , stringsAsFactors = FALSE\n )\n testDT <- data.table::as.data.table(testDF)\n for (input_data in list(testDF, testDT)) {\n conversion_result <- lgb.convert_with_rules(input_data)\n # dataset should have been converted to integer\n converted_dataset <- conversion_result[[\"data\"]]\n expect_identical(class(input_data), class(converted_dataset))\n expect_identical(class(converted_dataset[[\"col1\"]]), \"integer\")\n expect_identical(class(converted_dataset[[\"col2\"]]), \"integer\")\n expect_identical(converted_dataset[[\"col1\"]], c(1L, 2L, 3L))\n expect_identical(converted_dataset[[\"col2\"]], c(1L, 1L, 2L))\n # rules should be returned and correct\n rules <- conversion_result$rules\n expect_true(methods::is(rules, \"list\"))\n expect_length(rules, ncol(input_data))\n expect_identical(rules[[\"col1\"]], c(\"a\" = 1L, \"b\" = 2L, \"c\" = 3L))\n expect_identical(rules[[\"col2\"]], c(\"green\" = 1L, \"red\" = 2L))\n }\n})\n\ntest_that(\"lgb.convert_with_rules() should not change a dataset with only integer columns\", {\n testDF <- data.frame(\n col1 = 11L:15L\n , col2 = 16L:20L\n , stringsAsFactors = FALSE\n )\n testDT <- data.table::as.data.table(testDF)\n for (input_data in list(testDF, testDT)) {\n conversion_result <- lgb.convert_with_rules(input_data)\n # dataset should have been converted to integer\n converted_dataset <- conversion_result[[\"data\"]]\n expect_identical(converted_dataset, input_data)\n # rules should be returned and correct\n rules <- conversion_result$rules\n expect_identical(rules, list())\n }\n})\n\ntest_that(\"lgb.convert_with_rules() should work correctly for a dataset with numeric, factor, and character columns\", {\n testDF <- data.frame(\n character_col = c(\"a\", \"b\", \"c\")\n , numeric_col = c(1.0, 9.0, 10.0)\n , factor_col = as.factor(c(\"n\", \"n\", \"y\"))\n , stringsAsFactors = FALSE\n )\n testDT <- data.table::as.data.table(testDF)\n for (input_data in list(testDF, testDT)) {\n conversion_result <- lgb.convert_with_rules(input_data)\n # dataset should have been converted to numeric\n converted_dataset <- conversion_result[[\"data\"]]\n expect_identical(class(input_data), class(converted_dataset))\n expect_identical(class(converted_dataset[[\"character_col\"]]), \"integer\")\n expect_identical(class(converted_dataset[[\"factor_col\"]]), \"integer\")\n expect_identical(converted_dataset[[\"character_col\"]], c(1L, 2L, 3L))\n expect_identical(converted_dataset[[\"factor_col\"]], c(1L, 1L, 2L))\n # rules should be returned and correct\n rules <- conversion_result$rules\n expect_true(methods::is(rules, \"list\"))\n expect_length(rules, 2L)\n expect_identical(rules[[\"character_col\"]], c(\"a\" = 1L, \"b\" = 2L, \"c\" = 3L))\n expect_identical(rules[[\"factor_col\"]], c(\"n\" = 1L, \"y\" = 2L))\n\n # today, lgb.convert_with_rules() does not convert numeric columns\n expect_identical(class(converted_dataset[[\"numeric_col\"]]), \"numeric\")\n expect_identical(converted_dataset[[\"numeric_col\"]], c(1.0, 9.0, 10.0))\n }\n})\n\ntest_that(\"lgb.convert_with_rules() should convert missing values to the expected value\", {\n testDF <- data.frame(\n character_col = c(\"a\", NA_character_, \"c\")\n , na_col = rep(NA, 3L)\n , na_real_col = rep(NA_real_, 3L)\n , na_int_col = rep(NA_integer_, 3L)\n , na_character_col = rep(NA_character_, 3L)\n , numeric_col = c(1.0, 9.0, NA_real_)\n , factor_col = as.factor(c(\"n\", \"n\", \"y\"))\n , integer_col = c(1L, 9L, NA_integer_)\n , stringsAsFactors = FALSE\n )\n testDT <- data.table::as.data.table(testDF)\n for (input_data in list(testDF, testDT)) {\n conversion_result <- lgb.convert_with_rules(input_data)\n # dataset should have been converted to integer\n converted_dataset <- conversion_result[[\"data\"]]\n expect_identical(class(input_data), class(converted_dataset))\n\n expect_identical(class(converted_dataset[[\"character_col\"]]), \"integer\")\n expect_identical(converted_dataset[[\"character_col\"]], c(1L, 0L, 2L))\n\n # does not try to fill 0s in for already-integer columns\n expect_identical(class(converted_dataset[[\"integer_col\"]]), \"integer\")\n expect_identical(converted_dataset[[\"integer_col\"]], c(1L, 9L, NA_integer_))\n expect_identical(class(converted_dataset[[\"na_int_col\"]]), \"integer\")\n expect_identical(converted_dataset[[\"na_int_col\"]], rep(NA_integer_, nrow(converted_dataset)))\n\n expect_identical(class(converted_dataset[[\"factor_col\"]]), \"integer\")\n expect_identical(converted_dataset[[\"factor_col\"]], c(1L, 1L, 2L))\n\n # NAs in character columns should be converted to 0\n expect_identical(class(converted_dataset[[\"na_character_col\"]]), \"integer\")\n expect_identical(converted_dataset[[\"na_character_col\"]], rep(0L, nrow(converted_dataset)))\n\n # logical should be converted to integer\n expect_identical(class(converted_dataset[[\"na_col\"]]), \"integer\")\n expect_identical(converted_dataset[[\"na_col\"]], rep(-1L, 3L))\n\n # lgb.convert_with_rules() should not convert numeric columns to integer\n expect_identical(class(converted_dataset[[\"na_real_col\"]]), \"numeric\")\n expect_identical(converted_dataset[[\"na_real_col\"]], rep(NA_real_, nrow(converted_dataset)))\n expect_identical(class(converted_dataset[[\"numeric_col\"]]), \"numeric\")\n expect_identical(converted_dataset[[\"numeric_col\"]], c(1.0, 9.0, NA_real_))\n\n # rules should be returned and correct\n rules <- conversion_result$rules\n expect_true(methods::is(rules, \"list\"))\n expect_length(rules, 3L)\n expect_identical(rules[[\"character_col\"]], c(\"a\" = 1L, \"c\" = 2L))\n expect_identical(rules[[\"factor_col\"]], c(\"n\" = 1L, \"y\" = 2L))\n expect_identical(rules[[\"na_col\"]], stats::setNames(c(0L, 1L), c(FALSE, TRUE)))\n }\n})\n\ntest_that(\"lgb.convert_with_rules() should work correctly if you provide your own well-formed rules\", {\n testDF <- data.frame(\n character_col = c(\"a\", NA_character_, \"c\", \"a\", \"a\", \"c\")\n , na_col = rep(NA, 6L)\n , na_real_col = rep(NA_real_, 6L)\n , na_int_col = rep(NA_integer_, 6L)\n , na_character_col = rep(NA_character_, 6L)\n , numeric_col = c(1.0, 9.0, NA_real_, 10.0, 11.0, 12.0)\n , factor_col = as.factor(c(\"n\", \"n\", \"y\", \"y\", \"n\", \"n\"))\n , integer_col = c(1L, 9L, NA_integer_, 1L, 1L, 1L)\n , stringsAsFactors = FALSE\n )\n testDT <- data.table::as.data.table(testDF)\n\n # value used by lgb.convert_with_rules() when it encounters a categorical value that\n # is not in the provided rules\n UNKNOWN_FACTOR_VALUE <- 0L\n UNKNOWN_LOGICAL_VALUE <- -1L\n for (input_data in list(testDF, testDT)) {\n custom_rules <- list(\n \"character_col\" = c(\n \"a\" = 5L\n , \"c\" = -10L\n )\n , \"factor_col\" = c(\n \"n\" = 65L\n , \"y\" = 66L\n )\n )\n conversion_result <- lgb.convert_with_rules(\n data = input_data\n , rules = custom_rules\n )\n\n # dataset should have been converted to integer\n converted_dataset <- conversion_result[[\"data\"]]\n expect_identical(class(input_data), class(converted_dataset))\n\n expect_identical(class(converted_dataset[[\"character_col\"]]), \"integer\")\n expect_identical(converted_dataset[[\"character_col\"]], c(5L, UNKNOWN_FACTOR_VALUE, -10L, 5L, 5L, -10L))\n\n expect_identical(class(converted_dataset[[\"factor_col\"]]), \"integer\")\n expect_identical(converted_dataset[[\"factor_col\"]], c(65L, 65L, 66L, 66L, 65L, 65L))\n\n # columns not specified in rules are not going to be converted, unless they are all NA\n for (col in c(\"na_real_col\", \"na_int_col\", \"numeric_col\", \"integer_col\")) {\n expect_identical(converted_dataset[[col]], input_data[[col]])\n }\n\n # non-numeric/integer columns that are all NA should have been filled in\n expect_identical(converted_dataset[[\"na_col\"]], rep(UNKNOWN_LOGICAL_VALUE, 6L))\n expect_identical(converted_dataset[[\"na_character_col\"]], rep(UNKNOWN_FACTOR_VALUE, 6L))\n\n # the rules you passed in should be returned unchanged\n rules <- conversion_result$rules\n expect_identical(rules, custom_rules)\n }\n})\n\ntest_that(\"lgb.convert_with_rules() should modify data.tables in-place\", {\n testDT <- data.table::data.table(\n character_col = c(\"a\", NA_character_, \"c\")\n , na_col = rep(NA, 3L)\n , na_real_col = rep(NA_real_, 3L)\n , na_int_col = rep(NA_integer_, 3L)\n , na_character_col = rep(NA_character_, 3L)\n , numeric_col = c(1.0, 9.0, NA_real_)\n , factor_col = as.factor(c(\"n\", \"n\", \"y\"))\n , integer_col = c(1L, 9L, NA_integer_)\n )\n conversion_result <- lgb.convert_with_rules(testDT)\n resultDT <- conversion_result[[\"data\"]]\n expect_identical(resultDT, testDT)\n})\n" }, { "path": "R-package/tests/testthat/test_lgb.importance.R", "content": "test_that(\"lgb.importance() should reject bad inputs\", {\n bad_inputs <- list(\n .Machine$integer.max\n , Inf\n , -Inf\n , NA\n , NA_real_\n , -10L:10L\n , list(c(\"a\", \"b\", \"c\"))\n , data.frame(\n x = rnorm(20L)\n , y = sample(\n x = c(1L, 2L)\n , size = 20L\n , replace = TRUE\n )\n )\n , data.table::data.table(\n x = rnorm(20L)\n , y = sample(\n x = c(1L, 2L)\n , size = 20L\n , replace = TRUE\n )\n )\n , lgb.Dataset(\n data = matrix(rnorm(100L), ncol = 2L)\n , label = matrix(sample(c(0L, 1L), 50L, replace = TRUE))\n )\n , \"lightgbm.model\"\n )\n for (input in bad_inputs) {\n expect_error({\n lgb.importance(input)\n }, regexp = \"'model' has to be an object of class lgb\\\\.Booster\")\n }\n})\n" }, { "path": "R-package/tests/testthat/test_lgb.interprete.R", "content": ".sigmoid <- function(x) {\n 1.0 / (1.0 + exp(-x))\n}\n.logit <- function(x) {\n log(x / (1.0 - x))\n}\n\ntest_that(\"lgb.interprete works as expected for binary classification\", {\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n dtrain <- lgb.Dataset(train$data, label = train$label)\n set_field(\n dataset = dtrain\n , field_name = \"init_score\"\n , data = rep(\n .logit(mean(train$label))\n , length(train$label)\n )\n )\n data(agaricus.test, package = \"lightgbm\")\n test <- agaricus.test\n params <- list(\n objective = \"binary\"\n , learning_rate = 0.01\n , num_leaves = 63L\n , max_depth = -1L\n , min_data_in_leaf = 1L\n , min_sum_hessian_in_leaf = 1.0\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n model <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 3L\n )\n num_trees <- 5L\n tree_interpretation <- lgb.interprete(\n model = model\n , data = test$data\n , idxset = seq_len(num_trees)\n )\n expect_identical(class(tree_interpretation), \"list\")\n expect_true(length(tree_interpretation) == num_trees)\n expect_null(names(tree_interpretation))\n expect_true(all(\n sapply(\n X = tree_interpretation\n , FUN = function(treeDT) {\n checks <- c(\n data.table::is.data.table(treeDT)\n , identical(names(treeDT), c(\"Feature\", \"Contribution\"))\n , is.character(treeDT[, Feature])\n , is.numeric(treeDT[, Contribution])\n )\n return(all(checks))\n }\n )\n ))\n})\n\ntest_that(\"lgb.intereprete works as expected for multiclass classification\", {\n data(iris)\n\n # We must convert factors to numeric\n # They must be starting from number 0 to use multiclass\n # For instance: 0, 1, 2, 3, 4, 5...\n iris$Species <- as.numeric(as.factor(iris$Species)) - 1L\n\n # Create imbalanced training data (20, 30, 40 examples for classes 0, 1, 2)\n train <- as.matrix(iris[c(1L:20L, 51L:80L, 101L:140L), ])\n # The 10 last samples of each class are for validation\n test <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ])\n dtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L])\n dtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L])\n params <- list(\n objective = \"multiclass\"\n , metric = \"multi_logloss\"\n , num_class = 3L\n , learning_rate = 0.00001\n , min_data = 1L\n , verbose = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n model <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 3L\n )\n num_trees <- 5L\n tree_interpretation <- lgb.interprete(\n model = model\n , data = test[, 1L:4L]\n , idxset = seq_len(num_trees)\n )\n expect_identical(class(tree_interpretation), \"list\")\n expect_true(length(tree_interpretation) == num_trees)\n expect_null(names(tree_interpretation))\n expect_true(all(\n sapply(\n X = tree_interpretation\n , FUN = function(treeDT) {\n checks <- c(\n data.table::is.data.table(treeDT)\n , identical(names(treeDT), c(\"Feature\", \"Class 0\", \"Class 1\", \"Class 2\"))\n , is.character(treeDT[, Feature])\n , is.numeric(treeDT[, `Class 0`])\n , is.numeric(treeDT[, `Class 1`])\n , is.numeric(treeDT[, `Class 2`])\n )\n return(all(checks))\n }\n )\n ))\n})\n" }, { "path": "R-package/tests/testthat/test_lgb.model.dt.tree.R", "content": "NROUNDS <- 10L\nMAX_DEPTH <- 3L\nN <- nrow(iris)\nX <- data.matrix(iris[2L:4L])\nFEAT <- colnames(X)\nNCLASS <- nlevels(iris[, 5L])\n\nmodel_reg <- lgb.train(\n params = list(\n objective = \"regression\"\n , num_threads = .LGB_MAX_THREADS\n , max.depth = MAX_DEPTH\n )\n , data = lgb.Dataset(X, label = iris[, 1L])\n , verbose = .LGB_VERBOSITY\n , nrounds = NROUNDS\n)\n\nmodel_binary <- lgb.train(\n params = list(\n objective = \"binary\"\n , num_threads = .LGB_MAX_THREADS\n , max.depth = MAX_DEPTH\n )\n , data = lgb.Dataset(X, label = iris[, 5L] == \"setosa\")\n , verbose = .LGB_VERBOSITY\n , nrounds = NROUNDS\n)\n\nmodel_multiclass <- lgb.train(\n params = list(\n objective = \"multiclass\"\n , num_threads = .LGB_MAX_THREADS\n , max.depth = MAX_DEPTH\n , num_classes = NCLASS\n )\n , data = lgb.Dataset(X, label = as.integer(iris[, 5L]) - 1L)\n , verbose = .LGB_VERBOSITY\n , nrounds = NROUNDS\n)\n\nmodel_rank <- lgb.train(\n params = list(\n objective = \"lambdarank\"\n , num_threads = .LGB_MAX_THREADS\n , max.depth = MAX_DEPTH\n , lambdarank_truncation_level = 3L\n )\n , data = lgb.Dataset(\n X\n , label = as.integer(iris[, 1L] > 5.8)\n , group = rep(10L, times = 15L)\n )\n , verbose = .LGB_VERBOSITY\n , nrounds = NROUNDS\n)\n\nmodels <- list(\n reg = model_reg\n , bin = model_binary\n , multi = model_multiclass\n , rank = model_rank\n)\n\nfor (model_name in names(models)) {\n model <- models[[model_name]]\n expected_n_trees <- NROUNDS\n if (model_name == \"multi\") {\n expected_n_trees <- NROUNDS * NCLASS\n }\n df <- as.data.frame(lgb.model.dt.tree(model))\n df_list <- split(df, f = df$tree_index, drop = TRUE)\n\n df_leaf <- df[!is.na(df$leaf_index), ]\n df_internal <- df[is.na(df$leaf_index), ]\n\n test_that(\"lgb.model.dt.tree() returns the right number of trees\", {\n expect_equal(length(unique(df$tree_index)), expected_n_trees)\n })\n\n test_that(\"num_iteration can return less trees\", {\n expect_equal(\n length(unique(lgb.model.dt.tree(model, num_iteration = 2L)$tree_index))\n , 2L * (if (model_name == \"multi\") NCLASS else 1L)\n )\n })\n\n test_that(\"Tree index from lgb.model.dt.tree() is in 0:(NROUNS-1)\", {\n expect_equal(unique(df$tree_index), (0L:(expected_n_trees - 1L)))\n })\n\n test_that(\"Depth calculated from lgb.model.dt.tree() respects max.depth\", {\n expect_true(max(df$depth) <= MAX_DEPTH)\n })\n\n test_that(\"Each tree from lgb.model.dt.tree() has single root node\", {\n expect_equal(\n unname(sapply(df_list, function(df) sum(df$depth == 0L)))\n , rep(1L, expected_n_trees)\n )\n })\n\n test_that(\"Each tree from lgb.model.dt.tree() has two depth 1 nodes\", {\n expect_equal(\n unname(sapply(df_list, function(df) sum(df$depth == 1L)))\n , rep(2L, expected_n_trees)\n )\n })\n\n test_that(\"leaves from lgb.model.dt.tree() do not have split info\", {\n internal_node_cols <- c(\n \"split_index\"\n , \"split_feature\"\n , \"split_gain\"\n , \"threshold\"\n , \"decision_type\"\n , \"default_left\"\n , \"internal_value\"\n , \"internal_count\"\n )\n expect_true(all(is.na(df_leaf[internal_node_cols])))\n })\n\n test_that(\"leaves from lgb.model.dt.tree() have valid leaf info\", {\n expect_true(all(df_leaf$leaf_index %in% 0L:(2.0^MAX_DEPTH - 1.0)))\n expect_true(all(is.finite(df_leaf$leaf_value)))\n expect_true(all(df_leaf$leaf_count > 0L & df_leaf$leaf_count <= N))\n })\n\n test_that(\"non-leaves from lgb.model.dt.tree() do not have leaf info\", {\n leaf_node_cols <- c(\n \"leaf_index\", \"leaf_parent\", \"leaf_value\", \"leaf_count\"\n )\n expect_true(all(is.na(df_internal[leaf_node_cols])))\n })\n\n test_that(\"non-leaves from lgb.model.dt.tree() have valid split info\", {\n expect_true(\n all(\n sapply(\n split(df_internal, df_internal$tree_index),\n function(x) all(x$split_index %in% 0L:(nrow(x) - 1L))\n )\n )\n )\n\n expect_true(all(df_internal$split_feature %in% FEAT))\n\n num_cols <- c(\"split_gain\", \"threshold\", \"internal_value\")\n expect_true(all(is.finite(unlist(df_internal[, num_cols]))))\n\n # range of decision type?\n expect_true(all(df_internal$default_left %in% c(TRUE, FALSE)))\n\n counts <- df_internal$internal_count\n expect_true(all(counts > 1L & counts <= N))\n })\n}\n\ntest_that(\"num_iteration and start_iteration work as expected\", {\n set.seed(1L)\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n bst <- lightgbm(\n data = as.matrix(train$data)\n , label = train$label\n , params = list(objective = \"binary\", num_threads = .LGB_MAX_THREADS)\n , nrounds = 5L\n , verbose = .LGB_VERBOSITY\n )\n\n first2 <- lgb.model.dt.tree(bst, num_iteration = 2L)\n last3 <- lgb.model.dt.tree(bst, num_iteration = 3L, start_iteration = 3L)\n all5 <- lgb.model.dt.tree(bst)\n too_many <- lgb.model.dt.tree(bst, num_iteration = 10L)\n\n expect_equal(data.table::rbindlist(list(first2, last3)), all5)\n expect_equal(too_many, all5)\n\n # Check tree indices\n expect_equal(unique(first2[[\"tree_index\"]]), 0L:1L)\n expect_equal(unique(last3[[\"tree_index\"]]), 2L:4L)\n expect_equal(unique(all5[[\"tree_index\"]]), 0L:4L)\n})\n" }, { "path": "R-package/tests/testthat/test_lgb.plot.importance.R", "content": "test_that(\"lgb.plot.importance() should run without error for well-formed inputs\", {\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n dtrain <- lgb.Dataset(train$data, label = train$label)\n params <- list(\n objective = \"binary\"\n , learning_rate = 0.01\n , num_leaves = 63L\n , max_depth = -1L\n , min_data_in_leaf = 1L\n , min_sum_hessian_in_leaf = 1.0\n , verbosity = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n model <- lgb.train(params, dtrain, 3L)\n tree_imp <- lgb.importance(model, percentage = TRUE)\n\n # Check that there are no plots present before plotting\n expect_null(dev.list())\n\n args_no_cex <- list(\n \"tree_imp\" = tree_imp\n , top_n = 10L\n , measure = \"Gain\"\n )\n args_cex <- args_no_cex\n args_cex[[\"cex\"]] <- 0.75\n\n for (arg_list in list(args_no_cex, args_cex)) {\n\n resDT <- do.call(\n what = lgb.plot.importance\n , args = arg_list\n )\n\n # Check that lgb.plot.importance() returns the data.table of the plotted data\n expect_true(data.table::is.data.table(resDT))\n expect_named(resDT, c(\"Feature\", \"Gain\", \"Cover\", \"Frequency\"))\n\n # Check that a plot was produced\n expect_false(is.null(dev.list()))\n\n # remove all plots\n dev.off()\n expect_null(dev.list())\n }\n})\n" }, { "path": "R-package/tests/testthat/test_lgb.plot.interpretation.R", "content": ".sigmoid <- function(x) {\n 1.0 / (1.0 + exp(-x))\n}\n.logit <- function(x) {\n log(x / (1.0 - x))\n}\n\ntest_that(\"lgb.plot.interpretation works as expected for binary classification\", {\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n dtrain <- lgb.Dataset(train$data, label = train$label)\n set_field(\n dataset = dtrain\n , field_name = \"init_score\"\n , data = rep(\n .logit(mean(train$label))\n , length(train$label)\n )\n )\n data(agaricus.test, package = \"lightgbm\")\n test <- agaricus.test\n params <- list(\n objective = \"binary\"\n , learning_rate = 0.01\n , num_leaves = 63L\n , max_depth = -1L\n , min_data_in_leaf = 1L\n , min_sum_hessian_in_leaf = 1.0\n , verbosity = .LGB_VERBOSITY\n , num_threads = .LGB_MAX_THREADS\n )\n model <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 3L\n )\n num_trees <- 5L\n tree_interpretation <- lgb.interprete(\n model = model\n , data = test$data\n , idxset = seq_len(num_trees)\n )\n expect_true({\n lgb.plot.interpretation(\n tree_interpretation_dt = tree_interpretation[[1L]]\n , top_n = 5L\n )\n TRUE\n })\n\n # should also work when you explicitly pass cex\n plot_res <- lgb.plot.interpretation(\n tree_interpretation_dt = tree_interpretation[[1L]]\n , top_n = 5L\n , cex = 0.95\n )\n expect_null(plot_res)\n})\n\ntest_that(\"lgb.plot.interpretation works as expected for multiclass classification\", {\n data(iris)\n\n # We must convert factors to numeric\n # They must be starting from number 0 to use multiclass\n # For instance: 0, 1, 2, 3, 4, 5...\n iris$Species <- as.numeric(as.factor(iris$Species)) - 1L\n\n # Create imbalanced training data (20, 30, 40 examples for classes 0, 1, 2)\n train <- as.matrix(iris[c(1L:20L, 51L:80L, 101L:140L), ])\n # The 10 last samples of each class are for validation\n test <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ])\n dtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L])\n dtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L])\n params <- list(\n objective = \"multiclass\"\n , metric = \"multi_logloss\"\n , num_class = 3L\n , learning_rate = 0.00001\n , min_data = 1L\n , num_threads = .LGB_MAX_THREADS\n )\n model <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 3L\n , verbose = .LGB_VERBOSITY\n )\n num_trees <- 5L\n tree_interpretation <- lgb.interprete(\n model = model\n , data = test[, 1L:4L]\n , idxset = seq_len(num_trees)\n )\n plot_res <- lgb.plot.interpretation(\n tree_interpretation_dt = tree_interpretation[[1L]]\n , top_n = 5L\n )\n expect_null(plot_res)\n})\n" }, { "path": "R-package/tests/testthat/test_metrics.R", "content": "test_that(\".METRICS_HIGHER_BETTER() should be well formed\", {\n metrics <- .METRICS_HIGHER_BETTER()\n metric_names <- names(.METRICS_HIGHER_BETTER())\n # should be a logical vector\n expect_true(is.logical(metrics))\n # no metrics should be repeated\n expect_true(length(unique(metric_names)) == length(metrics))\n # should not be any NAs\n expect_false(anyNA(metrics))\n})\n" }, { "path": "R-package/tests/testthat/test_multithreading.R", "content": "test_that(\"getLGBMthreads() and setLGBMthreads() work as expected\", {\n # works with integer input\n ret <- setLGBMthreads(2L)\n expect_null(ret)\n expect_equal(getLGBMthreads(), 2L)\n\n # works with float input\n ret <- setLGBMthreads(1.0)\n expect_null(ret)\n expect_equal(getLGBMthreads(), 1L)\n\n # setting to any negative number sets max threads to -1\n ret <- setLGBMthreads(-312L)\n expect_null(ret)\n expect_equal(getLGBMthreads(), -1L)\n})\n" }, { "path": "R-package/tests/testthat/test_parameters.R", "content": "data(agaricus.train, package = \"lightgbm\")\ndata(agaricus.test, package = \"lightgbm\")\ntrain <- agaricus.train\ntest <- agaricus.test\n\ntest_that(\"Feature penalties work properly\", {\n # Fit a series of models with varying penalty on most important variable\n var_name <- \"odor=none\"\n var_index <- which(train$data@Dimnames[[2L]] == var_name)\n\n bst <- lapply(seq(1.0, 0.0, by = -0.1), function(x) {\n feature_penalties <- rep(1.0, ncol(train$data))\n feature_penalties[var_index] <- x\n lightgbm(\n data = train$data\n , label = train$label\n , params = list(\n num_leaves = 5L\n , learning_rate = 0.05\n , objective = \"binary\"\n , feature_penalty = paste(feature_penalties, collapse = \",\")\n , metric = \"binary_error\"\n , num_threads = .LGB_MAX_THREADS\n )\n , nrounds = 5L\n , verbose = -1L\n )\n })\n\n var_gain <- lapply(bst, function(x) lgb.importance(x)[Feature == var_name, Gain])\n var_cover <- lapply(bst, function(x) lgb.importance(x)[Feature == var_name, Cover])\n var_freq <- lapply(bst, function(x) lgb.importance(x)[Feature == var_name, Frequency])\n\n # Ensure that feature gain, cover, and frequency decreases with stronger penalties\n expect_true(all(diff(unlist(var_gain)) <= 0.0))\n expect_true(all(diff(unlist(var_cover)) <= 0.0))\n expect_true(all(diff(unlist(var_freq)) <= 0.0))\n\n expect_lt(min(diff(unlist(var_gain))), 0.0)\n expect_lt(min(diff(unlist(var_cover))), 0.0)\n expect_lt(min(diff(unlist(var_freq))), 0.0)\n\n # Ensure that feature is not used when feature_penalty = 0\n expect_length(var_gain[[length(var_gain)]], 0L)\n})\n\ntest_that(\".PARAMETER_ALIASES() returns a named list of character vectors, where names are unique\", {\n param_aliases <- .PARAMETER_ALIASES()\n expect_identical(class(param_aliases), \"list\")\n expect_true(length(param_aliases) > 100L)\n expect_true(is.character(names(param_aliases)))\n expect_true(is.character(param_aliases[[\"boosting\"]]))\n expect_true(is.character(param_aliases[[\"early_stopping_round\"]]))\n expect_true(is.character(param_aliases[[\"num_iterations\"]]))\n expect_true(is.character(param_aliases[[\"pre_partition\"]]))\n expect_true(length(names(param_aliases)) == length(param_aliases))\n expect_true(all(sapply(param_aliases, is.character)))\n expect_true(length(unique(names(param_aliases))) == length(param_aliases))\n expect_equal(sort(param_aliases[[\"task\"]]), c(\"task\", \"task_type\"))\n expect_equal(param_aliases[[\"bagging_fraction\"]], c(\"bagging_fraction\", \"bagging\", \"sub_row\", \"subsample\"))\n})\n\ntest_that(\".PARAMETER_ALIASES() uses the internal session cache\", {\n\n cache_key <- \"PARAMETER_ALIASES\"\n\n # clear cache, so this test isn't reliant on the order unit tests are run in\n if (exists(cache_key, where = .lgb_session_cache_env)) {\n rm(list = cache_key, envir = .lgb_session_cache_env)\n }\n expect_false(exists(cache_key, where = .lgb_session_cache_env))\n\n # check that result looks correct for at least one parameter\n iter_aliases <- .PARAMETER_ALIASES()[[\"num_iterations\"]]\n expect_true(is.character(iter_aliases))\n expect_true(all(c(\"num_round\", \"nrounds\") %in% iter_aliases))\n\n # patch the cache to check that .PARAMETER_ALIASES() checks it\n assign(\n x = cache_key\n , value = list(num_iterations = c(\"test\", \"other_test\"))\n , envir = .lgb_session_cache_env\n )\n iter_aliases <- .PARAMETER_ALIASES()[[\"num_iterations\"]]\n expect_equal(iter_aliases, c(\"test\", \"other_test\"))\n\n # re-set cache so this doesn't interfere with other unit tests\n if (exists(cache_key, where = .lgb_session_cache_env)) {\n rm(list = cache_key, envir = .lgb_session_cache_env)\n }\n expect_false(exists(cache_key, where = .lgb_session_cache_env))\n})\n\ntest_that(\"training should warn if you use 'dart' boosting with early stopping\", {\n for (boosting_param in .PARAMETER_ALIASES()[[\"boosting\"]]) {\n params <- list(\n num_leaves = 5L\n , learning_rate = 0.05\n , objective = \"binary\"\n , metric = \"binary_error\"\n , num_threads = .LGB_MAX_THREADS\n )\n params[[boosting_param]] <- \"dart\"\n\n # warning: early stopping requested\n expect_warning({\n result <- lightgbm(\n data = train$data\n , label = train$label\n , params = params\n , nrounds = 2L\n , verbose = .LGB_VERBOSITY\n , early_stopping_rounds = 1L\n )\n }, regexp = \"Early stopping is not available in 'dart' mode\")\n\n # no warning: early stopping not requested\n expect_silent({\n result <- lightgbm(\n data = train$data\n , label = train$label\n , params = params\n , nrounds = 2L\n , verbose = .LGB_VERBOSITY\n , early_stopping_rounds = NULL\n )\n })\n }\n})\n\ntest_that(\"lgb.cv() should warn if you use 'dart' boosting with early stopping\", {\n for (boosting_param in .PARAMETER_ALIASES()[[\"boosting\"]]) {\n params <- list(\n num_leaves = 5L\n , objective = \"binary\"\n , metric = \"binary_error\"\n , num_threads = .LGB_MAX_THREADS\n )\n params[[boosting_param]] <- \"dart\"\n\n # warning: early stopping requested\n expect_warning({\n result <- lgb.cv(\n data = lgb.Dataset(\n data = train$data\n , label = train$label\n )\n , params = params\n , nrounds = 2L\n , verbose = .LGB_VERBOSITY\n , early_stopping_rounds = 1L\n )\n }, regexp = \"Early stopping is not available in 'dart' mode\")\n\n # no warning: early stopping not requested\n expect_silent({\n result <- lgb.cv(\n data = lgb.Dataset(\n data = train$data\n , label = train$label\n )\n , params = params\n , nrounds = 2L\n , verbose = .LGB_VERBOSITY\n , early_stopping_rounds = NULL\n )\n })\n }\n})\n" }, { "path": "R-package/tests/testthat/test_utils.R", "content": "test_that(\".params2str() works as expected for empty lists\", {\n out_str <- .params2str(\n params = list()\n )\n expect_identical(class(out_str), \"character\")\n expect_equal(out_str, \"\")\n})\n\ntest_that(\".params2str() works as expected for a key in params with multiple different-length elements\", {\n metrics <- c(\"a\", \"ab\", \"abc\", \"abcdefg\")\n params <- list(\n objective = \"magic\"\n , metric = metrics\n , nrounds = 10L\n , learning_rate = 0.0000001\n )\n out_str <- .params2str(\n params = params\n )\n expect_identical(class(out_str), \"character\")\n expect_identical(\n out_str\n , \"objective=magic metric=a,ab,abc,abcdefg nrounds=10 learning_rate=0.0000001\"\n )\n})\n\ntest_that(\".params2str() passes through duplicated params\", {\n out_str <- .params2str(\n params = list(\n objective = \"regression\"\n , bagging_fraction = 0.8\n , bagging_fraction = 0.5 # nolint: duplicate_argument.\n )\n )\n expect_equal(out_str, \"objective=regression bagging_fraction=0.8 bagging_fraction=0.5\")\n})\n\ntest_that(\".check_eval works as expected with no metric\", {\n params <- .check_eval(\n params = list(device = \"cpu\")\n , eval = \"binary_error\"\n )\n expect_named(params, c(\"device\", \"metric\"))\n expect_identical(params[[\"metric\"]], list(\"binary_error\"))\n})\n\ntest_that(\".check_eval adds eval to metric in params\", {\n params <- .check_eval(\n params = list(metric = \"auc\")\n , eval = \"binary_error\"\n )\n expect_named(params, \"metric\")\n expect_identical(params[[\"metric\"]], list(\"auc\", \"binary_error\"))\n})\n\ntest_that(\".check_eval adds eval to metric in params if two evaluation names are provided\", {\n params <- .check_eval(\n params = list(metric = \"auc\")\n , eval = c(\"binary_error\", \"binary_logloss\")\n )\n expect_named(params, \"metric\")\n expect_identical(params[[\"metric\"]], list(\"auc\", \"binary_error\", \"binary_logloss\"))\n})\n\ntest_that(\".check_eval adds eval to metric in params if a list is provided\", {\n params <- .check_eval(\n params = list(metric = \"auc\")\n , eval = list(\"binary_error\", \"binary_logloss\")\n )\n expect_named(params, \"metric\")\n expect_identical(params[[\"metric\"]], list(\"auc\", \"binary_error\", \"binary_logloss\"))\n})\n\ntest_that(\".check_eval drops duplicate metrics and preserves order\", {\n params <- .check_eval(\n params = list(metric = \"l1\")\n , eval = list(\"l2\", \"rmse\", \"l1\", \"rmse\")\n )\n expect_named(params, \"metric\")\n expect_identical(params[[\"metric\"]], list(\"l1\", \"l2\", \"rmse\"))\n})\n\ntest_that(\".check_wrapper_param() uses passed-in keyword arg if no alias found in params\", {\n kwarg_val <- sample(seq_len(100L), size = 1L)\n params <- .check_wrapper_param(\n main_param_name = \"num_iterations\"\n , params = list()\n , alternative_kwarg_value = kwarg_val\n )\n expect_equal(params[[\"num_iterations\"]], kwarg_val)\n})\n\ntest_that(\".check_wrapper_param() prefers main parameter to alias and keyword arg\", {\n num_iterations <- sample(seq_len(100L), size = 1L)\n kwarg_val <- sample(seq_len(100L), size = 1L)\n params <- .check_wrapper_param(\n main_param_name = \"num_iterations\"\n , params = list(\n num_iterations = num_iterations\n , num_tree = sample(seq_len(100L), size = 1L)\n , n_estimators = sample(seq_len(100L), size = 1L)\n )\n , alternative_kwarg_value = kwarg_val\n )\n expect_equal(params[[\"num_iterations\"]], num_iterations)\n\n # aliases should be removed\n expect_identical(params, list(num_iterations = num_iterations))\n})\n\ntest_that(\".check_wrapper_param() prefers alias to keyword arg\", {\n n_estimators <- sample(seq_len(100L), size = 1L)\n num_tree <- sample(seq_len(100L), size = 1L)\n kwarg_val <- sample(seq_len(100L), size = 1L)\n params <- .check_wrapper_param(\n main_param_name = \"num_iterations\"\n , params = list(\n num_tree = num_tree\n , n_estimators = n_estimators\n )\n , alternative_kwarg_value = kwarg_val\n )\n expect_equal(params[[\"num_iterations\"]], num_tree)\n expect_identical(params, list(num_iterations = num_tree))\n\n # switching the order shouldn't switch which one is chosen\n params2 <- .check_wrapper_param(\n main_param_name = \"num_iterations\"\n , params = list(\n n_estimators = n_estimators\n , num_tree = num_tree\n )\n , alternative_kwarg_value = kwarg_val\n )\n expect_equal(params2[[\"num_iterations\"]], num_tree)\n expect_identical(params2, list(num_iterations = num_tree))\n})\n\ntest_that(\".equal_or_both_null produces expected results\", {\n expect_true(.equal_or_both_null(NULL, NULL))\n expect_false(.equal_or_both_null(1.0, NULL))\n expect_false(.equal_or_both_null(NULL, 1.0))\n expect_true(.equal_or_both_null(1.0, 1.0))\n expect_true(.equal_or_both_null(1.0, 1L))\n expect_false(.equal_or_both_null(NA, NULL))\n expect_false(.equal_or_both_null(NULL, NA))\n expect_false(.equal_or_both_null(10.0, 1L))\n expect_true(.equal_or_both_null(0L, 0L))\n})\n\ntest_that(\".check_interaction_constraints() adds skipped features\", {\n ref <- letters[1L:5L]\n ic_num <- list(1L, c(2L, 3L))\n ic_char <- list(\"a\", c(\"b\", \"c\"))\n expected <- list(\"[0]\", \"[1,2]\", \"[3,4]\")\n\n ic_checked_num <- .check_interaction_constraints(\n interaction_constraints = ic_num, column_names = ref\n )\n\n ic_checked_char <- .check_interaction_constraints(\n interaction_constraints = ic_char, column_names = ref\n )\n\n expect_equal(ic_checked_num, expected)\n expect_equal(ic_checked_char, expected)\n})\n" }, { "path": "R-package/tests/testthat/test_weighted_loss.R", "content": "test_that(\"Gamma regression reacts on 'weight'\", {\n n <- 100L\n set.seed(87L)\n X <- matrix(runif(2L * n), ncol = 2L)\n y <- X[, 1L] + X[, 2L] + runif(n)\n X_pred <- X[1L:5L, ]\n\n params <- list(objective = \"gamma\", num_threads = .LGB_MAX_THREADS)\n\n # Unweighted\n dtrain <- lgb.Dataset(X, label = y)\n bst <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 4L\n , verbose = .LGB_VERBOSITY\n )\n pred_unweighted <- predict(bst, X_pred)\n\n # Constant weight 1\n dtrain <- lgb.Dataset(\n X\n , label = y\n , weight = rep(1.0, n)\n )\n bst <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 4L\n , verbose = .LGB_VERBOSITY\n )\n pred_weighted_1 <- predict(bst, X_pred)\n\n # Constant weight 2\n dtrain <- lgb.Dataset(\n X\n , label = y\n , weight = rep(2.0, n)\n )\n bst <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 4L\n , verbose = .LGB_VERBOSITY\n )\n pred_weighted_2 <- predict(bst, X_pred)\n\n # Non-constant weights\n dtrain <- lgb.Dataset(\n X\n , label = y\n , weight = seq(0.0, 1.0, length.out = n)\n )\n bst <- lgb.train(\n params = params\n , data = dtrain\n , nrounds = 4L\n , verbose = .LGB_VERBOSITY\n )\n pred_weighted <- predict(bst, X_pred)\n\n expect_equal(pred_unweighted, pred_weighted_1)\n expect_equal(pred_weighted_1, pred_weighted_2)\n expect_false(all(pred_unweighted == pred_weighted))\n})\n" }, { "path": "R-package/tests/testthat.R", "content": "library(testthat)\nlibrary(lightgbm) # nolint: unused_import.\n\ntest_check(\n package = \"lightgbm\"\n , stop_on_failure = TRUE\n , stop_on_warning = FALSE\n , reporter = testthat::SummaryReporter$new()\n)\n" }, { "path": "R-package/vignettes/basic_walkthrough.Rmd", "content": "---\ntitle:\n \"Basic Walkthrough\"\ndescription: >\n This vignette describes how to train a LightGBM model for binary classification.\noutput:\n markdown::html_format:\n options:\n toc: true\n number_sections: true\nvignette: >\n %\\VignetteIndexEntry{Basic Walkthrough}\n %\\VignetteEngine{knitr::knitr}\n %\\VignetteEncoding{UTF-8}\n---\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n collapse = TRUE\n , comment = \"#>\"\n , warning = FALSE\n , message = FALSE\n)\n```\n\n## Introduction\n\nWelcome to the world of [LightGBM](https://lightgbm.readthedocs.io/en/latest/), a highly efficient gradient boosting implementation (Ke et al. 2017).\n\n```{r}\nlibrary(lightgbm)\n```\n\n```{r, include=FALSE}\n# limit number of threads used, to be respectful of CRAN's resources when it checks this vignette\ndata.table::setDTthreads(1L)\nsetLGBMthreads(2L)\n```\n\nThis vignette will guide you through its basic usage. It will show how to build a simple binary classification model based on a subset of the `bank` dataset (Moro, Cortez, and Rita 2014). You will use the two input features \"age\" and \"balance\" to predict whether a client has subscribed a term deposit.\n\n## The dataset\n\nThe dataset looks as follows.\n\n```{r}\ndata(bank, package = \"lightgbm\")\n\nbank[1L:5L, c(\"y\", \"age\", \"balance\")]\n\n# Distribution of the response\ntable(bank$y)\n```\n\n## Training the model\n\nThe R-package of LightGBM offers two functions to train a model:\n\n- `lgb.train()`: This is the main training logic. It offers full flexibility but requires a `Dataset` object created by the `lgb.Dataset()` function.\n- `lightgbm()`: Simpler, but less flexible. Data can be passed without having to bother with `lgb.Dataset()`.\n\n### Using the `lightgbm()` function\n\nIn a first step, you need to convert data to numeric. Afterwards, you are ready to fit the model by the `lightgbm()` function.\n\n```{r}\n# Numeric response and feature matrix\ny <- as.numeric(bank$y == \"yes\")\nX <- data.matrix(bank[, c(\"age\", \"balance\")])\n\n# Train\nfit <- lightgbm(\n data = X\n , label = y\n , params = list(\n num_leaves = 4L\n , learning_rate = 1.0\n , objective = \"binary\"\n )\n , nrounds = 10L\n , verbose = -1L\n)\n\n# Result\nsummary(predict(fit, X))\n```\n\nIt seems to have worked! And the predictions are indeed probabilities between 0 and 1.\n\n### Using the `lgb.train()` function\n\nAlternatively, you can go for the more flexible interface `lgb.train()`. Here, as an additional step, you need to prepare `y` and `X` by the data API `lgb.Dataset()` of LightGBM. Parameters are passed to `lgb.train()` as a named list.\n\n```{r}\n# Data interface\ndtrain <- lgb.Dataset(X, label = y)\n\n# Parameters\nparams <- list(\n objective = \"binary\"\n , num_leaves = 4L\n , learning_rate = 1.0\n)\n\n# Train\nfit <- lgb.train(\n params\n , data = dtrain\n , nrounds = 10L\n , verbose = -1L\n)\n```\n\nTry it out! If stuck, visit LightGBM's [documentation](https://lightgbm.readthedocs.io/en/latest/R/index.html) for more details.\n\n```{r, echo = FALSE, results = \"hide\"}\n# Cleanup\nif (file.exists(\"lightgbm.model\")) {\n file.remove(\"lightgbm.model\")\n}\n```\n\n## References\n\nKe, Guolin, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. \"LightGBM: A Highly Efficient Gradient Boosting Decision Tree.\" In Advances in Neural Information Processing Systems 30 (NIPS 2017).\n\nMoro, Sérgio, Paulo Cortez, and Paulo Rita. 2014. \"A Data-Driven Approach to Predict the Success of Bank Telemarketing.\" Decision Support Systems 62: 22–31.\n" }, { "path": "README.md", "content": "\n\n> [!NOTE]\n> This project moved from `Microsoft/LightGBM` to `lightgbm-org/LightGBM` in March 2026.\n> This repository is still the official LightGBM source code, managed by the same maintainers (including the creator of LightGBM).\n> For details, see https://github.com/lightgbm-org/LightGBM/issues/7187\n\nLight Gradient Boosting Machine\n===============================\n\n[![C++ GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/cpp.yml/badge.svg?branch=master)](https://github.com/lightgbm-org/LightGBM/actions/workflows/cpp.yml)\n[![Python-package GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/python_package.yml/badge.svg?branch=master)](https://github.com/lightgbm-org/LightGBM/actions/workflows/python_package.yml)\n[![R-package GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/r_package.yml/badge.svg?branch=master)](https://github.com/lightgbm-org/LightGBM/actions/workflows/r_package.yml)\n[![CUDA Version GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/cuda.yml/badge.svg?branch=master)](https://github.com/lightgbm-org/LightGBM/actions/workflows/cuda.yml)\n[![SWIG Wrapper GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/swig.yml/badge.svg?branch=master)](https://github.com/lightgbm-org/LightGBM/actions/workflows/swig.yml)\n[![Static Analysis GitHub Actions Build Status](https://github.com/lightgbm-org/LightGBM/actions/workflows/static_analysis.yml/badge.svg?branch=master)](https://github.com/lightgbm-org/LightGBM/actions/workflows/static_analysis.yml)\n[![Appveyor Build Status](https://ci.appveyor.com/api/projects/status/1ys5ot401m0fep6l/branch/master?svg=true)](https://ci.appveyor.com/project/guolinke/lightgbm/branch/master)\n[![Documentation Status](https://readthedocs.org/projects/lightgbm/badge/?version=latest)](https://lightgbm.readthedocs.io/)\n[![Link checks](https://github.com/lightgbm-org/LightGBM/actions/workflows/lychee.yml/badge.svg?branch=master)](https://github.com/lightgbm-org/LightGBM/actions/workflows/lychee.yml)\n[![License](https://img.shields.io/github/license/lightgbm-org/lightgbm.svg)](https://github.com/lightgbm-org/LightGBM/blob/master/LICENSE)\n[![EffVer Versioning](https://img.shields.io/badge/version_scheme-EffVer-0097a7)](https://jacobtomlinson.dev/effver)\n[![StackOverflow questions](https://img.shields.io/stackexchange/stackoverflow/t/lightgbm?logo=stackoverflow&logoColor=white&label=StackOverflow%20questions)](https://stackoverflow.com/questions/tagged/lightgbm?sort=votes)\n[![Python Versions](https://img.shields.io/pypi/pyversions/lightgbm.svg?logo=python&logoColor=white)](https://pypi.org/project/lightgbm)\n[![PyPI Version](https://img.shields.io/pypi/v/lightgbm.svg?logo=pypi&logoColor=white)](https://pypi.org/project/lightgbm)\n[![conda Version](https://img.shields.io/conda/vn/conda-forge/lightgbm?logo=conda-forge&logoColor=white&label=conda)](https://anaconda.org/conda-forge/lightgbm)\n[![CRAN Version](https://www.r-pkg.org/badges/version/lightgbm)](https://cran.r-project.org/package=lightgbm)\n[![NuGet Version](https://img.shields.io/nuget/v/lightgbm?logo=nuget&logoColor=white)](https://www.nuget.org/packages/LightGBM)\n[![Winget Version](https://img.shields.io/winget/v/Microsoft.LightGBM)](https://github.com/microsoft/winget-pkgs/tree/master/manifests/m/Microsoft/LightGBM)\n\nLightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:\n\n- Faster training speed and higher efficiency.\n- Lower memory usage.\n- Better accuracy.\n- Support of parallel, distributed, and GPU learning.\n- Capable of handling large-scale data.\n\nFor further details, please refer to [Features](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Features.rst).\n\nBenefiting from these advantages, LightGBM is being widely-used in many [winning solutions](https://github.com/lightgbm-org/LightGBM/blob/master/examples/README.md#machine-learning-challenge-winning-solutions) of machine learning competitions.\n\n[Comparison experiments](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Experiments.rst#comparison-experiment) on public datasets show that LightGBM can outperform existing boosting frameworks on both efficiency and accuracy, with significantly lower memory consumption. What's more, [distributed learning experiments](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Experiments.rst#parallel-experiment) show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.\n\nGet Started and Documentation\n-----------------------------\n\nOur primary documentation is at https://lightgbm.readthedocs.io/ and is generated from this repository. If you are new to LightGBM, follow [the installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html) on that site.\n\nNext you may want to read:\n\n- [**Examples**](https://github.com/lightgbm-org/LightGBM/tree/master/examples) showing command line usage of common tasks.\n- [**Features**](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Features.rst) and algorithms supported by LightGBM.\n- [**Parameters**](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Parameters.rst) is an exhaustive list of customization you can make.\n- [**Distributed Learning**](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst) and [**GPU Learning**](https://github.com/lightgbm-org/LightGBM/blob/master/docs/GPU-Tutorial.rst) can speed up computation.\n- [**FLAML**](https://www.microsoft.com/en-us/research/project/fast-and-lightweight-automl-for-large-scale-data/articles/flaml-a-fast-and-lightweight-automl-library/) provides automated tuning for LightGBM ([code examples](https://microsoft.github.io/FLAML/docs/Examples/AutoML-for-LightGBM/)).\n- [**Optuna Hyperparameter Tuner**](https://medium.com/optuna/lightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258) provides automated tuning for LightGBM hyperparameters ([code examples](https://github.com/optuna/optuna-examples/blob/main/lightgbm/lightgbm_tuner_simple.py)).\n- [**Understanding LightGBM Parameters (and How to Tune Them using Neptune)**](https://neptune.ai/blog/lightgbm-parameters-guide).\n\nDocumentation for contributors:\n\n- [**How we update readthedocs.io**](https://github.com/lightgbm-org/LightGBM/blob/master/docs/README.rst).\n- Check out the [**Development Guide**](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Development-Guide.rst).\n\nNews\n----\n\nPlease refer to changelogs at [GitHub releases](https://github.com/lightgbm-org/LightGBM/releases) page.\n\nExternal (Unofficial) Repositories\n----------------------------------\n\nProjects listed here offer alternative ways to use LightGBM.\nThey are not maintained or officially endorsed by the `LightGBM` development team.\n\nJPMML (Java PMML converter): https://github.com/jpmml/jpmml-lightgbm\n\nNyoka (Python PMML converter): https://github.com/SoftwareAG/nyoka\n\nTreelite (model compiler for efficient deployment): https://github.com/dmlc/treelite\n\nlleaves (LLVM-based model compiler for efficient inference): https://github.com/siboehm/lleaves\n\nHummingbird (model compiler into tensor computations): https://github.com/microsoft/hummingbird\n\nGBNet (use `LightGBM` as a [PyTorch Module](https://docs.pytorch.org/docs/stable/generated/torch.nn.Module.html)): https://github.com/mthorrell/gbnet\n\ncuML Forest Inference Library (GPU-accelerated inference): https://github.com/rapidsai/cuml\n\ndaal4py (Intel CPU-accelerated inference): https://github.com/intel/scikit-learn-intelex/tree/master/daal4py\n\nm2cgen (model appliers for various languages): https://github.com/BayesWitnesses/m2cgen\n\nleaves (Go model applier): https://github.com/dmitryikh/leaves\n\nONNXMLTools (ONNX converter): https://github.com/onnx/onnxmltools\n\nSHAP (model output explainer): https://github.com/slundberg/shap\n\nShapash (model visualization and interpretation): https://github.com/MAIF/shapash\n\ndtreeviz (decision tree visualization and model interpretation): https://github.com/parrt/dtreeviz\n\nsupertree (interactive visualization of decision trees): https://github.com/mljar/supertree\n\nSynapseML (LightGBM on Spark): https://github.com/microsoft/SynapseML\n\nKubeflow Fairing (LightGBM on Kubernetes): https://github.com/kubeflow/fairing\n\nKubeflow Operator (LightGBM on Kubernetes): https://github.com/kubeflow/xgboost-operator\n\nlightgbm_ray (LightGBM on Ray): https://github.com/ray-project/lightgbm_ray\n\nRay (distributed computing framework): https://github.com/ray-project/ray\n\nMars (LightGBM on Mars): https://github.com/mars-project/mars\n\nML.NET (.NET/C#-package): https://github.com/dotnet/machinelearning\n\nLightGBM.NET (.NET/C#-package): https://github.com/rca22/LightGBM.Net\n\nLightGBM Ruby (Ruby gem): https://github.com/ankane/lightgbm-ruby\n\nLightGBM4j (Java high-level binding): https://github.com/metarank/lightgbm4j\n\nLightGBM4J (JVM interface for LightGBM written in Scala): https://github.com/seek-oss/lightgbm4j\n\nJulia-package: https://github.com/IQVIA-ML/LightGBM.jl\n\nlightgbm3 (Rust binding): https://github.com/Mottl/lightgbm3-rs\n\nMLServer (inference server for LightGBM): https://github.com/SeldonIO/MLServer\n\nMLflow (experiment tracking, model monitoring framework): https://github.com/mlflow/mlflow\n\nFLAML (AutoML library for hyperparameter optimization): https://github.com/microsoft/FLAML\n\nMLJAR AutoML (AutoML on tabular data): https://github.com/mljar/mljar-supervised\n\nOptuna (hyperparameter optimization framework): https://github.com/optuna/optuna\n\nLightGBMLSS (probabilistic modelling with LightGBM): https://github.com/StatMixedML/LightGBMLSS\n\nmlforecast (time series forecasting with LightGBM): https://github.com/Nixtla/mlforecast\n\nskforecast (time series forecasting with LightGBM): https://github.com/JoaquinAmatRodrigo/skforecast\n\n`{bonsai}` (R `{parsnip}`-compliant interface): https://github.com/tidymodels/bonsai\n\n`{mlr3extralearners}` (R `{mlr3}`-compliant interface): https://github.com/mlr-org/mlr3extralearners\n\nlightgbm-transform (feature transformation binding): https://github.com/lightgbm-org/LightGBM-transform\n\n`postgresml` (LightGBM training and prediction in SQL, via a Postgres extension): https://github.com/postgresml/postgresml\n\n`pyodide` (run `lightgbm` Python-package in a web browser): https://github.com/pyodide/pyodide\n\n`vaex-ml` (Python DataFrame library with its own interface to LightGBM): https://github.com/vaexio/vaex\n\nSupport\n-------\n\n- Ask a question [on Stack Overflow with the `lightgbm` tag](https://stackoverflow.com/questions/ask?tags=lightgbm), we monitor this for new questions.\n- Open **bug reports** and **feature requests** on [GitHub issues](https://github.com/lightgbm-org/LightGBM/issues).\n\nHow to Contribute\n-----------------\n\nCheck [CONTRIBUTING](https://github.com/lightgbm-org/LightGBM/blob/master/CONTRIBUTING.md) page.\n\nMicrosoft Open Source Code of Conduct\n-------------------------------------\n\nThis project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.\n\nReference Papers\n----------------\n\nYu Shi, Guolin Ke, Zhuoming Chen, Shuxin Zheng, Tie-Yan Liu. \"Quantized Training of Gradient Boosting Decision Trees\" ([link](https://proceedings.neurips.cc/paper/2022/hash/77911ed9e6e864ca1a3d165b2c3cb258-Abstract.html)). Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pp. 18822-18833.\n\nGuolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu. \"[LightGBM: A Highly Efficient Gradient Boosting Decision Tree](https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html)\". Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149-3157.\n\nQi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu. \"[A Communication-Efficient Parallel Algorithm for Decision Tree](https://proceedings.neurips.cc/paper/2016/hash/10a5ab2db37feedfdeaab192ead4ac0e-Abstract.html)\". Advances in Neural Information Processing Systems 29 (NIPS 2016), pp. 1279-1287.\n\nHuan Zhang, Si Si and Cho-Jui Hsieh. \"[GPU Acceleration for Large-scale Tree Boosting](https://arxiv.org/abs/1706.08359)\". SysML Conference, 2018.\n\nLicense\n-------\n\nThis project is licensed under the terms of the MIT license. See [LICENSE](https://github.com/lightgbm-org/LightGBM/blob/master/LICENSE) for additional details.\n" }, { "path": "SECURITY.md", "content": "\n\n## Security\n\nMicrosoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/).\n\nIf you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/opensource/security/definition), please report it to us as described below.\n\n## Reporting Security Issues\n\n**Please do not report security vulnerabilities through public GitHub issues.**\n\nInstead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/opensource/security/create-report).\n\nIf you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com). If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/opensource/security/pgpkey).\n\nYou should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://aka.ms/opensource/security/msrc).\n\nPlease include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:\n\n * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)\n * Full paths of source file(s) related to the manifestation of the issue\n * The location of the affected source code (tag/branch/commit or direct URL)\n * Any special configuration required to reproduce the issue\n * Step-by-step instructions to reproduce the issue\n * Proof-of-concept or exploit code (if possible)\n * Impact of the issue, including how an attacker might exploit the issue\n\nThis information will help us triage your report more quickly.\n\nIf you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/opensource/security/bounty) page for more details about our active programs.\n\n## Preferred Languages\n\nWe prefer all communications to be in English.\n\n## Policy\n\nMicrosoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/opensource/security/cvd).\n\n\n" }, { "path": "VERSION.txt", "content": "4.6.0.99\n" }, { "path": "biome.json", "content": "{\n \"root\": false,\n \"vcs\": {\n \"enabled\": true,\n \"clientKind\": \"git\",\n \"useIgnoreFile\": true\n },\n \"files\": {\n \"includes\": [\n \"**\",\n \"!build\",\n \"!external_libs\",\n \"!lightgbm-python\",\n \"!lightgbm_r\"\n ]\n },\n \"formatter\": {\n \"enabled\": true,\n \"expand\": \"always\",\n \"useEditorconfig\": true,\n \"lineWidth\": 120\n },\n \"assist\": {\n \"enabled\": true,\n \"actions\": {\n \"recommended\": true\n }\n },\n \"linter\": {\n \"enabled\": true,\n \"domains\": {\n \"project\": \"all\"\n },\n \"rules\": {\n \"recommended\": true\n }\n }\n}\n" }, { "path": "build-cran-package.sh", "content": "#!/bin/sh\n\n# [description]\n# Prepare a source distribution of the R-package\n# to be submitted to CRAN.\n#\n# [arguments]\n#\n# --r-executable Customize the R executable used by `R CMD build`.\n# Useful if building the R-package in an environment with\n# non-standard builds of R, such as those provided in\n# https://github.com/wch/r-debug.\n#\n# --no-build-vignettes Pass this flag to skip creating vignettes.\n# You might want to do this to avoid installing\n# vignette-only dependencies, or to avoid\n# portability issues.\n#\n# [usage]\n#\n# # default usage\n# sh build-cran-package.sh\n#\n# # custom R build\n# sh build-cran-package.sh --r-executable=RDvalgrind\n#\n# # skip vignette building\n# sh build-cran-package.sh --no-build-vignettes\n\nset -e -u\n\n# Default values of arguments\nBUILD_VIGNETTES=true\nLGB_R_EXECUTABLE=R\n\nwhile [ $# -gt 0 ]; do\n case \"$1\" in\n --r-executable=*)\n LGB_R_EXECUTABLE=\"${1#*=}\"\n ;;\n --no-build-vignettes*)\n BUILD_VIGNETTES=false\n ;;\n *)\n echo \"invalid argument '${1}'\"\n exit 1\n ;;\n esac\n shift\ndone\n\necho \"Building lightgbm with R executable: ${LGB_R_EXECUTABLE}\"\n\nORIG_WD=\"$(pwd)\"\nTEMP_R_DIR=\"$(pwd)/lightgbm_r\"\n\nif test -d \"${TEMP_R_DIR}\"; then\n rm -r \"${TEMP_R_DIR}\"\nfi\nmkdir -p \"${TEMP_R_DIR}\"\n\nCURRENT_DATE=$(date +'%Y-%m-%d')\n\n# R packages cannot have versions like 3.0.0rc1, but\n# 3.0.0-1 is acceptable\nLGB_VERSION=$(head -1 ./VERSION.txt | sed \"s/rc/-/g\")\n\n# move relevant files\ncp -R R-package/* \"${TEMP_R_DIR}\"\ncp -R include \"${TEMP_R_DIR}/src/\"\ncp -R src/* \"${TEMP_R_DIR}/src/\"\n\nif ${BUILD_VIGNETTES} ; then\n cp docs/logo/LightGBM_logo_black_text.svg \"${TEMP_R_DIR}/vignettes/\"\nfi\n\ncp \\\n external_libs/fast_double_parser/include/fast_double_parser.h \\\n \"${TEMP_R_DIR}/src/include/LightGBM/utils\"\n\nmkdir -p \"${TEMP_R_DIR}/src/include/LightGBM/utils/fmt\"\ncp \\\n external_libs/fmt/include/fmt/*.h \\\n \"${TEMP_R_DIR}/src/include/LightGBM/utils/fmt\"\n\n# including only specific files from Eigen, to keep the R-package\n# small and avoid redistributing code with licenses incompatible with\n# LightGBM's license\nEIGEN_R_DIR=\"${TEMP_R_DIR}/src/include/Eigen\"\nmkdir -p \"${EIGEN_R_DIR}\"\n\nmodules=\"Cholesky Core Dense Eigenvalues Geometry Householder Jacobi LU QR SVD\"\nfor eigen_module in ${modules}; do\n cp \"external_libs/eigen/Eigen/${eigen_module}\" \"${EIGEN_R_DIR}/${eigen_module}\"\n if [ \"${eigen_module}\" != \"Dense\" ]; then\n mkdir -p \"${EIGEN_R_DIR}/src/${eigen_module}/\"\n cp -R \"external_libs/eigen/Eigen/src/${eigen_module}\"/* \"${EIGEN_R_DIR}/src/${eigen_module}/\"\n fi\ndone\n\nmkdir -p \"${EIGEN_R_DIR}/src/misc\"\ncp -R external_libs/eigen/Eigen/src/misc/* \"${EIGEN_R_DIR}/src/misc/\"\n\nmkdir -p \"${EIGEN_R_DIR}/src/plugins\"\ncp -R external_libs/eigen/Eigen/src/plugins/* \"${EIGEN_R_DIR}/src/plugins/\"\n\ncd \"${TEMP_R_DIR}\"\n\n # Remove files not needed for CRAN\n echo \"Removing files not needed for CRAN\"\n rm src/install.libs.R\n rm -r inst/\n rm -r pkgdown/\n rm cran-comments.md\n rm AUTOCONF_UBUNTU_VERSION\n rm recreate-configure.sh\n\n # files only used by the lightgbm CLI aren't needed for\n # the R-package\n rm src/application/application.cpp\n rm src/include/LightGBM/application.h\n rm src/main.cpp\n\n # configure.ac and DESCRIPTION have placeholders for version\n # and date so they don't have to be updated manually\n sed -i.bak -e \"s/~~VERSION~~/${LGB_VERSION}/\" configure.ac\n sed -i.bak -e \"s/~~VERSION~~/${LGB_VERSION}/\" DESCRIPTION\n sed -i.bak -e \"s/~~DATE~~/${CURRENT_DATE}/\" DESCRIPTION\n\n # Remove 'region', 'endregion', and 'warning' pragmas.\n # This won't change the correctness of the code. CRAN does\n # not allow you to use compiler flag '-Wno-unknown-pragmas' or\n # pragmas that suppress warnings.\n echo \"Removing unknown pragmas in headers\"\n find . \\( -name '*.h' -o -name '*.hpp' -o -name '*.cpp' \\) -exec \\\n sed \\\n -i.bak \\\n -e 's/^.*#pragma clang diagnostic.*$//' \\\n -e 's/^.*#pragma diag_suppress.*$//' \\\n -e 's/^.*#pragma GCC diagnostic.*$//' \\\n -e 's/^.*#pragma region.*$//' \\\n -e 's/^.*#pragma endregion.*$//' \\\n -e 's/^.*#pragma warning.*$//' \\\n {} +\n\n # 'processx' is listed as a 'Suggests' dependency in DESCRIPTION\n # because it is used in install.libs.R, a file that is not\n # included in the CRAN distribution of the package\n sed \\\n -i.bak \\\n '/processx/d' \\\n DESCRIPTION\n\n echo \"Cleaning sed backup files\"\n find . -name '*.bak' -exec rm {} \\;\n\ncd \"${ORIG_WD}\"\n\nif ${BUILD_VIGNETTES} ; then\n \"${LGB_R_EXECUTABLE}\" CMD build \\\n --keep-empty-dirs \\\n lightgbm_r\n\n echo \"removing object files created by vignettes\"\n rm -rf ./_tmp\n mkdir _tmp\n TARBALL_NAME=\"lightgbm_${LGB_VERSION}.tar.gz\"\n mv \"${TARBALL_NAME}\" _tmp/\n\n echo \"untarring ${TARBALL_NAME}\"\n cd _tmp\n tar -xf \"${TARBALL_NAME}\" > /dev/null 2>&1\n rm -f \"${TARBALL_NAME}\"\n echo \"done untarring ${TARBALL_NAME}\"\n\n # Object files are left behind from compiling the library to generate vignettes.\n # Approaches like using tar --exclude=*.so to exclude them are not portable\n # (for example, don't work with some versions of tar on Windows).\n #\n # Removing them manually here removes the need to use tar --exclude.\n #\n # For background, see https://github.com/lightgbm-org/LightGBM/pull/3946#pullrequestreview-799415812.\n rm -f ./lightgbm/src/*.o\n rm -f ./lightgbm/src/boosting/*.o\n rm -f ./lightgbm/src/io/*.o\n rm -f ./lightgbm/src/metric/*.o\n rm -f ./lightgbm/src/network/*.o\n rm -f ./lightgbm/src/objective/*.o\n rm -f ./lightgbm/src/treelearner/*.o\n rm -f ./lightgbm/src/utils/*.o\n\n echo \"re-tarring ${TARBALL_NAME}\"\n # --no-xattrs is the default in GNU tar but not some distributions of BSD tar.\n # Enable it here to avoid errors on macOS.\n # ref: https://stackoverflow.com/a/74373784/3986677\n tar \\\n -cz \\\n --no-xattrs \\\n -f \"${TARBALL_NAME}\" \\\n lightgbm \\\n > /dev/null 2>&1\n mv \"${TARBALL_NAME}\" ../\n cd ..\n echo \"Done creating ${TARBALL_NAME}\"\n\n rm -rf ./_tmp\nelse\n \"${LGB_R_EXECUTABLE}\" CMD build \\\n --keep-empty-dirs \\\n --no-build-vignettes \\\n lightgbm_r\nfi\n\necho \"Done building R-package\"\n" }, { "path": "build-python.sh", "content": "#!/bin/sh\n\n# [description]\n#\n# Prepare a source distribution (sdist) or built distribution (wheel)\n# of the Python-package, and optionally install it.\n#\n# [usage]\n#\n# # build sdist and put it in dist/\n# sh ./build-python.sh sdist\n#\n# # build wheel and put it in dist/\n# sh ./build-python.sh bdist_wheel [OPTIONS]\n#\n# # compile lib_lightgbm and install the Python-package wrapping it\n# sh ./build-python.sh install [OPTIONS]\n#\n# # install the Python-package using a pre-compiled lib_lightgbm\n# # (assumes lib_lightgbm.{dll,so} is located at the root of the repo)\n# sh ./build-python.sh install --precompile\n#\n# [options]\n#\n# --boost-dir=FILEPATH\n# Directory with Boost package configuration file.\n# --boost-include-dir=FILEPATH\n# Directory containing Boost headers.\n# --boost-librarydir=FILEPATH\n# Preferred Boost library directory.\n# --boost-root=FILEPATH\n# Boost preferred installation prefix.\n# --opencl-include-dir=FILEPATH\n# OpenCL include directory.\n# --opencl-library=FILEPATH\n# Path to OpenCL library.\n# --bit32\n# Compile 32-bit version.\n# --cuda\n# Compile CUDA version.\n# --gpu\n# Compile GPU version.\n# --integrated-opencl\n# Compile integrated OpenCL version.\n# --mingw\n# Compile with MinGW.\n# --mpi\n# Compile MPI version.\n# --no-isolation\n# Assume all build and install dependencies are already installed,\n# don't go to the internet to get them.\n# --nomp\n# Compile version without OpenMP support.\n# --precompile\n# Use precompiled library.\n# Only used with 'install' command.\n# --rocm\n# Compile ROCm version.\n# --time-costs\n# Compile version that outputs time costs for different internal routines.\n# --user\n# Install into user-specific instead of global site-packages directory.\n# Only used with 'install' command.\n\nset -e -u\n\necho \"[INFO] building lightgbm\"\n\n# Default values of arguments\nINSTALL=\"false\"\nBUILD_SDIST=\"false\"\nBUILD_WHEEL=\"false\"\n\nPIP_INSTALL_ARGS=\"\"\nBUILD_ARGS=\"\"\nPRECOMPILE=\"false\"\n\nwhile [ $# -gt 0 ]; do\n case \"$1\" in\n ############################\n # sub-commands of setup.py #\n ############################\n install)\n INSTALL=\"true\"\n ;;\n sdist)\n BUILD_SDIST=\"true\"\n ;;\n bdist_wheel)\n BUILD_WHEEL=\"true\"\n ;;\n ############################\n # customized library paths #\n ############################\n --boost-dir|--boost-dir=*)\n if echo \"$1\" | grep -q '^*=*$';\n then shift;\n fi\n BOOST_DIR=\"${1#*=}\"\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.define.Boost_DIR='${BOOST_DIR}'\"\n ;;\n --boost-include-dir|--boost-include-dir=*)\n if echo \"$1\" | grep -q '^*=*$';\n then shift;\n fi\n BOOST_INCLUDE_DIR=\"${1#*=}\"\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.define.Boost_INCLUDE_DIR='${BOOST_INCLUDE_DIR}'\"\n ;;\n --boost-librarydir|--boost-librarydir=*)\n if echo \"$1\" | grep -q '^*=*$';\n then shift;\n fi\n BOOST_LIBRARY_DIR=\"${1#*=}\"\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.define.BOOST_LIBRARYDIR='${BOOST_LIBRARY_DIR}'\"\n ;;\n --boost-root|--boost-root=*)\n if echo \"$1\" | grep -q '^*=*$';\n then shift;\n fi\n BOOST_ROOT=\"${1#*=}\"\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.define.Boost_ROOT='${BOOST_ROOT}'\"\n ;;\n --opencl-include-dir|--opencl-include-dir=*)\n if echo \"$1\" | grep -q '^*=*$';\n then shift;\n fi\n OPENCL_INCLUDE_DIR=\"${1#*=}\"\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.define.OpenCL_INCLUDE_DIR='${OPENCL_INCLUDE_DIR}'\"\n ;;\n --opencl-library|--opencl-library=*)\n if echo \"$1\" | grep -q '^*=*$';\n then shift;\n fi\n OPENCL_LIBRARY=\"${1#*=}\"\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.define.OpenCL_LIBRARY='${OPENCL_LIBRARY}'\"\n ;;\n #########\n # flags #\n #########\n --bit32)\n echo \"[INFO] Attempting to build 32-bit version of LightGBM, which is only supported on Windows with Visual Studio.\"\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.args=-AWin32\"\n ;;\n --cuda)\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.define.USE_CUDA=ON\"\n ;;\n --rocm)\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.define.USE_ROCM=ON\"\n ;;\n --gpu)\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.define.USE_GPU=ON\"\n ;;\n --integrated-opencl)\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.define.__INTEGRATE_OPENCL=ON\"\n ;;\n --mingw)\n # ref: https://stackoverflow.com/a/45104058/3986677\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.define.CMAKE_SH=CMAKE_SH-NOTFOUND\"\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.args=-G'MinGW Makefiles'\"\n ;;\n --mpi)\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.define.USE_MPI=ON\"\n ;;\n --no-isolation)\n BUILD_ARGS=\"${BUILD_ARGS} --no-isolation\"\n PIP_INSTALL_ARGS=\"${PIP_INSTALL_ARGS} --no-build-isolation\"\n ;;\n --nomp)\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.define.USE_OPENMP=OFF\"\n ;;\n --precompile)\n PRECOMPILE=\"true\"\n ;;\n --time-costs)\n BUILD_ARGS=\"${BUILD_ARGS} --config-setting=cmake.define.USE_TIMETAG=ON\"\n ;;\n --user)\n PIP_INSTALL_ARGS=\"${PIP_INSTALL_ARGS} --user\"\n ;;\n *)\n echo \"[ERROR] invalid argument '${1}'. Aborting\"\n exit 1\n ;;\n esac\n shift\ndone\n\npip install --prefer-binary 'build>=0.10.0'\n\n# create a new directory that just contains the files needed\n# to build the Python-package\ncreate_isolated_source_dir() {\n rm -rf \\\n ./lightgbm-python \\\n ./lightgbm \\\n ./python-package/build \\\n ./python-package/build_cpp \\\n ./python-package/compile \\\n ./python-package/dist \\\n ./python-package/lightgbm.egg-info\n\n cp -R ./python-package ./lightgbm-python\n\n cp LICENSE ./lightgbm-python/\n cp VERSION.txt ./lightgbm-python/lightgbm/VERSION.txt\n\n cp -R ./cmake ./lightgbm-python\n cp CMakeLists.txt ./lightgbm-python\n cp -R ./include ./lightgbm-python\n cp -R ./src ./lightgbm-python\n cp -R ./swig ./lightgbm-python\n\n # include only specific files from external_libs, to keep the package\n # small and avoid redistributing code with licenses incompatible with\n # LightGBM's license\n\n ######################\n # fast_double_parser #\n ######################\n mkdir -p ./lightgbm-python/external_libs/fast_double_parser\n cp \\\n external_libs/fast_double_parser/CMakeLists.txt \\\n ./lightgbm-python/external_libs/fast_double_parser/CMakeLists.txt\n cp \\\n external_libs/fast_double_parser/LICENSE* \\\n ./lightgbm-python/external_libs/fast_double_parser/\n\n mkdir -p ./lightgbm-python/external_libs/fast_double_parser/include/\n cp \\\n external_libs/fast_double_parser/include/fast_double_parser.h \\\n ./lightgbm-python/external_libs/fast_double_parser/include/\n\n #######\n # fmt #\n #######\n mkdir -p ./lightgbm-python/external_libs/fmt\n cp \\\n external_libs/fast_double_parser/CMakeLists.txt \\\n ./lightgbm-python/external_libs/fmt/CMakeLists.txt\n cp \\\n external_libs/fmt/LICENSE* \\\n ./lightgbm-python/external_libs/fmt/\n\n mkdir -p ./lightgbm-python/external_libs/fmt/include/fmt\n cp \\\n external_libs/fmt/include/fmt/*.h \\\n ./lightgbm-python/external_libs/fmt/include/fmt/\n\n #########\n # Eigen #\n #########\n mkdir -p ./lightgbm-python/external_libs/eigen/Eigen\n cp \\\n external_libs/eigen/CMakeLists.txt \\\n ./lightgbm-python/external_libs/eigen/CMakeLists.txt\n\n modules=\"Cholesky Core Dense Eigenvalues Geometry Householder Jacobi LU QR SVD\"\n for eigen_module in ${modules}; do\n cp \\\n \"external_libs/eigen/Eigen/${eigen_module}\" \\\n \"./lightgbm-python/external_libs/eigen/Eigen/${eigen_module}\"\n if [ \"${eigen_module}\" != \"Dense\" ]; then\n mkdir -p \"./lightgbm-python/external_libs/eigen/Eigen/src/${eigen_module}/\"\n cp \\\n -R \\\n \"external_libs/eigen/Eigen/src/${eigen_module}\"/* \\\n \"./lightgbm-python/external_libs/eigen/Eigen/src/${eigen_module}/\"\n fi\n done\n\n mkdir -p ./lightgbm-python/external_libs/eigen/Eigen/misc\n cp \\\n -R \\\n external_libs/eigen/Eigen/src/misc \\\n ./lightgbm-python/external_libs/eigen/Eigen/src/misc/\n\n mkdir -p ./lightgbm-python/external_libs/eigen/Eigen/plugins\n cp \\\n -R \\\n external_libs/eigen/Eigen/src/plugins \\\n ./lightgbm-python/external_libs/eigen/Eigen/src/plugins/\n\n ###################\n # compute (Boost) #\n ###################\n mkdir -p ./lightgbm-python/external_libs/compute\n cp \\\n -R \\\n external_libs/compute/include \\\n ./lightgbm-python/external_libs/compute/include/\n}\n\ncreate_isolated_source_dir\n\ncd ./lightgbm-python\n\n# if 'install' was passed, choose the type of package to build and install\nif test \"${INSTALL}\" = true; then\n if test \"${PRECOMPILE}\" = true; then\n BUILD_SDIST=false\n BUILD_WHEEL=true\n BUILD_ARGS=\"\"\n rm -rf \\\n ./cmake \\\n ./CMakeLists.txt \\\n ./external_libs \\\n ./include \\\n ./src \\\n ./swig\n # avoid trying to recompile, just use hatchling and copy in relevant files\n sed -i.bak -e '/start:build-system/,/end:build-system/d' pyproject.toml\n\n# replace build backend configuration\ncat >> ./pyproject.toml <=1.27.0\"]\nbuild-backend = \"hatchling.build\"\n\n[tool.hatch.build.targets.wheel]\n# do not consider .gitignore when choosing files to include / exclude\nignore-vcs = true\npackages = [\"lightgbm\"]\n\nEOF\n mkdir -p ./lightgbm/lib\n if test -f ../lib_lightgbm.so; then\n echo \"[INFO] found pre-compiled lib_lightgbm.so\"\n cp ../lib_lightgbm.so ./lightgbm/lib/lib_lightgbm.so\n elif test -f ../lib_lightgbm.dylib; then\n echo \"[INFO] found pre-compiled lib_lightgbm.dylib\"\n cp ../lib_lightgbm.dylib ./lightgbm/lib/lib_lightgbm.dylib\n elif test -f ../lib_lightgbm.dll; then\n echo \"[INFO] found pre-compiled lib_lightgbm.dll\"\n cp ../lib_lightgbm.dll ./lightgbm/lib/lib_lightgbm.dll\n elif test -f ../Release/lib_lightgbm.dll; then\n echo \"[INFO] found pre-compiled Release/lib_lightgbm.dll\"\n cp ../Release/lib_lightgbm.dll ./lightgbm/lib/lib_lightgbm.dll\n elif test -f ../windows/x64/DLL/lib_lightgbm.dll; then\n echo \"[INFO] found pre-compiled windows/x64/DLL/lib_lightgbm.dll\"\n cp ../windows/x64/DLL/lib_lightgbm.dll ./lightgbm/lib/lib_lightgbm.dll\n cp ../windows/x64/DLL/lib_lightgbm.lib ./lightgbm/lib/lib_lightgbm.lib\n elif test -f ../windows/x64/Debug_DLL/lib_lightgbm.dll; then\n echo \"[INFO] found pre-compiled windows/x64/Debug_DLL/lib_lightgbm.dll\"\n cp ../windows/x64/Debug_DLL/lib_lightgbm.dll ./lightgbm/lib/lib_lightgbm.dll\n cp ../windows/x64/Debug_DLL/lib_lightgbm.lib ./lightgbm/lib/lib_lightgbm.lib\n else\n echo \"[ERROR] cannot find pre-compiled library. Aborting\"\n exit 1\n fi\n rm -f ./*.bak\n fi\n\n # at this point, if 'install' was passed but the package type wasn't indicated, prefer wheel\n if test \"${BUILD_SDIST}\" = false && test \"${BUILD_WHEEL}\" = false; then\n echo \"[INFO] 'install' passed but no package type ('bdist_wheel', 'sdist') chosen. Defaulting to 'bdist_wheel'.\"\n BUILD_SDIST=\"false\"\n BUILD_WHEEL=\"true\"\n fi\nfi\n\nif test \"${BUILD_SDIST}\" = true; then\n echo \"[INFO] --- building sdist ---\"\n rm -f ../dist/*.tar.gz\n # use xargs to work with args that contain whitespaces\n # note that empty echo string leads to that xargs doesn't run the command\n # in some implementations of xargs\n # ref: https://stackoverflow.com/a/8296746\n echo \"--sdist --outdir ../dist ${BUILD_ARGS} .\" | xargs python -m build\nfi\n\nif test \"${BUILD_WHEEL}\" = true; then\n echo \"[INFO] --- building wheel ---\"\n rm -f ../dist/*.whl || true\n # use xargs to work with args that contain whitespaces\n # note that empty echo string leads to that xargs doesn't run the command\n # in some implementations of xargs\n # ref: https://stackoverflow.com/a/8296746\n echo \"--wheel --outdir ../dist ${BUILD_ARGS} .\" | xargs python -m build\nfi\n\nif test \"${INSTALL}\" = true; then\n echo \"[INFO] --- installing lightgbm ---\"\n cd ..\n if test \"${BUILD_WHEEL}\" = true; then\n PACKAGE_FILE=\"$(echo dist/lightgbm*.whl)\"\n else\n PACKAGE_FILE=\"$(echo dist/lightgbm*.tar.gz)\"\n fi\n # shellcheck disable=SC2086\n pip install \\\n ${PIP_INSTALL_ARGS} \\\n --force-reinstall \\\n --no-cache-dir \\\n --no-deps \\\n \"${PACKAGE_FILE}\"\nfi\n\necho \"[INFO] cleaning up\"\nrm -rf ./lightgbm-python\n" }, { "path": "build_r.R", "content": "# For macOS users who have decided to use gcc\n# (replace 8 with version of gcc installed on your machine)\n# NOTE: your gcc / g++ from Homebrew is probably in /usr/local/bin\n#export CXX=/usr/local/bin/g++-8 CC=/usr/local/bin/gcc-8\n# Sys.setenv(\"CXX\" = \"/usr/local/bin/g++-8\")\n# Sys.setenv(\"CC\" = \"/usr/local/bin/gcc-8\")\n\nargs <- commandArgs(trailingOnly = TRUE)\nINSTALL_AFTER_BUILD <- !(\"--skip-install\" %in% args)\nTEMP_R_DIR <- file.path(getwd(), \"lightgbm_r\")\nTEMP_SOURCE_DIR <- file.path(TEMP_R_DIR, \"src\")\n\n# [description]\n# Parse the content of commandArgs() into a structured\n# list. This returns a list with two sections.\n# * \"flags\" = a character of vector of flags like \"--use-gpu\"\n# * \"keyword_args\" = a named character vector, where names\n# refer to options and values are the option values. For\n# example, c(\"--boost-librarydir\" = \"/usr/lib/x86_64-linux-gnu\")\n.parse_args <- function(args) {\n out_list <- list(\n \"flags\" = character(0L)\n , \"keyword_args\" = character(0L)\n , \"make_args\" = character(0L)\n )\n for (arg in args) {\n if (any(grepl(\"^\\\\-j[0-9]+\", arg))) { # nolint: non_portable_path.\n out_list[[\"make_args\"]] <- arg\n } else if (any(grepl(\"=\", arg, fixed = TRUE))) {\n split_arg <- strsplit(arg, \"=\", fixed = TRUE)[[1L]]\n arg_name <- split_arg[[1L]]\n arg_value <- split_arg[[2L]]\n out_list[[\"keyword_args\"]][[arg_name]] <- arg_value\n } else {\n out_list[[\"flags\"]] <- c(out_list[[\"flags\"]], arg)\n }\n }\n return(out_list)\n}\nparsed_args <- .parse_args(args)\n\nSKIP_VIGNETTES <- \"--no-build-vignettes\" %in% parsed_args[[\"flags\"]]\nUSING_GPU <- \"--use-gpu\" %in% parsed_args[[\"flags\"]]\nUSING_MINGW <- \"--use-mingw\" %in% parsed_args[[\"flags\"]]\nUSING_MSYS2 <- \"--use-msys2\" %in% parsed_args[[\"flags\"]]\n\n# this maps command-line arguments to defines passed into CMake,\nARGS_TO_DEFINES <- c(\n \"--boost-root\" = \"-DBOOST_ROOT\"\n , \"--boost-dir\" = \"-DBoost_DIR\"\n , \"--boost-include-dir\" = \"-DBoost_INCLUDE_DIR\"\n , \"--boost-librarydir\" = \"-DBOOST_LIBRARYDIR\"\n , \"--opencl-include-dir\" = \"-DOpenCL_INCLUDE_DIR\"\n , \"--opencl-library\" = \"-DOpenCL_LIBRARY\"\n)\n\nrecognized_args <- c(\n \"--no-build-vignettes\"\n , \"--skip-install\"\n , \"--use-gpu\"\n , \"--use-mingw\"\n , \"--use-msys2\"\n , names(ARGS_TO_DEFINES)\n)\ngiven_args <- c(\n parsed_args[[\"flags\"]]\n , names(parsed_args[[\"keyword_args\"]])\n)\nunrecognized_args <- setdiff(given_args, recognized_args)\nif (length(unrecognized_args) > 0L) {\n msg <- paste0(\n \"Unrecognized arguments: \"\n , toString(unrecognized_args)\n )\n stop(msg)\n}\n\n# [description] Replace statements in install.libs.R code based on\n# command-line flags\n.replace_flag <- function(variable_name, value, content) {\n out <- gsub(\n pattern = paste0(variable_name, \" <-.*\")\n , replacement = paste0(variable_name, \" <- \", as.character(value))\n , x = content\n )\n return(out)\n}\n\ninstall_libs_content <- readLines(\n file.path(\"R-package\", \"src\", \"install.libs.R\")\n)\ninstall_libs_content <- .replace_flag(\"use_gpu\", USING_GPU, install_libs_content)\ninstall_libs_content <- .replace_flag(\"use_mingw\", USING_MINGW, install_libs_content)\ninstall_libs_content <- .replace_flag(\"use_msys2\", USING_MSYS2, install_libs_content)\n\n# set up extra flags based on keyword arguments\nkeyword_args <- parsed_args[[\"keyword_args\"]]\nif (length(keyword_args) > 0L) {\n cmake_args_to_add <- NULL\n for (i in seq_along(keyword_args)) {\n arg_name <- names(keyword_args)[[i]]\n define_name <- ARGS_TO_DEFINES[[arg_name]]\n arg_value <- shQuote(normalizePath(keyword_args[[arg_name]], winslash = \"/\"))\n cmake_args_to_add <- c(cmake_args_to_add, paste0(define_name, \"=\", arg_value))\n }\n install_libs_content <- gsub(\n pattern = paste0(\"command_line_args <- NULL\")\n , replacement = paste0(\n \"command_line_args <- c(\\'\"\n , paste(cmake_args_to_add, collapse = \"', '\")\n , \"')\"\n )\n , x = install_libs_content\n , fixed = TRUE\n )\n}\n\n# if provided, set '-j' in 'make' commands in install.libs.R\nif (length(parsed_args[[\"make_args\"]]) > 0L) {\n install_libs_content <- gsub(\n pattern = \"make_args_from_build_script <- character(0L)\"\n , replacement = paste0(\n \"make_args_from_build_script <- c(\\\"\"\n , paste(parsed_args[[\"make_args\"]], collapse = \"\\\", \\\"\")\n , \"\\\")\"\n )\n , x = install_libs_content\n , fixed = TRUE\n )\n}\n\n# R returns FALSE (not a non-zero exit code) if a file copy operation\n# breaks. Let's fix that\n.handle_result <- function(res) {\n if (!all(res)) {\n stop(\"Copying files failed!\")\n }\n return(invisible(NULL))\n}\n\n# system() will not raise an R exception if the process called\n# fails. Wrapping it here to get that behavior.\n#\n# system() introduces a lot of overhead, at least on Windows,\n# so trying processx if it is available\n.run_shell_command <- function(cmd, args, strict = TRUE) {\n on_windows <- .Platform$OS.type == \"windows\"\n has_processx <- suppressMessages({\n suppressWarnings({\n require(\"processx\") # nolint: undesirable_function, unused_import.\n })\n })\n if (has_processx && on_windows) {\n result <- processx::run(\n command = cmd\n , args = args\n , windows_verbatim_args = TRUE\n , error_on_status = FALSE\n , echo = TRUE\n )\n exit_code <- result$status\n } else {\n if (on_windows) {\n message(paste0(\n \"Using system() to run shell commands. Installing \"\n , \"'processx' with install.packages('processx') might \"\n , \"make this faster.\"\n ))\n }\n cmd <- paste0(cmd, \" \", paste(args, collapse = \" \"))\n exit_code <- system(cmd)\n }\n\n if (exit_code != 0L && isTRUE(strict)) {\n stop(paste0(\"Command failed with exit code: \", exit_code))\n }\n return(invisible(exit_code))\n}\n\n# Make a new temporary folder to work in\nunlink(x = TEMP_R_DIR, recursive = TRUE)\ndir.create(TEMP_R_DIR)\n\n# copy in the relevant files\nresult <- file.copy(\n from = \"R-package/./\"\n , to = sprintf(\"%s/\", TEMP_R_DIR)\n , recursive = TRUE\n , overwrite = TRUE\n)\n.handle_result(result)\n\n# overwrite src/install.libs.R with new content based on command-line flags\nwriteLines(\n text = install_libs_content\n , con = file.path(TEMP_SOURCE_DIR, \"install.libs.R\")\n)\n\n# Add blank Makevars files\nresult <- file.copy(\n from = file.path(TEMP_R_DIR, \"inst\", \"Makevars\")\n , to = file.path(TEMP_SOURCE_DIR, \"Makevars\")\n , overwrite = TRUE\n)\n.handle_result(result)\nresult <- file.copy(\n from = file.path(TEMP_R_DIR, \"inst\", \"Makevars.win\")\n , to = file.path(TEMP_SOURCE_DIR, \"Makevars.win\")\n , overwrite = TRUE\n)\n.handle_result(result)\n\nresult <- file.copy(\n from = \"include/\"\n , to = sprintf(\"%s/\", TEMP_SOURCE_DIR)\n , recursive = TRUE\n , overwrite = TRUE\n)\n.handle_result(result)\n\nresult <- file.copy(\n from = \"src/\"\n , to = sprintf(\"%s/\", TEMP_SOURCE_DIR)\n , recursive = TRUE\n , overwrite = TRUE\n)\n.handle_result(result)\n\nEIGEN_R_DIR <- file.path(TEMP_SOURCE_DIR, \"include\", \"Eigen\")\ndir.create(EIGEN_R_DIR)\n\neigen_modules <- c(\n \"Cholesky\"\n , \"Core\"\n , \"Dense\"\n , \"Eigenvalues\"\n , \"Geometry\"\n , \"Householder\"\n , \"Jacobi\"\n , \"LU\"\n , \"QR\"\n , \"SVD\"\n)\nfor (eigen_module in eigen_modules) {\n result <- file.copy(\n from = file.path(\"external_libs\", \"eigen\", \"Eigen\", eigen_module)\n , to = EIGEN_R_DIR\n , recursive = FALSE\n , overwrite = TRUE\n )\n .handle_result(result)\n}\n\ndir.create(file.path(EIGEN_R_DIR, \"src\"))\n\nfor (eigen_module in c(eigen_modules, \"misc\", \"plugins\")) {\n if (eigen_module == \"Dense\") {\n next\n }\n module_dir <- file.path(EIGEN_R_DIR, \"src\", eigen_module)\n dir.create(module_dir, recursive = TRUE)\n result <- file.copy(\n from = sprintf(\"%s/\", file.path(\"external_libs\", \"eigen\", \"Eigen\", \"src\", eigen_module))\n , to = sprintf(\"%s/\", file.path(EIGEN_R_DIR, \"src\"))\n , recursive = TRUE\n , overwrite = TRUE\n )\n .handle_result(result)\n}\n\n.replace_pragmas <- function(filepath) {\n pragma_patterns <- c(\n \"^.*#pragma clang diagnostic.*$\"\n , \"^.*#pragma diag_suppress.*$\"\n , \"^.*#pragma GCC diagnostic.*$\"\n , \"^.*#pragma region.*$\"\n , \"^.*#pragma endregion.*$\"\n , \"^.*#pragma warning.*$\"\n )\n content <- readLines(filepath)\n for (pragma_pattern in pragma_patterns) {\n content <- content[!grepl(pragma_pattern, content)]\n }\n writeLines(content, filepath)\n}\n\n# remove pragmas that suppress warnings, to appease R CMD check\n.replace_pragmas(\n file.path(EIGEN_R_DIR, \"src\", \"Core\", \"arch\", \"SSE\", \"Complex.h\")\n)\n.replace_pragmas(\n file.path(EIGEN_R_DIR, \"src\", \"Core\", \"util\", \"DisableStupidWarnings.h\")\n)\n\nresult <- file.copy(\n from = \"CMakeLists.txt\"\n , to = file.path(TEMP_R_DIR, \"inst\", \"bin/\")\n , overwrite = TRUE\n)\n.handle_result(result)\n\n# remove CRAN-specific files\nresult <- file.remove(\n file.path(TEMP_R_DIR, \"cleanup\")\n , file.path(TEMP_R_DIR, \"configure\")\n , file.path(TEMP_R_DIR, \"configure.ac\")\n , file.path(TEMP_R_DIR, \"configure.win\")\n , file.path(TEMP_SOURCE_DIR, \"Makevars.in\")\n , file.path(TEMP_SOURCE_DIR, \"Makevars.win.in\")\n)\n.handle_result(result)\n\n#------------#\n# submodules #\n#------------#\nEXTERNAL_LIBS_R_DIR <- file.path(TEMP_SOURCE_DIR, \"external_libs\")\ndir.create(EXTERNAL_LIBS_R_DIR)\nfor (submodule in list.dirs(\n path = \"external_libs\"\n , full.names = FALSE\n , recursive = FALSE\n)) {\n # compute/ is a submodule with boost, only needed if\n # building the R-package with GPU support;\n # eigen/ has a special treatment due to licensing aspects\n if ((submodule == \"compute\" && !USING_GPU) || submodule == \"eigen\") {\n next\n }\n result <- file.copy(\n from = sprintf(\"%s/\", file.path(\"external_libs\", submodule))\n , to = sprintf(\"%s/\", EXTERNAL_LIBS_R_DIR)\n , recursive = TRUE\n , overwrite = TRUE\n )\n .handle_result(result)\n}\n\n# copy files into the place CMake expects\nCMAKE_MODULES_R_DIR <- file.path(TEMP_SOURCE_DIR, \"cmake\", \"modules\")\ndir.create(CMAKE_MODULES_R_DIR, recursive = TRUE)\nresult <- file.copy(\n from = file.path(\"cmake\", \"modules\", \"FindLibR.cmake\")\n , to = sprintf(\"%s/\", CMAKE_MODULES_R_DIR)\n , overwrite = TRUE\n)\n.handle_result(result)\nfor (src_file in c(\"lightgbm_R.cpp\", \"lightgbm_R.h\")) {\n result <- file.copy(\n from = file.path(TEMP_SOURCE_DIR, src_file)\n , to = file.path(TEMP_SOURCE_DIR, \"src\", src_file)\n , overwrite = TRUE\n )\n .handle_result(result)\n result <- file.remove(\n file.path(TEMP_SOURCE_DIR, src_file)\n )\n .handle_result(result)\n}\n\nresult <- file.copy(\n from = file.path(\"R-package\", \"inst\", \"make-r-def.R\")\n , to = file.path(TEMP_R_DIR, \"inst\", \"bin/\")\n , overwrite = TRUE\n)\n.handle_result(result)\n\n# R packages cannot have versions like 3.0.0rc1, but\n# 3.0.0-1 is acceptable\nLGB_VERSION <- readLines(\"VERSION.txt\")[1L]\nLGB_VERSION <- gsub(\n pattern = \"rc\"\n , replacement = \"-\"\n , x = LGB_VERSION\n , fixed = TRUE\n)\n\n# DESCRIPTION has placeholders for version\n# and date so it doesn't have to be updated manually\nDESCRIPTION_FILE <- file.path(TEMP_R_DIR, \"DESCRIPTION\")\ndescription_contents <- readLines(DESCRIPTION_FILE)\ndescription_contents <- gsub(\n pattern = \"~~VERSION~~\"\n , replacement = LGB_VERSION\n , x = description_contents\n , fixed = TRUE\n)\ndescription_contents <- gsub(\n pattern = \"~~DATE~~\"\n , replacement = as.character(Sys.Date())\n , x = description_contents\n , fixed = TRUE\n)\nwriteLines(description_contents, DESCRIPTION_FILE)\n\n# NOTE: --keep-empty-dirs is necessary to keep the deep paths expected\n# by CMake while also meeting the CRAN req to create object files\n# on demand\nr_build_args <- c(\"CMD\", \"build\", TEMP_R_DIR, \"--keep-empty-dirs\")\nif (isTRUE(SKIP_VIGNETTES)) {\n r_build_args <- c(r_build_args, \"--no-build-vignettes\")\n}\n.run_shell_command(\"R\", r_build_args)\n\n# Install the package\nversion <- gsub(\n pattern = \"Version: \",\n replacement = \"\",\n x = grep(\n pattern = \"Version: \"\n , x = readLines(con = file.path(TEMP_R_DIR, \"DESCRIPTION\"))\n , value = TRUE\n , fixed = TRUE\n )\n , fixed = TRUE\n)\ntarball <- file.path(getwd(), sprintf(\"lightgbm_%s.tar.gz\", version))\n\ninstall_cmd <- \"R\"\ninstall_args <- c(\"CMD\", \"INSTALL\", \"--no-multiarch\", \"--with-keep.source\", tarball)\nif (INSTALL_AFTER_BUILD) {\n .run_shell_command(install_cmd, install_args)\n} else {\n cmd <- paste0(install_cmd, \" \", paste(install_args, collapse = \" \"))\n print(sprintf(\"Skipping installation. Install the package with command '%s'\", cmd))\n}\n" }, { "path": "cmake/IntegratedOpenCL.cmake", "content": "set(BUILD_SHARED_LIBS OFF CACHE BOOL \"\" FORCE)\nset(BOOST_VERSION_DOT \"1.74\")\nstring(REPLACE \".\" \"_\" BOOST_VERSION_UNDERSCORE ${BOOST_VERSION_DOT})\n\nset(OPENCL_HEADER_REPOSITORY \"https://github.com/KhronosGroup/OpenCL-Headers.git\")\nset(OPENCL_HEADER_TAG \"1b2a1850f410aaaaeaa56cead5a179b5aea4918e\")\n\nset(OPENCL_LOADER_REPOSITORY \"https://github.com/KhronosGroup/OpenCL-ICD-Loader.git\")\nset(OPENCL_LOADER_TAG \"98ca71fb9f8484f1cd1999f55224bf9e8d18693b\")\n\nset(BOOST_REPOSITORY \"https://github.com/boostorg/boost.git\")\nset(BOOST_TAG \"boost-${BOOST_VERSION_DOT}.0\")\n\n# Build Independent OpenCL library\ninclude(FetchContent)\n# lint_cmake: -readability/wonkycase\nFetchContent_Declare(OpenCL-Headers GIT_REPOSITORY ${OPENCL_HEADER_REPOSITORY} GIT_TAG ${OPENCL_HEADER_TAG})\nFetchContent_GetProperties(OpenCL-Headers)\n# lint_cmake: +readability/wonkycase\nif(NOT OpenCL-Headers_POPULATED)\n# lint_cmake: -readability/wonkycase\n FetchContent_MakeAvailable(OpenCL-Headers)\n# lint_cmake: +readability/wonkycase\n message(STATUS \"Populated OpenCL Headers\")\nendif()\nset(OPENCL_ICD_LOADER_HEADERS_DIR ${opencl-headers_SOURCE_DIR} CACHE PATH \"\") # for OpenCL ICD Loader\nset(OpenCL_INCLUDE_DIR ${opencl-headers_SOURCE_DIR} CACHE PATH \"\") # for Boost::Compute\n\n# lint_cmake: -readability/wonkycase\nFetchContent_Declare(\n# lint_cmake: +readability/wonkycase\n OpenCL-ICD-Loader\n GIT_REPOSITORY\n ${OPENCL_LOADER_REPOSITORY}\n GIT_TAG\n ${OPENCL_LOADER_TAG}\n EXCLUDE_FROM_ALL\n)\n# lint_cmake: -readability/wonkycase\nFetchContent_GetProperties(OpenCL-ICD-Loader)\n# lint_cmake: +readability/wonkycase\nif(NOT OpenCL-ICD-Loader_POPULATED)\n# lint_cmake: -readability/wonkycase\n FetchContent_MakeAvailable(OpenCL-ICD-Loader)\n# lint_cmake: +readability/wonkycase\n if(WIN32)\n set(USE_DYNAMIC_VCXX_RUNTIME ON)\n endif()\n message(STATUS \"Populated OpenCL ICD Loader\")\nendif()\nlist(APPEND INTEGRATED_OPENCL_INCLUDES ${OPENCL_ICD_LOADER_HEADERS_DIR})\nlist(APPEND INTEGRATED_OPENCL_DEFINITIONS CL_TARGET_OPENCL_VERSION=120)\nif(WIN32)\n list(\n APPEND\n INTEGRATED_OPENCL_LIBRARIES\n ${opencl-icd-loader_BINARY_DIR}/Release/OpenCL.lib\n cfgmgr32.lib\n runtimeobject.lib\n )\nelse()\n list(\n APPEND\n INTEGRATED_OPENCL_LIBRARIES\n ${opencl-icd-loader_BINARY_DIR}/libOpenCL.a\n )\n set_property(TARGET OpenCL PROPERTY POSITION_INDEPENDENT_CODE ON)\nendif()\n\n# Build Independent Boost libraries\ninclude(ExternalProject)\ninclude(ProcessorCount)\n# lint_cmake: -readability/wonkycase\nProcessorCount(J)\n# lint_cmake: +readability/wonkycase\nset(BOOST_BASE \"${PROJECT_BINARY_DIR}/Boost\")\nset(BOOST_INCLUDE \"${BOOST_BASE}/source\" CACHE PATH \"\")\nset(BOOST_LIBRARY \"${BOOST_BASE}/source/stage/lib\" CACHE PATH \"\")\nif(WIN32)\n if(MSVC)\n # references:\n #\n # * range of MSVC versions: https://learn.microsoft.com/en-us/cpp/overview/compiler-versions\n # * MSVC toolchain IDs: not sure...\n # comments like https://learn.microsoft.com/en-us/answers/questions/769911/visual-studio-2019-build-tools-v143\n #\n if(${MSVC_VERSION} GREATER 1929)\n set(MSVC_TOOLCHAIN_ID \"143\")\n elseif(${MSVC_VERSION} GREATER 1919)\n set(MSVC_TOOLCHAIN_ID \"142\")\n elseif(${MSVC_VERSION} GREATER 1909)\n set(MSVC_TOOLCHAIN_ID \"141\")\n else()\n message(FATAL_ERROR \"Unsupported MSVC version number: ${MSVC_VERSION}\")\n endif()\n list(\n APPEND\n BOOST_BUILD_BYPRODUCTS\n ${BOOST_LIBRARY}/libboost_filesystem-vc${MSVC_TOOLCHAIN_ID}-mt-x64-${BOOST_VERSION_UNDERSCORE}.lib\n ${BOOST_LIBRARY}/libboost_system-vc${MSVC_TOOLCHAIN_ID}-mt-x64-${BOOST_VERSION_UNDERSCORE}.lib\n ${BOOST_LIBRARY}/libboost_chrono-vc${MSVC_TOOLCHAIN_ID}-mt-x64-${BOOST_VERSION_UNDERSCORE}.lib\n )\n else()\n message(FATAL_ERROR \"Integrated OpenCL build is not yet available for MinGW\")\n endif()\n set(BOOST_BOOTSTRAP \"${BOOST_BASE}/source/bootstrap.bat\")\n set(BOOST_BUILD \"${BOOST_BASE}/source/b2.exe\")\n set(BOOST_FLAGS \"\")\nelse()\n set(BOOST_BOOTSTRAP \"${BOOST_BASE}/source/bootstrap.sh\")\n set(BOOST_BUILD \"${BOOST_BASE}/source/b2\")\n set(BOOST_FLAGS \"-fPIC\")\n list(\n APPEND\n BOOST_BUILD_BYPRODUCTS\n ${BOOST_LIBRARY}/libboost_filesystem.a\n ${BOOST_LIBRARY}/libboost_system.a\n ${BOOST_LIBRARY}/libboost_chrono.a\n )\nendif()\nlist(\n APPEND\n BOOST_SUBMODULES\n \"libs/algorithm\"\n \"libs/align\"\n \"libs/any\"\n \"libs/array\"\n \"libs/assert\"\n \"libs/bind\"\n \"libs/chrono\"\n \"libs/compute\"\n \"libs/concept_check\"\n \"libs/config\"\n \"libs/container\"\n \"libs/container_hash\"\n \"libs/core\"\n \"libs/detail\"\n \"libs/filesystem\"\n \"libs/foreach\"\n \"libs/format\"\n \"libs/function\"\n \"libs/function_types\"\n \"libs/fusion\"\n \"libs/headers\"\n \"libs/integer\"\n \"libs/io\"\n \"libs/iterator\"\n \"libs/lexical_cast\"\n \"libs/math\"\n \"libs/move\"\n \"libs/mpl\"\n \"libs/multi_index\"\n \"libs/numeric/conversion\"\n \"libs/optional\"\n \"libs/predef\"\n \"libs/preprocessor\"\n \"libs/property_tree\"\n \"libs/range\"\n \"libs/ratio\"\n \"libs/serialization\"\n \"libs/smart_ptr\"\n \"libs/static_assert\"\n \"libs/system\"\n \"libs/throw_exception\"\n \"libs/tuple\"\n \"libs/typeof\"\n \"libs/type_index\"\n \"libs/type_traits\"\n \"libs/utility\"\n \"libs/uuid\"\n \"libs/winapi\"\n \"tools/boost_install\"\n \"tools/build\"\n)\n# lint_cmake: -readability/wonkycase\nExternalProject_Add(\n# lint_cmake: +readability/wonkycase\n Boost\n TMP_DIR \"${BOOST_BASE}/tmp\"\n STAMP_DIR \"${BOOST_BASE}/stamp\"\n DOWNLOAD_DIR \"${BOOST_BASE}/download\"\n SOURCE_DIR \"${BOOST_BASE}/source\"\n BINARY_DIR \"${BOOST_BASE}/source\"\n INSTALL_DIR \"${BOOST_BASE}/install\"\n GIT_REPOSITORY ${BOOST_REPOSITORY}\n GIT_TAG ${BOOST_TAG}\n GIT_SUBMODULES ${BOOST_SUBMODULES}\n GIT_SHALLOW ON\n UPDATE_COMMAND \"\"\n PATCH_COMMAND \"\"\n CONFIGURE_COMMAND ${BOOST_BOOTSTRAP}\n BUILD_COMMAND\n ${BOOST_BUILD}\n -sBOOST_ROOT=${BOOST_BASE}/source\n -a\n -q\n -j ${J}\n --with-headers\n --with-chrono\n --with-filesystem\n --with-system\n link=static\n runtime-link=shared\n variant=release\n threading=multi\n cxxflags=\"${BOOST_FLAGS}\"\n INSTALL_COMMAND \"\"\n # BUILD_BYPRODUCTS is necessary to support 'Ninja' builds.\n # ref:\n # - https://cmake.org/cmake/help/latest/module/ExternalProject.html\n # - https://stackoverflow.com/a/65803911/3986677\n BUILD_BYPRODUCTS ${BOOST_BUILD_BYPRODUCTS}\n)\nlist(APPEND INTEGRATED_OPENCL_INCLUDES ${BOOST_INCLUDE})\nlist(APPEND INTEGRATED_OPENCL_LIBRARIES ${BOOST_BUILD_BYPRODUCTS})\n\nset(BUILD_SHARED_LIBS ON CACHE BOOL \"\" FORCE)\n" }, { "path": "cmake/Sanitizer.cmake", "content": "# Set appropriate compiler and linker flags for sanitizers.\n#\n# Usage of this module:\n# enable_sanitizers(\"address;leak\")\n\n# Add flags\nmacro(enable_sanitizer sanitizer)\n if(${sanitizer} MATCHES \"address\")\n set(SAN_COMPILE_FLAGS \"${SAN_COMPILE_FLAGS} -fsanitize=address\")\n\n elseif(${sanitizer} MATCHES \"thread\")\n set(SAN_COMPILE_FLAGS \"${SAN_COMPILE_FLAGS} -fsanitize=thread\")\n\n elseif(${sanitizer} MATCHES \"leak\")\n set(SAN_COMPILE_FLAGS \"${SAN_COMPILE_FLAGS} -fsanitize=leak\")\n\n elseif(${sanitizer} MATCHES \"undefined\")\n set(SAN_COMPILE_FLAGS \"${SAN_COMPILE_FLAGS} -fsanitize=undefined -fno-sanitize-recover=undefined\")\n\n else()\n message(FATAL_ERROR \"Sanitizer ${sanitizer} not supported.\")\n endif()\nendmacro()\n\nmacro(enable_sanitizers SANITIZERS)\n # Check sanitizers compatibility.\n foreach(_san ${SANITIZERS})\n string(TOLOWER ${_san} _san)\n if(_san MATCHES \"thread\")\n if(${_use_other_sanitizers})\n message(FATAL_ERROR \"thread sanitizer is not compatible with ${_san} sanitizer.\")\n endif()\n set(_use_thread_sanitizer 1)\n else()\n if(${_use_thread_sanitizer})\n message(FATAL_ERROR \"${_san} sanitizer is not compatible with thread sanitizer.\")\n endif()\n set(_use_other_sanitizers 1)\n endif()\n endforeach()\n\n message(STATUS \"Sanitizers: ${SANITIZERS}\")\n\n foreach(_san ${SANITIZERS})\n string(TOLOWER ${_san} _san)\n enable_sanitizer(${_san})\n endforeach()\n message(STATUS \"Sanitizers compile flags: ${SAN_COMPILE_FLAGS}\")\n set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} ${SAN_COMPILE_FLAGS}\")\n set(CMAKE_C_FLAGS \"${CMAKE_C_FLAGS} ${SAN_COMPILE_FLAGS}\")\nendmacro()\n" }, { "path": "cmake/modules/FindLibR.cmake", "content": "# CMake module used to find the location of R's\n# dll and header files.\n#\n# Borrows heavily from xgboost's R package:\n#\n# * https://github.com/dmlc/xgboost/blob/master/cmake/modules/FindLibR.cmake\n#\n# Defines the following:\n# LIBR_FOUND\n# LIBR_HOME\n# LIBR_EXECUTABLE\n# LIBR_MSVC_CORE_LIBRARY\n# LIBR_INCLUDE_DIRS\n# LIBR_LIBS_DIR\n# LIBR_CORE_LIBRARY\n# and a CMake function to create R.lib for MSVC\n\n# lint_cmake: -convention/filename\n\nif(NOT R_ARCH)\n if(\"${CMAKE_SIZEOF_VOID_P}\" STREQUAL \"4\")\n set(R_ARCH \"i386\")\n else()\n set(R_ARCH \"x64\")\n endif()\nendif()\n\nif(NOT (\"${R_ARCH}\" STREQUAL \"x64\"))\n message(FATAL_ERROR \"LightGBM's R-package currently only supports 64-bit operating systems\")\nendif()\n\n# Creates R.lib and R.def in the build directory for linking with MSVC\n# https://docs.microsoft.com/en-us/cpp/build/reference/link-input-files?redirectedfrom=MSDN&view=vs-2019\nfunction(create_rlib_for_msvc)\n\n message(STATUS \"Creating R.lib and R.def\")\n\n # various checks and warnings\n if(NOT WIN32 OR NOT MSVC)\n message(FATAL_ERROR \"create_rlib_for_msvc() can only be used with MSVC\")\n endif()\n\n if(NOT EXISTS \"${LIBR_CORE_LIBRARY}\")\n message(FATAL_ERROR \"LIBR_CORE_LIBRARY, '${LIBR_CORE_LIBRARY}', not found\")\n endif()\n\n find_program(DLLTOOL_EXE dlltool)\n\n if(NOT DLLTOOL_EXE)\n message(FATAL_ERROR \"dlltool.exe not found!\\nDo you have Rtools installed with its MinGW's bin/ in PATH?\")\n endif()\n\n set(LIBR_MSVC_CORE_LIBRARY \"${CMAKE_CURRENT_BINARY_DIR}/R.lib\" CACHE PATH \"R.lib filepath\")\n\n get_filename_component(\n LIBR_RSCRIPT_EXECUTABLE_DIR\n ${LIBR_EXECUTABLE}\n DIRECTORY\n )\n set(LIBR_RSCRIPT_EXECUTABLE \"${LIBR_RSCRIPT_EXECUTABLE_DIR}/Rscript\")\n\n execute_process(\n COMMAND ${LIBR_RSCRIPT_EXECUTABLE}\n \"${CMAKE_CURRENT_BINARY_DIR}/make-r-def.R\"\n \"${LIBR_CORE_LIBRARY}\" \"${CMAKE_CURRENT_BINARY_DIR}/R.def\"\n )\n execute_process(\n COMMAND ${DLLTOOL_EXE}\n \"--input-def\" \"${CMAKE_CURRENT_BINARY_DIR}/R.def\"\n \"--output-lib\" \"${LIBR_MSVC_CORE_LIBRARY}\"\n )\nendfunction()\n\n# R version information is used to search for R's libraries in\n# the registry on Windows. Since this code is orchestrated by\n# an R script (src/install.libs.R), that script uses R's built-ins to\n# find the version of R and pass it through as a CMake variable\nif(CMAKE_R_VERSION)\n message(STATUS \"R version passed into FindLibR.cmake: ${CMAKE_R_VERSION}\")\nelseif(WIN32)\n message(\n FATAL_ERROR\n \"Expected CMAKE_R_VERSION to be passed in on Windows but none was provided. Check src/install.libs.R\"\n )\nendif()\n\n\nif(NOT LIBR_EXECUTABLE)\n find_program(\n LIBR_EXECUTABLE\n NAMES R R.exe\n )\n\n # CRAN may run RD CMD CHECK instead of R CMD CHECK,\n # which can lead to this infamous error:\n # 'R' should not be used without a path -- see par. 1.6 of the manual\n if(LIBR_EXECUTABLE MATCHES \".*\\\\.Rcheck.*\")\n unset(LIBR_EXECUTABLE CACHE)\n endif()\n\n # ignore the R bundled with R.app on Mac, since that is GUI-only\n if(LIBR_EXECUTABLE MATCHES \".+R\\\\.app.*\")\n unset(LIBR_EXECUTABLE CACHE)\n endif()\nendif()\n\n# Find R executable unless it has been provided directly or already found\nif(NOT LIBR_EXECUTABLE)\n if(APPLE)\n\n find_library(LIBR_LIBRARIES R)\n\n if(LIBR_LIBRARIES MATCHES \".*\\\\.framework\")\n set(LIBR_HOME \"${LIBR_LIBRARIES}/Resources\")\n set(LIBR_EXECUTABLE \"${LIBR_HOME}/R\")\n else()\n get_filename_component(_LIBR_LIBRARIES \"${LIBR_LIBRARIES}\" REALPATH)\n get_filename_component(_LIBR_LIBRARIES_DIR \"${_LIBR_LIBRARIES}\" DIRECTORY)\n set(LIBR_EXECUTABLE \"${_LIBR_LIBRARIES_DIR}/../bin/R\")\n endif()\n\n elseif(UNIX)\n\n # attempt to find R executable\n if(NOT LIBR_EXECUTABLE)\n find_program(\n LIBR_EXECUTABLE\n NO_DEFAULT_PATH\n HINTS \"${CMAKE_CURRENT_BINARY_DIR}\" \"/usr/bin\" \"/usr/lib/\" \"/usr/local/bin/\"\n NAMES R\n )\n endif()\n\n # Windows\n else()\n\n # if R executable not available, query R_HOME path from registry\n if(NOT LIBR_HOME)\n\n # Try to find R's location in the registry\n # ref: https://cran.r-project.org/bin/windows/base/rw-FAQ.html#Does-R-use-the-Registry_003f\n get_filename_component(\n LIBR_HOME\n \"[HKEY_LOCAL_MACHINE\\\\SOFTWARE\\\\R-core\\\\R\\\\${CMAKE_R_VERSION};InstallPath]\"\n ABSOLUTE\n )\n endif()\n\n if(NOT LIBR_HOME)\n get_filename_component(\n LIBR_HOME\n \"[HKEY_CURRENT_USER\\\\SOFTWARE\\\\R-core\\\\R\\\\${CMAKE_R_VERSION};InstallPath]\"\n ABSOLUTE\n )\n endif()\n\n if(NOT LIBR_HOME)\n message(\n FATAL_ERROR\n \"Unable to locate R executable.\\\n\\nEither add its location to PATH or provide it through the LIBR_EXECUTABLE CMake variable\"\n )\n endif()\n\n # set exe location based on R_ARCH\n set(LIBR_EXECUTABLE \"${LIBR_HOME}/bin/${R_ARCH}/R.exe\")\n\n endif()\n\n if(NOT LIBR_EXECUTABLE)\n message(\n FATAL_ERROR\n \"Unable to locate R executable.\\\n\\nEither add its location to PATH or provide it through the LIBR_EXECUTABLE CMake variable\"\n )\n endif()\n\nendif()\n\n# ask R for the home path\nexecute_process(\n COMMAND ${LIBR_EXECUTABLE} \"--slave\" \"--vanilla\" \"-e\" \"cat(normalizePath(R.home(), winslash='/'))\"\n OUTPUT_VARIABLE LIBR_HOME\n)\n\n# ask R for the include dir\nexecute_process(\n COMMAND ${LIBR_EXECUTABLE} \"--slave\" \"--vanilla\" \"-e\" \"cat(normalizePath(R.home('include'), winslash='/'))\"\n OUTPUT_VARIABLE LIBR_INCLUDE_DIRS\n)\n\n# ask R for the lib dir\nexecute_process(\n COMMAND ${LIBR_EXECUTABLE} \"--slave\" \"--vanilla\" \"-e\" \"cat(normalizePath(R.home('lib'), winslash='/'))\"\n OUTPUT_VARIABLE LIBR_LIBS_DIR\n)\n\nset(LIBR_HOME ${LIBR_HOME} CACHE PATH \"R home directory\")\nset(LIBR_EXECUTABLE ${LIBR_EXECUTABLE} CACHE PATH \"R executable\")\nset(LIBR_INCLUDE_DIRS ${LIBR_INCLUDE_DIRS} CACHE PATH \"R include directory\")\nset(LIBR_LIBS_DIR ${LIBR_LIBS_DIR} CACHE PATH \"Where R stores vendored third-party libraries\")\n\n# where is R.so / R.dll / libR.so likely to be found?\nset(\n LIBR_PATH_HINTS\n \"${CMAKE_CURRENT_BINARY_DIR}\"\n \"${LIBR_HOME}/lib\"\n \"${LIBR_HOME}/bin/${R_ARCH}\"\n \"${LIBR_HOME}/bin\"\n \"${LIBR_LIBRARIES}\"\n)\n\n# look for the core R library\nfind_library(\n LIBR_CORE_LIBRARY\n NAMES R R.dll\n HINTS ${LIBR_PATH_HINTS}\n)\n\n# starting from CMake 3.17, find_library() will not find .dll files by default\n# https://cmake.org/cmake/help/v3.17/release/3.17.html#other-changes\nif(WIN32 AND NOT LIBR_CORE_LIBRARY)\n find_file(\n LIBR_CORE_LIBRARY\n NAME R.dll\n HINTS ${LIBR_PATH_HINTS}\n )\nendif()\n\nset(LIBR_CORE_LIBRARY ${LIBR_CORE_LIBRARY} CACHE PATH \"R core shared library\")\n\nif(WIN32 AND MSVC)\n\n # create a local R.lib import library for R.dll if it doesn't exist\n if(NOT EXISTS \"${CMAKE_CURRENT_BINARY_DIR}/R.lib\")\n create_rlib_for_msvc()\n endif()\n\nendif()\n\n# define find requirements\ninclude(FindPackageHandleStandardArgs)\n\nif(WIN32 AND MSVC)\n# lint_cmake: -package/stdargs\n find_package_handle_standard_args(\n# lint_cmake: +package/stdargs\n LibR DEFAULT_MSG\n LIBR_HOME\n LIBR_EXECUTABLE\n LIBR_INCLUDE_DIRS\n LIBR_LIBS_DIR\n LIBR_CORE_LIBRARY\n LIBR_MSVC_CORE_LIBRARY\n )\nelse()\n# lint_cmake: -package/stdargs\n find_package_handle_standard_args(\n# lint_cmake: +package/stdargs\n LibR DEFAULT_MSG\n LIBR_HOME\n LIBR_EXECUTABLE\n LIBR_INCLUDE_DIRS\n LIBR_LIBS_DIR\n LIBR_CORE_LIBRARY\n )\nendif()\n" }, { "path": "cmake/modules/FindNCCL.cmake", "content": "#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n# Tries to find NCCL headers and libraries.\n#\n# Usage of this module as follows:\n#\n# find_package(NCCL)\n#\n# Variables used by this module, they can change the default behaviour and need\n# to be set before calling find_package:\n#\n# NCCL_ROOT - When set, this path is inspected instead of standard library\n# locations as the root of the NCCL installation.\n# The environment variable NCCL_ROOT overrides this variable.\n#\n# This module defines\n# NCCL_FOUND, whether nccl has been found\n# NCCL_INCLUDE_DIR, directory containing header\n# NCCL_LIBRARY, directory containing nccl library\n# NCCL_LIB_NAME, nccl library name\n# USE_NCCL_LIB_PATH, when set, NCCL_LIBRARY path is also inspected for the\n# location of the nccl library. This would disable\n# switching between static and shared.\n#\n# This module assumes that the user has already called find_package(CUDA)\n\nif(NCCL_LIBRARY)\n if(NOT USE_NCCL_LIB_PATH)\n # Don't cache NCCL_LIBRARY to enable switching between static and shared.\n unset(NCCL_LIBRARY CACHE)\n endif()\nendif()\n\nif(BUILD_WITH_SHARED_NCCL)\n # libnccl.so\n set(NCCL_LIB_NAME nccl)\nelse()\n # libnccl_static.a\n set(NCCL_LIB_NAME nccl_static)\nendif()\n\nfind_path(NCCL_INCLUDE_DIR\n NAMES nccl.h\n PATHS $ENV{NCCL_ROOT}/include ${NCCL_ROOT}/include)\n\nfind_library(NCCL_LIBRARY\n NAMES ${NCCL_LIB_NAME}\n PATHS $ENV{NCCL_ROOT}/lib/ ${NCCL_ROOT}/lib)\n\nmessage(STATUS \"Using nccl library: ${NCCL_LIBRARY}\")\n\ninclude(FindPackageHandleStandardArgs)\nfind_package_handle_standard_args(NCCL DEFAULT_MSG\n NCCL_INCLUDE_DIR NCCL_LIBRARY)\n\nmark_as_advanced(\n NCCL_INCLUDE_DIR\n NCCL_LIBRARY\n)\n" }, { "path": "docker/README.md", "content": "# Using LightGBM via Docker\n\nThis directory contains `Dockerfile`s to make it easy to build and run LightGBM via [Docker](https://www.docker.com/).\n\nThese builds of LightGBM all train on the CPU. For GPU-enabled builds, see [the gpu/ directory](./gpu).\n\n## Installing Docker\n\nFollow the general installation instructions [on the Docker site](https://docs.docker.com/install/):\n\n* [macOS](https://docs.docker.com/docker-for-mac/install/)\n* [Ubuntu](https://docs.docker.com/install/linux/docker-ce/ubuntu/)\n* [Windows](https://docs.docker.com/docker-for-windows/install/)\n\n## Using CLI Version of LightGBM via Docker\n\nBuild an image with the LightGBM CLI.\n\n```shell\nmkdir lightgbm-docker\ncd lightgbm-docker\nwget https://raw.githubusercontent.com/lightgbm-org/LightGBM/master/docker/dockerfile-cli\ndocker build \\\n -t lightgbm-cli \\\n -f dockerfile-cli \\\n .\n```\n\nOnce that completes, the built image can be used to run the CLI in a container.\nTo try it out, run the following.\n\n```shell\n# configure the CLI\ncat << EOF > train.conf\ntask = train\nobjective = binary\ndata = binary.train\nnum_trees = 10\noutput_model = LightGBM-CLI-model.txt\nEOF\n\n# get training data\ncurl -O https://raw.githubusercontent.com/lightgbm-org/LightGBM/master/examples/binary_classification/binary.train\n\n# train, and save model to a text file\ndocker run \\\n --rm \\\n --volume \"${PWD}\":/opt/training \\\n --workdir /opt/training \\\n lightgbm-cli \\\n config=train.conf\n```\n\nAfter this runs, a LightGBM model can be found at `LightGBM-CLI-model.txt`.\n\nFor more details on how to configure and use the LightGBM CLI, see https://lightgbm.readthedocs.io/en/latest/Quick-Start.html.\n\n## Running the Python-package Container\n\nBuild an image with the LightGBM Python-package installed.\n\n```shell\nmkdir lightgbm-docker\ncd lightgbm-docker\nwget https://raw.githubusercontent.com/lightgbm-org/LightGBM/master/docker/dockerfile-python\ndocker build \\\n -t lightgbm-python \\\n -f dockerfile-python \\\n .\n```\n\nOnce that completes, the built image can be used to run LightGBM's Python-package in a container.\nRun the following to produce a model using the Python-package.\n\n```shell\n# get training data\ncurl -O https://raw.githubusercontent.com/lightgbm-org/LightGBM/master/examples/binary_classification/binary.train\n\n# create training script\ncat << EOF > train.py\nimport lightgbm as lgb\nimport numpy as np\nparams = {\n \"objective\": \"binary\",\n \"num_trees\": 10\n}\n\nbst = lgb.train(\n train_set=lgb.Dataset(\"binary.train\"),\n params=params\n)\nbst.save_model(\"LightGBM-python-model.txt\")\nEOF\n\n# run training in a container\ndocker run \\\n --rm \\\n --volume \"${PWD}\":/opt/training \\\n --workdir /opt/training \\\n lightgbm-python \\\n python train.py\n```\n\nAfter this runs, a LightGBM model can be found at `LightGBM-python-model.txt`.\n\nOr run an interactive Python session in a container.\n\n```shell\ndocker run \\\n --rm \\\n --volume \"${PWD}\":/opt/training \\\n --workdir /opt/training \\\n -it lightgbm-python \\\n python\n```\n\n## Running the R-package Container\n\nBuild an image with the LightGBM R-package installed.\n\n```shell\nmkdir lightgbm-docker\ncd lightgbm-docker\nwget https://raw.githubusercontent.com/lightgbm-org/LightGBM/master/docker/dockerfile-r\n\ndocker build \\\n -t lightgbm-r \\\n -f dockerfile-r \\\n .\n```\n\nOnce that completes, the built image can be used to run LightGBM's R-package in a container.\nRun the following to produce a model using the R-package.\n\n```shell\n# get training data\ncurl -O https://raw.githubusercontent.com/lightgbm-org/LightGBM/master/examples/binary_classification/binary.train\n\n# create training script\ncat << EOF > train.R\nlibrary(lightgbm)\nparams <- list(\n objective = \"binary\"\n , num_trees = 10L\n)\n\nbst <- lgb.train(\n data = lgb.Dataset(\"binary.train\"),\n params = params\n)\nlgb.save(bst, \"LightGBM-R-model.txt\")\nEOF\n\n# run training in a container\ndocker run \\\n --rm \\\n --volume \"${PWD}\":/opt/training \\\n --workdir /opt/training \\\n lightgbm-r \\\n Rscript train.R\n```\n\nAfter this runs, a LightGBM model can be found at `LightGBM-R-model.txt`.\n\nRun the following to get an interactive R session in a container.\n\n```shell\ndocker run \\\n --rm \\\n -it lightgbm-r \\\n R\n```\n\nTo use [RStudio](https://www.rstudio.com/products/rstudio/), an interactive development environment, run the following.\n\n```shell\ndocker run \\\n --rm \\\n --env PASSWORD=\"lightgbm\" \\\n -p 8787:8787 \\\n lightgbm-r\n```\n\nThen navigate to `localhost:8787` in your local web browser, and log in with username `rstudio` and password `lightgbm`.\n\nTo target a different R version, pass any [valid rocker/verse tag](https://hub.docker.com/r/rocker/verse/tags) to `docker build`.\n\nFor example, to test LightGBM with R 4.5:\n\n```shell\ndocker build \\\n -t lightgbm-r-45 \\\n -f dockerfile-r \\\n --build-arg R_VERSION=4.5 \\\n .\n```\n" }, { "path": "docker/dockerfile-cli", "content": "FROM ubuntu:20.04\n\nENV \\\n DEBIAN_FRONTEND=noninteractive \\\n LANG=C.UTF-8 \\\n LC_ALL=C.UTF-8\n\nRUN apt-get update -y && \\\n apt-get install -y --no-install-recommends \\\n ca-certificates \\\n curl \\\n build-essential \\\n gcc \\\n g++ \\\n git \\\n libomp-dev && \\\n rm -rf /var/lib/apt/lists/*\n\nRUN curl -L -o cmake.sh https://github.com/Kitware/CMake/releases/download/v3.29.2/cmake-3.29.2-linux-x86_64.sh && \\\n chmod +x cmake.sh && \\\n sh ./cmake.sh --prefix=/usr/local --skip-license && \\\n rm cmake.sh\n\nRUN git clone \\\n --recursive \\\n --branch stable \\\n --depth 1 \\\n https://github.com/lightgbm-org/LightGBM && \\\n cd ./LightGBM && \\\n cmake -B build -S . && \\\n cmake --build build -j4 && \\\n cmake --install build && \\\n cd \"${HOME}\" && \\\n rm -rf LightGBM\n\nENTRYPOINT [\"lightgbm\"]\n" }, { "path": "docker/dockerfile-python", "content": "FROM ubuntu:20.04\n\nARG CONDA_DIR=/opt/miniforge\n\nENV \\\n DEBIAN_FRONTEND=noninteractive \\\n LANG=C.UTF-8 \\\n LC_ALL=C.UTF-8 \\\n PATH=$CONDA_DIR/bin:$PATH\n\nRUN apt-get update && \\\n apt-get install -y --no-install-recommends \\\n ca-certificates \\\n cmake \\\n build-essential \\\n gcc \\\n g++ \\\n curl \\\n git \\\n libomp-dev && \\\n # python environment\n curl -sL https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh -o miniforge.sh && \\\n /bin/bash miniforge.sh -f -b -p $CONDA_DIR && \\\n export PATH=\"$CONDA_DIR/bin:$PATH\" && \\\n conda config --set always_yes yes --set changeps1 no && \\\n # lightgbm\n conda install -q -y numpy scipy scikit-learn pandas && \\\n git clone --recursive --branch stable --depth 1 https://github.com/lightgbm-org/LightGBM && \\\n cd ./LightGBM && \\\n sh ./build-python.sh install && \\\n # clean\n apt-get autoremove -y && apt-get clean && \\\n conda clean -a -y && \\\n rm -rf /usr/local/src/*\n" }, { "path": "docker/dockerfile-r", "content": "ARG R_VERSION=latest\nFROM rocker/verse:${R_VERSION}\n\nRUN apt-get update && \\\n apt-get install -y --no-install-recommends \\\n build-essential \\\n libomp-dev && \\\n git clone \\\n --recursive \\\n --branch stable \\\n --depth 1 https://github.com/lightgbm-org/LightGBM && \\\n cd ./LightGBM && \\\n sh build-cran-package.sh --no-build-vignettes && \\\n R CMD INSTALL ./lightgbm_*.tar.gz && \\\n cd .. && \\\n rm -rf ./LightGBM\n" }, { "path": "docker/gpu/README.md", "content": "# Tiny Distroless Dockerfile for LightGBM GPU CLI-only Version\r\n\r\n`dockerfile-cli-only-distroless.gpu` - A multi-stage build based on the `nvidia/opencl:devel-ubuntu18.04` (build) and `distroless/cc-debian10` (production) images. LightGBM (CLI-only) can be utilized in GPU and CPU modes. The resulting image size is around 15 MB.\r\n\r\n---\r\n\r\n# Small Dockerfile for LightGBM GPU CLI-only Version\r\n\r\n`dockerfile-cli-only.gpu` - A multi-stage build based on the `nvidia/opencl:devel` (build) and `nvidia/opencl:runtime` (production) images. LightGBM (CLI-only) can be utilized in GPU and CPU modes. The resulting image size is around 100 MB.\r\n\r\n---\r\n\r\n# Dockerfile for LightGBM GPU Version with Python\r\n\r\n`dockerfile.gpu` - A docker file with LightGBM utilizing nvidia-docker. The file is based on the `nvidia/cuda:8.0-cudnn5-devel` image.\r\nLightGBM can be utilized in GPU and CPU modes and via Python.\r\n\r\n## Contents\r\n\r\n- LightGBM (cpu + gpu)\r\n- Python (conda) + scikit-learn, notebooks, pandas, matplotlib\r\n\r\nRunning the container starts a Jupyter Notebook at `localhost:8888`.\r\n\r\nJupyter password: `keras`.\r\n\r\n## Requirements\r\n\r\nRequires docker and [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) on host machine.\r\n\r\n## Quickstart\r\n\r\n### Build Docker Image\r\n\r\n```sh\r\nmkdir lightgbm-docker\r\ncd lightgbm-docker\r\nwget https://raw.githubusercontent.com/lightgbm-org/LightGBM/master/docker/gpu/dockerfile.gpu\r\ndocker build -f dockerfile.gpu -t lightgbm-gpu .\r\n```\r\n\r\n### Run Image\r\n\r\n```sh\r\nnvidia-docker run --rm -d --name lightgbm-gpu -p 8888:8888 -v /home:/home lightgbm-gpu\r\n```\r\n\r\n### Attach with Command Line Access (if required)\r\n\r\n```sh\r\ndocker exec -it lightgbm-gpu bash\r\n```\r\n\r\n### Jupyter Notebook\r\n\r\n```sh\r\nlocalhost:8888\r\n```\r\n" }, { "path": "docker/gpu/dockerfile-cli-only-distroless.gpu", "content": "# Copyright (c) 2020 The Rector and Visitors of the University of Virginia\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\n# documentation files (the \"Software\"), to deal in the Software without restriction, including without\n# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the\n# Software, and to permit persons to whom the Software is furnished to do so, subject to the following\n# conditions:\n#\n# The above copyright notice and this permission notice shall be included in all copies or substantial portions\n# of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED\n# TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF\n# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n# DEALINGS IN THE SOFTWARE.\n\nFROM nvidia/opencl:devel-ubuntu18.04 AS build\nARG DEBIAN_FRONTEND=noninteractive\nARG OPENCL_LIBRARIES=/usr/lib/x86_64-linux-gnu\nARG OPENCL_INCLUDE_DIR=/usr/include/CL\n\n# SYSTEM\nRUN apt-get update && apt-get install -y --no-install-recommends \\\n build-essential \\\n git \\\n ca-certificates \\\n libglib2.0-0 \\\n libxext6 \\\n libsm6 \\\n libxrender1 \\\n cmake \\\n libboost-dev \\\n libboost-system-dev \\\n libboost-filesystem-dev \\\n gcc \\\n g++ && \\\n rm -rf /var/lib/apt/lists/*\n\n# LightGBM\nWORKDIR /opt\nRUN git clone --recursive --branch stable --depth 1 https://github.com/lightgbm-org/LightGBM && \\\n cd LightGBM && \\\n cmake -B build -S . -DUSE_GPU=1 -DOpenCL_LIBRARY=${OPENCL_LIBRARIES}/libOpenCL.so.1 -DOpenCL_INCLUDE_DIR=$OPENCL_INCLUDE_DIR && \\\n OPENCL_HEADERS=$OPENCL_INCLUDE_DIR LIBOPENCL=$OPENCL_LIBRARIES cmake --build build\n\nFROM gcr.io/distroless/cc-debian10\nCOPY --from=build \\\n /opt/LightGBM/lightgbm \\\n /opt/LightGBM/lib_lightgbm.so \\\n /opt/LightGBM/\nCOPY --from=build \\\n /usr/lib/x86_64-linux-gnu/libOpenCL.so.1 \\\n /usr/lib/x86_64-linux-gnu/libboost_filesystem.so.1.65.1 \\\n /usr/lib/x86_64-linux-gnu/libboost_system.so.1.65.1 \\\n /usr/lib/x86_64-linux-gnu/libgomp.so.1 \\\n /usr/lib/x86_64-linux-gnu/libstdc++.so.6 \\\n /usr/lib/x86_64-linux-gnu/\nCOPY --from=build \\\n /lib/x86_64-linux-gnu/libm.so.6 \\\n /lib/x86_64-linux-gnu/libgcc_s.so.1 \\\n /lib/x86_64-linux-gnu/libpthread.so.0 \\\n /lib/x86_64-linux-gnu/libc.so.6 \\\n /lib/x86_64-linux-gnu/libdl.so.2 \\\n /lib/x86_64-linux-gnu/\nCOPY --from=build \\\n /lib64/ld-linux-x86-64.so.2 \\\n /lib64/\nCOPY --from=build /etc/OpenCL/vendors/nvidia.icd /etc/OpenCL/vendors/nvidia.icd\n\nENV PATH /opt/LightGBM:${PATH}\nENV LANG C.UTF-8\nENV LC_ALL C.UTF-8\n\nENTRYPOINT [\"lightgbm\"]\n" }, { "path": "docker/gpu/dockerfile-cli-only.gpu", "content": "# Copyright (c) 2020 The Rector and Visitors of the University of Virginia\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated\n# documentation files (the \"Software\"), to deal in the Software without restriction, including without\n# limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the\n# Software, and to permit persons to whom the Software is furnished to do so, subject to the following\n# conditions:\n#\n# The above copyright notice and this permission notice shall be included in all copies or substantial portions\n# of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED\n# TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF\n# CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n# DEALINGS IN THE SOFTWARE.\n\nFROM nvidia/opencl:devel AS build\nARG DEBIAN_FRONTEND=noninteractive\nARG OPENCL_LIBRARIES=/usr/lib/x86_64-linux-gnu\nARG OPENCL_INCLUDE_DIR=/usr/include/CL\n\n# SYSTEM\nRUN apt-get update && apt-get install -y --no-install-recommends \\\n build-essential \\\n git \\\n ca-certificates \\\n libglib2.0-0 \\\n libxext6 \\\n libsm6 \\\n libxrender1 \\\n cmake \\\n libboost-dev \\\n libboost-system-dev \\\n libboost-filesystem-dev \\\n gcc \\\n g++ && \\\n rm -rf /var/lib/apt/lists/*\n\n# LightGBM\nWORKDIR /opt\nRUN git clone --recursive --branch stable --depth 1 https://github.com/lightgbm-org/LightGBM && \\\n cd LightGBM && \\\n cmake -B build -S . -DUSE_GPU=1 -DOpenCL_LIBRARY=${OPENCL_LIBRARIES}/libOpenCL.so.1 -DOpenCL_INCLUDE_DIR=$OPENCL_INCLUDE_DIR && \\\n OPENCL_HEADERS=$OPENCL_INCLUDE_DIR LIBOPENCL=$OPENCL_LIBRARIES cmake --build build\n\nFROM nvidia/opencl:runtime\nRUN apt-get update && apt-get install -y --no-install-recommends \\\n libxext6 \\\n libsm6 \\\n libxrender1 \\\n libboost-system-dev \\\n libboost-filesystem-dev \\\n gcc \\\n g++ && \\\n rm -rf /var/lib/apt/lists/*\n\nCOPY --from=build \\\n /opt/LightGBM/lightgbm \\\n /opt/LightGBM/lib_lightgbm.so \\\n /opt/LightGBM/\n\nENV PATH /opt/LightGBM:${PATH}\nENV LANG C.UTF-8\nENV LC_ALL C.UTF-8\n\nENTRYPOINT [\"lightgbm\"]\n" }, { "path": "docker/gpu/dockerfile.gpu", "content": "FROM nvidia/cuda:8.0-cudnn5-devel\n\n#################################################################################################################\n# Global\n#################################################################################################################\n# apt-get to skip any interactive post-install configuration steps with DEBIAN_FRONTEND=noninteractive and apt-get install -y\n\nENV LANG=C.UTF-8 LC_ALL=C.UTF-8\nARG DEBIAN_FRONTEND=noninteractive\n\n#################################################################################################################\n# Global Path Setting\n#################################################################################################################\n\nENV CUDA_HOME /usr/local/cuda\nENV LD_LIBRARY_PATH ${LD_LIBRARY_PATH}:${CUDA_HOME}/lib64\nENV LD_LIBRARY_PATH ${LD_LIBRARY_PATH}:/usr/local/lib\n\nENV OPENCL_LIBRARIES /usr/local/cuda/lib64\nENV OPENCL_INCLUDE_DIR /usr/local/cuda/include\n\n#################################################################################################################\n# TINI\n#################################################################################################################\n\n# Install tini\nENV TINI_VERSION v0.14.0\nADD https://github.com/krallin/tini/releases/download/${TINI_VERSION}/tini /tini\nRUN chmod +x /tini\n\n#################################################################################################################\n# SYSTEM\n#################################################################################################################\n# update: downloads the package lists from the repositories and \"updates\" them to get information on the newest versions of packages and their\n# dependencies. It will do this for all repositories and PPAs.\n\nRUN apt-get update && \\\napt-get install -y --no-install-recommends \\\nbuild-essential \\\ncurl \\\nbzip2 \\\nca-certificates \\\nlibglib2.0-0 \\\nlibxext6 \\\nlibsm6 \\\nlibxrender1 \\\ngit \\\nvim \\\nmercurial \\\nsubversion \\\ncmake \\\nlibboost-dev \\\nlibboost-system-dev \\\nlibboost-filesystem-dev \\\ngcc \\\ng++\n\n# Add OpenCL ICD files for LightGBM\nRUN mkdir -p /etc/OpenCL/vendors && \\\n echo \"libnvidia-opencl.so.1\" > /etc/OpenCL/vendors/nvidia.icd\n\n#################################################################################################################\n# CONDA\n#################################################################################################################\n\nARG CONDA_DIR=/opt/miniforge\n# add to path\nENV PATH $CONDA_DIR/bin:$PATH\n\n# Install miniforge\nRUN echo \"export PATH=$CONDA_DIR/bin:\"'$PATH' > /etc/profile.d/conda.sh && \\\n curl -sL https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh -o ~/miniforge.sh && \\\n /bin/bash ~/miniforge.sh -b -p $CONDA_DIR && \\\n rm ~/miniforge.sh\n\nRUN conda config --set always_yes yes --set changeps1 no && \\\n conda create -y -q -n py3 numpy scipy scikit-learn jupyter notebook ipython pandas matplotlib\n\n#################################################################################################################\n# LightGBM\n#################################################################################################################\n\nRUN cd /usr/local/src && mkdir lightgbm && cd lightgbm && \\\n git clone --recursive --branch stable --depth 1 https://github.com/lightgbm-org/LightGBM && \\\n cd LightGBM && \\\n cmake -B build -S . -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/ && \\\n OPENCL_HEADERS=/usr/local/cuda-8.0/targets/x86_64-linux/include LIBOPENCL=/usr/local/cuda-8.0/targets/x86_64-linux/lib cmake --build build\n\nENV PATH /usr/local/src/lightgbm/LightGBM:${PATH}\n\nRUN /bin/bash -c \"source activate py3 && cd /usr/local/src/lightgbm/LightGBM && sh ./build-python.sh install --precompile && source deactivate\"\n\n#################################################################################################################\n# System CleanUp\n#################################################################################################################\n# apt-get autoremove: used to remove packages that were automatically installed to satisfy dependencies for some package and that are no more needed.\n# apt-get clean: removes the aptitude cache in /var/cache/apt/archives. You'd be amazed how much is in there! the only drawback is that the packages\n# have to be downloaded again if you reinstall them.\n\nRUN apt-get autoremove -y && apt-get clean && \\\n rm -rf /var/lib/apt/lists/* && \\\n conda clean -a -y\n\n#################################################################################################################\n# JUPYTER\n#################################################################################################################\n\n# password: keras\n# password key: --NotebookApp.password='sha1:98b767162d34:8da1bc3c75a0f29145769edc977375a373407824'\n\n# Add a notebook profile.\nRUN mkdir -p -m 700 ~/.jupyter/ && \\\n echo \"c.NotebookApp.ip = '*'\" >> ~/.jupyter/jupyter_notebook_config.py\n\nVOLUME /home\nWORKDIR /home\n\n# IPython\nEXPOSE 8888\n\nENTRYPOINT [ \"/tini\", \"--\" ]\nCMD /bin/bash -c \"source activate py3 && jupyter notebook --allow-root --no-browser --NotebookApp.password='sha1:98b767162d34:8da1bc3c75a0f29145769edc977375a373407824' && source deactivate\"\n" }, { "path": "docs/.lychee.toml", "content": "verbose = \"info\"\nno_progress = false\ncache = false\nscheme = [\"http\", \"https\", \"file\"]\ninclude_mail = false\ninclude_fragments = true\nno_ignore = true\ninsecure = false\nrequire_https = true\naccept = [\"100..=103\", \"200..=299\"]\nuser_agent = \"curl/7.88.1\"\nheader = {\"User-Agent\" = \"curl/7.88.1\"}\ntimeout = 30\nretry_wait_time = 10\nmax_concurrency = 10\n# remove anchors from GitHub URLs to overcome https://github.com/lycheeverse/lychee/issues/1729\nremap = [\n '(?P^https://github\\.com)/(?P.*)#(?P.*)$ $host/$path/',\n]\nexclude = [\n '^https://www\\.swig\\.org/download\\.html$',\n '^https://proceedings\\.neurips\\.cc/.*',\n '^https://www\\.amd\\.com/en/support\\.html$',\n '^https://www\\.jstor\\.org/stable/2281952$',\n '^https://dl\\.acm\\.org/doi/10\\.1145/3298689\\.3347033$',\n '^https://packages\\.ubuntu\\.com/search.*',\n '^https://stackoverflow\\.com/.*',\n '^https://.*\\.stackexchange\\.com/.*',\n]\nexclude_path = [\n \"(^|/)docs/.*\\\\.rst\",\n]\n" }, { "path": "docs/Advanced-Topics.rst", "content": "Advanced Topics\n===============\n\nMissing Value Handle\n--------------------\n\n- LightGBM enables the missing value handle by default. Disable it by setting ``use_missing=false``.\n\n- LightGBM uses NA (NaN) to represent missing values by default. Change it to use zero by setting ``zero_as_missing=true``.\n\n- When ``zero_as_missing=false`` (default), the unrecorded values in sparse matrices (and LightSVM) are treated as zeros.\n\n- When ``zero_as_missing=true``, NA and zeros (including unrecorded values in sparse matrices (and LightSVM)) are treated as missing.\n\nCategorical Feature Support\n---------------------------\n\n- LightGBM offers good accuracy with integer-encoded categorical features. LightGBM applies\n `Fisher (1958) `_\n to find the optimal split over categories as\n `described here <./Features.rst#optimal-split-for-categorical-features>`_. This often performs better than one-hot encoding.\n\n- Use ``categorical_feature`` to specify the categorical features.\n Refer to the parameter ``categorical_feature`` in `Parameters <./Parameters.rst#categorical_feature>`__.\n\n- Categorical features will be cast to ``int32`` (integer codes will be extracted from pandas categoricals in the Python-package) so they must be encoded as non-negative integers (negative values will be treated as missing)\n less than ``Int32.MaxValue`` (2147483647).\n It is best to use a contiguous range of integers started from zero.\n Floating point numbers in categorical features will be rounded towards 0.\n\n- Use ``min_data_per_group``, ``cat_smooth`` to deal with over-fitting (when ``#data`` is small or ``#category`` is large).\n\n- For a categorical feature with high cardinality (``#category`` is large), it often works best to\n treat the feature as numeric, either by simply ignoring the categorical interpretation of the integers or\n by embedding the categories in a low-dimensional numeric space.\n\nLambdaRank\n----------\n\n- The label should be of type ``int``, such that larger numbers correspond to higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect).\n\n- Use ``label_gain`` to set the gain(weight) of ``int`` label.\n\n- Use ``lambdarank_truncation_level`` to truncate the max DCG.\n\nCost Efficient Gradient Boosting\n--------------------------------\n\n`Cost Efficient Gradient Boosting `_ (CEGB) makes it possible to penalise boosting based on the cost of obtaining feature values.\nCEGB penalises learning in the following ways:\n\n- Each time a tree is split, a penalty of ``cegb_penalty_split`` is applied.\n- When a feature is used for the first time, ``cegb_penalty_feature_coupled`` is applied. This penalty can be different for each feature and should be specified as one ``double`` per feature.\n- When a feature is used for the first time for a data row, ``cegb_penalty_feature_lazy`` is applied. Like ``cegb_penalty_feature_coupled``, this penalty is specified as one ``double`` per feature.\n\nEach of the penalties above is scaled by ``cegb_tradeoff``.\nUsing this parameter, it is possible to change the overall strength of the CEGB penalties by changing only one parameter.\n\nParameters Tuning\n-----------------\n\n- Refer to `Parameters Tuning <./Parameters-Tuning.rst>`__.\n\n.. _Parallel Learning:\n\nDistributed Learning\n--------------------\n\n- Refer to `Distributed Learning Guide <./Parallel-Learning-Guide.rst>`__.\n\nGPU Support\n-----------\n\n- Refer to `GPU Tutorial <./GPU-Tutorial.rst>`__ and `GPU Targets <./GPU-Targets.rst>`__.\n\nSupport for Position Bias Treatment\n------------------------------------\n\nOften the relevance labels provided in Learning-to-Rank tasks might be derived from implicit user feedback (e.g., clicks) and therefore might be biased due to their position/location on the screen when having been presented to a user.\nLightGBM can make use of positional data.\n\nFor example, consider the case where you expect that the first 3 results from a search engine will be visible in users' browsers without scrolling, and all other results for a query would require scrolling.\n\nLightGBM could be told to account for the position bias from results being \"above the fold\" by providing a ``positions`` array encoded as follows:\n\n::\n\n 0\n 0\n 0\n 1\n 1\n 0\n 0\n 0\n 1\n ...\n\nWhere ``0 = \"above the fold\"`` and ``1 = \"requires scrolling\"``.\nThe specific values are not important, as long as they are consistent across all observations in the training data.\nAn encoding like ``100 = \"above the fold\"`` and ``17 = \"requires scrolling\"`` would result in exactly the same trained model.\n\nIn that way, ``positions`` in LightGBM's API are similar to a categorical feature.\nJust as with non-ordinal categorical features, an integer representation is just used for memory and computational efficiency... LightGBM does not care about the absolute or relative magnitude of the values.\n\nUnlike a categorical feature, however, ``positions`` are used to adjust the target to reduce the bias in predictions made by the trained model.\n\nThe position file corresponds with training data file line by line, and has one position per line. And if the name of training data file is ``train.txt``, the position file should be named as ``train.txt.position`` and placed in the same folder as the data file.\nIn this case, LightGBM will load the position file automatically if it exists. The positions can also be specified through the ``Dataset`` constructor when using Python API. If the positions are specified in both approaches, the ``.position`` file will be ignored.\n\nCurrently, implemented is an approach to model position bias by using an idea of Generalized Additive Models (`GAM `_) to linearly decompose the document score ``s`` into the sum of a relevance component ``f`` and a positional component ``g``: ``s(x, pos) = f(x) + g(pos)`` where the former component depends on the original query-document features and the latter depends on the position of an item.\nDuring the training, the compound scoring function ``s(x, pos)`` is fit with a standard ranking algorithm (e.g., LambdaMART) which boils down to jointly learning the relevance component ``f(x)`` (it is later returned as an unbiased model) and the position factors ``g(pos)`` that help better explain the observed (biased) labels.\nSimilar score decomposition ideas have previously been applied for classification & pointwise ranking tasks with assumptions of binary labels and binary relevance (a.k.a. \"two-tower\" models, refer to the papers: `Towards Disentangling Relevance and Bias in Unbiased Learning to Rank `_, `PAL: a position-bias aware learning framework for CTR prediction in live recommender systems `_, `A General Framework for Debiasing in CTR Prediction `_).\nIn LightGBM, we adapt this idea to general pairwise Lerarning-to-Rank with arbitrary ordinal relevance labels.\nBesides, GAMs have been used in the context of explainable ML (`Accurate Intelligible Models with Pairwise Interactions `_) to linearly decompose the contribution of each feature (and possibly their pairwise interactions) to the overall score, for subsequent analysis and interpretation of their effects in the trained models.\n" }, { "path": "docs/C-API.rst", "content": "C API\n=====\n\n.. doxygenfile:: c_api.h\n" }, { "path": "docs/Development-Guide.rst", "content": "Development Guide\n=================\n\nAlgorithms\n----------\n\nRefer to `Features <./Features.rst>`__ for understanding of important algorithms used in LightGBM.\n\nClasses and Code Structure\n--------------------------\n\nImportant Classes\n~~~~~~~~~~~~~~~~~\n\n+-------------------------+----------------------------------------------------------------------------------------+\n| Class | Description |\n+=========================+========================================================================================+\n| ``Application`` | The entrance of application, including training and prediction logic |\n+-------------------------+----------------------------------------------------------------------------------------+\n| ``Bin`` | Data structure used for storing feature discrete values (converted from float values) |\n+-------------------------+----------------------------------------------------------------------------------------+\n| ``Boosting`` | Boosting interface (GBDT, DART, etc.) |\n+-------------------------+----------------------------------------------------------------------------------------+\n| ``Config`` | Stores parameters and configurations |\n+-------------------------+----------------------------------------------------------------------------------------+\n| ``Dataset`` | Stores information of dataset |\n+-------------------------+----------------------------------------------------------------------------------------+\n| ``DatasetLoader`` | Used to construct dataset |\n+-------------------------+----------------------------------------------------------------------------------------+\n| ``FeatureGroup`` | Stores the data of feature, could be multiple features |\n+-------------------------+----------------------------------------------------------------------------------------+\n| ``Metric`` | Evaluation metrics |\n+-------------------------+----------------------------------------------------------------------------------------+\n| ``Network`` | Network interfaces and communication algorithms |\n+-------------------------+----------------------------------------------------------------------------------------+\n| ``ObjectiveFunction`` | Objective functions used to train |\n+-------------------------+----------------------------------------------------------------------------------------+\n| ``Tree`` | Stores information of tree model |\n+-------------------------+----------------------------------------------------------------------------------------+\n| ``TreeLearner`` | Used to learn trees |\n+-------------------------+----------------------------------------------------------------------------------------+\n\nCode Structure\n~~~~~~~~~~~~~~\n\n+---------------------+------------------------------------------------------------------------------------------------------------------------------------+\n| Path | Description |\n+=====================+====================================================================================================================================+\n| ./include | Header files |\n+---------------------+------------------------------------------------------------------------------------------------------------------------------------+\n| ./include/utils | Some common functions |\n+---------------------+------------------------------------------------------------------------------------------------------------------------------------+\n| ./src/application | Implementations of training and prediction logic |\n+---------------------+------------------------------------------------------------------------------------------------------------------------------------+\n| ./src/boosting | Implementations of Boosting |\n+---------------------+------------------------------------------------------------------------------------------------------------------------------------+\n| ./src/io | Implementations of IO related classes, including ``Bin``, ``Config``, ``Dataset``, ``DatasetLoader``, ``Feature`` and ``Tree`` |\n+---------------------+------------------------------------------------------------------------------------------------------------------------------------+\n| ./src/metric | Implementations of metrics |\n+---------------------+------------------------------------------------------------------------------------------------------------------------------------+\n| ./src/network | Implementations of network functions |\n+---------------------+------------------------------------------------------------------------------------------------------------------------------------+\n| ./src/objective | Implementations of objective functions |\n+---------------------+------------------------------------------------------------------------------------------------------------------------------------+\n| ./src/treelearner | Implementations of tree learners |\n+---------------------+------------------------------------------------------------------------------------------------------------------------------------+\n\nDocuments API\n-------------\n\nRefer to `docs README <./README.rst>`__.\n\nC API\n-----\n\nRefer to `C API <./C-API.rst>`__ or the comments in `c\\_api.h `__ file, from which the documentation is generated.\n\nTests\n-----\n\nC++ unit tests are located in the ``./tests/cpp_tests`` folder and written with the help of Google Test framework.\nTo run tests locally first refer to the `Installation Guide <./Installation-Guide.rst#build-c-unit-tests>`__ for how to build tests and then simply run compiled executable file.\nIt is highly recommended to build tests with `sanitizers <./Installation-Guide.rst#sanitizers>`__.\n\nHigh Level Language Package\n---------------------------\n\nSee the implementations at `Python-package `__ and `R-package `__.\n\nQuestions\n---------\n\nRefer to `FAQ <./FAQ.rst>`__.\n\nAlso feel free to open `issues `__ if you met problems.\n" }, { "path": "docs/Experiments.rst", "content": "Experiments\n===========\n\nComparison Experiment\n---------------------\n\nFor the detailed experiment scripts and output logs, please refer to this `repo`_.\n\nHistory\n^^^^^^^\n\n08 Mar, 2020: update according to the latest master branch (`1b97eaf `__ for XGBoost, `bcad692 `__ for LightGBM). (``xgboost_exact`` is not updated for it is too slow.)\n\n27 Feb, 2017: first version.\n\nData\n^^^^\n\nWe used 5 datasets to conduct our comparison experiments. Details of data are listed in the following table:\n\n+-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+\n| Data | Task | Link | #Train\\_Set | #Feature | Comments |\n+===========+=======================+=================================================================================+=============+==========+==============================================+\n| Higgs | Binary classification | `link `__ | 10,500,000 | 28 | last 500,000 samples were used as test set |\n+-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+\n| Yahoo LTR | Learning to rank | `link `__ | 473,134 | 700 | set1.train as train, set1.test as test |\n+-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+\n| MS LTR | Learning to rank | `link `__ | 2,270,296 | 137 | {S1,S2,S3} as train set, {S5} as test set |\n+-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+\n| Expo | Binary classification | `link `__ | 11,000,000 | 700 | last 1,000,000 samples were used as test set |\n+-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+\n| Allstate | Binary classification | `link `__ | 13,184,290 | 4228 | last 1,000,000 samples were used as test set |\n+-----------+-----------------------+---------------------------------------------------------------------------------+-------------+----------+----------------------------------------------+\n\nEnvironment\n^^^^^^^^^^^\n\nWe ran all experiments on a single Linux server (Azure ND24s) with the following specifications:\n\n+------------------+-----------------+---------------------+\n| OS | CPU | Memory |\n+==================+=================+=====================+\n| Ubuntu 16.04 LTS | 2 \\* E5-2690 v4 | 448GB |\n+------------------+-----------------+---------------------+\n\nBaseline\n^^^^^^^^\n\nWe used `xgboost`_ as a baseline.\n\nBoth xgboost and LightGBM were built with OpenMP support.\n\nSettings\n^^^^^^^^\n\nWe set up total 3 settings for experiments. The parameters of these settings are:\n\n1. xgboost:\n\n .. code:: text\n\n eta = 0.1\n max_depth = 8\n num_round = 500\n nthread = 16\n tree_method = exact\n min_child_weight = 100\n\n2. xgboost\\_hist (using histogram based algorithm):\n\n .. code:: text\n\n eta = 0.1\n num_round = 500\n nthread = 16\n min_child_weight = 100\n tree_method = hist\n grow_policy = lossguide\n max_depth = 0\n max_leaves = 255\n\n3. LightGBM:\n\n .. code:: text\n\n learning_rate = 0.1\n num_leaves = 255\n num_trees = 500\n num_threads = 16\n min_data_in_leaf = 0\n min_sum_hessian_in_leaf = 100\n\nxgboost grows trees depth-wise and controls model complexity by ``max_depth``.\nLightGBM uses a leaf-wise algorithm instead and controls model complexity by ``num_leaves``.\nSo we cannot compare them in the exact same model setting. For the tradeoff, we use xgboost with ``max_depth=8``, which will have max number leaves to 255, to compare with LightGBM with ``num_leaves=255``.\n\nOther parameters are default values.\n\nResult\n^^^^^^\n\nSpeed\n'''''\n\nWe compared speed using only the training task without any test or metric output. We didn't count the time for IO.\nFor the ranking tasks, since XGBoost and LightGBM implement different ranking objective functions, we used ``regression`` objective for speed benchmark, for the fair comparison.\n\nThe following table is the comparison of time cost:\n\n+-----------+-----------+---------------+---------------+\n| Data | xgboost | xgboost\\_hist | LightGBM |\n+===========+===========+===============+===============+\n| Higgs | 3794.34 s | 165.575 s | **130.094 s** |\n+-----------+-----------+---------------+---------------+\n| Yahoo LTR | 674.322 s | 131.462 s | **76.229 s** |\n+-----------+-----------+---------------+---------------+\n| MS LTR | 1251.27 s | 98.386 s | **70.417 s** |\n+-----------+-----------+---------------+---------------+\n| Expo | 1607.35 s | 137.65 s | **62.607 s** |\n+-----------+-----------+---------------+---------------+\n| Allstate | 2867.22 s | 315.256 s | **148.231 s** |\n+-----------+-----------+---------------+---------------+\n\nLightGBM ran faster than xgboost on all experiment data sets.\n\nAccuracy\n''''''''\n\nWe computed all accuracy metrics only on the test data set.\n\n+-----------+-----------------+----------+-------------------+--------------+\n| Data | Metric | xgboost | xgboost\\_hist | LightGBM |\n+===========+=================+==========+===================+==============+\n| Higgs | AUC | 0.839593 | 0.845314 | **0.845724** |\n+-----------+-----------------+----------+-------------------+--------------+\n| Yahoo LTR | NDCG\\ :sub:`1` | 0.719748 | 0.720049 | **0.732981** |\n| +-----------------+----------+-------------------+--------------+\n| | NDCG\\ :sub:`3` | 0.717813 | 0.722573 | **0.735689** |\n| +-----------------+----------+-------------------+--------------+\n| | NDCG\\ :sub:`5` | 0.737849 | 0.740899 | **0.75352** |\n| +-----------------+----------+-------------------+--------------+\n| | NDCG\\ :sub:`10` | 0.78089 | 0.782957 | **0.793498** |\n+-----------+-----------------+----------+-------------------+--------------+\n| MS LTR | NDCG\\ :sub:`1` | 0.483956 | 0.485115 | **0.517767** |\n| +-----------------+----------+-------------------+--------------+\n| | NDCG\\ :sub:`3` | 0.467951 | 0.47313 | **0.501063** |\n| +-----------------+----------+-------------------+--------------+\n| | NDCG\\ :sub:`5` | 0.472476 | 0.476375 | **0.504648** |\n| +-----------------+----------+-------------------+--------------+\n| | NDCG\\ :sub:`10` | 0.492429 | 0.496553 | **0.524252** |\n+-----------+-----------------+----------+-------------------+--------------+\n| Expo | AUC | 0.756713 | 0.776224 | **0.776935** |\n+-----------+-----------------+----------+-------------------+--------------+\n| Allstate | AUC | 0.607201 | **0.609465** | 0.609072 |\n+-----------+-----------------+----------+-------------------+--------------+\n\nMemory Consumption\n''''''''''''''''''\n\nWe monitored RES while running training task. And we set ``two_round=true`` (this will increase data-loading time and\nreduce peak memory usage but not affect training speed or accuracy) in LightGBM to reduce peak memory usage.\n\n+-----------+---------+---------------+--------------------+--------------------+\n| Data | xgboost | xgboost\\_hist | LightGBM (col-wise)|LightGBM (row-wise) |\n+===========+=========+===============+====================+====================+\n| Higgs | 4.853GB | 7.335GB | **0.897GB** | 1.401GB |\n+-----------+---------+---------------+--------------------+--------------------+\n| Yahoo LTR | 1.907GB | 4.023GB | **1.741GB** | 2.161GB |\n+-----------+---------+---------------+--------------------+--------------------+\n| MS LTR | 5.469GB | 7.491GB | **0.940GB** | 1.296GB |\n+-----------+---------+---------------+--------------------+--------------------+\n| Expo | 1.553GB | 2.606GB | **0.555GB** | 0.711GB |\n+-----------+---------+---------------+--------------------+--------------------+\n| Allstate | 6.237GB | 12.090GB | **1.116GB** | 1.755GB |\n+-----------+---------+---------------+--------------------+--------------------+\n\nParallel Experiment\n-------------------\n\nHistory\n^^^^^^^\n\n27 Feb, 2017: first version.\n\nData\n^^^^\n\nWe used a terabyte click log dataset to conduct parallel experiments. Details are listed in following table:\n\n+--------+-----------------------+---------+---------------+----------+\n| Data | Task | Link | #Data | #Feature |\n+========+=======================+=========+===============+==========+\n| Criteo | Binary classification | `link`_ | 1,700,000,000 | 67 |\n+--------+-----------------------+---------+---------------+----------+\n\nThis data contains 13 integer features and 26 categorical features for 24 days of click logs.\nWe statisticized the click-through rate (CTR) and count for these 26 categorical features from the first ten days.\nThen we used next ten days' data, after replacing the categorical features by the corresponding CTR and count, as training data.\nThe processed training data have a total of 1.7 billions records and 67 features.\n\nEnvironment\n^^^^^^^^^^^\n\nWe ran our experiments on 16 Windows servers with the following specifications:\n\n+---------------------+-----------------+---------------------+-------------------------------------------+\n| OS | CPU | Memory | Network Adapter |\n+=====================+=================+=====================+===========================================+\n| Windows Server 2012 | 2 \\* E5-2670 v2 | DDR3 1600Mhz, 256GB | Mellanox ConnectX-3, 54Gbps, RDMA support |\n+---------------------+-----------------+---------------------+-------------------------------------------+\n\nSettings\n^^^^^^^^\n\n.. code:: text\n\n learning_rate = 0.1\n num_leaves = 255\n num_trees = 100\n num_thread = 16\n tree_learner = data\n\nWe used data parallel here because this data is large in ``#data`` but small in ``#feature``. Other parameters were default values.\n\nResults\n^^^^^^^\n\n+----------+---------------+---------------------------+\n| #Machine | Time per Tree | Memory Usage(per Machine) |\n+==========+===============+===========================+\n| 1 | 627.8 s | 176GB |\n+----------+---------------+---------------------------+\n| 2 | 311 s | 87GB |\n+----------+---------------+---------------------------+\n| 4 | 156 s | 43GB |\n+----------+---------------+---------------------------+\n| 8 | 80 s | 22GB |\n+----------+---------------+---------------------------+\n| 16 | 42 s | 11GB |\n+----------+---------------+---------------------------+\n\nThe results show that LightGBM achieves a linear speedup with distributed learning.\n\nGPU Experiments\n---------------\n\nRefer to `GPU Performance <./GPU-Performance.rst>`__.\n\n.. _repo: https://github.com/guolinke/boosting_tree_benchmarks\n\n.. _xgboost: https://github.com/dmlc/xgboost\n\n.. _link: https://ailab.criteo.com/download-criteo-1tb-click-logs-dataset/\n" }, { "path": "docs/FAQ.rst", "content": ".. role:: raw-html(raw)\n :format: html\n\nLightGBM FAQ\n############\n\n.. contents:: LightGBM Frequently Asked Questions\n :depth: 1\n :local:\n :backlinks: none\n\n------\n\nPlease post questions, feature requests, and bug reports at https://github.com/lightgbm-org/LightGBM/issues.\n\nThis project is mostly maintained by volunteers, so please be patient.\nIf your request is time-sensitive or more than a month goes by without a response, please tag the maintainers below for help.\n\n- `@guolinke `__ **Guolin Ke**\n- `@shiyu1994 `__ **Yu Shi**\n- `@jameslamb `__ **James Lamb**\n- `@jmoralez `__ **José Morales**\n\n--------------\n\nGeneral LightGBM Questions\n==========================\n\n.. contents::\n :local:\n :backlinks: none\n\n1. Where do I find more details about LightGBM parameters?\n----------------------------------------------------------\n\nTake a look at `Parameters <./Parameters.rst>`__.\n\n2. On datasets with millions of features, training does not start (or starts after a very long time).\n-----------------------------------------------------------------------------------------------------\n\nUse a smaller value for ``bin_construct_sample_cnt`` and a larger value for ``min_data``.\n\n3. When running LightGBM on a large dataset, my computer runs out of RAM.\n-------------------------------------------------------------------------\n\n**Multiple Solutions**: set the ``histogram_pool_size`` parameter to the MB you want to use for LightGBM (histogram\\_pool\\_size + dataset size = approximately RAM used),\nlower ``num_leaves`` or lower ``max_bin`` (see `lightgbm-org/LightGBM#562 `__).\n\n4. I am using Windows. Should I use Visual Studio or MinGW for compiling LightGBM?\n----------------------------------------------------------------------------------\n\nVisual Studio `performs best for LightGBM `__.\n\n5. When using LightGBM GPU, I cannot reproduce results over several runs.\n-------------------------------------------------------------------------\n\nThis is normal and expected behaviour, but you may try to use ``gpu_use_dp = true`` for reproducibility\n(see `lightgbm-org/LightGBM#560 `__).\nYou may also use the CPU version.\n\n6. Bagging is not reproducible when changing the number of threads.\n-------------------------------------------------------------------\n\n:raw-html:``\nLightGBM bagging is multithreaded, so its output depends on the number of threads used.\nThere is `no workaround currently `__.\n:raw-html:``\n\nStarting from `#2804 `__ bagging result doesn't depend on the number of threads.\nSo this issue should be solved in the latest version.\n\n7. I tried to use Random Forest mode, and LightGBM crashes!\n-----------------------------------------------------------\n\nThis is expected behaviour for arbitrary parameters. To enable Random Forest,\nyou must use ``bagging_fraction`` and ``feature_fraction`` different from 1, along with a ``bagging_freq``.\n`This thread `__ includes an example.\n\n8. CPU usage is low (like 10%) in Windows when using LightGBM on very large datasets with many-core systems.\n------------------------------------------------------------------------------------------------------------\n\nPlease use `Visual Studio `__\nas it may be `10x faster than MinGW `__ especially for very large trees.\n\n9. When I'm trying to specify a categorical column with the ``categorical_feature`` parameter, I get the following sequence of warnings, but there are no negative values in the column.\n----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n\n.. code-block:: console\n\n [LightGBM] [Warning] Met negative value in categorical features, will convert it to NaN\n [LightGBM] [Warning] There are no meaningful features, as all feature values are constant.\n\nThe column you're trying to pass via ``categorical_feature`` likely contains very large values.\nCategorical features in LightGBM are limited by int32 range,\nso you cannot pass values that are greater than ``Int32.MaxValue`` (2147483647) as categorical features (see `lightgbm-org/LightGBM#1359 `__).\nYou should convert them to integers ranging from zero to the number of categories first.\n\n10. LightGBM crashes randomly with the error like: ``Initializing libiomp5.dylib, but found libomp.dylib already initialized.``\n-------------------------------------------------------------------------------------------------------------------------------\n\n.. code-block:: console\n\n OMP: Error #15: Initializing libiomp5.dylib, but found libomp.dylib already initialized.\n OMP: Hint: This means that multiple copies of the OpenMP runtime have been linked into the program. That is dangerous, since it can degrade performance or cause incorrect results. The best thing to do is to ensure that only a single OpenMP runtime is linked into the process, e.g. by avoiding static linking of the OpenMP runtime in any library. As an unsafe, unsupported, undocumented workaround you can set the environment variable KMP_DUPLICATE_LIB_OK=TRUE to allow the program to continue to execute, but that may cause crashes or silently produce incorrect results. For more information, please see http://www.intel.com/software/products/support/.\n\n**Possible Cause**: This error means that you have multiple OpenMP libraries installed on your machine and they conflict with each other.\n(File extensions in the error message may differ depending on the operating system).\n\nIf you are using Python distributed by Conda, then it is highly likely that the error is caused by the ``numpy`` package from Conda which includes the ``mkl`` package which in turn conflicts with the system-wide library.\nIn this case you can update the ``numpy`` package in Conda or replace the Conda's OpenMP library instance with system-wide one by creating a symlink to it in Conda environment folder ``$CONDA_PREFIX/lib``.\n\n**Solution**: Assuming you are using macOS with Homebrew, the command which overwrites OpenMP library files in the current active Conda environment with symlinks to the system-wide library ones installed by Homebrew:\n\n.. code-block:: bash\n\n ln -sf `ls -d \"$(brew --cellar libomp)\"/*/lib`/* $CONDA_PREFIX/lib\n\nThe described above fix worked fine before the release of OpenMP 8.0.0 version.\nStarting from 8.0.0 version, Homebrew formula for OpenMP includes ``-DLIBOMP_INSTALL_ALIASES=OFF`` option which leads to that the fix doesn't work anymore.\nHowever, you can create symlinks to library aliases manually:\n\n.. code-block:: bash\n\n for LIBOMP_ALIAS in libgomp.dylib libiomp5.dylib libomp.dylib; do sudo ln -sf \"$(brew --cellar libomp)\"/*/lib/libomp.dylib $CONDA_PREFIX/lib/$LIBOMP_ALIAS; done\n\nAnother workaround would be removing MKL optimizations from Conda's packages completely:\n\n.. code-block:: bash\n\n conda install nomkl\n\nIf this is not your case, then you should find conflicting OpenMP library installations on your own and leave only one of them.\n\n11. LightGBM hangs when multithreading (OpenMP) and using forking in Linux at the same time.\n--------------------------------------------------------------------------------------------\n\nUse ``nthreads=1`` to disable multithreading of LightGBM. There is a bug with OpenMP which hangs forked sessions\nwith multithreading activated. A more expensive solution is to use new processes instead of using fork, however,\nkeep in mind it is creating new processes where you have to copy memory and load libraries (example: if you want to\nfork 16 times your current process, then you will require to make 16 copies of your dataset in memory)\n(see `lightgbm-org/LightGBM#1789 `__).\n\nAn alternative, if multithreading is really necessary inside the forked sessions, would be to compile LightGBM with\nIntel toolchain. Intel compilers are unaffected by this bug.\n\nFor C/C++ users, any OpenMP feature cannot be used before the fork happens. If an OpenMP feature is used before the\nfork happens (example: using OpenMP for forking), OpenMP will hang inside the forked sessions. Use new processes instead\nand copy memory as required by creating new processes instead of forking (or, use Intel compilers).\n\nCloud platform container services may cause LightGBM to hang, if they use Linux fork to run multiple containers on a\nsingle instance. For example, LightGBM hangs in AWS Batch array jobs, which `use the ECS agent\n`__ to manage multiple running jobs. Setting ``nthreads=1`` mitigates the issue.\n\n12. Why is early stopping not enabled by default in LightGBM?\n-------------------------------------------------------------\n\nEarly stopping involves choosing a validation set, a special type of holdout which is used to evaluate the current state of the model after each iteration to see if training can stop.\n\nIn ``LightGBM``, `we have decided to require that users specify this set directly <./Parameters.rst#valid>`_. Many options exist for splitting training data into training, test, and validation sets.\n\nThe appropriate splitting strategy depends on the task and domain of the data, information that a modeler has but which ``LightGBM`` as a general-purpose tool does not.\n\n13. Does LightGBM support direct loading data from zero-based or one-based LibSVM format file?\n----------------------------------------------------------------------------------------------\n\nLightGBM supports loading data from zero-based LibSVM format file directly.\n\n14. Why CMake cannot find the compiler when compiling LightGBM with MinGW?\n--------------------------------------------------------------------------\n\n.. code-block:: bash\n\n CMake Error: CMAKE_C_COMPILER not set, after EnableLanguage\n CMake Error: CMAKE_CXX_COMPILER not set, after EnableLanguage\n\nThis is a known issue of CMake when using MinGW. The easiest solution is to run again your ``cmake`` command to bypass the one time stopper from CMake. Or you can upgrade your version of CMake to at least version 3.17.0.\n\nSee `lightgbm-org/LightGBM#3060 `__ for more details.\n\n15. Where can I find LightGBM's logo to use it in my presentation?\n------------------------------------------------------------------\n\nYou can find LightGBM's logo in different file formats and resolutions `here `__.\n\n16. LightGBM crashes randomly or operating system hangs during or after running LightGBM.\n-----------------------------------------------------------------------------------------\n\n**Possible Cause**: This behavior may indicate that you have multiple OpenMP libraries installed on your machine and they conflict with each other, similarly to the ``FAQ #10``.\n\nIf you are using any Python-package that depends on ``threadpoolctl``, you also may see the following warning in your logs in this case:\n\n.. code-block:: console\n\n /root/miniconda/envs/test-env/lib/python3.8/site-packages/threadpoolctl.py:546: RuntimeWarning:\n Found Intel OpenMP ('libiomp') and LLVM OpenMP ('libomp') loaded at\n the same time. Both libraries are known to be incompatible and this\n can cause random crashes or deadlocks on Linux when loaded in the\n same Python program.\n Using threadpoolctl may cause crashes or deadlocks. For more\n information and possible workarounds, please see\n https://github.com/joblib/threadpoolctl/blob/master/multiple_openmp.md\n\nDetailed description of conflicts between multiple OpenMP instances is provided in the `following document `__.\n\n**Solution**: Assuming you are using LightGBM Python-package and conda as a package manager, we strongly recommend using ``conda-forge`` channel as the only source of all your Python package installations because it contains built-in patches to workaround OpenMP conflicts. Some other workarounds are listed `here `__ under the \"Workarounds for Intel OpenMP and LLVM OpenMP case\" section.\n\nIf this is not your case, then you should find conflicting OpenMP library installations on your own and leave only one of them.\n\n17. Loading LightGBM fails like: ``cannot allocate memory in static TLS block``\n-------------------------------------------------------------------------------\n\nWhen loading LightGBM, you may encounter errors like the following.\n\n.. code-block:: console\n\n lib/libgomp.so.1: cannot allocate memory in static TLS block\n\nThis most commonly happens on aarch64 Linux systems.\n\n``gcc``'s OpenMP library (``libgomp.so``) tries to allocate a small amount of static thread-local storage (\"TLS\")\nwhen it's dynamically loaded.\n\nThat error can happen when the loader isn't able to find a large enough block of memory.\n\nOn aarch64 Linux, processes and loaded libraries share the same pool of static TLS,\nwhich makes such failures more likely. See these discussions:\n\n* https://bugzilla.redhat.com/show_bug.cgi?id=1722181#c6\n* https://gcc.gcc.gnu.narkive.com/vOXMQqLA/failure-to-dlopen-libgomp-due-to-static-tls-data\n\nIf you are experiencing this issue when using the ``lightgbm`` Python-package, try upgrading\nto at least ``v4.6.0``.\n\nFor older versions of the Python-package, or for other LightGBM APIs, this issue can\noften be avoided by loading ``libgomp.so.1``. That can be done directly by setting environment\nvariable ``LD_PRELOAD``, like this:\n\n.. code-block:: console\n\n export LD_PRELOAD=/root/miniconda3/envs/test-env/lib/libgomp.so.1\n\nIt can also be done indirectly by changing the order that other libraries are loaded\ninto processes, which varies by programming language and application type.\n\nFor more details, see these discussions:\n\n* https://github.com/lightgbm-org/LightGBM/pull/6654#issuecomment-2352014275\n* https://github.com/lightgbm-org/LightGBM/issues/6509\n* https://maskray.me/blog/2021-02-14-all-about-thread-local-storage\n* https://bugzilla.redhat.com/show_bug.cgi?id=1722181#c6\n\n------\n\nR-package\n=========\n\n.. contents::\n :local:\n :backlinks: none\n\n1. Any training command using LightGBM does not work after an error occurred during the training of a previous LightGBM model.\n------------------------------------------------------------------------------------------------------------------------------\n\nIn older versions of the R-package (prior to ``v3.3.0``), this could happen occasionally and the solution was to run ``lgb.unloader(wipe = TRUE)`` to remove all LightGBM-related objects. Some conversation about this could be found in `lightgbm-org/LightGBM#698 `__.\n\nThat is no longer necessary as of ``v3.3.0``, and function ``lgb.unloader()`` has since been removed from the R-package.\n\n2. I used ``setinfo()``, tried to print my ``lgb.Dataset``, and now the R console froze!\n----------------------------------------------------------------------------------------\n\nAs of at least LightGBM v3.3.0, this issue has been resolved and printing a ``Dataset`` object does not cause the console to freeze.\n\nIn older versions, avoid printing the ``Dataset`` after calling ``setinfo()``.\n\nAs of LightGBM v4.0.0, ``setinfo()`` has been replaced by a new method, ``set_field()``.\n\n3. ``error in data.table::data.table()...argument 2 is NULL``.\n--------------------------------------------------------------\n\nIf you are experiencing this error when running ``lightgbm``, you may be facing the same issue reported in `#2715 `_ and later in `#2989 `_. We have seen that in some situations, using ``data.table`` 1.11.x results in this error. To get around this, you can upgrade your version of ``data.table`` to at least version 1.12.0.\n\n4. ``package/dependency ‘Matrix’ is not available ...``\n-------------------------------------------------------\n\nIn April 2024, ``Matrix==1.7-0`` was published to CRAN.\nThat version had a floor of ``R (>=4.4.0)``.\n``{Matrix}`` is a hard runtime dependency of ``{lightgbm}``, so on any version of R older than ``4.4.0``, running ``install.packages(\"lightgbm\")`` results in something like the following.\n\n.. code-block:: text\n\n package ‘Matrix’ is not available for this version of R\n\nTo fix that without upgrading to R 4.4.0 or greater, manually install an older version of ``{Matrix}``.\n\n.. code-block:: R\n\n install.packages('https://cran.r-project.org/src/contrib/Archive/Matrix/Matrix_1.6-5.tar.gz', repos = NULL)\n\n------\n\nPython-package\n==============\n\n.. contents::\n :local:\n :backlinks: none\n\n1. ``Error: setup script specifies an absolute path`` when installing from GitHub using ``python setup.py install``.\n--------------------------------------------------------------------------------------------------------------------\n\n.. note::\n As of v4.0.0, ``lightgbm`` does not support directly invoking ``setup.py``.\n This answer refers only to versions of ``lightgbm`` prior to v4.0.0.\n\n.. code-block:: console\n\n error: Error: setup script specifies an absolute path:\n /Users/Microsoft/LightGBM/python-package/lightgbm/../../lib_lightgbm.so\n setup() arguments must *always* be /-separated paths relative to the setup.py directory, *never* absolute paths.\n\nThis error should be solved in latest version.\nIf you still meet this error, try to remove ``lightgbm.egg-info`` folder in your Python-package and reinstall,\nor check `this thread on stackoverflow `__.\n\n2. Error messages: ``Cannot ... before construct dataset``.\n-----------------------------------------------------------\n\nI see error messages like...\n\n.. code-block:: console\n\n Cannot get/set label/weight/init_score/group/num_data/num_feature before construct dataset\n\nbut I've already constructed a dataset by some code like:\n\n.. code-block:: python\n\n train = lightgbm.Dataset(X_train, y_train)\n\nor error messages like\n\n.. code-block:: console\n\n Cannot set predictor/reference/categorical feature after freed raw data, set free_raw_data=False when construct Dataset to avoid this.\n\n**Solution**: Because LightGBM constructs bin mappers to build trees, and train and valid Datasets within one Booster share the same bin mappers,\ncategorical features and feature names etc., the Dataset objects are constructed when constructing a Booster.\nIf you set ``free_raw_data=True`` (default), the raw data (with Python data struct) will be freed.\nSo, if you want to:\n\n- get label (or weight/init\\_score/group/data) before constructing a dataset, it's same as get ``self.label``;\n\n- set label (or weight/init\\_score/group) before constructing a dataset, it's same as ``self.label=some_label_array``;\n\n- get num\\_data (or num\\_feature) before constructing a dataset, you can get data with ``self.data``.\n Then, if your data is ``numpy.ndarray``, use some code like ``self.data.shape``. But do not do this after subsetting the Dataset, because you'll get always ``None``;\n\n- set predictor (or reference/categorical feature) after constructing a dataset,\n you should set ``free_raw_data=False`` or init a Dataset object with the same raw data.\n\n3. I encounter segmentation faults (segfaults) randomly after installing LightGBM from PyPI using ``pip install lightgbm``.\n---------------------------------------------------------------------------------------------------------------------------\n\nWe are doing our best to provide universal wheels which have high running speed and are compatible with any hardware, OS, compiler, etc. at the same time.\nHowever, sometimes it's just impossible to guarantee the possibility of usage of LightGBM in any specific environment (see `lightgbm-org/LightGBM#1743 `__).\n\nTherefore, the first thing you should try in case of segfaults is **compiling from the source** using ``pip install --no-binary lightgbm lightgbm``.\nFor the OS-specific prerequisites see https://github.com/lightgbm-org/LightGBM/blob/master/python-package/README.rst.\n\nAlso, feel free to post a new issue in our GitHub repository. We always look at each case individually and try to find a root cause.\n\n4. I would like to install LightGBM from conda. What channel should I choose?\n-----------------------------------------------------------------------------\n\nWe strongly recommend installation from the ``conda-forge`` channel and not from the ``default`` one.\n\nFor some specific examples, see `this comment `__.\n\nIn addition, as of ``lightgbm==4.4.0``, the ``conda-forge`` package automatically supports CUDA-based GPU acceleration.\n\n5. How do I subclass ``scikit-learn`` estimators?\n-------------------------------------------------\n\nFor ``lightgbm <= 4.5.0``, copy all of the constructor arguments from the corresponding\n``lightgbm`` class into the constructor of your custom estimator.\n\nFor later versions, just ensure that the constructor of your custom estimator calls ``super().__init__()``.\n\nConsider the example below, which implements a regressor that allows creation of truncated predictions.\nThis pattern will work with ``lightgbm > 4.5.0``.\n\n.. code-block:: python\n\n import numpy as np\n from lightgbm import LGBMRegressor\n from sklearn.datasets import make_regression\n\n class TruncatedRegressor(LGBMRegressor):\n\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n def predict(self, X, max_score: float = np.inf):\n preds = super().predict(X)\n np.clip(preds, a_min=None, a_max=max_score, out=preds)\n return preds\n\n X, y = make_regression(n_samples=1_000, n_features=4)\n\n reg_trunc = TruncatedRegressor().fit(X, y)\n\n preds = reg_trunc.predict(X)\n print(f\"mean: {preds.mean():.2f}, max: {preds.max():.2f}\")\n # mean: -6.81, max: 345.10\n\n preds_trunc = reg_trunc.predict(X, max_score=preds.mean())\n print(f\"mean: {preds_trunc.mean():.2f}, max: {preds_trunc.max():.2f}\")\n # mean: -56.50, max: -6.81\n" }, { "path": "docs/Features.rst", "content": "Features\n========\n\nThis is a conceptual overview of how LightGBM works\\ `[1] <#references>`__. We assume familiarity with decision tree boosting algorithms to focus instead on aspects of LightGBM that may differ from other boosting packages. For detailed algorithms, please refer to the citations or source code.\n\nOptimization in Speed and Memory Usage\n--------------------------------------\n\nMany boosting tools use pre-sort-based algorithms\\ `[2, 3] <#references>`__ (e.g. default algorithm in xgboost) for decision tree learning. It is a simple solution, but not easy to optimize.\n\nLightGBM uses histogram-based algorithms\\ `[4, 5, 6] <#references>`__, which bucket continuous feature (attribute) values into discrete bins. This speeds up training and reduces memory usage. Advantages of histogram-based algorithms include the following:\n\n- **Reduced cost of calculating the gain for each split**\n\n - Pre-sort-based algorithms have time complexity ``O(#data)``\n\n - Computing the histogram has time complexity ``O(#data)``, but this involves only a fast sum-up operation. Once the histogram is constructed, a histogram-based algorithm has time complexity ``O(#bins)``, and ``#bins`` is far smaller than ``#data``.\n\n- **Use histogram subtraction for further speedup**\n\n - To get one leaf's histograms in a binary tree, use the histogram subtraction of its parent and its neighbor\n\n - So it needs to construct histograms for only one leaf (with smaller ``#data`` than its neighbor). It then can get histograms of its neighbor by histogram subtraction with small cost (``O(#bins)``)\n\n- **Reduce memory usage**\n\n - Replaces continuous values with discrete bins. If ``#bins`` is small, can use small data type, e.g. uint8\\_t, to store training data\n\n - No need to store additional information for pre-sorting feature values\n\n- **Reduce communication cost for distributed learning**\n\nSparse Optimization\n-------------------\n\n- Need only ``O(2 * #non_zero_data)`` to construct histogram for sparse features\n\nOptimization in Accuracy\n------------------------\n\nLeaf-wise (Best-first) Tree Growth\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nMost decision tree learning algorithms grow trees by level (depth)-wise, like the following image:\n\n.. image:: ./_static/images/level-wise.png\n :align: center\n :alt: A diagram depicting level wise tree growth in which the best possible node is split one level down. The strategy results in a symmetric tree, where every node in a level has child nodes resulting in an additional layer of depth.\n\nLightGBM grows trees leaf-wise (best-first)\\ `[7] <#references>`__. It will choose the leaf with max delta loss to grow.\nHolding ``#leaf`` fixed, leaf-wise algorithms tend to achieve lower loss than level-wise algorithms.\n\nLeaf-wise may cause over-fitting when ``#data`` is small, so LightGBM includes the ``max_depth`` parameter to limit tree depth. However, trees still grow leaf-wise even when ``max_depth`` is specified.\n\n.. image:: ./_static/images/leaf-wise.png\n :align: center\n :alt: A diagram depicting leaf wise tree growth in which only the node with the highest loss change is split and not bother with the rest of the nodes in the same level. This results in an asymmetrical tree where subsequent splitting is happening only on one side of the tree.\n\nOptimal Split for Categorical Features\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nIt is common to represent categorical features with one-hot encoding, but this approach is suboptimal for tree learners. Particularly for high-cardinality categorical features, a tree built on one-hot features tends to be unbalanced and needs to grow very deep to achieve good accuracy.\n\nInstead of one-hot encoding, the optimal solution is to split on a categorical feature by partitioning its categories into 2 subsets. If the feature has ``k`` categories, there are ``2^(k-1) - 1`` possible partitions.\nBut there is an efficient solution for regression trees\\ `[8] <#references>`__. It needs about ``O(k * log(k))`` to find the optimal partition.\n\nThe basic idea is to sort the categories according to the training objective at each split.\nMore specifically, LightGBM sorts the histogram (for a categorical feature) according to its accumulated values (``sum_gradient / sum_hessian``) and then finds the best split on the sorted histogram.\n\nOptimization in Network Communication\n-------------------------------------\n\nIt only needs to use some collective communication algorithms, like \"All reduce\", \"All gather\" and \"Reduce scatter\", in distributed learning of LightGBM.\nLightGBM implements state-of-the-art algorithms\\ `[9] <#references>`__.\nThese collective communication algorithms can provide much better performance than point-to-point communication.\n\n.. _Optimization in Parallel Learning:\n\nOptimization in Distributed Learning\n------------------------------------\n\nLightGBM provides the following distributed learning algorithms.\n\nFeature Parallel\n~~~~~~~~~~~~~~~~\n\nTraditional Algorithm\n^^^^^^^^^^^^^^^^^^^^^\n\nFeature parallel aims to parallelize the \"Find Best Split\" in the decision tree. The procedure of traditional feature parallel is:\n\n1. Partition data vertically (different machines have different feature set).\n\n2. Workers find local best split point {feature, threshold} on local feature set.\n\n3. Communicate local best splits with each other and get the best one.\n\n4. Worker with best split to perform split, then send the split result of data to other workers.\n\n5. Other workers split data according to received data.\n\nThe shortcomings of traditional feature parallel:\n\n- Has computation overhead, since it cannot speed up \"split\", whose time complexity is ``O(#data)``.\n Thus, feature parallel cannot speed up well when ``#data`` is large.\n\n- Need communication of split result, which costs about ``O(#data / 8)`` (one bit for one data).\n\nFeature Parallel in LightGBM\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nSince feature parallel cannot speed up well when ``#data`` is large, we make a little change: instead of partitioning data vertically, every worker holds the full data.\nThus, LightGBM doesn't need to communicate for split result of data since every worker knows how to split data.\nAnd ``#data`` won't be larger, so it is reasonable to hold the full data in every machine.\n\nThe procedure of feature parallel in LightGBM:\n\n1. Workers find local best split point {feature, threshold} on local feature set.\n\n2. Communicate local best splits with each other and get the best one.\n\n3. Perform best split.\n\nHowever, this feature parallel algorithm still suffers from computation overhead for \"split\" when ``#data`` is large.\nSo it will be better to use data parallel when ``#data`` is large.\n\nData Parallel\n~~~~~~~~~~~~~\n\nTraditional Algorithm\n^^^^^^^^^^^^^^^^^^^^^\n\nData parallel aims to parallelize the whole decision learning. The procedure of data parallel is:\n\n1. Partition data horizontally.\n\n2. Workers use local data to construct local histograms.\n\n3. Merge global histograms from all local histograms.\n\n4. Find best split from merged global histograms, then perform splits.\n\nThe shortcomings of traditional data parallel:\n\n- High communication cost.\n If using point-to-point communication algorithm, communication cost for one machine is about ``O(#machine * #feature * #bin)``.\n If using collective communication algorithm (e.g. \"All Reduce\"), communication cost is about ``O(2 * #feature * #bin)`` (check cost of \"All Reduce\" in chapter 4.5 at `[9] <#references>`__).\n\nData Parallel in LightGBM\n^^^^^^^^^^^^^^^^^^^^^^^^^\n\nWe reduce communication cost of data parallel in LightGBM:\n\n1. Instead of \"Merge global histograms from all local histograms\", LightGBM uses \"Reduce Scatter\" to merge histograms of different (non-overlapping) features for different workers.\n Then workers find the local best split on local merged histograms and sync up the global best split.\n\n2. As aforementioned, LightGBM uses histogram subtraction to speed up training.\n Based on this, we can communicate histograms only for one leaf, and get its neighbor's histograms by subtraction as well.\n\nAll things considered, data parallel in LightGBM has time complexity ``O(0.5 * #feature * #bin)``.\n\nVoting Parallel\n~~~~~~~~~~~~~~~\n\nVoting parallel further reduces the communication cost in `Data Parallel <#data-parallel>`__ to constant cost.\nIt uses two-stage voting to reduce the communication cost of feature histograms\\ `[10] <#references>`__.\n\nGPU Support\n-----------\n\nThanks `@huanzhang12 `__ for contributing this feature. Please read `[11] <#references>`__ to get more details.\n\n- `GPU Installation <./Installation-Guide.rst#build-gpu-version>`__\n\n- `GPU Tutorial <./GPU-Tutorial.rst>`__\n\nApplications and Metrics\n------------------------\n\nLightGBM supports the following applications:\n\n- regression, the objective function is L2 loss\n\n- binary classification, the objective function is logloss\n\n- multi classification\n\n- cross-entropy, the objective function is logloss and supports training on non-binary labels\n\n- LambdaRank, the objective function is LambdaRank with NDCG\n\nLightGBM supports the following metrics:\n\n- L1 loss\n\n- L2 loss\n\n- Log loss\n\n- Classification error rate\n\n- AUC\n\n- NDCG\n\n- MAP\n\n- Multi-class log loss\n\n- Multi-class error rate\n\n- AUC-mu ``(new in v3.0.0)``\n\n- Average precision ``(new in v3.1.0)``\n\n- Fair\n\n- Huber\n\n- Poisson\n\n- Quantile\n\n- MAPE\n\n- Kullback-Leibler\n\n- Gamma\n\n- Tweedie\n\nFor more details, please refer to `Parameters <./Parameters.rst#metric-parameters>`__.\n\nOther Features\n--------------\n\n- Limit ``max_depth`` of tree while grows tree leaf-wise\n\n- `DART `__\n\n- L1/L2 regularization\n\n- Bagging\n\n- Column (feature) sub-sample\n\n- Continued train with input GBDT model\n\n- Continued train with the input score file\n\n- Weighted training\n\n- Validation metric output during training\n\n- Multiple validation data\n\n- Multiple metrics\n\n- Early stopping (both training and prediction)\n\n- Prediction for leaf index\n\nFor more details, please refer to `Parameters <./Parameters.rst>`__.\n\nReferences\n----------\n\n[1] Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu. \"`LightGBM\\: A Highly Efficient Gradient Boosting Decision Tree`_.\" Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149-3157.\n\n[2] Mehta, Manish, Rakesh Agrawal, and Jorma Rissanen. \"SLIQ: A fast scalable classifier for data mining.\" International Conference on Extending Database Technology. Springer Berlin Heidelberg, 1996.\n\n[3] Shafer, John, Rakesh Agrawal, and Manish Mehta. \"SPRINT: A scalable parallel classifier for data mining.\" Proc. 1996 Int. Conf. Very Large Data Bases. 1996.\n\n[4] Ranka, Sanjay, and V. Singh. \"CLOUDS: A decision tree classifier for large datasets.\" Proceedings of the 4th Knowledge Discovery and Data Mining Conference. 1998.\n\n[5] Machado, F. P. \"Communication and memory efficient parallel decision tree construction.\" (2003).\n\n[6] Li, Ping, Qiang Wu, and Christopher J. Burges. \"Mcrank: Learning to rank using multiple classification and gradient boosting.\" Advances in Neural Information Processing Systems 20 (NIPS 2007).\n\n[7] Shi, Haijian. \"Best-first decision tree learning.\" Diss. The University of Waikato, 2007.\n\n[8] Walter D. Fisher. \"`On Grouping for Maximum Homogeneity`_.\" Journal of the American Statistical Association. Vol. 53, No. 284 (Dec., 1958), pp. 789-798.\n\n[9] Thakur, Rajeev, Rolf Rabenseifner, and William Gropp. \"`Optimization of collective communication operations in MPICH`_.\" International Journal of High Performance Computing Applications 19.1 (2005), pp. 49-66.\n\n[10] Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Tie-Yan Liu. \"`A Communication-Efficient Parallel Algorithm for Decision Tree`_.\" Advances in Neural Information Processing Systems 29 (NIPS 2016), pp. 1279-1287.\n\n[11] Huan Zhang, Si Si and Cho-Jui Hsieh. \"`GPU Acceleration for Large-scale Tree Boosting`_.\" SysML Conference, 2018.\n\n.. _LightGBM\\: A Highly Efficient Gradient Boosting Decision Tree: https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html\n\n.. _On Grouping for Maximum Homogeneity: https://www.jstor.org/stable/2281952\n\n.. _Optimization of collective communication operations in MPICH: https://www.mpich.org/2012/10/24/optimization-of-collective-communication-operations-in-mpich/\n\n.. _A Communication-Efficient Parallel Algorithm for Decision Tree: https://proceedings.neurips.cc/paper/2016/hash/10a5ab2db37feedfdeaab192ead4ac0e-Abstract.html\n\n.. _GPU Acceleration for Large-scale Tree Boosting: https://arxiv.org/abs/1706.08359\n" }, { "path": "docs/GPU-Performance.rst", "content": "GPU Tuning Guide and Performance Comparison\n===========================================\n\nHow It Works?\n-------------\n\nIn LightGBM, the main computation cost during training is building the feature histograms. We use an efficient algorithm on GPU to accelerate this process.\nThe implementation is highly modular, and works for all learning tasks (classification, ranking, regression, etc). GPU acceleration also works in distributed learning settings.\nGPU algorithm implementation is based on OpenCL and can work with a wide range of GPUs.\n\nSupported Hardware\n------------------\n\nWe target AMD Graphics Core Next (GCN) architecture and NVIDIA Maxwell and Pascal architectures.\nMost AMD GPUs released after 2012 and NVIDIA GPUs released after 2014 should be supported. We have tested the GPU implementation on the following GPUs:\n\n- AMD RX 480 with AMDGPU-pro driver 16.60 on Ubuntu 16.10\n\n- AMD R9 280X (aka Radeon HD 7970) with fglrx driver 15.302.2301 on Ubuntu 16.10\n\n- NVIDIA GTX 1080 with driver 375.39 and CUDA 8.0 on Ubuntu 16.10\n\n- NVIDIA Titan X (Pascal) with driver 367.48 and CUDA 8.0 on Ubuntu 16.04\n\n- NVIDIA Tesla M40 with driver 375.39 and CUDA 7.5 on Ubuntu 16.04\n\nUsing the following hardware is discouraged:\n\n- NVIDIA Kepler (K80, K40, K20, most GeForce GTX 700 series GPUs) or earlier NVIDIA GPUs. They don't support hardware atomic operations in local memory space and thus histogram construction will be slow.\n\n- AMD VLIW4-based GPUs, including Radeon HD 6xxx series and earlier GPUs. These GPUs have been discontinued for years and are rarely seen nowadays.\n\nHow to Achieve Good Speedup on GPU\n----------------------------------\n\n#. You want to run a few datasets that we have verified with good speedup (including Higgs, epsilon, Bosch, etc) to ensure your setup is correct.\n If you have multiple GPUs, make sure to set ``gpu_platform_id`` and ``gpu_device_id`` to use the desired GPU.\n Also make sure your system is idle (especially when using a shared computer) to get accuracy performance measurements.\n\n#. GPU works best on large scale and dense datasets. If dataset is too small, computing it on GPU is inefficient as the data transfer overhead can be significant.\n If you have categorical features, use the ``categorical_column`` option and input them into LightGBM directly; do not convert them into one-hot variables.\n\n#. To get good speedup with GPU, it is suggested to use a smaller number of bins.\n Setting ``max_bin=63`` is recommended, as it usually does not noticeably affect training accuracy on large datasets, but GPU training can be significantly faster than using the default bin size of 255.\n For some dataset, even using 15 bins is enough (``max_bin=15``); using 15 bins will maximize GPU performance. Make sure to check the run log and verify that the desired number of bins is used.\n\n#. Try to use single precision training (``gpu_use_dp=false``) when possible, because most GPUs (especially NVIDIA consumer GPUs) have poor double-precision performance.\n\nPerformance Comparison\n----------------------\n\nWe evaluate the training performance of GPU acceleration on the following datasets:\n\n+-----------+----------------+----------+------------+-----------+------------+\n| Data | Task | Link | #Examples | #Features | Comments |\n+===========+================+==========+============+===========+============+\n| Higgs | Binary | `link1`_ | 10,500,000 | 28 | use last |\n| | classification | | | | 500,000 |\n| | | | | | samples |\n| | | | | | as test |\n| | | | | | set |\n+-----------+----------------+----------+------------+-----------+------------+\n| Epsilon | Binary | `link2`_ | 400,000 | 2,000 | use the |\n| | classification | | | | provided |\n| | | | | | test set |\n+-----------+----------------+----------+------------+-----------+------------+\n| Bosch | Binary | `link3`_ | 1,000,000 | 968 | use the |\n| | classification | | | | provided |\n| | | | | | test set |\n+-----------+----------------+----------+------------+-----------+------------+\n| Yahoo LTR | Learning to | `link4`_ | 473,134 | 700 | set1.train |\n| | rank | | | | as train, |\n| | | | | | set1.test |\n| | | | | | as test |\n+-----------+----------------+----------+------------+-----------+------------+\n| MS LTR | Learning to | `link5`_ | 2,270,296 | 137 | {S1,S2,S3} |\n| | rank | | | | as train |\n| | | | | | set, {S5} |\n| | | | | | as test |\n| | | | | | set |\n+-----------+----------------+----------+------------+-----------+------------+\n| Expo | Binary | `link6`_ | 11,000,000 | 700 | use last |\n| | classification | | | | 1,000,000 |\n| | (Categorical) | | | | as test |\n| | | | | | set |\n+-----------+----------------+----------+------------+-----------+------------+\n\nWe used the following hardware to evaluate the performance of LightGBM GPU training.\nOur CPU reference is **a high-end dual socket Haswell-EP Xeon server with 28 cores**;\nGPUs include a budget GPU (RX 480) and a mainstream (GTX 1080) GPU installed on the same server.\nIt is worth mentioning that **the GPUs used are not the best GPUs in the market**;\nif you are using a better GPU (like AMD RX 580, NVIDIA GTX 1080 Ti, Titan X Pascal, Titan Xp, Tesla P100, etc), you are likely to get a better speedup.\n\n+--------------------------------+----------------+------------------+---------------+\n| Hardware | Peak FLOPS | Peak Memory BW | Cost (MSRP) |\n+================================+================+==================+===============+\n| AMD Radeon RX 480 | 5,161 GFLOPS | 256 GB/s | $199 |\n+--------------------------------+----------------+------------------+---------------+\n| NVIDIA GTX 1080 | 8,228 GFLOPS | 320 GB/s | $499 |\n+--------------------------------+----------------+------------------+---------------+\n| 2x Xeon E5-2683v3 (28 cores) | 1,792 GFLOPS | 133 GB/s | $3,692 |\n+--------------------------------+----------------+------------------+---------------+\n\nDuring benchmarking on CPU we used only 28 physical cores of the CPU, and did not use hyper-threading cores,\nbecause we found that using too many threads actually makes performance worse.\nThe following shows the training configuration we used:\n\n::\n\n max_bin = 63\n num_leaves = 255\n num_iterations = 500\n learning_rate = 0.1\n tree_learner = serial\n task = train\n is_training_metric = false\n min_data_in_leaf = 1\n min_sum_hessian_in_leaf = 100\n ndcg_eval_at = 1,3,5,10\n device = gpu\n gpu_platform_id = 0\n gpu_device_id = 0\n num_thread = 28\n\nWe use the configuration shown above, except for the Bosch dataset, we use a smaller ``learning_rate=0.015`` and set ``min_sum_hessian_in_leaf=5``.\nFor all GPU training we vary the max number of bins (255, 63 and 15).\nThe GPU implementation is from commit `0bb4a82`_ of LightGBM, when the GPU support was just merged in.\n\nThe following table lists the accuracy on test set that CPU and GPU learner can achieve after 500 iterations.\nGPU with the same number of bins can achieve a similar level of accuracy as on the CPU, despite using single precision arithmetic.\nFor most datasets, using 63 bins is sufficient.\n\n+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+\n| | CPU 255 bins | CPU 63 bins | CPU 15 bins | GPU 255 bins | GPU 63 bins | GPU 15 bins |\n+===========================+================+===============+===============+================+===============+===============+\n| Higgs AUC | 0.845612 | 0.845239 | 0.841066 | 0.845612 | 0.845209 | 0.840748 |\n+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+\n| Epsilon AUC | 0.950243 | 0.949952 | 0.948365 | 0.950057 | 0.949876 | 0.948365 |\n+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+\n| Yahoo-LTR NDCG\\ :sub:`1` | 0.730824 | 0.730165 | 0.729647 | 0.730936 | 0.732257 | 0.73114 |\n+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+\n| Yahoo-LTR NDCG\\ :sub:`3` | 0.738687 | 0.737243 | 0.736445 | 0.73698 | 0.739474 | 0.735868 |\n+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+\n| Yahoo-LTR NDCG\\ :sub:`5` | 0.756609 | 0.755729 | 0.754607 | 0.756206 | 0.757007 | 0.754203 |\n+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+\n| Yahoo-LTR NDCG\\ :sub:`10` | 0.79655 | 0.795827 | 0.795273 | 0.795894 | 0.797302 | 0.795584 |\n+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+\n| Expo AUC | 0.776217 | 0.771566 | 0.743329 | 0.776285 | 0.77098 | 0.744078 |\n+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+\n| MS-LTR NDCG\\ :sub:`1` | 0.521265 | 0.521392 | 0.518653 | 0.521789 | 0.522163 | 0.516388 |\n+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+\n| MS-LTR NDCG\\ :sub:`3` | 0.503153 | 0.505753 | 0.501697 | 0.503886 | 0.504089 | 0.501691 |\n+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+\n| MS-LTR NDCG\\ :sub:`5` | 0.509236 | 0.510391 | 0.507193 | 0.509861 | 0.510095 | 0.50663 |\n+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+\n| MS-LTR NDCG\\ :sub:`10` | 0.527835 | 0.527304 | 0.524603 | 0.528009 | 0.527059 | 0.524722 |\n+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+\n| Bosch AUC | 0.718115 | 0.721791 | 0.716677 | 0.717184 | 0.724761 | 0.717005 |\n+---------------------------+----------------+---------------+---------------+----------------+---------------+---------------+\n\nWe record the wall clock time after 500 iterations, as shown in the figure below:\n\n.. image:: ./_static/images/gpu-performance-comparison.png\n :align: center\n :target: ./_static/images/gpu-performance-comparison.png\n :alt: A performance chart which is a record of the wall clock time after 500 iterations on G P U for Higgs, epsilon, Bosch, Microsoft L T R, Expo and Yahoo L T R and bin size of 63 performs comparatively better.\n\nWhen using a GPU, it is advisable to use a bin size of 63 rather than 255, because it can speed up training significantly without noticeably affecting accuracy.\nOn CPU, using a smaller bin size only marginally improves performance, sometimes even slows down training,\nlike in Higgs (we can reproduce the same slowdown on two different machines, with different GCC versions).\nWe found that GPU can achieve impressive acceleration on large and dense datasets like Higgs and Epsilon.\nEven on smaller and sparse datasets, a *budget* GPU can still compete and be faster than a 28-core Haswell server.\n\nMemory Usage\n------------\n\nThe next table shows GPU memory usage reported by ``nvidia-smi`` during training with 63 bins.\nWe can see that even the largest dataset just uses about 1 GB of GPU memory,\nindicating that our GPU implementation can scale to huge datasets over 10x larger than Bosch or Epsilon.\nAlso, we can observe that generally a larger dataset (using more GPU memory, like Epsilon or Bosch) has better speedup,\nbecause the overhead of invoking GPU functions becomes significant when the dataset is small.\n\n+-------------------------+---------+-----------+---------+----------+--------+-------------+\n| Datasets | Higgs | Epsilon | Bosch | MS-LTR | Expo | Yahoo-LTR |\n+=========================+=========+===========+=========+==========+========+=============+\n| GPU Memory Usage (MB) | 611 | 901 | 1067 | 413 | 405 | 291 |\n+-------------------------+---------+-----------+---------+----------+--------+-------------+\n\nFurther Reading\n---------------\n\nYou can find more details about the GPU algorithm and benchmarks in the\nfollowing article:\n\nHuan Zhang, Si Si and Cho-Jui Hsieh. `GPU Acceleration for Large-scale Tree Boosting`_. SysML Conference, 2018.\n\n.. _link1: https://archive.ics.uci.edu/dataset/280/higgs\n\n.. _link2: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html\n\n.. _link3: https://www.kaggle.com/c/bosch-production-line-performance/data\n\n.. _link4: https://proceedings.mlr.press/v14/chapelle11a.html\n\n.. _link5: https://www.microsoft.com/en-us/research/project/mslr/\n\n.. _link6: https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2009\n\n.. _0bb4a82: https://github.com/lightgbm-org/LightGBM/commit/0bb4a82\n\n.. _GPU Acceleration for Large-scale Tree Boosting: https://arxiv.org/abs/1706.08359\n" }, { "path": "docs/GPU-Targets.rst", "content": "GPU SDK Correspondence and Device Targeting Table\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nGPU Targets Table\n=================\n\nOpenCL is a universal massively parallel programming framework that targets multiple backends (GPU, CPU, FPGA, etc).\nBasically, to use a device from a vendor, you have to install drivers from that specific vendor.\nIntel's and AMD's OpenCL runtime also include x86 CPU target support.\nNVIDIA's OpenCL runtime only supports NVIDIA GPU (no CPU support).\nIn general, OpenCL CPU backends are quite slow, and should be used for testing and debugging only.\n\nYou can find below a table of correspondence:\n\n+---------------------------+-----------------+-----------------+-----------------+--------------+\n| SDK | CPU Intel/AMD | GPU Intel | GPU AMD | GPU NVIDIA |\n+===========================+=================+=================+=================+==============+\n| `Intel SDK for OpenCL`_ | Supported | Supported | Not Supported | Not Supported|\n+---------------------------+-----------------+-----------------+-----------------+--------------+\n| AMD APP SDK \\* | Supported | Not Supported | Supported | Not Supported|\n+---------------------------+-----------------+-----------------+-----------------+--------------+\n| `PoCL`_ | Supported | Not Supported | Supported | Not Supported|\n+---------------------------+-----------------+-----------------+-----------------+--------------+\n| `NVIDIA CUDA Toolkit`_ | Not Supported | Not Supported | Not Supported | Supported |\n+---------------------------+-----------------+-----------------+-----------------+--------------+\n\nLegend:\n\n\\* AMD APP SDK is deprecated. On Windows, OpenCL is included in AMD graphics driver. On Linux, newer generation AMD cards are supported by the `ROCm`_ driver. You can download an archived copy of AMD APP SDK from our GitHub repo (`for Linux`_ and `for Windows`_).\n\n\n--------------\n\nQuery OpenCL Devices in Your System\n===================================\n\nYour system might have multiple GPUs from different vendors (\"platforms\") installed. Setting up LightGBM GPU device requires two parameters: `OpenCL Platform ID <./Parameters.rst#gpu_platform_id>`__ (``gpu_platform_id``) and `OpenCL Device ID <./Parameters.rst#gpu_device_id>`__ (``gpu_device_id``). Generally speaking, each vendor provides an OpenCL platform, and devices from the same vendor have different device IDs under that platform. For example, if your system has an Intel integrated GPU and two discrete GPUs from AMD, you will have two OpenCL platforms (with ``gpu_platform_id=0`` and ``gpu_platform_id=1``). If the platform 0 is Intel, it has one device (``gpu_device_id=0``) representing the Intel GPU; if the platform 1 is AMD, it has two devices (``gpu_device_id=0``, ``gpu_device_id=1``) representing the two AMD GPUs. If you have a discrete GPU by AMD/NVIDIA and an integrated GPU by Intel, make sure to select the correct ``gpu_platform_id`` to use the discrete GPU as it usually provides better performance.\n\nOn Windows, OpenCL devices can be queried using `GPUCapsViewer`_, under the OpenCL tab. Note that the platform and device IDs reported by this utility start from 1. So you should minus the reported IDs by 1.\n\nOn Linux, OpenCL devices can be listed using the ``clinfo`` command. On Ubuntu, you can install ``clinfo`` by executing ``sudo apt-get install clinfo``.\n\n\nExamples\n===============\n\nWe provide test R code below, but you can use the language of your choice with the examples of your choices:\n\n.. code:: r\n\n library(lightgbm)\n data(agaricus.train, package = \"lightgbm\")\n train <- agaricus.train\n train$data[, 1] <- 1:6513\n dtrain <- lgb.Dataset(train$data, label = train$label)\n data(agaricus.test, package = \"lightgbm\")\n test <- agaricus.test\n dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)\n valids <- list(test = dtest)\n\n params <- list(objective = \"regression\",\n metric = \"rmse\",\n device = \"gpu\",\n gpu_platform_id = 0,\n gpu_device_id = 0,\n nthread = 1,\n boost_from_average = FALSE,\n num_tree_per_iteration = 10,\n max_bin = 32)\n model <- lgb.train(params,\n dtrain,\n 2,\n valids,\n min_data = 1,\n learning_rate = 1,\n early_stopping_rounds = 10)\n\nMake sure you list the OpenCL devices in your system and set ``gpu_platform_id`` and ``gpu_device_id`` correctly. In the following examples, our system has 1 GPU platform (``gpu_platform_id = 0``) from AMD APP SDK. The first device ``gpu_device_id = 0`` is a GPU device (AMD Oland), and the second device ``gpu_device_id = 1`` is the x86 CPU backend.\n\nExample of using GPU (``gpu_platform_id = 0`` and ``gpu_device_id = 0`` in our system):\n\n.. code:: r\n\n > params <- list(objective = \"regression\",\n + metric = \"rmse\",\n + device = \"gpu\",\n + gpu_platform_id = 0,\n + gpu_device_id = 0,\n + nthread = 1,\n + boost_from_average = FALSE,\n + num_tree_per_iteration = 10,\n + max_bin = 32)\n > model <- lgb.train(params,\n + dtrain,\n + 2,\n + valids,\n + min_data = 1,\n + learning_rate = 1,\n + early_stopping_rounds = 10)\n [LightGBM] [Info] This is the GPU trainer!!\n [LightGBM] [Info] Total Bins 232\n [LightGBM] [Info] Number of data: 6513, number of used features: 116\n [LightGBM] [Info] Using GPU Device: Oland, Vendor: Advanced Micro Devices, Inc.\n [LightGBM] [Info] Compiling OpenCL Kernel with 16 bins...\n [LightGBM] [Info] GPU programs have been built\n [LightGBM] [Info] Size of histogram bin entry: 12\n [LightGBM] [Info] 40 dense feature groups (0.12 MB) transferred to GPU in 0.004211 secs. 76 sparse feature groups.\n [LightGBM] [Info] No further splits with positive gain, best gain: -inf\n [LightGBM] [Info] Trained a tree with leaves=16 and depth=8\n [1]: test's rmse:1.10643e-17\n [LightGBM] [Info] No further splits with positive gain, best gain: -inf\n [LightGBM] [Info] Trained a tree with leaves=7 and depth=5\n [2]: test's rmse:0\n\nRunning on OpenCL CPU backend devices is in generally slow, and we observe crashes on some Windows and macOS systems. Make sure you check the ``Using GPU Device`` line in the log and it is not using a CPU. The above log shows that we are using ``Oland`` GPU from AMD and not CPU.\n\nExample of using CPU (``gpu_platform_id = 0``, ``gpu_device_id = 1``). The GPU device reported is ``Intel(R) Core(TM) i7-4600U CPU``, so it is using the CPU backend rather than a real GPU.\n\n.. code:: r\n\n > params <- list(objective = \"regression\",\n + metric = \"rmse\",\n + device = \"gpu\",\n + gpu_platform_id = 0,\n + gpu_device_id = 1,\n + nthread = 1,\n + boost_from_average = FALSE,\n + num_tree_per_iteration = 10,\n + max_bin = 32)\n > model <- lgb.train(params,\n + dtrain,\n + 2,\n + valids,\n + min_data = 1,\n + learning_rate = 1,\n + early_stopping_rounds = 10)\n [LightGBM] [Info] This is the GPU trainer!!\n [LightGBM] [Info] Total Bins 232\n [LightGBM] [Info] Number of data: 6513, number of used features: 116\n [LightGBM] [Info] Using requested OpenCL platform 0 device 1\n [LightGBM] [Info] Using GPU Device: Intel(R) Core(TM) i7-4600U CPU @ 2.10GHz, Vendor: GenuineIntel\n [LightGBM] [Info] Compiling OpenCL Kernel with 16 bins...\n [LightGBM] [Info] GPU programs have been built\n [LightGBM] [Info] Size of histogram bin entry: 12\n [LightGBM] [Info] 40 dense feature groups (0.12 MB) transferred to GPU in 0.004540 secs. 76 sparse feature groups.\n [LightGBM] [Info] No further splits with positive gain, best gain: -inf\n [LightGBM] [Info] Trained a tree with leaves=16 and depth=8\n [1]: test's rmse:1.10643e-17\n [LightGBM] [Info] No further splits with positive gain, best gain: -inf\n [LightGBM] [Info] Trained a tree with leaves=7 and depth=5\n [2]: test's rmse:0\n\n\nKnown issues:\n\n- Using a bad combination of ``gpu_platform_id`` and ``gpu_device_id`` can potentially lead to a **crash** due to OpenCL driver issues on some machines (you will lose your entire session content). Beware of it.\n\n- On some systems, if you have integrated graphics card (Intel HD Graphics) and a dedicated graphics card (AMD, NVIDIA), the dedicated graphics card will automatically override the integrated graphics card. The workaround is to disable your dedicated graphics card to be able to use your integrated graphics card.\n\n.. _Intel SDK for OpenCL: https://software.intel.com/en-us/articles/opencl-drivers\n\n.. _ROCm: https://rocmdocs.amd.com/en/latest/\n\n.. _for Linux: https://github.com/lightgbm-org/LightGBM/releases/download/v2.0.12/AMD-APP-SDKInstaller-v3.0.130.136-GA-linux64.tar.bz2\n\n.. _for Windows: https://github.com/lightgbm-org/LightGBM/releases/download/v2.0.12/AMD-APP-SDKInstaller-v3.0.130.135-GA-windows-F-x64.exe\n\n.. _NVIDIA CUDA Toolkit: https://developer.nvidia.com/cuda-downloads\n\n.. _clinfo: https://github.com/Oblomov/clinfo\n\n.. _GPUCapsViewer: https://www.ozone3d.net/gpu_caps_viewer/\n\n.. _PoCL: https://portablecl.org/\n" }, { "path": "docs/GPU-Tutorial.rst", "content": "LightGBM GPU Tutorial\n=====================\n\nThe purpose of this document is to give you a quick step-by-step tutorial on GPU training.\n\nWe will use the GPU instance on `Microsoft Azure cloud computing platform`_ for demonstration,\nbut you can use any machine with modern AMD or NVIDIA GPUs.\n\nGPU Setup\n---------\n\nYou need to launch a ``NV`` type instance on Azure (available in East US, North Central US, South Central US, West Europe and Southeast Asia zones)\nand select Ubuntu 16.04 LTS as the operating system.\n\nFor testing, the smallest ``NV6`` type virtual machine is sufficient, which includes 1/2 M60 GPU, with 8 GB memory, 180 GB/s memory bandwidth and 4,825 GFLOPS peak computation power.\nDon't use the ``NC`` type instance as the GPUs (K80) are based on an older architecture (Kepler).\n\nFirst we need to install minimal NVIDIA drivers and OpenCL development environment:\n\n::\n\n sudo apt-get update\n sudo apt-get install --no-install-recommends nvidia-375\n sudo apt-get install --no-install-recommends nvidia-opencl-icd-375 nvidia-opencl-dev opencl-headers\n\nAfter installing the drivers you need to restart the server.\n\n::\n\n sudo init 6\n\nAfter about 30 seconds, the server should be up again.\n\nIf you are using an AMD GPU, you should download and install the `AMDGPU-Pro`_ driver and also install packages ``ocl-icd-libopencl1`` and ``ocl-icd-opencl-dev``.\n\nBuild LightGBM\n--------------\n\nNow install necessary building tools and dependencies:\n\n::\n\n sudo apt-get install --no-install-recommends git cmake build-essential libboost-dev libboost-system-dev libboost-filesystem-dev\n\nThe ``NV6`` GPU instance has a 320 GB ultra-fast SSD mounted at ``/mnt``.\nLet's use it as our workspace (skip this if you are using your own machine):\n\n::\n\n sudo mkdir -p /mnt/workspace\n sudo chown $(whoami):$(whoami) /mnt/workspace\n cd /mnt/workspace\n\nNow we are ready to checkout LightGBM and compile it with GPU support:\n\n::\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -DUSE_GPU=1\n   # if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following:\n # cmake -B build -S . -DUSE_GPU=1 -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/\n cmake --build build -j$(nproc)\n\nYou will see two binaries are generated, ``lightgbm`` and ``lib_lightgbm.so``.\n\nIf you are building on macOS, you probably need to remove macro ``BOOST_COMPUTE_USE_OFFLINE_CACHE`` in ``src/treelearner/gpu_tree_learner.h`` to avoid a known crash bug in Boost.Compute.\n\nInstall Python Interface (optional)\n-----------------------------------\n\nIf you want to use the Python interface of LightGBM, you can install it now (along with some necessary Python-package dependencies):\n\n::\n\n sudo apt-get -y install python3-pip python3-venv\n sudo -H pip install numpy scipy scikit-learn -U\n sudo sh ./build-python.sh install --precompile\n\nYou need to set an additional parameter ``\"device\" : \"gpu\"`` (along with your other options like ``learning_rate``, ``num_leaves``, etc) to use GPU in Python.\n\nYou can read our `Python-package Examples`_ for more information on how to use the Python interface.\n\nDataset Preparation\n-------------------\n\nUsing the following commands to prepare the Higgs dataset:\n\n::\n\n git clone https://github.com/guolinke/boosting_tree_benchmarks.git\n cd boosting_tree_benchmarks/data\n wget \"https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz\"\n gunzip HIGGS.csv.gz\n python higgs2libsvm.py\n cd ../..\n ln -s boosting_tree_benchmarks/data/higgs.train\n ln -s boosting_tree_benchmarks/data/higgs.test\n\nNow we create a configuration file for LightGBM by running the following commands (please copy the entire block and run it as a whole):\n\n::\n\n cat > lightgbm_gpu.conf <> lightgbm_gpu.conf\n\nGPU is enabled in the configuration file we just created by setting ``device=gpu``.\nIn this configuration we use the first GPU installed on the system (``gpu_platform_id=0`` and ``gpu_device_id=0``). If ``gpu_platform_id`` or ``gpu_device_id`` is not set, the default platform and GPU will be selected.\nYou might have multiple platforms (AMD/Intel/NVIDIA) or GPUs. You can use the `clinfo`_ utility to identify the GPUs on each platform. On Ubuntu, you can install ``clinfo`` by executing ``sudo apt-get install clinfo``. If you have a discrete GPU by AMD/NVIDIA and an integrated GPU by Intel, make sure to select the correct ``gpu_platform_id`` to use the discrete GPU.\n\nRun Your First Learning Task on GPU\n-----------------------------------\n\nNow we are ready to start GPU training!\n\nFirst we want to verify the GPU works correctly.\nRun the following command to train on GPU, and take a note of the AUC after 50 iterations:\n\n::\n\n ./lightgbm config=lightgbm_gpu.conf data=higgs.train valid=higgs.test objective=binary metric=auc\n\nNow train the same dataset on CPU using the following command. You should observe a similar AUC:\n\n::\n\n ./lightgbm config=lightgbm_gpu.conf data=higgs.train valid=higgs.test objective=binary metric=auc device=cpu\n\nNow we can make a speed test on GPU without calculating AUC after each iteration.\n\n::\n\n ./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=binary metric=auc\n\nSpeed test on CPU:\n\n::\n\n ./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=binary metric=auc device=cpu\n\nYou should observe over three times speedup on this GPU.\n\nThe GPU acceleration can be used on other tasks/metrics (regression, multi-class classification, ranking, etc) as well.\nFor example, we can train the Higgs dataset on GPU as a regression task:\n\n::\n\n ./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=regression_l2 metric=l2\n\nAlso, you can compare the training speed with CPU:\n\n::\n\n ./lightgbm config=lightgbm_gpu.conf data=higgs.train objective=regression_l2 metric=l2 device=cpu\n\nFurther Reading\n---------------\n\n- `GPU Tuning Guide and Performance Comparison <./GPU-Performance.rst>`__\n\n- `GPU SDK Correspondence and Device Targeting Table <./GPU-Targets.rst>`__\n\nReference\n---------\n\nPlease kindly cite the following article in your publications if you find the GPU acceleration useful:\n\nHuan Zhang, Si Si and Cho-Jui Hsieh. \"`GPU Acceleration for Large-scale Tree Boosting`_.\" SysML Conference, 2018.\n\n.. _Microsoft Azure cloud computing platform: https://azure.microsoft.com/\n\n.. _AMDGPU-Pro: https://www.amd.com/en/support.html\n\n.. _Python-package Examples: https://github.com/lightgbm-org/LightGBM/tree/master/examples/python-guide\n\n.. _GPU Acceleration for Large-scale Tree Boosting: https://arxiv.org/abs/1706.08359\n\n.. _clinfo: https://github.com/Oblomov/clinfo\n" }, { "path": "docs/GPU-Windows.rst", "content": "The content of this document was very outdated and is no longer available to avoid any misleadings.\n\nStarting from the ``3.2.0`` version LightGBM Python packages have been having built-in support of training on GPU devices.\n" }, { "path": "docs/Installation-Guide.rst", "content": "Installation Guide\n==================\n\nVersioning\n~~~~~~~~~~\n\nLightGBM releases use a 3-part version number, with this format:\n\n.. code::\n\n {major}.{minor}.{patch}\n\nThis version follows a scheme called Intended Effort Versioning (\"Effver\" for short).\nChanges to a component of the version indicate how much effort it will likely take to update\ncode using a previous version.\n\n* ``major`` = updating will require significant effort\n* ``minor`` = some effort\n* ``patch`` = no or very little effort\n\nThis means that **new minor versions can contain breaking changes**, but these are typically\nsmall or limited to less-frequently-used parts of the project.\n\nWhen built from source on an unreleased commit, this version takes the following form:\n\n.. code::\n\n {major}.{minor}.{patch}.99\n\nThat ``.99`` is added to ensure that a version built from an unreleased commit is considered \"newer\"\nthan all previous releases, and \"older\" than all future releases.\n\n.. _nightly-builds:\n\nTo download such artifacts, run the following from the root of this repository.\n\n.. code:: sh\n\n bash .ci/download-artifacts.sh ${COMMIT_ID}\n\nWhere `COMMIT_ID` is the full commit SHA pointing to a commit on ``master``.\nThe artifacts can then be found in the ``release-artifacts/`` directory.\n\nFor more details on why LightGBM uses EffVer instead of other schemes like semantic versioning,\nsee https://jacobtomlinson.dev/effver/.\n\nGeneral Installation Notes\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nAll instructions below are aimed at compiling the 64-bit version of LightGBM.\nIt is worth compiling the 32-bit version only in very rare special cases involving environmental limitations.\nThe 32-bit version is slow and untested, so use it at your own risk and don't forget to adjust some of the commands below when installing.\n\nBy default, instructions below will use **VS Build Tools** or **make** tool to compile the code.\nIt it possible to use `Ninja`_ tool instead of make on all platforms, but VS Build Tools cannot be replaced with Ninja.\nYou can add ``-G Ninja`` to CMake flags to use Ninja.\n\nBy default, instructions below will produce a shared library file and an executable file with command-line interface.\nYou can add ``-DBUILD_CLI=OFF`` to CMake flags to disable the executable compilation.\n\nIf you need to build a static library instead of a shared one, you can add ``-DBUILD_STATIC_LIB=ON`` to CMake flags.\n\nBy default, instructions below will place header files into system-wide folder.\nYou can add ``-DINSTALL_HEADERS=OFF`` to CMake flags to disable headers installation.\n\nBy default, on macOS, CMake is looking into Homebrew standard folders for finding dependencies (e.g. OpenMP).\nYou can add ``-DUSE_HOMEBREW_FALLBACK=OFF`` to CMake flags to disable this behaviour.\n\nUsers who want to perform benchmarking can make LightGBM output time costs for different internal routines by adding ``-DUSE_TIMETAG=ON`` to CMake flags.\n\nIt is possible to build LightGBM in debug mode.\nIn this mode all compiler optimizations are disabled and LightGBM performs more checks internally.\nTo enable debug mode you can add ``-DUSE_DEBUG=ON`` to CMake flags or choose ``Debug_*`` configuration (e.g. ``Debug_DLL``, ``Debug_mpi``) in Visual Studio depending on how you are building LightGBM.\n\n.. _sanitizers:\n\nIn addition to the debug mode, LightGBM can be built with compiler sanitizers.\nTo enable them add ``-DUSE_SANITIZER=ON -DENABLED_SANITIZERS=\"address;leak;undefined\"`` to CMake flags.\nThese values refer to the following supported sanitizers:\n\n- ``address`` - AddressSanitizer (ASan);\n- ``leak`` - LeakSanitizer (LSan);\n- ``undefined`` - UndefinedBehaviorSanitizer (UBSan);\n- ``thread`` - ThreadSanitizer (TSan).\n\nPlease note, that ThreadSanitizer cannot be used together with other sanitizers.\nFor more info and additional sanitizers' parameters please refer to the `following docs`_.\nIt is very useful to build `C++ unit tests <#build-c-unit-tests>`__ with sanitizers.\n\n.. contents:: **Contents**\n :depth: 1\n :local:\n :backlinks: none\n\nWindows\n~~~~~~~\n\nOn Windows, LightGBM can be built using\n\n- **Visual Studio**;\n- **CMake** and **VS Build Tools**;\n- **CMake** and **MinGW**.\n\nVisual Studio (or VS Build Tools)\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nWith GUI\n********\n\n1. Install `Visual Studio`_.\n\n2. Navigate to one of the releases at https://github.com/lightgbm-org/LightGBM/releases, download ``LightGBM-complete_source_code_zip.zip``, and unzip it.\n\n3. Go to ``LightGBM-complete_source_code_zip/windows`` folder.\n\n4. Open ``LightGBM.sln`` file with **Visual Studio**, choose ``Release`` configuration if you need executable file or ``DLL`` configuration if you need shared library and click ``Build`` -> ``Build Solution (Ctrl+Shift+B)``.\n\n If you have errors about **Platform Toolset**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the toolset installed on your machine.\n\n If you have errors about **Windows SDK Version**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the SDK installed on your machine.\n\nThe ``.exe`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/Release`` folder.\nThe ``.dll`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/DLL`` folder.\n\nFrom Command Line\n*****************\n\n1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed).\n\n2. Run the following commands:\n\n .. code:: console\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -A x64\n cmake --build build --target ALL_BUILD --config Release\n\nThe ``.exe`` and ``.dll`` files will be in ``LightGBM/Release`` folder.\n\nMinGW-w64\n^^^^^^^^^\n\n1. Install `Git for Windows`_, `CMake`_ and `MinGW-w64`_.\n\n2. Run the following commands:\n\n .. code:: console\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -G \"MinGW Makefiles\"\n cmake --build build -j4\n\nThe ``.exe`` and ``.dll`` files will be in ``LightGBM/`` folder.\n\n**Note**: You may need to run the ``cmake -B build -S . -G \"MinGW Makefiles\"`` one more time or add ``-DCMAKE_SH=CMAKE_SH-NOTFOUND`` to CMake flags if you encounter the ``sh.exe was found in your PATH`` error.\n\nIt is recommended that you use **Visual Studio** since it has better multithreading efficiency in **Windows** for many-core systems\n(see `Question 4 <./FAQ.rst#i-am-using-windows-should-i-use-visual-studio-or-mingw-for-compiling-lightgbm>`__ and `Question 8 <./FAQ.rst#cpu-usage-is-low-like-10-in-windows-when-using-lightgbm-on-very-large-datasets-with-many-core-systems>`__).\n\nLinux\n~~~~~\n\nOn Linux, LightGBM can be built using\n\n- **CMake** and **gcc**;\n- **CMake** and **Clang**.\n\nAfter compilation the executable and ``.so`` files will be in ``LightGBM/`` folder.\n\ngcc\n^^^\n\n1. Install `CMake`_ and **gcc**.\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S .\n cmake --build build -j4\n\nClang\n^^^^^\n\n1. Install `CMake`_, **Clang** and **OpenMP**.\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n export CXX=clang++-14 CC=clang-14 # replace \"14\" with version of Clang installed on your machine\n cmake -B build -S .\n cmake --build build -j4\n\nmacOS\n~~~~~\n\nOn macOS, LightGBM can be installed using\n\n- **Homebrew**;\n- **MacPorts**;\n\nor can be built using\n\n- **CMake** and **Apple Clang**;\n- **CMake** and **gcc**.\n\nInstall Using ``Homebrew``\n^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. code:: sh\n\n brew install lightgbm\n\nRefer to https://formulae.brew.sh/formula/lightgbm for more details.\n\nInstall Using ``MacPorts``\n^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n.. code:: sh\n\n sudo port install LightGBM\n\nRefer to https://ports.macports.org/port/LightGBM for more details.\n\n**Note**: Port for LightGBM is not maintained by LightGBM's maintainers.\n\nBuild from GitHub\n^^^^^^^^^^^^^^^^^\n\nAfter compilation the executable and ``.dylib`` files will be in ``LightGBM/`` folder.\n\nApple Clang\n***********\n\n1. Install `CMake`_ and **OpenMP**:\n\n .. code:: sh\n\n brew install cmake libomp\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S .\n cmake --build build -j4\n\ngcc\n***\n\n1. Install `CMake`_ and **gcc**:\n\n .. code:: sh\n\n brew install cmake gcc\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n export CXX=g++-7 CC=gcc-7 # replace \"7\" with version of gcc installed on your machine\n cmake -B build -S .\n cmake --build build -j4\n\nDocker\n~~~~~~\n\nRefer to `Docker folder `__.\n\nBuild Threadless Version (not Recommended)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe default build version of LightGBM is based on OpenMP.\nYou can build LightGBM without OpenMP support but it is **strongly not recommended**.\n\nWindows\n^^^^^^^\n\nOn Windows, a version of LightGBM without OpenMP support can be built using\n\n- **Visual Studio**;\n- **CMake** and **VS Build Tools**;\n- **CMake** and **MinGW**.\n\nVisual Studio (or VS Build Tools)\n*********************************\n\nWith GUI\n--------\n\n1. Install `Visual Studio`_.\n\n2. Navigate to one of the releases at https://github.com/lightgbm-org/LightGBM/releases, download ``LightGBM-complete_source_code_zip.zip``, and unzip it.\n\n3. Go to ``LightGBM-complete_source_code_zip/windows`` folder.\n\n4. Open ``LightGBM.sln`` file with **Visual Studio**, choose ``Release`` configuration if you need executable file or ``DLL`` configuration if you need shared library.\n\n5. Go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``C/C++`` -> ``Language`` and change the ``OpenMP Support`` property to ``No (/openmp-)``.\n\n6. Get back to the project's main screen and click ``Build`` -> ``Build Solution (Ctrl+Shift+B)``.\n\n If you have errors about **Platform Toolset**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the toolset installed on your machine.\n\n If you have errors about **Windows SDK Version**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the SDK installed on your machine.\n\nThe ``.exe`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/Release`` folder.\nThe ``.dll`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/DLL`` folder.\n\nFrom Command Line\n-----------------\n\n1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed).\n\n2. Run the following commands:\n\n .. code:: console\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -A x64 -DUSE_OPENMP=OFF\n cmake --build build --target ALL_BUILD --config Release\n\nThe ``.exe`` and ``.dll`` files will be in ``LightGBM/Release`` folder.\n\nMinGW-w64\n*********\n\n1. Install `Git for Windows`_, `CMake`_ and `MinGW-w64`_.\n\n2. Run the following commands:\n\n .. code:: console\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -G \"MinGW Makefiles\" -DUSE_OPENMP=OFF\n cmake --build build -j4\n\nThe ``.exe`` and ``.dll`` files will be in ``LightGBM/`` folder.\n\n**Note**: You may need to run the ``cmake -B build -S . -G \"MinGW Makefiles\" -DUSE_OPENMP=OFF`` one more time or add ``-DCMAKE_SH=CMAKE_SH-NOTFOUND`` to CMake flags if you encounter the ``sh.exe was found in your PATH`` error.\n\nLinux\n^^^^^\n\nOn Linux, a version of LightGBM without OpenMP support can be built using\n\n- **CMake** and **gcc**;\n- **CMake** and **Clang**.\n\nAfter compilation the executable and ``.so`` files will be in ``LightGBM/`` folder.\n\ngcc\n***\n\n1. Install `CMake`_ and **gcc**.\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -DUSE_OPENMP=OFF\n cmake --build build -j4\n\nClang\n*****\n\n1. Install `CMake`_ and **Clang**.\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n export CXX=clang++-14 CC=clang-14 # replace \"14\" with version of Clang installed on your machine\n cmake -B build -S . -DUSE_OPENMP=OFF\n cmake --build build -j4\n\nmacOS\n^^^^^\n\nOn macOS, a version of LightGBM without OpenMP support can be built using\n\n- **CMake** and **Apple Clang**;\n- **CMake** and **gcc**.\n\nAfter compilation the executable and ``.dylib`` files will be in ``LightGBM/`` folder.\n\nApple Clang\n***********\n\n1. Install `CMake`_:\n\n .. code:: sh\n\n brew install cmake\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -DUSE_OPENMP=OFF\n cmake --build build -j4\n\ngcc\n***\n\n1. Install `CMake`_ and **gcc**:\n\n .. code:: sh\n\n brew install cmake gcc\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n export CXX=g++-7 CC=gcc-7 # replace \"7\" with version of gcc installed on your machine\n cmake -B build -S . -DUSE_OPENMP=OFF\n cmake --build build -j4\n\nBuild MPI Version\n~~~~~~~~~~~~~~~~~\n\nThe default build version of LightGBM is based on socket. LightGBM also supports MPI.\n`MPI`_ is a high performance communication approach with `RDMA`_ support.\n\nIf you need to run a distributed learning application with high performance communication, you can build the LightGBM with MPI support.\n\nWindows\n^^^^^^^\n\nOn Windows, an MPI version of LightGBM can be built using\n\n- **MS MPI** and **Visual Studio**;\n- **MS MPI**, **CMake** and **VS Build Tools**.\n\n**Note**: Building MPI version by **MinGW** is not supported due to the miss of MPI library in it.\n\nWith GUI\n********\n\n1. You need to install `MS MPI`_ first. Both ``msmpisdk.msi`` and ``msmpisetup.exe`` are needed.\n\n2. Install `Visual Studio`_.\n\n3. Navigate to one of the releases at https://github.com/lightgbm-org/LightGBM/releases, download ``LightGBM-complete_source_code_zip.zip``, and unzip it.\n\n4. Go to ``LightGBM-complete_source_code_zip/windows`` folder.\n\n5. Open ``LightGBM.sln`` file with **Visual Studio**, choose ``Release_mpi`` configuration and click ``Build`` -> ``Build Solution (Ctrl+Shift+B)``.\n\n If you have errors about **Platform Toolset**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the toolset installed on your machine.\n\n If you have errors about **Windows SDK Version**, go to ``Project`` -> ``Properties`` -> ``Configuration Properties`` -> ``General`` and select the SDK installed on your machine.\n\nThe ``.exe`` file will be in ``LightGBM-complete_source_code_zip/windows/x64/Release_mpi`` folder.\n\nFrom Command Line\n*****************\n\n1. You need to install `MS MPI`_ first. Both ``msmpisdk.msi`` and ``msmpisetup.exe`` are needed.\n\n2. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed).\n\n3. Run the following commands:\n\n .. code:: console\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -A x64 -DUSE_MPI=ON\n cmake --build build --target ALL_BUILD --config Release\n\nThe ``.exe`` and ``.dll`` files will be in ``LightGBM/Release`` folder.\n\nLinux\n^^^^^\n\nOn Linux, an MPI version of LightGBM can be built using\n\n- **CMake**, **gcc** and **Open MPI**;\n- **CMake**, **Clang** and **Open MPI**.\n\nAfter compilation the executable and ``.so`` files will be in ``LightGBM/`` folder.\n\ngcc\n***\n\n1. Install `CMake`_, **gcc** and `Open MPI`_.\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -DUSE_MPI=ON\n cmake --build build -j4\n\nClang\n*****\n\n1. Install `CMake`_, **Clang**, **OpenMP** and `Open MPI`_.\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n export CXX=clang++-14 CC=clang-14 # replace \"14\" with version of Clang installed on your machine\n cmake -B build -S . -DUSE_MPI=ON\n cmake --build build -j4\n\nmacOS\n^^^^^\n\nOn macOS, an MPI version of LightGBM can be built using\n\n- **CMake**, **Open MPI** and **Apple Clang**;\n- **CMake**, **Open MPI** and **gcc**.\n\nAfter compilation the executable and ``.dylib`` files will be in ``LightGBM/`` folder.\n\nApple Clang\n***********\n\n1. Install `CMake`_, **OpenMP** and `Open MPI`_:\n\n .. code:: sh\n\n brew install cmake libomp open-mpi\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -DUSE_MPI=ON\n cmake --build build -j4\n\ngcc\n***\n\n1. Install `CMake`_, `Open MPI`_ and **gcc**:\n\n .. code:: sh\n\n brew install cmake open-mpi gcc\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n export CXX=g++-7 CC=gcc-7 # replace \"7\" with version of gcc installed on your machine\n cmake -B build -S . -DUSE_MPI=ON\n cmake --build build -j4\n\nBuild GPU Version\n~~~~~~~~~~~~~~~~~\n\nWindows\n^^^^^^^\n\nOn Windows, a GPU version of LightGBM (``device_type=gpu``) can be built using\n\n- **OpenCL**, **Boost**, **CMake** and **VS Build Tools**;\n- **OpenCL**, **Boost**, **CMake** and **MinGW**.\n\nIf you use **MinGW**, the build procedure is similar to the build on Linux.\n\nFollowing procedure is for the **MSVC** (Microsoft Visual C++) build.\n\n1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is installed).\n\n2. Install **OpenCL** for Windows. The installation depends on the brand (NVIDIA, AMD, Intel) of your GPU card.\n\n - For running on Intel, get `Intel SDK for OpenCL`_.\n\n - For running on AMD, get AMD APP SDK.\n\n - For running on NVIDIA, get `CUDA Toolkit`_.\n\n Further reading and correspondence table: `GPU SDK Correspondence and Device Targeting Table <./GPU-Targets.rst>`__.\n\n3. Install `Boost Binaries`_.\n\n **Note**: Match your Visual C++ version:\n\n Visual Studio 2017 -> ``msvc-14.1-64.exe``,\n\n Visual Studio 2019 -> ``msvc-14.2-64.exe``,\n\n Visual Studio 2022 -> ``msvc-14.3-64.exe``.\n\n4. Run the following commands:\n\n .. code:: console\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -A x64 -DUSE_GPU=ON -DBOOST_ROOT=C:/local/boost_1_63_0 -DBOOST_LIBRARYDIR=C:/local/boost_1_63_0/lib64-msvc-14.3\n # if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following:\n # cmake -B build -S . -A x64 -DUSE_GPU=ON -DBOOST_ROOT=C:/local/boost_1_63_0 -DBOOST_LIBRARYDIR=C:/local/boost_1_63_0/lib64-msvc-14.3 -DOpenCL_LIBRARY=\"C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.0/lib/x64/OpenCL.lib\" -DOpenCL_INCLUDE_DIR=\"C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.0/include\"\n cmake --build build --target ALL_BUILD --config Release\n\n **Note**: ``C:/local/boost_1_63_0`` and ``C:/local/boost_1_63_0/lib64-msvc-14.3`` are locations of your **Boost** binaries (assuming you've downloaded 1.63.0 version for Visual Studio 2022).\n\nThe ``.exe`` and ``.dll`` files will be in ``LightGBM/Release`` folder.\n\nLinux\n^^^^^\n\nOn Linux, a GPU version of LightGBM (``device_type=gpu``) can be built using\n\n- **CMake**, **OpenCL**, **Boost** and **gcc**;\n- **CMake**, **OpenCL**, **Boost** and **Clang**.\n\n**OpenCL** headers and libraries are usually provided by GPU manufacture.\nThe generic OpenCL ICD packages (for example, Debian packages ``ocl-icd-libopencl1``, ``ocl-icd-opencl-dev``, ``pocl-opencl-icd``) can also be used.\n\nRequired **Boost** libraries (Boost.Align, Boost.System, Boost.Filesystem, Boost.Chrono) should be provided by the following Debian packages: ``libboost-dev``, ``libboost-system-dev``, ``libboost-filesystem-dev``, ``libboost-chrono-dev``.\n\nAfter compilation the executable and ``.so`` files will be in ``LightGBM/`` folder.\n\ngcc\n***\n\n1. Install `CMake`_, **gcc**, **OpenCL** and **Boost**.\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -DUSE_GPU=ON\n # if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following:\n # cmake -B build -S . -DUSE_GPU=ON -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/\n cmake --build build -j4\n\nClang\n*****\n\n1. Install `CMake`_, **Clang**, **OpenMP**, **OpenCL** and **Boost**.\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n export CXX=clang++-14 CC=clang-14 # replace \"14\" with version of Clang installed on your machine\n cmake -B build -S . -DUSE_GPU=ON\n # if you have installed NVIDIA CUDA to a customized location, you should specify paths to OpenCL headers and library like the following:\n # cmake -B build -S . -DUSE_GPU=ON -DOpenCL_LIBRARY=/usr/local/cuda/lib64/libOpenCL.so -DOpenCL_INCLUDE_DIR=/usr/local/cuda/include/\n cmake --build build -j4\n\nmacOS\n^^^^^\n\nThe GPU version is not supported on macOS.\n\nDocker\n^^^^^^\n\nRefer to `GPU Docker folder `__.\n\nBuild CUDA Version\n~~~~~~~~~~~~~~~~~~\n\nThe `original GPU version <#build-gpu-version>`__ of LightGBM (``device_type=gpu``) is based on OpenCL, and only computes histograms on GPUs, with other parts of training in CPUs.\n\nThe CUDA-based version (``device_type=cuda``) is a separate implementation that runs significantly faster by putting all the training process on GPUs. It also supports multi-GPU, and multi-node multi-GPU training.\nUse this version in Linux environments with an NVIDIA GPU with compute capability 6.0 or higher.\n\nWindows\n^^^^^^^\n\nThe CUDA version is not supported on Windows.\nUse the `GPU version <#build-gpu-version>`__ (``device_type=gpu``) for GPU acceleration on Windows.\n\nLinux\n^^^^^\n\nOn Linux, a CUDA version of LightGBM can be built using\n\n- **CMake**, **gcc** and **CUDA**;\n- **CMake**, **Clang** and **CUDA**.\n\nPlease refer to `this detailed guide`_ for **CUDA** libraries installation.\n\nAfter compilation the executable and ``.so`` files will be in ``LightGBM/`` folder.\n\ngcc\n***\n\n1. Install `CMake`_, **gcc** and **CUDA**.\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -DUSE_CUDA=ON\n cmake --build build -j4\n\nClang\n*****\n\n1. Install `CMake`_, **Clang**, **OpenMP** and **CUDA**.\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n export CXX=clang++-14 CC=clang-14 # replace \"14\" with version of Clang installed on your machine\n cmake -B build -S . -DUSE_CUDA=ON\n cmake --build build -j4\n\nmacOS\n^^^^^\n\nThe CUDA version is not supported on macOS.\n\nBuild ROCm Version\n~~~~~~~~~~~~~~~~~~\n\nThe `original GPU version <#build-gpu-version>`__ of LightGBM (``device_type=gpu``) is based on OpenCL.\n\nThe ROCm-based version (``device_type=cuda``) is a separate implementation. Yes, the ROCm version reuses the ``device_type=cuda`` as a convenience for users. Use this version in Linux environments with an AMD GPU.\n\nWindows\n^^^^^^^\n\nThe ROCm version is not supported on Windows.\nUse the `GPU version <#build-gpu-version>`__ (``device_type=gpu``) for GPU acceleration on Windows.\n\nLinux\n^^^^^\n\nOn Linux, a ROCm version of LightGBM can be built using\n\n- **CMake**, **gcc** and **ROCm**;\n- **CMake**, **Clang** and **ROCm**.\n\nPlease refer to `the ROCm docs`_ for **ROCm** libraries installation.\n\nAfter compilation the executable and ``.so`` files will be in ``LightGBM/`` folder.\n\ngcc\n***\n\n1. Install `CMake`_, **gcc** and **ROCm**.\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -DUSE_ROCM=ON\n cmake --build build -j4\n\nClang\n*****\n\n1. Install `CMake`_, **Clang**, **OpenMP** and **ROCm**.\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n export CXX=clang++-14 CC=clang-14 # replace \"14\" with version of Clang installed on your machine\n cmake -B build -S . -DUSE_ROCM=ON\n cmake --build build -j4\n\nmacOS\n^^^^^\n\nThe ROCm version is not supported on macOS.\n\nBuild Java Wrapper\n~~~~~~~~~~~~~~~~~~\n\nUsing the following instructions you can generate a JAR file containing the LightGBM `C API <./Development-Guide.rst#c-api>`__ wrapped by **SWIG**.\n\nAfter compilation the ``.jar`` file will be in ``LightGBM/build`` folder.\n\nWindows\n^^^^^^^\n\nOn Windows, a Java wrapper of LightGBM can be built using\n\n- **Java**, **SWIG**, **CMake** and **VS Build Tools**;\n- **Java**, **SWIG**, **CMake** and **MinGW**.\n\nVS Build Tools\n**************\n\n1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed).\n\n2. Install `SWIG`_ and **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly).\n\n3. Run the following commands:\n\n .. code:: console\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -A x64 -DUSE_SWIG=ON\n cmake --build build --target ALL_BUILD --config Release\n\nMinGW-w64\n*********\n\n1. Install `Git for Windows`_, `CMake`_ and `MinGW-w64`_.\n\n2. Install `SWIG`_ and **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly).\n\n3. Run the following commands:\n\n .. code:: console\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -G \"MinGW Makefiles\" -DUSE_SWIG=ON\n cmake --build build -j4\n\n**Note**: You may need to run the ``cmake -B build -S . -G \"MinGW Makefiles\" -DUSE_SWIG=ON`` one more time or add ``-DCMAKE_SH=CMAKE_SH-NOTFOUND`` to CMake flags if you encounter the ``sh.exe was found in your PATH`` error.\n\nIt is recommended to use **VS Build Tools (Visual Studio)** since it has better multithreading efficiency in **Windows** for many-core systems\n(see `Question 4 <./FAQ.rst#i-am-using-windows-should-i-use-visual-studio-or-mingw-for-compiling-lightgbm>`__ and `Question 8 <./FAQ.rst#cpu-usage-is-low-like-10-in-windows-when-using-lightgbm-on-very-large-datasets-with-many-core-systems>`__).\n\nLinux\n^^^^^\n\nOn Linux, a Java wrapper of LightGBM can be built using\n\n- **CMake**, **gcc**, **Java** and **SWIG**;\n- **CMake**, **Clang**, **Java** and **SWIG**.\n\ngcc\n***\n\n1. Install `CMake`_, **gcc**, `SWIG`_ and **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly).\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -DUSE_SWIG=ON\n cmake --build build -j4\n\nClang\n*****\n\n1. Install `CMake`_, **Clang**, **OpenMP**, `SWIG`_ and **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly).\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n export CXX=clang++-14 CC=clang-14 # replace \"14\" with version of Clang installed on your machine\n cmake -B build -S . -DUSE_SWIG=ON\n cmake --build build -j4\n\nmacOS\n^^^^^\n\nOn macOS, a Java wrapper of LightGBM can be built using\n\n- **CMake**, **Java**, **SWIG** and **Apple Clang**;\n- **CMake**, **Java**, **SWIG** and **gcc**.\n\nApple Clang\n***********\n\n1. Install `CMake`_, **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly), `SWIG`_ and **OpenMP**:\n\n .. code:: sh\n\n brew install cmake openjdk swig libomp\n export JAVA_HOME=\"$(brew --prefix openjdk)/libexec/openjdk.jdk/Contents/Home/\"\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -DUSE_SWIG=ON\n cmake --build build -j4\n\ngcc\n***\n\n1. Install `CMake`_, **Java** (also make sure that ``JAVA_HOME`` environment variable is set properly), `SWIG`_ and **gcc**:\n\n .. code:: sh\n\n brew install cmake openjdk swig gcc\n export JAVA_HOME=\"$(brew --prefix openjdk)/libexec/openjdk.jdk/Contents/Home/\"\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n export CXX=g++-7 CC=gcc-7 # replace \"7\" with version of gcc installed on your machine\n cmake -B build -S . -DUSE_SWIG=ON\n cmake --build build -j4\n\nBuild Python-package\n~~~~~~~~~~~~~~~~~~~~\n\nRefer to `Python-package folder `__.\n\nBuild R-package\n~~~~~~~~~~~~~~~\n\nRefer to `R-package folder `__.\n\nBuild C++ Unit Tests\n~~~~~~~~~~~~~~~~~~~~\n\nWindows\n^^^^^^^\n\nOn Windows, C++ unit tests of LightGBM can be built using\n\n- **CMake** and **VS Build Tools**;\n- **CMake** and **MinGW**.\n\nVS Build Tools\n**************\n\n1. Install `Git for Windows`_, `CMake`_ and `VS Build Tools`_ (**VS Build Tools** is not needed if **Visual Studio** is already installed).\n\n2. Run the following commands:\n\n .. code:: console\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -A x64 -DBUILD_CPP_TEST=ON\n cmake --build build --target testlightgbm --config Debug\n\nThe ``.exe`` file will be in ``LightGBM/Debug`` folder.\n\nMinGW-w64\n*********\n\n1. Install `Git for Windows`_, `CMake`_ and `MinGW-w64`_.\n\n2. Run the following commands:\n\n .. code:: console\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -G \"MinGW Makefiles\" -DBUILD_CPP_TEST=ON\n cmake --build build --target testlightgbm -j4\n\nThe ``.exe`` file will be in ``LightGBM/`` folder.\n\n**Note**: You may need to run the ``cmake -B build -S . -G \"MinGW Makefiles\" -DBUILD_CPP_TEST=ON`` one more time or add ``-DCMAKE_SH=CMAKE_SH-NOTFOUND`` to CMake flags if you encounter the ``sh.exe was found in your PATH`` error.\n\nLinux\n^^^^^\n\nOn Linux, a C++ unit tests of LightGBM can be built using\n\n- **CMake** and **gcc**;\n- **CMake** and **Clang**.\n\nAfter compilation the executable file will be in ``LightGBM/`` folder.\n\ngcc\n***\n\n1. Install `CMake`_ and **gcc**.\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -DBUILD_CPP_TEST=ON\n cmake --build build --target testlightgbm -j4\n\nClang\n*****\n\n1. Install `CMake`_, **Clang** and **OpenMP**.\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n export CXX=clang++-14 CC=clang-14 # replace \"14\" with version of Clang installed on your machine\n cmake -B build -S . -DBUILD_CPP_TEST=ON\n cmake --build build --target testlightgbm -j4\n\nmacOS\n^^^^^\n\nOn macOS, a C++ unit tests of LightGBM can be built using\n\n- **CMake** and **Apple Clang**;\n- **CMake** and **gcc**.\n\nAfter compilation the executable file will be in ``LightGBM/`` folder.\n\nApple Clang\n***********\n\n1. Install `CMake`_ and **OpenMP**:\n\n .. code:: sh\n\n brew install cmake libomp\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n cmake -B build -S . -DBUILD_CPP_TEST=ON\n cmake --build build --target testlightgbm -j4\n\ngcc\n***\n\n1. Install `CMake`_ and **gcc**:\n\n .. code:: sh\n\n brew install cmake gcc\n\n2. Run the following commands:\n\n .. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM\n cd LightGBM\n export CXX=g++-7 CC=gcc-7 # replace \"7\" with version of gcc installed on your machine\n cmake -B build -S . -DBUILD_CPP_TEST=ON\n cmake --build build --target testlightgbm -j4\n\n.. _Visual Studio: https://visualstudio.microsoft.com/downloads/\n\n.. _Git for Windows: https://git-scm.com/download/win\n\n.. _CMake: https://cmake.org/\n\n.. _VS Build Tools: https://visualstudio.microsoft.com/downloads/\n\n.. _MinGW-w64: https://www.mingw-w64.org/downloads/\n\n.. _MPI: https://en.wikipedia.org/wiki/Message_Passing_Interface\n\n.. _RDMA: https://en.wikipedia.org/wiki/Remote_direct_memory_access\n\n.. _MS MPI: https://learn.microsoft.com/en-us/message-passing-interface/microsoft-mpi-release-notes\n\n.. _Open MPI: https://www.open-mpi.org/\n\n.. _Intel SDK for OpenCL: https://software.intel.com/en-us/articles/opencl-drivers\n\n.. _CUDA Toolkit: https://developer.nvidia.com/cuda-downloads\n\n.. _Boost Binaries: https://sourceforge.net/projects/boost/files/boost-binaries/\n\n.. _SWIG: https://www.swig.org/download.html\n\n.. _this detailed guide: https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html\n\n.. _the ROCm docs: https://rocm.docs.amd.com/projects/install-on-linux/en/latest/\n\n.. _following docs: https://github.com/google/sanitizers/wiki\n\n.. _Ninja: https://ninja-build.org\n" }, { "path": "docs/Key-Events.md", "content": "The content of this document was very outdated and is no longer available to avoid any misleadings.\n" }, { "path": "docs/Makefile", "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line.\nSPHINXOPTS = -W\nSPHINXBUILD = sphinx-build\nSPHINXPROJ = LightGBM\nSOURCEDIR = .\nBUILDDIR = _build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n" }, { "path": "docs/Parallel-Learning-Guide.rst", "content": "Distributed Learning Guide\n==========================\n\n.. _Parallel Learning Guide:\n\nThis guide describes distributed learning in LightGBM. Distributed learning allows the use of multiple machines to produce a single model.\n\nFollow the `Quick Start <./Quick-Start.rst>`__ to know how to use LightGBM first.\n\nHow Distributed LightGBM Works\n------------------------------\n\nThis section describes how distributed learning in LightGBM works. To learn how to do this in various programming languages and frameworks, please see `Integrations <#integrations>`__.\n\nChoose Appropriate Parallel Algorithm\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\nLightGBM provides 3 distributed learning algorithms now.\n\n+--------------------+---------------------------+\n| Parallel Algorithm | How to Use |\n+====================+===========================+\n| Data parallel | ``tree_learner=data`` |\n+--------------------+---------------------------+\n| Feature parallel | ``tree_learner=feature`` |\n+--------------------+---------------------------+\n| Voting parallel | ``tree_learner=voting`` |\n+--------------------+---------------------------+\n\nThese algorithms are suited for different scenarios, which is listed in the following table:\n\n+-------------------------+-------------------+-----------------+\n| | #data is small | #data is large |\n+=========================+===================+=================+\n| **#feature is small** | Feature Parallel | Data Parallel |\n+-------------------------+-------------------+-----------------+\n| **#feature is large** | Feature Parallel | Voting Parallel |\n+-------------------------+-------------------+-----------------+\n\nMore details about these parallel algorithms can be found in `optimization in distributed learning <./Features.rst#optimization-in-distributed-learning>`__.\n\nIntegrations\n------------\n\nThis section describes how to run distributed LightGBM training in various programming languages and frameworks. To learn how distributed learning in LightGBM works generally, please see `How Distributed LightGBM Works <#how-distributed-lightgbm-works>`__.\n\nApache Spark\n^^^^^^^^^^^^\n\nApache Spark users can use `SynapseML`_ for machine learning workflows with LightGBM. This project is not maintained by LightGBM's maintainers.\n\nSee `this SynapseML example`_ for additional information on using LightGBM on Spark.\n\n.. note::\n\n ``SynapseML`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should be directed to https://github.com/microsoft/SynapseML/issues.\n\nDask\n^^^^\n\n.. versionadded:: 3.2.0\n\nLightGBM's Python-package supports distributed learning via `Dask`_. This integration is maintained by LightGBM's maintainers.\n\n.. warning::\n\n Dask integration is only tested on macOS and Linux.\n\nDask Examples\n'''''''''''''\n\nFor sample code using ``lightgbm.dask``, see `these Dask examples`_.\n\nTraining with Dask\n''''''''''''''''''\n\nThis section contains detailed information on performing LightGBM distributed training using Dask.\n\nConfiguring the Dask Cluster\n****************************\n\n**Allocating Threads**\n\nWhen setting up a Dask cluster for training, give each Dask worker process at least two threads. If you do not do this, training might be substantially slower because communication work and training work will block each other.\n\nIf you do not have other significant processes competing with Dask for resources, just accept the default ``nthreads`` from your chosen ``dask.distributed`` cluster.\n\n.. code:: python\n\n from distributed import Client, LocalCluster\n\n cluster = LocalCluster(n_workers=3)\n client = Client(cluster)\n\n**Managing Memory**\n\nUse the Dask diagnostic dashboard or your preferred monitoring tool to monitor Dask workers' memory consumption during training. As described in `the Dask worker documentation`_, Dask workers will automatically start spilling data to disk if memory consumption gets too high. This can substantially slow down computations, since disk I/O is usually much slower than reading the same data from memory.\n\n `At 60% of memory load, [Dask will] spill least recently used data to disk`\n\nTo reduce the risk of hitting memory limits, consider restarting each worker process before running any data loading or training code.\n\n.. code:: python\n\n client.restart()\n\nSetting Up Training Data\n*************************\n\nThe estimators in ``lightgbm.dask`` expect that matrix-like or array-like data are provided in Dask DataFrame, Dask Array, or (in some cases) Dask Series format. See `the Dask DataFrame documentation`_ and `the Dask Array documentation`_ for more information on how to create such data structures.\n\n.. image:: ./_static/images/dask-initial-setup.svg\n :align: center\n :width: 600px\n :alt: On the left, rectangles showing a 5 by 5 grid for a local dataset. On the right, two circles representing Dask workers, one with a 3 by 5 grid and one with a 2 by 5 grid.\n :target: ./_static/images/dask-initial-setup.svg\n\nWhile setting up for training, ``lightgbm`` will concatenate all of the partitions on a worker into a single dataset. Distributed training then proceeds with one LightGBM worker process per Dask worker.\n\n.. image:: ./_static/images/dask-concat.svg\n :align: center\n :width: 600px\n :alt: A section labeled \"before\" showing two grids and a section labeled \"after\" showing a single grid that looks like the two from \"before\" stacked one on top of the other.\n :target: ./_static/images/dask-concat.svg\n\nWhen setting up data partitioning for LightGBM training with Dask, try to follow these suggestions:\n\n* ensure that each worker in the cluster has some of the training data\n* try to give each worker roughly the same amount of data, especially if your dataset is small\n* if you plan to train multiple models (for example, to tune hyperparameters) on the same data, use ``client.persist()`` before training to materialize the data one time\n\nUsing a Specific Dask Client\n****************************\n\nIn most situations, you should not need to tell ``lightgbm.dask`` to use a specific Dask client. By default, the client returned by ``distributed.default_client()`` will be used.\n\nHowever, you might want to explicitly control the Dask client used by LightGBM if you have multiple active clients in the same session. This is useful in more complex workflows like running multiple training jobs on different Dask clusters.\n\nLightGBM's Dask estimators support setting an attribute ``client`` to control the client that is used.\n\n.. code:: python\n\n import lightgbm as lgb\n from distributed import Client, LocalCluster\n\n cluster = LocalCluster()\n client = Client(cluster)\n\n # option 1: keyword argument in constructor\n dask_model = lgb.DaskLGBMClassifier(client=client)\n\n # option 2: set_params() after construction\n dask_model = lgb.DaskLGBMClassifier()\n dask_model.set_params(client=client)\n\nUsing Specific Ports\n********************\n\nAt the beginning of training, ``lightgbm.dask`` sets up a LightGBM network where each Dask worker runs one long-running task that acts as a LightGBM worker. During training, LightGBM workers communicate with each other over TCP sockets. By default, random open ports are used when creating these sockets.\n\nIf the communication between Dask workers in the cluster used for training is restricted by firewall rules, you must tell LightGBM exactly what ports to use.\n\n**Option 1: provide a specific list of addresses and ports**\n\nLightGBM supports a parameter ``machines``, a comma-delimited string where each entry refers to one worker (host name or IP) and a port that that worker will accept connections on. If you provide this parameter to the estimators in ``lightgbm.dask``, LightGBM will not search randomly for ports.\n\nFor example, consider the case where you are running one Dask worker process on each of the following IP addresses:\n\n.. code:: text\n\n 10.0.1.0\n 10.0.2.0\n 10.0.3.0\n\nYou could edit your firewall rules to allow traffic on one additional port on each of these hosts, then provide ``machines`` directly.\n\n.. code:: python\n\n import lightgbm as lgb\n\n machines = \"10.0.1.0:12401,10.0.2.0:12402,10.0.3.0:15000\"\n dask_model = lgb.DaskLGBMRegressor(machines=machines)\n\nIf you are running multiple Dask worker processes on physical host in the cluster, be sure that there are multiple entries for that IP address, with different ports. For example, if you were running a cluster with ``nprocs=2`` (2 Dask worker processes per machine), you might open two additional ports on each of these hosts, then provide ``machines`` as follows.\n\n.. code:: python\n\n import lightgbm as lgb\n\n machines = \",\".join([\n \"10.0.1.0:16000\",\n \"10.0.1.0:16001\",\n \"10.0.2.0:16000\",\n \"10.0.2.0:16001\",\n ])\n dask_model = lgb.DaskLGBMRegressor(machines=machines)\n\n.. warning::\n\n Providing ``machines`` gives you complete control over the networking details of training, but it also makes the training process fragile. Training will fail if you use ``machines`` and any of the following are true:\n\n * any of the ports mentioned in ``machines`` are not open when training begins\n * some partitions of the training data are held by machines that that are not present in ``machines``\n * some machines mentioned in ``machines`` do not hold any of the training data\n\n**Option 2: specify one port to use on every worker**\n\nIf you are only running one Dask worker process on each host, and if you can reliably identify a port that is open on every host, using ``machines`` is unnecessarily complicated. If ``local_listen_port`` is given and ``machines`` is not, LightGBM will not search for ports randomly, but it will limit the list of addresses in the LightGBM network to those Dask workers that have a piece of the training data.\n\nFor example, consider the case where you are running one Dask worker process on each of the following IP addresses:\n\n.. code:: text\n\n 10.0.1.0\n 10.0.2.0\n 10.0.3.0\n\nYou could edit your firewall rules to allow communication between any of the workers over one port, then provide that port via parameter ``local_listen_port``.\n\n.. code:: python\n\n import lightgbm as lgb\n\n dask_model = lgb.DaskLGBMRegressor(local_listen_port=12400)\n\n.. warning::\n\n Providing ``local_listen_port`` is slightly less fragile than ``machines`` because LightGBM will automatically figure out which workers have pieces of the training data. However, using this method, training can fail if any of the following are true:\n\n * the port ``local_listen_port`` is not open on any of the worker hosts\n * any machine has multiple Dask worker processes running on it\n\nUsing Custom Objective Functions with Dask\n******************************************\n\n.. versionadded:: 4.0.0\n\nIt is possible to customize the boosting process by providing a custom objective function written in Python.\nSee the Dask API's documentation for details on how to implement such functions.\n\n.. warning::\n\n Custom objective functions used with ``lightgbm.dask`` will be called by each worker process on only that worker's local data.\n\nFollow the example below to use a custom implementation of the ``regression_l2`` objective.\n\n.. code:: python\n\n import dask.array as da\n import lightgbm as lgb\n import numpy as np\n from distributed import Client, LocalCluster\n\n cluster = LocalCluster(n_workers=2)\n client = Client(cluster)\n\n X = da.random.random((1000, 10), (500, 10))\n y = da.random.random((1000,), (500,))\n\n def custom_l2_obj(y_true, y_pred):\n grad = y_pred - y_true\n hess = np.ones(len(y_true))\n return grad, hess\n\n dask_model = lgb.DaskLGBMRegressor(\n objective=custom_l2_obj\n )\n dask_model.fit(X, y)\n\nPrediction with Dask\n''''''''''''''''''''\n\nThe estimators from ``lightgbm.dask`` can be used to create predictions based on data stored in Dask collections. In that interface, ``.predict()`` expects a Dask Array or Dask DataFrame, and returns a Dask Array of predictions.\n\nSee `the Dask prediction example`_ for some sample code that shows how to perform Dask-based prediction.\n\nFor model evaluation, consider using `the metrics functions from dask-ml`_. Those functions are intended to provide the same API as equivalent functions in ``sklearn.metrics``, but they use distributed computation powered by Dask to compute metrics without all of the input data ever needing to be on a single machine.\n\nSaving Dask Models\n''''''''''''''''''\n\nAfter training with Dask, you have several options for saving a fitted model.\n\n**Option 1: pickle the Dask estimator**\n\nLightGBM's Dask estimators can be pickled directly with ``cloudpickle``, ``joblib``, or ``pickle``.\n\n.. code:: python\n\n import dask.array as da\n import pickle\n import lightgbm as lgb\n from distributed import Client, LocalCluster\n\n cluster = LocalCluster(n_workers=2)\n client = Client(cluster)\n\n X = da.random.random((1000, 10), (500, 10))\n y = da.random.random((1000,), (500,))\n\n dask_model = lgb.DaskLGBMRegressor()\n dask_model.fit(X, y)\n\n with open(\"dask-model.pkl\", \"wb\") as f:\n pickle.dump(dask_model, f)\n\nA model saved this way can then later be loaded with whichever serialization library you used to save it.\n\n.. code:: python\n\n import pickle\n with open(\"dask-model.pkl\", \"rb\") as f:\n dask_model = pickle.load(f)\n\n.. note::\n\n If you explicitly set a Dask client (see `Using a Specific Dask Client <#using-a-specific-dask-client>`__), it will not be saved when pickling the estimator. When loading a Dask estimator from disk, if you need to use a specific client you can add it after loading with ``dask_model.set_params(client=client)``.\n\n**Option 2: pickle the sklearn estimator**\n\nThe estimators available from ``lightgbm.dask`` can be converted to an instance of the equivalent class from ``lightgbm.sklearn``. Choosing this option allows you to use Dask for training but avoid depending on any Dask libraries at scoring time.\n\n.. code:: python\n\n import dask.array as da\n import joblib\n import lightgbm as lgb\n from distributed import Client, LocalCluster\n\n cluster = LocalCluster(n_workers=2)\n client = Client(cluster)\n\n X = da.random.random((1000, 10), (500, 10))\n y = da.random.random((1000,), (500,))\n\n dask_model = lgb.DaskLGBMRegressor()\n dask_model.fit(X, y)\n\n # convert to sklearn equivalent\n sklearn_model = dask_model.to_local()\n\n print(type(sklearn_model))\n #> lightgbm.sklearn.LGBMRegressor\n\n joblib.dump(sklearn_model, \"sklearn-model.joblib\")\n\nA model saved this way can then later be loaded with whichever serialization library you used to save it.\n\n.. code:: python\n\n import joblib\n\n sklearn_model = joblib.load(\"sklearn-model.joblib\")\n\n**Option 3: save the LightGBM Booster**\n\nThe lowest-level model object in LightGBM is the ``lightgbm.Booster``. After training, you can extract a Booster from the Dask estimator.\n\n.. code:: python\n\n import dask.array as da\n import lightgbm as lgb\n from distributed import Client, LocalCluster\n\n cluster = LocalCluster(n_workers=2)\n client = Client(cluster)\n\n X = da.random.random((1000, 10), (500, 10))\n y = da.random.random((1000,), (500,))\n\n dask_model = lgb.DaskLGBMRegressor()\n dask_model.fit(X, y)\n\n # get underlying Booster object\n bst = dask_model.booster_\n\nFrom the point forward, you can use any of the following methods to save the Booster:\n\n* serialize with ``cloudpickle``, ``joblib``, or ``pickle``\n* ``bst.dump_model()``: dump the model to a dictionary which could be written out as JSON\n* ``bst.model_to_string()``: dump the model to a string in memory\n* ``bst.save_model()``: write the output of ``bst.model_to_string()`` to a text file\n\nKubeflow\n^^^^^^^^\n\nKubeflow users can also use the `Kubeflow XGBoost Operator`_ for machine learning workflows with LightGBM. You can see `this example`_ for more details.\n\nKubeflow integrations for LightGBM are not maintained by LightGBM's maintainers.\n\n.. note::\n\n The Kubeflow integrations for LightGBM are not maintained by LightGBM's maintainers. Bug reports or feature requests should be directed to https://github.com/kubeflow/fairing/issues or https://github.com/kubeflow/xgboost-operator/issues.\n\nLightGBM CLI\n^^^^^^^^^^^^\n\n.. _Build Parallel Version:\n\nPreparation\n'''''''''''\n\nBy default, distributed learning with LightGBM uses socket-based communication.\n\nIf you need to build distributed version with MPI support, please refer to `Installation Guide <./Installation-Guide.rst#build-mpi-version>`__.\n\nSocket Version\n**************\n\nIt needs to collect IP of all machines that want to run distributed learning in and allocate one TCP port (assume 12345 here) for all machines,\nand change firewall rules to allow income of this port (12345). Then write these IP and ports in one file (assume ``mlist.txt``), like following:\n\n.. code:: text\n\n machine1_ip 12345\n machine2_ip 12345\n\nMPI Version\n***********\n\nIt needs to collect IP (or hostname) of all machines that want to run distributed learning in.\nThen write these IP in one file (assume ``mlist.txt``) like following:\n\n.. code:: text\n\n machine1_ip\n machine2_ip\n\n**Note**: For Windows users, need to start \"smpd\" to start MPI service. More details can be found `here`_.\n\nRun Distributed Learning\n''''''''''''''''''''''''\n\n.. _Run Parallel Learning:\n\nSocket Version\n**************\n\n1. Edit following parameters in config file:\n\n ``tree_learner=your_parallel_algorithm``, edit ``your_parallel_algorithm`` (e.g. feature/data) here.\n\n ``num_machines=your_num_machines``, edit ``your_num_machines`` (e.g. 4) here.\n\n ``machine_list_file=mlist.txt``, ``mlist.txt`` is created in `Preparation section <#preparation>`__.\n\n ``local_listen_port=12345``, ``12345`` is allocated in `Preparation section <#preparation>`__.\n\n2. Copy data file, executable file, config file and ``mlist.txt`` to all machines.\n\n3. Run following command on all machines, you need to change ``your_config_file`` to real config file.\n\n For Windows: ``lightgbm.exe config=your_config_file``\n\n For Linux: ``./lightgbm config=your_config_file``\n\nMPI Version\n***********\n\n1. Edit following parameters in config file:\n\n ``tree_learner=your_parallel_algorithm``, edit ``your_parallel_algorithm`` (e.g. feature/data) here.\n\n ``num_machines=your_num_machines``, edit ``your_num_machines`` (e.g. 4) here.\n\n2. Copy data file, executable file, config file and ``mlist.txt`` to all machines.\n\n **Note**: MPI needs to be run in the **same path on all machines**.\n\n3. Run following command on one machine (not need to run on all machines), need to change ``your_config_file`` to real config file.\n\n For Windows:\n\n .. code:: console\n\n mpiexec.exe /machinefile mlist.txt lightgbm.exe config=your_config_file\n\n For Linux:\n\n .. code:: console\n\n mpiexec --machinefile mlist.txt ./lightgbm config=your_config_file\n\nExample\n'''''''\n\n- `A simple distributed learning example`_\n\nRay\n^^^\n\n`Ray`_ is a Python-based framework for distributed computing. Ray provides LightGBM support through the Ray Train API with ``LightGBMTrainer`` and the `lightgbm_ray`_ project maintained within the official Ray GitHub organization.\n\nFor the Ray Train API, see `the Ray documentation`_ for usage examples.\n\nFor the lightgbm_ray project, see `the lightgbm_ray documentation`_ for usage examples.\n\n.. note::\n\n ``lightgbm_ray`` and ``ray`` are not maintained by LightGBM's maintainers. Bug reports or feature requests should be directed to https://github.com/ray-project/lightgbm_ray/issues and https://github.com/ray-project/ray/issues respectively.\n\nMars\n^^^^\n\n`Mars`_ is a tensor-based framework for large-scale data computation. LightGBM integration, maintained within the Mars GitHub repository, can be used to perform distributed LightGBM training using ``pymars``.\n\nSee `the mars documentation`_ for usage examples.\n\n.. note::\n\n ``Mars`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should be directed to https://github.com/mars-project/mars/issues.\n\n.. _Dask: https://docs.dask.org/en/latest/\n\n.. _SynapseML: https://aka.ms/spark\n\n.. _this SynapseML example: https://github.com/microsoft/SynapseML/tree/master/docs/Explore%20Algorithms/LightGBM\n\n.. _the Dask Array documentation: https://docs.dask.org/en/latest/array.html\n\n.. _the Dask DataFrame documentation: https://docs.dask.org/en/latest/dataframe.html\n\n.. _the Dask prediction example: https://github.com/lightgbm-org/LightGBM/blob/master/examples/python-guide/dask/prediction.py\n\n.. _the Dask worker documentation: https://distributed.dask.org/en/stable/worker-memory.html\n\n.. _the metrics functions from dask-ml: https://ml.dask.org/modules/api.html#dask-ml-metrics-metrics\n\n.. _these Dask examples: https://github.com/lightgbm-org/LightGBM/tree/master/examples/python-guide/dask\n\n.. _Kubeflow XGBoost Operator: https://github.com/kubeflow/xgboost-operator\n\n.. _this example: https://github.com/kubeflow/xgboost-operator/tree/master/config/samples/lightgbm-dist\n\n.. _here: https://www.youtube.com/watch?v=iqzXhp5TxUY\n\n.. _A simple distributed learning example: https://github.com/lightgbm-org/LightGBM/tree/master/examples/parallel_learning\n\n.. _lightgbm_ray: https://github.com/ray-project/lightgbm_ray\n\n.. _Ray: https://www.ray.io/\n\n.. _the lightgbm_ray documentation: https://docs.ray.io/en/latest/tune/api_docs/integration.html#lightgbm-tune-integration-lightgbm\n\n.. _the Ray documentation: https://docs.ray.io/en/latest/train/api/api.html#lightgbm\n\n.. _Mars: https://mars-project.readthedocs.io/en/latest/\n\n.. _the mars documentation: https://mars-project.readthedocs.io/en/latest/user_guide/learn/lightgbm.html\n" }, { "path": "docs/Parameters-Tuning.rst", "content": "Parameters Tuning\n=================\n\nThis page contains parameters tuning guides for different scenarios.\n\n**List of other helpful links**\n\n- `Parameters <./Parameters.rst>`__\n- `Python API <./Python-API.rst>`__\n- `FLAML`_ for automated hyperparameter tuning\n- `Optuna`_ for automated hyperparameter tuning\n\nTune Parameters for the Leaf-wise (Best-first) Tree\n---------------------------------------------------\n\nLightGBM uses the `leaf-wise <./Features.rst#leaf-wise-best-first-tree-growth>`__ tree growth algorithm, while many other popular tools use depth-wise tree growth.\nCompared with depth-wise growth, the leaf-wise algorithm can converge much faster.\nHowever, the leaf-wise growth may be over-fitting if not used with the appropriate parameters.\n\nTo get good results using a leaf-wise tree, these are some important parameters:\n\n1. ``num_leaves``. This is the main parameter to control the complexity of the tree model.\n Theoretically, we can set ``num_leaves = 2^(max_depth)`` to obtain the same number of leaves as depth-wise tree.\n However, this simple conversion is not good in practice.\n A leaf-wise tree is typically much deeper than a depth-wise tree for a fixed number of leaves. Unconstrained depth can induce over-fitting.\n Thus, when trying to tune the ``num_leaves``, we should let it be smaller than ``2^(max_depth)``.\n For example, when the ``max_depth=7`` the depth-wise tree can get good accuracy,\n but setting ``num_leaves`` to ``127`` may cause over-fitting, and setting it to ``70`` or ``80`` may get better accuracy than depth-wise.\n\n2. ``min_data_in_leaf``. This is a very important parameter to prevent over-fitting in a leaf-wise tree.\n Its optimal value depends on the number of training samples and ``num_leaves``.\n Setting it to a large value can avoid growing too deep a tree, but may cause under-fitting.\n In practice, setting it to hundreds or thousands is enough for a large dataset.\n\n3. ``max_depth``. You also can use ``max_depth`` to limit the tree depth explicitly.\n If you set ``max_depth``, also explicitly set ``num_leaves`` to some value ``<= 2^max_depth``.\n\nFor Faster Speed\n----------------\n\nAdd More Computational Resources\n''''''''''''''''''''''''''''''''\n\nOn systems where it is available, LightGBM uses OpenMP to parallelize many operations. The maximum number of threads used by LightGBM is controlled by the parameter ``num_threads``. By default, this will defer to the default behavior of OpenMP (one thread per real CPU core or the value in environment variable ``OMP_NUM_THREADS``, if it is set). For best performance, set this to the number of **real** CPU cores available.\n\nYou might be able to achieve faster training by moving to a machine with more available CPU cores.\n\nUsing distributed (multi-machine) training might also reduce training time. See the `Distributed Learning Guide <./Parallel-Learning-Guide.rst>`_ for details.\n\nUse a GPU-enabled version of LightGBM\n'''''''''''''''''''''''''''''''''''''\n\nYou might find that training is faster using a GPU-enabled build of LightGBM. See the `GPU Tutorial <./GPU-Tutorial.rst>`__ for details.\n\nGrow Shallower Trees\n''''''''''''''''''''\n\nThe total training time for LightGBM increases with the total number of tree nodes added. LightGBM comes with several parameters that can be used to control the number of nodes per tree.\n\nThe suggestions below will speed up training, but might hurt training accuracy.\n\nDecrease ``max_depth``\n**********************\n\nThis parameter is an integer that controls the maximum distance between the root node of each tree and a leaf node. Decrease ``max_depth`` to reduce training time.\n\nDecrease ``num_leaves``\n***********************\n\nLightGBM adds nodes to trees based on the gain from adding that node, regardless of depth. This figure from `the feature documentation <./Features.rst#leaf-wise-best-first-tree-growth>`__ illustrates the process.\n\n.. image:: ./_static/images/leaf-wise.png\n :align: center\n :alt: Three consecutive images of decision trees, where each shows the tree with an additional two leaf nodes added. Shows that leaf-wise growth can result in trees that have some branches which are longer than others.\n\nBecause of this growth strategy, it isn't straightforward to use ``max_depth`` alone to limit the complexity of trees. The ``num_leaves`` parameter sets the maximum number of nodes per tree. Decrease ``num_leaves`` to reduce training time.\n\nIncrease ``min_gain_to_split``\n******************************\n\nWhen adding a new tree node, LightGBM chooses the split point that has the largest gain. Gain is basically the reduction in training loss that results from adding a split point. By default, LightGBM sets ``min_gain_to_split`` to 0.0, which means \"there is no improvement that is too small\". However, in practice you might find that very small improvements in the training loss don't have a meaningful impact on the generalization error of the model. Increase ``min_gain_to_split`` to reduce training time.\n\nIncrease ``min_data_in_leaf`` and ``min_sum_hessian_in_leaf``\n*************************************************************\n\nDepending on the size of the training data and the distribution of features, it's possible for LightGBM to add tree nodes that only describe a small number of observations. In the most extreme case, consider the addition of a tree node that only a single observation from the training data falls into. This is very unlikely to generalize well, and probably is a sign of overfitting.\n\nThis can be prevented indirectly with parameters like ``max_depth`` and ``num_leaves``, but LightGBM also offers parameters to help you directly avoid adding these overly-specific tree nodes.\n\n- ``min_data_in_leaf``: Minimum number of observations that must fall into a tree node for it to be added.\n- ``min_sum_hessian_in_leaf``: Minimum sum of the Hessian (second derivative of the objective function evaluated for each observation) for observations in a leaf. For some regression objectives, this is just the minimum number of records that have to fall into each node. For classification objectives, it represents a sum over a distribution of probabilities. See `this Stack Overflow answer `_ for a good description of how to reason about values of this parameter.\n\nGrow Less Trees\n'''''''''''''''\n\nDecrease ``num_iterations``\n***************************\n\nThe ``num_iterations`` parameter controls the number of boosting rounds that will be performed. Since LightGBM uses decision trees as the learners, this can also be thought of as \"number of trees\".\n\nIf you try changing ``num_iterations``, change the ``learning_rate`` as well. ``learning_rate`` will not have any impact on training time, but it will impact the training accuracy. As a general rule, if you reduce ``num_iterations``, you should increase ``learning_rate``.\n\nChoosing the right value of ``num_iterations`` and ``learning_rate`` is highly dependent on the data and objective, so these parameters are often chosen from a set of possible values through hyperparameter tuning.\n\nDecrease ``num_iterations`` to reduce training time.\n\nUse Early Stopping\n******************\n\nIf early stopping is enabled, after each boosting round the model's training accuracy is evaluated against a validation set that contains data not available to the training process. That accuracy is then compared to the accuracy as of the previous boosting round. If the model's accuracy fails to improve for some number of consecutive rounds, LightGBM stops the training process.\n\nThat \"number of consecutive rounds\" is controlled by the parameter ``early_stopping_round``. For example, ``early_stopping_round=1`` says \"the first time accuracy on the validation set does not improve, stop training\".\n\nSet ``early_stopping_round`` and provide a validation set to possibly reduce training time.\n\nConsider Fewer Splits\n'''''''''''''''''''''\n\nThe parameters described in previous sections control how many trees are constructed and how many nodes are constructed per tree. Training time can be further reduced by reducing the amount of time needed to add a tree node to the model.\n\nThe suggestions below will speed up training, but might hurt training accuracy.\n\nEnable Feature Pre-Filtering When Creating Dataset\n**************************************************\n\nBy default, when a LightGBM ``Dataset`` object is constructed, some features will be filtered out based on the value of ``min_data_in_leaf``.\n\nFor a simple example, consider a 1000-observation dataset with a feature called ``feature_1``. ``feature_1`` takes on only two values: 25.0 (995 observations) and 50.0 (5 observations). If ``min_data_in_leaf = 10``, there is no split for this feature which will result in a valid split at least one of the leaf nodes will only have 5 observations.\n\nInstead of reconsidering this feature and then ignoring it every iteration, LightGBM filters this feature out at before training, when the ``Dataset`` is constructed.\n\nIf this default behavior has been overridden by setting ``feature_pre_filter=False``, set ``feature_pre_filter=True`` to reduce training time.\n\nDecrease ``max_bin`` or ``max_bin_by_feature`` When Creating Dataset\n********************************************************************\n\nLightGBM training `buckets continuous features into discrete bins <./Features.rst#optimization-in-speed-and-memory-usage>`_ to improve training speed and reduce memory requirements for training. This binning is done one time during ``Dataset`` construction. The number of splits considered when adding a node is ``O(#feature * #bin)``, so reducing the number of bins per feature can reduce the number of splits that need to be evaluated.\n\n``max_bin`` is controls the maximum number of bins that features will bucketed into. It is also possible to set this maximum feature-by-feature, by passing ``max_bin_by_feature``.\n\nReduce ``max_bin`` or ``max_bin_by_feature`` to reduce training time.\n\nIncrease ``min_data_in_bin`` When Creating Dataset\n**************************************************\n\nSome bins might contain a small number of observations, which might mean that the effort of evaluating that bin's boundaries as possible split points isn't likely to change the final model very much. You can control the granularity of the bins by setting ``min_data_in_bin``.\n\nIncrease ``min_data_in_bin`` to reduce training time.\n\nDecrease ``feature_fraction``\n*****************************\n\nBy default, LightGBM considers all features in a ``Dataset`` during the training process. This behavior can be changed by setting ``feature_fraction`` to a value ``> 0`` and ``<= 1.0``. Setting ``feature_fraction`` to ``0.5``, for example, tells LightGBM to randomly select ``50%`` of features at the beginning of constructing each tree. This reduces the total number of splits that have to be evaluated to add each tree node.\n\nDecrease ``feature_fraction`` to reduce training time.\n\nDecrease ``max_cat_threshold``\n******************************\n\nLightGBM uses a `custom approach for finding optimal splits for categorical features <./Advanced-Topics.html#categorical-feature-support>`_. In this process, LightGBM explores splits that break a categorical feature into two groups. These are sometimes called \"k-vs.-rest\" splits. Higher ``max_cat_threshold`` values correspond to more split points and larger possible group sizes to search.\n\nDecrease ``max_cat_threshold`` to reduce training time.\n\nUse Less Data\n'''''''''''''\n\nUse Bagging\n***********\n\nBy default, LightGBM uses all observations in the training data for each iteration. It is possible to instead tell LightGBM to randomly sample the training data. This process of training over multiple random samples without replacement is called \"bagging\".\n\nSet ``bagging_freq`` to an integer greater than 0 to control how often a new sample is drawn. Set ``bagging_fraction`` to a value ``> 0.0`` and ``< 1.0`` to control the size of the sample. For example, ``{\"bagging_freq\": 5, \"bagging_fraction\": 0.75}`` tells LightGBM \"re-sample without replacement every 5 iterations, and draw samples of 75% of the training data\".\n\nDecrease ``bagging_fraction`` to reduce training time.\n\n\nSave Constructed Datasets with ``save_binary``\n''''''''''''''''''''''''''''''''''''''''''''''\n\nThis only applies to the LightGBM CLI. If you pass parameter ``save_binary``, the training dataset and all validations sets will be saved in a binary format understood by LightGBM. This can speed up training next time, because binning and other work done when constructing a ``Dataset`` does not have to be re-done.\n\n\nFor Better Accuracy\n-------------------\n\n- Use large ``max_bin`` (may be slower)\n\n- Use small ``learning_rate`` with large ``num_iterations``\n\n- Use large ``num_leaves`` (may cause over-fitting)\n\n- Use bigger training data\n\n- Try ``dart``\n\nDeal with Over-fitting\n----------------------\n\n- Use small ``max_bin``\n\n- Use small ``num_leaves``\n\n- Use ``min_data_in_leaf`` and ``min_sum_hessian_in_leaf``\n\n- Use bagging by set ``bagging_fraction`` and ``bagging_freq``\n\n- Use feature sub-sampling by set ``feature_fraction``\n\n- Use bigger training data\n\n- Try ``lambda_l1``, ``lambda_l2`` and ``min_gain_to_split`` for regularization\n\n- Try ``max_depth`` to avoid growing deep tree\n\n- Try ``extra_trees``\n\n- Try increasing ``path_smooth``\n\n.. _Optuna: https://medium.com/optuna/lightgbm-tuner-new-optuna-integration-for-hyperparameter-optimization-8b7095e99258\n\n.. _FLAML: https://github.com/microsoft/FLAML\n" }, { "path": "docs/Parameters.rst", "content": ".. List of parameters is auto generated by LightGBM\\.ci\\parameter-generator.py from LightGBM\\include\\LightGBM\\config.h file.\n\n.. role:: raw-html(raw)\n :format: html\n\nParameters\n==========\n\nThis page contains descriptions of all parameters in LightGBM.\n\n**List of other helpful links**\n\n- `Python API <./Python-API.rst>`__\n\n- `Parameters Tuning <./Parameters-Tuning.rst>`__\n\nParameters Format\n-----------------\n\nParameters are merged together in the following order (later items overwrite earlier ones):\n\n1. LightGBM's default values\n2. special files for ``weight``, ``init_score``, ``query``, and ``positions`` (see `Others <#others>`__)\n3. (CLI only) configuration in a file passed like ``config=train.conf``\n4. (CLI only) configuration passed via the command line\n5. (Python, R) special keyword arguments to some functions (e.g. ``num_boost_round`` in ``train()``)\n6. (Python, R) ``params`` function argument (including ``**kwargs`` in Python and ``...`` in R)\n7. (C API) ``parameters`` or ``params`` function argument\n\nMany parameters have \"aliases\", alternative names which refer to the same configuration.\n\nWhere a mix of the primary parameter name and aliases are given, the primary parameter name is always preferred to any aliases.\n\nFor example, in Python:\n\n.. code-block:: python\n\n # use learning rate of 0.07, because 'learning_rate'\n # is the primary parameter name\n lgb.train(\n params={\n \"learning_rate\": 0.07,\n \"shrinkage_rate\": 0.12\n },\n train_set=dtrain\n )\n\nWhere multiple aliases are given, and the primary parameter name is not, the first alias\nappearing in the lists returned by ``Config::parameter2aliases()`` in the C++ library is used.\nThose lists are hard-coded in a fairly arbitrary way... wherever possible, avoid relying on this behavior.\n\nFor example, in Python:\n\n.. code-block:: python\n\n # use learning rate of 0.12, LightGBM has a hard-coded preference for 'shrinkage_rate'\n # over any other aliases, and 'learning_rate' is not provided\n lgb.train(\n params={\n \"eta\": 0.19,\n \"shrinkage_rate\": 0.12\n },\n train_set=dtrain\n )\n\n**CLI**\n\nThe parameters format is ``key1=value1 key2=value2 ...``.\nParameters can be set both in config file and command line.\nBy using command line, parameters should not have spaces before and after ``=``.\nBy using config files, one line can only contain one parameter. You can use ``#`` to comment.\n\n**Python**\n\nAny parameters that accept multiple values should be passed as a Python list.\n\n.. code-block:: python\n\n params = {\n \"monotone_constraints\": [-1, 0, 1]\n }\n\n\n**R**\n\nAny parameters that accept multiple values should be passed as an R list.\n\n.. code-block:: r\n\n params <- list(\n monotone_constraints = c(-1, 0, 1)\n )\n\n.. start params list\n\nCore Parameters\n---------------\n\n- ``config`` :raw-html:`🔗︎`, default = ``\"\"``, type = string, aliases: ``config_file``\n\n - path of config file\n\n - **Note**: can be used only in CLI version\n\n- ``task`` :raw-html:`🔗︎`, default = ``train``, type = enum, options: ``train``, ``predict``, ``convert_model``, ``refit``, aliases: ``task_type``\n\n - ``train``, for training, aliases: ``training``\n\n - ``predict``, for prediction, aliases: ``prediction``, ``test``\n\n - ``convert_model``, for converting model file into if-else format, see more information in `Convert Parameters <#convert-parameters>`__\n\n - ``refit``, for refitting existing models with new data, aliases: ``refit_tree``\n\n - ``save_binary``, load train (and validation) data then save dataset to binary file. Typical usage: ``save_binary`` first, then run multiple ``train`` tasks in parallel using the saved binary file\n\n - **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent functions\n\n- ``objective`` :raw-html:`🔗︎`, default = ``regression``, type = enum, options: ``regression``, ``regression_l1``, ``huber``, ``fair``, ``poisson``, ``quantile``, ``mape``, ``gamma``, ``tweedie``, ``binary``, ``multiclass``, ``multiclassova``, ``cross_entropy``, ``cross_entropy_lambda``, ``lambdarank``, ``rank_xendcg``, aliases: ``objective_type``, ``app``, ``application``, ``loss``\n\n - regression application\n\n - ``regression``, L2 loss, aliases: ``regression_l2``, ``l2``, ``mean_squared_error``, ``mse``, ``l2_root``, ``root_mean_squared_error``, ``rmse``\n\n - ``regression_l1``, L1 loss, aliases: ``l1``, ``mean_absolute_error``, ``mae``\n\n - ``huber``, `Huber loss `__\n\n - ``fair``, `Fair loss `__\n\n - ``poisson``, `Poisson regression `__\n\n - ``quantile``, `Quantile regression `__\n\n - ``mape``, `MAPE loss `__, aliases: ``mean_absolute_percentage_error``\n\n - ``gamma``, Gamma regression with log-link. It might be useful, e.g., for modeling insurance claims severity, or for any target that might be `gamma-distributed `__\n\n - ``tweedie``, Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any target that might be `tweedie-distributed `__\n\n - binary classification application\n\n - ``binary``, binary `log loss `__ classification (or logistic regression)\n\n - requires labels in {0, 1}; see ``cross-entropy`` application for general probability labels in [0, 1]\n\n - multi-class classification application\n\n - ``multiclass``, `softmax `__ objective function, aliases: ``softmax``\n\n - ``multiclassova``, `One-vs-All `__ binary objective function, aliases: ``multiclass_ova``, ``ova``, ``ovr``\n\n - ``num_class`` should be set as well\n\n - cross-entropy application\n\n - ``cross_entropy``, objective function for cross-entropy (with optional linear weights), aliases: ``xentropy``\n\n - ``cross_entropy_lambda``, alternative parameterization of cross-entropy, aliases: ``xentlambda``\n\n - label is anything in interval [0, 1]\n\n - ranking application\n\n - ``lambdarank``, `lambdarank `__ objective. `label_gain <#label_gain>`__ can be used to set the gain (weight) of ``int`` label and all values in ``label`` must be smaller than number of elements in ``label_gain``\n\n - ``rank_xendcg``, `XE_NDCG_MART `__ ranking objective function, aliases: ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``\n\n - ``rank_xendcg`` is faster than and achieves the similar performance as ``lambdarank``\n\n - label should be ``int`` type, and larger number represents the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect)\n\n - custom objective function (gradients and hessians not computed directly by LightGBM)\n\n - ``custom``\n\n - must be passed through parameters explicitly in the C API\n\n - **Note**: cannot be used in CLI version\n\n- ``boosting`` :raw-html:`🔗︎`, default = ``gbdt``, type = enum, options: ``gbdt``, ``rf``, ``dart``, aliases: ``boosting_type``, ``boost``\n\n - ``gbdt``, traditional Gradient Boosting Decision Tree, aliases: ``gbrt``\n\n - ``rf``, Random Forest, aliases: ``random_forest``\n\n - ``dart``, `Dropouts meet Multiple Additive Regression Trees `__\n\n - **Note**: internally, LightGBM uses ``gbdt`` mode for the first ``1 / learning_rate`` iterations\n\n- ``data_sample_strategy`` :raw-html:`🔗︎`, default = ``bagging``, type = enum, options: ``bagging``, ``goss``\n\n - ``bagging``, Randomly Bagging Sampling\n\n - **Note**: ``bagging`` is only effective when ``bagging_freq > 0`` and ``bagging_fraction < 1.0``\n\n - ``goss``, Gradient-based One-Side Sampling\n\n - *New in version 4.0.0*\n\n- ``data`` :raw-html:`🔗︎`, default = ``\"\"``, type = string, aliases: ``train``, ``train_data``, ``train_data_file``, ``data_filename``\n\n - path of training data, LightGBM will train from this data\n\n - **Note**: can be used only in CLI version\n\n- ``valid`` :raw-html:`🔗︎`, default = ``\"\"``, type = string, aliases: ``test``, ``valid_data``, ``valid_data_file``, ``test_data``, ``test_data_file``, ``valid_filenames``\n\n - path(s) of validation/test data, LightGBM will output metrics for these data\n\n - support multiple validation data, separated by ``,``\n\n - **Note**: can be used only in CLI version\n\n- ``num_iterations`` :raw-html:`🔗︎`, default = ``100``, type = int, aliases: ``num_iteration``, ``n_iter``, ``num_tree``, ``num_trees``, ``num_round``, ``num_rounds``, ``nrounds``, ``num_boost_round``, ``n_estimators``, ``max_iter``, constraints: ``num_iterations >= 0``\n\n - number of boosting iterations\n\n - **Note**: internally, LightGBM constructs ``num_class * num_iterations`` trees for multi-class classification problems\n\n- ``learning_rate`` :raw-html:`🔗︎`, default = ``0.1``, type = double, aliases: ``shrinkage_rate``, ``eta``, constraints: ``learning_rate > 0.0``\n\n - shrinkage rate\n\n - in ``dart``, it also affects on normalization weights of dropped trees\n\n- ``num_leaves`` :raw-html:`🔗︎`, default = ``31``, type = int, aliases: ``num_leaf``, ``max_leaves``, ``max_leaf``, ``max_leaf_nodes``, constraints: ``1 < num_leaves <= 131072``\n\n - max number of leaves in one tree\n\n- ``tree_learner`` :raw-html:`🔗︎`, default = ``serial``, type = enum, options: ``serial``, ``feature``, ``data``, ``voting``, aliases: ``tree``, ``tree_type``, ``tree_learner_type``\n\n - ``serial``, single machine tree learner\n\n - ``feature``, feature parallel tree learner, aliases: ``feature_parallel``\n\n - ``data``, data parallel tree learner, aliases: ``data_parallel``\n\n - ``voting``, voting parallel tree learner, aliases: ``voting_parallel``\n\n - refer to `Distributed Learning Guide <./Parallel-Learning-Guide.rst>`__ to get more details\n\n- ``num_threads`` :raw-html:`🔗︎`, default = ``0``, type = int, aliases: ``num_thread``, ``nthread``, ``nthreads``, ``n_jobs``\n\n - used only in ``train``, ``prediction`` and ``refit`` tasks or in correspondent functions of language-specific packages\n\n - number of threads for LightGBM\n\n - ``0`` means default number of threads in OpenMP\n\n - for the best speed, set this to the number of **real CPU cores**, not the number of threads (most CPUs use `hyper-threading `__ to generate 2 threads per CPU core)\n\n - do not set it too large if your dataset is small (for instance, do not use 64 threads for a dataset with 10,000 rows)\n\n - be aware a task manager or any similar CPU monitoring tool might report that cores not being fully utilized. **This is normal**\n\n - for distributed learning, do not use all CPU cores because this will cause poor performance for the network communication\n\n - **Note**: please **don't** change this during training, especially when running multiple jobs simultaneously by external packages, otherwise it may cause undesirable errors\n\n- ``device_type`` :raw-html:`🔗︎`, default = ``cpu``, type = enum, options: ``cpu``, ``gpu``, ``cuda``, aliases: ``device``\n\n - device for the tree learning\n\n - ``cpu`` supports all LightGBM functionality and is portable across the widest range of operating systems and hardware\n\n - ``cuda`` offers faster training than ``gpu`` or ``cpu``, but only works on GPUs supporting CUDA or ROCm\n\n - ``gpu`` can be faster than ``cpu`` and works on a wider range of GPUs than CUDA\n\n - **Note**: it is recommended to use the smaller ``max_bin`` (e.g. 63) to get the better speed up\n\n - **Note**: for the faster speed, GPU uses 32-bit float point to sum up by default, so this may affect the accuracy for some tasks. You can set ``gpu_use_dp=true`` to enable 64-bit float point, but it will slow down the training\n\n - **Note**: refer to `Installation Guide <./Installation-Guide.rst>`__ to build LightGBM with GPU, CUDA, or ROCm support\n\n- ``seed`` :raw-html:`🔗︎`, default = ``None``, type = int, aliases: ``random_seed``, ``random_state``\n\n - this seed is used to generate other seeds, e.g. ``data_random_seed``, ``feature_fraction_seed``, etc.\n\n - by default, this seed is unused in favor of default values of other seeds\n\n - this seed has lower priority in comparison with other seeds, which means that it will be overridden, if you set other seeds explicitly\n\n- ``deterministic`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - used only with ``cpu`` device type\n\n - setting this to ``true`` should ensure the stable results when using the same data and the same parameters (and different ``num_threads``)\n\n - when you use the different seeds, different LightGBM versions, the binaries compiled by different compilers, or in different systems, the results are expected to be different\n\n - you can `raise issues `__ in LightGBM GitHub repo when you meet the unstable results\n\n - **Note**: setting this to ``true`` may slow down the training\n\n - **Note**: to avoid potential instability due to numerical issues, please set ``force_col_wise=true`` or ``force_row_wise=true`` when setting ``deterministic=true``\n\nLearning Control Parameters\n---------------------------\n\n- ``force_col_wise`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - used only with ``cpu`` device type\n\n - set this to ``true`` to force col-wise histogram building\n\n - enabling this is recommended when:\n\n - the number of columns is large, or the total number of bins is large\n\n - ``num_threads`` is large, e.g. ``> 20``\n\n - you want to reduce memory cost\n\n - **Note**: when both ``force_col_wise`` and ``force_row_wise`` are ``false``, LightGBM will firstly try them both, and then use the faster one. To remove the overhead of testing set the faster one to ``true`` manually\n\n - **Note**: this parameter cannot be used at the same time with ``force_row_wise``, choose only one of them\n\n- ``force_row_wise`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - used only with ``cpu`` device type\n\n - set this to ``true`` to force row-wise histogram building\n\n - enabling this is recommended when:\n\n - the number of data points is large, and the total number of bins is relatively small\n\n - ``num_threads`` is relatively small, e.g. ``<= 16``\n\n - you want to use small ``bagging_fraction`` or ``goss`` sample strategy to speed up\n\n - **Note**: setting this to ``true`` will double the memory cost for Dataset object. If you have not enough memory, you can try setting ``force_col_wise=true``\n\n - **Note**: when both ``force_col_wise`` and ``force_row_wise`` are ``false``, LightGBM will firstly try them both, and then use the faster one. To remove the overhead of testing set the faster one to ``true`` manually\n\n - **Note**: this parameter cannot be used at the same time with ``force_col_wise``, choose only one of them\n\n- ``histogram_pool_size`` :raw-html:`🔗︎`, default = ``-1.0``, type = double, aliases: ``hist_pool_size``\n\n - max cache size in MB for historical histogram\n\n - ``< 0`` means no limit\n\n- ``max_depth`` :raw-html:`🔗︎`, default = ``-1``, type = int\n\n - limit the max depth for tree model. This is used to deal with over-fitting when ``#data`` is small. Tree still grows leaf-wise\n\n - ``<= 0`` means no limit\n\n- ``min_data_in_leaf`` :raw-html:`🔗︎`, default = ``20``, type = int, aliases: ``min_data_per_leaf``, ``min_data``, ``min_child_samples``, ``min_samples_leaf``, constraints: ``min_data_in_leaf >= 0``\n\n - minimal number of data in one leaf. Can be used to deal with over-fitting\n\n - **Note**: this is an approximation based on the Hessian, so occasionally you may observe splits which produce leaf nodes that have less than this many observations\n\n- ``min_sum_hessian_in_leaf`` :raw-html:`🔗︎`, default = ``1e-3``, type = double, aliases: ``min_sum_hessian_per_leaf``, ``min_sum_hessian``, ``min_hessian``, ``min_child_weight``, constraints: ``min_sum_hessian_in_leaf >= 0.0``\n\n - minimal sum hessian in one leaf. Like ``min_data_in_leaf``, it can be used to deal with over-fitting\n\n- ``bagging_fraction`` :raw-html:`🔗︎`, default = ``1.0``, type = double, aliases: ``sub_row``, ``subsample``, ``bagging``, constraints: ``0.0 < bagging_fraction <= 1.0``\n\n - like ``feature_fraction``, but this will randomly select part of data without resampling\n\n - can be used to speed up training\n\n - can be used to deal with over-fitting\n\n - **Note**: to enable bagging, ``bagging_freq`` should be set to a non zero value as well\n\n- ``pos_bagging_fraction`` :raw-html:`🔗︎`, default = ``1.0``, type = double, aliases: ``pos_sub_row``, ``pos_subsample``, ``pos_bagging``, constraints: ``0.0 < pos_bagging_fraction <= 1.0``\n\n - used only in ``binary`` application\n\n - used for imbalanced binary classification problem, will randomly sample ``#pos_samples * pos_bagging_fraction`` positive samples in bagging\n\n - should be used together with ``neg_bagging_fraction``\n\n - set this to ``1.0`` to disable\n\n - **Note**: to enable this, you need to set ``bagging_freq`` and ``neg_bagging_fraction`` as well\n\n - **Note**: if both ``pos_bagging_fraction`` and ``neg_bagging_fraction`` are set to ``1.0``, balanced bagging is disabled\n\n - **Note**: if balanced bagging is enabled, ``bagging_fraction`` will be ignored\n\n- ``neg_bagging_fraction`` :raw-html:`🔗︎`, default = ``1.0``, type = double, aliases: ``neg_sub_row``, ``neg_subsample``, ``neg_bagging``, constraints: ``0.0 < neg_bagging_fraction <= 1.0``\n\n - used only in ``binary`` application\n\n - used for imbalanced binary classification problem, will randomly sample ``#neg_samples * neg_bagging_fraction`` negative samples in bagging\n\n - should be used together with ``pos_bagging_fraction``\n\n - set this to ``1.0`` to disable\n\n - **Note**: to enable this, you need to set ``bagging_freq`` and ``pos_bagging_fraction`` as well\n\n - **Note**: if both ``pos_bagging_fraction`` and ``neg_bagging_fraction`` are set to ``1.0``, balanced bagging is disabled\n\n - **Note**: if balanced bagging is enabled, ``bagging_fraction`` will be ignored\n\n- ``bagging_freq`` :raw-html:`🔗︎`, default = ``0``, type = int, aliases: ``subsample_freq``\n\n - frequency for bagging\n\n - ``0`` means disable bagging; ``k`` means perform bagging at every ``k`` iteration. Every ``k``-th iteration, LightGBM will randomly select ``bagging_fraction * 100%`` of the data to use for the next ``k`` iterations\n\n - **Note**: bagging is only effective when ``0.0 < bagging_fraction < 1.0``\n\n- ``bagging_seed`` :raw-html:`🔗︎`, default = ``3``, type = int, aliases: ``bagging_fraction_seed``\n\n - random seed for bagging\n\n- ``bagging_by_query`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - whether to do bagging sample by query\n\n - *New in version 4.6.0*\n\n- ``feature_fraction`` :raw-html:`🔗︎`, default = ``1.0``, type = double, aliases: ``sub_feature``, ``colsample_bytree``, constraints: ``0.0 < feature_fraction <= 1.0``\n\n - LightGBM will randomly select a subset of features on each iteration (tree) if ``feature_fraction`` is smaller than ``1.0``. For example, if you set it to ``0.8``, LightGBM will select 80% of features before training each tree\n\n - can be used to speed up training\n\n - can be used to deal with over-fitting\n\n- ``feature_fraction_bynode`` :raw-html:`🔗︎`, default = ``1.0``, type = double, aliases: ``sub_feature_bynode``, ``colsample_bynode``, constraints: ``0.0 < feature_fraction_bynode <= 1.0``\n\n - LightGBM will randomly select a subset of features on each tree node if ``feature_fraction_bynode`` is smaller than ``1.0``. For example, if you set it to ``0.8``, LightGBM will select 80% of features at each tree node\n\n - can be used to deal with over-fitting\n\n - **Note**: unlike ``feature_fraction``, this cannot speed up training\n\n - **Note**: if both ``feature_fraction`` and ``feature_fraction_bynode`` are smaller than ``1.0``, the final fraction of each node is ``feature_fraction * feature_fraction_bynode``\n\n- ``feature_fraction_seed`` :raw-html:`🔗︎`, default = ``2``, type = int\n\n - random seed for ``feature_fraction``\n\n- ``extra_trees`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``extra_tree``\n\n - use extremely randomized trees\n\n - if set to ``true``, when evaluating node splits LightGBM will check only one randomly-chosen threshold for each feature\n\n - can be used to speed up training\n\n - can be used to deal with over-fitting\n\n- ``extra_seed`` :raw-html:`🔗︎`, default = ``6``, type = int\n\n - random seed for selecting thresholds when ``extra_trees`` is true\n\n- ``early_stopping_round`` :raw-html:`🔗︎`, default = ``0``, type = int, aliases: ``early_stopping_rounds``, ``early_stopping``, ``n_iter_no_change``\n\n - will stop training if one metric of one validation data doesn't improve in last ``early_stopping_round`` rounds\n\n - ``<= 0`` means disable\n\n - can be used to speed up training\n\n- ``early_stopping_min_delta`` :raw-html:`🔗︎`, default = ``0.0``, type = double, constraints: ``early_stopping_min_delta >= 0.0``\n\n - when early stopping is used (i.e. ``early_stopping_round > 0``), require the early stopping metric to improve by at least this delta to be considered an improvement\n\n - *New in version 4.4.0*\n\n- ``first_metric_only`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - LightGBM allows you to provide multiple evaluation metrics. Set this to ``true``, if you want to use only the first metric for early stopping\n\n- ``max_delta_step`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``max_tree_output``, ``max_leaf_output``\n\n - used to limit the max output of tree leaves\n\n - ``<= 0`` means no constraint\n\n - the final max output of leaves is ``learning_rate * max_delta_step``\n\n- ``lambda_l1`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``reg_alpha``, ``l1_regularization``, constraints: ``lambda_l1 >= 0.0``\n\n - L1 regularization\n\n- ``lambda_l2`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``reg_lambda``, ``lambda``, ``l2_regularization``, constraints: ``lambda_l2 >= 0.0``\n\n - L2 regularization\n\n- ``linear_lambda`` :raw-html:`🔗︎`, default = ``0.0``, type = double, constraints: ``linear_lambda >= 0.0``\n\n - linear tree regularization, corresponds to the parameter ``lambda`` in Eq. 3 of `Gradient Boosting with Piece-Wise Linear Regression Trees `__\n\n- ``min_gain_to_split`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``min_split_gain``, constraints: ``min_gain_to_split >= 0.0``\n\n - the minimal gain to perform split\n\n - can be used to speed up training\n\n- ``drop_rate`` :raw-html:`🔗︎`, default = ``0.1``, type = double, aliases: ``rate_drop``, constraints: ``0.0 <= drop_rate <= 1.0``\n\n - used only in ``dart``\n\n - dropout rate: a fraction of previous trees to drop during the dropout\n\n- ``max_drop`` :raw-html:`🔗︎`, default = ``50``, type = int\n\n - used only in ``dart``\n\n - max number of dropped trees during one boosting iteration\n\n - ``<=0`` means no limit\n\n- ``skip_drop`` :raw-html:`🔗︎`, default = ``0.5``, type = double, constraints: ``0.0 <= skip_drop <= 1.0``\n\n - used only in ``dart``\n\n - probability of skipping the dropout procedure during a boosting iteration\n\n- ``xgboost_dart_mode`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - used only in ``dart``\n\n - set this to ``true``, if you want to use XGBoost DART mode\n\n- ``uniform_drop`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - used only in ``dart``\n\n - set this to ``true``, if you want to use uniform drop\n\n- ``drop_seed`` :raw-html:`🔗︎`, default = ``4``, type = int\n\n - used only in ``dart``\n\n - random seed to choose dropping models\n\n- ``top_rate`` :raw-html:`🔗︎`, default = ``0.2``, type = double, constraints: ``0.0 <= top_rate <= 1.0``\n\n - used only in ``goss``\n\n - the retain ratio of large gradient data\n\n- ``other_rate`` :raw-html:`🔗︎`, default = ``0.1``, type = double, constraints: ``0.0 <= other_rate <= 1.0``\n\n - used only in ``goss``\n\n - the retain ratio of small gradient data\n\n- ``min_data_per_group`` :raw-html:`🔗︎`, default = ``100``, type = int, constraints: ``min_data_per_group > 0``\n\n - used for the categorical features\n\n - minimal number of data per categorical group\n\n- ``max_cat_threshold`` :raw-html:`🔗︎`, default = ``32``, type = int, constraints: ``max_cat_threshold > 0``\n\n - used for the categorical features\n\n - limit number of split points considered for categorical features. See `the documentation on how LightGBM finds optimal splits for categorical features <./Features.rst#optimal-split-for-categorical-features>`_ for more details\n\n - can be used to speed up training\n\n- ``cat_l2`` :raw-html:`🔗︎`, default = ``10.0``, type = double, constraints: ``cat_l2 >= 0.0``\n\n - used for the categorical features\n\n - L2 regularization in categorical split\n\n- ``cat_smooth`` :raw-html:`🔗︎`, default = ``10.0``, type = double, constraints: ``cat_smooth >= 0.0``\n\n - used for the categorical features\n\n - this can reduce the effect of noises in categorical features, especially for categories with few data\n\n- ``max_cat_to_onehot`` :raw-html:`🔗︎`, default = ``4``, type = int, constraints: ``max_cat_to_onehot > 0``\n\n - used for the categorical features\n\n - when number of categories of one feature smaller than or equal to ``max_cat_to_onehot``, one-vs-other split algorithm will be used\n\n- ``top_k`` :raw-html:`🔗︎`, default = ``20``, type = int, aliases: ``topk``, constraints: ``top_k > 0``\n\n - used only in ``voting`` tree learner, refer to `Voting parallel <./Parallel-Learning-Guide.rst#choose-appropriate-parallel-algorithm>`__\n\n - set this to larger value for more accurate result, but it will slow down the training speed\n\n- ``monotone_constraints`` :raw-html:`🔗︎`, default = ``None``, type = multi-int, aliases: ``mc``, ``monotone_constraint``, ``monotonic_cst``\n\n - used for constraints of monotonic features\n\n - ``1`` means increasing, ``-1`` means decreasing, ``0`` means non-constraint\n\n - you need to specify all features in order. For example, ``mc=-1,0,1`` means decreasing for the 1st feature, non-constraint for the 2nd feature and increasing for the 3rd feature\n\n- ``monotone_constraints_method`` :raw-html:`🔗︎`, default = ``basic``, type = enum, options: ``basic``, ``intermediate``, ``advanced``, aliases: ``monotone_constraining_method``, ``mc_method``\n\n - used only if ``monotone_constraints`` is set\n\n - monotone constraints method\n\n - ``basic``, the most basic monotone constraints method. It does not slow down the training speed at all, but over-constrains the predictions\n\n - ``intermediate``, a `more advanced method `__, which may slow down the training speed very slightly. However, this method is much less constraining than the basic method and should significantly improve the results\n\n - ``advanced``, an `even more advanced method `__, which may slow down the training speed. However, this method is even less constraining than the intermediate method and should again significantly improve the results\n\n- ``monotone_penalty`` :raw-html:`🔗︎`, default = ``0.0``, type = double, aliases: ``monotone_splits_penalty``, ``ms_penalty``, ``mc_penalty``, constraints: ``monotone_penalty >= 0.0``\n\n - used only if ``monotone_constraints`` is set\n\n - `monotone penalty `__: a penalization parameter X forbids any monotone splits on the first X (rounded down) level(s) of the tree. The penalty applied to monotone splits on a given depth is a continuous, increasing function the penalization parameter\n\n - if ``0.0`` (the default), no penalization is applied\n\n- ``feature_contri`` :raw-html:`🔗︎`, default = ``None``, type = multi-double, aliases: ``feature_contrib``, ``fc``, ``fp``, ``feature_penalty``\n\n - used to control feature's split gain, will use ``gain[i] = max(0, feature_contri[i]) * gain[i]`` to replace the split gain of i-th feature\n\n - you need to specify all features in order\n\n- ``forcedsplits_filename`` :raw-html:`🔗︎`, default = ``\"\"``, type = string, aliases: ``fs``, ``forced_splits_filename``, ``forced_splits_file``, ``forced_splits``\n\n - path to a ``.json`` file that specifies splits to force at the top of every decision tree before best-first learning commences\n\n - ``.json`` file can be arbitrarily nested, and each split contains ``feature``, ``threshold`` fields, as well as ``left`` and ``right`` fields representing subsplits\n\n - categorical splits are forced in a one-hot fashion, with ``left`` representing the split containing the feature value and ``right`` representing other values\n\n - **Note**: the forced split logic will be ignored, if the split makes gain worse\n\n - see `this file `__ as an example\n\n- ``refit_decay_rate`` :raw-html:`🔗︎`, default = ``0.9``, type = double, constraints: ``0.0 <= refit_decay_rate <= 1.0``\n\n - decay rate of ``refit`` task, will use ``leaf_output = refit_decay_rate * old_leaf_output + (1.0 - refit_decay_rate) * new_leaf_output`` to refit trees\n\n - used only in ``refit`` task in CLI version or as argument in ``refit`` function in language-specific package\n\n- ``cegb_tradeoff`` :raw-html:`🔗︎`, default = ``1.0``, type = double, constraints: ``cegb_tradeoff >= 0.0``\n\n - cost-effective gradient boosting multiplier for all penalties\n\n- ``cegb_penalty_split`` :raw-html:`🔗︎`, default = ``0.0``, type = double, constraints: ``cegb_penalty_split >= 0.0``\n\n - cost-effective gradient-boosting penalty for splitting a node\n\n- ``cegb_penalty_feature_lazy`` :raw-html:`🔗︎`, default = ``0,0,...,0``, type = multi-double\n\n - cost-effective gradient boosting penalty for using a feature\n\n - applied per data point\n\n- ``cegb_penalty_feature_coupled`` :raw-html:`🔗︎`, default = ``0,0,...,0``, type = multi-double\n\n - cost-effective gradient boosting penalty for using a feature\n\n - applied once per forest\n\n- ``path_smooth`` :raw-html:`🔗︎`, default = ``0``, type = double, constraints: ``path_smooth >= 0.0``\n\n - controls smoothing applied to tree nodes\n\n - helps prevent overfitting on leaves with few samples\n\n - if ``0.0`` (the default), no smoothing is applied\n\n - if ``path_smooth > 0`` then ``min_data_in_leaf`` must be at least ``2``\n\n - larger values give stronger regularization\n\n - the weight of each node is ``w * (n / path_smooth) / (n / path_smooth + 1) + w_p / (n / path_smooth + 1)``, where ``n`` is the number of samples in the node, ``w`` is the optimal node weight to minimise the loss (approximately ``-sum_gradients / sum_hessians``), and ``w_p`` is the weight of the parent node\n\n - note that the parent output ``w_p`` itself has smoothing applied, unless it is the root node, so that the smoothing effect accumulates with the tree depth\n\n- ``interaction_constraints`` :raw-html:`🔗︎`, default = ``\"\"``, type = string\n\n - controls which features can appear in the same branch\n\n - by default interaction constraints are disabled, to enable them you can specify\n\n - for CLI, lists separated by commas, e.g. ``[0,1,2],[2,3]``\n\n - for Python-package, list of lists, e.g. ``[[0, 1, 2], [2, 3]]``\n\n - for R-package, list of character or numeric vectors, e.g. ``list(c(\"var1\", \"var2\", \"var3\"), c(\"var3\", \"var4\"))`` or ``list(c(1L, 2L, 3L), c(3L, 4L))``. Numeric vectors should use 1-based indexing, where ``1L`` is the first feature, ``2L`` is the second feature, etc.\n\n - any two features can only appear in the same branch only if there exists a constraint containing both features\n\n- ``verbosity`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``verbose``\n\n - controls the level of LightGBM's verbosity\n\n - ``< 0``: Fatal, ``= 0``: Error (Warning), ``= 1``: Info, ``> 1``: Debug\n\n- ``input_model`` :raw-html:`🔗︎`, default = ``\"\"``, type = string, aliases: ``model_input``, ``model_in``\n\n - filename of input model\n\n - for ``prediction`` task, this model will be applied to prediction data\n\n - for ``train`` task, training will be continued from this model\n\n - **Note**: can be used only in CLI version\n\n- ``output_model`` :raw-html:`🔗︎`, default = ``LightGBM_model.txt``, type = string, aliases: ``model_output``, ``model_out``\n\n - filename of output model in training\n\n - **Note**: can be used only in CLI version\n\n- ``saved_feature_importance_type`` :raw-html:`🔗︎`, default = ``0``, type = int\n\n - the feature importance type in the saved model file\n\n - ``0``: count-based feature importance (numbers of splits are counted); ``1``: gain-based feature importance (values of gain are counted)\n\n - **Note**: can be used only in CLI version\n\n- ``snapshot_freq`` :raw-html:`🔗︎`, default = ``-1``, type = int, aliases: ``save_period``\n\n - frequency of saving model file snapshot\n\n - set this to positive value to enable this function. For example, the model file will be snapshotted at each iteration if ``snapshot_freq=1``\n\n - **Note**: can be used only in CLI version\n\n- ``use_quantized_grad`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - whether to use gradient quantization when training\n\n - enabling this will discretize (quantize) the gradients and hessians into bins of ``num_grad_quant_bins``\n\n - with quantized training, most arithmetics in the training process will be integer operations\n\n - gradient quantization can accelerate training, with little accuracy drop in most cases\n\n - **Note**: works only with ``cpu`` and ``cuda`` device type\n\n - *New in version 4.0.0*\n\n- ``num_grad_quant_bins`` :raw-html:`🔗︎`, default = ``4``, type = int\n\n - used only if ``use_quantized_grad=true``\n\n - number of bins to quantization gradients and hessians\n\n - with more bins, the quantized training will be closer to full precision training\n\n - **Note**: works only with ``cpu`` and ``cuda`` device type\n\n - *New in version 4.0.0*\n\n- ``quant_train_renew_leaf`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - used only if ``use_quantized_grad=true``\n\n - whether to renew the leaf values with original gradients when quantized training\n\n - renewing is very helpful for good quantized training accuracy for ranking objectives\n\n - **Note**: works only with ``cpu`` and ``cuda`` device type\n\n - *New in version 4.0.0*\n\n- ``stochastic_rounding`` :raw-html:`🔗︎`, default = ``true``, type = bool\n\n - used only if ``use_quantized_grad=true``\n\n - whether to use stochastic rounding in gradient quantization\n\n - **Note**: works only with ``cpu`` and ``cuda`` device type\n\n - *New in version 4.0.0*\n\nIO Parameters\n-------------\n\nDataset Parameters\n~~~~~~~~~~~~~~~~~~\n\n- ``linear_tree`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``linear_trees``\n\n - fit piecewise linear gradient boosting tree\n\n - tree splits are chosen in the usual way, but the model at each leaf is linear instead of constant\n\n - the linear model at each leaf includes all the numerical features in that leaf's branch\n\n - the first tree has constant leaf values\n\n - categorical features are used for splits as normal but are not used in the linear models\n\n - missing values should not be encoded as ``0``. Use ``np.nan`` for Python, ``NA`` for the CLI, and ``NA``, ``NA_real_``, or ``NA_integer_`` for R\n\n - it is recommended to rescale data before training so that features have similar mean and standard deviation\n\n - **Note**: works only with ``cpu``, ``gpu`` device type and ``serial`` tree learner\n\n - **Note**: ``regression_l1`` objective is not supported with linear tree boosting\n\n - **Note**: setting ``linear_tree=true`` significantly increases the memory use of LightGBM\n\n - **Note**: if you specify ``monotone_constraints``, constraints will be enforced when choosing the split points, but not when fitting the linear models on leaves\n\n- ``max_bin`` :raw-html:`🔗︎`, default = ``255``, type = int, aliases: ``max_bins``, constraints: ``max_bin > 1``\n\n - max number of bins that feature values will be bucketed in\n\n - small number of bins may reduce training accuracy but may increase general power (deal with over-fitting)\n\n - LightGBM will auto compress memory according to ``max_bin``. For example, LightGBM will use ``uint8_t`` for feature value if ``max_bin=255``\n\n- ``max_bin_by_feature`` :raw-html:`🔗︎`, default = ``None``, type = multi-int\n\n - max number of bins for each feature\n\n - if not specified, will use ``max_bin`` for all features\n\n- ``min_data_in_bin`` :raw-html:`🔗︎`, default = ``3``, type = int, constraints: ``min_data_in_bin > 0``\n\n - minimal number of data inside one bin\n\n - use this to avoid one-data-one-bin (potential over-fitting)\n\n- ``bin_construct_sample_cnt`` :raw-html:`🔗︎`, default = ``200000``, type = int, aliases: ``subsample_for_bin``, constraints: ``bin_construct_sample_cnt > 0``\n\n - number of data that sampled to construct feature discrete bins\n\n - setting this to larger value will give better training result, but may increase data loading time\n\n - set this to larger value if data is very sparse\n\n - **Note**: don't set this to small values, otherwise, you may encounter unexpected errors and poor accuracy\n\n- ``data_random_seed`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``data_seed``\n\n - random seed for sampling data to construct histogram bins\n\n- ``is_enable_sparse`` :raw-html:`🔗︎`, default = ``true``, type = bool, aliases: ``is_sparse``, ``enable_sparse``, ``sparse``\n\n - used to enable/disable sparse optimization\n\n- ``enable_bundle`` :raw-html:`🔗︎`, default = ``true``, type = bool, aliases: ``is_enable_bundle``, ``bundle``\n\n - set this to ``false`` to disable Exclusive Feature Bundling (EFB), which is described in `LightGBM: A Highly Efficient Gradient Boosting Decision Tree `__\n\n - **Note**: disabling this may cause the slow training speed for sparse datasets\n\n- ``use_missing`` :raw-html:`🔗︎`, default = ``true``, type = bool\n\n - set this to ``false`` to disable the special handle of missing value\n\n- ``zero_as_missing`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - set this to ``true`` to treat all zero as missing values (including the unshown values in LibSVM / sparse matrices)\n\n - set this to ``false`` to use ``na`` for representing missing values\n\n- ``feature_pre_filter`` :raw-html:`🔗︎`, default = ``true``, type = bool\n\n - set this to ``true`` (the default) to tell LightGBM to ignore the features that are unsplittable based on ``min_data_in_leaf``\n\n - as dataset object is initialized only once and cannot be changed after that, you may need to set this to ``false`` when searching parameters with ``min_data_in_leaf``, otherwise features are filtered by ``min_data_in_leaf`` firstly if you don't reconstruct dataset object\n\n - **Note**: setting this to ``false`` may slow down the training\n\n- ``pre_partition`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_pre_partition``\n\n - used for distributed learning (excluding the ``feature_parallel`` mode)\n\n - ``true`` if training data are pre-partitioned, and different machines use different partitions\n\n- ``two_round`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``two_round_loading``, ``use_two_round_loading``\n\n - set this to ``true`` if data file is too big to fit in memory\n\n - by default, LightGBM will map data file to memory and load features from memory. This will provide faster data loading speed, but may cause run out of memory error when the data file is very big\n\n - **Note**: works only in case of loading data directly from text file\n\n- ``header`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``has_header``\n\n - set this to ``true`` if input data has header\n\n - **Note**: works only in case of loading data directly from text file\n\n- ``label_column`` :raw-html:`🔗︎`, default = ``\"\"``, type = int or string, aliases: ``label``\n\n - used to specify the label column\n\n - use number for index, e.g. ``label=0`` means column\\_0 is the label\n\n - add a prefix ``name:`` for column name, e.g. ``label=name:is_click``\n\n - if omitted, the first column in the training data is used as the label\n\n - **Note**: works only in case of loading data directly from text file\n\n- ``weight_column`` :raw-html:`🔗︎`, default = ``\"\"``, type = int or string, aliases: ``weight``\n\n - used to specify the weight column\n\n - use number for index, e.g. ``weight=0`` means column\\_0 is the weight\n\n - add a prefix ``name:`` for column name, e.g. ``weight=name:weight``\n\n - **Note**: works only in case of loading data directly from text file\n\n - **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``, e.g. when label is column\\_0, and weight is column\\_1, the correct parameter is ``weight=0``\n\n - **Note**: weights should be non-negative\n\n- ``group_column`` :raw-html:`🔗︎`, default = ``\"\"``, type = int or string, aliases: ``group``, ``group_id``, ``query_column``, ``query``, ``query_id``\n\n - used to specify the query/group id column\n\n - use number for index, e.g. ``query=0`` means column\\_0 is the query id\n\n - add a prefix ``name:`` for column name, e.g. ``query=name:query_id``\n\n - **Note**: works only in case of loading data directly from text file\n\n - **Note**: data should be grouped by query\\_id, for more information, see `Query Data <#query-data>`__\n\n - **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``, e.g. when label is column\\_0 and query\\_id is column\\_1, the correct parameter is ``query=0``\n\n- ``ignore_column`` :raw-html:`🔗︎`, default = ``\"\"``, type = multi-int or string, aliases: ``ignore_feature``, ``blacklist``\n\n - used to specify some ignoring columns in training\n\n - use number for index, e.g. ``ignore_column=0,1,2`` means column\\_0, column\\_1 and column\\_2 will be ignored\n\n - add a prefix ``name:`` for column name, e.g. ``ignore_column=name:c1,c2,c3`` means c1, c2 and c3 will be ignored\n\n - **Note**: works only in case of loading data directly from text file\n\n - **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``\n\n - **Note**: despite the fact that specified columns will be completely ignored during the training, they still should have a valid format allowing LightGBM to load file successfully\n\n- ``categorical_feature`` :raw-html:`🔗︎`, default = ``\"\"``, type = multi-int or string, aliases: ``cat_feature``, ``categorical_column``, ``cat_column``, ``categorical_features``\n\n - used to specify categorical features\n\n - use number for index, e.g. ``categorical_feature=0,1,2`` means column\\_0, column\\_1 and column\\_2 are categorical features\n\n - add a prefix ``name:`` for column name, e.g. ``categorical_feature=name:c1,c2,c3`` means c1, c2 and c3 are categorical features\n\n - **Note**: all values will be cast to ``int32`` (integer codes will be extracted from pandas categoricals in the Python-package)\n\n - **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``\n\n - **Note**: all values should be less than ``Int32.MaxValue`` (2147483647)\n\n - **Note**: using large values could be memory consuming. Tree decision rule works best when categorical features are presented by consecutive integers starting from zero\n\n - **Note**: all negative values will be treated as **missing values**\n\n - **Note**: the output cannot be monotonically constrained with respect to a categorical feature\n\n - **Note**: floating point numbers in categorical features will be rounded towards 0\n\n- ``forcedbins_filename`` :raw-html:`🔗︎`, default = ``\"\"``, type = string\n\n - path to a ``.json`` file that specifies bin upper bounds for some or all features\n\n - ``.json`` file should contain an array of objects, each containing the word ``feature`` (integer feature index) and ``bin_upper_bound`` (array of thresholds for binning)\n\n - see `this file `__ as an example\n\n- ``save_binary`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_save_binary``, ``is_save_binary_file``\n\n - if ``true``, LightGBM will save the dataset (including validation data) to a binary file. This speed ups the data loading for the next time\n\n - **Note**: ``init_score`` is not saved in binary file\n\n - **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent function\n\n- ``precise_float_parser`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - use precise floating point number parsing for text parser (e.g. CSV, TSV, LibSVM input)\n\n - **Note**: setting this to ``true`` may lead to much slower text parsing\n\n- ``parser_config_file`` :raw-html:`🔗︎`, default = ``\"\"``, type = string\n\n - path to a ``.json`` file that specifies customized parser initialized configuration\n\n - see `lightgbm-transform `__ for usage examples\n\n - **Note**: ``lightgbm-transform`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should go to `issues page `__\n\n - *New in version 4.0.0*\n\nPredict Parameters\n~~~~~~~~~~~~~~~~~~\n\n- ``start_iteration_predict`` :raw-html:`🔗︎`, default = ``0``, type = int\n\n - used only in ``prediction`` task\n\n - used to specify from which iteration to start the prediction\n\n - ``<= 0`` means from the first iteration\n\n- ``num_iteration_predict`` :raw-html:`🔗︎`, default = ``-1``, type = int\n\n - used only in ``prediction`` task\n\n - used to specify how many trained iterations will be used in prediction\n\n - ``<= 0`` means no limit\n\n- ``predict_raw_score`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_predict_raw_score``, ``predict_rawscore``, ``raw_score``\n\n - used only in ``prediction`` task\n\n - set this to ``true`` to predict only the raw scores\n\n - set this to ``false`` to predict transformed scores\n\n- ``predict_leaf_index`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_predict_leaf_index``, ``leaf_index``\n\n - used only in ``prediction`` task\n\n - set this to ``true`` to predict with leaf index of all trees\n\n- ``predict_contrib`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``is_predict_contrib``, ``contrib``\n\n - used only in ``prediction`` task\n\n - set this to ``true`` to estimate `SHAP values `__, which represent how each feature contributes to each prediction\n\n - produces ``#features + 1`` values where the last value is the expected value of the model output over the training data\n\n - **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package `__\n\n - **Note**: unlike the shap package, with ``predict_contrib`` we return a matrix with an extra column, where the last column is the expected value\n\n - **Note**: this feature is not implemented for linear trees\n\n- ``predict_disable_shape_check`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - used only in ``prediction`` task\n\n - control whether or not LightGBM raises an error when you try to predict on data with a different number of features than the training data\n\n - if ``false`` (the default), a fatal error will be raised if the number of features in the dataset you predict on differs from the number seen during training\n\n - if ``true``, LightGBM will attempt to predict on whatever data you provide. This is dangerous because you might get incorrect predictions, but you could use it in situations where it is difficult or expensive to generate some features and you are very confident that they were never chosen for splits in the model\n\n - **Note**: be very careful setting this parameter to ``true``\n\n- ``pred_early_stop`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - used only in ``prediction`` task\n\n - used only in ``classification`` and ``ranking`` applications\n\n - used only for predicting normal or raw scores\n\n - if ``true``, will use early-stopping to speed up the prediction. May affect the accuracy\n\n - **Note**: cannot be used with ``rf`` boosting type or custom objective function\n\n- ``pred_early_stop_freq`` :raw-html:`🔗︎`, default = ``10``, type = int\n\n - used only in ``prediction`` task and if ``pred_early_stop=true``\n\n - the frequency of checking early-stopping prediction\n\n- ``pred_early_stop_margin`` :raw-html:`🔗︎`, default = ``10.0``, type = double\n\n - used only in ``prediction`` task and if ``pred_early_stop=true``\n\n - the threshold of margin in early-stopping prediction\n\n- ``output_result`` :raw-html:`🔗︎`, default = ``LightGBM_predict_result.txt``, type = string, aliases: ``predict_result``, ``prediction_result``, ``predict_name``, ``prediction_name``, ``pred_name``, ``name_pred``\n\n - used only in ``prediction`` task\n\n - filename of prediction result\n\n - **Note**: can be used only in CLI version\n\nConvert Parameters\n~~~~~~~~~~~~~~~~~~\n\n- ``convert_model_language`` :raw-html:`🔗︎`, default = ``\"\"``, type = string\n\n - used only in ``convert_model`` task\n\n - only ``cpp`` is supported yet; for conversion model to other languages consider using `m2cgen `__ utility\n\n - if ``convert_model_language`` is set and ``task=train``, the model will be also converted\n\n - **Note**: can be used only in CLI version\n\n- ``convert_model`` :raw-html:`🔗︎`, default = ``gbdt_prediction.cpp``, type = string, aliases: ``convert_model_file``\n\n - used only in ``convert_model`` task\n\n - output filename of converted model\n\n - **Note**: can be used only in CLI version\n\nObjective Parameters\n--------------------\n\n- ``objective_seed`` :raw-html:`🔗︎`, default = ``5``, type = int\n\n - used only in ``rank_xendcg`` objective\n\n - random seed for objectives, if random process is needed\n\n- ``num_class`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``num_classes``, constraints: ``num_class > 0``\n\n - used only in ``multi-class`` classification application\n\n- ``is_unbalance`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``unbalance``, ``unbalanced_sets``\n\n - used only in ``binary`` and ``multiclassova`` applications\n\n - set this to ``true`` if training data are unbalanced\n\n - **Note**: while enabling this should increase the overall performance metric of your model, it will also result in poor estimates of the individual class probabilities\n\n - **Note**: this parameter cannot be used at the same time with ``scale_pos_weight``, choose only **one** of them\n\n- ``scale_pos_weight`` :raw-html:`🔗︎`, default = ``1.0``, type = double, constraints: ``scale_pos_weight > 0.0``\n\n - used only in ``binary`` and ``multiclassova`` applications\n\n - weight of labels with positive class\n\n - **Note**: while enabling this should increase the overall performance metric of your model, it will also result in poor estimates of the individual class probabilities\n\n - **Note**: this parameter cannot be used at the same time with ``is_unbalance``, choose only **one** of them\n\n- ``sigmoid`` :raw-html:`🔗︎`, default = ``1.0``, type = double, constraints: ``sigmoid > 0.0``\n\n - used only in ``binary`` and ``multiclassova`` classification and in ``lambdarank`` applications\n\n - parameter for the sigmoid function\n\n- ``boost_from_average`` :raw-html:`🔗︎`, default = ``true``, type = bool\n\n - used only in ``regression``, ``binary``, ``multiclassova`` and ``cross-entropy`` applications\n\n - adjusts initial score to the mean of labels for faster convergence\n\n- ``reg_sqrt`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - used only in ``regression`` application\n\n - used to fit ``sqrt(label)`` instead of original values and prediction result will be also automatically converted to ``prediction^2``\n\n - might be useful in case of large-range labels\n\n- ``alpha`` :raw-html:`🔗︎`, default = ``0.9``, type = double, constraints: ``alpha > 0.0``\n\n - used only in ``huber`` and ``quantile`` ``regression`` applications\n\n - parameter for `Huber loss `__ and `Quantile regression `__\n\n- ``fair_c`` :raw-html:`🔗︎`, default = ``1.0``, type = double, constraints: ``fair_c > 0.0``\n\n - used only in ``fair`` ``regression`` application\n\n - parameter for `Fair loss `__\n\n- ``poisson_max_delta_step`` :raw-html:`🔗︎`, default = ``0.7``, type = double, constraints: ``poisson_max_delta_step > 0.0``\n\n - used only in ``poisson`` ``regression`` application\n\n - parameter for `Poisson regression `__ to safeguard optimization\n\n- ``tweedie_variance_power`` :raw-html:`🔗︎`, default = ``1.5``, type = double, constraints: ``1.0 <= tweedie_variance_power < 2.0``\n\n - used only in ``tweedie`` ``regression`` application\n\n - used to control the variance of the tweedie distribution\n\n - set this closer to ``2`` to shift towards a **Gamma** distribution\n\n - set this closer to ``1`` to shift towards a **Poisson** distribution\n\n- ``lambdarank_truncation_level`` :raw-html:`🔗︎`, default = ``30``, type = int, constraints: ``lambdarank_truncation_level > 0``\n\n - used only in ``lambdarank`` application\n\n - controls the number of top-results to focus on during training, refer to \"truncation level\" in the Sec. 3 of `LambdaMART paper `__\n\n - this parameter is closely related to the desirable cutoff ``k`` in the metric **NDCG@k** that we aim at optimizing the ranker for. The optimal setting for this parameter is likely to be slightly higher than ``k`` (e.g., ``k + 3``) to include more pairs of documents to train on, but perhaps not too high to avoid deviating too much from the desired target metric **NDCG@k**\n\n- ``lambdarank_norm`` :raw-html:`🔗︎`, default = ``true``, type = bool\n\n - used only in ``lambdarank`` application\n\n - set this to ``true`` to normalize the lambdas for different queries, and improve the performance for unbalanced data\n\n - set this to ``false`` to enforce the original lambdarank algorithm\n\n- ``label_gain`` :raw-html:`🔗︎`, default = ``0,1,3,7,15,31,63,...,2^30-1``, type = multi-double\n\n - used only in ``lambdarank`` application\n\n - relevant gain for labels. For example, the gain of label ``2`` is ``3`` in case of default label gains\n\n - separate by ``,``\n\n- ``lambdarank_position_bias_regularization`` :raw-html:`🔗︎`, default = ``0.0``, type = double, constraints: ``lambdarank_position_bias_regularization >= 0.0``\n\n - used only in ``lambdarank`` application when positional information is provided and position bias is modeled\n\n - larger values reduce the inferred position bias factors\n\n - *New in version 4.1.0*\n\nMetric Parameters\n-----------------\n\n- ``metric`` :raw-html:`🔗︎`, default = ``\"\"``, type = multi-enum, aliases: ``metrics``, ``metric_types``\n\n - metric(s) to be evaluated on the evaluation set(s)\n\n - ``\"\"`` (empty string or not specified) means that metric corresponding to specified ``objective`` will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added)\n\n - ``\"None\"`` (string, **not** a ``None`` value) means that no metric will be registered, aliases: ``na``, ``null``, ``custom``\n\n - ``l1``, absolute loss, aliases: ``mean_absolute_error``, ``mae``, ``regression_l1``\n\n - ``l2``, square loss, aliases: ``mean_squared_error``, ``mse``, ``regression_l2``, ``regression``\n\n - ``rmse``, root square loss, aliases: ``root_mean_squared_error``, ``l2_root``\n\n - ``quantile``, `Quantile regression `__\n\n - ``mape``, `MAPE loss `__, aliases: ``mean_absolute_percentage_error``\n\n - ``huber``, `Huber loss `__\n\n - ``fair``, `Fair loss `__\n\n - ``poisson``, negative log-likelihood for `Poisson regression `__\n\n - ``gamma``, negative log-likelihood for **Gamma** regression\n\n - ``gamma_deviance``, residual deviance for **Gamma** regression\n\n - ``tweedie``, negative log-likelihood for **Tweedie** regression\n\n - ``ndcg``, `NDCG `__, aliases: ``lambdarank``, ``rank_xendcg``, ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``\n\n - ``map``, `MAP `__, aliases: ``mean_average_precision``\n\n - ``auc``, `AUC `__\n\n - ``average_precision``, `average precision score `__\n\n - ``r2``, `R-squared `__\n\n - ``binary_logloss``, `log loss `__, aliases: ``binary``\n\n - ``binary_error``, for one sample: ``0`` for correct classification, ``1`` for error classification\n\n - ``auc_mu``, `AUC-mu `__\n\n - ``multi_logloss``, log loss for multi-class classification, aliases: ``multiclass``, ``softmax``, ``multiclassova``, ``multiclass_ova``, ``ova``, ``ovr``\n\n - ``multi_error``, error rate for multi-class classification\n\n - ``cross_entropy``, cross-entropy (with optional linear weights), aliases: ``xentropy``\n\n - ``cross_entropy_lambda``, \"intensity-weighted\" cross-entropy, aliases: ``xentlambda``\n\n - ``kullback_leibler``, `Kullback-Leibler divergence `__, aliases: ``kldiv``\n\n - support multiple metrics, separated by ``,``\n\n- ``metric_freq`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``output_freq``, constraints: ``metric_freq > 0``\n\n - frequency for metric output\n\n - **Note**: can be used only in CLI version\n\n- ``is_provide_training_metric`` :raw-html:`🔗︎`, default = ``false``, type = bool, aliases: ``training_metric``, ``is_training_metric``, ``train_metric``\n\n - set this to ``true`` to output metric result over training dataset\n\n - **Note**: can be used only in CLI version\n\n- ``eval_at`` :raw-html:`🔗︎`, default = ``1,2,3,4,5``, type = multi-int, aliases: ``ndcg_eval_at``, ``ndcg_at``, ``map_eval_at``, ``map_at``\n\n - used only with ``ndcg`` and ``map`` metrics\n\n - `NDCG `__ and `MAP `__ evaluation positions, separated by ``,``\n\n- ``multi_error_top_k`` :raw-html:`🔗︎`, default = ``1``, type = int, constraints: ``multi_error_top_k > 0``\n\n - used only with ``multi_error`` metric\n\n - threshold for top-k multi-error metric\n\n - the error on each sample is ``0`` if the true class is among the top ``multi_error_top_k`` predictions, and ``1`` otherwise\n\n - more precisely, the error on a sample is ``0`` if there are at least ``num_classes - multi_error_top_k`` predictions strictly less than the prediction on the true class\n\n - when ``multi_error_top_k=1`` this is equivalent to the usual multi-error metric\n\n- ``auc_mu_weights`` :raw-html:`🔗︎`, default = ``None``, type = multi-double\n\n - used only with ``auc_mu`` metric\n\n - list representing flattened matrix (in row-major order) giving loss weights for classification errors\n\n - list should have ``n * n`` elements, where ``n`` is the number of classes\n\n - the matrix co-ordinate ``[i, j]`` should correspond to the ``i * n + j``-th element of the list\n\n - if not specified, will use equal weights for all classes\n\nNetwork Parameters\n------------------\n\n- ``num_machines`` :raw-html:`🔗︎`, default = ``1``, type = int, aliases: ``num_machine``, constraints: ``num_machines > 0``\n\n - the number of machines for distributed learning application\n\n - this parameter is needed to be set in both **socket** and **MPI** versions\n\n- ``local_listen_port`` :raw-html:`🔗︎`, default = ``12400 (random for Dask-package)``, type = int, aliases: ``local_port``, ``port``, constraints: ``local_listen_port > 0``\n\n - TCP listen port for local machines\n\n - **Note**: don't forget to allow this port in firewall settings before training\n\n- ``time_out`` :raw-html:`🔗︎`, default = ``120``, type = int, constraints: ``time_out > 0``\n\n - socket time-out in minutes\n\n- ``machine_list_filename`` :raw-html:`🔗︎`, default = ``\"\"``, type = string, aliases: ``machine_list_file``, ``machine_list``, ``mlist``\n\n - path of file that lists machines for this distributed learning application\n\n - each line contains one IP and one port for one machine. The format is ``ip port`` (space as a separator)\n\n - **Note**: can be used only in CLI version\n\n- ``machines`` :raw-html:`🔗︎`, default = ``\"\"``, type = string, aliases: ``workers``, ``nodes``\n\n - list of machines in the following format: ``ip1:port1,ip2:port2``\n\nGPU Parameters\n--------------\n\n- ``gpu_platform_id`` :raw-html:`🔗︎`, default = ``-1``, type = int\n\n - used only with ``gpu`` device type\n\n - OpenCL platform ID. Usually each GPU vendor exposes one OpenCL platform\n\n - ``-1`` means the system-wide default platform\n\n - **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details\n\n- ``gpu_device_id`` :raw-html:`🔗︎`, default = ``-1``, type = int\n\n - OpenCL device ID in the specified platform or CUDA device ID. Each GPU in the selected platform has a unique device ID\n\n - ``-1`` means the default device in the selected platform\n\n - in multi-GPU case (``num_gpu>1``) means ID of the master GPU\n\n - **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details\n\n- ``gpu_device_id_list`` :raw-html:`🔗︎`, default = ``\"\"``, type = string\n\n - list of CUDA device IDs\n\n - **Note**: can be used only in CUDA implementation (``device_type=\"cuda\"``) and when ``num_gpu>1``\n\n - if empty, the devices with the smallest IDs will be used\n\n- ``gpu_use_dp`` :raw-html:`🔗︎`, default = ``false``, type = bool\n\n - set this to ``true`` to use double precision math on GPU (by default single precision is used)\n\n - **Note**: can be used only in OpenCL implementation (``device_type=\"gpu\"``), in CUDA implementation only double precision is currently supported\n\n- ``num_gpu`` :raw-html:`🔗︎`, default = ``1``, type = int, constraints: ``num_gpu > 0``\n\n - number of GPUs used for training in this node\n\n - **Note**: can be used only in CUDA implementation (``device_type=\"cuda\"``)\n\n - if ``0``, only 1 GPU will be used\n\n - used in both single-machine and distributed learning applications\n\n - in distributed learning application, each machine can use different number of GPUs\n\n.. end params list\n\nOthers\n------\n\nContinued Training with Input Score\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nLightGBM supports continued training with initial scores.\nIt uses an additional file to store these initial scores, like the following:\n\n::\n\n 0.5\n -0.1\n 0.9\n ...\n\nIt means the initial score of the first data row is ``0.5``, second is ``-0.1``, and so on.\nThe initial score file corresponds with data file line by line, and has per score per line.\n\nIf the name of data file is ``train.txt``, the initial score file should be named as ``train.txt.init`` and placed in the same folder as the data file.\nIn this case, LightGBM will auto load initial score file if it exists.\n\nIf binary data files exist for raw data file ``train.txt``, for example in the name ``train.txt.bin``, then the initial score file should be named as ``train.txt.bin.init``.\n\nWeight Data\n~~~~~~~~~~~\n\nLightGBM supports weighted training.\nIt uses an additional file to store weight data, like the following:\n\n::\n\n 1.0\n 0.5\n 0.8\n ...\n\nIt means the weight of the first data row is ``1.0``, second is ``0.5``, and so on. Weights should be non-negative.\n\nThe weight file corresponds with data file line by line, and has per weight per line.\n\nAnd if the name of data file is ``train.txt``, the weight file should be named as ``train.txt.weight`` and placed in the same folder as the data file.\nIn this case, LightGBM will load the weight file automatically if it exists.\n\nAlso, you can include weight column in your data file.\nPlease refer to the ``weight_column`` `parameter <#weight_column>`__ in above.\n\nQuery Data\n~~~~~~~~~~\n\nFor learning to rank, it needs query information for training data.\n\nLightGBM uses an additional file to store query data, like the following:\n\n::\n\n 27\n 18\n 67\n ...\n\nFor wrapper libraries like in Python and R, this information can also be provided as an array-like via the Dataset parameter ``group``.\n\n::\n\n [27, 18, 67, ...]\n\nFor example, if you have a 112-document dataset with ``group = [27, 18, 67]``, that means that you have 3 groups, where the first 27 records are in the first group, records 28-45 are in the second group, and records 46-112 are in the third group.\n\n**Note**: data should be ordered by the query.\n\nIf the name of data file is ``train.txt``, the query file should be named as ``train.txt.query`` and placed in the same folder as the data file.\nIn this case, LightGBM will load the query file automatically if it exists.\n\nAlso, you can include query/group id column in your data file.\nPlease refer to the ``group_column`` `parameter <#group_column>`__ in above.\n" }, { "path": "docs/Python-API.rst", "content": "Python API\n==========\n\n.. currentmodule:: lightgbm\n\nData Structure API\n------------------\n\n.. autosummary::\n :toctree: pythonapi/\n\n Dataset\n Booster\n CVBooster\n Sequence\n\nTraining API\n------------\n\n.. autosummary::\n :toctree: pythonapi/\n\n train\n cv\n\nScikit-learn API\n----------------\n\n.. autosummary::\n :toctree: pythonapi/\n\n LGBMModel\n LGBMClassifier\n LGBMRegressor\n LGBMRanker\n\nDask API\n--------\n\n.. versionadded:: 3.2.0\n\n.. autosummary::\n :toctree: pythonapi/\n\n DaskLGBMClassifier\n DaskLGBMRegressor\n DaskLGBMRanker\n\nCallbacks\n---------\n\n.. autosummary::\n :toctree: pythonapi/\n\n early_stopping\n log_evaluation\n record_evaluation\n reset_parameter\n\nPlotting\n--------\n\n.. autosummary::\n :toctree: pythonapi/\n\n plot_importance\n plot_split_value_histogram\n plot_metric\n plot_tree\n create_tree_digraph\n\nUtilities\n---------\n\n.. autosummary::\n :toctree: pythonapi/\n\n register_logger\n" }, { "path": "docs/Python-Intro.rst", "content": "Python-package Introduction\n===========================\n\nThis document gives a basic walk-through of LightGBM Python-package.\n\n**List of other helpful links**\n\n- `Python Examples `__\n\n- `Python API <./Python-API.rst>`__\n\n- `Parameters Tuning <./Parameters-Tuning.rst>`__\n\nInstall\n-------\n\nThe preferred way to install LightGBM is via pip:\n\n::\n\n pip install lightgbm\n\nRefer to `Python-package`_ folder for the detailed installation guide.\n\nTo verify your installation, try to ``import lightgbm`` in Python:\n\n::\n\n import lightgbm as lgb\n\nData Interface\n--------------\n\nThe LightGBM Python module can load data from:\n\n- LibSVM (zero-based) / TSV / CSV format text file\n\n- NumPy 2D array(s), pandas DataFrame, pyarrow Table, SciPy sparse matrix\n\n- LightGBM binary file\n\n- LightGBM ``Sequence`` object(s)\n\nThe data is stored in a ``Dataset`` object.\n\nMany of the examples in this page use functionality from ``numpy``. To run the examples, be sure to import ``numpy`` in your session.\n\n.. code:: python\n\n import numpy as np\n\n**To load a LibSVM (zero-based) text file or a LightGBM binary file into Dataset:**\n\n.. code:: python\n\n train_data = lgb.Dataset('train.svm.bin')\n\n**To load a numpy array into Dataset:**\n\n.. code:: python\n\n rng = np.random.default_rng()\n data = rng.uniform(size=(500, 10)) # 500 entities, each contains 10 features\n label = rng.integers(low=0, high=2, size=(500, )) # binary target\n train_data = lgb.Dataset(data, label=label)\n\n**To load a scipy.sparse.csr\\_matrix array into Dataset:**\n\n.. code:: python\n\n import scipy\n csr = scipy.sparse.csr_matrix((dat, (row, col)))\n train_data = lgb.Dataset(csr)\n\n**Load from Sequence objects:**\n\nWe can implement ``Sequence`` interface to read binary files. The following example shows reading HDF5 file with ``h5py``.\n\n.. code:: python\n\n import h5py\n\n class HDFSequence(lgb.Sequence):\n def __init__(self, hdf_dataset, batch_size):\n self.data = hdf_dataset\n self.batch_size = batch_size\n\n def __getitem__(self, idx):\n return self.data[idx]\n\n def __len__(self):\n return len(self.data)\n\n f = h5py.File('train.hdf5', 'r')\n train_data = lgb.Dataset(HDFSequence(f['X'], 8192), label=f['Y'][:])\n\nFeatures of using ``Sequence`` interface:\n\n- Data sampling uses random access, thus does not go through the whole dataset\n- Reading data in batch, thus saves memory when constructing ``Dataset`` object\n- Supports creating ``Dataset`` from multiple data files\n\nPlease refer to ``Sequence`` `API doc <./Python-API.rst#data-structure-api>`__.\n\n`dataset_from_multi_hdf5.py `__ is a detailed example.\n\n**Saving Dataset into a LightGBM binary file will make loading faster:**\n\n.. code:: python\n\n train_data = lgb.Dataset('train.svm.txt')\n train_data.save_binary('train.bin')\n\n**Create validation data:**\n\n.. code:: python\n\n validation_data = train_data.create_valid('validation.svm')\n\nor\n\n.. code:: python\n\n validation_data = lgb.Dataset('validation.svm', reference=train_data)\n\nIn LightGBM, the validation data should be aligned with training data.\n\n**Specific feature names and categorical features:**\n\n.. code:: python\n\n train_data = lgb.Dataset(data, label=label, feature_name=['c1', 'c2', 'c3'], categorical_feature=['c3'])\n\nLightGBM can use categorical features as input directly.\nIt doesn't need to convert to one-hot encoding, and is much faster than one-hot encoding (about 8x speed-up).\n\n**Note**: You should convert your categorical features to ``int`` type before you construct ``Dataset``.\n\n**Weights can be set when needed:**\n\n.. code:: python\n\n rng = np.random.default_rng()\n w = rng.uniform(size=(500, ))\n train_data = lgb.Dataset(data, label=label, weight=w)\n\nor\n\n.. code:: python\n\n train_data = lgb.Dataset(data, label=label)\n rng = np.random.default_rng()\n w = rng.uniform(size=(500, ))\n train_data.set_weight(w)\n\nAnd you can use ``Dataset.set_init_score()`` to set initial score, and ``Dataset.set_group()`` to set group/query data for ranking tasks.\n\n**Memory efficient usage:**\n\nThe ``Dataset`` object in LightGBM is very memory-efficient, it only needs to save discrete bins.\nHowever, Numpy/Array/Pandas object is memory expensive.\nIf you are concerned about your memory consumption, you can save memory by:\n\n1. Set ``free_raw_data=True`` (default is ``True``) when constructing the ``Dataset``\n\n2. Explicitly set ``raw_data=None`` after the ``Dataset`` has been constructed\n\n3. Call ``gc``\n\nSetting Parameters\n------------------\n\nLightGBM can use a dictionary to set `Parameters <./Parameters.rst>`__.\nFor instance:\n\n- Booster parameters:\n\n .. code:: python\n\n param = {'num_leaves': 31, 'objective': 'binary'}\n param['metric'] = 'auc'\n\n- You can also specify multiple eval metrics:\n\n .. code:: python\n\n param['metric'] = ['auc', 'binary_logloss']\n\nTraining\n--------\n\nTraining a model requires a parameter list and data set:\n\n.. code:: python\n\n num_round = 10\n bst = lgb.train(param, train_data, num_round, valid_sets=[validation_data])\n\nAfter training, the model can be saved:\n\n.. code:: python\n\n bst.save_model('model.txt')\n\nThe trained model can also be dumped to JSON format:\n\n.. code:: python\n\n json_model = bst.dump_model()\n\nA saved model can be loaded:\n\n.. code:: python\n\n bst = lgb.Booster(model_file='model.txt') # init model\n\nCV\n--\n\nTraining with 5-fold CV:\n\n.. code:: python\n\n lgb.cv(param, train_data, num_round, nfold=5)\n\nEarly Stopping\n--------------\n\nIf you have a validation set, you can use early stopping to find the optimal number of boosting rounds.\nEarly stopping requires at least one set in ``valid_sets``. If there is more than one, it will use all of them except the training data:\n\n.. code:: python\n\n bst = lgb.train(param, train_data, num_round, valid_sets=valid_sets, callbacks=[lgb.early_stopping(stopping_rounds=5)])\n bst.save_model('model.txt', num_iteration=bst.best_iteration)\n\nThe model will train until the validation score stops improving.\nValidation score needs to improve at least every ``stopping_rounds`` to continue training.\n\nThe index of iteration that has the best performance will be saved in the ``best_iteration`` field if early stopping logic is enabled by setting ``early_stopping`` callback.\nNote that ``train()`` will return a model from the best iteration.\n\nThis works with both metrics to minimize (L2, log loss, etc.) and to maximize (NDCG, AUC, etc.).\nNote that if you specify more than one evaluation metric, all of them will be used for early stopping.\nHowever, you can change this behavior and make LightGBM check only the first metric for early stopping by passing ``first_metric_only=True`` in ``early_stopping`` callback constructor.\n\nPrediction\n----------\n\nA model that has been trained or loaded can perform predictions on datasets:\n\n.. code:: python\n\n # 7 entities, each contains 10 features\n rng = np.random.default_rng()\n data = rng.uniform(size=(7, 10))\n ypred = bst.predict(data)\n\nIf early stopping is enabled during training, you can get predictions from the best iteration with ``bst.best_iteration``:\n\n.. code:: python\n\n ypred = bst.predict(data, num_iteration=bst.best_iteration)\n\n.. _Python-package: https://github.com/lightgbm-org/LightGBM/tree/master/python-package\n" }, { "path": "docs/Quick-Start.rst", "content": "Quick Start\n===========\n\nThis is a quick start guide for LightGBM CLI version.\n\nFollow the `Installation Guide <./Installation-Guide.rst>`__ to install LightGBM first.\n\n**List of other helpful links**\n\n- `Parameters <./Parameters.rst>`__\n\n- `Parameters Tuning <./Parameters-Tuning.rst>`__\n\n- `Python-package Quick Start <./Python-Intro.rst>`__\n\n- `Python API <./Python-API.rst>`__\n\nTraining Data Format\n--------------------\n\nLightGBM supports input data files with `CSV`_, `TSV`_ and `LibSVM`_ (zero-based) formats.\n\nFiles could be both with and without `headers <./Parameters.rst#header>`__.\n\n`Label column <./Parameters.rst#label_column>`__ could be specified both by index and by name.\n\nSome columns could be `ignored <./Parameters.rst#ignore_column>`__.\n\nCategorical Feature Support\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nLightGBM can use categorical features directly (without one-hot encoding).\nThe experiment on `Expo data`_ shows about 8x speed-up compared with one-hot encoding.\n\nFor the setting details, please refer to the ``categorical_feature`` `parameter <./Parameters.rst#categorical_feature>`__.\n\nWeight and Query/Group Data\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nLightGBM also supports weighted training, it needs an additional `weight data <./Parameters.rst#weight-data>`__.\nAnd it needs an additional `query data <./Parameters.rst#query-data>`_ for ranking task.\n\nAlso, `weight <./Parameters.rst#weight_column>`__ and `query <./Parameters.rst#group_column>`__ data could be specified as columns in training data in the same manner as label.\n\nParameters Quick Look\n---------------------\n\nThe parameters format is ``key1=value1 key2=value2 ...``.\n\nParameters can be set both in config file and command line.\nIf one parameter appears in both command line and config file, LightGBM will use the parameter from the command line.\n\nThe most important parameters which new users should take a look at are located into `Core Parameters <./Parameters.rst#core-parameters>`__\nand the top of `Learning Control Parameters <./Parameters.rst#learning-control-parameters>`__\nsections of the full detailed list of `LightGBM's parameters <./Parameters.rst>`__.\n\nRun LightGBM\n------------\n\n::\n\n lightgbm config=your_config_file other_args ...\n\nParameters can be set both in the config file and command line, and the parameters in command line have higher priority than in the config file.\nFor example, the following command line will keep ``num_trees=10`` and ignore the same parameter in the config file.\n\n::\n\n lightgbm config=train.conf num_trees=10\n\nExamples\n--------\n\n- `Binary Classification `__\n\n- `Regression `__\n\n- `Lambdarank `__\n\n- `Distributed Learning `__\n\n.. _CSV: https://en.wikipedia.org/wiki/Comma-separated_values\n\n.. _TSV: https://en.wikipedia.org/wiki/Tab-separated_values\n\n.. _LibSVM: https://www.csie.ntu.edu.tw/~cjlin/libsvm/\n\n.. _Expo data: https://community.amstat.org/jointscsg-section/dataexpo/dataexpo2009\n" }, { "path": "docs/README.rst", "content": "Documentation\n=============\n\nDocumentation for LightGBM is generated using `Sphinx `__\nand `Breathe `__, which works on top of `Doxygen `__ output.\n\nList of parameters and their descriptions in `Parameters.rst <./Parameters.rst>`__\nis generated automatically from comments in `config file `__\nby `this script `__.\n\nAfter each commit on ``master``, documentation is updated and published to `Read the Docs `__.\n\nBuild\n-----\n\nIt is not necessary to re-build this documentation while modifying LightGBM's source code.\nThe HTML files generated using ``Sphinx`` are not checked into source control.\nHowever, you may want to build them locally during development to test changes.\n\nDocker\n^^^^^^\n\nThe most reliable way to build the documentation locally is with Docker, using `the same images Read the Docs uses `_.\n\nRun the following from the root of this repository to pull the relevant image and run a container locally.\n\n.. code:: sh\n\n docker run \\\n --rm \\\n --user=0 \\\n -v $(pwd):/opt/LightGBM \\\n --env C_API=true \\\n --env CONDA=/opt/miniforge \\\n --env READTHEDOCS=true \\\n --workdir=/opt/LightGBM/docs \\\n --entrypoint=\"\" \\\n readthedocs/build:ubuntu-24.04-2024.06.17 \\\n /bin/bash build-docs.sh\n\nWhen that code completes, open ``docs/_build/html/index.html`` in your browser.\n\n.. note::\n\n The navigation in these locally-built docs does not link to the local copy of the R documentation. To view the local version of the R docs, open ``docs/_build/html/R/index.html`` in your browser.\n\nWithout Docker\n^^^^^^^^^^^^^^\n\nYou can build the documentation locally without Docker. Just install Doxygen and run in ``docs`` folder\n\n.. code:: sh\n\n pip install breathe sphinx 'sphinx_rtd_theme>=0.5'\n make html\n\nNote that this will not build the R documentation.\nConsider using common R utilities for documentation generation, if you need it.\nOr use the Docker-based approach described above to build the R documentation locally.\n\nOptionally, you may also install ``scikit-learn`` and get richer documentation for the classes in ``Scikit-learn API``.\n\nIf you faced any problems with Doxygen installation or you simply do not need documentation for C code, it is possible to build the documentation without it:\n\n.. code:: sh\n\n pip install sphinx 'sphinx_rtd_theme>=0.5'\n export C_API=NO || set C_API=NO\n make html\n" }, { "path": "docs/_static/js/script.js", "content": "$(() => {\n /* Use wider container for the page content */\n $(\".wy-nav-content\").each(function () {\n this.style.setProperty(\"max-width\", \"none\", \"important\");\n });\n\n /* List each class property item on a new line\n https://github.com/lightgbm-org/LightGBM/issues/5073 */\n if (window.location.pathname.toLocaleLowerCase().indexOf(\"pythonapi\") !== -1) {\n $(\".py.property\").each(function () {\n this.style.setProperty(\"display\", \"inline\", \"important\");\n });\n }\n\n /* Collapse specified sections in the installation guide */\n if (window.location.pathname.toLocaleLowerCase().indexOf(\"installation-guide\") !== -1) {\n $(\n '',\n ).appendTo(\"body\");\n const collapsible = [\n \"#build-threadless-version-not-recommended\",\n \"#build-mpi-version\",\n \"#build-gpu-version\",\n \"#build-cuda-version\",\n \"#build-rocm-version\",\n \"#build-java-wrapper\",\n \"#build-python-package\",\n \"#build-r-package\",\n \"#build-c-unit-tests\",\n ];\n $.each(collapsible, (_, val) => {\n const header = `${val} > :header:first`;\n const content = `${val} :not(:header:first)`;\n $(header).addClass(\"closed\");\n $(content).hide();\n $(header).click(() => {\n $(header).toggleClass(\"closed opened\");\n $(content).slideToggle(0);\n });\n });\n /* Uncollapse parent sections when nested section is specified in the URL or before navigate to it from navbar */\n function uncollapse(section) {\n section.parents().each((_, val) => {\n $(val).children(\".closed\").click();\n });\n }\n uncollapse($(window.location.hash));\n $(\".wy-menu.wy-menu-vertical li a.reference.internal\").click(function () {\n uncollapse($($(this).attr(\"href\")));\n });\n }\n});\n" }, { "path": "docs/build-docs.sh", "content": "#!/bin/bash\n\nset -e -E -u -o pipefail\n\nrm -f ./_FIRST_RUN.flag\n\nexport PATH=\"${CONDA}/bin:${PATH}\"\n\ncurl \\\n -sL \\\n -o \"${HOME}/miniforge.sh\" \\\n https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh\n\n/bin/bash \"${HOME}/miniforge.sh\" -b -p \"${CONDA}\"\n\nconda config --set always_yes yes --set changeps1 no\nconda update -q -y conda\n\nconda env create \\\n --name docs-env \\\n --file env.yml || exit 1\n\n# shellcheck disable=SC1091\nsource activate docs-env\nmake clean html || exit 1\n\necho \"Done building docs. Open docs/_build/html/index.html in a web browser to view them.\"\n" }, { "path": "docs/conf.py", "content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# LightGBM documentation build configuration file, created by\n# sphinx-quickstart on Thu May 4 14:30:58 2017.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute.\n\"\"\"Sphinx configuration file.\"\"\"\n\nimport datetime\nimport os\nimport sys\nfrom pathlib import Path\nfrom re import compile\nfrom shutil import copytree\nfrom subprocess import PIPE, Popen\nfrom typing import Any, List\n\nimport sphinx\nfrom docutils.nodes import reference\nfrom docutils.parsers.rst import Directive\nfrom docutils.transforms import Transform\nfrom sphinx.application import Sphinx\nfrom sphinx.errors import VersionRequirementError\n\nCURR_PATH = Path(__file__).absolute().parent\nLIB_PATH = CURR_PATH.parent / \"python-package\"\nsys.path.insert(0, str(LIB_PATH))\n\nINTERNAL_REF_REGEX = compile(r\"(?P\\.\\/.+)(?P\\.rst)(?P$|#)\")\nRTD_R_REF_REGEX = compile(r\"(?Phttps://.+/)(?Platest)(?P/R/reference/)\")\n\n\nclass InternalRefTransform(Transform):\n \"\"\"Replaces '.rst' with '.html' in all internal links like './[Something].rst[#anchor]'.\"\"\"\n\n default_priority = 210\n \"\"\"Numerical priority of this transform, 0 through 999.\"\"\"\n\n def apply(self, **kwargs: Any) -> None:\n \"\"\"Apply the transform to the document tree.\"\"\"\n for section in self.document.traverse(reference):\n if section.get(\"refuri\") is not None:\n section[\"refuri\"] = INTERNAL_REF_REGEX.sub(r\"\\g.html\\g\", section[\"refuri\"])\n\n\nclass IgnoredDirective(Directive):\n \"\"\"Stub for unknown directives.\"\"\"\n\n has_content = True\n\n def run(self) -> List:\n \"\"\"Do nothing.\"\"\"\n return []\n\n\n# -- General configuration ------------------------------------------------\n\nos.environ[\"LIGHTGBM_BUILD_DOC\"] = \"True\"\nC_API = os.environ.get(\"C_API\", \"\").lower().strip() != \"no\"\nRTD = bool(os.environ.get(\"READTHEDOCS\", \"\"))\nRTD_VERSION = os.environ.get(\"READTHEDOCS_VERSION\", \"stable\")\n\n# If your documentation needs a minimal Sphinx version, state it here.\nneeds_sphinx = \"2.1.0\" # Due to sphinx.ext.napoleon, autodoc_typehints\nif needs_sphinx > sphinx.__version__:\n message = f\"This project needs at least Sphinx v{needs_sphinx}\"\n raise VersionRequirementError(message)\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n \"sphinx.ext.autodoc\",\n \"sphinx.ext.autosummary\",\n \"sphinx.ext.todo\",\n \"sphinx.ext.viewcode\",\n \"sphinx.ext.napoleon\",\n \"sphinx.ext.intersphinx\",\n]\n\nautodoc_default_flags = [\"members\", \"inherited-members\", \"show-inheritance\"]\nautodoc_default_options = {\n \"members\": True,\n \"inherited-members\": True,\n \"show-inheritance\": True,\n}\n# mock out modules\nautodoc_mock_imports = [\n \"dask\",\n \"dask.distributed\",\n \"graphviz\",\n \"matplotlib\",\n \"numpy\",\n \"pandas\",\n \"scipy\",\n \"scipy.sparse\",\n]\ntry:\n import sklearn # noqa: F401\nexcept ImportError:\n autodoc_mock_imports.append(\"sklearn\")\n# hide type hints in API docs\nautodoc_typehints = \"none\"\n\n# Generate autosummary pages. Output should be set with: `:toctree: pythonapi/`\nautosummary_generate = [\"Python-API.rst\"]\n\n# Only the class' docstring is inserted.\nautoclass_content = \"class\"\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\n# The master toctree document.\nmaster_doc = \"index\"\n\n# General information about the project.\nproject = \"LightGBM\"\ncopyright = f\"{datetime.datetime.now().year}, Microsoft Corporation\"\nauthor = \"Microsoft Corporation\"\n\n# The name of an image file (relative to this directory) to place at the top\n# of the sidebar.\nhtml_logo = str(CURR_PATH / \"logo\" / \"LightGBM_logo_grey_text.svg\")\n\n# The name of an image file (relative to this directory) to use as a favicon of\n# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32\n# pixels large.\nhtml_favicon = str(CURR_PATH / \"_static\" / \"images\" / \"favicon.ico\")\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n# The short X.Y version.\nversion = (CURR_PATH.parent / \"VERSION.txt\").read_text(encoding=\"utf-8\").strip().replace(\"rc\", \"-rc\")\n# The full version, including alpha/beta/rc tags.\nrelease = version\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = \"en\"\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = [\"_build\", \"Thumbs.db\", \".DS_Store\"]\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = \"default\"\n\n# -- Configuration for C API docs generation ------------------------------\n\nif C_API:\n extensions.extend(\n [\n \"breathe\",\n ]\n )\n breathe_projects = {\"LightGBM\": str(CURR_PATH / \"doxyoutput\" / \"xml\")}\n breathe_default_project = \"LightGBM\"\n breathe_domain_by_extension = {\n \"h\": \"c\",\n }\n breathe_show_define_initializer = True\n c_id_attributes = [\"LIGHTGBM_C_EXPORT\"]\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\nhtml_theme = \"sphinx_rtd_theme\"\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\nhtml_theme_options = {\n \"includehidden\": False,\n \"logo_only\": True,\n}\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"_static\"]\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = \"LightGBMdoc\"\n\n# -- Options for LaTeX output ---------------------------------------------\n\n# The name of an image file (relative to this directory) to place at the top of\n# the title page.\nlatex_logo = str(CURR_PATH / \"logo\" / \"LightGBM_logo_black_text_small.png\")\n\n# intersphinx configuration\nintersphinx_mapping = {\n \"sklearn\": (\"https://scikit-learn.org/stable/\", None),\n}\n\n\ndef generate_doxygen_xml(app: Sphinx) -> None:\n \"\"\"Generate XML documentation for C API by Doxygen.\n\n Parameters\n ----------\n app : sphinx.application.Sphinx\n The application object representing the Sphinx process.\n \"\"\"\n doxygen_args = [\n f\"INPUT={CURR_PATH.parent / 'include' / 'LightGBM' / 'c_api.h'}\",\n f\"OUTPUT_DIRECTORY={CURR_PATH / 'doxyoutput'}\",\n \"GENERATE_HTML=NO\",\n \"GENERATE_LATEX=NO\",\n \"GENERATE_XML=YES\",\n \"XML_OUTPUT=xml\",\n \"XML_PROGRAMLISTING=YES\",\n r'ALIASES=\"rst=\\verbatim embed:rst:leading-asterisk\"',\n r'ALIASES+=\"endrst=\\endverbatim\"',\n \"ENABLE_PREPROCESSING=YES\",\n \"MACRO_EXPANSION=YES\",\n \"EXPAND_ONLY_PREDEF=NO\",\n \"SKIP_FUNCTION_MACROS=NO\",\n \"PREDEFINED=__cplusplus\",\n \"SORT_BRIEF_DOCS=YES\",\n \"WARN_AS_ERROR=YES\",\n ]\n doxygen_input = \"\\n\".join(doxygen_args)\n doxygen_input = bytes(doxygen_input, \"utf-8\")\n (CURR_PATH / \"doxyoutput\").mkdir(parents=True, exist_ok=True)\n try:\n # Warning! The following code can cause buffer overflows on RTD.\n # Consider suppressing output completely if RTD project silently fails.\n # Refer to https://github.com/svenevs/exhale\n # /blob/fe7644829057af622e467bb529db6c03a830da99/exhale/deploy.py#L99-L111\n process = Popen([\"doxygen\", \"-\"], stdin=PIPE, stdout=PIPE, stderr=PIPE)\n stdout, stderr = process.communicate(doxygen_input)\n output = \"\\n\".join([i.decode(\"utf-8\") for i in (stdout, stderr) if i is not None])\n if process.returncode != 0:\n raise RuntimeError(output)\n print(output)\n except BaseException as e:\n raise Exception(f\"An error has occurred while executing Doxygen\\n{e}\")\n\n\ndef generate_r_docs(app: Sphinx) -> None:\n \"\"\"Generate documentation for R-package.\n\n Parameters\n ----------\n app : sphinx.application.Sphinx\n The application object representing the Sphinx process.\n \"\"\"\n commands = f\"\"\"\n export TAR=/bin/tar\n cd {CURR_PATH.parent}\n export R_LIBS=\"$CONDA_PREFIX/lib/R/library\"\n sh build-cran-package.sh || exit 1\n R CMD INSTALL --with-keep.source lightgbm_*.tar.gz || exit 1\n cp -R \\\n {CURR_PATH.parent / \"R-package\" / \"pkgdown\"} \\\n {CURR_PATH.parent / \"lightgbm_r\" / \"pkgdown\"}\n cd {CURR_PATH.parent / \"lightgbm_r\"}\n Rscript -e \"roxygen2::roxygenize(load = 'installed')\" || exit 1\n Rscript -e \"pkgdown::build_site( \\\n lazy = FALSE \\\n , install = FALSE \\\n , devel = FALSE \\\n , examples = TRUE \\\n , run_dont_run = TRUE \\\n , seed = 42L \\\n , preview = FALSE \\\n , new_process = TRUE \\\n )\n \" || exit 1\n cd {CURR_PATH.parent}\n \"\"\"\n try:\n print(\"Building R-package documentation\")\n # Warning! The following code can cause buffer overflows on RTD.\n # Consider suppressing output completely if RTD project silently fails.\n # Refer to https://github.com/svenevs/exhale\n # /blob/fe7644829057af622e467bb529db6c03a830da99/exhale/deploy.py#L99-L111\n process = Popen([\"/bin/bash\"], stdin=PIPE, stdout=PIPE, stderr=PIPE, universal_newlines=True)\n stdout, stderr = process.communicate(commands)\n output = \"\\n\".join([i for i in (stdout, stderr) if i is not None])\n if process.returncode != 0:\n raise RuntimeError(output)\n print(output)\n print(\"Done building R-package documentation\")\n except BaseException as e:\n raise Exception(f\"An error has occurred while generating documentation for R-package\\n{e}\")\n\n\ndef replace_reference_to_r_docs(app: Sphinx) -> None:\n \"\"\"Make reference to R-package documentation point to the actual version.\n\n Parameters\n ----------\n app : sphinx.application.Sphinx\n The application object representing the Sphinx process.\n \"\"\"\n index_doc_path = CURR_PATH / \"index.rst\"\n with open(index_doc_path, \"r+t\", encoding=\"utf-8\") as index_doc:\n content = index_doc.read()\n content = RTD_R_REF_REGEX.sub(rf\"\\g{RTD_VERSION}\\g\", content)\n index_doc.seek(0)\n index_doc.write(content)\n\n\ndef setup(app: Sphinx) -> None:\n \"\"\"Add new elements at Sphinx initialization time.\n\n Parameters\n ----------\n app : sphinx.application.Sphinx\n The application object representing the Sphinx process.\n \"\"\"\n first_run = not (CURR_PATH / \"_FIRST_RUN.flag\").exists()\n if first_run and RTD:\n (CURR_PATH / \"_FIRST_RUN.flag\").touch()\n if C_API:\n app.connect(\"builder-inited\", generate_doxygen_xml)\n else:\n app.add_directive(\"doxygenfile\", IgnoredDirective)\n if RTD: # build R docs only on Read the Docs site\n if first_run:\n app.connect(\"builder-inited\", generate_r_docs)\n app.connect(\n \"build-finished\", lambda app, _: copytree(CURR_PATH.parent / \"lightgbm_r\" / \"docs\", Path(app.outdir) / \"R\")\n )\n app.connect(\"builder-inited\", replace_reference_to_r_docs)\n app.add_transform(InternalRefTransform)\n add_js_file = getattr(app, \"add_js_file\", False) or app.add_javascript\n add_js_file(\"js/script.js\")\n" }, { "path": "docs/env.yml", "content": "name: docs-env\nchannels:\n - nodefaults\n - conda-forge\ndependencies:\n - breathe>=4.36\n - doxygen>=1.13.2\n - python=3.12\n - r-base>=4.5.1\n - r-data.table=1.17.8\n - r-jsonlite=2.0.0\n - r-knitr=1.50\n - r-markdown=2.0\n - r-matrix=1.7_4\n - r-pkgdown=2.1.3\n - r-roxygen2=7.3.3\n # skipping scikit-learn 1.7.1 because of the problems described in\n # https://github.com/lightgbm-org/LightGBM/issues/6978\n - scikit-learn>=1.6.1,!=1.7.1\n - sphinx>=8.1.3\n - sphinx_rtd_theme>=3.0.1\n" }, { "path": "docs/gcc-Tips.rst", "content": "The content of this document was very outdated and is no longer available to avoid any misleadings.\n" }, { "path": "docs/index.rst", "content": ".. LightGBM documentation master file, created by\n sphinx-quickstart on Thu May 4 14:30:58 2017.\n You can adapt this file completely to your liking, but it should at least\n contain the root `toctree` directive.\n\n.. image:: ./logo/LightGBM_logo_black_text.svg\n :align: center\n :width: 600\n :alt: Light Gradient Boosting Machine logo.\n\n|\n\nWelcome to LightGBM's documentation!\n====================================\n\n**LightGBM** is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:\n\n- Faster training speed and higher efficiency.\n- Lower memory usage.\n- Better accuracy.\n- Support of parallel, distributed, and GPU learning.\n- Capable of handling large-scale data.\n\nFor more details, please refer to `Features <./Features.rst>`__.\n\n.. toctree::\n :maxdepth: 1\n :caption: Contents:\n\n Installation Guide \n Quick Start \n Python Quick Start \n Features \n Experiments \n Parameters \n Parameters Tuning \n C API \n Python API \n R API \n Distributed Learning Guide \n GPU Tutorial \n Advanced Topics \n FAQ \n Development Guide \n\n.. toctree::\n :hidden:\n\n GPU-Performance\n GPU-Targets\n GPU-Windows\n gcc-Tips\n README\n\nIndices and Tables\n==================\n\n* :ref:`genindex`\n" }, { "path": "docs/make.bat", "content": "@ECHO OFF\n\npushd %~dp0\n\nREM Command file for Sphinx documentation\n\nif \"%SPHINXBUILD%\" == \"\" (\n\tset SPHINXBUILD=sphinx-build\n)\nset SOURCEDIR=.\nset BUILDDIR=_build\nset SPHINXPROJ=LightGBM\nset SPHINXOPTS=-W\n\nif \"%1\" == \"\" goto help\n\n%SPHINXBUILD% >NUL 2>NUL\nif errorlevel 9009 (\n\techo.\n\techo.The 'sphinx-build' command was not found. Make sure you have Sphinx\n\techo.installed, then set the SPHINXBUILD environment variable to point\n\techo.to the full path of the 'sphinx-build' executable. Alternatively you\n\techo.may add the Sphinx directory to PATH.\n\techo.\n\techo.If you don't have Sphinx installed, grab it from\n\techo.https://www.sphinx-doc.org/\n\texit /b 1\n)\n\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\ngoto end\n\n:help\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\n\n:end\npopd\n" }, { "path": "examples/README.md", "content": "Examples\n========\n\nYou can learn how to use LightGBM by these examples.\n\nComments in configuration files might be outdated. Actual information about parameters always can be found [here](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Parameters.rst).\n\nMachine Learning Challenge Winning Solutions\n============================================\n\n**LightGBM is used in many winning solutions, but this table is updated very infrequently.**\n\n| Place         | Competition   | Solution | Date |\n|---------|:------------- | --------- | -----|\n| 3rd | [Water Supply Forecast Rodeo: Forecast Stage](https://drivendata.co/blog/water-supply-forecast-and-final-winners) | [link](https://github.com/drivendataorg/water-supply-forecast-rodeo/blob/main/overall/3rd%20place/) | 2024.3 |\n| 1st | [American Express - Default Prediction](https://www.drivendata.org/competitions/group/competition-nasa-airport-pushback/) | [link](https://www.kaggle.com/competitions/amex-default-prediction/writeups/lucky-shake-1st-solution-update-github-code) | 2022.8 |\n| 2nd | [American Express - Default Prediction](https://www.drivendata.org/competitions/group/competition-nasa-airport-pushback/) | [link](https://www.kaggle.com/competitions/amex-default-prediction/writeups/bydefault-junehomes-2nd-place-solution-team-juneho) | 2022.8 |\n| 3rd | [American Express - Default Prediction](https://www.drivendata.org/competitions/group/competition-nasa-airport-pushback/) | [link](https://www.kaggle.com/competitions/amex-default-prediction/writeups/aibank-3rd-solution-simple-is-the-best) | 2022.8 |\n| 1st | [Ubiquant Market Prediction](https://www.kaggle.com/competitions/ubiquant-market-prediction/) | [link](https://www.kaggle.com/competitions/ubiquant-market-prediction/writeups/k-i-y-1st-place-solution-our-betting-strategy) | 2022.7 |\n| 2nd | [Ubiquant Market Prediction](https://www.kaggle.com/competitions/ubiquant-market-prediction/) | [link](https://www.kaggle.com/competitions/ubiquant-market-prediction/writeups/davide-stenner-2nd-place-solution-robust-cv-and-lg) | 2022.7 |\n| 2nd | [G-Research Crypto Forecasting](https://www.kaggle.com/competitions/g-research-crypto-forecasting/) | [link](https://www.kaggle.com/competitions/g-research-crypto-forecasting/writeups/nathaniel-maddux-2nd-place-solution) | 2022.5 |\n| 3rd | [G-Research Crypto Forecasting](https://www.kaggle.com/competitions/g-research-crypto-forecasting/) | [link](https://www.kaggle.com/competitions/g-research-crypto-forecasting/writeups/gaba-3rd-place-solution) | 2022.5 |\n| 1st | [NASA Airathon: Predict Air Quality (Particulate Track)](https://www.drivendata.org/competitions/88/competition-air-quality-pm/) | [link](https://github.com/drivendataorg/nasa-airathon/tree/main/pm25/1st%20Place) | 2022.3 |\n| 2nd | [NASA Airathon: Predict Air Quality (Particulate Track)](https://www.drivendata.org/competitions/88/competition-air-quality-pm/) | [link](https://github.com/drivendataorg/nasa-airathon/tree/main/pm25/2nd%20Place) | 2022.3 |\n| 1st | [M5 Forecasting - Uncertainty](https://www.kaggle.com/c/m5-forecasting-uncertainty) | [link](https://www.kaggle.com/c/m5-forecasting-uncertainty/discussion/163368) | 2020.7 |\n| 3rd | [M5 Forecasting - Uncertainty](https://www.kaggle.com/c/m5-forecasting-uncertainty) | [link](https://www.kaggle.com/competitions/m5-forecasting-uncertainty/writeups/ouranos-3rd-place-solution) | 2020.7 |\n| 3rd | [ALASKA2 Image Steganalysis](https://www.kaggle.com/c/alaska2-image-steganalysis) | [link](https://www.kaggle.com/competitions/alaska2-image-steganalysis/writeups/kaizaburochubachi-3rd-place-solution) | 2020.7 |\n| 1st | [M5 Forecasting - Accuracy](https://www.kaggle.com/c/m5-forecasting-accuracy) | [link](https://www.kaggle.com/competitions/m5-forecasting-accuracy/writeups/yeonjun-in-stu-1st-place-solution) | 2020.6 |\n| 2nd | [COVID19 Global Forecasting (Week 5)](https://www.kaggle.com/c/covid19-global-forecasting-week-5) | [link](https://www.kaggle.com/competitions/covid19-global-forecasting-week-5/writeups/kaz-some-ml-a-lot-of-judgement-and-luck) | 2020.5 |\n| 3rd | [COVID19 Global Forecasting (Week 5)](https://www.kaggle.com/c/covid19-global-forecasting-week-5) | [link](https://www.kaggle.com/c/covid19-global-forecasting-week-5/discussion/143029) | 2020.5 |\n| 1st | [COVID19 Global Forecasting (Week 4)](https://www.kaggle.com/c/covid19-global-forecasting-week-4) | [link](https://www.kaggle.com/c/covid19-global-forecasting-week-5/discussion/154804) | 2020.5 |\n| 2nd | [COVID19 Global Forecasting (Week 4)](https://www.kaggle.com/c/covid19-global-forecasting-week-4) | [link](https://www.kaggle.com/c/covid19-global-forecasting-week-5/discussion/144081) | 2020.5 |\n| 2nd | [2019 Data Science Bowl](https://www.kaggle.com/c/data-science-bowl-2019) | [link](https://www.kaggle.com/competitions/data-science-bowl-2019/writeups/fuson-2nd-place-solution) | 2020.1 |\n| 3rd | [RSNA Intracranial Hemorrhage Detection](https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection) | [link](https://www.kaggle.com/competitions/rsna-intracranial-hemorrhage-detection/writeups/takuoko-3rd-place-solution-become-gm-updated-with-) | 2019.11 |\n| 1st | [IEEE-CIS Fraud Detection](https://www.kaggle.com/c/ieee-fraud-detection) | [link](https://www.kaggle.com/competitions/ieee-fraud-detection/writeups/fraudsquad-1st-place-solution-part-2) | 2019.10 |\n| 2nd | [IEEE-CIS Fraud Detection](https://www.kaggle.com/c/ieee-fraud-detection) | [link](https://www.kaggle.com/competitions/ieee-fraud-detection/writeups/2-uncles-and-3-puppies-2nd-solution-cpmp-view) | 2019.10 |\n| 2nd | [Kuzushiji Recognition](https://www.kaggle.com/c/kuzushiji-recognition) | [link](https://www.kaggle.com/c/kuzushiji-recognition/discussion/112712) | 2019.10 |\n| 1st | [Los Alamos National Laboratory Earthquake Prediction](https://www.kaggle.com/c/LANL-Earthquake-Prediction) | [link](https://www.kaggle.com/competitions/LANL-Earthquake-Prediction/writeups/the-zoo-1st-place-solution) | 2019.6 |\n| 3rd | [Los Alamos National Laboratory Earthquake Prediction](https://www.kaggle.com/c/LANL-Earthquake-Prediction) | [link](https://www.kaggle.com/competitions/LANL-Earthquake-Prediction/writeups/character-ranking-3rd-place-memo) | 2019.6 |\n| 1st | [Santander Customer Transaction Prediction](https://www.kaggle.com/c/santander-customer-transaction-prediction) | [link](https://www.kaggle.com/competitions/santander-customer-transaction-prediction/writeups/wizardry-1-solution) | 2019.4 |\n| 2nd | [Santander Customer Transaction Prediction](https://www.kaggle.com/c/santander-customer-transaction-prediction) | [link](https://www.kaggle.com/competitions/santander-customer-transaction-prediction/writeups/2nd-place-solution) | 2019.4 |\n| 3rd | [Santander Customer Transaction Prediction](https://www.kaggle.com/c/santander-customer-transaction-prediction) | [link](https://www.kaggle.com/competitions/santander-customer-transaction-prediction/writeups/rock-physics-science-3rd-place-solution-summary-an) | 2019.4 |\n| 1st | [PetFinder.my Adoption Prediction](https://www.kaggle.com/c/petfinder-adoption-prediction) | [link](https://www.kaggle.com/competitions/petfinder-adoption-prediction/writeups/kaggler-ja-wodori-1st-place-solution-summary) | 2019.4 |\n| 1st | [Google Analytics Customer Revenue Prediction](https://www.kaggle.com/c/ga-customer-revenue-prediction) | [link](https://www.kaggle.com/competitions/ga-customer-revenue-prediction/writeups/ml-keksika-winning-solution-link-to-kernel-inside) | 2019.3 |\n| 1st | [VSB Power Line Fault Detection](https://www.kaggle.com/c/vsb-power-line-fault-detection) | [link](https://www.kaggle.com/competitions/vsb-power-line-fault-detection/writeups/mark4h-overview-of-1st-place-solution) | 2019.3 |\n| 5th | [Elo Merchant Category Recommendation](https://www.kaggle.com/c/elo-merchant-category-recommendation) | [link](https://www.kaggle.com/competitions/elo-merchant-category-recommendation/writeups/evgeny-patekha-5-solution) | 2019.2 |\n| 2nd | [PLAsTiCC Astronomical Classification](https://www.kaggle.com/c/PLAsTiCC-2018) | [link](https://www.kaggle.com/competitions/PLAsTiCC-2018/writeups/mike-silogram-2nd-place-solution-notes) | 2018.12 |\n| 1st | [Google Research Doodle Recognition Challenge](https://www.kaggle.com/c/quickdraw-doodle-recognition) | [link](https://www.kaggle.com/competitions/quickdraw-doodle-recognition/writeups/ods-ai-pablos-1st-place-solution) | 2018.12 |\n| 1st | [Home Credit Group Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk) | [link](https://www.kaggle.com/competitions/home-credit-default-risk/writeups/home-aloan-1st-place-solution) | 2018.8 |\n| 2nd | [Home Credit Group Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk) | [link](https://www.kaggle.com/competitions/home-credit-default-risk/writeups/ikiri-ds-2nd-place-solution-team-ikiri-ds) | 2018.8 |\n| 3rd | [Home Credit Group Home Credit Default Risk](https://www.kaggle.com/c/home-credit-default-risk) | [link](https://www.kaggle.com/competitions/home-credit-default-risk/writeups/alijs-evgeny-3rd-place-solution) | 2018.8 |\n| 2nd | [Google AI Open Images - Visual Relationship Track](https://www.kaggle.com/c/google-ai-open-images-visual-relationship-track) | [link](https://www.kaggle.com/competitions/google-ai-open-images-visual-relationship-track/writeups/tito-brief-summary-of-2nd-place) | 2018.8 |\n| 2nd | [Santander Value Prediction Challenge](https://www.kaggle.com/c/santander-value-prediction-challenge) | [link](https://www.kaggle.com/competitions/santander-value-prediction-challenge/writeups/adilism-2nd-place-solution-overview) | 2018.8 |\n| 1st | [Avito Demand Prediction Challenge](https://www.kaggle.com/c/avito-demand-prediction) | [link](https://www.kaggle.com/competitions/avito-demand-prediction/writeups/dance-with-ensemble-dance-with-ensemble-sharing-th) | 2018.6 |\n| 2nd | [Avito Demand Prediction Challenge](https://www.kaggle.com/c/avito-demand-prediction) | [link](https://www.kaggle.com/competitions/avito-demand-prediction/writeups/song-and-dance-ensemble-second-place-solution) | 2018.6 |\n| 3rd | [Avito Demand Prediction Challenge](https://www.kaggle.com/c/avito-demand-prediction) | [link](https://www.kaggle.com/competitions/avito-demand-prediction/writeups/superanova-3-place-solution) | 2018.6 |\n| 1st | [TalkingData AdTracking Fraud Detection Challenge](https://www.kaggle.com/c/talkingdata-adtracking-fraud-detection) | [link](https://www.kaggle.com/competitions/talkingdata-adtracking-fraud-detection/writeups/flowlight-komaki-shuffle-1st-place-solution)| 2018.5 |\n| 1st | [DonorsChoose.org Application Screening](https://www.kaggle.com/c/donorschoose-application-screening)| [link](https://www.kaggle.com/shadowwarrior/1st-place-solution/notebook) | 2018.4 |\n| 1st | [Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge)| [link](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/writeups/toxic-crusaders-1st-place-solution-overview) | 2018.3 |\n| 1st | [Mercari Price Suggestion Challenge](https://www.kaggle.com/c/mercari-price-suggestion-challenge) | [link](https://www.kaggle.com/competitions/mercari-price-suggestion-challenge/writeups/pawe-and-konstantin-1st-place-solution) | 2018.2 |\n| 1st | [IEEE's Signal Processing Society, Camera Model Identification](https://www.kaggle.com/c/sp-society-camera-model-identification)| [link](https://www.kaggle.com/competitions/sp-society-camera-model-identification/writeups/ods-ai-stamp-1st-place-solution) | 2018.2 |\n| 1st | [Recruit Restaurant Visitor Forecasting](https://www.kaggle.com/c/recruit-restaurant-visitor-forecasting) | [link](https://www.kaggle.com/competitions/recruit-restaurant-visitor-forecasting/writeups/pppp-solution-public-0-471-private-0-505) | 2018.2|\n| 1st | [WSDM CUP 2018 - KKBox's Music Recommendation Challenge](https://www.kaggle.com/c/kkbox-music-recommendation-challenge) | [link](https://www.kaggle.com/competitions/kkbox-music-recommendation-challenge/writeups/bing-bai-a-brief-introduction-to-the-1st-place-sol) | 2017.12 |\n| 1st | [Porto Seguro’s Safe Driver Prediction](https://www.kaggle.com/c/porto-seguro-safe-driver-prediction) | [link](https://www.kaggle.com/competitions/porto-seguro-safe-driver-prediction/writeups/michael-jahrer-1st-place-with-representation-learn) |2017.11 |\n| 1st | [Quora Question Pairs](https://www.kaggle.com/c/quora-question-pairs) | [link](https://www.kaggle.com/competitions/quora-question-pairs/writeups/dl-guys-1st-place-solution) | 2017.6 |\n| 1st | [Two Sigma Connect: Rental Listing Inquiries](https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries) | [link](https://www.kaggle.com/competitions/two-sigma-connect-rental-listing-inquiries/writeups/plantsgo-my-best-single-model-and-solution) | 2017.4 |\n| 1st | [CIKM2017 AnalytiCup - Lazada Product Title Quality Challenge](https://cikm2017.org/CIKM_AnalytiCup_task3.html) | [link](https://arxiv.org/abs/1804.01000) | 2017.9 |\n| 2nd | [Two Sigma Connect: Rental Listing Inquiries](https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries) | [link](https://www.kaggle.com/competitions/two-sigma-connect-rental-listing-inquiries/writeups/faron-2nd-place-solution) | 2017.4 |\n| 3rd | [Two Sigma Connect: Rental Listing Inquiries](https://www.kaggle.com/c/two-sigma-connect-rental-listing-inquiries) | [link](https://www.kaggle.com/competitions/two-sigma-connect-rental-listing-inquiries/writeups/little-boat-3rd-place-solution-summary) | 2017.4 |\n| 3rd | [Dogs vs. Cats Redux: Kernels Edition](https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition) | [link](https://medium.com/kaggle-blog/dogs-vs-cats-redux-playground-competition-3rd-place-interview-marco-lugo-74893739b10f) | - |\n| 3rd | [Bosch Production Line Performance](https://www.kaggle.com/c/bosch-production-line-performance) | [link](https://www.kaggle.com/competitions/bosch-production-line-performance/writeups/data-property-avengers-3-place-solution) | 2016.11 |\n| 1st | [The 1st Di-Tech Competitions](https://web.archive.org/web/20170311212917/https://research.xiaojukeji.com/competition/main.action?competitionId=DiTech2016) | - | 2016.7 |\n" }, { "path": "examples/binary_classification/README.md", "content": "Binary Classification Example\n=============================\n\nHere is an example for LightGBM to run binary classification task.\n\n***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html)\nfor the following commands to work. The `lightgbm` binary must be built and available at the root of this project.***\n\nTraining\n--------\n\nRun the following command in this folder:\n\n```bash\n\"../../lightgbm\" config=train.conf\n```\n\nPrediction\n----------\n\nYou should finish training first.\n\nRun the following command in this folder:\n\n```bash\n\"../../lightgbm\" config=predict.conf\n```\n" }, { "path": "examples/binary_classification/binary.test", "content": 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n" }, { "path": "examples/binary_classification/forced_splits.json", "content": "{\n \"feature\": 25,\n \"threshold\": 1.3,\n \"left\": {\n \"feature\": 26,\n \"threshold\": 0.85\n },\n \"right\": {\n \"feature\": 26,\n \"threshold\": 0.85\n }\n}\n" }, { "path": "examples/binary_classification/predict.conf", "content": "task = predict\n\ndata = binary.test\n\ninput_model= LightGBM_model.txt\n" }, { "path": "examples/binary_classification/train.conf", "content": "# task type, support train and predict\ntask = train\n\n# boosting type, support gbdt for now, alias: boosting, boost\nboosting_type = gbdt\n\n# application type, support following application\n# regression , regression task\n# binary , binary classification task\n# lambdarank , LambdaRank task\n# alias: application, app\nobjective = binary\n\n# eval metrics, support multi metric, delimited by ',' , support following metrics\n# l1\n# l2 , default metric for regression\n# ndcg , default metric for lambdarank\n# auc\n# binary_logloss , default metric for binary\n# binary_error\nmetric = binary_logloss,auc\n\n# frequency for metric output\nmetric_freq = 1\n\n# true if need output metric for training data, alias: tranining_metric, train_metric\nis_training_metric = true\n\n# column in data to use as label\nlabel_column = 0\n\n# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.\nmax_bin = 255\n\n# training data\n# if existing weight file, should name to \"binary.train.weight\"\n# alias: train_data, train\ndata = binary.train\n\n# validation data, support multi validation data, separated by ','\n# if existing weight file, should name to \"binary.test.weight\"\n# alias: valid, test, test_data,\nvalid_data = binary.test\n\n# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds\nnum_trees = 100\n\n# shrinkage rate , alias: shrinkage_rate\nlearning_rate = 0.1\n\n# number of leaves for one tree, alias: num_leaf\nnum_leaves = 63\n\n# type of tree learner, support following types:\n# serial , single machine version\n# feature , use feature parallel to train\n# data , use data parallel to train\n# voting , use voting based parallel to train\n# alias: tree\ntree_learner = serial\n\n# number of threads for multi-threading. One thread will use each CPU. The default is the CPU count.\n# num_threads = 8\n\n# feature sub-sample, will random select 80% feature to train on each iteration\n# alias: sub_feature\nfeature_fraction = 0.8\n\n# Support bagging (data sub-sample), will perform bagging every 5 iterations\nbagging_freq = 5\n\n# Bagging fraction, will random select 80% data on bagging\n# alias: sub_row\nbagging_fraction = 0.8\n\n# minimal number data for one leaf, use this to deal with over-fit\n# alias : min_data_per_leaf, min_data\nmin_data_in_leaf = 50\n\n# minimal sum Hessians for one leaf, use this to deal with over-fit\nmin_sum_hessian_in_leaf = 5.0\n\n# save memory and faster speed for sparse feature, alias: is_sparse\nis_enable_sparse = true\n\n# when data is bigger than memory size, set this to true. otherwise set false will have faster speed\n# alias: two_round_loading, two_round\nuse_two_round_loading = false\n\n# true if need to save data to binary file and application will auto load data from binary file next time\n# alias: is_save_binary, save_binary\nis_save_binary_file = false\n\n# output model file\noutput_model = LightGBM_model.txt\n\n# support continuous train from trained gbdt model\n# input_model= trained_model.txt\n\n# output prediction file for predict task\n# output_result= prediction.txt\n\n\n# number of machines in distributed training, alias: num_machine\nnum_machines = 1\n\n# local listening port in distributed training, alias: local_port\nlocal_listen_port = 12400\n\n# machines list file for distributed training, alias: mlist\nmachine_list_file = mlist.txt\n\n# force splits\n# forced_splits = forced_splits.json\n" }, { "path": "examples/binary_classification/train_linear.conf", "content": "# task type, support train and predict\ntask = train\n\n# boosting type, support gbdt for now, alias: boosting, boost\nboosting_type = gbdt\n\n# application type, support following application\n# regression , regression task\n# binary , binary classification task\n# lambdarank , LambdaRank task\n# alias: application, app\nobjective = binary\n\nlinear_tree = true\n\n# eval metrics, support multi metric, delimited by ',' , support following metrics\n# l1\n# l2 , default metric for regression\n# ndcg , default metric for lambdarank\n# auc\n# binary_logloss , default metric for binary\n# binary_error\nmetric = binary_logloss,auc\n\n# frequency for metric output\nmetric_freq = 1\n\n# true if need output metric for training data, alias: tranining_metric, train_metric\nis_training_metric = true\n\n# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.\nmax_bin = 255\n\n# training data\n# if existing weight file, should name to \"binary.train.weight\"\n# alias: train_data, train\ndata = binary.train\n\n# validation data, support multi validation data, separated by ','\n# if existing weight file, should name to \"binary.test.weight\"\n# alias: valid, test, test_data,\nvalid_data = binary.test\n\n# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds\nnum_trees = 100\n\n# shrinkage rate , alias: shrinkage_rate\nlearning_rate = 0.1\n\n# number of leaves for one tree, alias: num_leaf\nnum_leaves = 63\n\n# type of tree learner, support following types:\n# serial , single machine version\n# feature , use feature parallel to train\n# data , use data parallel to train\n# voting , use voting based parallel to train\n# alias: tree\ntree_learner = serial\n\n# number of threads for multi-threading. One thread will use each CPU. The default is set to CPU count.\n# num_threads = 8\n\n# feature sub-sample, will random select 80% feature to train on each iteration\n# alias: sub_feature\nfeature_fraction = 0.8\n\n# Support bagging (data sub-sample), will perform bagging every 5 iterations\nbagging_freq = 5\n\n# Bagging fraction, will random select 80% data on bagging\n# alias: sub_row\nbagging_fraction = 0.8\n\n# minimal number data for one leaf, use this to deal with over-fit\n# alias : min_data_per_leaf, min_data\nmin_data_in_leaf = 50\n\n# minimal sum Hessians for one leaf, use this to deal with over-fit\nmin_sum_hessian_in_leaf = 5.0\n\n# save memory and faster speed for sparse feature, alias: is_sparse\nis_enable_sparse = true\n\n# when data is bigger than memory size, set this to true. otherwise set false will have faster speed\n# alias: two_round_loading, two_round\nuse_two_round_loading = false\n\n# true if need to save data to binary file and application will auto load data from binary file next time\n# alias: is_save_binary, save_binary\nis_save_binary_file = false\n\n# output model file\noutput_model = LightGBM_model.txt\n\n# support continuous train from trained gbdt model\n# input_model= trained_model.txt\n\n# output prediction file for predict task\n# output_result= prediction.txt\n\n\n# number of machines in distributed training, alias: num_machine\nnum_machines = 1\n\n# local listening port in distributed training, alias: local_port\nlocal_listen_port = 12400\n\n# machines list file for distributed training, alias: mlist\nmachine_list_file = mlist.txt\n\n# force splits\n# forced_splits = forced_splits.json\n" }, { "path": "examples/lambdarank/README.md", "content": "LambdaRank Example\n==================\n\nHere is an example for LightGBM to run LambdaRank task.\n\n***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html)\nfor the following commands to work. The `lightgbm` binary must be built and available at the root of this project.***\n\nTraining\n--------\n\nRun the following command in this folder:\n\n```bash\n\"../../lightgbm\" config=train.conf\n```\n\nPrediction\n----------\n\nYou should finish training first.\n\nRun the following command in this folder:\n\n```bash\n\"../../lightgbm\" config=predict.conf\n```\n\nData Format\n-----------\n\nTo learn more about the query format used in this example, check out the\n[query data format](https://lightgbm.readthedocs.io/en/latest/Parameters.html#query-data).\n" }, { "path": "examples/lambdarank/predict.conf", "content": "task = predict\n\ndata = rank.test\n\ninput_model= LightGBM_model.txt\n" }, { "path": "examples/lambdarank/rank.test", "content": "2 1:0.74 6:0.87 8:0.75 9:0.80 11:0.88 12:0.37 17:0.66 20:0.97 21:0.30 27:0.15 28:0.95 30:0.54 32:0.80 34:0.21 36:0.62 37:0.40 39:0.43 41:0.88 43:0.90 60:0.87 66:0.47 69:0.47 70:0.62 74:0.97 77:0.96 78:0.96 81:0.84 83:0.85 85:0.98 91:0.48 96:0.86 98:0.73 100:0.91 101:0.85 104:0.95 106:0.81 108:0.36 111:0.94 114:0.86 117:0.86 120:0.77 122:0.57 123:0.35 124:0.66 126:0.54 127:0.68 129:0.81 133:0.67 135:0.78 140:0.45 144:0.85 145:0.67 146:0.95 147:0.96 149:0.97 150:0.89 151:0.94 152:0.97 153:0.80 154:0.89 155:0.67 158:0.92 159:0.82 161:0.88 162:0.78 164:0.70 165:0.84 167:0.47 169:0.86 172:0.86 173:0.25 176:0.73 177:0.38 179:0.24 181:0.89 186:0.96 187:0.39 189:0.90 192:0.59 195:0.92 201:0.74 202:0.59 206:0.81 208:0.64 212:0.39 215:0.72 216:0.48 222:0.76 232:0.97 235:0.42 238:0.83 241:0.12 242:0.51 243:0.59 245:0.94 247:0.67 248:0.84 253:0.90 255:0.79 256:0.58 259:0.21 260:0.77 261:0.94 265:0.74 266:0.80 267:0.65 268:0.64 271:0.21 276:0.88 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.99 300:0.70\n3 1:0.74 6:0.81 8:0.60 9:0.80 11:0.88 12:0.37 17:0.47 20:0.99 21:0.30 27:0.15 28:0.95 30:0.17 34:0.80 36:1.00 37:0.81 39:0.43 41:0.95 43:0.77 55:0.71 60:0.97 66:0.34 69:0.80 70:0.68 74:0.79 78:0.99 81:0.84 83:0.84 85:0.80 91:0.38 96:0.91 98:0.43 100:0.97 101:0.73 104:0.98 108:0.64 111:0.98 114:0.86 117:0.86 120:0.84 122:0.67 123:0.62 124:0.77 126:0.54 127:0.37 129:0.59 133:0.76 135:0.60 138:0.98 140:0.45 144:0.85 145:0.74 146:0.67 147:0.72 149:0.66 150:0.89 151:0.97 152:0.93 153:0.89 154:0.69 155:0.67 158:0.92 159:0.64 161:0.88 162:0.65 163:0.81 164:0.82 165:0.84 167:0.53 169:0.96 172:0.37 173:0.71 176:0.73 177:0.38 179:0.67 181:0.89 186:0.98 187:0.87 189:0.83 192:0.59 201:0.74 202:0.77 208:0.64 212:0.19 215:0.72 216:0.54 222:0.76 232:0.95 235:0.42 238:0.90 241:0.46 242:0.63 243:0.50 245:0.57 247:0.47 248:0.90 253:0.92 255:0.79 256:0.81 259:0.21 260:0.84 261:0.96 265:0.45 266:0.75 267:0.65 268:0.79 271:0.21 276:0.47 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.43\n2 1:0.74 6:0.80 8:0.60 11:0.88 12:0.37 17:0.61 20:0.91 27:0.34 28:0.95 30:0.21 32:0.78 34:0.63 36:0.93 37:0.36 39:0.43 43:0.87 60:0.87 66:0.29 69:0.54 70:0.87 74:0.85 77:0.89 78:0.90 81:0.98 83:0.84 85:0.90 91:0.25 98:0.38 100:0.84 101:0.78 104:0.80 108:0.84 111:0.88 114:0.98 123:0.83 124:0.79 126:0.54 127:0.34 129:0.81 133:0.76 135:0.98 144:0.85 145:0.99 146:0.62 147:0.67 149:0.79 150:0.99 152:0.85 154:0.85 155:0.67 158:0.92 159:0.64 161:0.88 163:0.90 165:0.92 167:0.47 169:0.81 172:0.45 173:0.39 176:0.73 177:0.38 178:0.55 179:0.50 181:0.89 186:0.90 187:0.21 189:0.83 192:0.59 195:0.87 201:0.74 208:0.64 212:0.27 215:0.96 216:0.84 222:0.76 232:0.85 235:0.05 238:0.81 241:0.12 242:0.18 243:0.37 245:0.69 247:0.67 253:0.78 259:0.21 261:0.88 265:0.40 266:0.75 267:0.28 271:0.21 276:0.59 279:0.86 282:0.86 283:0.82 290:0.98 297:0.36 300:0.70\n0 1:0.74 6:0.84 7:0.81 8:0.66 9:0.80 11:0.88 12:0.37 17:0.84 20:0.92 21:0.05 27:0.27 28:0.95 30:0.73 32:0.80 34:0.29 36:0.72 37:0.35 39:0.43 41:0.82 43:0.96 60:0.87 66:0.27 69:0.10 70:0.40 74:0.96 77:0.70 78:0.86 81:0.95 83:0.85 85:0.98 91:0.15 96:0.77 98:0.51 100:0.83 101:0.85 104:0.79 106:0.81 108:0.90 111:0.84 114:0.95 117:0.86 120:0.65 121:0.99 122:0.57 123:0.09 124:0.71 126:0.54 127:0.71 129:0.81 133:0.67 135:0.63 140:0.45 144:0.85 145:0.70 146:0.80 147:0.83 149:0.97 150:0.98 151:0.77 152:0.91 153:0.48 154:0.96 155:0.67 158:0.92 159:0.78 161:0.88 162:0.86 164:0.57 165:0.90 167:0.48 169:0.81 172:0.73 173:0.10 176:0.73 177:0.38 179:0.33 181:0.89 186:0.86 187:0.21 189:0.86 192:0.59 195:0.90 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.73 215:0.91 216:0.70 220:0.62 222:0.76 230:0.87 232:0.85 233:0.76 235:0.12 238:0.82 241:0.21 242:0.57 243:0.46 245:0.91 247:0.55 248:0.71 253:0.85 255:0.79 256:0.45 259:0.21 260:0.66 261:0.87 265:0.44 266:0.80 267:0.84 268:0.46 271:0.21 276:0.76 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.55\n2 1:0.74 6:0.91 7:0.81 8:0.83 9:0.80 11:0.88 12:0.37 17:0.43 20:0.83 21:0.22 27:0.42 28:0.95 30:0.76 32:0.80 34:0.30 36:0.83 37:0.55 39:0.43 41:0.82 43:0.86 60:0.99 66:0.93 69:0.60 70:0.33 74:0.96 77:0.89 78:0.91 81:0.88 83:0.88 85:0.97 91:0.35 96:0.73 98:0.76 100:0.81 101:0.86 104:0.92 106:0.81 108:0.45 111:0.88 114:0.90 117:0.86 120:0.60 121:0.90 122:0.57 123:0.44 126:0.54 127:0.25 129:0.05 135:0.78 140:0.45 144:0.85 145:0.74 146:0.79 147:0.82 149:0.97 150:0.93 151:0.63 152:0.79 153:0.48 154:0.83 155:0.67 158:0.92 159:0.35 161:0.88 162:0.86 164:0.57 165:0.86 167:0.48 169:0.79 172:0.82 173:0.60 176:0.73 177:0.38 179:0.27 181:0.89 186:0.91 187:0.39 189:0.92 192:0.59 195:0.69 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.92 215:0.79 216:0.66 220:0.82 222:0.76 230:0.87 232:0.94 233:0.76 235:0.31 238:0.82 241:0.21 242:0.63 243:0.94 247:0.39 248:0.60 253:0.81 255:0.79 256:0.45 259:0.21 260:0.60 261:0.84 265:0.76 266:0.27 267:0.65 268:0.46 271:0.21 276:0.72 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.97 300:0.43\n1 1:0.74 11:0.88 12:0.37 17:0.62 20:0.80 21:0.40 27:0.33 28:0.95 30:0.70 32:0.80 34:0.63 36:0.91 37:0.78 39:0.43 43:0.86 66:0.46 69:0.60 70:0.43 74:0.94 77:0.89 78:0.85 81:0.87 83:0.76 85:0.98 91:0.09 98:0.55 100:0.73 101:0.84 104:0.84 106:0.81 108:0.46 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153:0.69 154:0.78 155:0.67 159:0.85 161:0.88 162:0.48 163:0.75 164:0.62 165:0.65 167:0.51 169:0.90 172:0.70 173:0.40 176:0.73 177:0.38 178:0.50 179:0.24 181:0.89 186:0.97 187:0.95 189:0.88 191:0.70 192:0.59 201:0.74 202:0.67 208:0.64 212:0.16 215:0.46 216:0.68 222:0.76 232:0.99 235:0.97 238:0.83 241:0.39 242:0.76 243:0.44 245:0.93 247:0.47 248:0.79 253:0.87 255:0.79 256:0.45 259:0.21 260:0.73 261:0.94 265:0.49 266:0.80 267:0.91 268:0.55 271:0.21 276:0.84 285:0.62 290:0.66 297:0.36 300:0.70\n0 1:0.74 6:0.86 7:0.81 8:0.68 9:0.80 11:0.88 12:0.37 17:0.73 20:0.95 21:0.30 27:0.25 28:0.95 30:0.57 34:0.37 36:0.55 37:0.55 39:0.43 41:0.88 43:0.85 60:0.97 66:0.93 69:0.61 70:0.64 74:0.94 78:0.95 81:0.84 83:0.87 85:0.95 91:0.38 96:0.82 98:0.88 100:0.88 101:0.85 108:0.47 111:0.92 114:0.86 120:0.72 121:0.97 122:0.57 123:0.45 126:0.54 127:0.41 129:0.05 135:0.65 140:0.45 144:0.85 146:0.85 147:0.88 149:0.94 150:0.89 151:0.87 152:0.88 153:0.48 154:0.82 155:0.67 158:0.87 159:0.42 161:0.88 162:0.86 164:0.67 165:0.84 167:0.47 169:0.86 172:0.81 173:0.64 176:0.73 177:0.38 179:0.40 181:0.89 186:0.94 187:0.39 189:0.87 192:0.59 201:0.74 202:0.50 204:0.89 208:0.64 212:0.60 215:0.72 216:0.38 222:0.76 230:0.87 233:0.76 235:0.42 238:0.82 241:0.15 242:0.44 243:0.93 247:0.67 248:0.79 253:0.88 255:0.79 256:0.58 259:0.21 260:0.73 261:0.92 265:0.88 266:0.54 267:0.23 268:0.59 271:0.21 276:0.70 279:0.86 283:0.71 285:0.62 290:0.86 297:0.36 300:0.55\n2 1:0.74 6:0.86 8:0.77 9:0.80 11:0.88 12:0.37 17:0.13 20:0.80 21:0.74 27:0.15 28:0.95 30:0.53 34:0.55 36:0.43 37:0.88 39:0.43 41:0.90 43:0.83 55:0.84 66:0.15 69:0.68 70:0.76 74:0.95 78:0.95 81:0.53 83:0.77 85:0.91 91:0.10 96:0.82 98:0.49 100:0.93 101:0.74 108:0.85 111:0.93 114:0.67 120:0.72 122:0.67 123:0.92 124:0.87 126:0.54 127:0.42 129:0.89 133:0.87 135:0.85 140:0.80 144:0.85 146:0.38 147:0.45 149:0.88 150:0.67 151:0.87 152:0.90 153:0.48 154:0.78 155:0.67 159:0.88 161:0.88 162:0.49 163:0.75 164:0.72 165:0.65 167:0.49 169:0.90 172:0.70 173:0.36 176:0.73 177:0.38 178:0.42 179:0.23 181:0.89 186:0.95 187:0.95 189:0.92 192:0.59 201:0.74 202:0.76 208:0.64 212:0.15 215:0.46 216:0.68 220:0.87 222:0.76 232:0.99 235:0.95 238:0.85 241:0.32 242:0.74 243:0.16 245:0.88 247:0.60 248:0.79 253:0.87 255:0.79 256:0.64 259:0.21 260:0.73 261:0.94 265:0.49 266:0.87 267:0.84 268:0.65 271:0.21 276:0.85 285:0.62 290:0.67 297:0.36 300:0.70\n1 1:0.74 11:0.88 12:0.37 17:0.43 21:0.54 27:0.45 28:0.95 30:0.95 34:0.81 36:0.19 37:0.50 39:0.43 43:0.82 66:0.54 69:0.69 74:0.43 81:0.70 83:0.74 85:0.72 91:0.12 98:0.09 101:0.72 108:0.52 114:0.77 123:0.50 126:0.54 127:0.06 129:0.05 135:0.60 144:0.85 145:0.49 146:0.13 147:0.18 149:0.46 150:0.80 154:0.78 155:0.67 159:0.08 161:0.88 165:0.79 167:0.47 172:0.10 173:0.67 176:0.73 177:0.38 178:0.55 179:0.08 181:0.89 187:0.68 192:0.59 201:0.74 215:0.58 216:0.77 222:0.76 235:0.68 241:0.15 243:0.63 253:0.59 259:0.21 265:0.09 267:0.28 271:0.21 290:0.77 297:0.36 300:0.08\n2 1:0.74 6:0.87 8:0.72 9:0.80 11:0.88 12:0.37 17:0.66 20:0.93 21:0.59 27:0.15 28:0.95 30:0.45 34:0.58 36:0.74 37:0.40 39:0.43 41:0.90 43:0.90 60:0.87 66:0.87 69:0.49 70:0.48 74:0.85 78:0.93 81:0.66 83:0.82 85:0.90 91:0.37 96:0.85 98:0.76 100:0.86 101:0.80 104:0.94 106:0.81 108:0.38 111:0.90 114:0.74 117:0.86 120:0.74 122:0.43 123:0.36 126:0.54 127:0.25 129:0.05 135:0.39 140:0.45 144:0.85 145:0.79 146:0.83 147:0.86 149:0.86 150:0.77 151:0.91 152:0.97 153:0.72 154:0.89 155:0.67 158:0.92 159:0.39 161:0.88 162:0.58 164:0.72 165:0.78 167:0.46 169:0.86 172:0.63 173:0.45 176:0.73 177:0.38 179:0.51 181:0.89 186:0.93 187:0.68 189:0.86 192:0.59 201:0.74 202:0.70 206:0.81 208:0.64 212:0.27 215:0.55 216:0.48 222:0.76 232:0.96 235:0.74 238:0.82 241:0.10 242:0.40 243:0.88 247:0.60 248:0.81 253:0.87 255:0.79 256:0.68 259:0.21 260:0.75 261:0.94 265:0.76 266:0.54 267:0.52 268:0.69 271:0.21 276:0.52 279:0.86 283:0.82 285:0.62 290:0.74 297:0.36 300:0.43\n1 11:0.88 12:0.37 17:0.47 21:0.78 27:0.45 28:0.95 30:0.95 34:0.69 36:0.23 37:0.50 39:0.43 43:0.79 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271:0.35 276:0.84 282:0.86 285:0.62 290:0.92 297:0.36 300:0.84\n2 1:0.56 8:0.60 10:0.94 11:0.57 12:0.86 17:0.36 18:0.96 21:0.71 27:0.48 29:0.86 30:0.44 34:0.88 36:0.91 37:0.42 39:0.51 43:0.65 45:0.98 46:0.93 64:0.77 66:0.43 69:0.81 70:0.77 71:0.57 74:0.94 81:0.42 83:0.56 86:0.97 91:0.20 97:0.89 98:0.71 99:0.83 101:0.96 107:0.97 108:0.65 110:0.97 114:0.66 122:0.86 123:0.63 124:0.68 125:0.88 126:0.32 127:0.49 129:0.59 133:0.67 135:0.56 139:0.96 146:0.96 147:0.59 149:0.91 150:0.37 154:0.27 155:0.46 158:0.76 159:0.84 160:0.88 165:0.65 170:0.82 172:0.94 173:0.59 174:0.96 176:0.63 177:0.70 178:0.55 179:0.13 187:0.21 192:0.44 197:0.92 201:0.54 208:0.50 212:0.78 216:0.70 219:0.91 222:0.35 235:0.88 239:0.92 241:0.27 242:0.18 243:0.15 244:0.61 245:0.99 247:0.97 253:0.67 257:0.65 259:0.21 265:0.71 266:0.54 267:0.59 271:0.38 276:0.96 279:0.75 289:0.94 290:0.66 292:0.88 300:0.70\n4 1:0.64 7:0.69 9:0.70 10:0.98 11:0.52 12:0.86 17:0.40 18:0.73 21:0.47 23:0.96 27:0.72 29:0.86 30:0.40 31:0.87 33:0.97 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17:0.43 18:0.64 21:0.13 27:0.72 29:0.86 30:0.68 34:0.90 36:0.62 37:0.92 39:0.51 43:0.68 45:0.73 46:0.96 55:0.71 64:0.77 66:0.79 69:0.52 70:0.31 71:0.57 74:0.41 81:0.70 83:0.69 86:0.70 91:0.27 98:0.80 99:0.95 101:0.65 107:0.92 108:0.40 110:0.92 114:0.92 122:0.41 123:0.38 125:0.88 126:0.51 127:0.28 129:0.05 135:0.37 139:0.67 146:0.55 147:0.61 149:0.47 150:0.58 154:0.58 155:0.50 159:0.20 160:0.88 165:0.81 170:0.82 172:0.41 173:0.73 174:0.83 176:0.70 177:0.88 178:0.55 179:0.80 187:0.39 192:0.50 197:0.88 201:0.63 208:0.61 212:0.42 215:0.84 216:0.24 222:0.35 235:0.31 239:0.87 241:0.41 242:0.19 243:0.80 244:0.61 247:0.55 253:0.63 259:0.21 265:0.80 266:0.27 267:0.52 271:0.38 276:0.32 289:0.95 290:0.92 297:0.36 300:0.25\n2 1:0.66 9:0.74 10:0.89 11:0.52 12:0.86 17:0.73 18:0.69 21:0.13 27:0.72 29:0.86 30:0.72 31:0.92 34:0.37 36:0.40 39:0.51 43:0.69 45:0.83 46:0.96 64:0.77 66:0.60 69:0.75 70:0.37 71:0.57 74:0.50 79:0.91 81:0.70 83:0.69 86:0.75 91:0.83 96:0.77 98:0.79 99:0.95 101:0.65 107:0.94 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36:0.40 37:0.95 39:0.54 43:0.57 44:0.93 45:0.66 55:0.42 64:0.77 66:0.27 69:0.83 70:0.86 71:0.92 74:0.49 77:0.70 79:0.90 81:0.59 83:0.60 86:0.59 91:0.25 96:0.75 97:0.89 98:0.16 99:0.83 101:0.52 108:0.68 114:0.65 122:0.80 123:0.66 124:0.70 126:0.54 127:0.17 129:0.05 131:0.88 133:0.60 135:0.80 137:0.88 139:0.75 140:0.45 144:0.57 145:0.47 146:0.31 147:0.38 149:0.28 150:0.74 151:0.65 153:0.48 154:0.46 155:0.67 157:0.61 158:0.72 159:0.48 162:0.86 165:0.70 166:0.93 172:0.21 173:0.68 176:0.73 177:0.70 179:0.53 182:0.61 187:0.68 190:0.77 192:0.87 195:0.93 196:0.89 201:0.93 202:0.36 204:0.85 208:0.64 212:0.16 215:0.44 216:0.87 219:0.87 220:0.62 222:0.76 227:0.87 229:0.61 230:0.63 231:0.90 233:0.73 235:0.52 241:0.49 242:0.40 243:0.46 244:0.61 245:0.50 246:0.61 247:0.47 248:0.63 253:0.52 255:0.74 256:0.45 259:0.21 265:0.17 266:0.54 267:0.44 268:0.46 276:0.31 279:0.82 281:0.91 282:0.88 284:0.86 285:0.56 287:0.61 290:0.65 292:0.95 297:0.36 300:0.55\n3 1:0.69 7:0.64 8:0.79 11:0.86 12:0.86 17:0.86 18:0.88 21:0.05 27:0.54 29:0.62 30:0.13 32:0.63 34:0.74 36:0.31 37:0.35 39:0.54 43:0.42 45:0.87 55:0.59 66:0.26 69:0.79 70:0.95 71:0.92 74:0.52 76:0.94 77:0.70 81:0.73 83:0.60 86:0.72 91:0.27 96:0.73 97:0.89 98:0.69 99:0.83 101:0.87 108:0.63 114:0.70 122:0.77 123:0.85 124:0.64 126:0.44 127:0.92 129:0.05 131:0.83 133:0.60 135:0.65 139:0.70 140:0.45 144:0.57 145:0.39 146:0.81 147:0.05 149:0.66 150:0.69 151:0.53 153:0.72 154:0.63 155:0.58 157:0.61 158:0.72 159:0.76 162:0.66 165:0.70 166:0.88 172:0.88 173:0.69 176:0.55 177:0.05 179:0.25 182:0.61 187:0.21 190:0.89 191:0.82 192:0.59 195:0.86 201:0.68 202:0.61 204:0.85 208:0.54 212:0.86 215:0.78 216:0.55 220:0.74 222:0.76 227:0.84 229:0.61 230:0.64 231:0.89 232:0.72 233:0.76 235:0.12 241:0.64 242:0.51 243:0.06 245:0.94 246:0.61 247:0.82 248:0.52 253:0.52 254:0.84 256:0.58 257:0.84 259:0.21 265:0.58 266:0.80 267:0.36 268:0.62 276:0.88 277:0.69 279:0.82 282:0.71 283:0.78 285:0.47 287:0.61 290:0.70 297:0.36 300:0.94\n1 1:0.74 7:0.63 11:0.64 12:0.86 17:0.59 18:0.70 21:0.05 22:0.93 27:0.72 29:0.62 30:0.33 34:0.92 36:0.76 39:0.54 43:0.79 47:0.93 60:0.87 64:0.77 66:0.79 69:0.74 70:0.49 71:0.92 74:0.55 76:0.94 81:0.83 83:0.91 85:0.72 86:0.74 91:0.35 98:0.60 101:0.87 108:0.57 114:0.89 117:0.86 122:0.57 123:0.55 126:0.54 127:0.18 129:0.05 131:0.61 135:0.56 139:0.80 144:0.57 145:0.74 146:0.40 147:0.47 149:0.55 150:0.89 154:0.72 155:0.67 157:0.87 158:0.92 159:0.15 165:0.93 166:0.93 172:0.41 173:0.93 176:0.73 177:0.70 178:0.55 179:0.59 182:0.98 187:0.68 192:0.87 201:0.93 208:0.64 212:0.42 215:0.78 216:0.08 219:0.95 222:0.76 227:0.61 229:0.61 230:0.63 231:0.89 232:0.99 233:0.76 235:0.22 241:0.12 242:0.19 243:0.80 246:0.93 247:0.55 253:0.60 259:0.21 262:0.93 265:0.61 266:0.38 267:0.52 276:0.31 279:0.86 283:0.82 287:0.61 290:0.89 292:0.95 297:0.36 300:0.33\n2 1:0.74 7:0.64 8:0.63 11:0.92 12:0.86 17:0.94 18:0.99 21:0.13 27:0.66 29:0.62 30:0.40 34:0.89 36:0.53 37:0.55 39:0.54 43:0.74 44:0.85 45:0.92 48:0.99 55:0.42 64:0.77 66:0.74 69:0.10 70:0.73 71:0.92 74:0.92 76:0.97 77:0.96 81:0.73 83:0.60 85:0.72 86:0.99 91:0.12 97:0.89 98:0.70 101:0.97 104:0.87 108:0.09 114:0.79 122:0.80 123:0.09 124:0.44 126:0.54 127:0.45 129:0.05 131:0.61 133:0.60 135:0.44 139:0.98 144:0.57 145:0.74 146:0.89 147:0.91 149:0.67 150:0.80 154:0.88 155:0.67 157:0.83 158:0.72 159:0.67 166:0.93 172:0.96 173:0.11 176:0.73 177:0.70 178:0.55 179:0.14 182:0.75 187:0.21 192:0.87 195:0.86 201:0.93 204:0.85 208:0.64 212:0.91 215:0.61 216:0.14 219:0.87 222:0.76 227:0.61 229:0.61 230:0.64 231:0.90 232:0.97 233:0.73 235:0.31 241:0.01 242:0.30 243:0.77 245:0.95 246:0.61 247:0.97 253:0.58 259:0.21 265:0.71 266:0.75 267:0.52 276:0.93 279:0.82 281:0.91 282:0.71 287:0.61 290:0.79 292:0.91 297:0.36 300:0.84\n2 8:0.71 11:0.85 12:0.86 17:0.59 18:0.86 21:0.13 27:0.49 29:0.62 30:0.14 32:0.62 34:0.61 36:0.43 37:0.29 39:0.54 43:0.18 45:0.73 55:0.52 66:0.64 69:0.10 70:0.94 71:0.92 74:0.29 81:0.45 86:0.61 91:0.72 96:0.68 98:0.50 101:0.87 108:0.09 120:0.61 122:0.57 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34:0.76 36:0.86 37:0.74 39:0.66 41:0.82 43:0.92 45:0.99 47:0.93 58:0.98 60:0.87 64:0.77 66:0.27 69:0.19 70:0.66 74:0.75 78:0.76 79:0.97 81:0.41 83:0.91 85:0.88 86:0.99 91:0.25 96:0.91 97:0.94 98:0.55 99:0.83 100:0.78 101:0.97 104:0.84 106:0.81 108:0.95 111:0.75 114:0.57 117:0.86 120:0.80 122:0.85 123:0.95 124:0.87 126:0.44 127:0.83 129:0.89 131:0.90 133:0.87 135:0.23 137:0.97 139:0.99 140:0.45 141:0.98 144:0.76 146:0.90 147:0.92 149:0.95 150:0.55 151:0.96 152:0.91 153:0.87 154:0.91 155:0.67 157:0.82 158:0.92 159:0.91 161:0.58 162:0.74 164:0.68 165:0.70 166:0.75 167:0.61 169:0.76 172:0.92 173:0.08 176:0.55 177:0.70 179:0.14 181:0.64 182:0.85 186:0.77 187:0.39 189:0.86 191:0.70 192:0.87 196:0.99 199:0.99 201:0.68 202:0.72 204:0.85 206:0.81 208:0.64 212:0.67 216:0.95 219:0.95 222:0.48 223:0.96 224:0.98 227:0.96 229:0.91 231:0.78 232:0.92 234:0.97 235:0.60 238:0.86 241:0.15 242:0.36 243:0.38 245:0.98 246:0.61 247:0.82 248:0.85 253:0.88 255:0.95 256:0.73 259:0.21 260:0.79 261:0.79 262:0.93 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216:0.45 219:0.92 222:0.48 223:0.96 224:0.98 227:0.96 229:0.92 230:0.63 231:0.78 232:0.72 233:0.73 234:0.98 235:0.52 238:0.95 241:0.95 242:0.63 243:0.77 246:0.96 247:0.35 248:0.97 253:0.96 254:0.90 255:0.95 256:0.93 259:0.21 260:0.95 261:0.94 265:0.75 266:0.12 267:0.79 268:0.93 271:0.64 274:1.00 276:0.27 279:0.82 281:0.91 282:0.88 284:0.99 285:0.62 287:0.97 290:0.65 292:0.91 297:0.36 300:0.25\n2 6:0.98 7:0.63 8:0.97 9:0.80 11:0.64 12:0.86 17:0.09 18:0.80 20:0.99 21:0.59 25:0.88 26:0.96 27:0.08 28:0.73 29:0.77 30:0.13 31:1.00 32:0.62 34:0.52 36:0.64 37:0.80 39:0.66 41:0.99 43:0.62 45:0.90 55:0.59 58:1.00 60:0.97 66:0.32 69:0.51 70:0.96 74:0.49 78:0.92 79:1.00 81:0.26 83:0.91 86:0.86 91:0.99 96:0.99 97:0.94 98:0.55 100:0.98 101:0.52 104:0.58 108:0.94 111:0.96 120:0.99 123:0.94 124:0.92 126:0.26 127:0.93 129:0.81 131:0.90 133:0.92 135:0.65 137:1.00 138:0.99 139:0.88 140:0.45 141:0.98 144:0.76 145:0.89 146:0.55 147:0.61 149:0.60 150:0.25 151:1.00 152:1.00 153:0.99 154:0.80 155:0.67 157:0.61 158:0.92 159:0.88 161:0.58 162:0.79 164:0.97 166:0.75 167:0.95 169:0.98 172:0.77 173:0.23 177:0.70 179:0.30 181:0.64 182:0.85 186:0.92 189:0.99 191:0.98 192:0.87 195:0.96 196:1.00 199:1.00 202:0.98 208:0.64 212:0.21 215:0.39 216:0.82 219:0.95 222:0.48 223:0.96 224:0.99 227:0.96 229:0.92 230:0.63 231:0.78 233:0.73 234:0.99 235:0.74 238:0.98 241:0.85 242:0.63 243:0.23 245:0.83 246:0.61 247:0.60 248:0.99 253:0.99 254:0.92 255:0.95 256:0.99 259:0.21 260:0.98 261:0.98 265:0.56 266:0.92 267:0.65 268:0.99 271:0.64 274:1.00 276:0.85 279:0.86 283:0.82 284:1.00 285:0.62 287:0.97 292:0.95 297:0.36 300:0.94\n2 9:0.80 11:0.81 12:0.86 17:0.07 18:0.88 21:0.30 25:0.88 26:0.96 27:0.08 28:0.73 29:0.77 30:0.74 31:0.95 34:0.50 36:0.86 37:0.80 39:0.66 41:0.82 43:0.72 45:0.87 58:0.98 66:0.87 69:0.17 70:0.44 74:0.44 79:0.94 81:0.31 83:0.91 86:0.91 91:0.31 96:0.84 98:0.94 101:0.52 104:0.58 108:0.14 120:0.81 122:0.80 123:0.13 126:0.26 127:0.62 129:0.05 131:0.90 135:0.61 137:0.95 139:0.88 140:0.80 141:0.98 144:0.76 146:0.74 147:0.78 149:0.78 150:0.29 151:0.92 153:0.72 154:0.68 155:0.67 157:0.61 159:0.30 161:0.58 162:0.78 164:0.65 166:0.75 167:0.74 172:0.63 173:0.38 177:0.70 179:0.71 181:0.64 182:0.85 187:0.87 192:0.87 196:0.96 199:0.96 202:0.59 208:0.64 212:0.48 215:0.44 216:0.82 222:0.48 223:0.96 224:1.00 227:0.96 229:0.92 231:0.77 232:0.86 234:1.00 235:0.31 241:0.60 242:0.58 243:0.88 246:0.61 247:0.39 248:0.81 253:0.78 254:0.80 255:0.95 256:0.58 259:0.21 260:0.76 265:0.94 266:0.38 267:0.69 268:0.64 271:0.64 274:0.98 276:0.49 284:0.91 285:0.62 287:0.97 297:0.36 300:0.43\n0 1:0.74 7:0.63 11:0.78 12:0.86 17:0.40 18:0.72 21:0.68 26:0.96 27:0.45 28:0.73 29:0.77 30:0.32 34:0.71 36:0.19 37:0.50 39:0.66 43:0.82 55:0.59 58:0.93 60:0.87 64:0.77 66:0.64 69:0.63 70:0.72 74:0.60 81:0.45 83:0.91 85:0.80 86:0.80 91:0.08 98:0.69 101:0.87 108:0.71 114:0.56 123:0.69 124:0.46 126:0.54 127:0.41 129:0.59 131:0.61 133:0.46 135:0.79 139:0.84 141:0.91 144:0.76 145:0.49 146:0.33 147:0.39 149:0.71 150:0.70 154:0.77 155:0.67 157:0.81 158:0.91 159:0.50 161:0.58 165:0.93 166:0.84 167:0.61 172:0.61 173:0.60 176:0.73 177:0.70 178:0.55 179:0.58 181:0.64 182:0.84 187:0.68 192:0.87 199:0.98 201:0.93 208:0.64 212:0.30 215:0.37 216:0.77 219:0.95 222:0.48 223:0.96 224:0.93 227:0.61 229:0.61 230:0.64 231:0.76 233:0.76 234:0.91 235:0.88 241:0.15 242:0.57 243:0.29 245:0.71 246:0.96 247:0.67 253:0.41 259:0.21 265:0.69 266:0.71 267:0.28 271:0.64 274:0.91 276:0.54 279:0.86 283:0.78 287:0.61 290:0.56 292:0.91 297:0.36 300:0.55\n2 1:0.69 6:0.93 8:0.95 9:0.80 11:0.64 12:0.86 17:0.24 18:0.90 20:0.74 21:0.13 26:0.96 27:0.09 28:0.73 29:0.77 30:0.67 31:0.96 34:0.92 36:0.89 37:0.86 39:0.66 41:0.88 43:0.71 44:0.90 45:0.91 58:0.98 64:0.77 66:0.47 69:0.32 70:0.63 74:0.35 77:0.70 78:0.72 79:0.95 81:0.74 83:0.91 86:0.96 91:0.38 96:0.86 97:0.89 98:0.44 99:0.83 100:0.73 101:0.52 108:0.25 111:0.72 114:0.64 120:0.75 122:0.80 123:0.24 124:0.66 126:0.44 127:0.77 129:0.59 131:0.90 133:0.66 135:0.54 137:0.96 139:0.96 140:0.45 141:0.98 144:0.76 145:0.79 146:0.61 147:0.66 149:0.62 150:0.70 151:0.92 152:0.99 153:0.72 154:0.80 155:0.67 157:0.61 158:0.72 159:0.65 161:0.58 162:0.86 164:0.70 165:0.70 166:0.75 167:0.75 169:0.92 172:0.63 173:0.25 176:0.55 177:0.70 179:0.54 181:0.64 182:0.85 186:0.73 187:0.39 192:0.87 195:0.80 196:0.98 199:0.96 201:0.68 202:0.59 208:0.64 212:0.57 216:0.45 219:0.92 222:0.48 223:0.96 224:0.99 227:0.96 229:0.92 231:0.78 232:0.72 234:0.99 235:0.22 241:0.64 242:0.32 243:0.59 245:0.78 246:0.61 247:0.60 248:0.83 253:0.80 254:0.86 255:0.95 256:0.64 259:0.21 260:0.76 261:0.94 265:0.46 266:0.75 267:0.59 268:0.68 271:0.64 274:0.98 276:0.61 279:0.82 282:0.71 284:0.94 285:0.62 287:0.97 290:0.64 292:0.91 300:0.70\n2 6:0.94 7:0.76 8:0.86 9:0.80 12:0.86 17:0.53 20:0.91 21:0.54 25:0.88 27:0.57 28:0.73 30:0.68 32:0.72 34:0.49 36:0.37 37:0.62 39:0.73 41:0.90 43:0.45 55:0.59 66:0.31 69:0.17 70:0.57 78:0.88 81:0.37 83:0.77 91:0.86 96:0.84 98:0.44 100:0.92 104:0.54 108:0.78 111:0.89 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230:0.71 233:0.66 235:0.84 236:0.94 239:0.88 241:0.74 242:0.22 243:0.31 244:0.93 245:0.60 247:0.76 248:0.77 253:0.96 255:0.78 256:0.98 257:0.93 259:0.21 260:0.93 264:0.95 265:0.21 266:0.75 267:0.99 268:0.98 271:0.43 276:0.29 277:0.87 279:0.79 282:0.74 284:0.99 285:0.62 290:0.69 292:0.95 294:0.95 295:0.99 297:0.36 300:0.55\n2 1:0.65 6:0.96 7:0.75 8:0.84 9:0.75 10:0.97 11:0.49 12:0.20 17:0.57 18:0.74 20:0.94 21:0.05 23:0.99 27:0.37 28:0.83 29:0.62 30:0.45 32:0.67 33:0.97 34:0.81 36:0.43 37:0.94 39:0.60 43:0.65 45:0.85 56:0.97 62:1.00 66:0.86 69:0.45 70:0.66 71:0.88 74:0.77 75:0.98 76:0.94 78:0.89 80:1.00 81:0.72 82:0.99 83:0.72 86:0.73 88:0.99 91:0.67 96:0.91 97:1.00 98:0.50 99:0.95 100:0.94 101:0.52 102:0.96 104:0.95 106:0.81 108:0.35 111:0.90 114:0.95 117:0.86 120:0.84 122:0.90 123:0.33 124:0.37 126:0.53 127:0.82 128:0.99 129:0.05 133:0.36 135:0.72 139:0.73 140:0.45 143:1.00 145:0.59 146:0.70 147:0.74 149:0.64 150:0.59 151:0.84 152:0.88 153:0.72 154:0.59 155:0.58 158:0.81 159:0.70 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120:0.77 123:0.94 124:0.72 126:0.43 127:0.74 128:0.99 129:0.05 133:0.65 135:0.54 137:0.89 139:0.61 140:0.87 143:1.00 145:0.76 146:0.53 147:0.34 149:0.08 150:0.39 151:0.64 153:0.83 154:0.19 155:0.57 159:0.87 161:0.87 162:0.64 164:0.85 167:0.66 168:0.99 170:0.87 172:0.41 173:0.98 174:0.86 175:0.75 176:0.63 177:0.70 179:0.73 181:0.91 187:0.68 190:0.95 191:0.70 192:0.87 193:0.95 195:0.96 196:0.87 197:0.85 201:0.59 202:0.88 205:1.00 208:0.54 212:0.27 215:0.53 216:0.75 220:0.91 222:0.76 226:0.96 230:0.68 232:0.75 233:0.66 235:0.88 236:0.94 239:0.88 241:0.67 242:0.12 243:0.33 244:0.93 245:0.66 247:0.86 248:0.68 253:0.81 254:0.80 255:0.73 256:0.89 257:0.84 259:0.21 260:0.77 264:0.95 265:0.25 266:0.63 267:0.23 268:0.89 271:0.43 276:0.27 277:0.69 282:0.73 283:0.67 284:0.88 285:0.62 290:0.69 297:0.36 300:0.55\n3 1:0.56 6:0.99 7:0.66 8:0.97 9:0.73 10:0.87 12:0.20 17:0.43 18:0.71 20:0.98 21:0.68 23:1.00 27:0.12 28:0.83 29:0.62 30:0.45 31:0.90 32:0.66 34:0.34 36:0.65 37:0.80 39:0.60 43:0.08 55:0.71 62:0.99 66:0.18 69:0.80 70:0.54 71:0.88 74:0.49 75:0.97 76:0.85 77:0.70 78:0.94 79:0.90 81:0.36 83:0.54 86:0.66 91:0.91 96:0.97 97:0.97 98:0.26 100:0.99 102:0.96 108:0.92 111:0.98 114:0.67 120:0.68 122:0.98 123:0.91 124:0.86 126:0.43 127:0.60 128:1.00 129:0.05 133:0.84 135:0.58 137:0.90 139:0.64 140:0.92 143:1.00 145:0.92 146:0.26 147:0.32 149:0.15 150:0.36 151:0.94 152:0.94 153:0.97 154:0.19 155:0.55 158:0.77 159:0.79 161:0.87 162:0.61 163:0.75 164:0.80 167:0.74 168:1.00 169:0.97 170:0.87 172:0.32 173:0.65 174:0.89 175:0.91 176:0.63 177:0.70 179:0.64 181:0.91 186:0.94 187:0.39 189:0.99 190:0.89 192:0.86 193:0.95 195:0.92 196:0.88 197:0.90 201:0.56 202:0.92 205:1.00 208:0.54 212:0.21 215:0.49 216:0.75 222:0.76 226:0.98 230:0.68 232:0.75 233:0.66 235:0.88 236:0.94 238:0.97 239:0.92 241:0.75 242:0.21 243:0.24 244:0.93 245:0.64 247:0.84 248:0.84 253:0.95 254:0.80 255:0.77 256:0.92 257:0.84 259:0.21 260:0.68 261:0.97 264:0.95 265:0.28 266:0.80 267:0.69 268:0.93 271:0.43 276:0.50 277:0.95 279:0.78 282:0.75 284:0.94 285:0.62 290:0.67 294:0.95 295:0.99 297:0.36 300:0.43\n2 6:0.95 7:0.69 8:0.89 9:0.74 10:0.98 11:0.54 12:0.20 17:0.47 18:0.87 20:0.91 21:0.54 23:0.99 27:0.21 28:0.83 29:0.62 30:0.44 33:0.97 34:0.68 36:0.77 37:0.74 39:0.60 43:0.33 45:0.88 55:0.52 62:0.99 66:0.51 69:0.19 70:0.76 71:0.88 74:0.58 75:0.97 76:0.85 78:0.82 81:0.33 83:0.60 86:0.85 91:0.49 96:0.85 97:0.94 98:0.52 100:0.93 101:0.87 104:0.84 106:0.81 108:0.93 111:0.87 120:0.74 122:0.98 123:0.93 124:0.69 126:0.31 127:0.90 128:0.99 129:0.05 133:0.74 135:0.18 139:0.79 140:0.97 143:1.00 146:0.90 147:0.92 149:0.41 150:0.34 151:0.80 152:0.97 153:0.46 154:0.52 155:0.58 159:0.90 161:0.87 162:0.56 164:0.75 167:0.51 168:0.99 169:0.86 170:0.87 172:0.94 173:0.09 174:0.99 177:0.96 179:0.16 181:0.91 186:0.83 187:0.39 190:0.77 192:0.98 193:0.96 197:0.98 201:0.54 202:0.76 205:0.99 206:0.81 208:0.54 212:0.87 215:0.58 216:0.95 220:0.74 222:0.76 226:0.98 230:0.68 232:0.93 233:0.65 235:0.68 236:0.92 239:0.98 241:0.37 242:0.12 243:0.43 244:0.61 245:0.97 247:1.00 248:0.75 253:0.82 254:0.90 255:0.78 256:0.79 259:0.21 260:0.74 261:0.90 264:0.95 265:0.53 266:0.87 267:0.73 268:0.75 271:0.43 275:0.95 276:0.92 277:0.69 279:0.77 283:0.66 285:0.61 291:0.97 294:0.97 295:0.99 297:0.36 300:0.98\n3 1:0.56 8:0.89 9:0.73 10:0.87 11:0.45 12:0.20 17:0.13 18:0.61 20:0.81 21:0.64 23:1.00 27:0.12 28:0.83 29:0.62 30:0.30 31:0.95 34:0.80 36:0.42 37:0.78 39:0.60 43:0.27 45:0.77 56:0.97 62:0.99 66:0.26 69:0.83 70:0.75 71:0.88 74:0.30 75:0.97 78:0.72 79:0.94 81:0.38 83:0.60 86:0.66 91:0.85 96:0.93 97:0.97 98:0.19 99:0.83 100:0.73 101:0.46 108:0.67 111:0.72 120:0.75 122:0.57 123:0.93 124:0.72 126:0.31 127:0.60 128:1.00 129:0.05 133:0.66 135:0.24 137:0.95 139:0.70 140:0.94 143:1.00 145:0.49 146:0.24 147:0.30 149:0.17 150:0.36 151:0.91 152:0.78 153:0.86 154:0.27 155:0.54 159:0.80 161:0.87 162:0.57 164:0.73 165:0.63 167:0.63 168:1.00 169:0.72 170:0.87 172:0.27 173:0.68 174:0.86 175:0.91 177:0.70 178:0.42 179:0.80 181:0.91 186:0.73 187:0.68 190:0.89 192:0.85 196:0.91 197:0.87 201:0.54 202:0.93 205:1.00 208:0.54 212:0.39 216:0.81 219:0.91 220:0.99 222:0.76 226:0.98 232:0.83 235:0.80 236:0.92 239:0.89 241:0.64 242:0.13 243:0.45 244:0.83 245:0.58 247:0.76 248:0.91 253:0.90 254:0.86 255:0.77 256:0.94 257:0.84 259:0.21 260:0.76 261:0.80 264:0.95 265:0.14 266:0.54 267:0.76 268:0.93 271:0.43 275:0.94 276:0.30 277:0.95 279:0.79 284:0.95 285:0.62 292:0.95 294:0.97 295:0.99 300:0.55\n2 8:0.92 9:0.74 10:0.86 11:0.45 12:0.20 17:0.12 18:0.63 21:0.68 23:0.99 27:0.05 28:0.83 29:0.62 30:0.15 31:0.87 34:0.24 36:0.43 37:0.78 39:0.60 43:0.14 45:0.66 55:0.45 62:0.98 66:0.33 69:0.96 70:0.84 71:0.88 74:0.37 75:0.97 79:0.87 81:0.25 83:0.60 86:0.62 91:0.86 96:0.92 97:0.94 98:0.33 101:0.46 104:0.94 106:0.81 108:0.92 120:0.73 122:0.75 123:0.92 124:0.71 126:0.23 127:0.45 128:1.00 129:0.05 133:0.65 135:0.23 137:0.87 139:0.61 140:0.98 143:1.00 146:0.63 147:0.24 149:0.11 150:0.27 151:0.76 153:0.72 154:0.16 155:0.58 158:0.77 159:0.76 161:0.87 162:0.57 163:0.80 164:0.83 167:0.56 168:1.00 170:0.87 172:0.27 173:0.98 174:0.86 175:0.75 177:0.38 179:0.82 181:0.91 187:0.87 190:0.89 192:0.98 196:0.87 197:0.86 202:0.88 205:0.99 206:0.81 208:0.54 212:0.15 215:0.49 216:0.83 220:0.62 222:0.76 226:0.98 232:0.88 235:0.80 239:0.88 241:0.53 242:0.14 243:0.28 244:0.93 245:0.53 247:0.67 248:0.70 253:0.87 255:0.78 256:0.88 257:0.93 259:0.21 260:0.74 264:0.95 265:0.35 266:0.63 267:0.36 268:0.88 271:0.43 275:0.91 276:0.29 277:0.69 279:0.77 283:0.66 284:0.86 285:0.62 294:0.95 295:0.99 297:0.36 300:0.55\n1 1:0.66 7:0.75 10:0.93 11:0.82 17:0.89 18:0.86 21:0.13 22:0.93 27:0.69 29:0.71 30:0.68 32:0.72 34:1.00 36:0.46 37:0.43 39:0.40 43:0.65 44:0.92 45:0.85 47:0.93 64:0.77 66:0.63 69:0.81 70:0.79 71:0.67 74:0.68 77:0.70 81:0.68 83:0.69 85:0.72 86:0.90 91:0.38 97:0.94 98:0.61 99:0.95 101:0.96 106:0.81 108:0.65 114:0.92 117:0.86 122:0.93 123:0.63 124:0.47 126:0.51 127:0.26 129:0.59 133:0.47 135:0.97 139:0.93 145:0.41 146:0.71 147:0.76 149:0.76 150:0.56 154:0.52 155:0.55 158:0.81 159:0.40 165:0.81 170:0.90 172:0.65 173:0.81 174:0.90 176:0.70 177:0.88 178:0.55 179:0.41 187:0.21 192:0.57 195:0.74 197:0.91 201:0.63 204:0.83 206:0.81 208:0.61 212:0.58 215:0.84 216:0.13 219:0.94 222:0.35 230:0.77 232:0.99 233:0.76 235:0.31 239:0.92 241:0.15 242:0.24 243:0.69 245:0.76 247:0.79 253:0.67 259:0.21 262:0.93 265:0.62 266:0.54 267:0.73 271:0.89 276:0.58 279:0.78 281:0.91 282:0.80 283:0.67 290:0.92 292:0.88 297:0.36 300:0.84\n1 1:0.60 8:0.81 9:0.70 10:0.98 11:0.52 17:0.12 18:0.82 21:0.74 23:0.96 27:0.45 29:0.71 30:0.09 31:0.86 32:0.66 34:0.59 36:0.19 37:0.50 39:0.40 43:0.24 44:0.88 45:0.81 55:0.45 62:0.96 64:0.77 66:0.29 69:0.71 70:0.94 71:0.67 74:0.76 75:0.98 79:0.86 81:0.52 83:0.56 86:0.88 91:0.23 97:0.94 98:0.46 99:0.95 101:0.65 102:0.96 108:0.54 120:0.54 122:0.83 123:0.85 124:0.82 126:0.51 127:0.55 128:0.95 129:0.59 133:0.80 135:0.89 137:0.86 139:0.87 140:0.45 145:0.54 146:0.72 147:0.76 149:0.27 150:0.41 151:0.47 153:0.48 154:0.60 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279:0.86 283:0.82 285:0.62 290:0.64 297:0.36 300:0.43\n4 1:0.74 6:0.98 7:0.81 8:0.98 9:0.80 11:0.57 12:0.01 17:0.01 20:0.99 21:0.47 27:0.09 28:0.96 30:0.85 32:0.80 34:0.66 36:0.86 37:0.78 39:0.83 41:1.00 43:0.92 60:1.00 66:0.35 69:0.40 70:0.41 74:0.90 76:0.85 77:0.70 78:0.92 81:0.57 83:0.91 85:0.96 91:0.89 96:1.00 98:0.44 100:0.99 101:0.82 108:0.85 111:0.99 114:0.80 120:0.99 121:0.97 122:0.43 123:0.84 124:0.83 126:0.54 127:0.62 129:0.93 133:0.84 135:0.89 138:1.00 140:0.45 145:0.67 146:0.37 147:0.44 149:0.91 150:0.74 151:1.00 152:0.99 153:0.99 154:0.91 155:0.67 158:0.92 159:0.79 161:0.48 162:0.66 164:0.98 165:0.80 167:0.79 169:0.98 172:0.54 173:0.21 176:0.73 177:0.38 179:0.56 181:0.92 186:0.92 187:0.68 189:0.99 191:0.92 192:0.59 195:0.91 201:0.74 202:0.97 204:0.89 208:0.64 212:0.42 215:0.63 216:0.89 222:0.76 230:0.87 232:0.88 233:0.76 235:0.60 238:0.99 241:0.71 242:0.63 243:0.33 245:0.68 247:0.30 248:1.00 253:1.00 255:0.79 256:0.98 259:0.21 260:0.99 261:0.99 265:0.46 266:0.63 267:0.84 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285:0.62 290:0.95 297:0.36 300:0.43\n1 7:0.81 9:0.80 11:0.57 12:0.01 17:0.36 21:0.13 27:0.43 28:0.96 30:0.85 34:0.19 36:0.98 37:0.84 39:0.83 41:0.97 43:0.85 60:0.95 66:0.46 69:0.62 70:0.44 74:0.90 78:0.91 81:0.56 83:0.89 85:0.96 91:0.78 96:0.97 98:0.29 101:0.82 108:0.47 111:0.97 114:0.74 120:0.93 121:0.90 122:0.43 123:0.45 124:0.67 126:0.32 127:0.65 129:0.81 133:0.67 135:0.85 140:0.45 145:0.39 146:0.53 147:0.59 149:0.93 150:0.42 151:0.98 153:0.93 154:0.81 155:0.67 158:0.92 159:0.77 161:0.48 162:0.81 164:0.89 167:0.78 169:0.97 172:0.59 173:0.44 176:0.56 177:0.38 179:0.56 181:0.92 187:0.21 191:0.77 192:0.59 202:0.83 204:0.89 208:0.64 212:0.76 216:0.50 222:0.76 230:0.87 232:0.74 233:0.76 235:0.12 238:0.98 241:0.70 242:0.63 243:0.58 245:0.75 247:0.30 248:0.96 253:0.98 255:0.79 256:0.88 260:0.94 265:0.31 266:0.71 267:0.36 268:0.87 271:0.42 276:0.44 279:0.86 283:0.82 285:0.62 290:0.74 300:0.70\n3 1:0.74 6:0.96 8:0.94 9:0.80 11:0.57 12:0.01 17:0.32 20:0.99 21:0.30 27:0.34 28:0.96 30:0.76 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78:0.80 81:0.77 91:0.94 100:0.82 104:0.74 111:0.79 120:0.94 126:0.54 135:0.37 140:0.45 150:0.57 151:0.83 152:0.74 153:0.95 161:0.45 162:0.76 164:0.94 167:0.95 169:0.79 175:0.75 181:0.87 186:0.81 189:0.89 191:0.93 202:0.90 215:0.91 216:0.27 235:0.05 238:0.87 241:0.81 244:0.61 248:0.92 256:0.92 257:0.65 259:0.21 260:0.95 261:0.74 267:0.85 268:0.93 277:0.69 283:0.82 285:0.62 297:0.36\n2 6:0.81 7:0.81 8:0.61 12:0.69 17:0.09 20:0.98 21:0.30 27:0.21 28:0.73 30:0.56 34:0.62 36:0.88 37:0.74 43:0.52 55:0.71 66:0.15 69:0.10 70:0.61 78:0.76 81:0.69 91:0.91 98:0.57 100:0.78 104:0.84 106:0.81 108:0.98 111:0.75 120:0.98 123:0.90 124:0.91 126:0.54 127:0.90 129:0.97 133:0.92 135:0.44 140:0.45 146:0.97 147:0.98 149:0.91 150:0.50 151:0.86 152:0.90 153:0.99 154:0.41 159:0.97 161:0.45 162:0.66 164:0.97 167:0.83 169:0.76 172:0.96 173:0.05 177:0.05 179:0.10 181:0.87 186:0.76 187:0.39 189:0.83 191:0.89 202:0.96 206:0.81 212:0.54 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.86 241:0.71 242:0.82 243:0.23 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248:0.49 253:0.52 255:0.95 256:0.45 259:0.21 260:0.55 261:0.97 265:0.67 266:0.17 267:0.23 268:0.46 271:0.31 276:0.17 284:0.89 285:0.62 290:0.71 297:0.36 300:0.18\n1 11:0.78 12:0.79 17:0.12 20:0.95 21:0.30 27:0.31 30:0.13 34:0.31 36:0.90 37:0.46 39:0.51 43:0.77 55:0.71 66:0.12 69:0.19 70:0.98 74:0.83 78:0.86 81:0.60 85:0.80 91:0.11 98:0.24 100:0.73 101:0.52 108:0.95 111:0.83 120:0.58 123:0.95 124:0.99 126:0.32 127:0.95 129:0.99 131:0.91 133:0.99 135:0.65 140:0.45 144:0.76 146:0.83 147:0.86 149:0.74 150:0.44 151:0.52 152:0.88 153:0.48 154:0.70 157:0.76 159:0.98 162:0.86 164:0.55 166:0.87 169:0.72 172:0.76 173:0.05 177:0.38 179:0.17 182:0.72 186:0.86 187:0.39 190:0.77 202:0.36 212:0.22 215:0.72 216:0.60 220:0.87 222:0.76 227:0.96 229:0.61 231:0.94 235:0.31 241:0.07 242:0.27 243:0.25 245:0.84 246:0.61 247:0.67 248:0.52 253:0.59 256:0.45 259:0.21 260:0.58 261:0.84 265:0.26 266:1.00 267:0.52 268:0.46 271:0.17 276:0.94 285:0.50 287:0.61 297:0.36 300:0.98\n2 6:0.85 8:0.75 9:0.80 12:0.79 17:0.15 20:0.91 21:0.78 27:0.32 30:0.56 34:0.24 36:0.91 37:0.42 39:0.51 41:0.82 43:0.27 55:0.96 66:0.19 69:0.78 70:0.42 74:0.63 78:0.94 83:0.76 91:0.72 96:0.80 98:0.23 100:0.92 108:0.83 111:0.92 120:0.69 122:0.67 123:0.82 124:0.90 127:0.37 129:0.05 131:0.98 133:0.89 135:0.79 140:0.92 144:0.76 146:0.36 147:0.43 149:0.40 151:0.87 152:0.85 153:0.48 154:0.19 155:0.67 157:0.61 159:0.73 162:0.49 163:0.93 164:0.57 166:0.61 169:0.90 172:0.21 173:0.62 177:0.38 178:0.51 179:0.73 182:0.96 186:0.93 187:0.39 189:0.86 192:0.59 202:0.66 208:0.64 212:0.23 216:0.55 222:0.76 227:0.99 229:0.98 231:0.95 235:0.22 238:0.86 241:0.37 242:0.63 243:0.33 244:0.61 245:0.48 246:0.61 247:0.39 248:0.78 253:0.79 255:0.79 256:0.45 259:0.21 260:0.70 261:0.90 265:0.25 266:0.71 267:0.88 268:0.46 271:0.17 276:0.44 277:0.69 285:0.62 287:0.99 300:0.33\n2 8:0.76 11:0.78 12:0.79 17:0.24 20:0.86 21:0.78 27:0.56 30:0.37 34:0.49 36:0.83 37:0.53 39:0.51 43:0.50 55:0.71 66:0.27 69:0.80 70:0.96 74:0.92 78:0.93 85:0.72 91:0.56 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8:0.62 12:0.37 17:0.40 20:0.88 21:0.71 28:0.66 30:0.37 32:0.80 34:0.40 36:0.28 37:0.97 43:0.30 55:0.71 66:0.62 69:0.73 70:0.89 78:0.74 81:0.76 91:0.54 98:0.47 100:0.77 104:0.80 108:0.70 111:0.73 120:0.93 123:0.68 124:0.75 126:0.54 127:0.66 129:0.95 133:0.87 135:0.69 140:0.45 145:0.63 146:0.40 147:0.46 149:0.71 150:0.57 151:0.80 152:0.83 153:0.93 154:0.22 159:0.89 161:0.61 162:0.72 164:0.92 167:0.55 169:0.75 172:0.77 173:0.44 177:0.05 179:0.43 181:0.91 186:0.75 187:0.21 195:0.97 202:0.87 212:0.42 215:0.48 216:0.98 235:0.84 241:0.49 242:0.82 243:0.23 245:0.70 247:0.12 248:0.90 256:0.89 259:0.21 260:0.93 261:0.75 265:0.49 266:0.75 267:0.44 268:0.90 271:0.55 276:0.70 283:0.60 285:0.62 297:0.36 300:0.70\n3 8:0.88 12:0.37 17:0.57 20:0.76 21:0.22 27:0.72 28:0.66 30:0.21 34:0.19 36:0.25 37:0.86 43:0.44 55:0.45 66:0.54 69:0.45 70:0.37 78:0.76 81:0.92 91:0.87 98:0.33 100:0.80 104:0.54 108:0.35 111:0.76 120:0.77 123:0.33 124:0.45 126:0.54 127:0.83 129:0.59 133:0.47 135:0.54 140:0.45 145:0.70 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242:0.82 243:0.16 245:0.48 247:0.12 248:0.79 256:0.85 259:0.21 260:0.86 261:0.82 265:0.84 266:0.54 267:0.36 268:0.86 271:0.55 276:0.41 283:0.60 285:0.62 300:0.33\n1 12:0.37 17:0.36 21:0.30 27:0.45 28:0.66 30:0.68 34:0.71 36:0.30 37:0.50 43:0.33 55:0.71 66:0.68 69:0.68 70:0.43 81:0.91 91:0.22 98:0.53 108:0.52 123:0.49 124:0.41 126:0.54 127:0.37 129:0.59 133:0.47 135:0.69 145:0.52 146:0.64 147:0.69 149:0.42 150:0.83 154:0.24 159:0.48 161:0.61 163:0.75 167:0.49 172:0.37 173:0.67 177:0.05 178:0.55 179:0.85 181:0.91 187:0.68 212:0.42 215:0.72 216:0.77 235:0.31 241:0.21 242:0.82 243:0.72 245:0.45 247:0.12 259:0.21 265:0.55 266:0.27 267:0.28 271:0.55 276:0.28 297:0.36 300:0.33\n3 6:0.85 7:0.81 8:0.78 12:0.37 17:0.73 20:0.76 21:0.74 27:0.43 28:0.66 30:0.21 32:0.80 34:0.59 36:0.62 37:0.69 43:0.49 55:0.45 66:0.54 69:0.38 70:0.37 78:0.89 81:0.73 91:0.76 98:0.49 100:0.95 104:0.58 108:0.59 111:0.91 120:0.85 123:0.56 124:0.45 126:0.54 127:0.75 129:0.59 133:0.47 135:0.42 140:0.45 145:0.98 146:0.05 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216:0.55 220:0.62 235:0.12 238:1.00 241:0.97 242:0.82 243:0.34 245:0.37 247:0.12 248:0.92 256:0.98 259:0.21 260:0.95 261:0.99 265:0.23 266:0.12 267:0.69 268:0.98 271:0.55 276:0.14 283:0.60 285:0.62 297:0.36 300:0.13\n3 8:1.00 12:0.37 17:0.77 20:0.80 21:0.13 27:0.54 28:0.66 30:0.76 34:0.45 36:0.97 37:0.85 43:0.32 55:0.92 66:0.72 69:0.70 70:0.22 78:0.72 81:0.94 91:0.85 98:0.78 100:0.78 104:0.72 108:0.53 111:0.73 120:0.85 123:0.51 126:0.54 127:0.26 129:0.05 135:0.74 140:0.45 146:0.66 147:0.71 149:0.31 150:0.89 151:0.78 152:0.78 153:0.89 154:0.23 159:0.25 161:0.61 162:0.79 163:0.90 164:0.88 167:0.83 169:0.75 172:0.27 173:0.82 177:0.05 179:0.91 181:0.91 186:0.73 191:0.90 202:0.81 212:0.42 215:0.84 216:0.24 220:0.74 235:0.12 241:0.89 242:0.82 243:0.75 247:0.12 248:0.79 256:0.83 259:0.21 260:0.86 261:0.75 265:0.78 266:0.23 267:0.28 268:0.85 271:0.55 276:0.24 283:0.60 285:0.62 297:0.36 300:0.18\n1 7:0.81 8:0.85 12:0.37 17:0.57 20:0.76 27:0.20 28:0.66 30:0.94 32:0.80 34:0.24 36:0.41 37:0.73 43:0.43 55:0.71 66:0.64 69:0.45 70:0.20 78:0.72 81:0.96 91:0.29 98:0.81 100:0.73 104:0.88 106:0.81 108:0.68 111:0.72 123:0.66 124:0.47 126:0.54 127:0.52 129:0.59 133:0.47 135:0.71 145:0.39 146:0.46 147:0.53 149:0.76 150:0.94 152:0.75 154:0.33 159:0.65 161:0.61 167:0.54 169:0.72 172:0.80 173:0.34 177:0.05 178:0.55 179:0.36 181:0.91 186:0.73 187:0.68 195:0.82 206:0.81 212:0.86 215:0.96 216:0.72 230:0.87 233:0.76 235:0.02 241:0.44 242:0.82 243:0.32 245:0.88 247:0.12 259:0.21 261:0.73 265:0.81 266:0.63 267:0.23 271:0.55 276:0.77 283:0.82 297:0.36 300:0.43\n2 7:0.81 8:0.93 12:0.37 17:0.13 21:0.54 27:0.54 28:0.66 30:0.33 32:0.80 34:0.55 36:0.56 37:0.95 43:0.50 55:0.52 66:0.86 69:0.27 70:0.69 81:0.94 91:0.67 98:0.92 104:0.58 108:0.21 120:0.75 123:0.20 126:0.54 127:0.54 129:0.05 135:0.60 140:0.45 145:0.47 146:0.57 147:0.63 149:0.65 150:0.90 151:0.73 153:0.78 154:0.39 159:0.21 161:0.61 162:0.60 164:0.73 167:0.73 172:0.61 173:0.58 177:0.05 179:0.71 181:0.91 187:0.39 191:0.82 195:0.56 202:0.67 212:0.85 215:0.58 216:0.24 230:0.87 233:0.76 235:0.68 241:0.74 242:0.82 243:0.87 247:0.12 248:0.68 256:0.64 259:0.21 260:0.76 265:0.92 266:0.27 267:0.28 268:0.67 271:0.55 276:0.51 283:0.60 285:0.62 297:0.36 300:0.55\n2 7:0.81 12:0.37 17:0.49 20:0.81 21:0.22 27:0.64 28:0.66 30:0.54 32:0.80 34:0.92 36:0.90 37:0.55 43:0.49 55:0.71 66:0.88 69:0.27 70:0.42 78:0.74 81:0.92 91:0.74 98:0.98 100:0.79 104:0.58 108:0.21 111:0.74 120:0.73 123:0.20 126:0.54 127:0.80 129:0.05 135:0.77 140:0.45 145:0.84 146:0.85 147:0.88 149:0.62 150:0.86 151:0.66 152:0.82 153:0.48 154:0.38 159:0.42 161:0.61 162:0.74 164:0.67 167:0.83 169:0.75 172:0.67 173:0.37 177:0.05 179:0.69 181:0.91 186:0.75 187:0.21 195:0.67 202:0.56 212:0.61 215:0.79 216:0.21 230:0.87 233:0.76 235:0.22 241:0.91 242:0.82 243:0.89 247:0.12 248:0.66 256:0.58 259:0.21 260:0.74 261:0.76 265:0.98 266:0.54 267:0.59 268:0.59 271:0.55 276:0.56 285:0.62 297:0.36 300:0.33\n2 7:0.81 8:0.80 12:0.37 17:0.36 20:0.83 21:0.05 25:0.88 27:0.37 28:0.66 30:0.72 32:0.80 34:0.42 36:0.78 37:0.67 43:0.49 55:0.71 66:0.84 69:0.25 70:0.29 78:0.75 81:0.95 91:0.83 98:0.98 100:0.79 104:0.58 108:0.20 111:0.75 120:0.64 123:0.19 126:0.54 127:0.85 129:0.05 135:0.39 140:0.45 145:0.76 146:0.82 147:0.85 149:0.54 150:0.92 151:0.59 152:0.79 153:0.91 154:0.37 159:0.38 161:0.61 162:0.69 163:0.75 164:0.82 167:0.83 169:0.77 172:0.54 173:0.39 177:0.05 179:0.83 181:0.91 186:0.76 191:0.73 195:0.63 202:0.74 212:0.39 215:0.91 216:0.24 220:0.62 230:0.65 233:0.63 235:0.05 241:0.90 242:0.82 243:0.85 247:0.12 248:0.58 256:0.75 259:0.21 260:0.65 261:0.76 265:0.98 266:0.47 267:0.28 268:0.77 271:0.55 276:0.45 283:0.60 285:0.62 297:0.36 300:0.25\n3 6:0.94 7:0.81 8:0.94 12:0.37 17:0.20 20:0.99 21:0.05 27:0.16 28:0.66 30:0.91 32:0.80 34:0.64 36:0.73 37:0.90 43:0.42 55:0.59 66:0.69 69:0.47 70:0.13 78:0.78 81:0.95 91:0.98 98:0.44 100:0.90 104:0.58 108:0.36 111:0.83 120:0.97 123:0.35 126:0.54 127:0.14 129:0.05 135:0.34 140:0.45 145:0.47 146:0.32 147:0.39 149:0.33 150:0.92 151:0.83 152:0.91 153:0.96 154:0.32 159:0.13 161:0.61 162:0.80 164:0.96 167:0.84 169:0.87 172:0.21 173:0.68 177:0.05 179:0.69 181:0.91 186:0.78 189:0.95 191:0.88 195:0.65 202:0.93 212:0.61 215:0.91 216:0.48 220:0.62 230:0.65 233:0.63 235:0.05 238:0.98 241:0.93 242:0.82 243:0.73 247:0.12 248:0.95 256:0.95 259:0.21 260:0.97 261:0.88 265:0.46 266:0.17 267:0.79 268:0.95 271:0.55 276:0.23 283:0.82 285:0.62 297:0.36 300:0.13\n2 7:0.81 8:0.92 12:0.37 17:0.32 20:0.84 21:0.22 27:0.64 28:0.66 30:0.54 32:0.80 34:0.91 36:0.90 37:0.55 43:0.49 55:0.71 66:0.88 69:0.27 70:0.42 78:0.74 81:0.92 91:0.76 98:0.98 100:0.73 104:0.58 108:0.21 111:0.73 120:0.78 123:0.20 126:0.54 127:0.80 129:0.05 135:0.78 140:0.45 145:0.84 146:0.85 147:0.88 149:0.62 150:0.86 151:0.73 152:0.82 153:0.78 154:0.38 159:0.42 161:0.61 162:0.73 164:0.85 167:0.84 169:0.75 172:0.67 173:0.37 177:0.05 179:0.69 181:0.91 186:0.75 195:0.67 202:0.77 212:0.61 215:0.79 216:0.21 220:0.82 230:0.87 233:0.76 235:0.22 241:0.93 242:0.82 243:0.89 247:0.12 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247:0.39 253:0.29 254:0.86 257:0.65 259:0.21 265:0.44 266:0.63 267:0.28 271:0.18 276:0.49 297:0.36 300:0.33\n2 1:0.74 8:0.92 9:0.80 12:0.69 17:0.67 21:0.68 27:0.09 28:0.78 30:0.62 32:0.64 34:0.82 36:0.94 37:0.89 39:0.50 43:0.18 55:0.59 66:0.61 69:0.27 70:0.23 81:0.35 91:0.62 96:0.67 98:0.36 104:0.58 108:0.77 114:0.55 120:0.53 123:0.75 124:0.43 126:0.54 127:0.61 129:0.59 133:0.47 135:0.54 140:0.93 145:0.61 146:0.05 147:0.05 149:0.14 150:0.62 151:0.46 153:0.48 154:0.12 155:0.67 159:0.47 161:0.90 162:0.47 164:0.54 167:0.83 172:0.27 173:0.31 175:0.75 176:0.66 177:0.05 178:0.52 179:0.94 181:0.48 187:0.21 190:0.77 191:0.75 192:0.59 195:0.67 201:0.74 202:0.76 208:0.64 212:0.61 215:0.37 216:0.52 220:0.99 222:0.60 235:0.84 241:0.74 242:0.82 243:0.23 244:0.83 245:0.42 247:0.12 248:0.46 253:0.37 255:0.79 256:0.45 257:0.65 259:0.21 260:0.53 265:0.38 266:0.23 267:0.87 268:0.46 271:0.18 276:0.17 277:0.69 285:0.62 290:0.55 297:0.36 300:0.18\n2 7:0.72 8:0.73 11:0.57 12:0.69 17:0.90 20:0.90 21:0.64 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266:0.71 267:0.82 276:0.50 279:0.86 282:0.88 283:0.82 290:0.98 297:0.36 299:0.97 300:0.43\n3 1:0.74 6:0.87 7:0.81 8:0.77 9:0.80 11:0.57 12:0.51 17:0.79 20:0.98 21:0.64 27:0.37 28:0.99 30:0.43 32:0.80 34:0.83 36:0.29 37:0.36 39:0.39 41:0.90 43:0.89 60:0.87 66:0.23 69:0.52 70:0.73 74:0.98 77:0.70 78:0.98 81:0.70 83:0.81 85:0.96 91:0.85 96:0.88 98:0.71 100:0.97 101:0.85 104:0.58 108:0.40 111:0.97 114:0.72 120:0.79 121:0.90 122:0.57 123:0.69 124:0.55 126:0.54 127:0.47 129:0.59 133:0.47 135:0.61 140:0.45 145:0.72 146:0.61 147:0.66 149:0.96 150:0.75 151:0.97 152:0.96 153:0.88 154:0.87 155:0.67 158:0.92 159:0.52 161:0.88 162:0.73 164:0.81 167:0.83 169:0.94 172:0.76 173:0.49 176:0.73 177:0.38 179:0.35 181:0.47 186:0.98 189:0.90 191:0.76 192:0.59 195:0.72 201:0.74 202:0.74 204:0.89 208:0.64 212:0.77 215:0.53 216:0.15 222:0.48 230:0.83 232:0.79 233:0.66 235:0.84 238:0.89 241:0.85 242:0.28 243:0.61 245:0.90 247:0.60 248:0.86 253:0.92 255:0.79 256:0.75 259:0.21 260:0.80 261:0.95 265:0.52 266:0.54 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290:0.92 297:0.36 300:0.18\n2 1:0.74 6:0.97 7:0.81 8:0.84 9:0.80 11:0.87 12:0.51 17:0.15 18:0.98 20:0.99 21:0.30 22:0.93 27:0.21 28:0.73 29:0.62 30:0.42 31:0.99 34:0.44 36:0.84 37:0.74 39:0.67 41:0.95 43:0.97 45:0.93 47:0.93 60:0.98 64:0.77 66:0.43 69:0.15 70:0.82 71:0.73 74:0.85 78:0.85 79:0.99 81:0.59 83:0.91 85:0.90 86:0.96 91:0.73 96:0.96 97:0.89 98:0.58 100:0.91 101:0.97 104:0.84 106:0.81 108:0.12 111:0.86 114:0.65 117:0.86 120:0.92 121:0.90 122:0.85 123:0.12 124:0.79 126:0.54 127:0.81 129:0.59 131:0.93 133:0.81 135:0.44 137:0.99 139:0.97 140:0.45 144:0.76 146:0.88 147:0.90 149:0.95 150:0.76 151:0.99 152:0.99 153:0.95 154:0.97 155:0.67 157:0.98 158:0.92 159:0.82 161:0.71 162:0.61 164:0.86 165:0.93 166:0.89 167:0.73 169:0.86 172:0.87 173:0.10 176:0.73 177:0.70 179:0.22 181:0.73 182:0.99 186:0.85 187:0.39 189:0.98 192:0.87 196:0.99 201:0.93 202:0.88 204:0.89 206:0.81 208:0.64 212:0.61 215:0.44 216:0.95 219:0.95 222:0.90 227:0.96 229:0.99 230:0.87 231:0.85 232:0.90 233:0.76 235:0.52 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39:0.67 43:0.82 64:0.77 66:0.69 69:0.70 70:0.13 71:0.73 74:0.43 81:0.48 83:0.91 85:0.72 86:0.74 91:0.20 98:0.34 101:0.87 108:0.53 114:0.59 122:0.57 123:0.51 126:0.54 127:0.12 129:0.05 131:0.61 135:0.26 139:0.72 144:0.76 145:0.52 146:0.42 147:0.49 149:0.58 150:0.72 154:0.77 155:0.67 157:0.85 159:0.16 161:0.71 163:0.75 165:0.93 166:0.89 167:0.65 172:0.21 173:0.70 176:0.73 177:0.70 178:0.55 179:0.54 181:0.73 182:0.97 187:0.68 192:0.87 201:0.93 208:0.64 212:0.61 215:0.39 216:0.77 222:0.90 227:0.61 229:0.61 231:0.83 235:0.74 241:0.12 242:0.63 243:0.73 246:0.99 247:0.30 253:0.42 259:0.21 265:0.36 266:0.17 267:0.23 271:0.69 276:0.13 287:0.61 290:0.59 297:0.36 300:0.13\n1 1:0.74 11:0.78 12:0.51 17:0.24 18:0.83 21:0.54 27:0.45 28:0.73 29:0.62 30:0.44 34:0.86 36:0.18 37:0.50 39:0.67 43:0.82 55:0.71 64:0.77 66:0.10 69:0.69 70:0.82 71:0.73 74:0.43 81:0.48 83:0.91 85:0.72 86:0.75 91:0.18 98:0.25 101:0.87 108:0.53 114:0.59 123:0.88 124:0.90 126:0.54 127:0.38 129:0.05 131:0.61 133:0.89 135:0.85 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151:0.82 152:0.82 153:0.46 154:0.65 155:0.67 157:0.97 158:0.92 159:0.81 161:0.71 162:0.77 164:0.54 166:0.74 167:0.67 169:0.72 172:0.75 173:0.59 177:0.70 179:0.20 181:0.73 182:0.98 186:0.77 187:0.68 192:0.87 195:0.97 196:0.96 202:0.55 208:0.64 212:0.58 216:0.72 219:0.95 220:0.62 222:0.90 227:0.96 229:0.99 231:0.84 235:0.31 241:0.24 242:0.21 243:0.17 245:0.89 246:0.61 247:0.90 248:0.74 253:0.79 255:0.95 256:0.58 259:0.21 260:0.68 261:0.77 265:0.38 266:0.63 267:0.36 268:0.58 271:0.69 276:0.82 279:0.86 282:0.88 283:0.82 284:0.87 285:0.62 287:0.99 292:0.95 299:0.88 300:0.94\n3 1:0.74 6:0.99 7:0.63 8:0.99 9:0.80 12:0.51 17:0.13 20:0.95 21:0.30 27:0.17 28:0.73 29:0.62 30:0.11 31:0.99 32:0.80 34:0.62 36:0.73 37:1.00 39:0.67 41:0.98 43:0.14 44:0.93 48:0.91 55:0.45 60:0.87 64:0.77 66:0.61 69:0.63 70:0.54 71:0.73 74:0.59 76:0.85 77:0.70 78:0.96 79:0.99 81:0.59 83:0.91 91:0.92 96:0.97 97:0.99 98:0.54 100:1.00 108:0.48 111:0.99 114:0.65 120:0.92 123:0.46 124:0.43 126:0.54 127:0.48 129:0.59 131:0.93 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20:0.75 21:0.78 27:0.12 28:0.76 29:0.56 30:0.38 32:0.69 34:0.21 36:0.41 37:0.29 39:0.25 43:0.62 45:0.83 55:0.92 66:0.24 69:0.70 70:0.70 71:0.97 74:0.60 76:0.85 77:0.70 78:0.82 83:0.60 91:0.73 96:0.98 98:0.67 100:0.91 101:0.52 104:0.58 108:0.84 111:0.85 120:0.95 123:0.64 124:0.78 127:0.81 129:0.81 133:0.76 135:0.28 138:0.99 140:0.45 144:0.85 145:0.74 146:0.29 147:0.05 149:0.61 151:0.90 152:0.74 153:0.83 154:0.52 155:0.58 159:0.62 161:0.78 162:0.72 164:0.90 167:0.79 169:0.89 172:0.45 173:0.67 175:0.75 177:0.05 179:0.64 181:0.96 186:0.83 190:0.77 191:0.77 192:0.59 195:0.78 202:0.87 204:0.85 208:0.54 212:0.18 216:0.15 220:0.62 222:0.60 230:0.75 232:0.78 233:0.76 235:0.05 241:0.84 242:0.18 243:0.10 244:0.61 245:0.68 247:0.67 248:0.97 253:0.96 255:0.74 256:0.90 257:0.84 259:0.21 260:0.94 261:0.76 265:0.44 266:0.80 267:0.65 268:0.90 271:0.52 276:0.60 277:0.69 282:0.77 285:0.56 300:0.55\n2 1:0.69 6:0.87 7:0.72 8:0.81 9:0.76 11:0.91 12:0.51 17:0.49 20:0.78 21:0.05 27:0.18 28:0.76 29:0.56 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282:0.77 283:0.82 285:0.56 290:0.85 297:0.36 300:0.70\n0 1:0.74 11:0.91 12:0.51 17:0.36 18:0.76 21:0.59 27:0.45 28:0.76 29:0.56 30:0.33 32:0.80 34:0.81 36:0.17 37:0.50 39:0.25 43:0.32 44:0.93 45:0.73 64:0.77 66:0.45 69:0.69 70:0.69 71:0.97 74:0.67 77:0.70 81:0.45 83:0.60 86:0.79 91:0.17 97:0.89 98:0.30 99:0.83 101:0.52 108:0.53 114:0.58 122:0.41 123:0.51 124:0.66 126:0.54 127:0.34 129:0.05 133:0.60 135:0.86 139:0.88 144:0.85 145:0.47 146:0.39 147:0.46 149:0.24 150:0.67 154:0.76 155:0.67 158:0.91 159:0.47 161:0.78 163:0.75 165:0.70 167:0.46 172:0.27 173:0.68 176:0.73 177:0.70 178:0.55 179:0.84 181:0.96 187:0.68 192:0.87 195:0.73 201:0.93 208:0.64 212:0.19 215:0.39 216:0.77 219:0.95 222:0.60 235:0.80 241:0.12 242:0.19 243:0.58 245:0.47 247:0.55 253:0.43 254:0.74 259:0.21 265:0.32 266:0.47 267:0.28 271:0.52 276:0.30 279:0.86 282:0.88 283:0.77 290:0.58 292:0.91 297:0.36 300:0.43\n2 1:0.69 6:0.84 8:0.77 9:0.76 11:0.91 12:0.51 17:0.03 18:0.60 20:0.94 21:0.05 27:0.14 28:0.76 29:0.56 30:0.41 32:0.69 34:0.37 36:0.25 37:0.67 39:0.25 43:0.67 45:0.81 66:0.49 69:0.57 70:0.45 71:0.97 74:0.58 77:0.70 78:0.81 81:0.82 83:0.60 86:0.62 91:0.44 96:0.79 98:0.36 99:0.83 100:0.84 101:0.52 108:0.61 111:0.81 114:0.85 120:0.68 122:0.51 123:0.59 124:0.66 126:0.44 127:0.18 129:0.81 133:0.67 135:0.95 139:0.60 140:0.45 144:0.85 145:0.47 146:0.22 147:0.28 149:0.64 150:0.79 151:0.70 152:0.96 153:0.72 154:0.57 155:0.58 159:0.48 161:0.78 162:0.55 164:0.65 165:0.70 167:0.47 169:0.82 172:0.45 173:0.37 175:0.75 176:0.66 177:0.38 179:0.39 181:0.96 186:0.82 187:0.68 189:0.86 190:0.77 192:0.59 195:0.91 201:0.68 202:0.65 208:0.54 212:0.19 215:0.78 216:0.93 220:0.62 222:0.60 235:0.12 238:0.88 241:0.18 242:0.33 243:0.29 244:0.61 245:0.60 247:0.55 248:0.72 253:0.75 255:0.74 256:0.58 257:0.65 259:0.21 260:0.67 261:0.90 265:0.38 266:0.54 267:0.36 268:0.62 271:0.52 276:0.48 277:0.69 282:0.77 285:0.56 290:0.85 297:0.36 300:0.33\n1 1:0.69 6:0.89 7:0.72 8:0.81 9:0.76 11:0.91 12:0.51 17:0.91 18:0.70 20:0.94 21:0.30 27:0.35 28:0.76 29:0.56 30:0.75 32:0.69 34:0.39 36:0.47 37:0.55 39:0.25 43:0.72 45:0.98 66:0.77 69:0.80 70:0.41 71:0.97 74:0.92 77:0.70 78:0.85 81:0.54 83:0.60 86:0.79 91:0.67 96:0.76 97:0.89 98:0.76 99:0.83 100:0.87 101:0.52 104:0.91 108:0.77 111:0.85 114:0.63 120:0.64 122:0.51 123:0.75 124:0.42 126:0.44 127:0.81 129:0.59 133:0.66 135:0.37 138:0.96 139:0.75 140:0.45 144:0.85 145:0.41 146:0.37 147:0.18 149:0.94 150:0.58 151:0.81 152:0.88 153:0.48 154:0.65 155:0.58 158:0.79 159:0.66 161:0.78 162:0.81 164:0.68 165:0.70 167:0.47 169:0.85 172:0.93 173:0.77 176:0.66 177:0.38 179:0.23 181:0.96 186:0.85 187:0.39 189:0.90 190:0.77 192:0.59 195:0.79 201:0.68 202:0.61 204:0.85 208:0.54 212:0.92 215:0.44 216:0.52 220:0.87 222:0.60 230:0.74 232:0.93 233:0.73 235:0.42 238:0.88 241:0.15 242:0.60 243:0.08 245:0.89 247:0.60 248:0.74 253:0.71 254:0.80 255:0.74 256:0.64 257:0.65 259:0.21 260:0.62 261:0.90 265:0.76 266:0.54 267:0.36 268:0.66 271:0.52 276:0.88 279:0.82 282:0.77 283:0.82 285:0.56 290:0.63 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289:0.92\n2 1:0.74 8:0.95 9:0.80 11:0.57 12:0.79 17:0.78 27:0.72 28:0.80 30:0.91 34:0.36 36:0.40 37:0.87 39:0.79 41:0.82 43:0.89 46:0.99 66:0.69 69:0.58 70:0.13 74:0.56 81:0.83 83:0.81 85:0.80 91:0.88 96:0.84 98:0.52 101:0.81 107:0.92 108:0.44 110:0.91 114:0.98 120:0.75 122:0.43 123:0.43 125:0.99 126:0.54 127:0.16 129:0.05 135:0.51 140:0.45 145:0.96 146:0.26 147:0.32 149:0.69 150:0.90 151:0.93 153:0.77 154:0.88 155:0.67 159:0.11 160:0.96 161:0.66 162:0.68 164:0.64 165:0.92 167:1.00 172:0.21 173:0.95 176:0.73 177:0.38 179:0.77 181:0.52 192:0.59 201:0.74 202:0.54 208:0.64 212:0.61 215:0.96 216:0.07 222:0.76 235:0.22 241:0.99 242:0.63 243:0.73 247:0.30 248:0.82 253:0.84 255:0.79 256:0.45 259:0.21 260:0.75 265:0.54 266:0.17 267:0.59 268:0.57 271:0.95 276:0.15 285:0.62 289:0.92 290:0.98 297:1.00 300:0.13\n3 1:0.74 6:0.98 7:0.81 8:0.97 9:0.80 11:0.57 12:0.79 17:0.15 20:0.97 21:0.13 27:0.02 28:0.80 30:0.86 34:0.25 36:0.49 37:0.96 39:0.79 41:0.98 43:0.98 46:1.00 66:0.86 69:0.08 70:0.46 74:0.86 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55:0.45 58:0.98 62:0.96 64:0.77 66:0.91 69:0.88 70:0.41 71:0.92 74:0.54 77:0.70 79:0.87 81:0.54 83:0.69 86:0.89 89:0.99 91:0.38 98:0.77 101:0.65 104:0.78 106:0.81 108:0.77 120:0.57 123:0.75 124:0.37 126:0.51 127:0.38 128:0.95 129:0.05 133:0.39 135:0.51 137:0.88 139:0.92 140:0.45 141:0.98 145:0.77 146:0.88 147:0.23 149:0.74 150:0.50 151:0.54 153:0.48 154:0.25 155:0.55 159:0.55 161:0.90 162:0.86 164:0.56 167:0.52 168:0.95 170:0.77 172:0.94 173:0.89 174:0.94 177:0.38 179:0.19 181:0.50 187:0.39 190:0.77 192:0.58 193:0.96 195:0.80 196:0.91 197:0.94 199:0.99 201:0.65 202:0.36 204:0.81 205:0.95 206:0.81 208:0.64 212:0.91 215:0.67 216:0.70 219:0.91 220:0.91 222:0.21 223:0.95 224:0.99 225:0.99 230:0.77 233:0.66 234:0.99 235:0.74 236:0.95 239:0.94 240:0.99 241:0.32 242:0.63 243:0.08 244:0.61 245:0.82 247:0.91 248:0.53 253:0.43 254:0.80 255:0.70 256:0.45 257:0.84 259:0.21 260:0.57 262:0.93 265:0.77 266:0.54 267:0.79 268:0.46 271:0.66 274:0.98 276:0.87 281:0.86 282:0.80 283:0.67 284:0.88 285:0.60 291:0.97 292:0.88 297:0.36 300:0.70\n2 1:0.69 6:0.83 7:0.81 8:0.69 9:0.80 11:0.52 12:0.86 17:0.89 18:0.96 20:0.81 22:0.93 23:0.96 26:0.95 27:0.61 28:0.76 29:0.86 30:0.85 31:0.94 32:0.72 34:0.64 36:0.55 37:0.43 39:0.99 41:0.82 43:0.73 44:0.92 45:0.95 47:0.93 48:0.98 58:0.99 62:0.97 64:0.77 66:0.97 69:0.21 70:0.41 71:0.92 74:0.82 77:0.70 78:0.91 79:0.93 81:0.86 83:0.91 86:0.98 89:0.95 91:0.46 96:0.80 97:0.94 98:0.97 99:0.95 100:0.89 101:0.65 104:0.91 106:0.81 108:0.17 111:0.89 114:0.98 120:0.69 121:0.90 122:0.65 123:0.16 126:0.54 127:0.75 128:0.98 129:0.05 135:0.42 137:0.94 139:0.98 140:0.45 141:0.98 145:0.89 146:0.91 147:0.93 149:0.94 150:0.83 151:0.86 152:0.79 153:0.48 154:0.63 155:0.67 158:0.82 159:0.53 161:0.90 162:0.86 164:0.57 165:0.93 167:0.56 168:0.98 169:0.86 170:0.77 172:0.92 173:0.26 175:0.75 176:0.73 177:0.70 179:0.28 181:0.50 186:0.91 187:0.39 189:0.84 192:0.99 193:1.00 195:0.71 196:0.97 199:0.99 201:0.74 202:0.36 204:0.85 205:0.95 206:0.81 208:0.64 212:0.78 215:0.96 216:0.30 219:0.94 222:0.21 223:0.95 224:1.00 225:0.98 230:0.87 233:0.76 234:1.00 235:0.68 236:0.97 238:0.84 239:0.90 240:0.99 241:0.49 242:0.18 243:0.97 244:0.61 247:0.84 248:0.77 253:0.86 255:1.00 256:0.45 257:0.65 259:0.21 260:0.70 261:0.84 262:0.93 265:0.97 266:0.38 267:0.28 268:0.46 271:0.66 274:0.98 276:0.85 277:0.69 279:0.78 281:0.91 282:0.80 283:0.82 284:0.89 285:0.62 290:0.98 292:0.95 297:0.36 300:0.43\n1 1:0.63 7:0.81 8:0.83 9:0.73 10:0.90 11:0.46 12:0.86 17:0.90 18:0.92 23:0.96 26:0.95 27:0.68 28:0.76 29:0.86 30:0.11 31:0.94 32:0.71 34:0.45 36:0.77 37:0.87 39:0.99 43:0.59 44:0.92 45:0.66 48:0.99 55:0.84 58:0.98 62:0.98 64:0.77 66:0.16 69:0.73 70:0.97 71:0.92 74:0.47 79:0.94 81:0.66 83:0.91 86:0.92 89:0.97 91:0.23 98:0.48 99:0.83 101:0.47 102:0.97 104:0.81 106:0.81 108:0.56 120:0.72 121:0.90 122:0.92 123:0.72 124:0.89 126:0.51 127:0.53 128:0.98 129:0.05 133:0.89 135:0.85 137:0.94 139:0.94 140:0.45 141:0.98 145:0.94 146:0.50 147:0.05 149:0.38 150:0.57 151:0.90 153:0.69 154:0.49 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276:0.33 283:0.71 297:0.36 300:0.43\n3 6:0.92 8:0.88 9:0.80 12:0.20 17:0.49 20:0.99 25:0.88 27:0.26 28:0.48 30:0.76 34:0.34 36:0.53 37:0.60 39:0.39 41:0.88 43:0.42 55:0.52 66:0.80 69:0.21 70:0.21 74:0.87 78:0.89 81:0.80 83:0.76 91:0.72 96:0.97 98:0.92 100:0.93 104:0.58 108:0.17 111:0.90 114:0.80 117:0.86 120:0.87 122:0.67 123:0.16 126:0.32 127:0.54 129:0.05 135:0.61 138:0.98 140:0.95 146:0.48 147:0.54 149:0.51 150:0.60 151:0.77 152:0.99 153:0.95 154:0.32 155:0.67 159:0.17 161:0.54 162:0.84 164:0.87 167:0.87 169:0.88 172:0.45 173:0.62 175:0.75 176:0.56 177:0.05 179:0.86 181:0.73 186:0.89 187:0.21 189:0.96 190:0.77 191:0.93 192:0.59 202:0.85 208:0.64 212:0.95 216:0.83 222:0.25 232:0.81 235:0.12 238:0.94 241:0.76 242:0.82 243:0.82 244:0.61 247:0.12 248:0.85 253:0.98 255:0.79 256:0.89 257:0.65 259:0.21 260:0.87 261:0.92 265:0.92 266:0.12 267:0.44 268:0.90 271:0.74 276:0.37 277:0.69 283:0.71 285:0.62 290:0.80 300:0.18\n1 12:0.20 17:0.34 21:0.47 27:0.45 28:0.48 30:0.20 34:0.77 36:0.21 37:0.50 39:0.39 43:0.30 55:0.52 66:0.83 69:0.68 70:0.84 74:0.69 81:0.39 91:0.11 98:0.64 108:0.52 123:0.50 124:0.37 126:0.32 127:0.34 129:0.05 133:0.39 135:0.72 145:0.47 146:0.82 147:0.85 149:0.54 150:0.34 154:0.63 159:0.57 161:0.54 167:0.59 172:0.71 173:0.60 177:0.38 178:0.55 179:0.48 181:0.73 187:0.68 212:0.77 215:0.63 216:0.77 222:0.25 235:0.52 241:0.10 242:0.63 243:0.84 245:0.57 247:0.55 253:0.52 254:0.88 259:0.21 265:0.65 266:0.54 267:0.19 271:0.74 276:0.56 283:0.71 297:0.36 300:0.70\n2 7:0.78 12:0.20 17:0.34 21:0.30 25:0.88 27:0.26 28:0.48 30:0.36 32:0.75 34:0.47 36:0.80 37:0.60 39:0.39 43:0.45 55:0.71 66:0.71 69:0.77 70:0.48 74:0.51 76:0.85 81:0.51 91:0.74 98:0.84 104:0.58 108:0.60 123:0.57 124:0.43 126:0.32 127:0.65 129:0.05 133:0.39 135:0.49 145:0.58 146:0.87 147:0.49 149:0.68 150:0.40 154:0.34 159:0.54 161:0.54 167:0.92 172:0.71 173:0.78 177:0.05 178:0.55 179:0.55 181:0.73 187:0.21 195:0.75 212:0.27 215:0.72 216:0.83 222:0.25 230:0.81 233:0.76 235:0.31 241:0.80 242:0.82 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255:0.79 256:0.64 259:0.21 260:0.73 261:0.87 265:0.85 266:0.23 267:0.19 268:0.65 271:0.16 276:0.25 285:0.62 290:0.92 297:0.36 300:0.13\n2 7:0.78 8:0.91 9:0.80 11:0.57 12:0.20 17:0.90 21:0.78 27:0.54 28:0.98 30:0.76 34:0.91 36:0.54 37:0.66 39:0.61 43:0.94 55:0.71 60:0.87 66:0.16 69:0.21 70:0.36 74:0.80 83:0.78 85:0.72 91:0.48 96:0.75 98:0.26 101:0.70 104:0.81 108:0.69 120:0.77 122:0.67 123:0.67 124:0.82 127:0.27 129:0.81 133:0.76 135:0.84 140:0.90 145:0.91 146:0.17 147:0.22 149:0.64 151:0.69 153:0.83 154:0.94 155:0.67 158:0.92 159:0.65 161:0.90 162:0.54 163:0.90 164:0.74 167:0.64 172:0.21 173:0.13 177:0.38 179:0.62 181:0.67 187:0.21 190:0.77 191:0.87 192:0.59 202:0.74 208:0.64 212:0.36 216:0.28 220:0.62 222:0.60 230:0.81 233:0.69 235:0.52 241:0.47 242:0.61 243:0.23 245:0.57 247:0.47 248:0.64 253:0.74 255:0.79 256:0.68 259:0.21 260:0.78 265:0.28 266:0.47 267:0.89 268:0.71 271:0.16 276:0.44 279:0.86 283:0.82 285:0.62 300:0.33\n2 6:0.87 7:0.78 8:0.66 11:0.57 12:0.20 17:0.97 20:0.81 21:0.59 27:0.36 28:0.98 30:0.15 32:0.80 34:0.90 36:0.55 37:0.56 39:0.61 43:0.28 55:0.92 66:0.16 69:0.99 70:0.96 74:0.55 77:0.70 78:0.85 81:0.43 85:0.72 91:0.23 98:0.19 100:0.85 101:0.64 104:0.58 108:0.98 111:0.84 122:0.41 123:0.56 124:0.84 126:0.32 127:0.89 129:0.05 133:0.82 135:0.49 145:0.59 146:0.28 147:0.63 149:0.30 150:0.36 152:0.97 154:0.13 159:0.94 161:0.90 167:0.52 169:0.94 172:0.21 173:1.00 177:0.05 178:0.55 179:0.87 181:0.67 186:0.85 187:0.39 195:0.99 212:0.15 215:0.55 216:0.37 222:0.60 230:0.81 232:0.80 233:0.69 235:0.74 241:0.02 242:0.33 243:0.37 245:0.47 247:0.55 253:0.44 257:0.65 259:0.21 261:0.96 265:0.12 266:0.63 267:0.91 271:0.16 276:0.31 277:0.69 282:0.88 297:0.36 299:0.88 300:0.70\n2 11:0.57 12:0.20 17:0.89 21:0.22 27:0.63 28:0.98 30:0.38 32:0.80 34:0.93 36:0.36 37:0.45 39:0.61 43:0.63 55:0.42 66:0.09 69:0.91 70:0.63 74:0.76 77:0.89 81:0.73 85:0.85 91:0.67 98:0.25 101:0.78 104:0.58 106:0.81 108:0.81 122:0.43 123:0.54 124:0.74 126:0.32 127:0.62 129:0.05 133:0.74 135:0.58 145:0.69 146:0.31 147:0.05 149:0.65 150:0.54 154:0.53 159:0.51 161:0.90 167:0.57 172:0.37 173:0.98 177:0.05 178:0.55 179:0.82 181:0.67 187:0.21 195:0.69 202:0.36 206:0.81 212:0.57 215:0.79 216:0.28 222:0.60 232:0.80 235:0.31 241:0.21 242:0.29 243:0.16 245:0.49 247:0.60 253:0.55 257:0.65 259:0.21 265:0.27 266:0.47 267:0.52 271:0.16 276:0.39 277:0.69 282:0.88 283:0.71 297:0.36 299:0.88 300:0.43\n2 1:0.74 6:0.83 7:0.81 8:0.70 9:0.80 11:0.57 12:0.20 17:0.90 20:0.97 21:0.05 27:0.63 28:0.98 30:0.88 32:0.80 34:0.36 36:0.49 37:0.45 39:0.61 41:0.97 43:0.61 55:0.92 60:0.87 66:0.24 69:0.75 70:0.29 74:0.87 76:0.85 77:0.89 78:0.98 81:0.91 83:0.88 85:0.72 91:0.91 96:0.96 98:0.67 100:0.95 101:0.72 108:0.80 111:0.96 114:0.95 120:0.93 121:0.90 122:0.67 123:0.56 124:0.67 126:0.54 127:0.80 129:0.59 133:0.64 135:0.66 140:0.80 145:0.72 146:0.73 147:0.05 149:0.51 150:0.95 151:0.98 152:0.95 153:0.91 154:0.54 155:0.67 158:0.87 159:0.55 161:0.90 162:0.64 163:0.81 164:0.89 165:0.90 167:0.91 169:0.91 172:0.37 173:0.77 176:0.73 177:0.05 178:0.42 179:0.80 181:0.67 186:0.98 189:0.85 191:0.83 192:0.59 195:0.71 201:0.74 202:0.84 204:0.89 208:0.64 212:0.22 215:0.91 216:0.28 220:0.62 222:0.60 230:0.87 232:0.80 233:0.76 235:0.22 238:0.85 241:0.89 242:0.38 243:0.14 245:0.59 247:0.60 248:0.96 253:0.97 255:0.79 256:0.85 257:0.65 259:0.21 260:0.93 261:0.95 265:0.56 266:0.54 267:0.65 268:0.86 271:0.16 276:0.47 279:0.86 282:0.88 283:0.71 285:0.62 290:0.95 297:0.36 299:0.88 300:0.33\n3 7:0.78 8:0.91 9:0.80 11:0.57 12:0.20 17:0.88 21:0.78 27:0.54 28:0.98 30:0.73 34:0.91 36:0.59 37:0.66 39:0.61 43:0.94 55:0.71 60:0.87 66:0.15 69:0.21 70:0.37 74:0.84 83:0.78 85:0.72 91:0.48 96:0.75 98:0.26 101:0.70 104:0.81 108:0.69 120:0.77 122:0.67 123:0.67 124:0.82 127:0.27 129:0.81 133:0.76 135:0.84 140:0.90 145:0.91 146:0.31 147:0.38 149:0.66 151:0.69 153:0.83 154:0.94 155:0.67 158:0.92 159:0.65 161:0.90 162:0.54 163:0.90 164:0.74 167:0.67 172:0.21 173:0.13 177:0.38 179:0.58 181:0.67 187:0.21 190:0.77 191:0.87 192:0.59 202:0.74 208:0.64 212:0.30 216:0.28 220:0.62 222:0.60 230:0.81 233:0.69 235:0.52 241:0.54 242:0.63 243:0.29 245:0.59 247:0.47 248:0.64 253:0.76 255:0.79 256:0.68 259:0.21 260:0.78 265:0.28 266:0.63 267:0.91 268:0.71 271:0.16 276:0.47 279:0.86 283:0.82 285:0.62 300:0.33\n2 6:0.82 7:0.81 8:0.61 12:0.20 17:0.43 20:0.87 21:0.30 27:0.21 28:0.78 30:0.30 34:0.58 36:0.98 37:0.74 43:0.52 55:0.52 66:0.44 69:0.12 70:0.60 78:0.74 81:0.95 91:0.57 98:0.44 100:0.77 106:0.81 108:0.60 111:0.74 120:0.77 123:0.57 124:0.58 126:0.54 127:0.27 129:0.59 133:0.47 135:0.20 140:0.45 146:0.42 147:0.48 149:0.57 150:0.92 151:0.74 152:0.82 153:0.84 154:0.41 159:0.29 161:0.54 162:0.69 164:0.79 167:0.72 169:0.75 172:0.37 173:0.25 177:0.05 179:0.64 181:0.52 186:0.75 187:0.39 189:0.84 202:0.71 206:0.81 212:0.73 215:0.72 216:0.95 220:0.87 230:0.87 233:0.76 235:0.42 238:0.85 241:0.52 242:0.82 243:0.45 245:0.64 247:0.12 248:0.69 256:0.68 259:0.21 260:0.78 261:0.76 265:0.46 266:0.38 267:0.85 268:0.74 271:0.61 276:0.43 283:0.82 285:0.62 297:0.36 300:0.43\n1 6:0.90 7:0.81 8:0.89 12:0.20 17:0.75 20:0.94 21:0.71 25:0.88 27:0.06 28:0.78 30:0.95 32:0.80 34:0.53 36:0.80 37:0.95 43:0.37 55:0.84 66:0.54 69:0.61 78:0.82 81:0.83 91:0.93 98:0.54 100:0.86 104:0.58 108:0.46 111:0.85 120:0.96 123:0.45 126:0.54 127:0.16 129:0.05 135:0.26 140:0.45 145:0.89 146:0.33 147:0.39 149:0.21 150:0.63 151:0.83 152:0.88 153:0.95 154:0.28 159:0.13 161:0.54 162:0.60 164:0.95 167:0.97 169:0.90 172:0.10 173:0.86 177:0.05 179:0.97 181:0.52 186:0.81 189:0.91 191:0.95 195:0.58 202:0.93 215:0.48 216:0.52 220:0.62 230:0.87 233:0.76 235:0.84 238:0.97 241:0.86 243:0.63 248:0.94 256:0.93 260:0.96 261:0.84 265:0.55 267:0.76 268:0.94 271:0.61 283:0.60 285:0.62 297:0.36 300:0.08\n1 12:0.20 17:0.64 21:0.30 25:0.88 27:0.05 28:0.78 34:0.67 36:0.41 37:0.96 81:0.95 91:0.62 104:0.58 126:0.54 132:0.97 135:0.49 150:0.92 161:0.54 167:0.74 175:0.75 178:0.55 181:0.52 187:0.39 215:0.72 216:0.64 235:0.42 241:0.61 244:0.61 257:0.65 267:0.93 271:0.61 277:0.69 297:0.36\n2 6:0.81 8:0.82 12:0.20 17:0.55 20:0.86 21:0.22 25:0.88 27:0.16 28:0.78 30:0.76 34:0.56 36:0.74 37:0.95 43:0.48 55:0.52 66:0.64 69:0.34 70:0.15 78:0.77 81:0.96 91:0.88 98:0.83 100:0.80 104:0.75 108:0.27 111:0.77 120:0.62 123:0.26 126:0.54 127:0.33 129:0.05 135:0.51 140:0.45 146:0.30 147:0.36 149:0.29 150:0.94 151:0.56 152:0.81 153:0.72 154:0.36 159:0.13 161:0.54 162:0.85 164:0.81 167:0.98 169:0.78 172:0.16 173:0.86 177:0.05 179:0.98 181:0.52 186:0.78 189:0.83 191:0.89 202:0.70 212:0.95 215:0.79 216:0.32 220:0.87 235:0.22 238:0.90 241:0.92 242:0.82 243:0.69 247:0.12 248:0.56 256:0.73 259:0.21 260:0.63 261:0.79 265:0.83 266:0.12 267:0.36 268:0.77 271:0.61 276:0.16 283:0.60 285:0.62 297:0.36 300:0.13\n1 6:0.84 7:0.81 8:0.89 12:0.20 17:0.96 20:0.80 21:0.76 27:0.24 28:0.78 30:0.42 32:0.80 34:0.50 36:0.83 37:0.68 43:0.47 55:0.84 66:0.88 69:0.47 70:0.48 78:0.76 81:0.78 91:0.66 98:0.95 100:0.78 104:0.58 108:0.36 111:0.75 120:0.73 123:0.34 126:0.54 127:0.67 129:0.05 135:0.47 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36:0.63 37:0.97 39:0.86 41:0.95 43:0.68 46:0.98 66:0.75 69:0.89 70:0.60 74:0.84 76:0.85 77:0.70 83:0.82 85:0.96 91:0.80 96:0.94 98:0.37 101:0.85 104:0.58 107:0.98 108:0.86 110:0.92 120:0.89 122:0.57 123:0.85 124:0.42 125:0.99 127:0.25 129:0.59 133:0.64 135:0.89 140:0.45 145:0.56 146:0.13 147:0.18 149:0.87 151:0.93 153:0.78 154:0.58 155:0.67 159:0.46 160:0.99 161:0.55 162:0.65 164:0.84 167:0.83 172:0.75 173:0.89 177:0.38 179:0.33 181:0.48 187:0.68 192:0.59 195:0.81 202:0.78 208:0.64 212:0.82 216:0.85 220:0.62 222:0.25 232:0.76 235:0.05 241:0.72 242:0.63 243:0.14 245:0.65 247:0.39 248:0.94 253:0.95 255:0.79 256:0.78 259:0.21 260:0.90 265:0.39 266:0.54 267:0.85 268:0.80 271:0.90 276:0.58 282:0.88 285:0.62 289:0.90 299:0.88 300:0.84\n1 1:0.74 7:0.66 11:0.64 12:0.06 17:0.77 18:0.93 22:0.93 27:0.65 28:0.73 29:0.91 30:0.32 31:0.86 32:0.80 34:0.97 36:0.17 37:0.43 39:0.52 43:0.78 44:0.93 46:0.96 47:0.93 48:0.91 55:0.59 60:0.95 64:0.77 66:0.94 69:0.40 70:0.91 71:0.95 74:0.76 77:0.70 79:0.86 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30:0.60 34:0.52 36:0.83 37:0.59 43:0.42 55:0.84 66:0.34 69:0.48 70:0.41 78:0.76 81:0.99 91:0.63 98:0.76 100:0.79 108:0.84 111:0.76 120:0.93 123:0.83 124:0.68 126:0.54 127:0.81 129:0.81 133:0.67 135:0.61 140:0.45 146:0.87 147:0.89 149:0.75 150:0.98 151:0.79 152:0.75 153:0.90 154:0.32 159:0.78 161:0.50 162:0.86 163:0.75 164:0.86 167:0.66 169:0.77 172:0.68 173:0.30 177:0.05 179:0.40 181:0.63 186:0.77 187:0.68 189:0.87 202:0.75 212:0.22 215:0.91 216:0.34 235:0.05 238:0.90 241:0.27 242:0.82 243:0.49 245:0.87 247:0.12 248:0.90 256:0.79 259:0.21 260:0.93 261:0.75 265:0.76 266:0.63 267:0.91 268:0.82 271:0.71 276:0.78 285:0.62 297:0.36 300:0.55\n1 12:0.37 17:0.78 25:0.88 27:0.72 28:0.76 30:0.95 34:0.18 36:0.36 43:0.29 55:0.98 66:0.20 69:0.75 81:0.99 91:0.81 98:0.09 104:0.54 108:0.58 123:0.56 124:0.61 126:0.54 127:0.33 129:0.59 133:0.47 135:0.56 145:0.69 146:0.05 147:0.05 149:0.17 150:0.99 154:0.21 159:0.30 161:0.50 167:0.95 172:0.05 173:0.86 177:0.05 178:0.55 179:1.00 181:0.63 215:0.96 235:0.05 241:0.80 243:0.40 245:0.36 259:0.21 265:0.09 267:0.44 271:0.71 297:0.36 300:0.08\n0 12:0.37 17:0.47 21:0.59 27:0.45 28:0.76 34:0.85 36:0.22 37:0.50 43:0.32 66:0.36 69:0.70 81:0.91 91:0.20 98:0.06 108:0.53 123:0.51 126:0.54 127:0.05 129:0.05 135:0.58 145:0.50 146:0.05 147:0.05 149:0.13 150:0.83 154:0.23 159:0.05 161:0.50 167:0.72 172:0.05 173:0.51 177:0.05 178:0.55 181:0.63 187:0.68 215:0.55 216:0.77 235:0.68 241:0.51 243:0.51 259:0.21 265:0.06 267:0.73 271:0.71 283:0.60 297:0.36\n2 6:0.98 8:0.94 12:0.37 17:0.20 20:0.75 21:0.13 25:0.88 27:0.03 28:0.76 30:0.95 34:0.31 36:0.30 37:0.95 43:0.27 55:0.99 66:0.20 69:0.79 78:0.94 81:0.98 91:0.86 98:0.07 100:0.96 104:0.58 108:0.62 111:0.95 120:0.89 123:0.60 124:0.61 126:0.54 127:0.41 129:0.59 133:0.47 135:0.42 140:0.87 145:0.76 146:0.05 147:0.05 149:0.15 150:0.97 151:0.75 152:1.00 153:0.84 154:0.20 159:0.53 161:0.50 162:0.69 164:0.85 167:0.86 169:0.96 172:0.05 173:0.77 177:0.05 178:0.48 179:1.00 181:0.63 186:0.93 187:0.21 189:0.98 191:0.98 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147:0.05 149:0.14 150:0.97 151:0.49 153:0.48 154:0.18 159:0.53 161:0.50 162:0.62 164:0.67 167:0.88 172:0.05 173:0.81 177:0.05 178:0.42 179:1.00 181:0.63 187:0.39 202:0.60 206:0.81 215:0.84 216:0.65 220:0.74 235:0.12 241:0.75 243:0.40 245:0.36 248:0.50 256:0.58 259:0.21 260:0.58 265:0.07 267:0.89 268:0.59 271:0.71 285:0.62 297:0.36 300:0.08\n2 6:0.98 8:0.66 12:0.37 17:0.32 20:0.74 21:0.13 25:0.88 27:0.03 28:0.76 30:0.45 34:0.29 36:0.28 37:0.95 43:0.31 55:0.52 66:0.27 69:0.70 70:0.25 78:0.89 81:0.98 91:0.48 98:0.07 100:0.91 104:0.58 108:0.92 111:0.88 120:0.95 123:0.92 124:0.72 126:0.54 127:0.93 129:0.81 133:0.67 135:0.94 140:0.45 145:0.84 146:0.05 147:0.05 149:0.18 150:0.97 151:0.80 152:1.00 153:0.93 154:0.23 159:0.96 161:0.50 162:0.85 163:0.97 164:0.90 167:0.69 169:0.96 172:0.10 173:0.22 177:0.05 179:0.98 181:0.63 186:0.85 187:0.21 189:0.87 191:0.84 202:0.81 212:0.61 215:0.84 216:0.65 235:0.12 238:0.87 241:0.41 242:0.82 243:0.29 245:0.38 247:0.12 248:0.92 256:0.86 259:0.21 260:0.95 261:1.00 265:0.07 266:0.17 267:0.91 268:0.87 271:0.71 276:0.17 285:0.62 297:0.36 300:0.18\n2 7:0.81 8:0.80 12:0.37 17:0.78 20:0.75 21:0.30 27:0.43 28:0.76 30:0.68 32:0.80 34:0.64 36:0.47 37:0.63 43:0.51 55:0.52 66:0.77 69:0.19 70:0.61 78:0.78 81:0.97 91:0.51 98:0.75 100:0.80 106:0.81 108:0.58 111:0.77 120:0.69 123:0.56 124:0.38 126:0.54 127:0.45 129:0.59 133:0.47 135:0.58 140:0.45 145:0.65 146:0.05 147:0.05 149:0.59 150:0.95 151:0.66 152:0.74 153:0.48 154:0.40 159:0.41 161:0.50 162:0.86 164:0.57 167:0.64 169:0.78 172:0.59 173:0.28 177:0.05 179:0.68 181:0.63 186:0.79 187:0.21 195:0.67 202:0.36 206:0.81 212:0.69 215:0.72 216:0.69 230:0.87 233:0.76 235:0.42 241:0.18 242:0.82 243:0.13 245:0.52 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.74 265:0.75 266:0.47 267:0.23 268:0.46 271:0.71 276:0.48 283:0.60 285:0.62 297:0.36 300:0.55\n2 6:0.81 7:0.81 8:0.61 12:0.37 17:0.45 20:0.90 21:0.30 27:0.21 28:0.76 30:0.58 34:0.47 36:0.67 37:0.74 43:0.52 55:0.84 66:0.36 69:0.10 70:0.33 78:0.76 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159:0.90 161:0.50 162:0.71 163:0.75 164:0.81 167:0.63 169:0.76 172:0.54 173:0.39 177:0.05 179:0.42 181:0.63 186:0.77 187:0.21 189:0.88 191:0.73 195:0.98 202:0.73 212:0.15 215:0.46 216:0.98 220:0.96 235:0.95 238:0.89 241:0.15 242:0.82 243:0.29 245:0.70 247:0.12 248:0.68 256:0.77 259:0.21 260:0.77 261:0.79 265:0.33 266:0.97 267:0.18 268:0.76 271:0.71 276:0.73 283:0.82 285:0.62 297:0.36 300:0.70\n3 6:0.98 8:0.97 12:0.37 17:0.36 20:0.97 21:0.13 25:0.88 27:0.03 28:0.76 30:0.45 34:0.29 36:0.73 37:0.95 43:0.51 55:0.71 66:0.27 69:0.10 70:0.25 78:0.99 81:0.98 91:0.99 98:0.21 100:0.99 104:0.58 108:0.09 111:1.00 120:0.98 123:0.09 124:0.72 126:0.54 127:0.65 129:0.81 133:0.67 135:0.82 140:0.45 145:0.76 146:0.28 147:0.34 149:0.29 150:0.97 151:0.85 152:1.00 153:0.98 154:0.40 159:0.50 161:0.50 162:0.61 163:0.79 164:0.97 167:0.99 169:0.96 172:0.10 173:0.19 177:0.05 179:0.98 181:0.63 186:0.99 189:0.99 191:1.00 202:0.96 212:0.61 215:0.84 216:0.65 220:0.62 235:0.12 238:1.00 241:0.93 242:0.82 243:0.46 245:0.38 247:0.12 248:0.97 256:0.98 259:0.21 260:0.98 261:1.00 265:0.23 266:0.17 267:0.95 268:0.97 271:0.71 276:0.20 283:0.82 285:0.62 297:0.36 300:0.18\n4 12:0.37 17:0.88 21:0.13 27:0.53 28:0.76 30:0.21 34:0.53 36:0.38 37:0.90 43:0.51 55:0.71 66:0.16 69:0.62 70:0.37 81:0.98 91:0.37 98:0.35 104:0.95 106:0.81 108:0.53 123:0.82 124:0.81 126:0.54 127:0.65 129:0.89 133:0.78 135:0.96 145:0.76 146:0.05 147:0.05 149:0.36 150:0.97 154:0.40 159:0.56 161:0.50 163:0.89 167:0.85 172:0.10 173:0.60 177:0.05 178:0.55 179:0.96 181:0.63 187:0.39 202:0.36 206:0.81 212:0.42 215:0.84 216:0.27 235:0.12 241:0.73 242:0.82 243:0.23 245:0.42 247:0.12 259:0.21 265:0.21 266:0.23 267:0.82 271:0.71 276:0.24 297:0.36 300:0.25\n2 6:0.81 8:0.60 12:0.37 17:0.93 20:0.84 21:0.59 25:0.88 27:0.72 28:0.76 30:0.76 32:0.80 34:0.39 36:0.36 43:0.49 55:0.52 66:0.64 69:0.37 70:0.15 78:0.74 81:0.98 91:0.65 98:0.65 100:0.77 104:0.54 108:0.29 111:0.74 120:0.68 123:0.28 126:0.54 127:0.19 129:0.05 135:0.69 140:0.45 145:0.54 146:0.29 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37:0.28 39:0.31 43:0.79 66:0.75 69:0.19 70:0.54 74:0.59 81:0.95 83:0.79 85:0.72 91:0.53 98:0.85 101:0.75 104:0.74 108:0.15 114:0.95 120:0.65 122:0.41 123:0.15 126:0.54 127:0.35 129:0.05 131:0.96 135:0.60 140:0.45 144:0.57 146:0.60 147:0.65 149:0.43 150:0.97 151:0.61 153:0.48 154:0.84 155:0.67 157:0.87 159:0.22 162:0.86 163:0.81 164:0.55 165:0.90 166:0.99 172:0.32 173:0.45 176:0.73 177:0.38 179:0.91 182:0.98 187:0.21 190:0.77 192:0.59 201:0.74 202:0.36 212:0.61 215:0.91 216:0.15 220:0.62 222:0.76 227:0.61 229:0.61 231:0.99 235:0.12 241:0.27 242:0.33 243:0.77 246:0.61 247:0.39 248:0.59 253:0.72 256:0.45 259:0.21 260:0.65 265:0.85 266:0.23 267:0.28 268:0.46 271:0.26 276:0.26 285:0.50 287:0.61 290:0.95 297:0.36 300:0.33\n2 1:0.74 7:0.76 8:0.61 11:0.42 12:0.69 17:0.66 21:0.13 27:0.72 30:0.95 32:0.78 34:0.81 36:0.72 37:0.26 39:0.31 43:0.86 55:0.52 66:0.54 69:0.10 74:0.64 76:0.94 77:0.98 81:0.90 91:0.81 98:0.46 104:0.58 108:0.09 114:0.92 123:0.09 126:0.54 127:0.14 129:0.05 131:0.61 135:0.21 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215:0.53 216:0.28 220:0.74 222:0.76 227:0.61 229:0.61 230:0.83 231:0.99 233:0.69 235:0.84 241:0.89 242:0.60 243:0.84 246:0.61 247:0.39 248:0.55 253:0.65 254:0.80 255:0.79 256:0.78 259:0.21 260:0.83 265:0.97 266:0.27 267:0.52 268:0.80 271:0.26 276:0.38 282:0.86 283:0.71 285:0.62 287:0.61 290:0.72 297:0.36 300:0.55\n1 1:0.74 7:0.76 11:0.42 12:0.69 17:0.51 21:0.47 27:0.58 30:0.76 32:0.78 34:0.73 36:0.69 37:0.47 39:0.31 43:0.34 55:0.45 66:0.72 69:0.49 70:0.22 74:0.64 76:0.85 77:0.70 81:0.78 91:0.49 98:0.63 104:0.58 108:0.38 114:0.80 122:0.41 123:0.36 126:0.54 127:0.18 129:0.05 131:0.61 135:0.54 144:0.57 145:0.88 146:0.31 147:0.37 149:0.13 150:0.80 154:0.83 155:0.67 157:0.61 159:0.13 166:0.99 172:0.27 173:0.84 176:0.73 177:0.38 178:0.55 179:0.79 182:0.61 187:0.21 192:0.59 195:0.53 201:0.74 212:0.78 215:0.63 216:0.28 222:0.76 227:0.61 229:0.61 230:0.78 231:0.99 233:0.69 235:0.68 241:0.44 242:0.45 243:0.75 246:0.61 247:0.35 253:0.64 254:0.80 259:0.21 265:0.63 266:0.17 267:0.23 271:0.26 276:0.19 282:0.86 287:0.61 290:0.80 297:0.36 300:0.18\n2 1:0.74 7:0.81 9:0.80 11:0.42 12:0.69 17:0.53 21:0.64 27:0.58 30:0.21 32:0.78 34:0.76 36:0.91 37:0.47 39:0.31 43:0.44 55:0.45 66:0.80 69:0.30 70:0.67 74:0.65 76:0.94 77:0.70 81:0.65 91:0.58 96:0.73 98:0.86 104:0.58 108:0.24 114:0.72 120:0.67 123:0.23 126:0.54 127:0.38 129:0.05 131:1.00 135:0.51 140:0.45 144:0.57 145:0.88 146:0.53 147:0.59 149:0.33 150:0.70 151:0.62 153:0.48 154:0.75 155:0.67 157:0.61 159:0.19 162:0.86 164:0.66 166:0.99 172:0.45 173:0.61 176:0.73 177:0.38 179:0.82 182:0.61 187:0.21 190:0.77 192:0.59 195:0.55 201:0.74 202:0.50 204:0.89 208:0.64 212:0.71 215:0.53 216:0.28 220:0.82 222:0.76 227:0.61 229:0.61 230:0.83 231:0.99 233:0.69 235:0.84 241:0.53 242:0.72 243:0.82 246:0.61 247:0.39 248:0.60 253:0.66 254:0.80 255:0.79 256:0.58 259:0.21 260:0.67 265:0.86 266:0.27 267:0.59 268:0.59 271:0.26 276:0.36 282:0.86 285:0.62 287:0.61 290:0.72 297:0.36 300:0.43\n2 7:0.76 8:0.73 9:0.80 11:0.64 12:0.69 17:0.79 21:0.71 27:0.26 30:0.33 32:0.78 34:0.89 36:0.96 37:0.94 39:0.31 41:0.82 43:0.84 55:0.45 66:0.20 69:0.65 70:0.49 74:0.68 77:0.98 81:0.38 83:0.72 85:0.72 91:0.68 96:0.71 98:0.44 101:0.73 104:0.76 108:0.49 120:0.58 123:0.74 124:0.56 126:0.32 127:0.55 129:0.05 131:1.00 133:0.39 135:0.24 140:0.80 144:0.57 145:1.00 146:0.41 147:0.48 149:0.56 150:0.34 151:0.58 153:0.48 154:0.80 155:0.67 157:0.86 159:0.42 162:0.62 164:0.57 166:0.94 172:0.27 173:0.72 177:0.38 178:0.42 179:0.89 182:0.98 187:0.39 191:0.84 192:0.59 195:0.83 202:0.50 208:0.64 212:0.19 215:0.48 216:0.29 220:0.87 222:0.76 227:0.61 229:0.99 230:0.78 231:0.99 233:0.69 235:0.95 241:0.75 242:0.19 243:0.58 245:0.53 246:0.61 247:0.55 248:0.57 253:0.56 255:0.79 256:0.45 259:0.21 260:0.59 265:0.43 266:0.47 267:0.65 268:0.46 271:0.26 276:0.28 277:0.69 282:0.86 285:0.62 287:0.61 297:1.00 300:0.33\n2 7:0.76 8:0.87 11:0.42 12:0.69 17:0.78 21:0.71 27:0.26 30:0.30 32:0.78 34:0.88 36:0.80 37:0.94 39:0.31 43:0.33 55:0.59 66:0.64 69:0.49 70:0.83 74:0.84 77:0.70 81:0.40 91:0.37 98:0.78 104:0.76 108:0.60 122:0.55 123:0.58 124:0.47 126:0.32 127:0.46 129:0.59 131:0.61 133:0.47 135:0.30 144:0.57 145:0.80 146:0.34 147:0.41 149:0.45 150:0.34 154:0.84 157:0.61 159:0.44 166:0.93 172:0.67 173:0.51 177:0.38 178:0.55 179:0.53 182:0.61 187:0.21 195:0.70 202:0.36 212:0.55 215:0.48 216:0.29 222:0.76 227:0.61 229:0.61 230:0.78 231:0.99 233:0.69 235:0.88 241:0.39 242:0.24 243:0.31 245:0.77 246:0.61 247:0.67 253:0.60 254:0.74 259:0.21 265:0.78 266:0.54 267:0.44 271:0.26 276:0.62 282:0.86 287:0.61 297:0.36 300:0.55\n3 1:0.74 7:0.76 8:0.61 11:0.42 12:0.69 17:0.66 21:0.13 27:0.72 30:0.95 32:0.78 34:0.81 36:0.72 37:0.26 39:0.31 43:0.86 55:0.52 66:0.54 69:0.10 74:0.64 76:0.94 77:0.98 81:0.90 91:0.81 98:0.46 104:0.58 108:0.09 114:0.92 123:0.09 126:0.54 127:0.14 129:0.05 131:0.61 135:0.21 144:0.57 145:0.89 146:0.19 147:0.25 149:0.49 150:0.92 154:0.83 155:0.67 157:0.61 159:0.10 166:0.99 172:0.10 173:0.79 176:0.73 177:0.38 178:0.55 179:0.93 182:0.61 187:0.21 192:0.59 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248:0.97 253:0.97 255:0.79 256:0.86 260:0.92 265:0.32 266:0.63 267:0.69 268:0.86 271:0.38 276:0.65 279:0.86 282:0.83 283:0.82 285:0.62 286:0.99 287:0.90 290:0.66 297:0.36 300:0.70\n1 8:0.89 12:0.37 17:0.49 21:0.78 27:0.21 30:0.30 34:0.29 36:0.74 37:0.74 39:0.39 43:0.38 55:0.92 66:0.06 69:0.92 70:0.79 74:0.84 91:0.80 96:0.96 98:0.21 108:0.88 120:0.67 122:0.67 123:0.98 124:0.98 127:0.87 129:0.05 131:0.98 133:0.97 135:0.54 140:0.90 146:0.59 147:0.05 149:0.55 151:0.66 153:0.48 154:0.28 157:0.61 159:0.95 162:0.52 163:0.94 164:0.54 166:0.61 172:0.27 173:0.64 177:0.05 178:0.50 179:0.37 182:0.61 187:0.39 190:0.77 191:0.73 202:0.91 212:0.16 216:0.95 220:0.62 222:0.76 227:0.92 229:0.61 231:0.96 235:0.31 241:0.15 242:0.80 243:0.07 244:0.61 245:0.70 246:0.61 247:0.39 248:0.89 253:0.96 256:0.89 257:0.65 259:0.21 260:0.68 265:0.20 266:0.98 267:0.44 268:0.88 271:0.38 276:0.80 277:0.69 283:0.82 285:0.62 287:0.61 300:0.70\n1 1:0.74 11:0.88 12:0.37 17:0.40 21:0.54 27:0.45 28:0.78 30:0.87 32:0.80 34:0.79 36:0.17 37:0.50 39:0.72 43:0.82 66:0.24 69:0.70 70:0.27 74:0.73 77:0.70 81:0.58 83:0.74 85:0.90 91:0.31 98:0.27 101:0.80 108:0.53 114:0.77 122:0.43 123:0.75 124:0.73 126:0.54 127:0.23 129:0.81 133:0.67 135:0.73 144:0.85 145:0.44 146:0.38 147:0.45 149:0.80 150:0.74 154:0.77 155:0.67 159:0.40 161:0.88 163:0.81 165:0.79 167:0.47 172:0.21 173:0.65 176:0.73 177:0.38 178:0.55 179:0.65 181:0.89 187:0.68 192:0.59 195:0.77 201:0.74 212:0.50 215:0.58 216:0.77 222:0.48 235:0.68 241:0.12 242:0.63 243:0.44 245:0.54 247:0.30 253:0.66 259:0.21 265:0.29 266:0.38 267:0.19 271:0.53 276:0.36 282:0.88 290:0.77 297:0.36 299:0.88 300:0.25\n3 1:0.74 6:0.85 8:0.92 9:0.80 11:0.88 12:0.37 17:0.59 20:0.81 21:0.68 28:0.78 30:0.55 32:0.80 34:0.45 36:0.22 37:0.97 39:0.72 41:0.82 43:0.80 60:0.95 66:0.35 69:0.59 70:0.85 74:0.94 77:0.70 78:0.86 81:0.50 83:0.87 85:0.98 91:0.33 96:0.80 98:0.46 100:0.86 101:0.83 104:0.80 108:0.78 111:0.85 114:0.70 120:0.69 122:0.43 123:0.77 124:0.87 126:0.54 127:0.76 129:0.81 133:0.89 135:0.51 140:0.45 144:0.85 145:0.59 146:0.78 147:0.81 149:0.96 150:0.67 151:0.87 152:0.84 153:0.48 154:0.74 155:0.67 158:0.92 159:0.86 161:0.88 162:0.86 164:0.57 165:0.70 167:0.45 169:0.82 172:0.76 173:0.32 176:0.73 177:0.38 179:0.32 181:0.89 186:0.86 187:0.21 192:0.59 195:0.95 201:0.74 202:0.36 208:0.64 212:0.51 215:0.49 216:0.98 222:0.48 232:0.85 235:0.84 242:0.52 243:0.43 245:0.82 247:0.60 248:0.78 253:0.87 255:0.79 256:0.45 259:0.21 260:0.70 261:0.84 265:0.48 266:0.89 267:0.73 268:0.46 271:0.53 276:0.81 279:0.86 282:0.88 283:0.82 285:0.62 290:0.70 297:0.36 299:0.88 300:0.94\n2 1:0.74 11:0.88 12:0.37 17:0.38 21:0.59 27:0.45 28:0.78 30:0.76 34:0.74 36:0.19 37:0.50 39:0.72 43:0.82 66:0.72 69:0.70 70:0.22 74:0.52 81:0.55 83:0.73 85:0.80 91:0.11 98:0.44 101:0.74 108:0.53 114:0.74 122:0.43 123:0.51 126:0.54 127:0.14 129:0.05 135:0.88 144:0.85 145:0.52 146:0.56 147:0.62 149:0.65 150:0.71 154:0.77 155:0.67 159:0.20 161:0.88 163:0.92 165:0.78 167:0.49 172:0.27 173:0.67 176:0.73 177:0.38 178:0.55 179:0.58 181:0.89 187:0.68 192:0.59 201:0.74 212:0.78 215:0.55 216:0.77 222:0.48 235:0.74 241:0.24 242:0.75 243:0.75 247:0.30 253:0.59 259:0.21 265:0.46 266:0.17 267:0.36 271:0.53 276:0.22 290:0.74 297:0.36 300:0.18\n3 1:0.74 9:0.80 11:0.88 12:0.37 17:0.02 21:0.05 27:0.33 28:0.78 30:0.74 34:0.86 36:0.26 37:0.41 39:0.72 41:0.82 43:0.86 60:0.87 66:0.35 69:0.57 70:0.53 74:0.87 77:0.89 81:0.86 83:0.86 85:0.94 91:0.37 96:0.80 98:0.28 101:0.82 108:0.86 114:0.95 120:0.69 122:0.57 123:0.85 124:0.71 126:0.54 127:0.51 129:0.81 133:0.67 135:0.60 140:0.45 144:0.85 145:0.84 146:0.30 147:0.37 149:0.89 150:0.92 151:0.87 153:0.48 154:0.84 155:0.67 158:0.92 159:0.64 161:0.88 162:0.86 164:0.57 165:0.90 167:0.50 172:0.48 173:0.47 176:0.73 177:0.38 179:0.60 181:0.89 187:0.39 192:0.59 195:0.82 201:0.74 202:0.36 208:0.64 212:0.51 215:0.91 216:0.88 222:0.48 235:0.12 241:0.27 242:0.60 243:0.37 245:0.72 247:0.47 248:0.78 253:0.86 255:0.79 256:0.45 259:0.21 260:0.70 265:0.31 266:0.75 267:0.36 268:0.46 271:0.53 276:0.46 279:0.86 282:0.81 283:0.82 285:0.62 290:0.95 297:0.36 300:0.55\n0 1:0.74 8:0.60 9:0.80 11:0.88 12:0.37 17:0.12 20:0.92 21:0.22 27:0.64 28:0.78 30:0.32 34:0.34 36:0.55 37:0.45 39:0.72 41:0.99 43:0.93 55:0.92 60:1.00 66:0.06 69:0.32 70:0.86 74:0.66 78:0.83 81:0.76 83:0.90 85:0.80 91:0.76 96:0.98 98:0.16 100:0.78 101:0.68 104:0.79 106:0.81 108:0.25 111:0.82 114:0.90 117:0.86 120:0.96 123:0.97 124:0.96 126:0.54 127:0.95 129:0.05 133:0.96 135:0.30 140:0.45 144:0.85 145:0.41 146:0.22 147:0.27 149:0.73 150:0.85 151:0.99 152:0.92 153:0.95 154:0.93 155:0.67 158:0.92 159:0.94 161:0.88 162:0.65 164:0.95 165:0.86 167:0.65 169:0.79 172:0.21 173:0.08 176:0.73 177:0.38 179:0.69 181:0.89 186:0.79 187:0.21 192:0.59 201:0.74 202:0.92 206:0.81 208:0.64 212:0.14 215:0.79 216:0.87 222:0.48 232:0.85 235:0.31 238:0.83 241:0.66 242:0.78 243:0.29 245:0.52 247:0.39 248:0.98 253:0.98 255:0.79 256:0.94 259:0.21 260:0.96 261:0.81 265:0.09 266:0.89 267:0.97 268:0.93 271:0.53 276:0.56 277:0.69 279:0.86 283:0.82 285:0.62 290:0.90 297:0.36 300:0.70\n0 1:0.74 6:0.79 7:0.81 8:0.60 9:0.80 11:0.88 12:0.37 17:0.20 20:0.92 21:0.22 27:0.57 28:0.78 30:0.45 32:0.80 34:0.86 36:0.40 37:0.23 39:0.72 41:0.98 43:0.75 60:0.99 66:0.82 69:0.82 70:0.42 74:0.77 77:0.70 78:0.91 81:0.76 83:0.83 85:0.85 91:0.89 96:0.93 98:0.70 100:0.90 101:0.79 104:0.58 108:0.67 111:0.90 114:0.90 120:0.88 121:0.90 122:0.43 123:0.65 126:0.54 127:0.21 129:0.05 135:0.34 140:0.45 144:0.85 145:0.88 146:0.64 147:0.69 149:0.68 150:0.85 151:0.98 152:0.86 153:0.93 154:0.66 155:0.67 158:0.92 159:0.24 161:0.88 162:0.60 164:0.89 165:0.86 167:0.83 169:0.87 172:0.48 173:0.92 176:0.73 177:0.38 179:0.62 181:0.89 186:0.89 189:0.82 191:0.78 192:0.59 195:0.64 201:0.74 202:0.86 204:0.89 208:0.64 212:0.61 215:0.79 216:0.28 222:0.48 230:0.87 232:0.77 233:0.76 235:0.31 238:0.85 241:0.91 242:0.53 243:0.83 247:0.47 248:0.93 253:0.94 255:0.79 256:0.88 259:0.21 260:0.89 261:0.90 265:0.70 266:0.38 267:0.92 268:0.87 271:0.53 276:0.36 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.33\n2 1:0.74 7:0.81 8:0.78 9:0.80 11:0.88 12:0.37 17:0.06 21:0.40 27:0.64 28:0.78 30:0.95 32:0.80 34:0.47 36:0.87 37:0.70 39:0.72 41:0.98 43:0.96 60:0.95 66:0.54 69:0.21 74:0.74 77:0.70 78:0.96 81:0.66 83:0.90 85:0.72 91:0.88 96:0.97 98:0.23 101:0.77 104:0.58 108:0.17 111:0.94 114:0.82 120:0.94 121:0.90 123:0.16 126:0.54 127:0.11 129:0.05 132:0.97 135:0.42 140:0.45 144:0.85 145:0.61 146:0.13 147:0.18 149:0.53 150:0.79 151:0.93 153:0.78 154:0.96 155:0.67 158:0.87 159:0.08 161:0.88 162:0.76 164:0.92 165:0.83 167:0.83 169:0.91 172:0.10 173:0.84 176:0.73 177:0.38 179:0.42 181:0.89 191:0.75 192:0.59 195:0.53 201:0.74 202:0.86 204:0.89 208:0.64 215:0.67 216:0.19 220:0.87 222:0.48 230:0.87 232:0.77 233:0.76 235:0.52 238:0.86 241:0.90 243:0.63 248:0.97 253:0.97 255:0.79 256:0.89 259:0.21 260:0.94 265:0.25 267:0.69 268:0.89 271:0.53 279:0.86 282:0.88 283:0.71 285:0.62 290:0.82 297:0.36 299:0.88 300:0.08\n2 1:0.74 6:0.86 7:0.81 8:0.78 9:0.80 11:0.88 12:0.37 17:0.26 20:0.85 21:0.30 27:0.57 28:0.78 30:0.62 32:0.80 34:0.55 36:0.98 37:0.31 39:0.72 41:0.99 43:0.98 55:0.71 60:0.97 66:0.44 69:0.12 70:0.66 74:0.88 77:0.70 78:0.93 81:0.71 83:0.90 85:0.90 91:0.58 96:0.97 98:0.72 100:0.90 101:0.80 108:0.73 111:0.92 114:0.86 120:0.94 121:0.90 123:0.71 124:0.60 126:0.54 127:0.67 129:0.59 133:0.47 135:0.90 138:0.98 140:0.45 144:0.85 145:0.58 146:0.73 147:0.77 149:0.85 150:0.82 151:0.98 152:0.89 153:0.91 154:0.98 155:0.67 158:0.92 159:0.57 161:0.88 162:0.73 164:0.93 165:0.84 167:0.64 169:0.87 172:0.65 173:0.18 176:0.73 177:0.38 179:0.47 181:0.89 186:0.89 187:0.21 189:0.91 191:0.77 192:0.59 195:0.73 201:0.74 202:0.89 204:0.89 208:0.64 212:0.58 215:0.72 216:0.90 220:0.87 222:0.48 230:0.87 233:0.76 235:0.42 238:0.89 241:0.64 242:0.57 243:0.49 245:0.87 247:0.60 248:0.97 253:0.98 255:0.79 256:0.91 259:0.21 260:0.95 261:0.89 265:0.72 266:0.47 267:0.73 268:0.91 271:0.53 276:0.71 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.88 300:0.70\n2 1:0.74 6:0.98 7:0.81 8:0.95 9:0.80 11:0.88 12:0.37 17:0.43 20:0.97 21:0.30 27:0.21 28:0.78 30:0.36 34:0.69 36:0.79 37:0.74 39:0.72 41:0.98 43:0.98 55:0.71 60:0.97 66:0.20 69:0.12 70:0.83 74:0.99 78:0.94 81:0.71 83:0.90 85:0.99 91:0.76 96:0.98 98:0.62 100:0.99 101:0.79 104:0.84 106:0.81 108:0.95 111:0.98 114:0.86 117:0.86 120:0.96 121:0.90 122:0.67 123:0.94 124:0.93 126:0.54 127:0.71 129:0.97 133:0.93 135:0.26 140:0.45 144:0.85 146:0.35 147:0.42 149:0.95 150:0.82 151:0.99 152:0.95 153:0.96 154:0.98 155:0.67 158:0.92 159:0.96 161:0.88 162:0.67 164:0.92 165:0.84 167:0.49 169:0.97 172:0.95 173:0.06 176:0.73 177:0.38 179:0.11 181:0.89 186:0.93 187:0.39 189:0.99 191:0.73 192:0.59 201:0.74 202:0.89 204:0.89 206:0.81 208:0.64 212:0.48 215:0.72 216:0.95 222:0.48 230:0.87 232:0.89 233:0.76 235:0.42 238:0.98 241:0.24 242:0.70 243:0.11 245:0.98 247:0.60 248:0.98 253:0.99 255:0.79 256:0.91 259:0.21 260:0.96 261:0.97 265:0.63 266:0.89 267:0.89 268:0.90 271:0.53 276:0.98 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.94\n2 1:0.74 6:0.93 8:0.86 9:0.80 11:0.88 12:0.37 17:0.36 20:0.97 21:0.40 27:0.39 28:0.78 30:0.10 34:0.44 36:1.00 37:0.57 39:0.72 41:0.98 43:0.98 55:0.92 60:0.87 66:0.20 69:0.15 70:0.88 74:0.82 78:0.93 81:0.66 83:0.89 85:0.94 91:0.78 96:0.97 98:0.52 100:0.94 101:0.78 108:0.83 111:0.92 114:0.82 120:0.94 123:0.82 124:0.92 126:0.54 127:0.93 129:0.96 133:0.91 135:0.92 140:0.45 144:0.85 146:0.57 147:0.63 149:0.86 150:0.79 151:0.97 152:0.89 153:0.90 154:0.98 155:0.67 158:0.92 159:0.86 161:0.88 162:0.79 164:0.91 165:0.83 167:0.56 169:0.91 172:0.67 173:0.09 176:0.73 177:0.38 179:0.33 181:0.89 186:0.92 187:0.39 189:0.94 192:0.59 201:0.74 202:0.85 208:0.64 212:0.18 215:0.67 216:0.26 220:0.74 222:0.48 232:0.95 235:0.52 238:0.90 241:0.51 242:0.41 243:0.17 245:0.83 247:0.60 248:0.97 253:0.97 255:0.79 256:0.88 259:0.21 260:0.95 261:0.93 265:0.54 266:0.91 267:0.23 268:0.89 271:0.53 276:0.83 279:0.86 283:0.82 285:0.62 290:0.82 297:0.36 300:0.70\n" }, { "path": "examples/lambdarank/rank.train.query", "content": "1\n13\n5\n8\n19\n12\n18\n5\n14\n13\n8\n9\n16\n11\n21\n14\n21\n9\n14\n11\n20\n18\n13\n20\n22\n22\n13\n17\n10\n13\n12\n13\n13\n23\n18\n13\n20\n12\n22\n14\n13\n23\n13\n14\n14\n5\n13\n15\n14\n14\n16\n16\n15\n21\n22\n10\n22\n18\n25\n16\n12\n12\n15\n15\n25\n13\n9\n12\n8\n16\n25\n19\n24\n12\n16\n10\n16\n9\n17\n15\n7\n9\n15\n14\n16\n17\n8\n17\n12\n18\n23\n10\n12\n12\n4\n14\n12\n15\n27\n16\n20\n13\n19\n13\n17\n17\n16\n12\n15\n14\n14\n19\n12\n23\n18\n16\n9\n23\n11\n15\n8\n10\n10\n16\n11\n15\n22\n16\n17\n23\n16\n22\n17\n14\n12\n14\n20\n15\n17\n15\n15\n22\n9\n21\n9\n17\n16\n15\n13\n13\n15\n14\n18\n21\n14\n17\n15\n14\n16\n12\n17\n19\n16\n11\n18\n11\n13\n14\n9\n16\n15\n16\n25\n9\n13\n22\n16\n18\n20\n14\n11\n9\n16\n19\n19\n11\n11\n13\n14\n14\n13\n16\n6\n21\n16\n12\n16\n11\n24\n12\n10\n" }, { "path": "examples/lambdarank/train.conf", "content": "# task type, support train and predict\ntask = train\n\n# boosting type, support gbdt for now, alias: boosting, boost\nboosting_type = gbdt\n\n# application type, support following application\n# regression , regression task\n# binary , binary classification task\n# lambdarank , LambdaRank task\n# alias: application, app\nobjective = lambdarank\n\n# eval metrics, support multi metric, delimited by ',' , support following metrics\n# l1\n# l2 , default metric for regression\n# ndcg , default metric for lambdarank\n# auc\n# binary_logloss , default metric for binary\n# binary_error\nmetric = ndcg\n\n# evaluation position for ndcg metric, alias : ndcg_at\nndcg_eval_at = 1,3,5\n\n# frequency for metric output\nmetric_freq = 1\n\n# true if need output metric for training data, alias: tranining_metric, train_metric\nis_training_metric = true\n\n# column in data to use as label\nlabel_column = 0\n\n# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.\nmax_bin = 255\n\n# training data\n# if existing weight file, should name to \"rank.train.weight\"\n# if existing query file, should name to \"rank.train.query\"\n# alias: train_data, train\ndata = rank.train\n\n# validation data, support multi validation data, separated by ','\n# if existing weight file, should name to \"rank.test.weight\"\n# if existing query file, should name to \"rank.test.query\"\n# alias: valid, test, test_data,\nvalid_data = rank.test\n\n# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds\nnum_trees = 100\n\n# shrinkage rate , alias: shrinkage_rate\nlearning_rate = 0.1\n\n# number of leaves for one tree, alias: num_leaf\nnum_leaves = 31\n\n# type of tree learner, support following types:\n# serial , single machine version\n# feature , use feature parallel to train\n# data , use data parallel to train\n# voting , use voting based parallel to train\n# alias: tree\ntree_learner = serial\n\n# number of threads for multi-threading. One thread will use one CPU, default is set to #cpu.\n# num_threads = 8\n\n# feature sub-sample, will random select 80% feature to train on each iteration\n# alias: sub_feature\nfeature_fraction = 1.0\n\n# Support bagging (data sub-sample), will perform bagging every 5 iterations\nbagging_freq = 1\n\n# Bagging fraction, will random select 80% data on bagging\n# alias: sub_row\nbagging_fraction = 0.9\n\n# minimal number data for one leaf, use this to deal with over-fit\n# alias : min_data_per_leaf, min_data\nmin_data_in_leaf = 50\n\n# minimal sum Hessians for one leaf, use this to deal with over-fit\nmin_sum_hessian_in_leaf = 5.0\n\n# save memory and faster speed for sparse feature, alias: is_sparse\nis_enable_sparse = true\n\n# when data is bigger than memory size, set this to true. otherwise set false will have faster speed\n# alias: two_round_loading, two_round\nuse_two_round_loading = false\n\n# true if need to save data to binary file and application will auto load data from binary file next time\n# alias: is_save_binary, save_binary\nis_save_binary_file = false\n\n# output model file\noutput_model = LightGBM_model.txt\n\n# support continuous train from trained gbdt model\n# input_model= trained_model.txt\n\n# output prediction file for predict task\n# output_result= prediction.txt\n\n\n# number of machines in distributed training, alias: num_machine\nnum_machines = 1\n\n# local listening port in distributed training, alias: local_port\nlocal_listen_port = 12400\n\n# machines list file for distributed training, alias: mlist\nmachine_list_file = mlist.txt\n" }, { "path": "examples/multiclass_classification/README.md", "content": "Multiclass Classification Example\n=================================\n\nHere is an example for LightGBM to run multiclass classification task.\n\n***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html)\nfor the following commands to work. The `lightgbm` binary must be built and available at the root of this project.***\n\nTraining\n--------\n\nRun the following command in this folder:\n\n```bash\n\"../../lightgbm\" config=train.conf\n```\n\nPrediction\n----------\n\nYou should finish training first.\n\nRun the following command in this folder:\n\n```bash\n\"../../lightgbm\" config=predict.conf\n```\n" }, { "path": "examples/multiclass_classification/multiclass.test", "content": "1\t0.109\t1.261\t-0.274\t2.605\t0.472\t-0.429\t-0.983\t1.000\t-0.095\t-1.219\t-0.369\t-0.312\t-0.840\t1.281\t-0.618\t-0.532\t-0.132\t0.443\t0.028\t2.201\t0.044\t1.671\t0.660\t-0.114\t0.574\t0.276\t0.680\t-0.670\n4\t-0.294\t-0.659\t-0.569\t2.351\t0.775\t-0.339\t-2.436\t-1.704\t0.086\t-0.291\t-1.152\t-0.132\t-0.745\t-0.641\t-0.173\t-1.016\t-1.386\t-1.354\t-0.405\t-1.583\t2.190\t0.933\t0.720\t-0.885\t-0.848\t-1.416\t1.838\t1.343\n2\t0.194\t1.849\t-1.486\t0.190\t0.331\t-1.080\t-2.175\t1.073\t0.371\t-1.200\t0.022\t0.784\t-1.400\t-0.130\t-1.631\t-0.166\t0.156\t0.944\t0.360\t-0.081\t-0.736\t0.534\t-1.664\t0.267\t-0.813\t0.871\t0.924\t-0.935\n0\t0.261\t-0.391\t-0.138\t-0.822\t0.774\t-0.146\t-0.738\t-0.121\t1.735\t0.335\t1.076\t-1.612\t-0.197\t-1.000\t-0.519\t-0.767\t1.289\t0.717\t-0.755\t-0.465\t0.363\t0.891\t1.041\t0.635\t1.814\t0.427\t-0.445\t-0.815\n2\t0.547\t-0.093\t-0.077\t-2.957\t-0.713\t0.933\t0.970\t-0.735\t0.165\t-0.189\t0.690\t2.209\t0.708\t-0.670\t-0.078\t1.402\t-1.409\t-0.615\t0.710\t1.294\t0.609\t-0.607\t0.537\t0.414\t0.351\t-0.097\t0.053\t-0.907\n4\t-0.544\t1.656\t0.213\t2.566\t-1.318\t-0.301\t-0.304\t-0.777\t-0.351\t-0.480\t-0.979\t1.795\t-0.083\t0.927\t0.990\t1.428\t-0.982\t1.266\t-0.074\t2.036\t0.525\t-1.153\t1.258\t1.159\t-1.292\t0.745\t1.604\t0.479\n2\t0.499\t0.546\t-0.268\t0.686\t-0.923\t-2.454\t0.707\t0.166\t-0.610\t-1.471\t-2.037\t0.052\t1.649\t-0.139\t-0.361\t-0.024\t2.179\t0.752\t-0.934\t0.028\t0.332\t-1.056\t0.759\t-0.818\t-1.511\t0.076\t-0.082\t-0.001\n0\t-0.651\t-0.487\t-0.592\t-0.864\t0.049\t-0.831\t0.270\t-0.050\t-0.239\t-0.908\t-0.577\t0.755\t0.501\t-0.978\t0.099\t0.751\t-1.669\t0.543\t-0.663\t0.571\t-0.763\t-1.805\t-1.628\t0.048\t0.260\t-0.904\t0.639\t-1.662\n4\t-1.463\t0.326\t1.539\t0.616\t0.623\t-0.292\t1.767\t0.728\t0.818\t0.100\t0.842\t-0.257\t1.059\t0.309\t-0.462\t1.705\t-0.238\t-0.841\t2.439\t0.076\t-0.003\t0.519\t0.449\t-0.589\t-2.275\t1.666\t-0.145\t1.938\n2\t0.837\t0.724\t-0.514\t0.910\t-1.161\t1.077\t-0.261\t1.684\t-1.521\t1.763\t-0.361\t-0.379\t0.195\t-0.697\t-0.072\t-0.738\t-0.701\t0.487\t-0.902\t-0.768\t-1.373\t-0.504\t1.312\t-0.846\t-0.732\t0.194\t-0.371\t2.228\n1\t-0.350\t-0.612\t0.361\t-0.562\t-0.930\t-1.303\t0.705\t-0.769\t0.447\t-0.075\t1.981\t0.363\t-1.274\t-0.208\t0.410\t-0.552\t0.526\t-0.348\t0.143\t0.604\t0.265\t-0.976\t-1.465\t2.297\t-1.661\t0.787\t0.096\t-0.769\n2\t-0.852\t0.442\t0.536\t1.362\t-0.641\t-0.848\t1.189\t-0.225\t-0.899\t-0.495\t-1.386\t-0.028\t-2.881\t0.266\t-1.542\t1.269\t-0.706\t-0.828\t0.042\t0.393\t0.035\t-1.621\t0.118\t-0.079\t-0.117\t0.579\t-0.773\t-1.133\n0\t-1.667\t0.223\t-1.121\t0.194\t0.025\t0.773\t-1.064\t-0.295\t-1.464\t0.098\t-0.681\t-0.536\t0.098\t-1.381\t0.109\t-1.370\t-0.347\t-1.482\t0.666\t0.646\t-0.322\t0.344\t0.915\t0.802\t-1.206\t0.740\t-1.173\t0.267\n1\t0.068\t0.818\t-1.738\t0.085\t-0.070\t0.725\t-0.188\t-1.039\t0.007\t-1.083\t-1.925\t0.727\t0.579\t0.467\t1.303\t-0.665\t-1.431\t-0.070\t-0.348\t-0.842\t-0.080\t-0.612\t-1.836\t-0.441\t-1.136\t0.232\t-0.656\t1.112\n4\t0.009\t-0.265\t-1.209\t0.797\t-2.475\t-0.172\t1.974\t-0.278\t1.191\t-1.676\t-0.027\t-0.851\t-1.736\t0.834\t-0.205\t0.103\t-1.528\t-0.716\t0.647\t-1.237\t-1.428\t1.648\t-1.814\t0.095\t1.461\t-0.233\t-1.034\t-0.994\n2\t0.726\t1.481\t1.267\t0.788\t-1.491\t-2.417\t0.117\t0.229\t-2.124\t-0.887\t1.691\t0.393\t-0.295\t-0.137\t0.296\t-0.308\t0.281\t-0.229\t0.286\t-0.140\t-2.183\t0.244\t1.006\t0.018\t-0.536\t1.063\t0.160\t-0.036\n4\t-0.374\t-0.465\t-1.333\t-0.949\t0.981\t0.504\t0.273\t-1.335\t0.101\t-2.304\t0.843\t0.259\t-1.345\t1.616\t-1.796\t0.530\t0.555\t-1.240\t-0.038\t-1.637\t-2.846\t-1.068\t-0.617\t0.644\t0.067\t0.094\t-1.014\t-0.530\n0\t1.231\t-0.064\t0.033\t0.833\t-0.892\t-0.583\t-0.094\t-0.657\t-1.489\t-0.029\t0.136\t-0.243\t1.020\t0.129\t0.756\t0.265\t0.854\t1.836\t0.166\t0.881\t0.361\t1.008\t0.637\t-0.645\t0.166\t-0.404\t0.274\t0.939\n4\t-0.647\t-0.659\t-1.209\t-0.148\t1.131\t1.019\t-0.026\t1.784\t-0.055\t-0.341\t-1.977\t0.199\t-0.896\t1.452\t-1.243\t1.287\t0.205\t-1.149\t-1.661\t0.370\t-0.921\t-1.971\t1.979\t0.868\t-1.145\t-1.817\t1.181\t-1.354\n2\t0.314\t-0.396\t-1.002\t1.413\t0.361\t0.356\t-0.549\t-0.580\t-0.445\t-0.045\t2.310\t-0.581\t0.389\t-2.498\t-0.284\t-0.588\t-0.132\t0.580\t0.916\t1.235\t2.289\t-1.210\t-0.520\t0.079\t0.005\t0.951\t0.216\t-1.186\n3\t-0.653\t-0.269\t0.167\t-1.778\t-0.499\t0.294\t0.597\t1.236\t0.016\t0.421\t1.926\t0.748\t-0.621\t-0.607\t0.991\t-2.071\t-0.566\t-0.159\t-0.548\t-0.582\t0.196\t0.978\t0.174\t0.773\t-1.765\t1.876\t1.908\t-0.310\n3\t-0.033\t-1.985\t-2.104\t-0.444\t-1.038\t-1.375\t0.260\t-0.999\t-1.046\t1.118\t0.341\t-0.548\t0.095\t0.233\t-0.446\t-0.502\t1.835\t-0.209\t1.280\t-0.305\t0.140\t-0.627\t-0.211\t0.256\t2.191\t0.856\t0.963\t-1.432\n3\t-0.127\t-0.265\t-0.637\t1.229\t-1.362\t-0.085\t-0.844\t0.567\t0.134\t1.182\t0.735\t0.082\t0.744\t0.836\t-0.913\t0.773\t0.297\t0.489\t-0.408\t-0.854\t-1.034\t-3.408\t-0.709\t2.439\t0.822\t0.318\t0.961\t-0.260\n4\t-2.755\t0.924\t-1.861\t0.345\t0.034\t-1.552\t1.405\t-2.208\t1.390\t0.034\t-0.175\t-0.780\t-1.042\t-1.789\t0.008\t-0.425\t0.499\t0.297\t0.941\t0.205\t0.910\t0.567\t0.908\t-1.248\t0.338\t-1.810\t0.097\t-0.171\n2\t-0.202\t0.177\t-1.019\t0.758\t-1.104\t-1.135\t0.949\t1.259\t0.287\t-0.281\t-1.108\t-1.077\t-0.438\t-0.440\t-2.299\t0.380\t-1.768\t-1.403\t-0.553\t0.245\t0.105\t-0.044\t-1.096\t-0.056\t1.254\t0.007\t0.647\t-2.126\n4\t0.175\t0.528\t0.853\t-1.185\t-0.018\t0.140\t3.120\t0.901\t0.122\t-1.231\t-0.183\t2.311\t-1.496\t-0.209\t1.321\t2.220\t-0.193\t0.623\t0.985\t0.698\t0.314\t1.398\t1.208\t0.499\t1.745\t0.190\t0.233\t-0.202\n4\t-0.052\t-0.478\t0.395\t-0.274\t-2.571\t-1.746\t-0.255\t-0.102\t0.447\t-1.724\t-0.401\t-0.156\t0.292\t1.451\t0.229\t-1.350\t1.062\t1.287\t-1.010\t0.417\t2.362\t1.323\t-0.552\t1.960\t-0.098\t0.590\t2.763\t0.381\n0\t-0.607\t0.753\t0.541\t-0.371\t1.879\t-0.843\t0.458\t-1.308\t0.302\t0.515\t-0.197\t0.045\t-0.477\t0.931\t0.875\t0.438\t0.700\t0.553\t-0.410\t0.356\t0.754\t-0.204\t0.570\t-1.322\t-1.185\t0.083\t-0.312\t1.101\n2\t-0.369\t-1.453\t1.285\t-0.066\t-0.155\t-0.311\t0.256\t-1.681\t-1.971\t0.452\t0.560\t1.672\t-0.159\t0.085\t0.035\t-0.307\t-0.558\t-2.198\t0.301\t-0.725\t1.913\t0.997\t-0.588\t0.871\t-0.643\t-0.672\t-0.416\t0.290\n4\t-1.068\t1.667\t-0.093\t-1.823\t0.985\t-1.302\t1.507\t-1.712\t-1.619\t-1.039\t-0.824\t-0.102\t-2.205\t0.194\t-0.502\t-1.238\t0.153\t1.491\t1.334\t0.329\t-0.399\t-1.305\t-1.170\t1.561\t0.425\t-0.069\t0.284\t-1.353\n4\t-1.824\t0.659\t0.617\t0.814\t2.506\t-1.299\t-1.488\t0.706\t-0.337\t-0.986\t-0.283\t-0.618\t1.725\t2.262\t-1.636\t-1.477\t-0.759\t-0.690\t-0.775\t-0.194\t-0.447\t-1.354\t-1.027\t1.546\t-0.357\t-0.409\t-1.520\t-0.463\n0\t-1.368\t0.589\t-0.055\t-0.378\t-1.288\t-0.300\t-0.415\t0.318\t0.167\t0.792\t-0.485\t-1.860\t0.991\t-0.936\t-1.107\t-0.043\t0.302\t-0.531\t-0.539\t-0.391\t1.417\t-0.003\t-0.968\t-0.958\t-0.251\t0.238\t0.484\t0.124\n0\t-0.965\t-1.354\t-0.158\t-0.337\t-0.611\t-1.336\t-1.668\t-1.137\t0.871\t0.029\t-0.928\t-0.098\t0.258\t0.103\t-0.543\t-0.539\t-0.470\t1.209\t0.592\t1.290\t0.117\t-0.988\t1.295\t-0.330\t-1.114\t-0.967\t0.690\t0.138\n1\t-0.290\t-1.035\t-0.820\t-1.467\t0.141\t-0.862\t-0.495\t-0.128\t1.044\t0.929\t-0.055\t1.266\t-2.012\t1.877\t0.692\t0.103\t-0.891\t-0.438\t0.609\t0.632\t0.668\t-0.728\t-0.930\t0.195\t0.770\t0.003\t1.481\t0.033\n1\t-1.215\t2.249\t1.060\t-0.160\t1.110\t-0.347\t0.475\t0.592\t0.862\t-0.460\t0.883\t-0.442\t0.731\t-0.018\t-1.824\t-0.737\t0.848\t-0.304\t1.214\t-1.213\t0.696\t-0.048\t-0.247\t-1.178\t0.210\t-1.009\t-0.636\t-0.141\n1\t-1.617\t-0.229\t-0.249\t0.627\t0.046\t0.623\t0.703\t2.155\t-0.315\t-0.941\t0.246\t-0.802\t1.482\t-0.283\t-0.566\t-0.471\t0.718\t-0.158\t-0.642\t1.665\t-0.856\t0.036\t0.652\t-1.631\t0.480\t-1.055\t0.823\t-0.963\n2\t1.022\t-0.268\t0.245\t-2.234\t0.225\t-1.043\t-0.351\t-1.567\t-0.882\t-0.164\t-0.272\t0.885\t-1.868\t-0.051\t-0.099\t-0.458\t-0.913\t1.102\t2.048\t-1.714\t-0.924\t-1.004\t-0.282\t1.256\t0.528\t-0.154\t0.016\t-0.319\n3\t-0.153\t-0.452\t0.570\t0.579\t-0.310\t1.099\t-1.020\t0.408\t1.547\t1.248\t-1.612\t1.060\t-1.243\t-2.433\t0.479\t0.931\t-1.346\t1.494\t-0.244\t-0.874\t0.362\t0.725\t0.883\t-0.750\t0.366\t0.165\t-1.291\t-1.055\n1\t-0.803\t1.518\t1.136\t2.244\t0.580\t-0.334\t-1.220\t0.130\t0.103\t1.263\t-0.188\t-0.724\t-1.792\t-0.099\t-0.711\t-0.397\t-0.203\t0.096\t-0.910\t-0.686\t1.451\t-0.261\t0.716\t-0.386\t-0.257\t1.023\t-0.531\t0.872\n1\t-2.040\t-0.141\t-0.711\t0.734\t-0.479\t0.467\t0.625\t-0.335\t0.490\t-0.255\t0.343\t-0.949\t-0.944\t-1.166\t0.263\t1.207\t0.961\t-0.241\t-0.162\t0.820\t1.318\t-2.157\t-0.507\t0.156\t-0.042\t-0.072\t1.906\t-0.838\n3\t1.358\t0.042\t0.877\t-0.775\t-0.615\t-1.600\t1.109\t-0.576\t-1.328\t0.501\t1.343\t-0.371\t-1.771\t-0.324\t0.239\t0.595\t0.777\t0.076\t-0.259\t0.686\t-1.323\t-0.969\t-0.727\t-0.442\t-1.383\t0.972\t-1.212\t-2.688\n0\t-0.174\t-0.956\t0.877\t-0.937\t0.865\t0.054\t0.284\t0.040\t0.238\t-0.603\t1.559\t1.015\t-0.592\t-0.263\t0.450\t-0.356\t-0.501\t0.597\t0.028\t-0.081\t-0.037\t-1.521\t1.920\t-0.712\t-0.832\t-1.960\t-0.520\t0.920\n4\t-0.969\t-0.131\t-0.226\t2.224\t-0.489\t0.449\t-0.080\t2.200\t0.299\t1.428\t1.351\t-1.764\t-0.067\t-0.233\t-1.742\t-1.553\t0.529\t1.406\t-0.126\t-1.170\t0.624\t-0.069\t-0.241\t-0.352\t-2.241\t0.544\t1.581\t-0.561\n4\t1.007\t0.023\t-0.864\t1.590\t-0.750\t-2.162\t0.458\t-1.105\t0.989\t-0.381\t0.604\t1.402\t-1.209\t-0.566\t-0.076\t1.538\t-0.658\t-0.595\t0.478\t-0.577\t0.153\t-1.552\t2.214\t-1.348\t-0.322\t-0.032\t1.913\t-1.325\n4\t-0.451\t2.086\t-1.295\t1.459\t1.215\t1.901\t-1.588\t-0.869\t1.492\t-1.877\t-1.606\t-0.488\t0.192\t1.592\t-0.185\t-0.608\t0.311\t0.470\t0.441\t-0.178\t-1.955\t-0.918\t1.420\t0.187\t-0.323\t1.118\t-1.130\t-0.225\n4\t0.893\t1.292\t0.102\t-0.997\t-0.715\t0.629\t-1.135\t-0.582\t0.513\t-0.651\t1.421\t-1.038\t-3.288\t-0.778\t0.310\t0.863\t-0.408\t-0.277\t-0.031\t1.742\t0.379\t-0.493\t2.052\t0.011\t-2.075\t-1.387\t0.558\t-1.777\n4\t0.711\t-1.612\t0.990\t-1.148\t-0.725\t-0.018\t2.103\t2.309\t-0.713\t-0.173\t0.996\t-0.361\t0.703\t-0.812\t1.110\t-1.219\t-1.618\t1.175\t-0.115\t-0.129\t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t-0.085\t-1.840\t0.755\t0.387\t0.127\t0.916\t1.702\n4\t2.101\t2.078\t-2.316\t0.629\t-0.282\t-1.064\t1.455\t-0.138\t0.923\t1.016\t0.674\t-0.334\t-0.324\t0.095\t1.455\t1.037\t-0.245\t-0.086\t1.785\t-1.232\t-1.875\t-0.865\t2.116\t-1.195\t-0.926\t0.307\t0.182\t1.160\n0\t-0.553\t0.680\t0.948\t0.586\t-0.652\t0.643\t1.208\t0.402\t0.835\t1.790\t0.898\t-0.727\t0.140\t-0.332\t0.173\t1.705\t-0.424\t-1.267\t-0.128\t-0.884\t-0.147\t-0.149\t0.620\t-0.942\t1.031\t-1.169\t-1.141\t-0.244\n3\t1.302\t0.276\t0.815\t0.393\t-0.847\t0.248\t-0.332\t-1.063\t0.920\t-1.305\t-0.659\t2.217\t0.701\t0.245\t-2.173\t-0.118\t-1.165\t-0.078\t-0.190\t-0.857\t1.172\t0.955\t-1.126\t1.574\t-1.150\t1.664\t-0.211\t-0.400\n3\t1.898\t-2.365\t0.365\t0.166\t0.355\t-0.558\t0.003\t-1.094\t1.038\t1.007\t1.641\t-0.774\t0.270\t2.043\t-0.675\t-1.009\t0.300\t1.165\t-0.667\t1.933\t-0.916\t0.658\t-0.677\t-0.466\t0.823\t-0.209\t-1.519\t-0.799\n4\t-0.415\t-2.426\t0.880\t2.158\t1.974\t-0.476\t-0.287\t0.874\t0.092\t0.129\t-2.197\t-0.817\t-0.204\t1.778\t1.764\t-1.111\t-1.231\t1.682\t1.117\t-0.474\t-0.333\t-1.144\t-1.214\t-1.605\t-0.136\t-0.417\t-2.040\t-0.365\n4\t-0.772\t-0.327\t1.185\t-0.448\t0.400\t0.072\t-0.274\t0.650\t0.375\t0.437\t1.485\t-2.901\t0.143\t0.372\t-1.317\t-1.662\t-1.742\t3.620\t-2.126\t-1.778\t-0.169\t1.344\t0.730\t-0.153\t0.789\t0.805\t-0.096\t-1.300\n3\t-0.732\t0.735\t1.397\t-0.673\t1.475\t-0.378\t0.225\t-0.363\t0.250\t-1.050\t1.029\t-1.635\t-0.934\t1.297\t2.263\t-0.528\t1.534\t1.293\t0.408\t-0.480\t1.403\t-0.188\t0.465\t-2.162\t-0.403\t-0.611\t0.126\t-0.314\n4\t-0.458\t0.142\t-0.771\t0.017\t0.079\t-0.182\t-1.644\t-1.051\t-0.162\t-2.136\t1.025\t1.300\t0.852\t0.282\t-1.233\t0.307\t1.405\t-1.133\t2.554\t0.818\t-1.061\t-1.010\t-0.505\t-0.054\t-0.377\t-2.801\t-1.302\t-0.007\n3\t0.777\t0.503\t-0.640\t0.811\t-1.283\t-2.228\t-1.562\t-0.922\t-0.755\t-0.435\t-0.390\t-0.650\t-2.116\t0.192\t-0.265\t-0.696\t1.398\t-0.242\t0.059\t0.796\t-1.490\t-0.023\t0.793\t-0.091\t-1.303\t-0.482\t0.189\t-2.107\n1\t1.429\t0.321\t-0.218\t0.096\t1.137\t0.057\t-0.020\t0.128\t0.872\t-0.467\t0.189\t0.215\t0.578\t0.993\t0.078\t-0.471\t0.679\t-1.569\t-1.077\t-1.013\t-0.716\t-1.391\t0.957\t-0.342\t-1.724\t-1.203\t0.314\t-1.634\n3\t0.886\t-1.530\t0.249\t-0.936\t0.655\t1.079\t-1.891\t0.265\t-1.558\t-1.717\t0.763\t-0.098\t-0.143\t1.559\t-0.271\t-0.789\t-0.506\t0.406\t-1.060\t-0.253\t0.964\t-1.597\t-1.641\t-0.339\t0.679\t1.481\t-0.791\t-0.476\n2\t-0.247\t0.291\t-0.750\t1.254\t0.817\t0.281\t0.714\t1.157\t-0.525\t0.047\t-1.185\t2.254\t-0.510\t-2.002\t-0.424\t-0.455\t0.714\t0.656\t0.839\t-0.118\t0.436\t-2.222\t-0.313\t1.628\t1.647\t-0.770\t-0.076\t-0.251\n0\t-1.384\t-1.904\t0.874\t0.258\t1.549\t0.971\t-0.623\t0.247\t-0.792\t-1.613\t1.196\t-0.194\t1.348\t0.109\t0.278\t0.577\t-0.184\t-0.237\t0.242\t1.246\t0.505\t0.017\t-0.411\t-0.118\t-0.864\t-0.982\t-0.238\t0.006\n2\t-0.966\t-0.107\t-0.665\t-1.543\t-0.182\t0.106\t1.268\t0.937\t1.220\t0.720\t0.333\t-1.229\t0.150\t0.597\t1.457\t0.686\t1.240\t-1.032\t-0.326\t0.429\t1.161\t-0.145\t-0.082\t0.687\t-1.126\t1.607\t-0.879\t-1.919\n2\t-1.283\t0.717\t-0.453\t0.330\t-0.624\t1.474\t-1.434\t1.034\t-1.913\t0.097\t-1.387\t0.713\t0.342\t0.525\t-0.270\t-0.265\t-0.135\t-1.174\t2.576\t-0.797\t-0.400\t0.321\t-0.448\t0.286\t-1.851\t-0.036\t-0.940\t0.402\n0\t1.013\t-0.161\t1.329\t-1.660\t0.446\t0.138\t0.834\t-0.076\t-0.984\t-1.092\t-0.012\t-0.260\t0.857\t0.954\t0.048\t0.026\t1.065\t-0.245\t-0.204\t-1.323\t0.879\t-0.090\t1.137\t-1.207\t-0.491\t0.946\t0.326\t-1.183\n4\t-1.772\t0.941\t-0.361\t-0.467\t1.478\t0.170\t0.904\t-0.623\t-0.725\t-0.365\t0.670\t0.051\t0.468\t-2.603\t-1.882\t-2.154\t-0.379\t-1.398\t1.690\t-0.021\t0.217\t-1.090\t-1.121\t1.551\t1.782\t0.416\t-2.032\t-0.425\n1\t-0.952\t-1.032\t0.604\t0.771\t-0.250\t-0.597\t0.569\t-1.482\t1.950\t0.452\t-0.159\t0.830\t0.631\t0.235\t-1.890\t0.411\t1.456\t-0.555\t0.959\t0.849\t0.294\t0.170\t0.560\t0.416\t1.891\t0.967\t0.707\t1.057\n2\t1.566\t0.434\t1.083\t0.387\t0.379\t0.753\t-0.567\t-0.556\t0.350\t1.941\t-1.541\t-2.029\t-0.264\t1.031\t0.051\t-1.169\t0.079\t-1.733\t0.629\t-0.309\t-0.082\t0.427\t0.206\t-0.541\t1.034\t0.797\t-1.774\t-0.230\n3\t0.633\t-1.243\t-2.269\t-0.977\t-0.243\t2.580\t0.543\t-1.808\t-0.690\t-0.053\t0.254\t-1.315\t-0.135\t-0.718\t-1.104\t0.260\t0.774\t0.841\t0.574\t-0.497\t0.715\t0.309\t0.134\t-0.612\t-0.497\t0.074\t2.440\t0.301\n0\t0.148\t-0.643\t0.262\t-0.331\t0.880\t0.333\t-0.315\t0.232\t1.315\t-0.570\t0.403\t-0.070\t0.427\t-0.655\t0.110\t1.108\t-0.937\t-1.329\t-1.291\t-1.171\t0.595\t-1.406\t-0.298\t0.618\t0.417\t-0.386\t0.191\t0.107\n0\t-0.719\t-0.234\t1.263\t-0.775\t0.483\t0.747\t0.077\t0.040\t-1.025\t0.731\t-1.254\t0.391\t-0.518\t-0.395\t-0.201\t0.499\t1.562\t0.111\t-0.720\t-1.440\t1.160\t0.175\t-0.232\t0.059\t0.767\t-0.410\t-0.666\t-1.310\n3\t0.265\t-0.096\t0.818\t-1.546\t-0.614\t-0.791\t0.123\t-1.861\t-1.498\t1.854\t-0.448\t-1.560\t0.247\t1.735\t0.266\t-0.284\t0.287\t0.635\t-0.347\t-0.244\t0.348\t0.150\t-2.634\t0.734\t0.967\t-0.010\t0.872\t-0.477\n2\t1.473\t2.152\t-0.709\t0.140\t-0.400\t-0.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658\t-1.474\t-0.635\t-0.365\t-0.469\t-0.649\t-1.038\t0.512\t-1.613\t-0.532\t0.791\t0.397\n2\t-1.429\t1.286\t0.121\t-0.203\t0.431\t0.237\t0.767\t0.538\t0.718\t1.351\t0.884\t-0.160\t-1.123\t1.668\t-0.238\t-0.473\t-1.737\t0.338\t0.149\t-1.515\t0.453\t-0.498\t2.690\t-0.569\t-0.435\t-0.875\t1.278\t-0.373\n2\t-0.699\t1.247\t0.314\t-1.193\t1.339\t0.108\t0.286\t-1.216\t1.878\t1.293\t0.527\t0.508\t0.599\t0.050\t-0.563\t2.102\t0.100\t-1.871\t0.311\t-0.585\t-0.159\t-1.166\t1.209\t-0.466\t-0.375\t-0.557\t0.964\t0.777\n2\t0.347\t-0.184\t1.339\t0.658\t-0.317\t-1.351\t1.346\t0.782\t1.288\t0.964\t-1.219\t0.461\t0.953\t0.111\t-0.324\t-0.465\t-0.764\t-0.107\t-1.252\t-1.826\t-0.550\t-0.585\t2.238\t1.364\t-0.204\t-0.030\t1.052\t-0.014\n0\t0.698\t1.842\t-0.340\t-0.117\t0.949\t0.611\t0.944\t-0.602\t-0.560\t-0.839\t-1.064\t-0.254\t0.032\t-1.757\t0.037\t-0.908\t0.283\t0.520\t-0.365\t0.543\t-0.919\t0.058\t-1.105\t0.706\t-0.463\t-0.509\t-0.886\t1.832\n4\t-1.450\t-1.283\t-0.922\t1.434\t0.541\t1.966\t-1.912\t0.607\t0.463\t-1.634\t0.449\t0.493\t-0.645\t0.527\t-1.090\t0.733\t1.188\t-0.836\t0.926\t-1.460\t0.118\t0.045\t-0.355\t-0.375\t1.646\t-2.578\t1.555\t-1.306\n1\t0.138\t-1.072\t-1.138\t-2.150\t0.842\t0.490\t0.199\t0.254\t1.126\t0.691\t0.596\t1.034\t0.373\t0.137\t-0.731\t-1.910\t-0.892\t0.655\t-0.494\t-0.309\t0.709\t-1.793\t0.757\t0.807\t-0.072\t-0.046\t-0.184\t0.662\n4\t1.327\t-0.327\t-0.250\t-0.162\t0.275\t-0.581\t-2.894\t1.193\t1.795\t-0.612\t-1.067\t-0.137\t-0.188\t0.996\t0.055\t-0.793\t-1.318\t0.866\t0.600\t1.061\t-0.968\t1.838\t-1.744\t0.710\t-1.910\t-0.790\t1.561\t0.831\n2\t0.366\t-0.507\t-0.189\t0.161\t-0.007\t0.736\t0.487\t-1.508\t-1.358\t-0.370\t1.829\t-0.852\t-0.208\t-0.536\t-0.554\t0.178\t1.401\t0.456\t-0.804\t2.753\t0.079\t-0.577\t1.412\t-0.469\t1.663\t-0.871\t1.430\t0.201\n3\t-1.313\t0.624\t1.626\t0.497\t-1.879\t-0.325\t0.890\t-0.392\t0.682\t-1.426\t-1.990\t0.022\t-2.174\t1.214\t-0.757\t0.350\t1.265\t0.042\t-0.837\t-0.684\t0.282\t-0.880\t-0.162\t0.063\t-0.397\t-2.039\t0.275\t0.554\n1\t1.155\t0.571\t-0.154\t-0.544\t0.771\t0.275\t-0.861\t1.302\t0.262\t0.653\t-0.392\t1.131\t-1.585\t1.675\t-1.939\t-1.377\t0.252\t-0.531\t-0.328\t-0.531\t-0.783\t0.317\t-0.049\t0.885\t0.080\t-0.417\t0.774\t-1.832\n4\t1.116\t0.766\t-1.191\t-0.179\t0.540\t-2.216\t-1.627\t-1.394\t-1.241\t1.364\t-0.761\t1.809\t0.578\t-0.726\t0.127\t-1.594\t-0.897\t0.344\t1.192\t-1.437\t-1.020\t-0.293\t2.070\t0.593\t0.002\t-0.619\t-0.201\t-0.024\n1\t1.479\t0.664\t2.139\t-1.457\t0.119\t-0.612\t-0.091\t-0.270\t-0.086\t0.852\t0.662\t0.177\t0.086\t0.109\t-0.434\t1.240\t0.273\t1.276\t1.505\t-0.249\t-0.563\t-0.550\t1.856\t-0.640\t-0.545\t0.246\t-0.187\t-0.047\n0\t0.028\t-1.116\t-0.366\t0.203\t-1.551\t1.923\t1.126\t-1.264\t1.276\t0.658\t-0.915\t-0.478\t-1.186\t-0.563\t0.669\t-0.433\t0.052\t0.228\t0.931\t0.060\t1.666\t0.548\t-0.244\t-0.611\t-0.898\t0.007\t-0.003\t-0.430\n3\t-0.282\t0.325\t-2.009\t1.822\t-0.670\t-0.296\t0.260\t-0.196\t0.961\t-0.370\t-0.580\t0.933\t-2.732\t-1.275\t0.243\t0.838\t0.847\t-2.723\t-0.484\t0.860\t-1.027\t-0.233\t0.451\t0.065\t0.489\t-0.173\t0.570\t0.593\n1\t-1.716\t-0.486\t0.862\t1.005\t-1.143\t-0.860\t0.231\t0.248\t0.018\t-1.470\t-0.688\t-0.993\t-0.831\t-0.948\t-0.545\t0.881\t1.075\t-0.490\t0.097\t-0.322\t1.239\t1.151\t-0.477\t0.577\t1.343\t-0.241\t-1.088\t1.270\n0\t0.716\t0.805\t-1.525\t-0.641\t-0.801\t-0.699\t1.255\t0.088\t-0.862\t-0.843\t-0.603\t0.093\t-0.942\t-0.509\t-0.593\t0.117\t0.902\t0.725\t0.375\t-0.387\t-0.905\t-0.380\t0.385\t0.700\t-0.400\t0.488\t0.178\t0.032\n0\t0.672\t-0.096\t0.529\t-0.126\t0.217\t0.575\t-0.398\t-1.234\t0.276\t1.189\t0.299\t0.318\t-0.042\t0.739\t-0.186\t-0.454\t1.105\t-0.531\t0.889\t1.106\t0.567\t-1.249\t0.027\t-0.202\t-0.496\t0.143\t0.769\t-0.459\n3\t0.497\t0.791\t1.134\t0.659\t-0.407\t-1.810\t0.991\t-0.525\t-1.447\t1.872\t-0.061\t0.489\t0.228\t-0.014\t-0.387\t-0.584\t2.225\t-1.458\t-0.433\t-0.890\t0.518\t-0.414\t-0.499\t0.642\t1.935\t-0.830\t-1.551\t0.063\n1\t-0.005\t0.140\t0.260\t-0.162\t0.363\t-0.375\t0.292\t-1.201\t0.740\t-0.944\t0.770\t0.874\t1.145\t1.723\t0.336\t-1.014\t1.691\t0.944\t0.280\t-2.281\t-0.678\t1.068\t-0.440\t1.824\t-0.884\t0.183\t-0.572\t0.182\n3\t1.125\t-0.957\t-2.429\t1.806\t0.808\t-0.305\t1.265\t0.080\t0.162\t1.593\t0.332\t1.110\t-0.512\t-1.308\t-0.314\t0.696\t1.547\t0.116\t2.162\t-0.678\t-0.860\t0.185\t0.275\t-0.285\t-0.381\t-0.706\t0.758\t0.186\n3\t-1.992\t-0.652\t2.554\t-0.205\t1.139\t-0.028\t-0.330\t0.933\t1.197\t-0.892\t-1.098\t-1.368\t-0.878\t1.136\t0.031\t1.184\t-1.033\t-1.003\t-0.392\t0.297\t-0.140\t0.698\t0.414\t0.488\t0.685\t1.096\t-2.460\t-0.012\n1\t0.543\t0.374\t0.927\t1.441\t-1.064\t0.300\t-0.722\t1.070\t0.724\t-0.970\t-0.007\t-1.501\t0.950\t-0.536\t-1.130\t-0.346\t0.584\t0.287\t-0.489\t-0.105\t-0.292\t-0.934\t-1.866\t-0.006\t0.585\t-0.016\t2.050\t-0.596\n3\t-0.312\t1.000\t2.085\t-0.398\t0.746\t0.791\t-2.211\t0.479\t-0.610\t-1.343\t1.161\t0.397\t2.040\t0.758\t-0.109\t-0.550\t-1.531\t1.120\t0.776\t0.268\t1.265\t-0.087\t-0.454\t0.048\t0.327\t1.500\t-0.942\t-0.441\n1\t0.722\t0.397\t0.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n0\t-1.472\t-0.599\t-0.164\t0.195\t1.446\t0.073\t0.320\t0.350\t-0.328\t-0.192\t-0.107\t0.626\t-0.913\t-0.507\t0.356\t-1.076\t0.114\t-0.546\t0.505\t1.374\t-0.545\t0.758\t0.201\t-0.384\t0.007\t1.298\t-0.172\t1.309\n1\t1.259\t-0.139\t-0.607\t-0.262\t-0.644\t1.341\t-0.335\t0.456\t0.790\t-0.661\t-1.804\t-0.481\t0.637\t-1.695\t0.253\t0.840\t1.392\t0.678\t0.293\t0.186\t-1.208\t-0.947\t-1.732\t0.072\t0.441\t0.118\t0.284\t0.760\n2\t-1.486\t-1.459\t0.751\t0.786\t0.380\t1.098\t-0.914\t-1.581\t0.433\t1.680\t-0.920\t-1.741\t0.046\t0.974\t0.221\t0.945\t0.488\t0.047\t-0.111\t-0.805\t0.309\t-0.460\t-1.764\t1.255\t-1.037\t-0.306\t0.873\t-0.769\n0\t0.191\t0.206\t-0.017\t0.441\t1.021\t-1.064\t0.378\t1.014\t1.080\t-1.650\t0.820\t0.818\t0.112\t-0.473\t-0.987\t-0.136\t1.195\t1.137\t0.578\t0.497\t0.543\t-1.753\t-0.905\t0.521\t0.250\t0.456\t-0.688\t-0.579\n1\t0.909\t-0.522\t0.253\t-0.577\t-0.232\t0.092\t-2.234\t1.608\t0.532\t0.418\t1.032\t0.863\t1.318\t0.040\t-0.360\t-0.572\t-2.087\t0.113\t0.090\t-0.341\t1.066\t-0.973\t-0.779\t0.109\t1.132\t0.301\t0.223\t0.063\n0\t0.935\t0.649\t-1.080\t-0.600\t1.333\t0.188\t0.442\t-0.186\t1.493\t-0.844\t0.906\t0.976\t1.360\t0.125\t0.934\t0.806\t-0.239\t0.643\t0.280\t1.514\t-0.104\t0.812\t0.718\t-0.839\t-0.548\t-0.955\t0.417\t0.917\n4\t2.332\t0.727\t-0.653\t-0.779\t-0.446\t-2.064\t0.278\t0.694\t-0.967\t-1.552\t-0.911\t0.997\t0.803\t0.215\t1.642\t-1.689\t-0.208\t0.560\t0.790\t0.804\t0.724\t1.319\t0.761\t-1.154\t-0.455\t-1.695\t0.996\t1.195\n2\t0.577\t-0.129\t1.407\t-0.484\t0.955\t-0.039\t-0.608\t0.593\t0.410\t0.357\t0.081\t-0.038\t2.388\t1.184\t0.723\t0.112\t1.007\t0.518\t0.026\t-0.304\t-0.081\t-3.170\t0.211\t-0.488\t1.593\t0.019\t-1.189\t-0.050\n4\t-0.454\t1.726\t-0.887\t0.772\t0.356\t1.262\t-0.667\t-0.733\t1.096\t0.425\t-1.545\t-0.539\t0.386\t0.838\t-0.679\t-2.914\t0.523\t-1.220\t-2.017\t-2.832\t0.814\t0.129\t-0.226\t1.389\t0.637\t1.616\t-1.393\t-0.035\n0\t-0.324\t0.117\t0.648\t-1.262\t1.752\t-1.062\t-0.940\t-0.257\t0.552\t0.045\t0.050\t0.760\t0.135\t0.402\t-0.269\t0.925\t-1.057\t0.570\t-0.357\t0.057\t0.159\t0.871\t-0.082\t-1.754\t-1.005\t-0.098\t0.837\t0.205\n2\t-0.849\t-1.309\t-0.154\t-0.866\t0.907\t0.406\t-0.070\t-0.797\t0.381\t0.691\t0.143\t1.604\t0.460\t0.914\t0.134\t0.676\t-0.797\t-0.458\t0.349\t0.342\t0.508\t1.100\t-1.425\t-0.974\t0.230\t-0.724\t3.378\t0.516\n2\t1.118\t1.003\t0.147\t-0.919\t-0.483\t-0.243\t-0.915\t1.081\t1.028\t-1.396\t-0.322\t2.062\t1.163\t0.188\t1.096\t1.649\t0.095\t-1.357\t0.860\t0.911\t-1.006\t0.261\t-0.791\t-1.645\t0.221\t1.452\t0.321\t0.177\n3\t-0.911\t1.576\t0.084\t-1.499\t-0.308\t0.439\t0.732\t0.199\t0.528\t-0.140\t-0.137\t3.293\t-0.602\t-1.259\t-0.438\t-0.109\t-1.566\t0.501\t-0.610\t-2.313\t-0.021\t-0.812\t1.014\t-1.010\t-0.541\t0.345\t-0.369\t-0.945\n2\t-0.374\t-1.496\t-0.494\t-0.346\t-1.837\t-0.320\t-0.171\t-0.851\t0.158\t1.139\t2.159\t-0.234\t-0.769\t-0.862\t0.443\t-1.860\t1.328\t-0.949\t0.823\t-1.358\t1.068\t0.467\t-0.103\t0.045\t-0.272\t0.311\t-1.744\t0.300\n0\t-0.805\t0.727\t0.083\t-1.749\t-0.606\t-0.268\t1.198\t1.744\t0.771\t-0.365\t-0.151\t0.993\t1.576\t0.278\t0.587\t-0.040\t1.159\t0.159\t-0.105\t0.838\t-1.165\t0.579\t0.178\t-0.343\t0.068\t0.098\t-1.113\t-1.560\n1\t-1.327\t0.406\t-0.396\t1.087\t-0.329\t-1.594\t-0.583\t-0.091\t1.246\t0.120\t-0.194\t0.008\t1.348\t0.704\t0.955\t0.599\t-0.040\t0.301\t-0.110\t-0.060\t-1.058\t-2.342\t-1.239\t-0.328\t0.198\t-0.349\t1.663\t-0.049\n0\t0.404\t0.625\t0.476\t-0.424\t-1.091\t-1.308\t0.114\t-0.221\t-0.265\t-1.507\t0.409\t-0.519\t0.719\t-0.018\t0.395\t-2.069\t1.221\t0.306\t-1.181\t0.222\t-1.190\t0.376\t-0.288\t-1.127\t0.261\t-1.432\t0.900\t1.020\n3\t0.590\t-1.385\t0.661\t-1.555\t-0.772\t1.033\t0.984\t-0.944\t-1.140\t0.026\t2.003\t0.561\t2.353\t-0.165\t0.883\t-0.316\t-0.800\t-0.952\t0.354\t1.295\t-0.327\t1.421\t1.831\t0.868\t0.327\t-0.670\t1.303\t-1.141\n2\t-0.021\t0.323\t1.446\t0.009\t0.510\t-3.200\t-0.511\t0.575\t-0.608\t-0.955\t2.165\t0.715\t-0.546\t1.127\t-0.413\t-1.364\t-0.546\t-0.777\t-0.631\t-1.094\t-0.022\t0.690\t-0.197\t-0.234\t0.419\t-0.249\t0.584\t-0.183\n2\t0.567\t0.994\t-0.127\t0.976\t-2.038\t-1.056\t0.164\t0.330\t-0.912\t0.259\t-0.624\t-0.277\t1.025\t0.909\t-0.997\t-1.057\t-0.411\t-0.027\t-0.658\t1.041\t0.347\t0.406\t-1.267\t1.167\t-2.820\t0.834\t0.934\t-0.432\n1\t0.108\t0.046\t1.468\t-1.058\t-0.284\t-0.870\t-0.549\t-1.546\t-1.195\t0.670\t-1.420\t-2.068\t0.775\t-0.848\t1.242\t-0.116\t0.018\t-1.497\t-0.605\t-0.415\t0.905\t0.262\t0.174\t-0.606\t-0.056\t1.176\t0.488\t-0.047\n0\t0.422\t1.073\t0.264\t0.638\t-0.647\t-1.283\t-0.819\t1.201\t-0.289\t-0.488\t0.007\t-0.199\t-1.804\t0.633\t0.080\t0.707\t0.013\t0.066\t0.013\t1.449\t-0.160\t0.055\t1.630\t-0.367\t0.233\t-0.901\t-1.266\t-0.576\n2\t0.136\t-0.970\t-0.516\t-1.078\t0.603\t1.513\t-0.623\t-1.102\t-2.147\t-1.083\t-1.162\t1.863\t-1.503\t0.520\t0.395\t-0.089\t-0.403\t1.806\t0.383\t-1.382\t-0.326\t-0.117\t-0.673\t0.053\t0.836\t0.172\t0.679\t0.568\n1\t0.384\t0.518\t-0.209\t1.203\t0.081\t-0.550\t1.304\t0.157\t-0.129\t-0.616\t0.757\t0.664\t-0.564\t0.681\t1.392\t0.228\t0.302\t-0.644\t-0.826\t-1.197\t-0.370\t-0.467\t-0.488\t2.411\t-0.785\t-1.808\t0.465\t1.059\n3\t1.551\t2.221\t0.102\t0.239\t2.394\t-1.093\t-0.522\t0.581\t1.148\t0.038\t-0.814\t1.288\t-0.008\t0.001\t0.930\t1.506\t0.240\t0.478\t1.046\t-0.226\t-0.548\t-0.368\t-0.920\t-1.088\t2.221\t-0.895\t0.271\t-0.086\n2\t-0.119\t-1.571\t-0.745\t2.019\t-1.508\t-0.251\t-0.016\t0.320\t-1.497\t0.174\t-1.686\t0.097\t-0.652\t-0.866\t0.835\t-0.236\t0.474\t0.507\t1.270\t-1.208\t0.919\t0.016\t-0.378\t-1.149\t0.607\t-0.922\t0.518\t1.928\n2\t0.661\t0.173\t-1.456\t0.259\t-0.230\t1.137\t-1.059\t-0.640\t0.017\t-0.663\t-1.531\t-0.060\t-1.493\t-0.855\t1.253\t-0.392\t0.083\t-0.237\t-1.146\t0.723\t0.234\t-0.511\t1.419\t-0.261\t-1.967\t1.528\t0.356\t-2.046\n3\t-0.459\t-0.260\t1.045\t0.524\t-1.866\t-1.289\t-0.135\t0.536\t-0.229\t-0.503\t-0.984\t0.913\t0.100\t1.680\t-1.300\t0.691\t0.548\t0.847\t0.827\t-0.507\t-0.181\t0.166\t-1.003\t1.032\t-2.475\t-1.162\t-1.678\t1.100\n0\t-1.947\t-0.821\t0.820\t0.593\t-0.160\t1.069\t0.608\t0.344\t-0.017\t0.896\t0.755\t-0.273\t0.813\t-0.654\t0.235\t0.400\t-0.335\t0.134\t0.608\t-1.082\t0.184\t2.215\t-0.826\t1.030\t-0.096\t1.183\t-0.723\t0.006\n4\t1.851\t-0.095\t-0.931\t0.133\t-0.622\t-0.410\t1.629\t0.126\t-1.279\t-0.440\t-1.851\t-0.759\t1.003\t-0.723\t2.642\t-2.613\t-0.443\t0.113\t0.061\t0.563\t-0.275\t1.253\t-1.230\t-0.238\t-1.114\t-0.495\t-0.257\t0.619\n3\t-1.171\t0.254\t-2.459\t0.414\t-1.409\t-0.096\t-1.195\t0.003\t0.578\t1.503\t-1.541\t0.975\t0.131\t-0.451\t0.760\t-0.236\t0.718\t-0.992\t-0.633\t0.944\t1.485\t-0.035\t-2.787\t-0.199\t0.032\t-0.233\t0.336\t-0.477\n4\t2.025\t-0.668\t-0.099\t1.892\t-1.012\t0.578\t2.092\t0.315\t2.121\t0.094\t1.062\t-0.119\t-0.762\t0.218\t-2.254\t-0.956\t-0.177\t0.378\t1.763\t-0.270\t-0.731\t0.286\t-0.648\t-1.642\t-0.680\t-1.103\t0.764\t-0.719\n0\t1.330\t-0.121\t-1.340\t-0.486\t-1.488\t-1.125\t0.389\t-1.174\t1.113\t-0.071\t0.086\t-0.278\t0.773\t0.783\t0.335\t0.565\t-0.212\t0.542\t-0.338\t0.002\t-0.275\t-0.457\t-0.689\t0.468\t1.311\t1.171\t-1.257\t1.289\n0\t-0.125\t0.235\t-0.211\t-1.040\t0.297\t-1.566\t0.138\t-0.995\t-0.081\t0.191\t-0.353\t-1.641\t-1.188\t0.458\t-0.557\t0.561\t0.010\t-0.198\t0.348\t-0.675\t0.400\t-0.420\t2.536\t-1.134\t-0.357\t-0.317\t-1.352\t0.726\n0\t1.220\t0.389\t-0.502\t-0.606\t-0.590\t-1.016\t1.112\t-0.518\t1.353\t-0.829\t-0.599\t-0.452\t-0.899\t-0.586\t0.551\t1.202\t0.299\t-1.191\t-0.600\t-0.579\t1.701\t0.986\t1.214\t-0.709\t-1.273\t0.573\t-0.460\t0.292\n2\t0.548\t1.883\t-0.804\t0.255\t0.031\t0.788\t-0.452\t-0.566\t-0.064\t0.475\t-0.457\t-2.655\t-0.952\t1.414\t1.547\t-0.496\t1.797\t-0.327\t-1.535\t-0.306\t0.725\t-0.565\t0.603\t0.267\t-0.286\t0.776\t1.229\t0.182\n4\t0.857\t1.525\t-2.802\t0.434\t-1.427\t0.499\t0.128\t0.636\t-0.499\t-1.873\t-0.450\t0.407\t1.015\t-1.172\t1.027\t-0.648\t0.362\t-0.510\t0.182\t0.892\t-1.227\t-0.680\t0.185\t2.402\t-0.863\t2.040\t2.004\t-0.097\n4\t-1.475\t-0.866\t0.012\t-2.449\t-0.380\t-1.165\t-1.499\t-0.802\t-1.069\t0.469\t1.197\t-0.364\t-0.596\t-0.414\t-1.108\t-1.289\t-1.646\t-1.330\t1.632\t-0.690\t-2.942\t-0.587\t-1.590\t0.059\t-0.048\t0.056\t-0.041\t-0.373\n0\t-0.993\t0.458\t-0.038\t-0.444\t-0.889\t1.204\t-0.186\t-0.648\t2.455\t-1.334\t-0.945\t-0.171\t-0.147\t-0.961\t-0.200\t-0.550\t-0.278\t-0.194\t0.897\t-0.505\t-0.789\t1.144\t-0.479\t-0.810\t-0.905\t1.322\t0.547\t0.021\n3\t-0.197\t-1.532\t0.016\t-0.250\t0.512\t0.081\t-0.549\t0.926\t-2.221\t-0.064\t-1.763\t-0.912\t0.525\t-1.274\t-1.424\t2.382\t-1.359\t1.651\t-0.172\t-0.525\t0.567\t1.337\t-1.146\t-0.510\t0.847\t0.288\t1.102\t0.515\n2\t-0.121\t-0.434\t-1.526\t-0.654\t-1.748\t-2.103\t0.166\t0.112\t-1.681\t0.192\t-1.309\t0.146\t0.499\t-2.119\t-0.043\t0.628\t-1.160\t-0.355\t0.799\t-0.381\t1.024\t1.420\t1.367\t-0.538\t-1.074\t0.604\t-0.151\t-0.113\n1\t-0.183\t0.270\t0.003\t0.327\t0.831\t-1.441\t0.536\t1.804\t-1.607\t-0.802\t-0.621\t0.703\t-0.293\t0.432\t0.618\t-0.931\t0.166\t-1.256\t0.166\t1.289\t-0.731\t-0.741\t0.433\t1.125\t-0.667\t1.510\t1.527\t0.151\n2\t-1.866\t0.028\t0.979\t-0.643\t-1.227\t-0.186\t-0.061\t-0.909\t2.096\t-0.479\t-1.154\t-0.094\t0.908\t0.017\t0.486\t-0.872\t1.488\t0.562\t1.048\t0.365\t0.825\t-0.873\t-0.491\t1.346\t1.560\t-1.147\t0.250\t-0.431\n1\t0.056\t-0.380\t0.346\t-1.126\t-0.366\t1.683\t-1.048\t1.233\t-0.832\t-1.053\t0.107\t0.807\t-0.742\t-2.019\t1.290\t-0.747\t-1.237\t0.253\t-0.575\t0.227\t-0.901\t-0.678\t-1.065\t-0.976\t-0.193\t1.793\t0.159\t-0.367\n3\t1.474\t0.180\t0.143\t-0.028\t-0.596\t-0.311\t0.102\t-0.560\t-0.387\t0.408\t-1.431\t0.559\t2.352\t2.014\t0.295\t-1.249\t1.383\t-0.190\t1.423\t-0.764\t-0.588\t0.481\t-0.687\t1.877\t0.564\t1.773\t-1.136\t-0.873\n4\t1.051\t2.023\t1.384\t0.203\t-0.258\t3.016\t0.487\t-1.107\t0.278\t0.038\t0.306\t1.413\t0.010\t-0.452\t1.768\t1.453\t1.511\t-0.822\t-0.274\t1.590\t-1.351\t-0.192\t-1.119\t-0.819\t1.651\t-1.733\t1.274\t0.751\n3\t-1.694\t-0.456\t-0.737\t-0.145\t1.161\t-0.838\t0.089\t1.568\t-0.751\t-0.951\t-0.332\t-2.227\t-1.043\t-0.951\t-2.232\t-0.295\t0.079\t-0.512\t-0.054\t-1.514\t2.014\t-0.285\t0.792\t1.028\t-0.418\t-0.142\t0.884\t-0.579\n1\t1.273\t-0.063\t0.062\t0.340\t3.187\t1.344\t0.701\t-0.913\t0.144\t-0.813\t0.822\t0.140\t-1.125\t0.081\t-0.849\t0.151\t-0.859\t0.027\t0.750\t-0.053\t-0.391\t0.203\t0.697\t-1.107\t0.261\t1.318\t0.574\t0.058\n4\t-0.456\t1.301\t0.568\t-0.803\t0.106\t1.749\t1.016\t-1.383\t1.765\t1.136\t0.602\t1.449\t0.299\t-0.586\t1.543\t-1.143\t-0.731\t1.972\t-0.795\t0.480\t0.331\t-1.303\t-0.444\t-1.273\t0.166\t-0.620\t0.945\t2.148\n2\t0.176\t0.679\t-0.443\t1.029\t0.386\t-0.880\t1.466\t-0.106\t0.703\t0.442\t0.044\t0.193\t-0.145\t0.829\t-2.284\t1.765\t0.335\t-0.257\t-0.520\t0.595\t1.377\t1.751\t-1.234\t2.076\t0.883\t-1.035\t-0.483\t0.571\n2\t0.803\t-0.702\t-0.056\t-1.873\t-0.825\t0.510\t-2.571\t-0.593\t0.106\t0.196\t0.256\t0.273\t0.879\t0.307\t1.591\t-0.395\t1.256\t-0.079\t0.914\t0.585\t-0.363\t-0.510\t0.307\t1.706\t0.322\t0.885\t1.610\t0.577\n2\t-1.042\t-0.640\t0.880\t-0.312\t0.859\t-0.157\t-0.963\t0.480\t0.560\t-1.230\t1.136\t1.521\t0.627\t0.077\t2.012\t0.539\t0.197\t1.700\t1.603\t-1.439\t-0.504\t0.586\t1.227\t-1.166\t0.908\t-0.405\t0.717\t-0.567\n3\t0.042\t-0.613\t-0.075\t-1.052\t1.050\t-0.781\t1.199\t-0.061\t0.856\t-0.767\t-1.967\t-1.649\t2.503\t-0.598\t1.096\t-1.298\t0.179\t0.348\t2.836\t-0.318\t-0.711\t0.351\t-0.925\t0.410\t-0.053\t-0.754\t0.494\t0.108\n4\t-1.315\t2.003\t0.121\t-1.236\t-0.202\t-0.483\t-0.795\t0.425\t2.230\t1.869\t0.201\t-0.781\t0.280\t0.738\t0.204\t0.745\t0.444\t0.285\t-1.216\t-0.130\t1.911\t0.490\t1.168\t2.011\t-1.179\t2.599\t0.973\t1.162\n4\t-0.279\t-1.437\t-0.082\t1.295\t-2.067\t0.736\t0.158\t2.593\t2.295\t-1.764\t1.381\t-0.390\t-0.194\t-0.576\t-0.699\t1.318\t-2.354\t-0.665\t0.676\t-2.039\t-0.230\t-1.208\t0.234\t0.050\t0.933\t-2.319\t0.430\t-0.750\n0\t1.683\t-1.699\t-0.346\t-0.438\t-0.009\t-1.227\t-0.580\t0.789\t-0.269\t-1.070\t2.080\t0.049\t0.613\t0.764\t-0.071\t-0.790\t0.199\t-0.700\t0.372\t-0.164\t1.029\t-1.142\t0.719\t0.614\t-0.239\t0.730\t0.419\t0.667\n1\t1.116\t-0.430\t-0.334\t0.958\t0.165\t0.095\t2.135\t-1.639\t-0.797\t0.580\t-0.413\t-0.660\t-2.036\t-0.974\t-0.830\t-0.240\t0.664\t0.301\t1.321\t-0.228\t0.507\t-0.168\t-1.089\t1.105\t-0.958\t0.894\t-0.032\t0.569\n2\t0.262\t0.110\t-0.592\t-0.937\t-0.697\t-0.202\t0.591\t0.574\t0.211\t-1.335\t-1.695\t-0.814\t0.815\t-0.980\t0.162\t0.028\t0.015\t0.243\t-1.306\t0.830\t-1.076\t-1.025\t-2.107\t1.811\t0.198\t-0.477\t-1.215\t-1.443\n3\t0.970\t-0.419\t-0.556\t1.233\t0.243\t0.983\t-1.411\t-0.806\t-0.523\t1.298\t1.260\t0.088\t0.481\t0.168\t-0.997\t0.060\t-0.903\t-0.381\t-0.225\t1.651\t0.158\t-0.407\t0.614\t-0.661\t2.657\t0.430\t-2.472\t-0.804\n3\t2.301\t0.739\t-0.856\t1.212\t-1.152\t-0.137\t-0.247\t0.182\t-0.942\t0.128\t-1.748\t-0.377\t1.335\t1.025\t-0.696\t-1.526\t-2.183\t0.617\t0.278\t0.386\t1.691\t0.153\t0.228\t-1.397\t1.624\t-0.164\t-0.817\t0.208\n1\t0.479\t0.444\t0.060\t-2.084\t0.440\t-0.145\t0.547\t1.120\t0.385\t-0.260\t-1.776\t-0.872\t0.696\t0.283\t0.928\t0.470\t0.270\t-0.346\t0.207\t-0.192\t0.095\t-1.162\t1.425\t-0.293\t-2.270\t-1.011\t-0.317\t0.961\n1\t1.086\t0.708\t-1.581\t0.187\t0.144\t0.714\t-0.335\t-1.429\t-0.867\t1.080\t0.156\t-0.261\t-1.016\t-0.951\t-1.340\t-0.364\t0.791\t-2.246\t-0.575\t-1.397\t-0.751\t0.866\t1.093\t0.863\t-0.049\t-0.636\t0.544\t-0.734\n0\t1.755\t-0.331\t1.391\t-0.316\t-0.966\t1.746\t0.521\t0.004\t0.687\t0.274\t-0.069\t-1.326\t-0.271\t0.623\t-0.123\t0.902\t0.384\t-0.878\t-1.319\t-0.157\t-0.524\t0.336\t-0.718\t-0.615\t1.053\t0.148\t-0.703\t0.018\n1\t-0.417\t0.425\t-0.924\t-0.020\t2.081\t1.126\t-0.685\t1.866\t0.794\t0.965\t0.466\t-0.132\t1.584\t0.690\t0.986\t-0.389\t-0.028\t-0.635\t0.331\t0.355\t0.386\t0.018\t0.002\t1.015\t0.750\t1.562\t0.221\t-1.388\n1\t-0.356\t-0.064\t0.370\t1.473\t-0.039\t-1.132\t0.626\t-0.493\t-0.190\t1.612\t-0.579\t-0.525\t-0.714\t-0.961\t-0.615\t0.241\t-2.332\t-0.804\t-0.305\t0.982\t1.968\t-0.962\t0.581\t0.548\t-0.070\t1.756\t-0.123\t0.665\n1\t-1.096\t0.685\t-0.491\t-2.104\t0.261\t-0.010\t0.886\t0.766\t-2.511\t0.454\t-1.142\t0.304\t0.085\t-0.055\t-0.965\t0.612\t1.844\t-0.268\t0.096\t0.898\t-0.753\t0.585\t0.104\t1.187\t0.922\t0.015\t0.239\t-0.364\n0\t0.968\t-0.498\t-1.309\t-0.051\t0.223\t-0.472\t0.278\t0.330\t-0.910\t0.395\t-0.267\t-0.700\t0.787\t-0.777\t-0.457\t0.072\t1.374\t0.193\t0.528\t-0.298\t-0.078\t-0.049\t0.293\t-0.296\t1.340\t-0.373\t-1.707\t2.743\n4\t1.286\t0.057\t0.002\t0.635\t1.576\t1.512\t0.593\t0.517\t0.696\t0.986\t1.299\t-0.237\t1.318\t0.343\t0.651\t1.382\t0.747\t0.916\t-0.389\t1.498\t0.696\t0.126\t1.101\t2.588\t-1.169\t-0.804\t2.685\t0.427\n2\t0.084\t1.327\t-1.119\t-1.038\t0.033\t-1.636\t0.244\t0.016\t0.545\t2.238\t0.059\t-0.540\t0.962\t-0.012\t-0.164\t1.787\t0.751\t-0.952\t-0.922\t-1.335\t-1.100\t1.128\t-0.508\t-1.786\t0.458\t0.169\t-0.448\t-0.637\n1\t-0.596\t-0.872\t-0.671\t1.605\t-0.026\t1.862\t-0.547\t1.256\t-0.854\t1.295\t1.000\t-0.029\t-1.446\t0.699\t0.158\t-0.831\t1.241\t0.335\t0.182\t0.010\t-0.827\t0.147\t-0.110\t1.143\t-0.815\t0.678\t-0.880\t-0.114\n3\t1.290\t1.299\t-1.300\t-0.545\t0.724\t-0.627\t0.105\t-0.716\t-1.089\t-0.896\t0.956\t0.014\t1.272\t0.520\t1.606\t0.818\t0.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0.936\t0.701\t0.123\t-1.090\t-1.435\t0.107\t-0.543\t0.919\t0.746\t-0.185\t-1.158\t0.511\t-1.018\t-0.758\t-0.979\t-0.139\t1.170\t-0.005\t-0.443\t-1.526\t0.552\t-0.780\t0.118\t-0.354\t0.838\t-0.157\t-0.059\n4\t-0.311\t0.154\t2.081\t-0.282\t-0.247\t0.150\t-0.183\t0.791\t1.469\t-0.247\t-0.857\t1.975\t0.120\t-1.705\t1.862\t1.126\t1.601\t1.434\t2.645\t-1.037\t-0.796\t-0.492\t-0.521\t0.123\t-0.490\t-1.190\t-0.874\t-0.294\n4\t-0.942\t-1.023\t2.026\t-0.379\t-1.015\t1.190\t-0.147\t-2.027\t2.083\t0.584\t-1.512\t1.396\t-1.048\t-1.108\t-0.892\t1.940\t2.045\t-0.548\t1.706\t-2.287\t0.914\t1.364\t1.506\t0.077\t0.566\t-0.717\t0.025\t-0.164\n2\t-0.360\t-1.173\t0.666\t0.814\t-0.539\t-0.512\t-0.852\t0.705\t1.295\t-0.967\t-0.927\t-0.148\t0.830\t0.504\t1.183\t-2.353\t-0.468\t1.076\t1.681\t-0.197\t-0.516\t-0.856\t0.727\t1.197\t0.339\t-2.218\t0.408\t-0.500\n1\t0.453\t1.017\t1.868\t-1.376\t0.597\t1.034\t1.105\t-1.139\t1.256\t0.156\t-0.211\t0.287\t-0.266\t-0.259\t-0.451\t0.107\t0.702\t-0.297\t-1.193\t-0.939\t0.874\t-1.344\t-0.801\t-0.278\t0.265\t-1.082\t1.048\t1.836\n4\t0.702\t-0.454\t-2.021\t0.657\t1.248\t-0.903\t0.433\t1.218\t-0.679\t-1.910\t1.307\t-1.562\t0.169\t0.553\t-0.261\t2.156\t0.116\t0.523\t-0.373\t0.364\t0.432\t-0.234\t-0.911\t-1.357\t-0.717\t1.679\t-1.711\t1.624\n3\t-0.730\t1.274\t-0.622\t0.031\t1.119\t-0.737\t-0.527\t1.498\t-0.446\t1.457\t-1.048\t-0.463\t0.045\t-1.769\t-0.424\t0.207\t-0.157\t-0.243\t2.189\t-1.105\t1.988\t1.169\t0.416\t-0.601\t-2.108\t-0.792\t1.057\t-1.369\n" }, { "path": "examples/multiclass_classification/multiclass.train", "content": 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1.191\t-0.358\t0.767\t0.266\n4\t-0.573\t-1.484\t-0.626\t0.749\t1.370\t-0.495\t-0.913\t-0.522\t1.229\t1.805\t-1.660\t2.326\t-1.372\t0.474\t2.029\t-0.608\t0.368\t-1.096\t1.659\t-2.070\t-2.184\t-0.056\t-0.193\t0.710\t0.334\t-0.261\t0.026\t-2.769\n0\t0.551\t-1.128\t0.758\t-1.441\t0.749\t-0.313\t-0.655\t0.299\t0.329\t-1.614\t0.167\t0.475\t0.028\t0.434\t0.847\t-0.993\t-0.996\t-0.003\t-0.989\t-0.546\t0.870\t-0.220\t-0.077\t1.608\t0.163\t1.119\t1.020\t-0.472\n4\t0.924\t-3.128\t0.273\t2.366\t-0.446\t-0.361\t-1.064\t0.374\t1.294\t-0.330\t-1.284\t1.031\t-0.686\t0.050\t1.687\t0.173\t1.035\t0.868\t-0.619\t-2.266\t1.620\t-0.655\t1.028\t0.468\t0.621\t-0.861\t-1.138\t-1.172\n2\t0.628\t1.538\t1.195\t0.958\t0.237\t0.721\t-0.411\t-0.221\t1.721\t-0.557\t-0.631\t-0.362\t-0.549\t-0.465\t0.242\t0.275\t0.911\t2.188\t-0.368\t-0.066\t-1.838\t-1.292\t-0.146\t0.464\t0.491\t0.617\t1.257\t1.706\n4\t0.057\t0.167\t0.626\t-0.756\t2.587\t1.332\t-0.532\t-1.091\t-0.088\t0.182\t1.066\t-1.169\t0.285\t-1.529\t0.436\t-0.108\t1.937\t-1.546\t1.371\t-1.704\t-1.327\t-1.677\t0.250\t-1.174\t-1.348\t0.203\t0.452\t0.040\n1\t0.197\t0.311\t1.700\t1.072\t0.191\t0.941\t-1.033\t0.398\t1.809\t-0.218\t-0.848\t-0.652\t-1.090\t-0.785\t-0.371\t-1.406\t0.016\t0.902\t-0.908\t1.519\t0.511\t1.031\t-0.658\t0.855\t-1.092\t0.890\t0.172\t0.553\n2\t1.381\t0.078\t0.304\t-1.233\t-0.426\t-2.033\t-0.581\t0.324\t1.575\t0.404\t-1.806\t0.893\t-1.026\t-0.665\t-1.216\t0.388\t0.892\t1.043\t1.193\t-0.414\t-0.532\t1.602\t0.748\t0.542\t-1.002\t-0.815\t0.751\t0.513\n1\t-1.080\t1.415\t-0.709\t0.336\t-1.271\t-0.122\t-0.111\t-0.315\t0.385\t-1.007\t-0.884\t0.438\t1.342\t-0.023\t0.504\t0.758\t0.831\t-0.954\t-0.777\t-0.690\t1.929\t0.508\t0.463\t0.366\t-1.321\t1.938\t0.479\t0.723\n2\t-0.650\t0.624\t1.416\t0.461\t0.960\t2.345\t0.225\t-0.653\t-0.136\t0.645\t0.632\t-1.023\t0.151\t0.908\t0.117\t-0.754\t-0.539\t-0.642\t-0.135\t1.130\t0.665\t-1.032\t-2.322\t-1.998\t-0.529\t-0.107\t-0.353\t1.185\n2\t0.223\t-0.727\t-1.880\t0.807\t-1.299\t-0.408\t1.674\t0.611\t0.390\t0.944\t0.907\t1.581\t-0.602\t-0.377\t-0.922\t-0.033\t-0.132\t-1.755\t-0.654\t1.439\t0.244\t0.715\t-0.121\t-1.887\t-0.129\t1.042\t-1.186\t0.747\n1\t-1.065\t-1.213\t1.365\t0.319\t0.353\t0.264\t0.148\t-0.225\t-0.709\t-0.539\t2.166\t1.804\t0.927\t-0.067\t0.565\t-1.424\t0.996\t-0.240\t0.015\t0.098\t-0.179\t0.804\t1.595\t0.857\t0.066\t-1.066\t0.040\t-0.077\n3\t0.983\t0.012\t0.211\t1.046\t-0.477\t-1.374\t0.070\t0.308\t-0.263\t1.474\t-2.486\t1.120\t0.791\t0.457\t-3.336\t1.284\t-1.026\t-0.508\t-0.171\t0.407\t0.639\t0.893\t0.816\t0.253\t0.416\t-1.091\t-0.751\t0.895\n0\t0.754\t0.070\t1.055\t-0.516\t1.360\t0.055\t-0.148\t0.902\t-1.316\t-0.479\t0.302\t0.482\t-2.282\t-0.803\t-0.074\t-0.098\t0.613\t-0.346\t-1.025\t0.545\t-1.098\t-0.521\t-0.798\t-0.483\t-1.755\t-0.438\t0.890\t-0.215\n4\t-0.621\t1.433\t-0.673\t-2.307\t-0.526\t0.104\t0.163\t3.180\t-0.109\t-0.607\t-0.717\t1.549\t-0.031\t0.071\t-1.516\t-0.208\t-0.384\t0.382\t-2.356\t0.542\t1.469\t-0.448\t-1.149\t0.997\t-1.409\t1.039\t1.496\t0.145\n3\t0.546\t0.905\t1.070\t-1.347\t2.293\t1.000\t-0.573\t0.163\t-1.697\t-0.617\t0.538\t0.063\t1.743\t-1.479\t-0.117\t-1.137\t1.535\t0.265\t0.008\t-0.133\t-0.877\t1.198\t0.171\t0.245\t0.582\t-0.862\t1.995\t1.570\n2\t-0.962\t-1.447\t0.535\t0.514\t1.154\t-2.144\t1.172\t1.113\t1.209\t0.120\t0.222\t0.217\t-0.913\t-0.871\t0.226\t-0.249\t0.716\t-0.357\t-1.469\t-1.422\t-0.761\t0.726\t-0.519\t0.298\t-1.672\t0.162\t0.038\t1.190\n0\t0.679\t-0.346\t-0.005\t1.078\t-0.025\t-1.036\t-0.066\t-0.045\t0.427\t1.432\t-1.178\t-0.010\t-0.471\t0.528\t0.431\t-0.262\t-0.713\t1.555\t-0.090\t-0.729\t-2.561\t0.193\t-0.034\t0.720\t0.326\t0.496\t-1.105\t-0.909\n0\t0.382\t1.600\t0.194\t-1.585\t-1.172\t0.097\t-0.549\t1.046\t-0.411\t-0.570\t0.058\t0.433\t0.023\t-0.404\t-1.386\t-1.649\t-0.239\t-1.138\t0.326\t-0.656\t-0.146\t0.720\t0.049\t0.416\t0.304\t-0.589\t-0.664\t1.513\n2\t0.532\t0.189\t0.491\t-0.137\t-1.183\t-2.042\t-0.617\t0.791\t-1.039\t1.061\t1.391\t0.473\t-0.166\t1.025\t1.157\t-0.225\t0.085\t-0.813\t0.286\t-2.067\t-0.293\t-0.853\t-0.088\t-0.795\t-1.913\t-0.654\t-0.476\t1.065\n2\t-0.605\t0.498\t0.537\t0.642\t-1.694\t1.007\t-0.570\t-0.358\t2.638\t0.302\t-0.386\t-2.205\t1.390\t-1.345\t-1.003\t-0.331\t1.416\t0.773\t-0.158\t0.308\t-0.600\t0.140\t0.073\t0.969\t0.967\t0.494\t-0.362\t0.742\n0\t-0.320\t-1.176\t-0.253\t1.290\t0.227\t0.627\t1.069\t0.387\t-0.368\t0.100\t-1.073\t0.057\t0.122\t1.317\t-0.324\t-0.324\t-1.516\t0.648\t-1.469\t-0.498\t-0.710\t0.891\t0.488\t0.708\t0.946\t-0.639\t-0.868\t-1.684\n2\t-0.188\t1.276\t-0.796\t-1.977\t-0.008\t0.504\t-0.350\t0.221\t-0.776\t-0.927\t0.569\t0.615\t0.584\t0.555\t0.418\t-0.144\t0.698\t-0.825\t1.245\t0.898\t-1.484\t0.818\t0.818\t2.408\t-1.039\t-1.325\t1.020\t0.447\n1\t0.284\t-0.389\t-1.370\t0.373\t-0.981\t0.737\t-0.833\t-1.053\t-1.004\t0.291\t0.048\t1.282\t-1.152\t1.374\t0.711\t1.000\t0.589\t1.364\t1.496\t0.103\t0.262\t1.481\t-0.539\t-0.354\t1.173\t-0.718\t-0.505\t-0.720\n4\t1.691\t-0.581\t0.207\t-1.798\t2.468\t1.997\t-0.838\t1.120\t1.211\t-0.519\t0.710\t-1.257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49\t0.359\t-1.851\t-0.906\t0.502\t-1.017\n1\t-0.723\t-0.121\t0.552\t1.740\t-0.391\t-1.327\t0.660\t0.813\t-0.241\t0.339\t-0.981\t-0.126\t0.953\t-0.210\t-1.781\t-1.363\t0.026\t-0.365\t-0.613\t0.456\t1.093\t-1.824\t-0.609\t-1.219\t-0.345\t0.610\t-0.041\t-0.922\n2\t0.054\t-0.286\t2.526\t0.483\t0.210\t-0.183\t-0.897\t-1.951\t1.521\t-0.779\t-0.190\t-0.106\t1.433\t-0.766\t0.257\t0.512\t-1.384\t-1.478\t-0.715\t0.244\t0.116\t-0.119\t0.652\t-0.857\t-0.171\t-0.114\t-0.830\t2.302\n2\t-0.332\t-0.306\t1.344\t-1.063\t-0.482\t-0.165\t0.935\t1.697\t1.293\t1.806\t-2.974\t-0.576\t0.286\t-0.605\t-0.223\t-0.361\t0.204\t0.489\t-1.572\t-0.524\t-0.585\t0.315\t0.447\t-0.736\t-0.289\t-0.661\t0.360\t-1.103\n3\t-1.470\t1.284\t2.097\t-0.161\t0.771\t0.408\t-0.644\t-0.223\t-0.164\t-0.523\t-1.163\t-2.125\t0.260\t-0.187\t-0.377\t-1.160\t-0.444\t-0.512\t0.292\t1.289\t1.118\t2.014\t-0.046\t0.419\t1.350\t-1.905\t-0.828\t0.696\n2\t0.372\t1.132\t0.443\t1.780\t-1.361\t-0.917\t-1.794\t-0.431\t1.680\t0.636\t0.810\t0.327\t-0.669\t1.486\t-0.542\t-0.172\t1.912\t0.551\t0.878\t-0.460\t-1.379\t-1.337\t-0.622\t-0.280\t-0.008\t0.226\t0.113\t0.667\n1\t1.341\t0.114\t-1.617\t0.032\t-1.659\t-0.580\t-1.549\t0.048\t0.994\t-0.808\t-0.665\t1.036\t-0.160\t-0.578\t0.622\t0.270\t-0.737\t0.535\t-2.001\t-0.044\t-0.438\t-0.792\t-1.330\t0.310\t1.212\t-0.401\t-1.039\t-0.503\n4\t2.151\t1.638\t1.752\t0.402\t-0.057\t-0.768\t-1.571\t0.964\t0.489\t-0.110\t-0.508\t-0.716\t-0.768\t-0.977\t3.151\t-1.061\t-1.958\t-0.331\t-0.339\t-0.176\t0.552\t1.935\t1.068\t2.863\t-0.100\t1.300\t-1.160\t1.990\n2\t0.334\t-0.366\t1.189\t0.642\t1.297\t-0.046\t-0.581\t0.489\t0.738\t1.307\t0.071\t0.310\t-0.346\t0.143\t1.105\t-1.455\t-0.617\t-1.745\t0.004\t0.397\t-2.380\t-1.347\t0.163\t-2.073\t0.282\t0.550\t0.386\t-0.747\n3\t-1.456\t-0.075\t1.019\t1.814\t-0.238\t-0.130\t0.500\t1.493\t-0.486\t0.693\t-0.823\t0.330\t0.971\t-1.291\t-0.525\t0.955\t0.616\t1.036\t0.848\t-0.341\t2.199\t0.137\t-3.056\t-0.282\t-0.469\t0.795\t-0.451\t1.099\n1\t0.316\t-0.578\t-0.524\t0.302\t2.261\t1.032\t-0.078\t0.074\t1.360\t1.087\t0.383\t-1.185\t0.098\t1.018\t0.581\t-0.021\t-0.163\t0.850\t-0.119\t-1.259\t-1.175\t-1.214\t-0.519\t1.893\t-0.047\t0.803\t-0.469\t-1.193\n3\t1.028\t-0.410\t1.257\t1.289\t0.968\t0.202\t-0.238\t-0.750\t-0.224\t-0.070\t0.755\t-0.498\t1.592\t0.618\t-0.279\t0.588\t2.000\t0.239\t0.615\t1.010\t0.080\t0.360\t-1.244\t-0.585\t-0.269\t-1.087\t-3.018\t1.270\n1\t0.393\t0.829\t1.710\t0.405\t-0.730\t0.611\t-0.151\t-1.345\t-0.048\t0.283\t1.434\t0.071\t-0.815\t-1.887\t-1.229\t-0.970\t1.666\t-0.191\t-0.090\t1.101\t0.603\t-0.262\t1.146\t-0.684\t0.284\t-1.255\t0.260\t1.018\n2\t-0.517\t-0.587\t1.289\t-0.191\t0.165\t-1.638\t-0.251\t1.665\t-1.513\t-0.166\t0.596\t0.518\t1.475\t-1.390\t0.976\t0.695\t0.499\t-1.625\t1.460\t-0.692\t-1.986\t-0.336\t-0.840\t-0.067\t0.042\t1.063\t0.599\t0.117\n3\t-1.358\t0.786\t1.262\t-0.130\t0.408\t1.633\t-0.318\t0.729\t0.381\t-1.309\t1.536\t-0.302\t-1.522\t0.367\t-2.807\t-2.041\t0.063\t0.469\t-0.007\t-0.497\t0.509\t0.999\t0.085\t0.277\t-1.295\t-1.292\t1.014\t0.598\n2\t-1.353\t-1.188\t-0.575\t0.867\t-0.423\t-0.810\t-1.188\t-1.261\t-0.529\t0.992\t-0.945\t-1.230\t-0.588\t1.015\t-0.366\t-0.494\t0.238\t1.390\t-0.032\t-1.940\t-0.200\t-1.215\t-0.867\t0.972\t-0.415\t-1.329\t0.429\t-1.196\n1\t0.406\t-0.815\t1.482\t-0.036\t0.220\t0.536\t-1.711\t0.275\t-0.631\t-0.871\t0.459\t1.129\t-0.001\t1.536\t-0.235\t-1.722\t-1.165\t1.526\t1.782\t0.363\t-0.617\t-0.307\t-0.043\t0.014\t0.558\t0.343\t-1.192\t-1.128\n0\t0.658\t-0.070\t-1.187\t1.058\t-0.589\t-0.936\t-0.178\t-0.913\t-0.417\t0.344\t0.348\t-0.898\t-0.488\t1.074\t0.496\t2.075\t1.180\t1.144\t-1.388\t0.764\t-0.053\t-0.551\t0.168\t0.945\t0.501\t-0.878\t0.742\t-0.638\n1\t-0.052\t0.281\t-0.345\t0.227\t-0.592\t1.274\t-0.114\t-0.383\t-0.925\t0.269\t0.187\t0.388\t-1.027\t-1.601\t0.626\t1.485\t-1.026\t1.589\t0.284\t-0.007\t-0.398\t-1.385\t0.988\t-0.776\t-0.780\t0.992\t-1.883\t-1.524\n2\t0.876\t0.114\t1.073\t1.419\t-1.095\t1.686\t0.552\t0.310\t-0.686\t0.340\t-2.221\t-1.103\t-1.862\t-0.139\t1.359\t0.260\t0.593\t-0.232\t0.011\t-1.793\t1.053\t0.510\t0.783\t0.344\t-0.459\t0.513\t-0.297\t-0.082\n4\t-0.028\t-0.739\t-0.306\t-1.407\t-0.718\t1.754\t0.208\t0.630\t-1.792\t1.861\t0.763\t-0.294\t1.702\t-1.734\t2.510\t-0.189\t-1.437\t-0.726\t-1.325\t0.991\t-0.431\t-2.282\t-1.679\t-1.800\t1.033\t0.366\t-1.148\t-0.441\n3\t0.545\t0.776\t0.654\t-0.795\t-0.018\t-0.418\t0.369\t-1.627\t-0.024\t-1.763\t-1.085\t1.632\t1.694\t-0.651\t0.493\t0.482\t1.366\t0.246\t-1.048\t-0.820\t0.396\t-1.827\t-1.715\t0.845\t2.642\t-0.624\t-0.161\t-0.081\n2\t2.448\t0.486\t-0.337\t-0.766\t-0.305\t-0.075\t0.238\t-0.614\t-1.197\t1.131\t1.171\t0.197\t1.119\t-0.769\t0.148\t0.686\t1.285\t-0.263\t2.352\t0.544\t-1.065\t0.950\t-1.035\t-0.926\t-0.075\t0.131\t-0.121\t-0.403\n0\t0.687\t-0.783\t-0.989\t1.561\t0.732\t-0.663\t-0.255\t-0.969\t-1.820\t0.421\t-0.128\t-0.922\t-2.078\t0.465\t1.006\t-0.312\t0.675\t0.264\t0.274\t0.363\t0.443\t0.361\t0.436\t0.290\t1.487\t-0.492\t-0.992\t-0.419\n3\t0.075\t-1.248\t-0.165\t-0.545\t-1.861\t0.426\t0.328\t1.262\t-1.224\t1.081\t-2.319\t-0.147\t1.976\t-0.029\t-0.735\t-0.083\t0.848\t-0.282\t0.392\t0.381\t0.549\t1.036\t1.980\t0.087\t0.323\t-1.187\t0.945\t1.361\n4\t1.518\t-2.143\t0.465\t1.218\t-2.010\t1.366\t2.209\t0.184\t0.541\t-0.969\t2.047\t0.283\t-2.559\t0.966\t-0.907\t-0.504\t0.289\t0.026\t0.333\t0.447\t1.346\t-1.353\t0.536\t0.167\t-0.226\t-1.259\t0.397\t-0.952\n2\t1.459\t0.010\t0.071\t-1.208\t-2.426\t0.414\t-0.531\t0.479\t0.733\t0.452\t0.443\t0.680\t2.462\t-0.173\t-0.315\t-1.374\t0.630\t0.558\t0.125\t1.047\t0.051\t-0.729\t1.514\t-0.689\t-0.677\t-0.723\t0.570\t0.944\n1\t0.584\t0.371\t-0.302\t1.825\t-0.245\t0.590\t-0.409\t-0.424\t-1.136\t0.541\t-1.307\t-0.410\t-1.017\t1.647\t0.419\t1.428\t0.353\t-0.078\t1.395\t0.618\t0.468\t-0.923\t0.729\t-0.368\t-1.469\t1.311\t-1.100\t-0.507\n2\t0.117\t1.739\t-2.409\t0.355\t-0.384\t0.344\t0.326\t0.835\t0.732\t0.180\t-1.174\t0.597\t-1.214\t-0.869\t0.478\t0.187\t-0.979\t-1.304\t0.121\t-0.215\t-0.547\t0.655\t-0.030\t1.020\t0.591\t-1.543\t-1.825\t0.827\n2\t-0.089\t-1.587\t-1.601\t-0.376\t-1.190\t-1.619\t-0.427\t0.797\t-0.463\t0.330\t1.437\t0.383\t-0.933\t-0.025\t-0.438\t0.503\t0.187\t-0.283\t0.080\t-0.320\t-1.598\t-1.939\t0.874\t1.028\t0.187\t0.943\t1.715\t-0.452\n1\t-0.902\t0.636\t-1.276\t1.661\t0.306\t-1.459\t-0.102\t0.629\t-0.356\t0.021\t-1.009\t-2.223\t0.785\t-0.252\t0.036\t-2.226\t-0.385\t-0.263\t-0.158\t-0.219\t-0.015\t0.710\t0.943\t0.010\t0.382\t1.385\t1.003\t0.495\n2\t0.612\t-0.979\t-1.382\t-1.798\t-2.224\t-0.518\t-0.301\t-0.631\t1.243\t1.228\t-0.335\t-1.794\t-0.567\t-0.600\t-0.657\t-0.698\t-0.653\t-0.387\t-0.636\t0.208\t0.360\t-0.911\t1.271\t-0.980\t-1.618\t0.381\t-0.817\t0.321\n1\t-0.048\t-1.172\t-0.356\t-0.705\t0.101\t0.885\t1.374\t0.086\t-1.011\t1.415\t0.391\t-0.566\t-0.333\t-0.668\t0.724\t0.981\t0.332\t1.093\t-0.064\t2.350\t-0.127\t-1.934\t0.914\t-0.402\t1.074\t0.293\t-0.684\t-0.097\n0\t0.444\t-0.427\t0.799\t-0.024\t0.494\t0.555\t1.227\t1.225\t0.070\t0.404\t0.399\t-0.284\t-0.103\t0.536\t-0.019\t0.338\t-0.417\t1.824\t0.801\t0.809\t-0.218\t-1.259\t-1.484\t0.799\t-0.609\t0.639\t0.483\t0.643\n1\t-0.732\t0.602\t-1.380\t-1.780\t0.983\t0.003\t-1.165\t0.520\t-1.114\t-0.220\t-0.856\t-1.843\t1.061\t-0.731\t1.411\t0.107\t-0.525\t-1.096\t0.042\t0.499\t0.969\t0.595\t-0.251\t0.530\t-0.398\t1.501\t-0.338\t0.536\n2\t0.344\t-0.420\t0.956\t0.514\t-0.437\t0.971\t0.369\t-1.661\t-0.992\t-1.329\t1.031\t-0.702\t0.627\t2.172\t0.794\t-0.419\t-0.744\t-0.306\t-1.028\t-1.144\t-0.805\t0.532\t-0.521\t-0.225\t0.469\t0.727\t-1.929\t-1.723\n4\t-2.125\t-0.566\t0.890\t0.723\t-0.210\t-1.491\t-0.976\t-1.088\t-1.842\t1.600\t2.041\t-0.733\t-1.927\t0.162\t0.048\t0.949\t0.241\t0.132\t-1.069\t-0.174\t1.052\t1.702\t0.162\t0.426\t-0.230\t0.502\t-1.583\t0.871\n4\t0.106\t0.584\t1.976\t-1.564\t1.617\t0.104\t-0.899\t-1.330\t-0.189\t0.922\t-0.128\t1.511\t-1.451\t-0.012\t-1.252\t0.364\t0.887\t-0.421\t-2.604\t0.199\t0.437\t0.404\t1.236\t-1.071\t0.680\t1.193\t-1.779\t0.320\n1\t-0.005\t0.911\t0.170\t0.215\t-0.922\t-0.596\t-0.018\t2.581\t-0.851\t-0.906\t0.060\t1.894\t1.305\t-0.627\t-0.563\t0.292\t-0.460\t1.772\t0.505\t-1.115\t-0.313\t0.451\t0.557\t0.113\t-0.283\t0.858\t1.450\t0.791\n1\t-1.044\t0.848\t1.866\t-0.323\t0.841\t1.229\t1.017\t0.126\t-0.698\t-0.081\t0.459\t0.908\t0.334\t-0.380\t0.555\t0.860\t0.380\t-0.836\t-0.181\t0.948\t-1.383\t-2.093\t1.508\t0.511\t0.607\t-1.077\t0.738\t0.275\n0\t-0.698\t0.867\t-0.459\t0.410\t0.379\t-0.052\t0.453\t0.644\t-1.249\t-0.480\t1.114\t0.421\t1.117\t-1.702\t1.146\t-0.074\t-0.513\t0.389\t1.577\t-1.496\t0.363\t-1.236\t-0.791\t-0.689\t-0.322\t0.118\t-0.334\t-0.231\n3\t-0.711\t-0.384\t-0.411\t1.022\t-0.379\t2.083\t0.431\t-1.276\t-0.129\t-0.652\t-2.194\t1.599\t0.703\t0.533\t0.165\t-1.382\t-0.484\t0.842\t-1.051\t-2.532\t-0.227\t-0.142\t0.136\t0.729\t0.638\t0.259\t1.196\t-1.519\n4\t-2.888\t1.229\t0.439\t0.505\t-1.093\t-1.013\t1.150\t0.243\t-0.117\t0.221\t1.509\t0.724\t-0.475\t-1.403\t0.588\t-0.924\t-1.182\t1.316\t0.872\t0.386\t-1.238\t1.789\t1.029\t-0.304\t-0.515\t0.078\t-0.478\t2.212\n1\t0.137\t1.473\t0.123\t0.614\t-0.780\t0.042\t0.140\t-0.141\t-0.022\t0.126\t0.481\t1.169\t-2.266\t1.114\t-1.711\t0.309\t2.356\t-0.043\t0.180\t-0.310\t0.667\t0.362\t-0.676\t-0.114\t-1.118\t-0.119\t-0.419\t0.137\n0\t-0.362\t-1.120\t-1.295\t1.161\t-0.468\t0.347\t-0.047\t0.477\t0.077\t-1.283\t0.996\t-0.494\t-1.557\t-0.428\t1.501\t0.850\t-0.349\t-0.349\t-0.322\t2.077\t0.382\t0.430\t1.030\t0.239\t-0.259\t-0.196\t-0.072\t-0.037\n0\t-0.530\t0.555\t-0.115\t0.756\t0.219\t-1.890\t0.604\t0.624\t-1.148\t-0.414\t-0.453\t-0.697\t-1.385\t0.728\t0.510\t0.886\t-0.145\t1.167\t-1.480\t0.413\t0.990\t-0.922\t-0.429\t-1.343\t0.767\t-0.878\t0.003\t-0.450\n4\t-0.140\t-1.472\t-0.236\t0.154\t2.270\t0.201\t-1.704\t2.236\t0.368\t0.017\t-0.513\t-0.105\t-1.236\t-0.665\t-0.036\t1.057\t0.426\t1.037\t-0.352\t1.437\t-0.724\t-1.526\t-1.356\t2.467\t1.070\t-0.545\t-0.812\t-0.033\n3\t-1.347\t0.455\t-0.990\t0.244\t-1.128\t2.783\t0.162\t0.276\t-0.365\t-1.071\t0.958\t0.446\t1.217\t1.682\t0.013\t1.179\t-1.846\t-1.397\t0.354\t-0.904\t1.023\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t-1.640\t-1.275\t1.343\t-0.094\t-0.218\t-0.790\t0.019\t-0.488\n3\t0.582\t-0.114\t-0.718\t1.056\t-0.110\t-1.036\t1.552\t2.471\t0.157\t0.356\t1.812\t-0.298\t1.125\t-0.572\t0.481\t-0.965\t-0.256\t-1.658\t0.491\t0.400\t-1.021\t2.195\t0.076\t-0.141\t-0.978\t0.781\t-0.897\t0.765\n1\t1.063\t-0.139\t0.839\t-2.127\t-0.091\t-0.706\t1.121\t0.072\t-0.329\t1.267\t-0.153\t0.574\t0.489\t0.388\t0.039\t2.108\t-0.493\t-0.465\t-0.884\t0.207\t1.002\t1.640\t-0.169\t0.274\t1.522\t1.069\t-1.199\t-0.075\n3\t0.258\t-3.564\t-0.969\t-0.150\t0.965\t0.312\t0.590\t-0.910\t-0.431\t-1.797\t0.660\t-0.551\t-0.954\t0.311\t1.164\t0.672\t0.093\t-1.057\t-0.022\t0.321\t-0.501\t0.707\t-0.987\t-1.249\t-0.823\t-0.656\t0.400\t1.334\n3\t0.783\t1.288\t2.530\t-0.935\t0.702\t0.460\t-0.386\t-0.647\t1.652\t-1.022\t-1.409\t-0.563\t-1.782\t-0.846\t-1.053\t0.086\t1.445\t-0.589\t0.865\t0.751\t-2.220\t-0.552\t-0.376\t0.381\t0.570\t0.331\t-0.895\t-0.633\n4\t0.880\t-2.102\t1.272\t-0.889\t-0.911\t2.488\t0.057\t-0.255\t-1.153\t0.443\t0.793\t-0.351\t-2.148\t1.627\t-0.289\t-0.311\t-0.035\t-0.805\t1.274\t0.726\t0.488\t2.311\t-0.697\t1.277\t-2.224\t0.644\t2.258\t-1.050\n1\t0.565\t-0.223\t1.044\t1.347\t-1.296\t1.116\t2.237\t0.526\t0.984\t-0.647\t-0.504\t-0.210\t-0.402\t-1.312\t-0.173\t0.836\t-1.299\t0.179\t0.148\t-0.301\t0.497\t0.403\t-0.372\t1.061\t0.561\t1.573\t-0.066\t-0.322\n0\t-0.357\t0.217\t0.352\t-0.031\t0.188\t0.659\t0.613\t1.534\t1.043\t0.018\t0.641\t0.424\t0.164\t-0.869\t0.162\t-2.065\t1.072\t0.350\t-0.159\t-0.421\t0.655\t1.838\t0.375\t1.902\t0.784\t-0.805\t0.488\t0.390\n3\t-1.563\t-0.529\t0.794\t-1.254\t0.294\t-1.357\t0.466\t-0.036\t-1.615\t1.165\t-0.735\t-0.810\t0.201\t1.149\t-1.016\t0.062\t0.429\t0.693\t0.176\t-0.367\t-0.828\t0.086\t-1.072\t-2.921\t0.437\t0.904\t-2.363\t-1.010\n1\t-0.460\t2.452\t-0.608\t-0.325\t-1.255\t-2.299\t-0.451\t-0.066\t0.019\t-0.818\t-0.065\t-0.761\t-0.488\t0.959\t1.668\t0.448\t-0.074\t0.102\t-0.754\t-0.257\t0.899\t1.157\t-0.618\t-1.197\t0.129\t0.719\t0.926\t0.539\n4\t0.950\t-1.485\t-2.554\t0.934\t-1.367\t-0.225\t-1.170\t-1.802\t0.541\t0.759\t-0.577\t-2.591\t-0.546\t0.392\t-1.479\t0.183\t-0.015\t0.579\t0.120\t-0.973\t1.197\t-0.159\t-0.027\t-0.933\t-0.443\t-0.885\t-0.173\t1.712\n4\t0.238\t1.032\t1.537\t-0.120\t-1.337\t-1.303\t-0.691\t-0.935\t-1.682\t1.848\t0.528\t-0.524\t-0.033\t-1.786\t1.157\t-1.950\t0.510\t-1.838\t-0.414\t1.243\t-1.634\t-0.149\t0.628\t0.464\t0.070\t0.496\t-0.611\t1.933\n4\t-0.833\t2.085\t-0.220\t0.939\t0.741\t-1.185\t1.700\t-0.192\t0.905\t-2.201\t-0.492\t-0.121\t0.829\t-0.039\t1.761\t-1.753\t-0.946\t-0.772\t-1.187\t-0.246\t-1.760\t-1.366\t-1.975\t0.744\t-0.837\t-0.213\t0.355\t-0.394\n0\t1.207\t-1.129\t-1.126\t-0.536\t0.099\t0.048\t1.240\t1.295\t0.006\t-0.228\t0.517\t1.063\t0.552\t-0.432\t-0.612\t-1.111\t0.193\t0.623\t0.735\t1.152\t1.480\t-1.375\t0.405\t0.439\t-0.074\t-0.828\t1.445\t-0.589\n3\t0.464\t-0.242\t-0.462\t-1.779\t0.690\t-0.273\t-0.218\t0.589\t1.366\t-0.498\t0.183\t1.676\t0.308\t-0.271\t1.822\t-1.298\t0.298\t-0.469\t0.611\t0.493\t-3.536\t1.032\t0.333\t1.504\t-0.992\t-0.248\t0.803\t-0.117\n0\t-0.689\t-0.685\t-0.549\t-0.594\t-1.280\t-1.124\t-0.829\t0.592\t-0.381\t-0.207\t1.270\t1.295\t-0.282\t-0.631\t-0.864\t0.693\t-0.205\t-0.386\t-0.814\t-0.770\t0.320\t-1.315\t0.995\t-0.534\t-0.150\t-1.337\t-0.012\t-0.328\n1\t-0.432\t2.366\t-1.415\t-0.130\t0.273\t0.154\t-0.561\t1.232\t0.667\t0.740\t-0.668\t-0.403\t0.488\t-1.101\t0.666\t-0.101\t1.065\t-0.428\t1.814\t0.411\t0.520\t-0.693\t0.253\t-0.549\t-1.540\t-0.196\t1.163\t-0.064\n3\t-0.800\t1.530\t-1.119\t-2.351\t-1.298\t0.386\t-0.118\t0.752\t0.502\t-0.636\t-0.489\t-0.385\t1.884\t0.691\t1.892\t0.841\t-0.064\t-1.019\t0.202\t-0.061\t-0.094\t-0.209\t-2.052\t1.261\t-0.050\t-1.414\t0.482\t-0.856\n4\t-2.291\t-1.187\t1.766\t0.326\t-2.700\t0.070\t1.803\t0.274\t0.044\t-1.828\t-0.271\t-1.643\t-1.222\t-1.451\t1.739\t0.728\t1.401\t-1.323\t0.191\t-1.008\t-0.901\t0.469\t0.179\t0.286\t-1.256\t0.128\t0.348\t-0.675\n4\t2.973\t-2.141\t-1.389\t1.175\t0.915\t0.128\t1.120\t-1.117\t1.955\t1.387\t1.685\t-1.574\t1.067\t0.002\t2.388\t0.261\t-0.888\t0.584\t-1.693\t1.058\t-0.425\t-0.179\t0.249\t-0.313\t1.627\t-0.282\t0.517\t0.560\n1\t0.786\t-0.055\t-0.119\t-0.314\t-1.380\t-0.283\t0.006\t-1.325\t-1.102\t2.327\t1.625\t-0.735\t-0.736\t0.545\t-0.415\t0.086\t-0.046\t-0.494\t-1.496\t-0.828\t-0.390\t-0.636\t-1.254\t1.664\t0.497\t-0.220\t0.640\t-0.262\n0\t0.449\t-0.613\t0.977\t0.407\t-0.068\t0.313\t-0.265\t0.687\t-0.954\t0.855\t-1.237\t1.826\t1.341\t-0.557\t-1.742\t-1.479\t0.154\t0.276\t0.649\t-0.838\t-0.602\t0.612\t-0.451\t-0.730\t-0.215\t0.906\t0.438\t0.987\n3\t-1.054\t-0.330\t-0.331\t-2.091\t-0.640\t-0.861\t-0.538\t-1.080\t0.126\t-0.047\t0.043\t0.306\t-0.006\t0.965\t-1.423\t-0.854\t0.616\t0.636\t1.863\t-1.634\t-1.552\t0.161\t-2.329\t-0.845\t-0.043\t-0.365\t1.052\t1.253\n2\t0.235\t-0.055\t0.441\t-0.012\t0.835\t-0.763\t0.756\t0.472\t1.089\t1.157\t-0.952\t0.517\t-1.166\t1.160\t-0.853\t-0.380\t0.218\t0.738\t0.371\t0.766\t0.012\t0.244\t-1.767\t1.026\t-0.291\t-0.430\t1.937\t-2.709\n3\t1.128\t0.390\t-0.670\t-0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832\t-0.191\t-1.411\t0.822\t1.310\t1.034\t-0.969\t-0.865\t1.656\t-1.209\t0.478\t-1.778\t-0.300\t0.585\t-0.030\t0.524\t-1.226\t-0.820\t0.482\t0.697\t0.220\t0.947\t-1.657\t-0.466\t1.823\t1.283\n4\t0.036\t-0.052\t-0.311\t-0.326\t0.384\t1.134\t-0.481\t2.438\t1.961\t1.329\t0.622\t2.008\t-0.290\t-0.833\t-0.526\t-1.003\t0.122\t0.300\t0.201\t-0.012\t0.133\t1.736\t0.620\t0.923\t0.191\t-1.616\t-1.814\t2.083\n4\t0.751\t-2.635\t-1.021\t-0.110\t0.289\t2.740\t0.968\t2.435\t-0.296\t-0.916\t-0.434\t-0.081\t-1.761\t-0.004\t0.130\t-1.970\t0.010\t-1.550\t-0.283\t0.559\t-1.707\t-0.447\t1.046\t0.488\t0.112\t0.386\t-1.215\t-0.786\n4\t0.032\t0.459\t0.334\t0.856\t-1.072\t-1.193\t1.360\t0.695\t0.447\t-1.672\t1.542\t0.484\t0.501\t2.779\t2.954\t0.550\t-2.212\t-0.306\t-0.366\t-0.126\t0.838\t0.388\t0.758\t0.958\t0.656\t1.256\t1.854\t-0.503\n4\t1.906\t-1.640\t-0.464\t-1.122\t0.683\t1.857\t0.904\t-1.392\t0.883\t1.685\t-1.488\t0.460\t-0.265\t-2.796\t0.706\t1.095\t-1.031\t1.464\t0.824\t3.002\t-0.260\t-1.189\t-0.151\t-0.241\t-1.127\t-0.752\t0.631\t-0.290\n4\t0.801\t0.596\t-0.013\t-1.047\t0.558\t-1.096\t0.040\t0.080\t0.787\t1.040\t0.246\t0.938\t-0.439\t1.293\t-0.298\t-1.856\t1.164\t2.038\t-1.158\t1.349\t-0.718\t-1.110\t-2.256\t1.381\t-0.742\t3.482\t0.992\t0.987\n1\t0.411\t-0.986\t1.162\t1.458\t0.297\t-0.320\t1.613\t1.546\t-0.885\t-0.264\t-0.054\t1.640\t0.892\t-0.322\t-0.005\t-0.109\t-0.762\t0.520\t0.453\t0.592\t0.223\t-1.086\t-0.635\t0.063\t0.900\t0.281\t-1.230\t-2.022\n3\t-0.043\t-1.171\t1.735\t1.826\t0.502\t0.121\t1.695\t0.908\t-0.347\t0.580\t-1.104\t-0.469\t0.352\t0.174\t-0.842\t-1.013\t-0.304\t-0.854\t-0.581\t-1.012\t-1.438\t1.554\t2.240\t-0.430\t0.344\t-0.549\t-1.386\t-1.052\n0\t-0.153\t-1.132\t-0.358\t1.294\t-0.228\t-0.612\t0.326\t1.227\t-0.247\t0.004\t0.791\t0.628\t0.287\t-1.258\t0.231\t1.203\t-0.993\t-0.233\t0.016\t0.115\t0.788\t-0.769\t-1.633\t-0.452\t0.115\t0.082\t0.791\t0.723\n0\t1.724\t-0.981\t-0.249\t0.629\t0.493\t-0.240\t-0.247\t0.877\t0.688\t0.225\t0.383\t0.396\t0.100\t-0.832\t0.275\t-1.817\t-0.591\t0.141\t0.133\t-0.321\t0.977\t-0.493\t-0.272\t-1.565\t1.007\t-1.619\t1.013\t-0.235\n1\t0.590\t-1.344\t1.200\t0.772\t0.182\t1.160\t0.872\t-0.558\t-1.813\t0.120\t0.528\t-0.025\t-0.651\t-0.012\t-0.101\t0.062\t2.176\t0.706\t1.295\t0.744\t1.809\t0.114\t0.846\t-1.225\t0.385\t-0.630\t-0.144\t1.176\n1\t-0.236\t0.292\t1.009\t1.026\t-0.869\t-1.113\t1.778\t1.783\t0.733\t-0.296\t-0.293\t-0.980\t0.217\t-0.122\t-0.094\t-1.150\t0.954\t0.449\t0.456\t-1.786\t-1.517\t-1.099\t0.773\t-0.489\t-1.034\t0.654\t-0.324\t0.373\n1\t0.684\t-1.260\t-0.607\t-0.593\t0.942\t0.528\t0.904\t1.339\t-0.306\t-1.187\t-1.181\t-1.838\t-0.459\t-0.384\t0.788\t0.017\t-0.490\t-0.116\t-1.039\t1.025\t-0.786\t-1.140\t0.818\t0.574\t-1.724\t0.855\t-0.742\t-0.374\n3\t-2.172\t-1.552\t-0.075\t0.296\t-0.282\t-0.542\t-0.082\t0.527\t-1.147\t-1.114\t-0.866\t1.320\t0.373\t0.572\t1.591\t0.787\t0.156\t1.558\t-0.972\t-0.265\t1.156\t-2.194\t-0.797\t0.790\t0.107\t0.514\t-1.916\t-0.841\n2\t0.919\t1.316\t-0.327\t-0.065\t0.479\t0.660\t-0.081\t-0.587\t-0.160\t-0.388\t0.237\t-1.083\t1.167\t1.971\t-0.724\t-0.785\t-0.851\t-0.475\t1.743\t-0.576\t0.128\t-0.469\t-2.052\t-0.556\t-0.047\t1.521\t-1.750\t0.944\n2\t1.726\t0.662\t-0.743\t0.335\t0.535\t0.760\t-0.427\t-1.953\t1.450\t-0.099\t-2.098\t-0.276\t0.742\t-1.070\t-1.083\t-0.250\t0.106\t-0.581\t-0.368\t0.812\t0.014\t-1.483\t1.187\t-0.185\t-0.908\t-0.188\t-1.325\t0.010\n1\t1.199\t0.309\t-2.056\t1.069\t0.048\t0.939\t-1.347\t0.211\t-0.106\t0.542\t-0.098\t-1.273\t-0.295\t0.223\t-2.057\t-1.625\t-0.324\t0.807\t-0.408\t-1.974\t-0.275\t-0.157\t-0.736\t-0.178\t-0.254\t-0.272\t0.208\t-0.818\n0\t0.617\t0.091\t-0.334\t0.471\t0.189\t0.299\t-0.068\t0.535\t0.918\t0.904\t0.457\t-0.004\t0.445\t1.195\t-0.154\t-0.272\t0.494\t0.738\t-0.100\t0.451\t-0.840\t-0.011\t1.553\t0.608\t-0.647\t-0.513\t-1.244\t1.623\n4\t-1.424\t-1.682\t1.826\t-0.919\t1.684\t-1.362\t0.489\t-0.346\t0.379\t-1.555\t-0.446\t1.983\t-0.759\t-0.489\t-1.586\t-1.362\t0.969\t1.466\t-0.412\t-0.679\t0.677\t-0.948\t-0.018\t1.758\t0.039\t-0.922\t0.099\t1.461\n3\t-0.239\t1.307\t-1.066\t-0.356\t1.258\t1.709\t0.766\t1.556\t1.171\t-1.709\t0.563\t0.239\t-1.034\t-1.704\t0.828\t-0.117\t0.136\t0.627\t1.714\t-0.640\t-0.356\t-0.320\t-0.662\t1.853\t0.979\t0.653\t0.579\t-0.103\n3\t-0.732\t-0.129\t0.148\t-1.054\t-1.036\t1.804\t-0.479\t1.323\t0.628\t0.217\t1.341\t0.026\t-0.370\t1.828\t-0.328\t0.581\t0.176\t-0.418\t-0.051\t1.600\t-2.347\t0.921\t-0.507\t0.859\t2.601\t-0.670\t0.819\t0.014\n0\t0.792\t0.030\t-0.460\t0.725\t1.216\t0.700\t-0.556\t1.067\t0.025\t0.107\t0.960\t0.598\t0.648\t0.141\t1.222\t-0.431\t-0.850\t1.361\t-0.073\t0.488\t0.219\t-0.240\t-1.199\t0.451\t-0.643\t-1.474\t0.143\t0.504\n2\t-0.870\t1.553\t1.298\t1.618\t0.683\t-0.029\t1.615\t-0.681\t-0.871\t-0.602\t0.523\t-0.762\t-0.155\t-1.602\t0.481\t-1.517\t-0.932\t1.217\t1.180\t0.374\t0.276\t-0.531\t-0.773\t-0.362\t-1.514\t0.252\t0.049\t-0.933\n3\t-1.533\t1.177\t0.101\t0.598\t-1.552\t-0.753\t1.469\t-1.253\t0.548\t-0.335\t0.938\t0.917\t-1.116\t0.522\t-1.176\t1.054\t2.108\t-1.071\t0.469\t-0.445\t0.510\t-0.356\t0.484\t0.506\t1.272\t1.607\t-1.324\t-0.488\n0\t0.292\t-1.707\t-0.630\t-0.303\t1.053\t0.684\t1.272\t0.604\t-0.638\t-0.208\t-0.589\t-0.073\t-1.823\t-0.338\t0.027\t0.795\t0.011\t1.007\t1.211\t0.695\t-0.337\t0.305\t-0.178\t1.640\t0.156\t-0.095\t1.260\t-0.676\n3\t-0.552\t0.866\t1.650\t-1.219\t-0.507\t-1.583\t0.403\t-0.695\t-1.208\t-1.441\t-0.843\t0.040\t1.074\t-0.758\t0.290\t-0.708\t-1.429\t1.575\t1.860\t2.223\t-0.959\t-0.017\t-0.984\t-1.187\t0.853\t-0.397\t-0.333\t0.774\n3\t1.585\t0.499\t0.140\t-1.098\t0.428\t-0.291\t0.266\t0.513\t-0.468\t0.282\t1.847\t1.261\t0.909\t2.381\t1.958\t1.521\t-1.043\t-1.187\t-1.266\t0.855\t0.755\t0.455\t-1.062\t0.965\t-0.296\t-0.213\t0.716\t-0.165\n2\t0.211\t0.408\t-0.746\t-1.553\t0.616\t0.118\t1.302\t-0.912\t-1.282\t0.711\t-0.658\t-1.551\t-0.582\t-1.044\t-1.531\t0.425\t0.574\t-0.308\t1.845\t-0.250\t0.640\t-1.539\t-1.152\t1.074\t1.183\t-0.215\t0.614\t0.318\n3\t0.011\t-1.298\t-0.294\t-0.010\t1.606\t1.013\t0.351\t-1.348\t0.436\t-0.645\t-2.385\t0.956\t0.642\t-0.759\t0.518\t0.016\t-0.522\t0.537\t1.167\t-0.453\t-0.860\t1.196\t1.622\t1.723\t0.879\t-1.003\t-1.339\t1.246\n3\t0.773\t-1.056\t-0.706\t0.757\t-0.859\t-1.082\t-1.179\t0.352\t0.309\t-0.684\t0.005\t1.527\t-0.974\t-1.952\t0.455\t-0.110\t-0.710\t0.652\t0.839\t-2.208\t-0.653\t2.318\t0.583\t0.676\t1.173\t0.017\t0.797\t0.718\n2\t0.001\t0.692\t1.354\t1.475\t-0.295\t-0.987\t-0.319\t-1.182\t-0.217\t-0.342\t0.727\t-2.206\t-0.734\t-0.726\t1.187\t-1.114\t0.227\t-0.128\t1.043\t0.596\t0.121\t-0.555\t-1.852\t0.247\t0.530\t-1.575\t-1.234\t0.252\n1\t1.801\t0.148\t-0.473\t0.406\t-1.589\t-0.972\t-1.407\t-0.368\t1.609\t0.597\t0.479\t-0.660\t0.780\t-0.776\t-0.204\t0.755\t-0.081\t0.915\t-0.201\t1.358\t0.417\t-0.882\t-0.543\t-0.223\t0.957\t1.837\t-1.487\t-0.386\n1\t0.112\t-1.047\t1.678\t0.413\t-0.153\t-0.603\t0.512\t0.302\t0.063\t-1.189\t1.397\t-0.137\t2.206\t-0.843\t-1.052\t-0.691\t-1.039\t-0.654\t-0.543\t-0.828\t-0.105\t0.418\t0.823\t1.302\t0.722\t-0.098\t0.974\t-0.742\n2\t1.613\t-0.235\t1.409\t-1.285\t0.127\t0.999\t-0.175\t0.221\t0.268\t0.777\t0.118\t1.508\t0.386\t0.699\t-0.378\t-0.255\t2.000\t-2.187\t-0.073\t-1.170\t0.815\t0.719\t-0.480\t-0.019\t0.404\t1.613\t-1.105\t-1.032\n3\t1.295\t-1.894\t-0.292\t-1.553\t-0.241\t-1.244\t-1.134\t1.486\t-0.819\t-0.278\t-0.661\t1.592\t0.307\t-0.367\t0.201\t-0.186\t-0.769\t-0.585\t1.723\t-1.337\t0.802\t-2.760\t-0.249\t-0.440\t0.114\t-0.106\t-0.284\t-1.292\n0\t-0.157\t-0.037\t-1.223\t-0.140\t0.066\t-0.660\t0.633\t0.502\t-0.290\t1.189\t-0.275\t-1.063\t0.117\t-0.177\t1.178\t0.166\t1.489\t-0.027\t-0.636\t-0.283\t1.523\t0.356\t-0.451\t-0.337\t-0.962\t-0.752\t0.143\t-1.250\n1\t-0.189\t-0.480\t-1.481\t-0.725\t1.446\t0.041\t-0.593\t-0.026\t-0.367\t0.440\t-0.354\t-0.051\t-2.037\t-0.983\t0.309\t-0.149\t0.583\t0.250\t1.404\t-2.905\t-0.060\t0.641\t0.080\t-0.259\t-0.439\t-0.644\t-0.440\t-0.918\n4\t3.452\t0.034\t2.889\t-0.126\t-1.053\t-0.317\t1.101\t0.619\t0.308\t-0.061\t0.129\t-0.149\t1.197\t0.916\t-0.942\t-1.477\t-1.226\t-0.356\t0.221\t-0.055\t0.630\t-0.015\t1.596\t0.509\t0.018\t-1.511\t0.350\t0.499\n4\t-0.361\t0.101\t0.272\t2.523\t-1.039\t-1.308\t-0.588\t-3.035\t-1.892\t-1.326\t1.133\t-1.352\t-1.697\t-0.844\t0.400\t-0.513\t-0.342\t-0.574\t-1.324\t-0.924\t-0.818\t0.634\t-1.120\t1.020\t0.684\t-1.564\t0.386\t0.056\n0\t-0.286\t-0.879\t-0.864\t-0.039\t-0.587\t0.127\t-0.838\t0.707\t-0.460\t1.637\t-0.051\t-0.102\t1.604\t-0.495\t-0.407\t-0.701\t0.204\t-0.735\t0.777\t-0.047\t0.839\t0.726\t-0.512\t-0.737\t-0.328\t0.009\t0.334\t0.445\n1\t1.342\t0.232\t-0.787\t0.306\t-0.815\t1.196\t-0.115\t-0.801\t-0.555\t-0.789\t1.537\t-1.128\t-0.068\t0.257\t-0.441\t-1.349\t2.093\t-0.325\t-1.312\t-0.778\t0.120\t-0.174\t-1.403\t0.305\t-1.170\t0.774\t0.389\t-0.301\n4\t-1.670\t-1.946\t0.439\t-1.176\t-0.044\t0.639\t-0.554\t-0.238\t0.095\t0.512\t0.071\t-0.645\t0.481\t0.396\t1.708\t0.506\t-1.107\t-1.055\t-0.579\t-2.323\t-1.698\t0.101\t-1.136\t1.964\t-0.001\t1.515\t-1.618\t-0.685\n1\t-0.387\t-0.191\t1.960\t-0.184\t-0.825\t0.476\t0.416\t1.269\t1.597\t1.000\t-1.434\t0.263\t-1.413\t-1.605\t1.042\t0.041\t-0.131\t0.464\t1.364\t1.180\t0.389\t-0.296\t0.422\t0.493\t0.736\t-0.041\t0.845\t0.117\n3\t0.720\t-0.078\t0.298\t-0.101\t-1.532\t0.478\t1.029\t1.506\t-0.019\t-1.922\t0.016\t-0.177\t0.697\t-1.348\t-0.700\t-0.908\t-0.358\t-0.182\t-0.814\t0.998\t1.532\t-2.361\t0.191\t2.219\t0.142\t-0.940\t-0.644\t1.216\n3\t-0.709\t0.284\t1.726\t0.504\t0.173\t1.773\t1.179\t-0.816\t-0.648\t-1.402\t0.625\t0.309\t0.490\t-1.760\t-0.245\t1.391\t-2.168\t0.023\t-0.052\t-1.393\t-0.935\t-0.300\t0.198\t1.383\t-0.359\t0.367\t-1.782\t0.008\n4\t1.227\t1.441\t0.128\t-0.543\t-2.194\t-0.455\t-1.275\t0.028\t0.812\t0.742\t0.826\t-1.490\t-1.473\t1.286\t-2.024\t0.698\t-0.190\t-0.335\t0.196\t1.810\t-0.794\t-0.401\t1.123\t-0.878\t0.848\t-0.045\t-2.241\t0.115\n0\t0.752\t-0.588\t1.089\t0.382\t-2.342\t0.300\t-0.406\t0.537\t0.944\t0.195\t1.665\t-0.179\t0.625\t0.805\t-0.097\t0.779\t-0.595\t-0.423\t-1.312\t0.241\t0.325\t1.179\t0.791\t-0.285\t-1.178\t0.433\t1.111\t0.755\n4\t0.157\t0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1.045\t0.135\t-0.447\t-0.146\t-0.325\t-0.754\t0.124\t1.171\t-0.717\t-0.768\t-1.442\t-0.893\t1.202\t1.856\t1.330\t0.371\t0.125\t0.863\t-0.150\t0.114\t-1.880\t2.151\t0.346\t1.268\t-0.796\t0.082\t-0.153\t-1.545\n4\t-2.160\t1.241\t0.800\t0.808\t0.096\t1.334\t-0.448\t0.392\t0.073\t-0.232\t0.122\t-0.949\t-0.664\t1.580\t2.882\t0.520\t0.834\t0.252\t0.455\t0.544\t1.035\t1.537\t-0.884\t2.828\t0.138\t0.139\t-0.413\t-0.409\n1\t0.153\t1.695\t-0.193\t-0.405\t-0.813\t1.236\t0.090\t-0.026\t-0.297\t1.485\t0.238\t-0.565\t1.814\t-1.153\t0.877\t-0.287\t0.875\t-0.138\t1.560\t-0.842\t0.191\t-0.958\t1.140\t-1.112\t0.150\t0.114\t-0.714\t0.683\n3\t-2.378\t0.498\t0.330\t1.517\t0.436\t0.639\t-1.930\t-1.221\t0.313\t0.324\t-0.547\t-0.924\t-0.337\t-1.561\t-0.967\t0.588\t-1.555\t1.018\t0.024\t0.395\t-0.203\t-0.070\t-1.041\t1.222\t0.261\t2.051\t0.963\t-0.497\n4\t0.192\t0.285\t0.498\t-0.390\t-0.583\t0.810\t-0.190\t-1.014\t-0.739\t2.003\t1.724\t1.829\t2.496\t-0.467\t1.368\t-0.322\t-0.557\t1.250\t-0.001\t-1.467\t-0.518\t-2.266\t0.151\t0.702\t1.565\t1.221\t2.011\t-0.172\n2\t0.206\t-0.296\t0.233\t1.593\t-0.344\t-0.211\t0.334\t1.271\t-0.155\t-2.812\t-1.207\t-2.247\t0.556\t-0.997\t-0.875\t-1.156\t0.340\t-0.007\t-1.257\t-0.194\t-1.110\t0.611\t0.334\t0.570\t-0.120\t-0.236\t0.746\t-0.406\n2\t-0.512\t0.537\t-0.073\t-0.695\t0.341\t0.668\t-0.089\t1.650\t1.236\t0.521\t-0.365\t-1.820\t-1.217\t0.890\t-0.924\t0.410\t-0.894\t-0.181\t-0.480\t0.836\t0.907\t-1.617\t1.349\t-0.262\t1.086\t1.315\t-0.564\t1.620\n2\t-1.818\t-0.051\t-0.334\t-0.102\t0.260\t0.259\t0.399\t-0.275\t-0.350\t0.124\t-0.959\t0.462\t1.306\t1.354\t-0.616\t-0.753\t0.188\t1.433\t-0.813\t0.072\t-1.140\t2.697\t0.169\t1.884\t-0.366\t1.119\t-0.624\t0.594\n2\t0.224\t0.789\t-0.044\t-1.211\t0.678\t1.253\t0.317\t0.489\t-1.235\t0.707\t1.346\t0.405\t-1.797\t1.636\t-0.945\t0.388\t0.953\t1.080\t-1.800\t0.117\t0.051\t1.439\t0.459\t1.448\t0.540\t-0.074\t0.194\t1.524\n2\t0.936\t-0.193\t-0.773\t-0.114\t-0.340\t1.953\t-1.226\t0.135\t-0.018\t-0.117\t-0.109\t-0.116\t0.873\t0.609\t0.419\t-3.034\t0.300\t-1.656\t0.646\t1.474\t-0.926\t-0.262\t-0.599\t1.235\t0.095\t1.728\t0.414\t0.242\n1\t-0.636\t1.341\t1.164\t0.279\t0.471\t0.880\t0.930\t-0.482\t0.559\t-0.779\t0.559\t-0.397\t0.052\t-0.676\t0.341\t-0.499\t2.619\t-0.833\t1.544\t0.109\t0.206\t0.848\t-0.820\t1.246\t0.173\t-2.084\t0.346\t0.079\n0\t-0.789\t0.648\t0.036\t0.937\t-0.124\t0.482\t-1.812\t-1.609\t-0.803\t0.311\t-0.781\t0.728\t0.387\t-0.459\t0.224\t-0.444\t0.315\t-0.510\t0.396\t1.361\t-1.236\t-0.694\t-0.653\t0.311\t0.838\t-1.740\t-0.183\t0.028\n3\t0.875\t-0.470\t-0.296\t0.534\t-1.510\t-1.089\t-0.990\t-1.796\t1.085\t-1.988\t0.343\t0.812\t-0.294\t-0.668\t0.233\t0.002\t1.004\t-2.629\t-0.919\t0.562\t0.050\t0.752\t0.262\t-0.135\t-0.302\t2.325\t1.535\t0.496\n2\t0.043\t-0.590\t1.066\t-1.230\t-0.036\t-0.569\t0.842\t-0.947\t-0.409\t0.044\t1.488\t-0.533\t-0.401\t-1.348\t-0.983\t0.413\t0.032\t0.048\t1.741\t-1.672\t2.318\t1.178\t-0.326\t0.391\t-0.683\t0.319\t2.150\t-0.646\n0\t-0.051\t0.512\t0.192\t-0.671\t0.547\t2.257\t-0.526\t-0.211\t-1.835\t0.616\t0.597\t0.566\t-0.271\t-0.396\t0.191\t0.521\t-0.013\t-0.473\t-1.587\t0.971\t0.135\t-1.418\t0.624\t0.663\t-0.485\t-1.041\t1.046\t-0.489\n2\t0.271\t0.029\t-0.510\t1.251\t1.897\t1.167\t0.558\t0.762\t1.856\t0.760\t-0.585\t0.392\t0.626\t-0.995\t-0.285\t1.100\t-1.823\t-1.250\t-0.176\t0.354\t-1.358\t-0.127\t1.154\t0.069\t0.939\t-0.592\t-0.810\t0.849\n3\t-0.130\t-0.374\t-1.051\t-0.339\t-0.780\t-0.771\t0.586\t0.971\t-0.226\t0.502\t2.096\t-0.841\t-1.634\t0.833\t-0.734\t-0.527\t0.123\t1.239\t0.131\t-2.120\t0.358\t0.232\t-0.860\t-1.702\t-0.382\t-2.303\t-1.787\t-1.351\n2\t0.121\t1.234\t0.697\t-0.104\t-0.687\t0.325\t0.530\t-1.254\t-1.019\t-0.290\t-1.332\t-1.811\t-1.031\t0.383\t0.102\t-0.445\t-0.936\t0.369\t0.316\t1.243\t-2.511\t-1.746\t0.938\t0.474\t0.934\t-0.892\t-0.389\t-1.278\n3\t1.098\t1.081\t-0.303\t-0.120\t-1.260\t1.372\t-0.051\t1.636\t1.457\t-0.184\t-1.591\t0.076\t-0.803\t-0.172\t0.838\t1.541\t-0.460\t2.687\t-1.077\t-0.519\t1.197\t-0.984\t1.123\t0.213\t-0.445\t1.304\t-0.794\t0.353\n1\t1.144\t0.340\t-1.574\t0.543\t-0.100\t0.131\t-0.651\t-0.412\t1.456\t-0.237\t-0.969\t-0.779\t1.125\t1.310\t0.438\t0.370\t1.158\t0.004\t-0.571\t-2.272\t-0.326\t-0.109\t1.214\t-0.402\t-0.352\t-1.794\t0.548\t0.645\n2\t2.380\t0.161\t0.227\t1.445\t-0.149\t0.247\t-2.421\t1.738\t0.391\t-0.107\t0.907\t-1.181\t-0.551\t0.553\t0.044\t0.447\t0.249\t0.631\t-1.321\t1.278\t-0.484\t0.058\t-0.601\t-0.287\t0.229\t0.735\t0.901\t0.433\n3\t1.704\t-1.336\t1.074\t1.594\t-1.095\t0.650\t-1.694\t-0.026\t-1.039\t0.682\t-0.050\t-0.349\t0.610\t-0.853\t0.020\t0.696\t-0.332\t0.131\t-0.255\t1.431\t1.412\t-0.082\t-0.117\t-1.011\t2.257\t0.783\t0.901\t1.426\n2\t0.259\t-1.860\t-0.660\t0.137\t0.199\t-1.626\t2.576\t0.122\t-0.051\t-0.058\t-0.484\t-0.495\t-1.363\t0.815\t-0.304\t1.183\t-1.064\t1.563\t-0.408\t1.002\t-0.436\t-1.285\t-0.211\t-0.494\t0.800\t0.445\t0.232\t1.841\n2\t-0.107\t-1.638\t-0.688\t-0.021\t0.321\t0.347\t-1.971\t-0.630\t-1.530\t-0.006\t-0.431\t-0.625\t3.167\t-1.052\t-0.017\t0.479\t0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2.433\t-1.036\n1\t-0.269\t-1.499\t0.328\t0.970\t-0.068\t0.596\t0.816\t-0.499\t-0.806\t-0.342\t-2.157\t-0.450\t0.276\t1.139\t-0.828\t0.488\t-0.635\t-1.679\t0.401\t-0.354\t1.314\t1.553\t-0.113\t0.141\t-1.380\t-0.318\t0.771\t-0.343\n3\t1.248\t0.817\t0.974\t-0.800\t-2.059\t0.325\t-0.736\t-0.848\t-0.848\t-1.386\t0.592\t0.811\t-1.546\t1.051\t-0.346\t0.734\t-1.111\t1.698\t-0.895\t-0.393\t-0.314\t-0.287\t0.507\t-1.972\t-0.586\t0.776\t-0.522\t-1.669\n1\t-0.121\t0.845\t-0.819\t0.172\t0.492\t0.398\t-0.196\t-0.113\t1.001\t0.665\t0.364\t-2.587\t0.740\t-1.620\t-0.060\t0.173\t1.013\t0.898\t0.616\t0.370\t1.586\t-1.029\t1.391\t0.869\t0.173\t-0.247\t-0.661\t-0.001\n3\t-0.500\t-0.081\t-0.499\t1.608\t0.319\t-0.526\t-0.981\t1.569\t1.303\t-1.185\t1.065\t1.998\t0.136\t-0.457\t1.268\t-0.667\t-2.259\t-0.921\t-0.340\t0.824\t-0.440\t-1.304\t1.320\t-0.079\t-0.299\t-0.710\t0.599\t-1.051\n3\t-0.905\t-0.297\t1.111\t1.602\t1.378\t0.610\t-0.409\t-0.606\t0.209\t-0.587\t-1.450\t0.599\t0.200\t-0.159\t-0.142\t1.030\t1.378\t0.840\t0.962\t-0.932\t1.408\t-0.151\t1.056\t-1.827\t1.330\t-1.564\t1.873\t-0.813\n0\t0.748\t0.386\t0.464\t-1.148\t-0.688\t0.718\t-0.749\t-1.363\t0.521\t0.365\t-0.031\t-0.104\t-0.230\t-0.550\t1.109\t0.066\t-1.886\t-0.011\t-2.246\t0.728\t-0.678\t-0.407\t0.930\t1.079\t-0.644\t0.642\t-0.144\t0.865\n0\t0.549\t0.642\t0.772\t0.899\t0.776\t1.127\t0.167\t1.072\t1.892\t0.196\t0.481\t-0.580\t-0.165\t0.353\t-0.389\t0.092\t0.341\t1.521\t-0.864\t0.420\t-0.604\t0.766\t0.845\t1.677\t-0.426\t0.146\t0.804\t0.659\n4\t-0.237\t-0.270\t-1.131\t0.791\t-0.361\t-0.045\t1.190\t-1.047\t-0.087\t-1.558\t-0.524\t1.288\t-1.541\t0.236\t1.604\t0.549\t-0.584\t-1.112\t-1.930\t1.562\t-0.421\t-1.802\t-1.021\t-1.078\t-0.495\t2.388\t0.923\t0.484\n1\t0.058\t-0.967\t-0.581\t-0.614\t-0.277\t-1.658\t1.090\t-0.016\t0.202\t0.914\t0.838\t-0.575\t0.604\t0.594\t0.316\t1.061\t0.800\t0.727\t1.365\t0.029\t0.058\t0.819\t-0.178\t0.482\t-0.560\t-2.279\t0.598\t-1.549\n2\t0.033\t-1.120\t-1.103\t0.368\t0.947\t-0.194\t1.649\t0.212\t-0.777\t-1.087\t1.018\t0.128\t0.676\t-2.658\t0.245\t-0.512\t-1.394\t-0.063\t1.092\t2.266\t0.242\t-0.709\t-0.770\t0.963\t-0.997\t0.622\t0.294\t0.183\n1\t1.492\t1.246\t-1.323\t-0.806\t1.038\t0.198\t0.906\t-0.109\t-1.858\t-0.378\t0.882\t1.189\t0.343\t-0.872\t0.469\t0.880\t0.738\t0.240\t-0.972\t1.015\t-1.103\t-0.300\t0.855\t1.326\t-0.602\t-0.543\t-0.380\t0.005\n4\t1.738\t-0.723\t-0.014\t0.275\t-2.183\t0.298\t1.228\t-0.898\t-0.138\t0.768\t-0.926\t-0.043\t0.899\t-1.968\t0.265\t-0.545\t0.993\t1.836\t-1.421\t-1.484\t-0.702\t0.590\t-0.068\t1.559\t-1.373\t-0.888\t1.860\t-0.140\n1\t0.976\t0.671\t0.584\t-1.184\t2.887\t1.051\t0.517\t-1.164\t0.948\t-0.501\t-0.194\t-1.681\t-1.241\t0.439\t1.006\t0.685\t0.651\t-0.065\t-0.407\t0.289\t0.821\t0.005\t1.210\t-0.215\t-0.980\t-0.010\t0.106\t-0.082\n4\t0.485\t1.042\t0.473\t-0.542\t3.611\t1.199\t0.933\t-0.353\t-0.625\t-0.280\t1.081\t-0.991\t-1.068\t0.540\t0.319\t-0.770\t-1.091\t1.438\t0.357\t-1.364\t-1.294\t-0.362\t0.733\t0.595\t1.132\t-1.490\t0.224\t1.426\n4\t-0.057\t-1.821\t1.533\t1.169\t0.372\t-0.420\t0.010\t-2.644\t-1.107\t1.767\t0.517\t-1.949\t-0.828\t-1.925\t-0.780\t-0.165\t1.602\t0.785\t-0.302\t-0.253\t-0.801\t1.090\t1.210\t-1.338\t-1.721\t-0.319\t-0.196\t0.259\n1\t-0.219\t-0.031\t0.700\t0.270\t0.830\t1.587\t-0.575\t-0.130\t0.372\t1.011\t1.093\t1.238\t-1.006\t0.264\t-2.401\t0.035\t1.112\t-1.609\t0.567\t-0.310\t0.478\t0.062\t0.120\t-0.396\t0.318\t0.576\t-0.188\t-1.463\n1\t-0.788\t1.391\t-0.972\t1.181\t-0.862\t0.294\t-0.358\t-0.285\t-0.748\t0.667\t0.922\t1.185\t-0.852\t1.159\t0.838\t-2.040\t0.183\t0.955\t0.244\t-0.629\t-1.600\t-0.988\t0.462\t-1.339\t-0.528\t0.323\t-0.663\t-1.068\n4\t-0.877\t1.481\t2.150\t-0.273\t-1.171\t-1.044\t-1.208\t-0.282\t1.630\t-0.126\t-0.543\t0.503\t0.326\t-0.437\t-0.238\t0.258\t0.512\t-0.544\t1.354\t1.266\t0.528\t-0.930\t-2.113\t-2.249\t0.618\t0.632\t1.277\t-1.356\n2\t2.665\t0.470\t0.741\t0.652\t1.178\t0.621\t0.467\t-0.065\t1.614\t0.600\t-1.166\t0.821\t0.368\t-0.050\t0.521\t-1.677\t-0.625\t1.091\t0.313\t0.113\t0.290\t-0.210\t-0.101\t0.414\t-0.855\t-1.183\t1.478\t-1.117\n4\t-2.508\t1.543\t0.015\t1.539\t1.804\t-0.155\t1.349\t-1.060\t0.433\t-0.473\t0.696\t0.883\t-0.160\t0.545\t0.945\t0.188\t2.539\t-0.137\t1.656\t1.145\t-1.860\t1.493\t1.074\t0.114\t0.369\t0.493\t1.375\t-0.413\n4\t-0.564\t0.889\t-1.445\t-1.339\t0.875\t-0.260\t0.821\t0.457\t-0.066\t-0.346\t-0.237\t0.133\t-1.271\t0.633\t-1.497\t2.360\t1.102\t-0.747\t1.507\t-0.839\t1.288\t0.351\t1.407\t-1.268\t1.057\t-1.881\t-0.091\t-2.089\n4\t0.016\t0.595\t1.775\t0.081\t-1.491\t1.182\t-1.357\t-0.496\t1.193\t-0.335\t-0.572\t0.281\t0.010\t-0.693\t0.878\t1.228\t1.388\t0.552\t-0.701\t-1.255\t-2.317\t1.033\t0.195\t-0.419\t-1.933\t-1.183\t-1.765\t1.442\n4\t0.754\t0.508\t-0.704\t0.885\t-1.530\t-1.735\t-1.276\t-0.033\t0.675\t-2.976\t1.323\t0.131\t-1.568\t0.101\t1.093\t0.599\t1.099\t-1.366\t-0.414\t-0.925\t1.718\t0.639\t2.847\t-0.468\t-0.725\t-0.143\t1.378\t-0.366\n4\t-0.727\t0.060\t-0.130\t1.096\t-0.231\t0.839\t-1.472\t-0.524\t0.695\t2.067\t-0.142\t0.829\t1.336\t0.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0.875\t-0.603\t0.263\t0.698\t1.353\t0.044\t-0.214\t-0.055\t-1.194\t0.770\t-1.457\t0.391\t0.085\t-1.551\t-1.494\n3\t0.537\t0.413\t1.025\t1.836\t-0.467\t-1.445\t-0.578\t-0.310\t0.997\t-0.490\t0.926\t-0.531\t0.282\t-0.502\t-2.164\t-1.363\t-1.331\t-0.508\t1.464\t0.176\t-0.554\t0.378\t1.632\t-2.494\t0.080\t-1.288\t1.342\t0.353\n4\t-0.957\t0.440\t0.318\t-1.489\t-0.746\t-0.691\t-0.444\t-0.956\t0.939\t1.061\t-0.788\t-1.154\t-0.013\t1.330\t2.154\t-0.675\t-2.275\t-1.487\t-1.782\t0.417\t0.098\t0.217\t1.126\t1.629\t0.843\t0.735\t-1.474\t-0.509\n3\t1.310\t0.048\t0.095\t-0.788\t-1.884\t-0.819\t0.570\t-0.201\t-0.333\t-1.699\t1.811\t-0.059\t2.342\t0.367\t1.551\t-0.718\t-1.514\t-0.186\t0.560\t0.669\t0.395\t2.292\t0.392\t-0.310\t-0.234\t-0.805\t0.293\t1.082\n3\t1.847\t-0.469\t-2.419\t0.743\t0.427\t0.109\t-0.232\t1.351\t-1.546\t1.532\t1.734\t0.150\t0.284\t-1.186\t1.004\t1.271\t0.764\t-1.240\t-1.609\t-0.240\t0.079\t1.015\t-0.241\t1.063\t1.129\t0.474\t0.869\t-0.416\n0\t-0.226\t-0.049\t-0.424\t0.330\t0.369\t1.765\t-0.808\t0.902\t-0.030\t0.615\t0.480\t-0.053\t0.669\t-0.926\t0.574\t-1.470\t0.444\t0.665\t-1.817\t-1.220\t-0.788\t0.990\t1.017\t-0.398\t0.274\t-1.094\t-0.232\t0.872\n3\t0.710\t0.008\t0.395\t1.826\t-0.196\t-1.092\t-2.526\t1.936\t1.981\t-0.991\t1.286\t1.033\t0.641\t0.503\t-0.745\t0.129\t0.348\t0.070\t-0.055\t1.300\t1.016\t0.874\t0.932\t0.229\t1.166\t-1.198\t0.070\t0.708\n1\t-0.046\t0.119\t-0.099\t-0.370\t-0.412\t-1.827\t-0.277\t-0.373\t-0.678\t-0.626\t0.041\t-0.381\t0.756\t-1.454\t0.943\t1.321\t0.784\t-2.177\t0.215\t-1.177\t-0.920\t-0.374\t0.162\t0.861\t0.209\t-0.628\t1.554\t1.111\n0\t-0.435\t-0.036\t0.527\t2.479\t0.896\t-0.656\t-0.764\t0.002\t-0.075\t0.770\t0.001\t0.217\t-0.754\t-0.144\t0.492\t2.179\t-0.037\t0.276\t-0.495\t-0.778\t0.512\t0.477\t-1.310\t-1.363\t0.956\t-0.768\t0.142\t0.292\n0\t-0.268\t-0.709\t-0.526\t0.707\t-0.138\t-0.958\t-0.399\t0.830\t-0.320\t-2.570\t0.905\t-0.245\t0.794\t-0.409\t0.479\t0.572\t-0.738\t-1.332\t-1.145\t0.203\t-0.152\t-0.020\t0.427\t-0.324\t-0.453\t-0.825\t0.374\t1.700\n3\t-1.660\t-1.256\t-0.460\t-0.240\t-0.894\t-1.159\t1.510\t0.263\t0.585\t-0.137\t0.289\t0.933\t-0.996\t0.265\t0.696\t1.545\t1.948\t0.070\t1.139\t-2.670\t1.552\t-0.726\t-0.241\t0.441\t-0.153\t0.196\t1.588\t-0.384\n2\t0.969\t0.427\t-0.646\t1.775\t-1.194\t0.919\t1.001\t-0.671\t1.392\t-0.250\t0.289\t0.260\t-0.134\t0.811\t0.793\t-1.749\t1.304\t-1.662\t1.033\t1.127\t-1.091\t-0.411\t-1.106\t-0.215\t-0.308\t0.780\t1.310\t1.396\n0\t0.788\t0.754\t0.603\t-0.034\t1.722\t0.700\t0.222\t0.567\t-0.399\t-0.057\t-2.082\t-0.387\t1.760\t0.148\t0.107\t0.999\t0.358\t-0.786\t0.024\t-1.135\t0.184\t-0.838\t0.928\t-0.510\t-0.825\t-0.113\t0.365\t-1.207\n0\t1.113\t-0.002\t-0.023\t0.181\t0.004\t-0.770\t0.208\t0.509\t-0.545\t0.477\t-0.737\t-0.332\t-0.158\t1.075\t0.311\t0.823\t0.246\t0.503\t-0.248\t0.023\t1.012\t-1.499\t0.006\t0.181\t0.519\t0.193\t0.089\t-1.017\n2\t1.818\t0.498\t-1.502\t-0.228\t0.245\t-1.685\t-0.536\t-1.405\t-1.030\t0.339\t-0.049\t-1.095\t-0.067\t-0.362\t0.883\t-2.074\t-0.741\t1.517\t-1.164\t-1.081\t-0.518\t1.269\t0.402\t-1.062\t-0.084\t-0.352\t0.450\t-0.042\n2\t-0.224\t0.191\t0.317\t-1.978\t0.370\t0.375\t0.012\t0.765\t0.328\t-1.258\t1.552\t0.444\t-1.281\t-0.571\t-0.489\t0.427\t0.790\t0.144\t0.496\t0.396\t-2.255\t-0.953\t-2.418\t-0.786\t-0.235\t-1.300\t-0.386\t0.385\n1\t-0.226\t0.499\t0.719\t-1.400\t-0.190\t-1.072\t-0.565\t1.273\t1.153\t0.589\t-1.405\t0.187\t-0.576\t0.992\t-0.563\t-0.828\t0.361\t1.214\t1.068\t1.299\t1.308\t1.633\t0.181\t-0.445\t-0.826\t1.195\t-0.173\t-0.910\n2\t1.487\t0.815\t0.242\t-0.459\t-0.549\t-0.336\t0.457\t0.836\t-1.444\t-1.947\t-0.951\t-0.408\t1.196\t-0.280\t0.255\t0.418\t-1.024\t1.460\t-0.665\t1.560\t-0.126\t-0.338\t-1.111\t0.197\t2.254\t0.414\t0.270\t-0.667\n2\t0.134\t-2.809\t-0.589\t1.407\t0.982\t-0.509\t-0.433\t0.567\t0.935\t-0.533\t-0.234\t-0.566\t-1.331\t-0.220\t-0.296\t-1.261\t0.646\t0.483\t0.696\t-0.768\t-1.984\t0.467\t-0.664\t-0.891\t-1.257\t-0.275\t1.577\t-0.505\n2\t-0.007\t0.654\t0.300\t-0.160\t-0.415\t0.932\t-0.931\t0.773\t0.808\t-0.445\t0.303\t0.553\t-0.111\t-0.877\t-1.309\t-1.049\t0.823\t-1.359\t0.323\t-0.419\t-1.041\t-1.409\t-0.156\t1.282\t0.079\t2.771\t1.826\t-0.969\n4\t-0.020\t-2.806\t0.460\t0.553\t-0.713\t-0.138\t0.914\t-0.473\t-1.644\t-1.999\t-1.183\t2.036\t1.204\t-1.055\t-1.515\t0.872\t-1.056\t-0.363\t0.240\t0.415\t-0.953\t-0.321\t0.408\t-2.044\t-0.186\t1.377\t0.406\t-0.823\n0\t-1.146\t-0.460\t1.027\t0.773\t-0.588\t0.467\t-0.451\t0.384\t0.782\t-0.718\t-0.361\t0.100\t-0.490\t-0.609\t0.359\t1.080\t-0.581\t-0.749\t-0.838\t-0.091\t1.344\t-0.399\t1.324\t-0.484\t-1.310\t1.707\t-0.141\t0.947\n3\t-0.900\t1.307\t-0.260\t-2.423\t-0.924\t1.963\t0.640\t-0.396\t0.809\t-1.438\t0.855\t1.461\t-0.758\t0.641\t1.130\t-2.088\t-0.986\t-0.244\t0.659\t0.295\t0.071\t-0.015\t-0.740\t-1.605\t0.106\t-0.224\t-0.036\t-0.253\n3\t-1.542\t0.502\t1.440\t0.053\t-0.427\t0.805\t-0.161\t-0.275\t0.316\t0.753\t-1.073\t-1.747\t-1.189\t-0.811\t1.953\t-0.461\t0.520\t-0.569\t-0.679\t1.087\t-1.801\t1.310\t-0.005\t0.109\t1.083\t1.419\t-0.888\t-1.525\n0\t0.220\t0.132\t0.564\t-0.492\t-0.214\t0.810\t0.277\t-0.114\t-1.401\t1.498\t-0.661\t0.987\t0.176\t-1.293\t1.252\t0.255\t1.335\t-0.182\t0.704\t-1.763\t-0.500\t-1.273\t-0.375\t0.472\t-0.605\t0.864\t-0.857\t0.885\n2\t-0.303\t-0.931\t-0.018\t0.123\t0.157\t-1.561\t1.262\t0.247\t-0.227\t-1.078\t1.245\t2.138\t2.150\t0.231\t1.059\t0.518\t-0.843\t0.664\t1.188\t-1.775\t-0.960\t-0.884\t0.628\t-1.403\t-0.302\t0.042\t-0.755\t0.493\n4\t-1.210\t-0.115\t-1.503\t-0.200\t-1.249\t0.207\t-0.679\t0.291\t1.082\t1.589\t-2.528\t-0.834\t-0.303\t0.460\t-1.650\t-0.982\t0.130\t0.085\t0.378\t0.444\t0.339\t0.194\t0.056\t0.665\t-2.239\t1.544\t-2.204\t-1.079\n3\t0.326\t-0.491\t0.950\t-2.250\t-0.512\t1.331\t1.182\t-0.106\t-1.048\t-0.666\t-0.558\t-1.145\t0.728\t-0.010\t1.477\t-1.323\t-0.995\t0.730\t1.870\t-1.170\t0.122\t-0.735\t-1.560\t-0.775\t-0.551\t0.049\t-1.538\t0.337\n4\t1.123\t0.416\t-0.064\t1.365\t0.390\t0.209\t1.934\t-1.105\t-1.498\t1.986\t-1.772\t1.887\t0.107\t-1.320\t0.422\t-1.004\t-0.613\t0.467\t-0.858\t-0.878\t0.839\t-0.643\t-0.992\t-1.991\t-0.925\t-0.876\t-1.000\t1.512\n4\t-2.078\t1.318\t-1.470\t-0.147\t-0.183\t0.533\t0.168\t-0.533\t1.713\t1.709\t0.356\t-0.131\t1.514\t1.056\t-1.271\t2.227\t-0.961\t-0.453\t-1.484\t-0.521\t1.179\t0.030\t0.217\t-0.810\t0.704\t-1.920\t-0.333\t-0.146\n3\t1.520\t-2.293\t-0.952\t-0.323\t0.763\t-0.647\t-0.684\t0.156\t-0.474\t-1.345\t1.188\t0.609\t0.882\t0.186\t-0.108\t1.398\t-0.744\t0.504\t0.181\t0.102\t-1.497\t-0.958\t1.846\t0.612\t-1.169\t-1.705\t-1.137\t-0.891\n3\t-0.357\t-0.485\t-0.723\t0.416\t-0.502\t0.010\t-0.889\t-0.558\t1.914\t-0.800\t1.495\t-1.110\t1.585\t-0.148\t0.359\t-0.932\t0.261\t0.365\t0.009\t-1.419\t-0.872\t-1.266\t-0.051\t-0.987\t-1.201\t-1.179\t-0.900\t2.525\n4\t1.617\t-1.622\t-2.033\t0.167\t0.840\t-1.711\t-1.300\t0.793\t-1.152\t-0.256\t0.210\t0.955\t-0.306\t0.492\t-1.240\t0.589\t-0.909\t-1.381\t2.065\t1.830\t0.148\t1.547\t-0.326\t-0.136\t-0.177\t-0.913\t-2.344\t-0.309\n2\t1.295\t0.681\t0.815\t-0.027\t0.785\t1.884\t0.988\t0.670\t-2.202\t0.640\t0.302\t-0.530\t-0.632\t0.764\t1.528\t-0.133\t-0.480\t-0.917\t-1.548\t-0.104\t0.713\t0.508\t-0.975\t0.517\t-0.208\t-1.411\t-1.906\t0.193\n4\t0.396\t0.690\t0.330\t-0.497\t1.549\t-0.475\t0.388\t-0.173\t-1.084\t-0.576\t0.634\t2.456\t0.311\t0.372\t-1.070\t2.409\t-0.555\t1.027\t-0.651\t-0.001\t-0.599\t-0.850\t0.840\t1.300\t-1.804\t1.251\t1.770\t-1.730\n1\t-1.338\t0.103\t-0.269\t-0.540\t1.291\t1.977\t0.641\t-0.638\t1.569\t-1.936\t-0.236\t0.315\t-1.408\t-0.832\t-0.568\t0.784\t0.094\t-0.025\t-0.458\t0.450\t1.113\t-0.198\t0.828\t0.926\t-0.380\t0.246\t0.747\t-0.621\n3\t2.175\t-0.986\t-0.064\t-0.112\t-1.686\t0.486\t0.313\t1.111\t0.411\t0.193\t-0.683\t-1.836\t0.606\t1.550\t-1.428\t0.718\t-0.068\t-0.968\t1.515\t0.300\t0.252\t-1.516\t1.651\t-0.701\t0.334\t0.990\t0.363\t0.852\n4\t1.060\t-0.652\t1.759\t1.896\t0.578\t-2.229\t-0.843\t-1.533\t-0.148\t-0.638\t0.443\t-1.242\t-2.361\t0.973\t1.049\t-2.309\t-0.540\t-0.408\t-0.600\t1.281\t0.151\t-1.779\t0.718\t0.127\t0.067\t0.660\t-1.929\t-0.023\n4\t-0.552\t-1.177\t1.450\t0.540\t0.989\t-0.983\t1.334\t-2.897\t-1.282\t0.164\t0.907\t-0.679\t-1.973\t-0.897\t-1.123\t2.796\t0.689\t-1.021\t-1.577\t-0.820\t0.162\t-0.482\t2.154\t-0.745\t0.619\t-0.706\t0.835\t-0.440\n1\t-1.213\t-0.183\t-0.478\t-1.750\t0.595\t-0.954\t0.268\t1.871\t-0.958\t0.575\t-0.741\t-1.561\t1.615\t0.178\t0.380\t-1.407\t-0.703\t-0.471\t-0.063\t0.861\t0.482\t-0.458\t-0.791\t-0.388\t-0.179\t-0.474\t-0.224\t1.145\n4\t0.010\t1.698\t0.586\t0.751\t0.041\t-0.152\t-1.545\t-2.259\t0.196\t1.313\t0.344\t0.624\t1.584\t0.329\t-0.123\t-0.819\t2.022\t0.527\t-0.944\t-1.171\t1.253\t-1.037\t1.854\t-2.756\t1.423\t0.487\t0.119\t-0.175\n3\t-1.854\t-0.070\t-1.585\t-0.378\t-1.241\t-0.070\t-0.915\t-0.693\t1.064\t-0.448\t-2.739\t-0.166\t1.414\t0.360\t0.924\t-0.611\t0.637\t-0.458\t0.319\t1.172\t-1.075\t-1.797\t-0.421\t0.329\t1.647\t0.088\t0.627\t-0.686\n3\t0.471\t-0.079\t0.626\t0.798\t0.642\t-1.549\t2.033\t2.403\t-1.909\t0.511\t2.046\t1.002\t1.013\t-1.439\t0.791\t0.239\t0.780\t-1.036\t-1.071\t-0.143\t-0.379\t-0.157\t-0.480\t-0.201\t-0.666\t-0.048\t-1.424\t0.514\n4\t-0.243\t0.115\t1.298\t0.646\t0.701\t0.723\t-0.154\t0.319\t-1.085\t1.269\t-2.783\t-0.087\t1.141\t-1.364\t0.400\t-0.681\t-0.369\t0.946\t0.198\t-2.034\t-0.976\t0.975\t0.449\t1.748\t1.683\t-0.342\t2.503\t0.024\n4\t-0.107\t0.657\t-0.300\t-1.099\t0.851\t-2.220\t-0.488\t-1.099\t1.071\t0.224\t-1.957\t0.554\t0.051\t-0.903\t-0.644\t0.618\t0.889\t-0.352\t0.534\t-0.869\t1.709\t2.086\t-1.427\t1.416\t1.343\t0.675\t-2.660\t1.202\n2\t1.279\t-1.467\t-1.246\t-0.014\t-0.479\t0.705\t0.457\t1.576\t0.543\t0.470\t-1.136\t-0.254\t0.927\t2.938\t-0.842\t1.177\t0.016\t0.119\t-0.819\t-0.478\t-0.091\t0.884\t1.509\t-0.483\t-0.552\t0.753\t-0.247\t1.070\n4\t-0.734\t-0.496\t0.392\t1.784\t0.604\t-0.387\t-0.305\t-0.149\t-1.341\t-0.458\t-0.502\t2.778\t0.927\t-1.073\t-1.569\t-1.306\t1.414\t-1.349\t1.610\t0.240\t-0.246\t-1.145\t1.264\t0.546\t0.900\t-0.273\t0.667\t1.482\n0\t0.441\t0.303\t-0.778\t-1.144\t1.085\t0.492\t1.286\t-1.059\t0.075\t0.794\t1.799\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1.066\t1.327\t-0.343\t-0.847\t0.215\t-0.504\t-0.702\t-0.855\t-1.894\t-1.154\t1.393\t0.927\t-0.095\t-0.069\t-1.389\t1.032\t-0.581\t0.707\t-0.394\t-0.739\t1.682\t-0.073\t0.846\t0.422\t-0.755\n0\t1.345\t0.651\t0.473\t0.616\t-0.400\t-0.055\t-0.902\t-0.333\t-0.367\t-0.635\t0.137\t-0.324\t0.502\t-1.622\t-0.113\t0.132\t-0.311\t-0.172\t-1.585\t0.356\t0.648\t0.494\t1.039\t0.202\t0.479\t0.156\t0.688\t-1.577\n3\t-2.268\t0.331\t0.842\t1.700\t-1.464\t1.206\t1.644\t-2.017\t0.479\t0.618\t-0.327\t-0.490\t0.668\t-0.615\t-0.217\t0.812\t1.754\t0.538\t0.133\t-0.516\t-1.011\t1.110\t-0.153\t1.151\t-0.330\t-0.649\t0.421\t-0.077\n1\t-1.364\t1.704\t0.474\t-1.050\t0.337\t-0.832\t-1.920\t-0.345\t-0.009\t-1.567\t-1.257\t-1.002\t-0.204\t0.129\t-1.443\t-0.582\t-0.471\t-1.738\t0.225\t0.597\t0.312\t-1.322\t0.598\t-0.101\t0.983\t0.478\t0.312\t-0.282\n1\t-1.774\t0.029\t0.131\t0.651\t-0.403\t-0.431\t0.533\t-0.370\t-0.672\t1.107\t-1.021\t-0.715\t0.354\t0.733\t-0.239\t0.470\t0.206\t-0.290\t-0.976\t0.987\t-0.990\t2.683\t-0.519\t1.602\t1.093\t-0.291\t1.034\t0.976\n2\t0.509\t0.341\t-0.349\t0.274\t0.428\t1.435\t2.500\t-1.062\t-1.297\t0.103\t-2.079\t0.649\t0.113\t0.367\t-0.304\t0.255\t0.472\t0.200\t-1.345\t0.172\t1.854\t0.241\t0.137\t1.560\t-0.746\t0.890\t-0.530\t1.087\n4\t0.649\t0.042\t0.165\t-0.707\t1.741\t2.843\t-0.279\t0.353\t0.097\t-0.260\t-0.832\t-1.085\t-1.136\t0.405\t-0.520\t-2.578\t0.445\t-0.038\t1.347\t0.373\t-1.479\t-2.230\t-0.719\t-0.625\t0.943\t-0.009\t1.134\t-1.325\n4\t-1.927\t-0.827\t1.018\t-1.329\t0.848\t0.870\t0.777\t2.347\t-0.501\t0.556\t-1.481\t-0.211\t-1.296\t-1.290\t1.143\t0.028\t0.910\t-0.238\t0.397\t2.487\t1.286\t-1.017\t0.797\t-0.629\t-0.051\t1.945\t-0.762\t0.052\n2\t-0.354\t0.753\t-0.862\t-0.505\t1.436\t-0.379\t0.486\t0.185\t-0.086\t1.014\t0.830\t-0.782\t1.014\t-0.630\t1.202\t-1.701\t-0.967\t-0.681\t1.136\t-1.522\t-0.716\t-0.672\t2.692\t-0.645\t0.186\t-0.378\t-0.610\t-1.313\n3\t-0.708\t1.326\t-1.230\t-0.521\t0.588\t-1.569\t0.388\t1.139\t1.677\t-0.390\t-0.440\t0.715\t-0.618\t-0.309\t0.322\t0.372\t-1.907\t-1.166\t-1.833\t-1.668\t-1.119\t-0.222\t-0.125\t-2.152\t-0.933\t-1.518\t-0.200\t-0.656\n1\t-0.250\t0.331\t-0.927\t-1.966\t-0.383\t-1.431\t0.712\t-1.154\t1.485\t-0.006\t0.059\t-1.981\t0.145\t-0.080\t-1.299\t0.087\t-0.398\t-0.217\t0.026\t-0.471\t0.246\t-0.087\t0.473\t-0.529\t-1.065\t-0.709\t1.443\t0.956\n1\t0.812\t0.851\t-0.116\t-1.726\t0.752\t-1.266\t0.858\t-0.248\t-0.021\t-0.070\t-0.453\t0.287\t-1.197\t0.542\t-1.622\t0.307\t-1.317\t-1.350\t-1.241\t-0.790\t0.131\t0.599\t-1.213\t-0.838\t-1.638\t1.109\t-0.516\t-0.221\n1\t1.426\t-0.003\t0.400\t0.529\t0.863\t-0.182\t0.063\t1.163\t0.922\t0.073\t-1.026\t0.771\t-1.035\t0.618\t1.202\t0.515\t0.090\t-0.383\t1.064\t0.588\t2.799\t-0.267\t-0.403\t-0.782\t0.117\t0.870\t-0.504\t-1.894\n2\t-0.043\t0.494\t-0.534\t-0.247\t-1.035\t1.428\t0.682\t0.674\t-0.358\t1.789\t-1.557\t0.326\t-0.219\t-0.004\t-0.158\t-1.583\t-0.810\t1.659\t-0.880\t0.258\t-0.670\t-0.230\t-0.174\t-1.467\t1.087\t1.911\t0.844\t-1.051\n4\t-0.429\t-0.469\t-0.003\t-0.993\t0.664\t-0.296\t-0.067\t-0.175\t0.514\t0.286\t1.906\t-0.384\t1.793\t-1.812\t0.490\t-1.586\t-1.024\t-0.975\t-1.052\t-0.608\t2.201\t-0.878\t-1.614\t0.494\t0.242\t2.216\t-0.041\t-2.287\n3\t-0.757\t0.351\t0.205\t-0.729\t-0.761\t1.943\t-0.246\t-0.118\t-0.531\t-1.769\t-0.264\t-1.796\t-2.491\t-0.273\t0.239\t-0.520\t0.011\t-1.087\t0.827\t0.872\t0.438\t-1.075\t1.726\t0.882\t1.785\t0.796\t-0.135\t-1.499\n3\t-0.309\t-1.097\t-0.114\t-0.813\t-1.074\t1.132\t0.672\t-0.882\t0.945\t0.309\t-0.315\t1.591\t0.305\t1.778\t0.566\t0.652\t-1.076\t-0.557\t1.457\t1.162\t-0.358\t-1.353\t1.660\t1.067\t-1.144\t-1.145\t1.834\t-0.508\n4\t1.703\t0.171\t0.070\t-1.348\t-0.051\t-2.180\t0.442\t-1.157\t-2.171\t-0.305\t-0.045\t-1.267\t0.769\t-2.645\t2.301\t0.412\t-1.156\t-0.927\t-0.112\t-0.374\t-0.602\t0.547\t1.313\t-1.104\t-0.780\t-0.345\t0.826\t0.134\n1\t0.882\t-0.452\t-0.470\t0.266\t-0.437\t-0.066\t2.100\t-0.247\t-0.358\t-0.648\t0.744\t-0.181\t-0.649\t1.321\t1.420\t-0.600\t-1.867\t1.008\t-0.685\t0.791\t-1.970\t0.893\t-1.211\t0.731\t0.014\t-0.954\t-0.407\t0.686\n4\t-1.618\t-1.384\t1.038\t-0.158\t0.765\t1.422\t1.014\t0.788\t-0.560\t2.198\t-0.946\t0.815\t0.337\t0.559\t-1.319\t1.888\t-2.322\t-2.504\t-1.805\t1.047\t-1.883\t-0.895\t-0.023\t0.470\t-0.449\t1.572\t-0.460\t0.877\n3\t0.957\t-0.743\t-2.396\t0.896\t-0.006\t0.962\t0.092\t0.021\t1.498\t0.118\t-0.476\t-0.088\t0.158\t-2.845\t0.491\t1.359\t-0.049\t-0.421\t0.388\t1.744\t0.469\t0.214\t-0.391\t-1.069\t0.023\t0.042\t-1.932\t-0.727\n4\t0.091\t1.872\t-1.043\t0.085\t0.689\t-1.141\t-1.559\t0.320\t-2.120\t0.748\t-1.048\t0.176\t-1.672\t-0.196\t-0.099\t-1.761\t1.537\t-0.633\t-0.065\t-1.216\t-0.044\t-0.701\t-0.153\t1.207\t1.172\t-1.156\t2.082\t1.429\n1\t1.126\t-0.496\t0.003\t-0.111\t-1.039\t-0.379\t-1.007\t0.920\t-1.661\t-0.650\t-0.989\t1.774\t0.794\t0.502\t1.099\t0.042\t-0.151\t-0.127\t-0.468\t0.695\t0.960\t0.681\t-0.445\t-0.565\t2.237\t-0.363\t1.405\t-0.394\n4\t-0.594\t-1.992\t-1.709\t-1.976\t-0.695\t-0.310\t0.347\t0.978\t-1.449\t1.006\t-0.323\t0.650\t0.646\t0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n4\t-0.727\t1.045\t1.215\t-0.925\t0.307\t1.834\t0.415\t1.422\t0.999\t-0.910\t-0.190\t0.035\t0.104\t0.916\t0.409\t-2.936\t-1.313\t-2.187\t0.175\t0.240\t0.283\t-0.959\t0.674\t0.716\t-1.255\t-1.466\t-0.000\t1.792\n3\t0.641\t-0.222\t2.318\t0.231\t1.399\t-0.422\t1.068\t-0.546\t0.323\t-0.607\t1.901\t-0.221\t-1.108\t0.095\t0.537\t-0.027\t-0.813\t1.037\t1.259\t-1.648\t-0.877\t-0.159\t-0.531\t0.526\t1.839\t-0.381\t1.577\t-1.468\n1\t0.391\t0.294\t-0.502\t0.717\t-0.363\t-1.081\t-0.773\t-1.705\t-0.240\t-1.085\t-0.621\t-1.457\t-1.573\t1.958\t0.095\t-0.320\t-0.626\t0.346\t0.033\t-0.297\t0.390\t0.575\t-2.196\t0.028\t0.388\t-0.244\t-1.498\t-0.356\n3\t-0.649\t0.324\t0.262\t-1.517\t-1.247\t-0.262\t1.699\t-1.319\t0.615\t1.071\t-2.243\t0.319\t2.132\t1.031\t1.583\t1.305\t-1.893\t-0.107\t0.015\t0.370\t-0.863\t-0.430\t0.883\t0.068\t1.293\t0.140\t-0.081\t1.059\n2\t-0.854\t0.800\t-0.239\t-0.985\t0.956\t0.310\t-0.840\t1.129\t-0.475\t-0.667\t0.364\t-1.108\t-0.507\t-2.001\t0.172\t-2.403\t1.358\t0.287\t-0.147\t-0.128\t-1.268\t1.367\t1.115\t-1.072\t-0.288\t1.233\t-0.254\t-0.344\n4\t1.196\t-0.083\t1.974\t1.583\t-1.984\t-1.030\t-2.255\t-0.325\t-1.211\t-0.814\t1.091\t-1.755\t0.130\t0.124\t-0.082\t0.131\t-2.232\t-0.810\t1.543\t-1.197\t1.667\t-1.407\t-1.527\t-0.952\t1.129\t-0.546\t0.345\t0.074\n1\t0.689\t0.137\t1.238\t0.727\t-0.327\t-0.053\t0.901\t0.239\t0.426\t-1.253\t-0.526\t-0.057\t1.132\t1.891\t-0.900\t-0.621\t0.447\t2.127\t-0.996\t-1.963\t0.164\t-1.349\t0.568\t0.781\t-0.220\t-0.450\t-0.712\t0.065\n3\t0.770\t1.207\t0.234\t0.916\t-0.056\t-0.785\t-0.098\t1.557\t0.387\t-0.722\t0.907\t0.794\t1.305\t-1.100\t-0.691\t1.449\t-0.237\t-2.662\t-1.553\t0.871\t-0.496\t-0.660\t1.651\t0.233\t1.066\t0.217\t-0.401\t-1.637\n0\t0.138\t-0.441\t0.101\t-0.188\t-1.399\t0.181\t-1.328\t0.166\t1.374\t-0.281\t-1.171\t-0.127\t-0.832\t0.595\t-0.148\t1.710\t1.163\t-0.153\t0.716\t-0.393\t0.034\t-0.361\t0.766\t1.433\t0.799\t0.922\t0.923\t0.551\n0\t-0.749\t0.231\t-0.987\t-0.510\t0.705\t2.352\t-0.898\t1.249\t1.340\t0.638\t0.767\t-0.707\t-0.969\t-0.473\t0.267\t-0.301\t-0.747\t-0.703\t-0.304\t0.860\t-1.079\t0.566\t0.525\t-0.522\t0.296\t1.186\t-0.324\t-0.772\n3\t0.779\t-1.574\t2.242\t0.629\t0.982\t1.961\t0.503\t-0.252\t-0.905\t-0.015\t0.653\t0.434\t-0.305\t-0.149\t1.323\t-0.596\t1.249\t0.956\t-0.768\t1.510\t1.314\t0.029\t2.025\t-1.408\t0.291\t-0.789\t-1.288\t0.460\n4\t-0.604\t0.988\t-0.240\t1.662\t3.003\t1.374\t-1.058\t1.405\t-0.573\t0.499\t1.383\t-1.121\t0.121\t0.118\t-0.872\t-1.814\t0.717\t-1.415\t0.651\t-0.192\t1.340\t-1.131\t-0.076\t2.095\t-0.709\t1.639\t-0.784\t0.161\n3\t0.267\t0.451\t-1.027\t0.486\t-1.560\t-2.285\t0.456\t0.132\t1.383\t-1.784\t0.821\t-0.227\t0.703\t-1.420\t0.468\t-1.557\t1.378\t-0.577\t-0.208\t0.357\t-0.910\t0.737\t1.157\t-1.573\t-0.462\t-0.590\t-1.036\t-0.059\n2\t1.379\t0.294\t0.593\t-0.752\t1.026\t2.318\t-0.791\t-1.182\t0.176\t0.876\t-0.091\t-1.053\t-0.109\t0.598\t-0.407\t0.607\t1.165\t0.240\t-0.028\t0.829\t-0.856\t-0.955\t-0.267\t-0.492\t-0.803\t1.830\t2.172\t-0.354\n4\t-1.050\t-0.972\t-0.450\t-0.756\t0.224\t-1.947\t-1.118\t1.539\t-0.928\t1.243\t0.355\t-1.809\t0.635\t-0.118\t-0.455\t0.245\t0.475\t0.708\t1.178\t0.408\t-1.376\t-0.474\t2.943\t0.724\t2.369\t1.395\t0.242\t0.301\n0\t-0.127\t-0.275\t0.308\t-0.224\t-0.891\t-1.490\t-0.783\t-0.481\t0.478\t0.289\t-0.205\t-0.097\t-0.091\t-0.071\t1.323\t0.532\t-1.516\t0.870\t1.032\t0.675\t0.308\t-0.196\t0.516\t-0.730\t-0.503\t1.317\t0.160\t-1.354\n4\t1.031\t1.019\t1.708\t0.082\t0.336\t-0.217\t0.592\t1.755\t-2.238\t1.042\t1.687\t1.294\t-1.337\t-1.524\t0.404\t-1.465\t-3.097\t0.706\t1.592\t1.186\t1.299\t-0.435\t-0.173\t-0.734\t-1.087\t0.303\t0.582\t-1.312\n2\t-1.618\t0.676\t-0.058\t-0.834\t1.193\t1.350\t-0.956\t-1.283\t-0.143\t0.139\t-0.179\t0.146\t-0.741\t-0.881\t1.004\t1.418\t-1.043\t-1.191\t0.124\t-0.829\t1.535\t-1.074\t-0.061\t-1.347\t-0.585\t-0.156\t-2.159\t-0.711\n1\t0.143\t0.569\t1.791\t-1.014\t-1.121\t-0.417\t1.050\t0.066\t1.262\t-0.888\t-0.148\t-1.072\t-0.379\t-0.673\t-1.253\t0.481\t1.824\t-0.266\t-1.703\t-0.832\t-0.129\t-0.254\t0.889\t-1.443\t0.190\t-1.049\t-0.738\t-0.856\n2\t0.401\t-1.325\t1.395\t-0.440\t0.082\t1.089\t-0.781\t0.180\t-1.155\t0.083\t0.288\t-2.136\t0.712\t-1.105\t-0.360\t-0.256\t-1.298\t0.354\t0.055\t-1.329\t1.460\t2.649\t-0.401\t-0.707\t-0.715\t-0.137\t0.532\t-0.857\n4\t0.760\t-0.581\t0.412\t-0.468\t-0.030\t-0.045\t2.448\t-2.284\t2.588\t-2.788\t0.364\t1.316\t0.265\t2.536\t-0.831\t-0.753\t-0.148\t2.612\t0.244\t1.017\t-2.688\t2.741\t-0.506\t-0.786\t-0.498\t0.447\t-1.375\t-0.915\n2\t0.325\t-0.205\t-1.366\t-2.055\t0.172\t-1.693\t-0.524\t0.684\t0.416\t-0.597\t0.895\t0.526\t-0.488\t1.521\t0.487\t-0.780\t-2.053\t0.224\t-0.505\t0.021\t-0.237\t-2.198\t-0.292\t-1.103\t-0.508\t-0.579\t0.064\t0.300\n2\t-0.345\t1.922\t0.535\t0.492\t0.176\t0.421\t-0.376\t-1.125\t0.918\t-0.627\t1.425\t-0.533\t1.158\t0.413\t-0.559\t0.054\t0.560\t0.220\t-0.261\t1.351\t0.228\t-0.878\t2.667\t0.985\t1.531\t0.880\t0.016\t-0.645\n4\t-1.005\t-1.673\t-0.515\t-0.510\t0.074\t1.695\t-0.895\t0.116\t-0.262\t1.346\t0.231\t1.203\t-1.000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n1\t-0.257\t0.983\t-0.608\t0.265\t0.194\t2.806\t-0.182\t1.498\t0.419\t0.219\t-0.439\t0.421\t1.088\t1.090\t0.008\t0.378\t1.773\t-0.404\t0.340\t1.282\t0.717\t0.350\t-0.429\t0.348\t0.100\t1.102\t0.344\t0.166\n1\t0.742\t1.953\t0.333\t0.625\t0.436\t0.257\t-0.107\t-0.819\t-1.184\t0.369\t-0.837\t0.517\t1.746\t-0.555\t-1.208\t-0.361\t0.371\t0.619\t-0.784\t-0.549\t-0.859\t1.511\t-1.471\t0.480\t-0.434\t0.416\t-0.520\t-1.310\n0\t-0.270\t-1.542\t1.221\t-0.341\t-0.964\t-1.475\t0.095\t0.121\t-0.651\t0.814\t0.282\t-1.020\t-1.626\t-0.174\t-0.522\t0.408\t0.834\t1.174\t-1.031\t-0.465\t-0.644\t-1.187\t0.880\t-0.093\t0.855\t-0.139\t0.702\t-0.579\n3\t-0.042\t-2.118\t0.502\t-1.571\t0.982\t1.048\t-1.670\t0.737\t-0.187\t-1.235\t0.302\t-0.814\t-2.504\t1.243\t0.371\t0.715\t1.642\t0.274\t0.902\t0.879\t-0.462\t-0.346\t0.012\t1.040\t-0.317\t-1.272\t0.529\t1.505\n3\t3.250\t-0.748\t-0.505\t0.244\t0.388\t-1.062\t0.344\t1.262\t-0.223\t1.785\t1.885\t-0.259\t-0.689\t0.650\t-0.148\t1.198\t0.099\t0.048\t0.840\t-0.513\t-0.292\t-0.147\t-0.510\t-0.340\t1.077\t0.187\t1.499\t-0.974\n4\t0.073\t0.157\t-1.479\t-1.542\t-1.839\t-1.637\t0.828\t-1.967\t0.385\t1.317\t0.833\t-1.031\t2.366\t0.297\t-0.330\t1.831\t1.010\t-0.277\t0.426\t0.734\t1.222\t-0.924\t1.775\t-0.610\t0.260\t-0.969\t-1.897\t-0.929\n2\t-0.926\t1.178\t0.567\t-1.363\t-1.427\t0.119\t1.143\t-0.825\t0.667\t2.041\t1.229\t1.750\t0.932\t-1.179\t0.438\t-0.268\t0.122\t-0.112\t1.876\t0.132\t-0.386\t0.736\t0.234\t0.190\t-0.518\t-0.058\t0.964\t-1.102\n0\t-0.184\t-0.214\t-0.152\t1.257\t-0.888\t-1.946\t-0.534\t-0.668\t-2.231\t0.245\t-1.186\t-0.083\t-0.363\t-1.074\t0.891\t-0.281\t0.054\t0.586\t0.289\t0.145\t-0.824\t-1.112\t0.158\t-0.962\t0.794\t-0.130\t0.649\t-0.986\n0\t-0.699\t1.830\t0.019\t0.079\t0.270\t-0.783\t0.340\t0.348\t-0.010\t-0.067\t-0.222\t-0.380\t0.727\t0.290\t0.931\t-0.521\t1.128\t0.891\t-0.243\t-0.666\t0.595\t-1.586\t-0.415\t0.275\t-0.150\t-1.071\t0.090\t-0.999\n2\t-0.193\t0.480\t0.397\t-0.608\t0.843\t-0.938\t-0.591\t-0.342\t-0.644\t1.450\t0.459\t0.508\t-0.570\t2.604\t-1.293\t0.873\t1.956\t0.422\t0.204\t-0.324\t0.639\t-0.038\t1.654\t-0.567\t-0.726\t-0.955\t1.579\t0.511\n1\t0.985\t-0.199\t-1.975\t-0.311\t0.673\t-1.452\t0.559\t0.230\t-1.378\t-0.455\t-1.100\t0.066\t-1.358\t1.752\t-0.121\t0.088\t0.829\t-0.670\t0.137\t0.153\t-0.806\t-0.639\t0.741\t-0.323\t-0.347\t1.686\t1.586\t-0.768\n0\t0.760\t1.652\t-0.502\t-0.412\t-1.346\t0.521\t-0.645\t0.346\t1.165\t-0.684\t-0.474\t1.015\t0.707\t0.601\t-0.391\t-1.436\t-0.712\t-1.009\t0.582\t0.285\t-0.599\t-0.428\t0.476\t-0.319\t-0.581\t1.979\t-0.550\t0.669\n1\t-0.619\t0.200\t-0.096\t0.636\t0.757\t-0.240\t-0.962\t1.548\t-1.355\t-0.595\t-0.850\t0.746\t1.317\t-0.174\t0.230\t0.429\t1.804\t-1.774\t0.744\t0.377\t-0.371\t0.326\t0.741\t0.583\t2.159\t0.127\t0.027\t-0.181\n4\t0.670\t-0.269\t-1.218\t-1.521\t0.435\t-1.017\t2.225\t-1.000\t0.354\t0.169\t-1.398\t1.530\t-0.964\t2.518\t-1.041\t1.649\t-0.658\t1.170\t0.445\t-1.766\t1.885\t-1.251\t0.004\t-0.865\t0.082\t0.750\t-0.293\t-1.330\n3\t-1.578\t-0.704\t-0.200\t-0.867\t0.667\t0.813\t1.263\t-0.620\t-1.625\t-0.396\t-0.495\t-2.179\t0.477\t-1.740\t0.249\t0.956\t-0.306\t-0.604\t1.214\t-0.687\t1.434\t-0.972\t0.081\t1.357\t1.406\t-0.120\t-0.974\t1.473\n4\t1.166\t0.531\t-2.178\t-0.529\t-0.562\t0.479\t-2.625\t2.549\t0.531\t0.149\t1.182\t2.344\t0.871\t0.949\t0.131\t-0.142\t-0.476\t-0.826\t-0.687\t-1.351\t-0.076\t-1.685\t0.191\t-0.036\t0.147\t-0.522\t3.077\t1.360\n3\t-0.266\t0.388\t-0.851\t0.475\t-0.584\t-1.119\t1.136\t0.084\t0.640\t-0.781\t-0.675\t1.823\t-0.597\t1.294\t-0.210\t0.122\t1.837\t-0.130\t0.156\t-2.177\t0.926\t0.281\t-0.723\t2.598\t-0.627\t-0.825\t-0.101\t-1.273\n0\t-0.058\t-0.206\t1.291\t0.717\t1.009\t-1.394\t0.842\t0.751\t0.219\t-0.327\t0.327\t-0.856\t0.455\t0.302\t1.172\t-0.032\t-1.438\t-0.262\t1.925\t-0.127\t0.110\t0.722\t-1.524\t0.403\t-0.777\t0.769\t-1.413\t0.105\n2\t-0.685\t1.345\t0.613\t-1.429\t0.246\t1.657\t-0.598\t1.658\t0.386\t1.340\t0.159\t-0.124\t1.837\t0.721\t-0.235\t0.424\t-0.565\t0.108\t1.196\t-0.162\t-0.875\t-0.967\t0.074\t-0.808\t-1.433\t-1.199\t1.175\t-0.142\n0\t-0.839\t-1.253\t-0.102\t-0.167\t0.011\t-0.048\t-0.973\t0.791\t-0.447\t1.908\t0.581\t0.933\t0.188\t1.887\t0.770\t1.315\t0.686\t0.192\t-0.099\t0.287\t-0.122\t0.952\t0.281\t0.796\t0.558\t-0.318\t-0.678\t0.946\n1\t0.643\t-1.098\t-0.384\t0.422\t0.760\t1.213\t-0.496\t1.034\t-0.232\t0.702\t-1.856\t1.643\t0.853\t-0.467\t0.640\t0.762\t1.615\t-0.322\t-0.909\t0.533\t-0.358\t1.618\t-0.298\t0.289\t-0.559\t0.207\t0.063\t-1.209\n4\t0.664\t0.222\t-1.901\t0.083\t0.318\t-0.329\t-0.061\t0.190\t0.765\t-1.575\t-0.244\t1.508\t1.417\t0.851\t0.537\t1.655\t1.918\t-0.129\t-0.969\t-0.270\t3.097\t-1.569\t-0.471\t-1.413\t0.125\t-1.203\t-0.109\t-1.950\n4\t0.767\t0.550\t0.680\t-1.402\t0.586\t-0.274\t1.899\t1.412\t-0.790\t-2.343\t-0.281\t0.983\t0.548\t0.099\t-1.175\t-0.386\t1.309\t1.196\t-0.499\t0.147\t-2.189\t2.171\t0.365\t0.359\t1.099\t-2.069\t0.404\t2.033\n2\t0.374\t0.246\t-0.213\t-1.928\t1.728\t0.149\t-1.858\t-1.434\t1.290\t0.759\t-0.107\t0.721\t-0.050\t0.373\t-0.215\t-0.476\t1.253\t-0.017\t-1.644\t0.249\t0.195\t0.447\t-1.184\t0.930\t0.587\t1.733\t-0.387\t-0.088\n3\t-0.897\t-0.794\t-1.517\t0.110\t-0.239\t-0.779\t0.811\t0.492\t0.857\t0.053\t-0.789\t-0.718\t0.591\t-1.279\t-0.429\t0.286\t-0.837\t-0.223\t-1.071\t-0.521\t1.783\t-1.399\t0.801\t0.109\t-3.681\t-1.382\t-0.661\t-0.863\n1\t-0.409\t-0.141\t2.362\t-0.384\t-0.276\t-0.436\t0.809\t0.758\t-0.075\t0.059\t-1.493\t0.260\t-0.337\t0.500\t-2.195\t-0.794\t0.439\t-0.223\t0.533\t-1.269\t-0.496\t-0.085\t1.810\t-0.372\t0.668\t0.610\t-1.457\t0.988\n2\t0.130\t0.246\t0.556\t-1.351\t1.415\t0.497\t-1.055\t0.239\t-0.752\t-1.240\t1.086\t-0.698\t1.311\t-2.313\t-0.750\t-0.821\t-0.547\t-1.233\t0.545\t1.254\t1.436\t-0.002\t-0.392\t0.984\t1.109\t-0.899\t-0.507\t-0.514\n1\t-0.633\t-0.885\t-0.527\t0.252\t1.492\t1.618\t-0.353\t-0.702\t0.620\t-0.788\t-1.545\t-0.202\t-1.677\t1.061\t-0.054\t-0.605\t0.763\t0.024\t-1.611\t-0.391\t-1.415\t0.149\t0.363\t1.597\t0.115\t-0.949\t-0.878\t-0.612\n4\t-0.253\t-1.274\t-1.057\t0.971\t-0.074\t-1.135\t0.075\t0.434\t0.067\t0.087\t-0.555\t-0.745\t-3.298\t2.111\t1.414\t-0.444\t-1.616\t-0.346\t-0.871\t-0.254\t0.112\t-0.834\t-0.558\t0.933\t2.006\t0.424\t-0.601\t0.911\n3\t-0.617\t1.345\t-1.792\t1.405\t1.073\t0.541\t-2.004\t-0.362\t1.008\t-1.687\t-0.522\t0.396\t-1.159\t1.301\t1.103\t0.242\t0.434\t-0.540\t0.590\t-0.598\t-0.197\t-0.492\t1.436\t1.207\t-1.003\t1.338\t-0.798\t1.853\n0\t-0.344\t1.218\t-0.326\t0.959\t-0.994\t-0.787\t-0.957\t-0.626\t-1.648\t-1.016\t-0.979\t1.936\t-0.138\t-0.401\t0.744\t-0.459\t0.300\t0.659\t0.604\t-0.034\t0.105\t0.356\t0.528\t0.341\t0.156\t-0.941\t0.560\t-1.211\n0\t-0.941\t-0.507\t1.562\t-0.158\t0.242\t0.273\t1.588\t-0.576\t-0.223\t-0.106\t1.137\t1.344\t0.475\t1.233\t0.178\t-0.173\t1.179\t0.488\t-0.736\t0.997\t-0.208\t-0.331\t0.893\t-0.089\t0.389\t0.662\t-0.783\t0.385\n4\t0.843\t2.524\t-0.100\t-2.198\t1.425\t-1.218\t0.461\t0.538\t0.126\t-2.134\t-0.363\t-1.570\t0.454\t-0.885\t-2.124\t0.712\t-1.267\t0.135\t-0.737\t0.868\t0.826\t0.238\t-0.107\t-0.441\t-1.205\t-0.588\t0.478\t1.955\n1\t0.418\t-1.620\t-0.373\t-0.382\t0.962\t0.693\t-1.683\t-0.693\t-0.690\t-0.217\t-1.098\t0.452\t-0.694\t0.589\t-0.003\t-0.973\t1.388\t1.418\t0.290\t-0.239\t-1.056\t1.071\t0.823\t-0.814\t-0.687\t0.457\t-1.175\t-0.626\n1\t0.148\t1.311\t-0.486\t-0.987\t0.051\t1.320\t2.005\t-0.616\t0.132\t0.275\t-1.421\t-1.476\t0.324\t-0.376\t-1.401\t0.687\t0.061\t0.012\t-1.095\t-1.706\t-0.598\t0.049\t-0.709\t-1.097\t-0.372\t0.035\t1.415\t0.150\n0\t0.745\t-0.837\t-0.581\t0.310\t0.661\t-1.809\t-0.028\t1.129\t0.791\t0.277\t0.331\t-0.596\t-0.013\t0.163\t1.461\t0.596\t0.471\t-0.812\t0.065\t-0.625\t-0.353\t0.582\t0.590\t-0.364\t-0.225\t-0.408\t-0.095\t0.070\n0\t-0.621\t1.963\t0.138\t-0.257\t-1.477\t-0.932\t-0.642\t0.439\t-0.356\t0.015\t-1.433\t1.404\t-0.737\t0.235\t-0.531\t-0.318\t-0.794\t0.296\t-0.980\t0.314\t-0.119\t0.630\t0.729\t-0.318\t0.366\t-0.448\t1.316\t-0.359\n0\t0.968\t-0.179\t0.891\t-0.722\t-0.413\t-0.714\t0.249\t0.825\t0.009\t-2.141\t1.042\t-0.630\t-0.974\t1.221\t-0.085\t-0.292\t0.327\t0.088\t-0.210\t-0.477\t-0.675\t-1.382\t-0.139\t0.759\t-0.313\t-0.185\t1.000\t0.017\n0\t-1.312\t-0.213\t-0.027\t-0.679\t-0.562\t0.843\t0.470\t0.798\t0.337\t-0.873\t0.423\t-0.952\t-0.312\t-1.036\t-0.611\t0.090\t-0.457\t-0.883\t1.138\t-0.120\t-0.528\t-1.082\t-1.872\t-0.222\t-1.378\t0.712\t0.029\t1.558\n2\t0.012\t-0.656\t0.305\t-0.719\t-0.543\t-0.195\t0.388\t-1.667\t-2.030\t-0.860\t0.522\t-1.265\t1.834\t0.852\t0.669\t0.855\t0.427\t0.507\t1.242\t1.000\t0.300\t1.924\t0.077\t-0.455\t-1.483\t-0.722\t1.202\t-0.034\n0\t-0.004\t0.003\t-0.739\t-0.976\t0.900\t0.016\t0.480\t0.425\t-1.293\t-1.217\t0.786\t0.432\t-0.119\t0.399\t1.450\t0.923\t0.263\t0.448\t1.474\t-0.411\t0.230\t-0.024\t-0.635\t-0.201\t-1.017\t1.063\t-0.436\t0.212\n4\t1.428\t-2.009\t-0.152\t-0.108\t-1.087\t-0.156\t-1.639\t-0.584\t-0.268\t-0.005\t1.875\t-0.816\t-1.325\t0.992\t-4.463\t-0.619\t-0.326\t-0.941\t-0.095\t-0.649\t-1.741\t0.618\t0.657\t-0.987\t-0.541\t1.856\t0.448\t0.000\n2\t0.730\t-0.711\t-1.963\t-0.429\t-1.290\t2.009\t-0.769\t0.122\t0.340\t-0.227\t-0.109\t-0.545\t-1.486\t1.763\t-0.461\t-1.213\t-0.129\t-0.314\t1.504\t-0.792\t0.473\t1.225\t-0.848\t-0.124\t-0.856\t-0.564\t-1.272\t-0.341\n2\t1.006\t0.691\t0.051\t-0.261\t-1.773\t1.292\t-0.447\t1.555\t0.196\t2.025\t0.606\t-1.432\t-0.614\t-0.169\t-0.874\t-0.377\t-1.367\t0.006\t0.237\t-1.004\t1.940\t0.064\t-0.817\t-1.630\t0.060\t-0.260\t0.783\t-0.143\n0\t0.251\t0.360\t-0.265\t-0.893\t-0.139\t0.259\t-0.026\t-0.177\t-0.112\t0.655\t-0.189\t-0.922\t-0.523\t1.010\t0.186\t-1.102\t0.363\t-0.784\t0.062\t1.026\t-0.744\t-1.076\t0.438\t-0.112\t0.143\t1.123\t-0.638\t0.291\n4\t0.768\t-1.961\t-2.204\t-2.005\t-2.143\t1.357\t0.787\t-1.199\t-0.106\t1.466\t0.500\t2.114\t-0.114\t-0.655\t-1.081\t0.024\t0.936\t0.099\t-0.358\t1.257\t0.529\t-0.113\t-0.767\t-0.254\t0.473\t-0.780\t-1.234\t-0.480\n4\t-0.990\t-0.572\t0.835\t-1.388\t0.207\t0.574\t0.182\t-0.438\t0.172\t0.321\t-0.131\t-0.836\t1.040\t-1.109\t1.239\t1.816\t-1.502\t-0.054\t1.564\t2.059\t0.582\t0.663\t-1.010\t1.932\t1.720\t-0.3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0.061\t0.474\t-1.489\t0.125\t-1.289\t0.028\t-0.270\t-0.252\t0.091\t0.155\t-1.163\n1\t-0.175\t-0.772\t-0.039\t0.721\t1.767\t-0.244\t-0.437\t0.716\t-0.663\t0.209\t-1.188\t-1.948\t0.049\t0.546\t-0.468\t1.112\t0.382\t-1.961\t-0.356\t1.284\t-0.708\t-0.096\t0.471\t-0.720\t1.404\t-0.415\t-1.052\t-1.104\n4\t-0.157\t0.330\t1.443\t-1.748\t-1.012\t-0.085\t-1.140\t-1.489\t1.310\t-1.048\t-1.309\t-1.148\t1.086\t-0.695\t-1.386\t-0.840\t-0.048\t0.892\t0.808\t-0.778\t-0.531\t2.103\t0.996\t-0.252\t-2.295\t-0.459\t-1.327\t-0.863\n0\t-0.284\t-0.615\t1.267\t0.502\t0.970\t-0.432\t-1.191\t-0.421\t1.151\t1.451\t0.441\t0.473\t-1.569\t0.252\t-0.440\t-0.568\t0.137\t0.160\t-0.362\t-0.258\t-0.352\t0.135\t1.350\t1.389\t-1.302\t-1.229\t-0.260\t-0.798\n1\t0.963\t-2.233\t-1.048\t-0.865\t-0.199\t0.459\t0.063\t-1.808\t0.888\t0.274\t-0.849\t0.068\t0.090\t-1.441\t-0.347\t0.299\t-1.048\t0.197\t-0.058\t0.357\t-0.092\t-1.236\t-1.556\t1.347\t0.968\t-0.881\t1.114\t0.256\n0\t0.096\t-0.615\t-0.055\t-1.024\t-0.785\t0.514\t-0.463\t-0.738\t0.449\t0.380\t-0.215\t-0.149\t0.416\t0.969\t0.201\t-1.192\t-0.221\t-0.407\t-1.053\t0.498\t0.575\t0.726\t-0.991\t0.157\t-0.006\t0.307\t0.130\t-0.013\n2\t1.637\t0.018\t-1.637\t0.950\t0.828\t-0.962\t-1.912\t-0.529\t-0.534\t0.934\t0.611\t1.099\t-0.203\t0.043\t-0.121\t0.542\t-0.028\t1.277\t1.247\t-0.992\t0.519\t-0.084\t-0.812\t-0.849\t-0.396\t1.298\t-0.232\t1.933\n2\t-0.441\t-1.620\t0.180\t-0.321\t-0.295\t-1.710\t-1.509\t-0.548\t-0.928\t-0.569\t0.833\t-1.064\t-0.906\t-0.527\t-0.963\t-0.215\t1.028\t-0.287\t0.258\t-2.321\t0.082\t1.476\t0.098\t0.128\t0.286\t1.568\t1.354\t-1.022\n2\t-0.051\t-0.442\t0.293\t-1.996\t-0.826\t0.475\t0.184\t-0.664\t0.103\t1.723\t1.089\t0.972\t0.550\t0.181\t-0.684\t0.056\t1.967\t0.920\t-0.358\t-2.096\t0.757\t0.936\t-0.543\t-0.668\t-1.949\t1.256\t-0.415\t0.261\n0\t-0.224\t0.671\t1.510\t-0.906\t-0.836\t1.111\t0.525\t0.475\t-0.041\t-0.609\t-1.219\t0.780\t-1.018\t0.511\t0.028\t-0.170\t-0.082\t-0.736\t0.594\t-0.087\t0.002\t-1.051\t-0.265\t0.192\t0.382\t0.118\t-0.022\t-0.334\n4\t-1.029\t-2.905\t0.867\t2.266\t0.920\t1.010\t0.946\t-1.243\t-0.180\t0.026\t1.732\t-0.688\t0.686\t-0.754\t-1.202\t-1.126\t-1.657\t-0.118\t0.768\t2.550\t0.755\t-0.844\t1.052\t0.826\t-0.159\t-1.040\t-1.734\t-0.826\n4\t-1.051\t-1.518\t0.447\t-1.291\t0.432\t1.228\t0.502\t-1.830\t0.870\t-1.261\t-1.548\t-1.204\t0.886\t1.045\t1.076\t0.844\t-1.294\t0.580\t1.041\t-0.686\t0.132\t0.722\t2.548\t0.882\t-1.291\t-1.119\t-0.687\t-0.297\n2\t-0.854\t0.576\t-1.656\t1.601\t1.001\t-1.618\t0.244\t-0.012\t0.555\t-1.393\t0.502\t-0.558\t-1.738\t-0.472\t1.332\t0.470\t0.236\t-0.968\t-0.960\t1.239\t-1.490\t0.074\t-1.008\t0.369\t-1.477\t-0.279\t0.469\t0.488\n4\t0.396\t1.561\t0.340\t-0.263\t-1.296\t-0.099\t-1.332\t-0.310\t-0.063\t-2.361\t1.388\t-1.239\t-0.186\t0.534\t1.309\t-0.792\t-2.509\t1.079\t-1.249\t0.359\t0.422\t-3.252\t-1.615\t-0.461\t-2.584\t-1.338\t0.123\t-0.088\n1\t0.783\t-0.101\t1.258\t0.866\t-0.062\t0.043\t-1.842\t1.210\t-0.330\t-0.400\t-0.550\t0.457\t-1.129\t-0.984\t-0.053\t-0.627\t0.335\t-0.590\t1.595\t0.693\t0.681\t-0.605\t2.406\t-0.439\t-0.714\t0.707\t0.195\t-0.818\n2\t0.313\t-0.228\t-2.120\t1.850\t-0.835\t-1.341\t-0.031\t1.184\t0.513\t-1.316\t-1.481\t-2.192\t-0.292\t-0.473\t0.514\t-0.686\t0.974\t0.655\t-0.729\t0.391\t1.329\t-0.961\t-0.213\t-0.233\t-0.358\t0.802\t0.881\t0.407\n0\t-1.195\t-0.457\t0.687\t0.439\t-0.344\t0.544\t-1.181\t0.065\t-0.016\t1.323\t0.381\t-2.001\t-1.261\t0.385\t-1.940\t-0.057\t-0.536\t-0.046\t-0.660\t0.897\t-0.388\t0.459\t0.438\t-1.222\t0.284\t-0.570\t0.394\t-1.090\n4\t0.596\t-2.007\t-0.027\t-1.548\t-2.827\t2.345\t-0.034\t0.500\t-0.749\t-0.304\t-0.440\t1.275\t-0.015\t1.211\t-1.405\t0.307\t-0.051\t0.481\t-1.024\t-0.664\t-0.161\t0.316\t0.066\t0.225\t-2.076\t-0.843\t-1.188\t-0.158\n1\t-0.517\t0.521\t-2.187\t0.233\t-0.143\t1.158\t0.349\t-0.242\t1.226\t0.156\t0.457\t-0.117\t0.239\t0.098\t0.675\t-1.530\t0.449\t-0.668\t0.382\t-2.221\t-0.713\t-0.071\t-0.335\t-1.749\t0.057\t-0.436\t-0.822\t0.658\n2\t3.024\t-1.208\t-0.710\t0.482\t-1.552\t-0.492\t-0.374\t0.936\t0.237\t1.713\t0.778\t-0.517\t0.699\t0.028\t0.677\t-0.070\t-2.001\t0.156\t0.467\t-0.404\t-0.756\t0.517\t-0.794\t-0.174\t1.216\t-0.296\t0.455\t0.959\n4\t0.317\t1.333\t0.373\t-2.136\t-1.437\t-1.067\t-0.749\t-0.140\t-0.215\t-1.823\t-1.609\t-2.119\t-1.554\t0.991\t-0.270\t-0.953\t-1.918\t-1.013\t0.607\t0.476\t-0.252\t-0.267\t-1.771\t-0.776\t0.556\t-0.675\t1.623\t1.206\n3\t-2.246\t0.195\t1.919\t-0.353\t-0.225\t0.296\t-0.559\t-0.312\t-0.338\t-0.627\t1.229\t0.461\t0.251\t0.809\t-0.264\t-0.774\t-0.603\t0.002\t-1.059\t-0.500\t-0.516\t2.125\t-2.774\t-0.180\t1.726\t0.122\t0.753\t0.100\n2\t0.452\t-0.464\t-1.427\t0.748\t-1.228\t-0.262\t0.364\t-0.678\t1.570\t1.972\t-0.810\t-0.770\t-0.439\t1.153\t0.486\t0.751\t1.315\t-0.906\t0.503\t0.051\t0.430\t1.149\t-0.152\t-0.697\t-1.051\t1.754\t-0.042\t-1.368\n0\t0.946\t0.417\t0.803\t1.426\t0.041\t-0.331\t0.326\t-0.092\t-0.696\t-0.305\t0.439\t-0.817\t-1.744\t-1.476\t0.438\t-0.115\t0.149\t-0.154\t0.408\t0.914\t0.727\t-1.419\t-0.883\t0.414\t-0.702\t1.047\t1.31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0.786\t1.255\t-0.161\t0.722\t0.455\t2.395\t-0.162\t0.875\t0.311\t-0.034\t0.328\t0.120\t0.411\t0.078\t0.908\n1\t-0.153\t-0.294\t1.774\t0.573\t1.071\t-0.187\t1.798\t1.003\t-0.895\t-1.193\t0.057\t0.524\t-0.366\t-0.733\t0.968\t0.363\t-0.078\t-0.732\t-0.066\t-0.755\t0.919\t0.746\t-0.946\t-0.917\t1.307\t0.804\t0.694\t1.142\n3\t0.455\t0.641\t-0.493\t-0.369\t0.004\t-1.289\t0.575\t-0.358\t-2.120\t1.887\t0.265\t0.326\t-0.691\t0.332\t0.736\t-1.454\t0.604\t-1.059\t-1.286\t-0.880\t2.101\t0.783\t-0.018\t1.702\t-0.923\t-1.027\t-0.916\t1.878\n2\t0.671\t-0.264\t0.443\t0.288\t-0.145\t1.209\t-1.275\t0.117\t-0.580\t1.642\t-0.174\t1.923\t-0.422\t0.109\t1.088\t-0.956\t1.509\t0.026\t0.412\t0.546\t-0.076\t-0.810\t0.094\t-2.011\t-1.311\t-0.140\t1.547\t1.517\n4\t1.273\t2.163\t1.878\t-1.085\t-0.714\t-0.369\t0.411\t-1.277\t-0.545\t0.441\t-0.869\t0.452\t0.975\t1.117\t0.404\t0.870\t-1.149\t0.609\t1.844\t-1.531\t0.921\t0.454\t1.368\t-1.114\t-0.887\t1.422\t-1.762\t-1.746\n4\t1.077\t0.467\t-1.273\t-2.380\t-1.428\t0.193\t0.121\t0.856\t1.091\t-1.961\t-0.065\t1.019\t-0.186\t2.830\t-1.688\t-0.332\t0.814\t0.385\t-0.225\t-0.471\t-1.546\t-0.069\t0.494\t-0.499\t0.366\t0.040\t0.961\t-1.445\n4\t0.523\t1.334\t-0.252\t-2.157\t-0.225\t-0.487\t1.252\t0.890\t0.814\t0.319\t-0.322\t-0.695\t0.708\t1.558\t-1.881\t-1.300\t0.929\t-1.290\t-0.306\t-0.948\t-0.558\t1.709\t-2.408\t-2.225\t1.840\t0.367\t0.471\t1.531\n3\t-0.902\t1.151\t0.212\t0.343\t0.690\t-0.042\t1.013\t-1.197\t-1.086\t-1.141\t0.422\t0.506\t-0.473\t-0.232\t1.268\t-0.694\t0.295\t-0.686\t1.096\t0.628\t-1.399\t-0.261\t-2.079\t0.900\t2.047\t-2.180\t-1.427\t0.167\n3\t0.542\t0.292\t0.429\t0.093\t-0.084\t0.982\t-0.328\t-0.511\t0.355\t-0.555\t0.998\t-2.161\t1.759\t-1.740\t1.416\t-0.913\t0.769\t-0.412\t0.770\t-1.203\t0.191\t-0.812\t-2.465\t0.369\t-0.351\t-0.653\t-1.432\t-0.162\n3\t0.199\t-1.058\t0.449\t-0.462\t-0.795\t-0.270\t-1.165\t-3.101\t0.710\t-0.569\t0.285\t-0.157\t1.019\t-0.468\t-1.261\t0.852\t-2.376\t0.222\t0.210\t0.647\t2.409\t-0.380\t0.470\t0.644\t0.093\t-0.825\t-1.544\t-0.692\n3\t-1.178\t-0.056\t1.090\t0.726\t0.466\t0.985\t-0.925\t-0.457\t0.615\t-0.475\t-0.420\t1.862\t0.095\t1.764\t-1.823\t-0.631\t0.409\t0.094\t-2.056\t-0.230\t2.473\t0.247\t0.604\t-0.644\t0.243\t-0.012\t1.119\t1.087\n0\t0.484\t-0.981\t-1.127\t-0.459\t-0.439\t-0.666\t-0.305\t-0.417\t-0.536\t-1.530\t0.959\t-1.176\t-0.415\t-1.188\t-0.122\t0.052\t0.592\t0.188\t-1.365\t0.453\t0.570\t0.543\t0.164\t-0.628\t1.375\t1.356\t-0.340\t0.552\n3\t0.704\t-0.160\t1.057\t-1.495\t-0.063\t-1.188\t0.257\t-0.762\t-0.312\t1.051\t-0.117\t-3.010\t-1.127\t0.339\t2.224\t0.181\t-0.700\t-0.624\t-0.550\t0.862\t-0.984\t-0.528\t0.372\t0.999\t0.258\t-0.169\t-1.271\t-1.325\n2\t-0.312\t0.390\t0.324\t-0.918\t0.284\t0.410\t-1.413\t-1.683\t0.600\t-1.181\t0.098\t-0.163\t-0.523\t1.562\t-1.032\t-0.820\t0.653\t0.315\t0.135\t0.279\t-0.575\t-1.062\t1.564\t-1.137\t-1.989\t-0.729\t0.807\t-2.017\n0\t0.929\t0.332\t0.145\t-0.106\t-0.149\t0.301\t1.298\t-0.952\t0.073\t0.345\t1.656\t-0.432\t-0.780\t1.510\t0.510\t-0.423\t1.056\t-1.877\t-0.054\t0.294\t0.590\t-0.826\t-0.843\t1.540\t0.016\t0.159\t-0.729\t-0.917\n3\t-0.864\t-0.550\t-0.095\t1.593\t-1.961\t0.129\t-0.816\t-1.608\t-0.674\t-0.003\t0.030\t-0.628\t0.561\t-1.748\t-0.515\t0.809\t0.606\t2.150\t0.747\t-1.355\t0.999\t-0.461\t1.521\t1.263\t-1.448\t0.275\t-0.255\t-0.316\n0\t0.588\t-2.018\t0.995\t-0.225\t-0.387\t0.141\t-0.404\t-0.028\t-1.017\t-0.368\t-0.719\t0.473\t-0.114\t-0.077\t-0.972\t0.682\t0.445\t1.171\t0.342\t-0.080\t0.618\t-0.714\t-0.431\t-0.539\t1.246\t-0.094\t0.119\t-0.626\n2\t-0.000\t0.631\t0.838\t-0.799\t1.039\t-0.534\t-0.164\t-0.433\t-1.136\t1.570\t0.048\t1.087\t1.045\t-0.968\t1.216\t0.503\t-1.041\t0.449\t-1.023\t1.606\t0.814\t-0.071\t1.265\t-1.582\t-0.749\t-0.252\t2.236\t-1.070\n1\t-0.391\t-0.609\t1.134\t-1.500\t0.543\t1.387\t-0.579\t1.472\t1.157\t-0.349\t0.819\t-0.827\t-1.102\t-0.464\t1.350\t-0.458\t0.042\t-1.004\t0.867\t0.831\t-0.704\t-2.317\t-0.362\t0.443\t-0.013\t0.080\t0.350\t-0.511\n1\t-0.626\t-0.993\t0.684\t-0.280\t-1.495\t0.860\t0.483\t-0.084\t-0.017\t0.940\t-0.479\t-1.478\t-1.232\t1.006\t0.001\t0.311\t-0.316\t-1.317\t-0.547\t1.381\t-0.024\t-0.377\t1.989\t-0.820\t-0.486\t-1.409\t0.368\t-1.386\n0\t-0.036\t-0.301\t0.640\t1.120\t1.750\t0.645\t0.137\t0.137\t-1.881\t-0.430\t0.361\t-2.258\t0.180\t-0.812\t1.577\t-0.285\t-0.264\t-0.137\t-0.005\t0.793\t-0.344\t0.023\t0.799\t0.009\t0.655\t-0.446\t0.351\t1.309\n4\t0.137\t-0.208\t1.383\t-1.835\t0.604\t-0.430\t2.027\t-0.455\t0.628\t-0.577\t-0.529\t2.021\t-0.250\t0.631\t-1.072\t1.676\t-0.594\t-1.049\t-1.744\t0.765\t2.038\t0.155\t-0.141\t1.225\t-0.882\t1.273\t-1.521\t0.822\n1\t-0.710\t-0.151\t0.943\t-0.052\t1.067\t-1.060\t-0.245\t0.011\t1.915\t0.011\t-0.497\t-1.463\t0.088\t1.128\t0.382\t-0.967\t-1.061\t-0.336\t-1.260\t-0.573\t0.296\t0.039\t-0.765\t-0.598\t-0.475\t-0.944\t-0.446\t-1.995\n3\t-0.894\t1.269\t-0.123\t-0.662\t0.170\t0.098\t1.007\t2.109\t-0.576\t-2.418\t1.661\t1.016\t0.684\t0.227\t0.996\t-0.709\t0.783\t-0.589\t2.047\t-0.554\t-1.318\t0.711\t0.872\t0.257\t-0.531\t-0.843\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10\t-0.036\t-1.861\t1.308\t-0.922\t-1.117\t-0.088\t0.917\t0.195\t1.699\t1.234\t0.586\t-0.329\t-0.894\t-0.080\t0.347\t0.470\t-0.206\t-0.171\t-0.541\t0.594\t0.439\t0.133\t1.057\t3.095\t-0.363\n1\t-0.680\t1.496\t0.502\t1.556\t0.440\t0.995\t0.510\t-0.622\t0.342\t0.516\t1.531\t0.536\t-0.964\t-0.358\t0.885\t-0.398\t-0.102\t0.135\t-1.694\t0.332\t1.416\t-2.250\t-0.197\t-1.301\t0.171\t0.196\t0.334\t0.614\n4\t2.901\t0.877\t-0.127\t1.243\t-1.796\t-1.051\t-1.883\t-1.583\t0.014\t-1.379\t-1.006\t-1.716\t0.465\t-1.226\t0.872\t-0.137\t1.395\t-0.357\t0.867\t0.206\t0.608\t0.948\t0.184\t0.380\t0.561\t-1.612\t0.370\t0.837\n3\t-0.948\t1.200\t-0.535\t-1.202\t-0.252\t-0.146\t0.575\t-1.284\t1.699\t-0.542\t1.727\t0.203\t0.567\t0.826\t-0.414\t-1.357\t0.717\t2.170\t0.011\t0.057\t0.333\t-1.460\t1.177\t-1.242\t1.496\t0.261\t1.280\t-1.784\n2\t-0.630\t0.576\t-1.042\t-1.463\t-0.691\t0.113\t1.462\t1.008\t0.451\t1.611\t-1.053\t0.475\t1.044\t-1.364\t-0.406\t0.419\t-0.405\t-0.315\t0.846\t1.492\t-1.308\t1.355\t-0.889\t-0.210\t0.839\t0.848\t-1.399\t1.527\n3\t-1.018\t0.090\t0.574\t1.294\t1.063\t-0.392\t-1.466\t0.048\t-0.055\t0.822\t0.994\t-0.373\t-0.916\t0.977\t-0.711\t-0.906\t-1.173\t-1.179\t2.493\t0.075\t-0.413\t-1.606\t-0.512\t1.918\t-0.652\t0.101\t-0.407\t1.276\n4\t-0.189\t-0.920\t1.795\t-0.853\t0.448\t0.906\t-0.136\t0.191\t-0.180\t3.018\t1.680\t0.917\t0.464\t-0.608\t-1.175\t0.411\t0.662\t-2.267\t-1.129\t0.179\t-0.574\t0.362\t-0.315\t0.506\t1.551\t-1.269\t0.141\t2.202\n3\t0.945\t-0.553\t-1.873\t2.044\t-0.997\t-1.059\t1.104\t-0.384\t-0.063\t0.483\t1.084\t-0.333\t1.520\t-2.136\t0.760\t-0.840\t1.372\t0.142\t-0.673\t0.663\t0.893\t0.642\t-1.030\t-0.484\t-0.004\t-0.041\t0.750\t-2.015\n4\t-0.348\t0.595\t-0.849\t-0.867\t-0.003\t-0.922\t0.259\t-2.039\t1.176\t-2.785\t0.905\t-0.581\t-1.450\t0.170\t0.886\t1.159\t1.126\t-0.858\t0.205\t-1.778\t0.079\t0.338\t-0.375\t-1.836\t-0.594\t1.907\t0.391\t-0.485\n1\t1.399\t-0.092\t-0.304\t-0.393\t0.637\t0.084\t0.190\t-0.325\t-0.198\t-0.148\t1.422\t-1.801\t0.448\t1.088\t-1.839\t-0.071\t0.982\t-0.753\t0.269\t-0.932\t0.033\t1.278\t1.696\t0.255\t-1.279\t-0.310\t-1.813\t-0.294\n0\t-1.017\t-0.522\t-0.279\t0.493\t-1.628\t0.247\t-0.985\t0.447\t-1.491\t-0.278\t-0.921\t0.103\t1.573\t0.594\t0.625\t-0.071\t-0.196\t-0.140\t-0.252\t-0.625\t-0.734\t-0.752\t-0.287\t-0.953\t0.205\t-1.516\t0.355\t-0.024\n2\t-0.049\t-1.818\t2.228\t-1.316\t-0.707\t0.696\t0.246\t1.643\t0.720\t-0.081\t1.044\t1.161\t-0.660\t0.963\t-0.222\t-0.103\t0.143\t-0.381\t-0.435\t0.985\t-1.529\t-1.062\t0.675\t-0.073\t0.992\t0.358\t-1.334\t0.063\n3\t1.709\t0.736\t-1.018\t-1.883\t-1.744\t0.869\t-0.758\t0.818\t-0.455\t0.388\t0.701\t-0.783\t0.823\t-0.660\t-0.702\t0.821\t0.632\t0.460\t-1.103\t-0.384\t0.689\t2.284\t1.811\t-0.468\t-0.659\t1.730\t0.601\t-0.468\n2\t-0.454\t-0.015\t1.190\t-0.569\t1.365\t1.494\t-0.849\t1.567\t-1.098\t-2.009\t-0.112\t-0.230\t-0.618\t-0.802\t0.464\t-1.645\t0.464\t-0.193\t-1.231\t-0.722\t-1.233\t0.768\t-0.417\t-1.371\t0.607\t0.489\t-0.613\t0.680\n2\t-1.318\t0.601\t0.139\t0.782\t0.669\t-0.421\t-0.070\t-0.511\t-0.210\t-0.967\t0.390\t1.075\t-0.817\t1.281\t0.114\t0.241\t-1.699\t-1.807\t0.144\t0.790\t-0.671\t1.670\t0.735\t-0.908\t0.031\t2.477\t-1.243\t0.528\n0\t0.583\t0.077\t0.460\t-0.336\t-0.965\t-0.823\t-0.784\t-0.003\t-1.556\t-1.247\t0.136\t-0.128\t0.190\t-0.045\t2.519\t0.127\t-0.379\t0.101\t1.020\t-0.050\t0.685\t0.396\t-0.290\t0.111\t1.599\t0.243\t-0.986\t-0.320\n4\t-1.114\t1.201\t-1.397\t-0.020\t0.276\t-1.268\t0.061\t-1.306\t-0.934\t-0.888\t1.083\t0.712\t-1.699\t-0.067\t0.298\t0.589\t1.142\t1.462\t0.165\t2.510\t-1.066\t-0.006\t0.174\t-1.293\t0.237\t-1.372\t1.547\t-2.008\n1\t-1.495\t-0.283\t0.951\t-0.209\t0.993\t0.565\t1.097\t0.519\t-0.935\t0.071\t0.188\t0.007\t-0.738\t-0.239\t0.589\t0.045\t-2.365\t1.297\t-0.446\t-1.632\t0.001\t-0.616\t-0.891\t0.175\t1.392\t0.465\t-1.364\t-0.264\n2\t0.289\t0.485\t0.087\t-0.653\t0.385\t1.865\t-0.520\t0.867\t0.181\t-0.590\t0.895\t0.779\t0.580\t1.749\t1.836\t0.054\t-0.106\t-1.427\t1.533\t-0.073\t-0.609\t-0.933\t-0.941\t1.759\t0.898\t-1.488\t-1.448\t0.122\n0\t0.074\t-1.457\t-0.353\t-0.241\t0.293\t1.839\t0.125\t-0.251\t0.159\t-1.471\t0.563\t-0.989\t0.159\t-0.888\t0.136\t0.245\t0.751\t0.627\t0.913\t0.106\t-0.844\t-0.481\t0.258\t1.052\t0.525\t1.006\t0.520\t-0.029\n1\t-0.227\t0.673\t-2.995\t-0.028\t0.792\t0.995\t-0.697\t-0.301\t-0.616\t-0.393\t-1.032\t-0.889\t0.434\t0.132\t0.492\t0.214\t0.704\t0.878\t-1.742\t-0.854\t-0.231\t-0.023\t-0.670\t0.912\t1.980\t0.186\t0.084\t0.849\n2\t-0.551\t-0.717\t0.254\t-0.867\t0.686\t0.929\t0.182\t1.037\t0.871\t-0.894\t-2.188\t-2.466\t0.608\t-0.741\t-0.239\t1.047\t-0.430\t-0.348\t-0.363\t-0.792\t-0.488\t1.219\t-0.943\t0.349\t0.767\t1.173\t1.614\t-0.214\n1\t-0.386\t-1.115\t0.855\t-0.464\t1.798\t-0.488\t0.309\t-0.667\t0.488\t0.510\t0.888\t-0.134\t1.270\t0.300\t-0.643\t-0.636\t0.729\t1.264\t-1.122\t0.751\t-1.486\t-0.617\t0.490\t-0.848\t1.466\t0.609\t-1.182\t0.655\n1\t-1.149\t-0.819\t0.703\t0.994\t0.018\t2.475\t0.824\t-0.243\t-0.816\t-0.975\t0.335\t-0.489\t1.674\t1.466\t0.633\t-1.179\t-0.672\t1.256\t-0.563\t1.122\t0.475\t0.052\t-0.703\t-0.094\t0.190\t1.033\t-0.886\t-0.263\n1\t0.239\t-0.079\t0.601\t-0.702\t-0.137\t0.702\t0.757\t-0.027\t-0.579\t0.866\t1.019\t0.034\t1.575\t-0.977\t-1.212\t-0.480\t-1.089\t-1.106\t-1.545\t0.585\t0.712\t0.978\t1.763\t-0.188\t-0.025\t0.390\t-1.539\t1.416\n3\t0.431\t0.699\t-0.775\t0.964\t0.471\t2.233\t0.806\t-0.598\t-1.350\t0.218\t-1.029\t-0.735\t-0.966\t-0.581\t1.456\t-0.967\t-0.425\t-1.431\t-1.706\t-0.279\t0.207\t-1.150\t-1.519\t-1.871\t0.145\t0.935\t-1.428\t-0.648\n2\t-0.202\t-0.469\t-0.733\t2.034\t0.486\t0.068\t-0.476\t1.113\t-0.546\t0.121\t2.203\t-0.957\t0.540\t0.289\t0.528\t-0.285\t-0.592\t0.226\t-0.456\t-2.332\t-0.824\t-0.317\t-1.078\t0.151\t-0.758\t-0.043\t2.168\t1.227\n3\t-0.069\t-0.046\t0.674\t-0.806\t0.596\t0.109\t-1.344\t-0.638\t0.583\t-0.258\t-0.051\t0.120\t0.162\t1.427\t1.310\t-1.345\t0.546\t0.483\t-0.301\t-1.212\t0.184\t-0.725\t-2.252\t3.248\t-0.331\t-1.858\t-0.842\t0.435\n1\t0.586\t0.643\t-0.301\t-0.140\t0.011\t0.626\t0.456\t-1.205\t0.949\t-0.322\t-3.065\t-0.233\t0.321\t-0.231\t0.139\t-0.041\t-0.720\t2.284\t-0.879\t-0.488\t-0.279\t-0.020\t0.792\t-0.399\t-1.539\t-0.889\t-0.100\t0.541\n4\t-0.898\t0.128\t0.282\t1.234\t-1.635\t0.126\t-0.464\t-0.342\t2.044\t1.035\t1.158\t0.272\t0.124\t-1.152\t1.712\t-0.959\t0.425\t-0.387\t-0.345\t-1.430\t0.285\t-1.831\t-2.464\t-0.569\t0.434\t-1.160\t-1.972\t1.832\n4\t-1.099\t-1.448\t1.496\t-0.389\t-0.750\t2.902\t0.985\t-0.721\t0.737\t-1.605\t-0.319\t-0.838\t0.418\t1.657\t-1.694\t1.857\t0.750\t-0.999\t-0.980\t-0.715\t-0.866\t-0.835\t-1.814\t-0.195\t0.747\t-0.574\t0.297\t1.125\n3\t-0.434\t0.927\t0.905\t1.636\t-0.958\t1.120\t-1.827\t-0.310\t-0.666\t0.673\t-1.932\t0.427\t-1.834\t0.192\t0.245\t-0.286\t1.144\t-0.832\t0.388\t0.101\t1.552\t-1.608\t1.584\t1.227\t0.385\t0.057\t-0.531\t-0.309\n0\t-1.471\t-0.221\t0.244\t1.039\t0.107\t0.635\t-0.028\t-0.032\t0.085\t-0.189\t-1.687\t-0.768\t0.245\t-0.249\t-0.156\t-0.323\t-0.864\t-0.493\t-0.307\t0.005\t0.393\t-0.181\t-1.030\t-1.398\t2.597\t-1.130\t-0.519\t0.517\n4\t-1.817\t1.730\t-1.334\t1.889\t-0.839\t-0.849\t-0.398\t-0.439\t-0.605\t-0.371\t0.092\t-0.897\t0.841\t1.412\t-1.945\t1.855\t0.773\t0.258\t-0.574\t0.167\t0.897\t2.610\t2.192\t-1.529\t-0.208\t0.078\t1.344\t-0.084\n3\t-0.427\t-0.842\t0.522\t0.644\t-0.556\t1.432\t-0.002\t0.913\t0.909\t-1.324\t1.158\t1.023\t0.335\t-2.412\t0.234\t-0.909\t-1.365\t-0.157\t-2.180\t-0.104\t1.092\t0.549\t1.172\t-0.318\t0.453\t-1.088\t-1.946\t-1.111\n2\t2.511\t-0.020\t-0.313\t-0.522\t0.376\t0.387\t0.951\t-0.926\t-0.312\t2.031\t0.225\t-0.173\t-0.942\t0.506\t1.031\t0.875\t-0.259\t0.361\t1.672\t1.071\t-0.784\t-2.546\t0.297\t0.799\t-1.089\t-0.009\t-0.264\t0.104\n2\t-0.388\t-0.284\t1.269\t0.770\t0.533\t0.222\t-0.434\t-0.854\t-1.197\t-0.695\t-0.043\t-1.155\t0.170\t-0.194\t-0.397\t1.162\t-2.461\t-0.384\t-2.352\t0.239\t0.762\t-1.351\t-1.223\t-1.698\t0.064\t-0.341\t-0.279\t-0.368\n4\t-0.050\t-1.451\t-0.529\t-0.258\t1.860\t-0.160\t0.273\t2.151\t-1.579\t-1.534\t-0.778\t-0.333\t-0.232\t-0.511\t-1.234\t0.974\t-0.454\t1.184\t-1.017\t1.960\t-1.666\t-0.014\t-0.998\t0.036\t-0.980\t-2.145\t-0.781\t1.596\n0\t0.970\t0.812\t-0.035\t-1.442\t-0.992\t-1.122\t0.146\t0.542\t-0.669\t-0.876\t1.003\t-0.431\t0.133\t0.772\t0.324\t0.821\t0.545\t-0.137\t0.637\t-0.125\t-0.003\t-1.672\t0.152\t0.768\t-0.375\t1.053\t1.665\t-0.871\n1\t0.581\t1.541\t0.939\t0.273\t-1.810\t-0.285\t0.262\t-0.106\t0.018\t-0.558\t0.704\t0.702\t1.305\t-1.019\t0.855\t-1.165\t-0.819\t2.308\t-0.753\t-0.985\t0.444\t0.505\t-0.755\t-0.690\t-0.959\t0.380\t0.440\t-0.479\n0\t-0.098\t0.229\t-0.066\t-0.320\t-0.888\t0.077\t-1.372\t-1.445\t-1.026\t-0.691\t0.086\t1.689\t-0.005\t0.148\t-0.379\t0.396\t-0.415\t-1.010\t-0.644\t-0.490\t1.168\t0.184\t1.248\t-0.331\t0.451\t0.227\t-1.232\t0.450\n1\t0.883\t-1.743\t-1.035\t0.362\t-0.588\t-0.130\t0.746\t-0.357\t-0.353\t-1.038\t0.369\t0.636\t1.431\t-1.409\t-1.078\t-1.187\t-1.234\t1.165\t-1.354\t-0.881\t-0.106\t-0.785\t0.982\t0.267\t-0.392\t1.134\t0.765\t1.345\n4\t0.009\t-0.238\t0.077\t-0.512\t-2.105\t2.042\t0.155\t0.398\t-0.058\t-0.735\t-0.186\t-1.599\t2.277\t-1.235\t-1.032\t-0.440\t1.288\t0.015\t-1.236\t1.254\t0.731\t0.538\t0.716\t2.394\t2.185\t-0.687\t1.624\t0.383\n2\t0.063\t-0.728\t-0.913\t0.701\t0.845\t0.604\t1.515\t-0.542\t1.674\t-0.901\t-1.013\t-1.760\t-0.446\t-0.504\t0.526\t0.244\t-1.193\t-0.393\t-0.371\t-1.776\t-0.981\t-0.771\t1.434\t0.191\t0.662\t-1.499\t1.194\t1.301\n0\t0.742\t-1.439\t-1.026\t-0.773\t-0.861\t1.088\t-0.067\t1.158\t0.059\t-0.794\t-1.400\t-0.373\t-0.626\t0.494\t0.992\t1.395\t0.465\t-0.959\t0.678\t0.689\t-0.827\t0.212\t-0.533\t-0.866\t0.961\t0.121\t1.004\t-0.163\n0\t-0.933\t-1.043\t-1.116\t0.025\t-1.178\t-0.425\t0.903\t-1.029\t-0.504\t0.072\t0.247\t-0.499\t0.787\t0.590\t1.140\t-0.082\t-0.513\t-0.683\t0.307\t-0.377\t-0.569\t-0.448\t-0.684\t0.199\t-1.413\t0.303\t0.677\t-0.581\n0\t0.949\t1.339\t-1.119\t-1.080\t-0.330\t-1.199\t0.993\t-0.052\t0.345\t1.696\t-0.659\t0.008\t-1.013\t0.025\t-0.920\t0.466\t-0.233\t1.155\t0.064\t1.061\t-0.639\t-0.507\t-0.193\t-0.275\t-0.344\t0.194\t-0.429\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54\t2.152\t-0.546\t1.454\t-1.354\t0.814\t1.437\t-1.907\t0.139\t0.715\t0.252\t0.378\t0.136\t-1.094\t2.368\n2\t0.577\t0.585\t-2.514\t0.383\t0.632\t-1.824\t-0.790\t1.493\t0.125\t0.434\t-1.067\t1.642\t0.202\t1.105\t-0.731\t-0.668\t0.399\t1.311\t0.492\t-0.652\t-0.901\t-0.095\t0.148\t0.004\t-0.779\t-1.013\t-0.079\t-1.269\n1\t-0.169\t0.480\t1.571\t0.705\t-0.129\t1.676\t1.580\t1.140\t0.282\t-0.771\t-2.366\t0.211\t0.774\t-1.337\t0.415\t-0.445\t0.510\t-0.617\t-0.726\t-0.061\t-1.176\t0.962\t0.020\t-1.145\t-0.616\t0.590\t-0.301\t0.710\n4\t0.882\t-0.111\t-1.360\t-2.093\t2.148\t-0.986\t-0.686\t1.228\t-1.209\t0.531\t1.822\t2.114\t-0.447\t-0.012\t-0.396\t-0.018\t1.298\t-1.399\t0.648\t-0.195\t1.369\t1.315\t-0.407\t-0.111\t0.893\t0.983\t-2.472\t0.890\n3\t-1.994\t1.478\t0.419\t0.021\t0.019\t0.975\t2.185\t0.903\t0.368\t0.286\t0.364\t-1.004\t0.585\t-1.311\t0.442\t-0.472\t0.866\t0.381\t-0.351\t-1.011\t1.476\t-0.086\t1.740\t-1.231\t-0.253\t-2.251\t0.756\t0.838\n2\t-0.690\t0.671\t-1.489\t0.272\t0.981\t0.570\t-0.592\t0.798\t-1.753\t-1.158\t-0.955\t-1.825\t1.046\t1.479\t-0.215\t1.214\t-1.018\t-0.446\t-0.383\t-1.126\t0.487\t-0.091\t-0.495\t0.081\t0.882\t-0.399\t2.243\t0.697\n2\t0.062\t0.075\t1.175\t-1.404\t0.468\t1.539\t1.432\t0.569\t-0.757\t0.777\t1.325\t-0.515\t0.358\t-0.205\t-0.614\t-0.315\t0.129\t-0.918\t-1.082\t-0.890\t0.328\t-1.466\t-2.217\t1.373\t-1.013\t-0.194\t0.922\t-1.438\n2\t1.061\t0.040\t1.245\t0.144\t-1.457\t0.481\t-0.593\t-0.998\t-0.645\t-1.532\t0.773\t0.419\t0.237\t-1.191\t0.506\t2.034\t0.187\t-0.580\t-1.133\t-0.750\t1.750\t-0.454\t-1.626\t0.317\t-0.456\t-1.009\t0.634\t-0.342\n0\t-1.645\t-0.821\t-0.404\t1.707\t1.111\t0.355\t0.294\t0.251\t1.284\t-1.642\t0.678\t-0.233\t0.598\t0.176\t-0.174\t0.000\t-0.007\t-0.688\t-0.136\t-1.456\t-0.600\t-0.273\t-0.250\t0.268\t-0.858\t-0.548\t0.530\t1.297\n3\t0.242\t1.416\t0.405\t0.262\t-1.064\t0.516\t-0.629\t-0.796\t-1.654\t-0.482\t-1.410\t-0.742\t-0.261\t1.168\t-1.735\t-0.010\t0.421\t-0.785\t-0.715\t1.419\t-1.101\t1.681\t0.471\t1.722\t0.673\t-2.018\t0.599\t-1.068\n0\t0.693\t1.106\t0.861\t0.251\t-0.231\t-0.576\t1.471\t-0.699\t-1.037\t0.665\t-0.796\t-0.950\t-1.444\t-0.483\t-0.083\t0.243\t0.951\t0.527\t-0.191\t-0.198\t0.510\t1.273\t0.126\t-0.469\t-0.993\t-1.445\t0.666\t0.578\n1\t0.282\t-1.417\t-0.741\t0.580\t-0.444\t-1.670\t-1.355\t1.128\t-0.353\t-0.330\t-0.611\t-1.520\t-0.268\t1.237\t-0.778\t-1.888\t0.774\t0.084\t0.190\t0.379\t-1.745\t-0.113\t0.242\t-0.043\t-0.050\t1.157\t0.440\t0.628\n2\t-0.887\t-1.174\t-0.154\t-0.966\t1.394\t-1.376\t2.337\t0.302\t0.069\t-0.540\t0.657\t-0.204\t0.300\t0.842\t0.669\t1.240\t0.538\t-0.951\t-2.080\t0.211\t0.225\t1.926\t-0.116\t0.374\t0.777\t0.513\t0.844\t-0.818\n0\t1.002\t-0.154\t-0.431\t-0.422\t-0.494\t0.606\t-0.623\t0.616\t0.042\t0.529\t-1.022\t-0.333\t-0.874\t2.121\t-0.286\t-1.163\t-0.898\t-0.618\t0.457\t0.009\t0.958\t0.591\t1.237\t-0.863\t-1.076\t0.485\t1.548\t0.701\n0\t-0.093\t2.176\t0.150\t-0.650\t0.406\t-1.248\t0.647\t0.951\t-0.443\t-0.021\t0.270\t0.089\t0.943\t0.902\t-0.270\t-0.700\t-1.063\t-0.744\t-1.176\t1.261\t0.395\t-0.878\t0.116\t-0.102\t0.687\t0.884\t1.284\t-1.071\n0\t0.432\t-1.417\t0.700\t-1.484\t0.701\t0.599\t1.421\t-0.862\t-0.987\t1.107\t-1.075\t-1.173\t0.209\t-0.047\t0.155\t0.538\t0.543\t-0.078\t0.048\t-0.067\t0.035\t-1.387\t0.756\t1.049\t0.637\t0.561\t-0.991\t-1.415\n1\t0.916\t-0.129\t-0.712\t1.156\t-1.234\t0.189\t-1.486\t-1.026\t0.745\t-0.679\t-1.877\t-0.723\t-0.830\t-1.262\t0.550\t-1.547\t-0.588\t0.133\t-0.343\t-0.404\t-0.723\t-1.219\t-0.637\t0.658\t-0.434\t0.167\t1.117\t0.600\n1\t-0.037\t0.660\t1.090\t-0.748\t-1.967\t-0.021\t0.294\t0.249\t-0.327\t0.125\t1.515\t0.382\t-0.373\t-0.500\t-0.170\t1.637\t-0.183\t-0.020\t-0.482\t1.099\t1.960\t0.451\t0.083\t0.037\t-2.384\t0.745\t0.134\t0.428\n4\t0.097\t1.845\t1.029\t-0.922\t2.418\t-0.031\t1.246\t-1.155\t-2.398\t-1.408\t-3.250\t2.112\t0.215\t-0.925\t0.453\t0.189\t0.621\t1.210\t-0.290\t-0.880\t0.828\t0.802\t0.101\t2.178\t-0.438\t-0.900\t0.418\t-0.675\n3\t0.437\t1.611\t-0.780\t-2.302\t0.125\t2.461\t-0.759\t2.061\t0.911\t-0.222\t-0.238\t0.543\t-1.871\t-0.104\t0.571\t0.291\t0.393\t0.255\t0.270\t-0.125\t-1.458\t0.787\t-0.181\t-0.023\t0.472\t-1.151\t0.047\t0.776\n0\t2.387\t1.779\t0.158\t0.114\t0.347\t-0.304\t-0.790\t-0.591\t-0.495\t-0.364\t0.658\t0.913\t0.274\t0.205\t0.225\t-1.153\t1.973\t1.668\t-0.274\t-0.032\t-0.256\t0.135\t0.411\t-0.610\t-0.205\t0.264\t-0.531\t0.433\n1\t-0.956\t-0.237\t0.598\t1.461\t-1.080\t-0.446\t1.382\t0.464\t0.671\t-1.051\t-0.379\t-0.168\t-0.858\t-1.675\t-0.510\t1.354\t0.059\t1.374\t0.179\t-0.765\t1.278\t0.212\t0.962\t-1.068\t1.039\t1.069\t0.290\t-0.807\n1\t1.419\t0.181\t-0.171\t-1.819\t0.192\t-0.005\t-1.356\t-0.781\t0.093\t0.467\t-1.018\t-1.009\t0.116\t-0.841\t-0.201\t1.775\t-0.043\t1.023\t-1.246\t-0.768\t-0.789\t-1.104\t-1.768\t0.193\t0.387\t-0.946\t-0.745\t0.078\n1\t-1.169\t0.320\t-0.244\t-0.476\t-0.885\t0.480\t1.609\t-0.140\t-1.952\t0.100\t0.515\t0.090\t1.037\t-0.993\t0.125\t-0.671\t-0.453\t-0.741\t-1.244\t-1.412\t-0.685\t0.150\t-0.615\t-0.526\t-0.693\t-0.223\t-1.262\t1.933\n2\t0.332\t1.395\t-1.176\t0.171\t-0.699\t1.628\t2.250\t0.555\t0.878\t1.366\t0.765\t-0.823\t-0.445\t-0.452\t-1.733\t0.978\t-1.252\t0.829\t-0.727\t-0.222\t0.558\t1.528\t0.869\t-0.118\t-0.034\t-1.177\t0.637\t-0.082\n4\t1.560\t-1.528\t-1.061\t0.789\t-0.770\t1.996\t1.503\t-0.268\t1.722\t1.054\t1.885\t-0.767\t-0.463\t-0.358\t-0.032\t0.745\t1.472\t-0.852\t-0.135\t0.264\t-0.856\t1.311\t-2.823\t1.641\t-2.668\t1.923\t0.694\t1.386\n3\t-0.594\t-0.866\t0.274\t1.430\t-0.316\t1.606\t1.070\t-0.556\t-0.110\t1.956\t0.490\t-0.809\t0.873\t-0.092\t0.215\t1.359\t0.361\t0.119\t-0.144\t0.554\t-1.246\t-0.914\t1.097\t2.814\t0.442\t1.479\t-0.595\t0.403\n3\t-0.428\t0.418\t0.863\t0.790\t-2.237\t1.365\t-1.826\t1.089\t-0.600\t-0.342\t-0.286\t0.100\t1.263\t1.299\t0.190\t0.834\t-0.228\t-0.351\t0.865\t1.040\t-0.591\t1.630\t1.322\t-1.767\t-0.844\t-0.635\t-0.366\t-1.087\n0\t-0.884\t0.699\t0.034\t0.494\t-0.434\t-0.076\t-0.570\t-0.236\t-1.451\t-0.776\t-0.168\t0.340\t0.766\t0.578\t-0.145\t-0.417\t-0.414\t-0.422\t-0.153\t-0.504\t0.134\t-0.844\t-0.996\t-2.173\t0.224\t-0.646\t-0.485\t0.858\n2\t0.524\t-1.954\t-1.057\t-0.158\t0.147\t0.861\t0.043\t0.051\t0.238\t-0.255\t0.087\t1.126\t-0.084\t-0.030\t-0.034\t1.298\t-1.108\t1.776\t-1.176\t-0.654\t0.745\t1.030\t-1.403\t-1.093\t0.278\t2.344\t1.246\t-0.248\n2\t1.291\t1.902\t-1.133\t1.276\t-1.409\t-1.047\t-0.803\t0.504\t0.033\t-0.167\t0.146\t0.591\t0.668\t1.094\t1.820\t-0.021\t-0.521\t0.614\t-1.144\t-0.679\t0.999\t-0.124\t-1.208\t-1.490\t0.049\t1.142\t1.129\t0.111\n4\t-1.794\t1.269\t2.333\t1.344\t1.162\t-0.485\t0.485\t-1.495\t2.262\t-1.760\t0.584\t0.842\t0.329\t0.254\t-0.937\t-1.816\t0.733\t0.990\t0.444\t-1.693\t-0.312\t-0.457\t-2.586\t-0.192\t-0.096\t1.890\t0.449\t-1.525\n2\t-0.021\t0.289\t-1.179\t-0.220\t0.610\t-1.555\t0.201\t0.101\t0.041\t1.037\t0.334\t1.478\t-0.994\t0.136\t0.084\t-1.069\t0.956\t2.325\t-0.017\t0.505\t0.085\t-0.714\t-1.821\t-0.706\t-1.392\t1.704\t-0.115\t-0.346\n1\t0.504\t0.386\t-0.110\t0.049\t-1.404\t-0.584\t0.050\t-0.736\t0.860\t-0.659\t0.216\t0.117\t0.746\t0.901\t0.694\t-2.106\t1.194\t1.527\t-0.482\t0.237\t-0.891\t-1.948\t-0.739\t-0.540\t-0.355\t-0.959\t-0.733\t-0.011\n4\t1.004\t-0.800\t1.462\t0.051\t0.509\t1.488\t0.769\t-0.000\t-0.672\t2.709\t-0.789\t0.318\t0.510\t0.557\t2.128\t0.048\t-0.095\t1.552\t-0.136\t0.876\t0.194\t0.921\t0.725\t-0.403\t-1.844\t-1.674\t-1.840\t-0.091\n0\t0.226\t-0.762\t-1.142\t-0.200\t-1.375\t-0.800\t0.464\t1.076\t0.862\t-0.662\t-0.399\t-0.473\t0.197\t-0.871\t0.050\t-0.112\t0.592\t-1.124\t-0.959\t0.302\t0.126\t-1.152\t-1.867\t0.523\t0.120\t-0.247\t0.515\t-0.809\n4\t1.003\t0.211\t0.481\t-0.061\t-1.000\t0.453\t1.106\t1.613\t-3.353\t-0.472\t-0.931\t-0.552\t-0.438\t0.384\t-0.815\t-0.610\t1.693\t0.197\t0.151\t-1.015\t0.872\t1.158\t-1.105\t2.422\t0.549\t-0.396\t-0.382\t-0.870\n3\t-0.924\t-0.484\t1.663\t0.273\t-0.202\t-1.015\t2.002\t0.017\t1.168\t-1.122\t1.927\t-1.338\t1.230\t0.192\t0.276\t0.272\t1.720\t0.670\t-1.000\t-1.021\t-0.770\t-0.705\t1.806\t0.376\t0.019\t0.399\t0.745\t1.253\n3\t-0.853\t2.403\t0.009\t0.799\t0.140\t0.297\t-0.504\t1.233\t2.347\t-0.629\t1.938\t0.597\t-1.008\t-1.398\t1.040\t-0.554\t0.444\t0.723\t1.503\t-0.338\t-0.264\t0.965\t0.364\t0.339\t0.182\t-0.335\t0.919\t0.739\n0\t-0.676\t-1.184\t0.585\t0.687\t-0.343\t-0.152\t0.288\t1.194\t-0.692\t-0.227\t0.763\t-0.794\t0.215\t0.126\t0.320\t0.114\t-0.442\t0.731\t-1.132\t0.679\t-1.451\t-0.178\t1.204\t1.381\t0.970\t0.548\t0.143\t-0.840\n1\t0.877\t1.295\t0.146\t0.104\t0.597\t-1.316\t-0.358\t0.979\t0.864\t2.199\t-0.076\t1.217\t-0.059\t-1.100\t0.177\t-0.219\t-0.421\t-1.074\t-0.259\t-0.839\t1.984\t0.907\t-1.168\t-0.597\t-0.395\t-0.208\t-0.838\t-0.111\n1\t-0.293\t-0.998\t0.895\t-0.391\t-0.726\t-1.690\t1.682\t-1.554\t0.287\t0.662\t-0.388\t0.419\t0.496\t0.841\t0.604\t-0.313\t-0.400\t2.049\t1.094\t-0.698\t0.330\t0.395\t-0.018\t1.756\t-0.103\t-0.082\t0.823\t-0.945\n2\t0.543\t-0.586\t-1.667\t1.145\t-2.399\t0.796\t0.130\t1.051\t0.626\t0.613\t0.898\t0.838\t0.451\t-0.335\t-0.229\t0.107\t-0.266\t2.186\t-0.087\t-1.150\t-0.121\t-0.838\t1.958\t0.098\t0.635\t0.515\t0.946\t0.238\n2\t-0.116\t-0.393\t1.343\t-0.264\t0.353\t0.278\t0.489\t-1.100\t0.440\t-0.882\t-0.599\t0.929\t-0.503\t-0.638\t0.204\t1.347\t2.405\t1.142\t1.129\t-0.024\t2.560\t0.199\t-0.036\t0.383\t0.264\t-1.314\t-0.697\t0.381\n2\t0.546\t-0.481\t1.001\t-0.022\t-1.243\t-0.254\t2.318\t-0.190\t0.237\t0.288\t-0.043\t1.532\t0.733\t0.653\t-0.269\t0.695\t-0.308\t0.427\t2.065\t1.562\t-0.117\t0.224\t-1.098\t-0.243\t-2.224\t0.914\t-0.748\t-0.096\n0\t-0.064\t1.579\t-0.310\t-1.074\t0.817\t-0.235\t-0.173\t-0.225\t-0.155\t0.497\t-0.305\t0.308\t1.044\t1.478\t-0.903\t1.021\t1.133\t1.261\t0.049\t-0.239\t-1.545\t0.099\t1.782\t0.314\t-0.216\t0.580\t-1.186\t-0.273\n1\t-0.786\t0.537\t-0.489\t-0.707\t0.824\t-0.828\t2.482\t1.594\t-0.213\t1.250\t0.187\t-0.416\t1.978\t0.390\t-0.894\t0.460\t1.677\t0.002\t0.210\t-0.300\t0.352\t-0.978\t-0.796\t-0.342\t0.168\t1.112\t-0.147\t0.827\n1\t0.400\t-0.879\t-0.272\t1.513\t0.718\t0.238\t0.281\t-0.014\t1.025\t-1.280\t0.211\t-0.810\t-1.048\t-0.050\t-0.882\t1.619\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416\t-0.090\t-0.203\t-0.586\t1.685\t2.464\t1.268\t0.232\n2\t-1.805\t0.211\t0.544\t-0.939\t2.026\t-0.624\t-0.511\t0.952\t0.631\t0.190\t0.451\t-0.365\t-0.484\t-1.105\t1.939\t-1.351\t-0.047\t-1.318\t-0.166\t0.454\t0.033\t1.883\t0.431\t0.026\t0.658\t-0.493\t-0.168\t-1.280\n1\t1.360\t-0.886\t0.504\t0.085\t0.818\t-0.234\t0.473\t1.102\t-0.651\t0.242\t-1.888\t0.703\t1.955\t-1.244\t-0.091\t0.880\t0.031\t0.165\t-0.391\t-0.428\t-0.087\t0.042\t-1.181\t-0.066\t1.569\t-0.419\t-1.237\t-0.151\n1\t0.893\t0.752\t0.651\t-1.272\t-0.822\t0.294\t0.472\t1.404\t-0.575\t-0.003\t1.171\t-0.139\t0.166\t-0.044\t0.570\t0.505\t-1.607\t0.646\t1.677\t-1.387\t0.340\t0.578\t-1.320\t-0.762\t-0.682\t0.217\t-1.364\t-0.686\n0\t1.145\t-0.748\t-0.056\t0.891\t-0.276\t0.549\t-0.912\t0.485\t0.735\t-0.567\t0.132\t0.259\t-0.245\t-0.593\t-0.308\t-0.113\t0.031\t0.295\t0.138\t-0.354\t-0.396\t0.598\t0.237\t0.184\t-0.250\t-1.054\t-0.175\t0.111\n1\t1.540\t-0.290\t0.499\t-0.122\t1.199\t-0.438\t-0.497\t0.406\t0.472\t0.794\t-0.521\t-0.681\t1.700\t0.880\t-1.438\t-1.110\t-0.871\t1.148\t0.098\t-0.886\t0.440\t1.328\t0.987\t0.717\t-1.749\t-0.466\t0.603\t-1.137\n2\t2.412\t-1.169\t-1.258\t0.693\t0.065\t1.194\t0.738\t-0.005\t0.329\t0.667\t-0.970\t0.106\t-0.636\t-0.204\t0.760\t-0.834\t-0.068\t-0.888\t-0.439\t1.348\t-0.161\t-0.236\t-2.046\t-0.931\t-0.815\t1.029\t-0.231\t-1.456\n2\t-1.164\t-0.102\t0.061\t0.799\t1.846\t0.498\t2.303\t0.191\t-0.794\t-0.710\t-0.074\t-0.727\t0.881\t-0.149\t0.322\t-0.968\t0.741\t-0.613\t1.164\t-0.612\t-0.663\t-0.909\t-1.312\t1.335\t1.323\t0.824\t-0.326\t-1.574\n2\t-0.290\t0.848\t-0.167\t0.554\t-1.126\t1.918\t0.197\t-0.218\t-1.890\t0.053\t0.258\t-0.203\t0.496\t0.265\t-0.924\t1.094\t-0.133\t0.235\t1.395\t-1.028\t-0.017\t-0.198\t-1.787\t0.278\t2.212\t0.709\t-0.063\t-1.481\n4\t-0.584\t1.401\t-0.486\t-0.654\t-0.589\t-0.307\t0.613\t-0.135\t1.621\t-0.085\t-1.033\t0.218\t2.027\t-0.014\t-1.733\t-0.863\t0.329\t-1.888\t1.620\t-0.408\t-2.900\t2.460\t1.472\t-0.591\t-0.895\t-0.094\t1.644\t0.014\n0\t1.773\t0.394\t0.525\t0.317\t-0.376\t0.601\t-0.386\t0.407\t-1.437\t1.151\t-0.647\t-0.979\t0.352\t0.092\t-0.385\t-0.576\t-0.956\t0.605\t0.142\t0.838\t0.540\t0.114\t-0.648\t1.619\t-0.751\t0.307\t1.320\t0.951\n1\t-0.353\t0.318\t-1.156\t0.805\t-1.627\t0.644\t-0.503\t0.462\t-0.706\t-0.852\t1.588\t1.702\t-1.144\t0.159\t0.774\t-0.066\t-0.032\t0.723\t0.547\t-0.449\t0.527\t-0.264\t-0.355\t-0.737\t-0.734\t0.259\t-1.530\t-1.558\n1\t-0.300\t0.118\t0.091\t0.006\t-1.016\t-0.590\t0.367\t0.651\t0.234\t0.239\t2.039\t1.381\t-0.447\t1.934\t0.862\t1.681\t0.685\t-0.697\t0.625\t-0.422\t0.825\t-1.480\t0.376\t0.392\t1.127\t0.962\t-0.406\t-0.544\n0\t0.366\t-0.480\t0.828\t-0.127\t0.270\t-0.079\t0.770\t0.164\t-0.928\t0.967\t0.836\t0.462\t0.245\t-0.802\t0.041\t-0.672\t0.502\t0.707\t1.123\t0.153\t0.506\t-0.344\t-1.153\t-0.618\t0.624\t-0.272\t-1.559\t-0.068\n0\t0.809\t-0.129\t0.065\t-0.224\t0.423\t0.685\t-0.123\t-1.498\t0.297\t-0.340\t0.857\t-0.896\t-0.314\t-1.482\t0.783\t1.225\t-0.488\t-0.194\t-0.810\t-0.713\t0.694\t-0.504\t0.475\t-0.015\t0.751\t-0.392\t1.085\t1.875\n0\t0.727\t-1.129\t0.011\t0.427\t-0.981\t-0.460\t0.759\t-0.059\t-2.017\t0.774\t0.089\t-1.606\t-0.878\t-0.126\t0.312\t1.029\t-0.901\t-0.451\t0.884\t1.045\t-0.323\t-0.533\t0.500\t-2.071\t-0.014\t-0.027\t-0.468\t0.472\n3\t-1.462\t0.015\t-0.409\t-1.498\t-0.293\t0.134\t0.443\t-1.349\t-1.460\t1.011\t1.863\t-1.055\t-0.944\t-0.675\t-1.020\t-0.414\t-0.816\t0.381\t-0.198\t0.243\t1.533\t1.066\t-0.316\t0.605\t2.275\t-1.750\t-0.234\t-0.342\n3\t-0.085\t-0.104\t0.013\t0.294\t0.714\t-0.483\t-0.214\t1.301\t0.955\t-1.167\t1.073\t0.521\t1.797\t-1.197\t0.400\t0.302\t2.039\t0.217\t0.429\t1.231\t1.723\t-0.755\t-0.541\t0.300\t1.131\t-1.524\t-0.440\t2.152\n3\t0.999\t1.288\t0.029\t-0.593\t-2.198\t0.431\t-0.301\t-2.100\t0.645\t-0.615\t0.369\t-0.078\t0.341\t-1.480\t-0.303\t-2.530\t0.361\t1.968\t1.610\t-0.292\t-0.424\t-1.113\t-0.698\t1.041\t0.646\t-0.261\t0.345\t0.981\n2\t0.563\t-0.138\t0.780\t-1.174\t-0.356\t1.648\t0.068\t0.126\t2.306\t-1.399\t0.796\t1.664\t-0.325\t0.860\t-0.982\t-0.687\t0.385\t0.799\t1.121\t1.570\t-0.067\t1.194\t-0.342\t0.902\t0.207\t0.446\t0.424\t-1.605\n4\t0.369\t-1.186\t-0.801\t1.565\t-0.925\t0.934\t-0.478\t-0.003\t0.295\t-2.101\t-1.463\t0.086\t-0.724\t0.997\t-1.606\t0.565\t-1.376\t-0.352\t-1.064\t1.902\t-1.339\t-0.122\t-1.727\t1.600\t0.401\t0.137\t1.474\t0.147\n1\t2.545\t-0.067\t0.029\t0.771\t-1.276\t-0.978\t-1.597\t0.837\t-0.861\t1.002\t-0.462\t0.004\t-0.171\t0.190\t-0.489\t1.040\t-0.168\t0.364\t0.110\t-1.014\t1.349\t-0.652\t0.809\t0.729\t-1.499\t-0.180\t0.580\t-0.290\n1\t-0.192\t-0.037\t-0.321\t0.540\t-0.102\t-0.607\t-0.396\t1.218\t0.317\t-1.676\t1.511\t0.434\t0.639\t0.929\t0.981\t-0.919\t-1.070\t2.396\t-0.698\t0.379\t1.631\t-0.444\t-0.360\t0.926\t0.213\t0.495\t-0.080\t-0.526\n1\t-0.554\t-1.721\t-0.041\t-0.419\t0.116\t-1.080\t0.161\t2.415\t-1.249\t0.065\t0.320\t0.066\t-0.025\t0.078\t-0.430\t1.680\t0.414\t-0.623\t1.211\t-0.724\t-1.135\t-0.421\t1.435\t-1.118\t-0.286\t-0.584\t0.857\t-1.075\n0\t0.367\t0.767\t0.018\t-0.347\t0.462\t-1.083\t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0.921\t-1.018\t0.575\t-0.217\t-2.869\t1.923\n4\t1.088\t-0.175\t1.533\t2.876\t-2.514\t0.660\t1.388\t1.698\t-1.112\t-1.058\t-0.757\t0.530\t-0.133\t1.464\t0.685\t0.517\t0.047\t0.506\t0.085\t-0.129\t-0.545\t0.346\t1.343\t-0.668\t-0.614\t0.194\t1.213\t-1.720\n4\t0.698\t-0.625\t0.376\t-1.011\t-1.555\t0.877\t-0.981\t-0.181\t0.206\t-0.182\t-1.028\t0.061\t0.148\t0.871\t2.445\t-1.976\t-0.799\t0.223\t1.011\t0.346\t1.123\t-1.480\t-0.318\t1.462\t-0.860\t-2.899\t-0.817\t0.437\n2\t0.359\t0.415\t-2.383\t-0.240\t-0.038\t1.281\t0.048\t-1.647\t0.430\t-0.804\t-1.217\t-0.834\t0.543\t1.259\t0.483\t0.632\t0.473\t0.101\t0.697\t-2.419\t0.111\t-1.045\t1.163\t-0.147\t0.031\t-1.505\t1.017\t0.627\n1\t0.285\t0.683\t-0.384\t-0.530\t-0.320\t-1.531\t-0.509\t-1.015\t-0.705\t-1.258\t-2.108\t-0.027\t1.084\t-1.294\t-1.258\t-0.920\t-0.016\t0.549\t-0.592\t-1.519\t0.652\t-0.405\t0.701\t0.680\t-0.484\t-0.305\t-0.930\t-0.813\n1\t1.076\t-0.762\t-0.443\t-0.386\t2.079\t0.283\t0.054\t-1.691\t-1.020\t-0.159\t-0.885\t0.755\t-0.017\t-1.086\t-1.155\t-0.265\t-1.427\t0.425\t-0.872\t0.011\t0.397\t1.394\t-0.870\t0.537\t0.059\t-0.738\t-1.252\t0.483\n2\t-0.340\t1.896\t-0.565\t0.682\t-1.580\t-1.015\t1.029\t1.421\t-0.150\t2.280\t1.309\t-0.696\t-0.378\t-1.386\t-0.443\t0.239\t0.040\t0.031\t0.997\t0.167\t0.685\t0.447\t0.287\t0.922\t0.802\t-1.126\t1.819\t0.120\n4\t-1.439\t-0.633\t0.961\t-0.022\t-0.008\t-0.295\t0.562\t1.432\t2.984\t-0.148\t1.526\t0.999\t0.267\t-2.210\t-0.378\t-0.338\t0.359\t-0.197\t-0.563\t2.030\t1.308\t-2.023\t-0.281\t0.631\t1.331\t-0.420\t-0.215\t0.095\n3\t-0.117\t1.911\t0.512\t0.461\t-0.430\t-0.207\t0.106\t-2.049\t0.552\t0.322\t-0.668\t-0.356\t1.085\t-2.634\t-0.916\t-0.394\t0.416\t-0.386\t-0.803\t0.485\t-1.760\t0.216\t-1.226\t-1.392\t1.287\t-0.549\t-0.457\t1.075\n2\t0.234\t-0.296\t0.847\t0.340\t1.732\t1.886\t-1.605\t1.657\t0.032\t0.119\t-1.356\t-0.312\t0.844\t-0.749\t-1.798\t-0.865\t0.369\t0.854\t0.685\t-0.428\t-0.797\t-0.832\t0.231\t0.352\t-0.794\t0.522\t1.196\t1.180\n0\t0.275\t-0.640\t0.699\t1.918\t-1.590\t-0.088\t-0.388\t0.012\t-1.171\t-0.278\t0.729\t0.065\t1.191\t1.001\t-0.391\t0.220\t-1.356\t-0.295\t1.552\t-0.554\t0.081\t-1.012\t-1.144\t1.151\t-0.114\t0.026\t-0.831\t-0.209\n2\t1.185\t-0.154\t0.178\t-0.330\t0.504\t-0.145\t-0.167\t-0.463\t-0.821\t-0.301\t-2.847\t-1.317\t-1.627\t0.697\t1.456\t1.060\t-0.319\t1.028\t-0.931\t1.595\t-0.578\t-0.476\t0.961\t-0.654\t-1.300\t-0.615\t0.187\t-0.345\n4\t-0.715\t-1.338\t0.139\t-0.710\t-0.625\t0.153\t-1.533\t-1.742\t0.671\t-1.931\t-0.977\t-1.800\t-0.486\t0.428\t0.112\t2.392\t1.087\t-0.931\t-1.197\t2.707\t1.063\t0.589\t0.075\t0.274\t-1.482\t0.157\t0.071\t-0.904\n1\t-1.111\t2.029\t-1.175\t1.024\t-1.273\t0.879\t-0.942\t0.784\t0.046\t0.481\t0.620\t0.917\t0.077\t0.085\t-0.147\t-0.316\t1.234\t0.214\t-0.258\t0.967\t0.734\t1.291\t-0.757\t-1.223\t0.141\t-1.776\t-0.189\t-0.589\n0\t-1.610\t0.328\t-0.777\t-0.248\t0.309\t-0.213\t0.230\t0.673\t0.295\t-0.647\t-0.629\t-1.440\t-0.854\t1.962\t-0.792\t-1.193\t0.320\t-0.110\t-0.196\t1.409\t0.549\t-0.530\t-0.424\t-0.503\t0.875\t-0.308\t-1.793\t-0.048\n3\t1.047\t0.491\t0.995\t0.844\t-0.772\t2.823\t-2.261\t-0.864\t0.681\t0.291\t0.134\t0.716\t-0.843\t0.840\t-0.129\t1.401\t-0.305\t0.468\t0.637\t0.905\t0.037\t-0.287\t-0.775\t0.486\t-1.897\t0.309\t0.383\t1.874\n3\t-0.847\t-1.362\t-2.517\t1.040\t-0.251\t2.034\t0.028\t-0.630\t-0.279\t1.045\t-0.546\t0.213\t1.006\t0.136\t1.124\t-0.978\t-0.271\t-0.562\t-2.467\t-0.138\t1.129\t0.227\t-0.545\t-0.231\t-0.838\t0.423\t-0.112\t-0.992\n2\t0.170\t-0.173\t0.221\t0.553\t-0.294\t-0.737\t-2.006\t0.771\t0.079\t-0.176\t1.172\t-1.289\t0.056\t-0.964\t0.656\t-2.226\t0.804\t0.763\t-0.923\t0.066\t-0.902\t0.428\t0.820\t-1.005\t-1.297\t1.096\t1.941\t0.494\n2\t1.533\t1.630\t-0.933\t0.753\t-1.383\t-0.463\t-1.174\t-1.153\t0.242\t-0.692\t0.287\t-1.209\t0.484\t-0.430\t-0.292\t-1.300\t1.019\t0.140\t0.540\t-1.277\t0.676\t0.041\t-0.496\t1.377\t0.409\t0.453\t-1.965\t1.081\n4\t-0.607\t-0.440\t-0.740\t-0.813\t-2.429\t0.087\t0.068\t-0.797\t0.360\t1.397\t0.024\t1.330\t2.238\t2.531\t-1.188\t-1.183\t-0.444\t0.129\t1.587\t0.627\t-1.203\t-0.612\t-0.042\t-1.489\t0.014\t-1.188\t-1.651\t-0.947\n0\t0.614\t-0.919\t-0.453\t0.271\t0.274\t-0.472\t0.311\t-0.002\t0.096\t1.367\t-1.080\t0.481\t1.225\t0.237\t0.112\t-0.255\t0.993\t0.614\t1.010\t1.799\t-0.158\t-0.687\t-0.286\t1.048\t1.482\t-0.429\t0.465\t-1.848\n4\t-0.503\t0.447\t1.320\t-0.676\t1.755\t-0.931\t0.555\t-1.111\t0.526\t-0.656\t-2.306\t1.395\t0.207\t-0.538\t-0.234\t-0.192\t-0.357\t-0.389\t-0.139\t1.989\t-2.279\t-2.013\t0.449\t-2.584\t-0.500\t-0.785\t-0.039\t-0.626\n3\t0.876\t0.935\t0.172\t-0.281\t-1.347\t-0.472\t-1.855\t-0.353\t0.047\t1.086\t-0.528\t0.252\t1.142\t-1.138\t0.566\t-1.659\t-0.954\t-0.861\t-0.691\t-0.717\t0.339\t-0.209\t-1.035\t-1.520\t0.679\t0.958\t-0.010\t-2.798\n3\t-0.979\t-0.337\t-0.351\t-0.690\t0.307\t0.613\t0.612\t0.893\t-0.095\t1.362\t0.103\t-1.677\t-0.405\t-0.333\t-1.331\t0.770\t-0.102\t-2.255\t-2.178\t-1.842\t0.034\t0.631\t0.287\t0.161\t-1.079\t-1.642\t1.887\t0.035\n3\t-0.867\t0.106\t1.621\t-0.980\t1.001\t-0.107\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138\t0.420\t-1.296\t-1.036\t0.832\t0.738\t0.430\t0.116\t-0.104\t-0.779\t0.389\t1.618\t-0.030\t0.262\t1.026\t-0.521\t0.412\t0.800\t-0.734\t-1.299\t-0.226\t0.855\t-1.469\t0.242\t-0.186\n1\t-0.262\t-0.964\t-0.468\t-0.423\t-0.647\t-0.689\t1.122\t1.408\t0.392\t-0.647\t-0.584\t-2.073\t0.380\t-0.683\t1.471\t-1.378\t-0.592\t-0.096\t0.465\t1.803\t-1.370\t-0.302\t-1.428\t0.790\t-0.647\t-0.103\t-0.172\t0.116\n3\t0.050\t0.689\t-1.758\t0.307\t0.694\t0.099\t-0.958\t0.711\t-0.638\t1.705\t1.060\t0.254\t-0.265\t-1.454\t1.823\t-0.594\t1.657\t-2.208\t-0.071\t-0.572\t-0.977\t0.324\t1.635\t-0.398\t0.222\t-0.760\t-0.912\t-2.159\n4\t0.516\t0.533\t-2.021\t1.250\t-0.560\t-0.724\t-0.695\t1.230\t1.474\t0.472\t-0.481\t2.909\t-0.018\t-0.653\t1.025\t-0.121\t2.091\t-0.846\t-0.181\t1.533\t0.488\t-0.296\t-0.923\t-1.136\t-1.295\t0.972\t0.786\t0.521\n1\t0.348\t-1.223\t-0.334\t-0.092\t0.987\t-0.204\t-0.729\t0.335\t-0.658\t0.697\t-0.998\t-0.149\t0.331\t0.223\t-0.325\t0.463\t2.309\t0.039\t-0.626\t-0.841\t0.033\t-0.211\t1.213\t-2.190\t0.874\t0.781\t-1.531\t1.118\n1\t0.576\t-0.785\t0.056\t-1.664\t0.162\t0.203\t-0.432\t0.862\t1.029\t-0.959\t1.096\t0.061\t-0.785\t1.270\t0.100\t-1.022\t-0.669\t0.106\t0.438\t1.043\t0.400\t-1.191\t-0.571\t2.237\t1.930\t0.253\t0.756\t0.443\n1\t-1.075\t-0.824\t-0.179\t-0.596\t-0.143\t-0.273\t1.065\t-1.151\t-0.097\t1.775\t0.864\t-0.817\t1.132\t0.921\t-0.041\t0.765\t0.025\t0.210\t1.912\t0.866\t0.423\t0.289\t-1.795\t-0.526\t-1.549\t0.469\t1.214\t0.697\n3\t0.604\t-1.059\t0.619\t0.725\t0.489\t2.478\t0.726\t0.968\t1.876\t0.850\t-0.551\t1.243\t-0.401\t-0.983\t-0.897\t1.231\t0.620\t1.061\t0.625\t0.189\t0.318\t0.591\t-0.602\t-1.290\t-0.485\t0.544\t-0.648\t2.130\n4\t1.024\t-0.236\t-1.298\t1.401\t1.549\t1.998\t1.967\t-0.264\t-0.365\t-1.870\t-0.951\t0.363\t-1.547\t0.585\t0.442\t-0.762\t0.581\t0.654\t-1.085\t-0.474\t0.254\t-0.960\t-2.899\t0.214\t-0.862\t-1.627\t-1.052\t-1.518\n1\t-1.320\t1.101\t0.630\t-1.309\t-0.173\t-1.143\t0.837\t-1.018\t-0.934\t0.082\t2.071\t1.221\t-0.989\t-1.225\t-0.591\t-1.402\t0.787\t0.659\t0.914\t0.049\t0.120\t-1.066\t0.275\t1.264\t-0.430\t0.407\t0.068\t-0.533\n1\t-0.601\t-0.292\t-0.602\t1.852\t-0.013\t-1.058\t0.823\t-1.221\t0.209\t-1.960\t-1.328\t0.197\t0.738\t0.171\t-0.116\t-0.301\t-1.479\t-0.720\t-0.461\t1.057\t0.344\t-1.763\t0.324\t-0.385\t-0.677\t0.612\t1.031\t0.931\n2\t-0.974\t-0.975\t-0.660\t-0.239\t1.488\t0.278\t0.395\t0.234\t1.195\t0.055\t-0.378\t-0.300\t0.920\t2.237\t-0.278\t-0.106\t-0.250\t-0.478\t1.243\t0.840\t0.113\t0.954\t0.969\t-0.230\t-0.486\t-2.381\t1.658\t-0.830\n1\t0.832\t-0.015\t1.616\t1.460\t-1.463\t-0.154\t0.867\t-0.860\t0.203\t-1.269\t-0.642\t-0.391\t-1.587\t0.518\t0.264\t-1.440\t1.062\t0.058\t-1.379\t-0.339\t1.196\t0.566\t-0.378\t-0.378\t0.082\t1.025\t0.446\t0.455\n3\t0.999\t-0.181\t-0.714\t-0.000\t-0.573\t-2.734\t-0.147\t0.022\t-0.868\t-2.057\t0.041\t0.095\t-0.864\t1.960\t-0.038\t-0.859\t1.164\t-0.990\t0.944\t0.704\t-1.139\t1.387\t-0.290\t-0.506\t-0.728\t-1.257\t0.383\t0.237\n2\t0.522\t-0.608\t-1.698\t-0.088\t-0.169\t1.620\t0.296\t0.562\t-0.412\t-0.596\t-0.835\t-1.260\t0.726\t0.307\t1.477\t0.550\t0.243\t-1.981\t1.908\t0.039\t-2.084\t0.226\t-0.811\t-0.847\t0.267\t-0.587\t1.027\t-0.115\n4\t-1.209\t-0.284\t0.795\t-0.980\t-2.154\t0.530\t-0.942\t-0.639\t0.131\t0.077\t1.022\t-0.242\t0.068\t-1.693\t0.628\t-0.691\t3.231\t-0.214\t0.424\t0.638\t-0.089\t0.437\t2.255\t-0.754\t-2.243\t-1.303\t0.997\t-0.479\n4\t1.064\t-0.121\t-1.621\t1.022\t-1.776\t-1.546\t-0.178\t0.252\t0.422\t-0.066\t1.760\t0.065\t1.035\t0.787\t-1.293\t-0.170\t2.123\t-0.504\t-0.675\t0.171\t-0.469\t-0.794\t-0.483\t1.432\t-0.137\t-1.328\t-1.512\t2.207\n2\t2.133\t1.161\t-1.261\t0.333\t0.323\t-1.068\t1.203\t1.202\t-1.327\t0.522\t-0.667\t-0.285\t-0.345\t-0.325\t1.221\t0.915\t1.064\t0.425\t-0.284\t1.256\t1.073\t-1.727\t1.039\t-0.011\t0.087\t-0.790\t-0.784\t1.278\n4\t2.333\t1.296\t0.013\t0.867\t-1.637\t2.523\t-0.590\t-0.120\t-1.856\t0.486\t-1.106\t0.102\t-0.544\t-1.078\t-0.804\t0.226\t-1.281\t-0.749\t0.663\t2.602\t0.478\t-0.917\t-0.367\t0.032\t-0.349\t0.903\t0.217\t0.908\n2\t-0.143\t-0.095\t1.612\t0.890\t0.791\t-1.578\t-0.656\t1.189\t-1.171\t0.084\t-1.065\t-0.965\t1.681\t0.719\t0.983\t0.784\t-0.139\t-0.692\t-0.402\t-0.192\t1.776\t-1.336\t-0.554\t-0.913\t1.057\t1.854\t0.309\t0.391\n4\t-0.081\t-1.034\t-0.612\t-0.061\t-0.678\t0.278\t-1.537\t1.604\t-2.389\t-1.359\t-0.358\t-1.986\t-1.394\t1.752\t0.091\t-1.286\t-0.523\t0.222\t-1.908\t-2.336\t0.546\t-0.399\t-0.113\t-0.209\t0.387\t-0.274\t-0.014\t-0.465\n1\t-0.858\t-0.385\t0.556\t2.054\t0.605\t0.473\t0.531\t-1.073\t0.116\t-0.030\t0.322\t0.117\t0.380\t-0.311\t0.059\t-1.228\t-0.320\t1.391\t0.319\t-0.021\t1.896\t-1.361\t-1.109\t1.713\t0.046\t-0.243\t-1.199\t0.448\n3\t0.185\t-2.108\t-1.517\t0.070\t-0.625\t-0.142\t1.642\t0.091\t-1.540\t0.833\t-0.206\t1.436\t0.166\t0.676\t0.780\t-0.340\t-1.447\t1.550\t0.732\t0.435\t-0.698\t-1.082\t-0.283\t1.178\t0.886\t-1.825\t0.794\t-1.246\n1\t1.049\t0.116\t1.387\t-0.129\t0.245\t0.751\t0.199\t0.527\t0.284\t-1.414\t0.766\t1.178\t-0.130\t-0.205\t0.293\t1.946\t-0.285\t0.622\t-0.175\t0.598\t-1.276\t1.796\t0.367\t-0.279\t-0.252\t0.278\t2.003\t1.547\n1\t-1.034\t-0.968\t0.555\t1.483\t1.041\t1.458\t0.784\t-0.034\t-0.526\t0.577\t-1.680\t-0.605\t-0.788\t1.244\t-1.378\t0.930\t0.387\t0.400\t-0.468\t-1.463\t0.500\t0.886\t0.497\t-0.144\t-1.138\t-0.623\t0.450\t-0.790\n2\t-1.957\t-0.557\t-0.476\t0.906\t0.197\t-0.418\t-1.478\t2.285\t-0.450\t-0.949\t-0.414\t0.011\t0.127\t-0.937\t-1.232\t-1.183\t-1.478\t-0.169\t-0.428\t-0.392\t0.671\t-0.286\t-2.661\t-0.610\t0.346\t0.067\t-0.209\t-0.247\n4\t0.781\t1.516\t-0.766\t-1.685\t0.683\t2.052\t0.134\t-1.073\t0.458\t-1.618\t0.135\t-1.890\t-0.605\t2.248\t-1.272\t0.471\t0.642\t-0.999\t1.635\t-0.551\t0.041\t-0.059\t0.762\t2.526\t-0.886\t0.367\t1.215\t-1.033\n4\t0.030\t-1.878\t-0.719\t0.930\t-0.993\t-0.100\t-0.687\t0.217\t-0.498\t0.635\t1.976\t-0.628\t1.424\t1.162\t-0.233\t0.338\t-0.007\t-1.515\t0.364\t1.230\t-1.315\t2.733\t0.021\t0.954\t-2.040\t0.706\t1.755\t-0.235\n4\t-0.901\t0.972\t-1.032\t0.504\t0.576\t0.055\t-0.495\t0.141\t0.267\t1.790\t-0.872\t-0.556\t0.285\t-0.256\t-0.100\t1.070\t-2.820\t-0.598\t-1.742\t-1.196\t2.405\t0.598\t0.952\t-0.081\t0.759\t-1.767\t1.038\t-0.820\n3\t1.160\t0.557\t-0.013\t0.030\t0.755\t-0.261\t1.621\t0.812\t0.781\t-0.449\t0.414\t-0.754\t2.254\t1.497\t-0.040\t-0.326\t1.175\t1.502\t-0.451\t0.130\t0.639\t0.303\t1.076\t2.679\t0.491\t1.444\t-0.793\t1.242\n2\t-0.905\t0.824\t0.166\t-1.091\t0.155\t-1.000\t-0.364\t0.181\t1.065\t1.479\t0.558\t-0.905\t-0.448\t1.863\t-1.966\t-1.585\t0.864\t-0.432\t1.258\t-0.830\t-0.123\t0.804\t-0.616\t-0.982\t-1.559\t-0.705\t1.104\t0.250\n4\t1.193\t-1.494\t-0.018\t-0.700\t-0.569\t0.961\t1.367\t-1.331\t-0.176\t-0.652\t0.668\t1.112\t2.266\t1.197\t1.146\t-1.001\t-0.676\t0.056\t2.568\t0.076\t1.431\t-0.419\t-0.024\t-0.021\t-0.493\t0.687\t-1.294\t-1.828\n0\t0.251\t-0.327\t1.061\t0.598\t0.600\t-0.270\t-0.895\t-1.010\t0.103\t0.008\t0.483\t-0.425\t1.280\t-1.326\t0.418\t-0.909\t-0.243\t-0.921\t-0.368\t-1.070\t-1.001\t0.964\t-1.389\t0.650\t0.852\t0.730\t-0.027\t-1.092\n2\t-0.803\t1.300\t-0.754\t1.424\t-0.251\t0.432\t-0.866\t-1.600\t0.345\t-0.120\t-1.592\t0.588\t-0.776\t-2.118\t-0.811\t1.761\t0.735\t0.497\t0.778\t-0.734\t-1.676\t-0.487\t0.405\t0.097\t-0.301\t0.283\t0.738\t-0.589\n1\t1.476\t1.572\t1.277\t0.235\t-0.338\t1.276\t-0.791\t0.271\t-0.044\t-0.891\t0.278\t0.742\t-0.931\t0.460\t-2.229\t-1.049\t-0.457\t0.357\t0.869\t0.083\t0.567\t-0.161\t0.405\t0.352\t-0.509\t-0.355\t0.600\t1.812\n2\t0.274\t-0.601\t1.069\t-0.208\t-0.788\t0.226\t1.964\t0.335\t-0.363\t-1.527\t-0.219\t0.235\t-0.388\t-1.665\t-0.851\t1.455\t1.434\t-0.887\t0.673\t-1.179\t1.404\t0.245\t0.950\t-1.715\t0.678\t-0.008\t0.852\t0.915\n4\t1.087\t-0.571\t0.369\t0.554\t0.170\t-0.006\t1.817\t-0.340\t-0.063\t-0.417\t0.385\t-1.590\t1.087\t-1.316\t0.430\t-0.583\t1.612\t0.293\t-2.374\t-0.227\t0.434\t-0.296\t-1.020\t-2.636\t1.935\t2.659\t-1.041\t0.248\n0\t-1.004\t-0.409\t0.210\t-0.632\t0.577\t0.720\t0.536\t1.025\t-0.905\t0.933\t-0.696\t-0.941\t0.532\t1.216\t0.488\t0.021\t0.310\t0.136\t-1.126\t-1.243\t-0.079\t0.080\t0.011\t0.676\t-0.215\t0.563\t-0.145\t1.823\n2\t0.600\t0.545\t0.617\t-0.835\t-1.237\t-0.009\t-0.861\t0.867\t-1.038\t-1.242\t0.946\t0.159\t-0.145\t0.911\t-1.299\t-0.527\t0.690\t0.799\t0.088\t-1.151\t0.830\t-1.000\t-0.837\t-0.168\t-1.377\t-2.821\t0.881\t-0.253\n2\t0.765\t1.606\t0.228\t-0.034\t-0.258\t-1.745\t-1.250\t-1.622\t-0.539\t-0.393\t1.728\t0.100\t0.153\t-0.751\t-0.142\t-2.207\t0.640\t-0.412\t-1.217\t-0.171\t0.667\t0.772\t1.797\t-0.122\t-1.141\t0.347\t0.978\t0.492\n3\t0.075\t1.722\t-1.775\t-1.660\t1.521\t0.328\t-0.019\t0.907\t1.758\t0.272\t-0.011\t-1.886\t-0.891\t-1.857\t0.032\t1.419\t-0.418\t0.455\t0.875\t0.395\t-0.089\t-1.362\t0.714\t1.194\t0.028\t-0.337\t0.813\t0.347\n1\t1.243\t0.018\t0.150\t-1.165\t1.321\t-0.988\t0.845\t1.352\t0.048\t0.968\t1.848\t0.268\t-0.175\t-0.067\t-0.743\t-0.766\t0.442\t-0.164\t0.328\t-1.490\t-0.093\t1.030\t-1.119\t0.017\t-1.855\t-1.305\t0.808\t-0.309\n3\t0.467\t-1.210\t1.406\t0.066\t0.276\t0.117\t0.210\t1.647\t1.240\t1.604\t-0.195\t-2.125\t0.747\t-1.116\t-0.362\t-0.062\t0.002\t0.644\t-0.923\t0.627\t-0.190\t2.602\t-1.590\t0.127\t0.615\t-0.411\t-0.574\t1.325\n1\t-0.385\t0.448\t1.702\t-1.321\t-0.457\t-0.853\t-0.741\t1.223\t-0.158\t0.723\t-0.667\t-0.645\t-1.018\t1.445\t0.775\t-1.051\t1.142\t-0.260\t0.453\t0.047\t-1.437\t-0.828\t1.354\t0.939\t-1.022\t0.208\t-0.215\t1.142\n0\t-0.340\t-0.407\t0.200\t0.346\t0.307\t0.365\t-0.179\t-0.199\t-1.449\t-0.689\t0.183\t-0.446\t-0.820\t2.831\t0.711\t0.631\t-1.061\t-0.292\t-1.013\t-0.357\t0.121\t0.350\t0.379\t-0.110\t-0.368\t-0.548\t0.014\t0.188\n4\t-0.941\t-1.061\t-0.087\t-1.636\t2.013\t-0.938\t0.878\t0.599\t2.153\t-0.858\t0.714\t-1.022\t-0.262\t-2.318\t-0.025\t-1.074\t-2.683\t-0.813\t-0.154\t-0.029\t-0.208\t-0.293\t1.583\t-1.249\t0.192\t-0.263\t-0.976\t-0.113\n1\t-0.921\t-0.383\t-0.371\t-2.061\t0.287\t1.405\t0.960\t-0.376\t1.055\t-1.734\t0.832\t-0.731\t0.968\t0.036\t-1.204\t0.247\t0.001\t0.238\t-0.802\t-1.171\t0.614\t-0.067\t0.573\t0.555\t0.708\t1.619\t0.199\t-0.997\n2\t0.073\t1.781\t-0.199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t-0.853\t0.965\t0.704\t1.852\t-0.418\t-1.098\t-0.362\t1.436\t-1.828\t-2.068\t0.089\t-0.443\t0.008\n4\t0.056\t0.223\t0.833\t-2.785\t-0.055\t-0.494\t0.348\t0.337\t0.256\t0.126\t-0.072\t0.122\t-1.851\t3.054\t0.652\t1.666\t0.005\t0.547\t-1.610\t1.668\t1.346\t-0.948\t-0.605\t-0.177\t0.401\t-0.217\t0.693\t0.244\n1\t-0.266\t1.300\t-0.217\t-0.006\t-1.845\t-0.460\t0.555\t-0.800\t-1.751\t-0.961\t1.264\t0.367\t0.892\t0.599\t2.490\t1.019\t-0.036\t-0.255\t0.483\t1.214\t-0.829\t0.452\t0.190\t0.316\t-0.304\t0.571\t1.309\t0.364\n0\t-0.018\t-1.173\t2.311\t0.153\t1.572\t0.817\t0.610\t0.611\t-0.555\t-0.590\t-0.100\t-0.163\t-2.452\t-0.016\t0.571\t-1.302\t-1.069\t0.065\t0.411\t-0.167\t0.346\t0.133\t-0.385\t-0.035\t-0.031\t0.023\t-0.149\t0.341\n4\t-1.417\t0.581\t-1.573\t0.152\t-0.447\t1.219\t-0.393\t1.107\t-0.514\t1.000\t2.060\t-1.068\t0.292\t2.749\t0.067\t0.062\t0.788\t0.041\t-1.707\t1.132\t-2.003\t-0.417\t1.207\t-0.357\t-0.694\t0.879\t-0.851\t-1.222\n3\t1.190\t-0.437\t0.870\t-1.621\t-0.206\t1.324\t0.078\t0.317\t0.568\t0.883\t1.455\t-2.165\t-0.339\t-2.893\t0.839\t1.334\t-0.356\t0.473\t-0.713\t1.349\t0.936\t-0.054\t-0.992\t-0.142\t1.527\t0.623\t0.530\t-0.087\n1\t-0.375\t0.518\t-0.086\t-0.324\t0.381\t2.058\t-0.317\t0.408\t0.496\t-0.758\t-0.685\t0.687\t1.221\t1.994\t1.118\t-0.621\t0.658\t-0.072\t-1.069\t-0.362\t0.131\t1.531\t0.578\t-0.849\t0.802\t1.784\t0.305\t0.291\n2\t0.535\t0.234\t-0.468\t-0.274\t-0.560\t-1.325\t-1.463\t0.390\t-0.871\t0.576\t-0.595\t-1.313\t-0.408\t2.094\t-0.096\t0.646\t0.169\t-1.405\t1.449\t-0.248\t-1.311\t-0.512\t-0.206\t0.749\t-0.681\t2.620\t-0.937\t0.612\n2\t0.568\t-0.382\t-0.546\t0.010\t0.288\t-1.348\t-0.125\t-2.049\t-0.025\t1.457\t-1.301\t-0.140\t-0.217\t-1.124\t-0.453\t-1.321\t-0.968\t0.164\t0.387\t-0.329\t1.342\t0.732\t1.968\t-0.118\t-1.049\t-0.189\t2.016\t-1.002\n1\t1.696\t-0.640\t1.186\t-1.660\t-0.834\t0.347\t-0.317\t0.826\t0.263\t1.443\t-0.499\t-1.094\t-0.225\t-0.409\t-0.671\t-0.868\t-1.433\t1.293\t-1.485\t-0.084\t0.129\t0.359\t-0.046\t-1.437\t-0.792\t-0.054\t-0.007\t-1.198\n0\t-2.111\t0.388\t-0.679\t-0.266\t-0.465\t-0.430\t-0.294\t1.668\t1.098\t0.608\t-0.312\t0.305\t-0.684\t0.347\t-0.100\t0.493\t-0.463\t0.982\t0.050\t-0.191\t0.760\t1.610\t0.017\t-0.186\t0.412\t-0.465\t-1.100\t-0.667\n3\t-0.822\t0.080\t0.083\t0.344\t-0.679\t0.598\t1.608\t0.973\t-1.936\t-0.643\t0.151\t0.059\t1.459\t1.455\t-1.050\t-0.617\t0.839\t1.734\t0.797\t0.444\t-0.145\t1.461\t-1.466\t-1.593\t1.811\t-0.104\t-0.523\t1.320\n0\t-1.742\t-0.099\t-0.877\t-0.062\t-1.095\t0.050\t-0.297\t-0.626\t0.289\t0.375\t-0.049\t0.569\t-0.413\t0.400\t-0.042\t-0.999\t-0.801\t1.165\t1.576\t0.713\t-0.583\t1.428\t0.613\t0.336\t1.842\t-0.860\t0.773\t-0.815\n1\t-0.068\t-0.907\t-1.198\t0.529\t1.362\t1.269\t0.370\t0.871\t-0.928\t1.947\t0.811\t0.785\t-0.312\t0.130\t1.363\t-1.096\t0.341\t-1.623\t-0.483\t0.404\t0.322\t0.621\t-0.609\t0.476\t-0.166\t0.949\t0.780\t-0.658\n3\t0.997\t-0.466\t0.640\t-0.191\t0.324\t-1.148\t0.085\t-2.991\t-0.187\t-1.630\t1.206\t0.777\t0.467\t1.521\t-0.949\t1.747\t0.932\t-0.237\t1.136\t-1.106\t-0.825\t-0.609\t-0.529\t-1.057\t1.223\t-0.259\t0.353\t-0.570\n1\t-1.051\t1.258\t0.665\t-0.860\t0.827\t1.612\t0.288\t0.955\t1.345\t-0.784\t0.599\t-1.364\t-0.080\t-0.507\t-0.459\t-0.818\t-0.881\t-1.122\t-1.658\t-0.398\t-1.413\t-0.748\t-0.953\t-1.448\t0.305\t0.084\t0.244\t0.044\n2\t-0.206\t0.719\t0.317\t0.619\t0.232\t-0.466\t0.274\t-0.525\t1.646\t0.813\t2.964\t-0.157\t0.957\t1.906\t0.266\t0.062\t-0.327\t-0.838\t0.918\t0.530\t1.085\t1.143\t-1.448\t0.189\t0.210\t0.525\t-0.958\t0.467\n0\t-0.458\t-0.383\t-1.209\t-1.052\t-0.690\t1.429\t-0.424\t-0.353\t0.665\t-0.006\t-0.481\t-0.089\t-0.908\t-1.198\t0.965\t0.313\t-1.937\t0.799\t-0.211\t-0.262\t0.923\t-0.984\t0.054\t1.432\t-0.294\t-0.422\t1.644\t-0.544\n4\t0.595\t-1.056\t0.520\t0.755\t1.677\t0.585\t1.102\t0.616\t-1.355\t0.829\t0.120\t1.997\t-1.439\t-1.132\t0.725\t1.512\t-0.120\t-0.115\t0.207\t-0.484\t0.988\t-0.483\t0.700\t-1.323\t1.719\t-2.120\t-1.063\t2.253\n4\t0.686\t0.973\t0.669\t0.443\t-0.460\t1.170\t-0.369\t-0.711\t-0.424\t-0.491\t-2.219\t0.340\t-0.596\t0.282\t1.979\t-3.357\t-1.163\t0.429\t0.699\t-1.274\t-1.481\t-0.362\t-0.835\t-0.773\t-1.339\t1.046\t0.721\t-0.799\n4\t0.727\t-0.522\t1.298\t-0.931\t2.132\t2.029\t-0.095\t1.146\t-0.400\t1.013\t-0.176\t-1.285\t-3.533\t-0.269\t0.279\t1.330\t0.037\t1.003\t-0.454\t-0.337\t1.054\t-2.465\t0.013\t-0.400\t1.361\t-0.528\t0.517\t0.922\n3\t-1.838\t1.565\t0.348\t0.681\t0.063\t1.178\t0.536\t1.305\t0.846\t1.118\t-0.715\t-0.381\t0.016\t0.003\t0.575\t-0.750\t-0.006\t0.627\t-1.915\t1.057\t2.045\t-0.915\t1.480\t0.227\t-0.886\t0.828\t1.940\t-0.403\n2\t0.206\t0.833\t-0.275\t-0.707\t-0.187\t-0.496\t0.656\t-0.543\t1.994\t-0.351\t-0.646\t-0.270\t2.391\t0.519\t0.709\t-0.332\t-1.164\t-1.151\t-0.471\t0.625\t-1.140\t-0.734\t-0.474\t2.142\t1.041\t-0.836\t-0.353\t-0.571\n3\t-1.374\t0.091\t-0.612\t-0.274\t1.985\t-1.223\t0.300\t-0.878\t-0.308\t0.745\t0.318\t-0.225\t-0.650\t0.316\t1.708\t-0.901\t1.912\t1.199\t1.369\t0.308\t1.171\t-1.009\t-0.668\t0.766\t0.296\t-0.148\t-2.503\t-0.435\n1\t0.097\t0.692\t1.907\t0.549\t-0.257\t0.265\t-1.128\t-0.057\t0.264\t-0.245\t-1.081\t-0.393\t-0.399\t-0.953\t-1.297\t-1.273\t0.632\t-0.771\t-0.792\t1.605\t-0.750\t1.359\t1.394\t-0.266\t0.435\t-1.189\t-1.090\t0.728\n4\t2.059\t2.092\t-0.119\t1.423\t-0.161\t0.433\t1.536\t-0.233\t-1.000\t0.471\t-0.808\t0.036\t0.328\t0.681\t0.693\t0.307\t0.860\t1.562\t0.853\t1.058\t-0.889\t0.228\t0.421\t2.329\t-2.190\t0.892\t0.555\t0.740\n0\t-0.387\t-0.575\t0.873\t0.537\t-0.383\t0.476\t-1.095\t-1.263\t-0.094\t0.102\t-0.140\t1.356\t0.687\t1.035\t-0.553\t0.333\t-1.043\t-0.731\t0.331\t-0.890\t-0.791\t0.431\t-0.764\t0.962\t-0.214\t-0.173\t0.251\t-0.135\n3\t0.797\t0.152\t1.430\t0.652\t-0.557\t0.596\t0.432\t-0.395\t1.243\t-1.265\t0.372\t-0.432\t-1.606\t0.117\t-0.414\t-0.584\t-1.060\t2.010\t-2.460\t-2.162\t0.523\t0.463\t-1.389\t-0.598\t0.627\t-0.276\t0.796\t-0.046\n1\t1.054\t-0.900\t-0.214\t-0.794\t-0.566\t2.111\t-1.230\t2.235\t-1.096\t-1.374\t-0.296\t-1.129\t0.713\t0.740\t-0.986\t-1.008\t0.577\t-0.201\t-0.645\t-0.331\t0.380\t0.627\t-0.378\t0.586\t0.155\t-0.579\t0.430\t-0.059\n1\t0.194\t-0.353\t0.338\t-0.295\t0.168\t1.318\t-1.007\t1.140\t1.317\t-0.118\t-2.122\t-0.608\t1.297\t-0.023\t-0.999\t-0.505\t0.841\t0.547\t-0.239\t-0.367\t-0.392\t-0.922\t1.615\t-0.322\t1.217\t1.521\t0.998\t-0.432\n2\t0.466\t0.063\t-0.038\t0.137\t0.698\t0.940\t-0.498\t-0.406\t1.451\t0.502\t-0.824\t-0.256\t1.258\t-1.553\t0.244\t-0.458\t0.997\t-0.608\t-1.792\t-0.927\t1.087\t-2.789\t-0.189\t-1.470\t-0.154\t0.446\t0.339\t0.020\n1\t-0.027\t-0.145\t0.371\t0.133\t-0.194\t-0.641\t0.500\t0.430\t0.177\t-0.829\t-1.131\t0.676\t-0.892\t-0.338\t-1.556\t0.443\t-1.240\t-1.291\t-0.989\t0.429\t-0.216\t-0.634\t1.653\t0.522\t-1.992\t-0.873\t-1.978\t-0.478\n3\t0.896\t-1.717\t1.614\t-0.337\t-0.799\t-0.541\t-0.865\t1.515\t0.672\t0.097\t-1.203\t-0.055\t-0.771\t-2.631\t-0.694\t0.605\t-0.426\t-0.145\t1.041\t0.862\t1.961\t-2.194\t-0.842\t0.908\t0.431\t0.099\t-0.605\t0.226\n4\t0.119\t0.281\t-0.259\t-0.326\t-0.974\t0.693\t-1.797\t-1.307\t1.867\t-0.116\t0.140\t-1.212\t0.485\t1.374\t1.451\t-1.517\t1.169\t0.010\t-2.527\t-1.540\t-1.881\t0.602\t-0.187\t-0.651\t-0.095\t1.756\t-0.295\t0.329\n4\t1.961\t-0.491\t-2.881\t0.286\t-0.564\t-0.188\t-0.922\t0.537\t-0.549\t0.987\t1.517\t-0.277\t-1.977\t0.428\t-1.817\t-0.482\t0.916\t0.094\t-1.342\t-0.709\t-0.551\t-0.788\t0.445\t0.078\t-1.341\t-2.144\t0.515\t-0.114\n1\t-1.283\t0.186\t-1.182\t-0.110\t-1.440\t1.094\t-0.395\t0.906\t-0.076\t-1.844\t-0.484\t-0.531\t-0.015\t1.508\t1.674\t-0.231\t-1.260\t-0.744\t0.480\t-0.108\t-1.575\t-0.504\t1.312\t0.292\t0.047\t-0.389\t-0.460\t0.544\n4\t-0.616\t-1.736\t-0.288\t-0.504\t-1.525\t0.251\t-1.083\t1.324\t0.168\t-0.261\t1.254\t0.260\t0.347\t-0.065\t-0.314\t0.404\t1.524\t-3.420\t1.868\t-1.008\t0.192\t-1.717\t1.491\t-1.383\t1.122\t-0.241\t-0.163\t-1.259\n4\t1.734\t-0.637\t-0.024\t0.117\t-0.139\t0.994\t-2.388\t-0.474\t0.715\t-0.904\t-0.807\t-0.395\t1.473\t0.220\t-0.145\t-1.162\t0.211\t-1.393\t-2.146\t-0.409\t-2.356\t-0.291\t1.110\t1.565\t0.867\t1.001\t-0.324\t-0.752\n2\t-0.619\t-0.455\t0.488\t-0.516\t0.547\t0.095\t-0.084\t-1.573\t-0.820\t0.603\t-0.682\t1.205\t2.346\t0.656\t1.493\t0.721\t-0.534\t0.200\t2.013\t-0.524\t-1.039\t-1.134\t-0.218\t-1.003\t1.674\t0.216\t0.972\t-0.299\n0\t0.066\t1.330\t0.065\t-0.727\t-0.218\t-1.020\t-0.198\t-0.818\t-1.333\t-0.478\t-1.505\t0.749\t0.637\t1.016\t-0.532\t0.453\t-1.141\t-0.571\t1.025\t1.175\t0.123\t0.679\t-0.233\t1.006\t-0.910\t0.120\t1.184\t-0.506\n4\t-0.989\t1.377\t-1.213\t-1.186\t0.138\t0.053\t0.580\t-1.176\t0.353\t-1.678\t-2.195\t1.828\t1.047\t-0.004\t-2.023\t-0.458\t0.449\t-0.460\t1.561\t0.629\t-0.923\t-1.596\t0.875\t0.841\t0.756\t1.685\t0.458\t0.094\n1\t-0.817\t0.445\t-1.156\t1.962\t0.327\t1.047\t-0.262\t0.768\t0.537\t-0.591\t-0.275\t1.492\t1.144\t-1.413\t1.019\t-0.303\t-0.246\t-0.345\t0.864\t1.213\t0.763\t-0.087\t0.075\t-0.317\t-1.258\t0.215\t1.247\t0.986\n4\t1.197\t0.667\t-2.353\t-1.333\t2.159\t1.180\t-0.583\t-1.488\t-1.250\t0.509\t1.977\t1.913\t-1.576\t-0.296\t-0.857\t0.137\t-1.331\t-0.546\t-0.867\t0.524\t-0.515\t-0.371\t0.511\t1.643\t2.116\t0.456\t-0.089\t1.316\n0\t0.995\t0.880\t-1.232\t-0.831\t-0.226\t0.953\t0.608\t0.955\t-0.440\t-0.426\t0.568\t-0.296\t-0.378\t-0.609\t0.662\t0.613\t1.810\t-0.676\t0.335\t0.645\t-0.638\t0.299\t-0.648\t1.167\t-0.183\t0.899\t-0.563\t-0.981\n2\t0.662\t-0.470\t-0.833\t-0.082\t-1.761\t0.424\t-1.253\t0.358\t-0.400\t0.398\t-0.477\t-0.082\t-0.713\t0.260\t0.922\t1.208\t-0.366\t-2.659\t-0.503\t0.695\t1.019\t1.903\t-0.665\t1.555\t-0.312\t0.451\t0.424\t1.261\n2\t0.071\t-1.294\t-0.696\t-0.918\t1.240\t-0.396\t1.068\t0.604\t2.304\t-1.479\t1.259\t1.146\t-0.973\t1.007\t0.336\t-0.227\t-0.824\t-0.719\t2.050\t0.001\t0.782\t-0.791\t-0.780\t1.082\t-1.350\t-0.415\t0.034\t0.111\n4\t-0.317\t0.872\t-1.221\t-1.918\t-0.544\t-0.248\t-0.542\t-1.482\t0.265\t-0.886\t0.760\t0.502\t-0.186\t0.342\t0.967\t2.239\t-0.783\t0.607\t0.953\t-2.086\t1.005\t-1.621\t-0.021\t1.879\t0.255\t-0.039\t-0.779\t2.323\n2\t-0.963\t-1.352\t-1.364\t1.466\t-0.853\t0.874\t0.356\t-1.028\t-0.617\t-1.121\t0.272\t-0.31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1\t0.159\t-1.843\t0.145\t-0.019\t0.398\t-0.524\t0.539\t0.949\t-0.198\t-1.496\t0.451\t-0.468\t0.310\t0.029\t-1.670\t0.244\t0.890\t-0.537\t-2.085\t0.409\t-0.251\t1.820\t-1.201\t2.041\t0.899\t-0.001\n1\t0.146\t1.644\t0.494\t-1.376\t1.270\t0.795\t1.000\t1.133\t-0.543\t1.059\t-0.646\t-1.182\t-0.829\t0.693\t-0.409\t1.476\t1.114\t0.674\t1.380\t-1.175\t0.957\t0.574\t-1.273\t-0.720\t-0.159\t-0.187\t-0.041\t-0.619\n2\t1.383\t0.407\t-0.555\t1.332\t0.552\t0.484\t1.399\t-0.370\t0.664\t-0.493\t0.001\t-1.048\t0.282\t0.758\t-0.080\t0.350\t-2.222\t0.452\t-1.313\t1.031\t0.928\t-0.887\t-0.541\t1.376\t1.341\t0.445\t-0.515\t-1.829\n2\t-0.442\t1.484\t0.972\t0.334\t-0.379\t-0.936\t-0.530\t0.637\t-1.983\t1.795\t1.239\t1.216\t0.615\t0.108\t0.378\t-0.366\t2.118\t-0.138\t-0.476\t-0.265\t-0.723\t0.809\t-0.519\t-0.126\t1.336\t1.071\t-1.024\t1.181\n2\t-1.308\t1.635\t-0.797\t0.724\t-1.129\t-1.233\t0.427\t-0.480\t-0.573\t-0.321\t-0.245\t1.724\t0.597\t0.696\t1.868\t1.689\t-0.265\t-0.204\t0.254\t0.132\t0.983\t1.753\t0.349\t-0.659\t-0.040\t0.547\t-0.640\t1.873\n3\t-1.690\t-2.087\t-1.357\t-0.485\t-1.780\t-1.897\t0.780\t-0.673\t-1.535\t-0.337\t-0.976\t0.439\t0.343\t0.766\t0.831\t-1.132\t-1.150\t-0.473\t-0.814\t-0.837\t-0.829\t-0.698\t0.005\t0.822\t0.992\t-0.925\t1.068\t1.462\n4\t-1.989\t-0.368\t0.259\t1.708\t0.785\t2.393\t-0.482\t0.471\t-0.107\t2.502\t0.606\t-2.349\t-0.617\t-1.500\t1.308\t-0.808\t0.426\t-0.150\t-0.398\t-0.960\t-0.489\t0.052\t-0.326\t0.513\t-1.736\t-1.455\t0.757\t-1.030\n1\t-1.077\t0.160\t-0.136\t-0.506\t-1.266\t0.889\t-0.469\t-2.935\t0.356\t0.381\t0.933\t0.413\t0.138\t-0.487\t-0.484\t0.676\t-0.157\t-1.437\t-0.344\t-0.054\t-1.991\t0.747\t0.001\t-1.363\t-0.297\t-0.102\t0.946\t0.917\n0\t-1.709\t-0.377\t-0.803\t0.815\t-0.739\t0.426\t0.122\t-0.307\t-0.645\t0.089\t0.070\t-0.767\t-0.258\t-0.290\t0.484\t0.025\t0.222\t0.096\t-0.368\t-0.525\t1.263\t-0.794\t1.672\t1.699\t-0.663\t0.575\t0.557\t0.754\n4\t-0.274\t0.519\t-1.921\t-0.006\t-1.302\t0.135\t-0.901\t0.402\t-0.125\t2.689\t1.061\t1.535\t-1.737\t0.333\t-0.094\t1.295\t-0.646\t-1.272\t-2.396\t0.621\t-1.502\t0.674\t-0.526\t0.119\t-1.864\t0.163\t0.246\t-1.472\n4\t-0.073\t-3.397\t0.118\t-0.710\t-1.743\t0.225\t1.067\t0.015\t1.032\t2.138\t-0.052\t0.226\t0.321\t0.836\t-0.719\t1.052\t1.066\t0.339\t1.490\t1.064\t-1.082\t0.032\t0.833\t1.066\t-2.560\t-0.107\t1.240\t1.034\n4\t-0.351\t0.734\t-2.001\t0.440\t0.778\t-2.613\t-0.527\t0.344\t1.303\t-1.379\t1.387\t0.439\t-1.003\t0.993\t2.303\t-0.335\t-0.418\t-0.952\t1.838\t-0.730\t-0.969\t-0.624\t-0.612\t-0.578\t-0.789\t-0.716\t0.143\t1.350\n3\t-1.766\t0.780\t-0.356\t-1.323\t-0.954\t-1.265\t-1.282\t0.061\t-0.387\t0.334\t1.676\t-0.130\t-2.245\t-0.242\t1.193\t-0.086\t1.529\t0.453\t-1.838\t0.110\t0.297\t1.080\t1.820\t0.908\t0.440\t0.676\t-0.168\t-1.463\n2\t1.110\t0.855\t-0.556\t0.281\t-1.494\t-0.770\t1.669\t-1.154\t0.010\t-1.396\t0.338\t0.563\t0.724\t0.468\t1.427\t-0.554\t-1.058\t-0.587\t-1.346\t0.079\t-0.140\t0.478\t-1.616\t-0.013\t1.214\t0.572\t-1.896\t-0.012\n0\t0.232\t1.071\t-0.226\t0.791\t1.074\t-1.431\t0.541\t-0.519\t0.081\t-0.702\t-0.075\t-0.879\t-0.686\t-1.137\t0.402\t1.096\t-1.106\t1.743\t-0.647\t-0.141\t-0.765\t0.885\t0.199\t0.189\t-0.778\t0.228\t0.623\t-0.222\n4\t2.124\t-0.464\t-2.665\t1.312\t-0.245\t0.203\t0.078\t1.056\t-1.111\t-0.486\t-0.875\t-0.120\t1.985\t-0.827\t0.419\t0.606\t0.308\t0.141\t-0.052\t0.455\t0.880\t-0.975\t1.777\t-0.980\t1.613\t1.277\t1.288\t0.799\n3\t1.161\t1.147\t0.106\t1.331\t0.617\t-0.293\t0.385\t-1.565\t0.432\t-0.633\t0.589\t-2.360\t0.733\t0.040\t-0.857\t-0.344\t-0.143\t0.039\t-1.527\t0.387\t-1.966\t-0.474\t-1.613\t0.362\t-0.474\t-0.934\t-2.750\t0.483\n0\t0.402\t0.463\t-1.097\t-1.083\t-0.393\t-0.693\t-0.597\t-0.095\t-0.092\t-2.560\t0.096\t-0.252\t0.226\t-0.641\t-0.635\t-0.391\t1.069\t-0.417\t1.111\t0.977\t-0.338\t-0.511\t-0.476\t-0.144\t-0.559\t0.736\t-0.646\t0.863\n0\t0.486\t2.107\t0.888\t-0.089\t0.003\t-0.525\t-0.950\t-0.553\t-0.772\t0.653\t-1.071\t1.333\t0.752\t0.021\t0.000\t-0.557\t0.270\t1.088\t-0.703\t-1.533\t0.703\t0.324\t-1.120\t-0.733\t-0.859\t-0.725\t-0.324\t-0.267\n3\t0.177\t0.641\t-2.557\t-0.433\t-2.710\t-2.767\t0.945\t0.221\t0.401\t0.997\t-0.511\t0.476\t0.060\t-0.998\t-1.326\t1.030\t-0.682\t0.619\t1.393\t-0.807\t-0.175\t0.296\t0.139\t0.518\t0.550\t-0.124\t-0.156\t0.714\n3\t0.038\t1.657\t-0.066\t0.631\t0.902\t1.600\t1.429\t-0.712\t-0.077\t-0.286\t-0.908\t-0.278\t-1.342\t0.126\t-2.733\t0.594\t-1.101\t-1.174\t0.805\t-0.191\t-0.397\t-0.976\t-0.751\t1.210\t0.241\t0.699\t-1.507\t1.420\n3\t-0.943\t0.103\t-0.667\t-0.372\t0.621\t0.944\t0.537\t1.149\t-2.662\t2.016\t0.817\t-0.356\t-1.477\t-0.176\t-0.797\t1.211\t0.679\t-0.645\t-1.400\t-0.248\t1.004\t0.546\t-1.403\t-0.525\t0.980\t-0.132\t-0.052\t1.890\n4\t-0.027\t-0.002\t-1.626\t2.332\t0.232\t0.438\t-0.340\t-0.874\t0.151\t0.289\t1.302\t2.078\t1.477\t-1.767\t-0.460\t0.336\t-0.400\t-0.044\t-2.145\t0.914\t-1.875\t-1.572\t-1.015\t-0.098\t-1.623\t-1.060\t1.149\t-0.112\n0\t1.817\t-0.442\t-0.955\t0.889\t0.710\t0.149\t0.033\t-0.496\t-0.735\t-0.606\t0.313\t-0.207\t-1.491\t-0.421\t-0.213\t0.510\t0.292\t1.755\t-0.980\t0.553\t0.635\t0.279\t-0.297\t-0.684\t-0.041\t-1.788\t-0.924\t-0.975\n2\t0.297\t-1.261\t0.118\t0.474\t0.838\t-2.247\t-0.644\t1.529\t-1.290\t-0.298\t0.859\t0.611\t0.757\t-0.508\t-1.219\t1.201\t-0.741\t0.858\t1.313\t-0.767\t-0.279\t1.614\t0.988\t0.526\t0.451\t-0.561\t1.441\t-0.758\n4\t-2.198\t0.571\t-0.455\t-0.188\t-0.300\t0.768\t1.402\t1.799\t1.385\t-0.197\t0.189\t-1.962\t0.699\t-0.487\t-1.510\t1.346\t0.724\t1.535\t-0.650\t-1.695\t1.448\t-0.656\t-0.113\t1.461\t-1.146\t-0.427\t-0.972\t-0.057\n3\t0.251\t-0.825\t-2.018\t-1.364\t-0.448\t0.898\t0.347\t0.140\t-2.010\t1.270\t0.224\t-0.119\t1.132\t1.247\t1.155\t-0.247\t-1.003\t0.989\t-1.365\t1.947\t0.028\t-1.061\t-1.232\t-1.278\t0.140\t-0.006\t-1.236\t-0.356\n2\t0.464\t2.028\t0.239\t0.282\t-0.795\t-1.697\t-0.886\t-0.819\t0.565\t0.230\t-0.399\t0.519\t-0.086\t0.722\t-1.073\t-0.616\t-0.641\t1.964\t-1.678\t0.058\t-0.765\t0.343\t0.229\t0.882\t-1.262\t-0.165\t-1.738\t0.465\n1\t-0.024\t1.422\t0.135\t-0.595\t-1.060\t-1.279\t-0.263\t-0.199\t0.552\t-0.357\t1.389\t-1.294\t-1.124\t0.410\t-0.489\t-0.560\t-0.782\t-1.385\t-2.103\t-0.671\t-1.136\t0.296\t-0.056\t-0.648\t-0.512\t1.092\t-0.819\t0.939\n1\t0.195\t-0.056\t-1.315\t0.563\t-1.020\t0.554\t0.111\t1.326\t-0.865\t-1.285\t-0.615\t0.347\t1.455\t-0.478\t-0.209\t0.147\t-0.875\t-1.309\t-0.078\t0.847\t-0.308\t0.876\t-1.591\t-0.094\t-2.179\t0.334\t-0.950\t0.790\n1\t2.362\t-0.182\t0.750\t-1.321\t0.925\t1.161\t-0.683\t-1.598\t1.732\t-0.516\t-0.030\t-0.165\t-0.099\t-0.204\t0.051\t-0.700\t-0.293\t0.547\t-0.060\t0.476\t0.412\t0.935\t0.354\t-1.360\t-0.626\t0.695\t1.693\t0.437\n1\t-0.006\t1.256\t-0.972\t-1.039\t-0.874\t-0.309\t-1.467\t-0.081\t0.573\t0.985\t0.959\t-0.228\t0.919\t1.128\t-0.059\t0.754\t-0.149\t1.576\t-1.558\t0.456\t0.074\t0.403\t1.917\t-1.106\t0.296\t-0.871\t0.082\t1.154\n2\t1.570\t-0.125\t1.148\t-2.044\t-1.849\t-0.750\t0.285\t0.375\t0.594\t-0.452\t1.174\t-2.001\t-0.176\t-1.663\t0.053\t0.143\t1.071\t-0.978\t-0.624\t-0.554\t0.133\t0.362\t0.003\t-0.665\t0.628\t-0.921\t-1.612\t-0.417\n0\t0.545\t0.701\t-0.277\t1.175\t1.151\t-1.177\t-0.697\t-0.330\t1.024\t1.299\t0.299\t1.423\t-0.348\t1.502\t0.949\t0.175\t0.655\t1.196\t-0.442\t-0.629\t0.548\t-0.192\t0.003\t1.550\t-0.803\t0.878\t0.275\t-1.109\n2\t-0.674\t2.291\t-2.030\t0.029\t0.103\t-0.209\t0.526\t-0.202\t-1.495\t-0.089\t0.165\t0.912\t1.418\t-0.855\t1.672\t-0.428\t-0.511\t-0.411\t-1.951\t-0.423\t1.311\t0.477\t0.228\t0.351\t1.261\t-0.878\t0.904\t0.067\n3\t0.180\t1.779\t-0.032\t0.474\t-1.335\t-0.475\t-0.222\t1.104\t2.062\t0.343\t-1.727\t-1.179\t1.159\t-0.699\t0.778\t0.358\t0.045\t0.278\t0.183\t-1.324\t-0.285\t2.136\t-1.101\t-1.130\t-0.451\t1.895\t-0.906\t-0.486\n3\t-0.454\t-0.487\t0.637\t-1.503\t1.506\t1.163\t-1.339\t-0.508\t-0.508\t0.539\t-0.752\t-2.379\t1.227\t-2.289\t1.689\t-0.387\t-0.016\t-0.265\t-0.445\t0.071\t-0.267\t-0.845\t0.612\t-0.230\t1.974\t-0.884\t0.494\t-0.532\n2\t-1.803\t-1.312\t-1.107\t-1.020\t-0.325\t2.109\t-0.266\t-1.746\t-0.411\t-0.652\t0.614\t1.100\t0.911\t0.347\t-0.252\t0.300\t-0.209\t-0.284\t1.002\t-0.009\t0.454\t-1.153\t1.942\t-0.031\t0.286\t-0.715\t-0.517\t1.741\n0\t0.338\t-0.993\t0.020\t-1.466\t-1.769\t0.367\t-0.384\t0.202\t-0.018\t-0.976\t-0.045\t-1.233\t1.391\t-1.356\t-0.196\t-1.042\t0.570\t-0.331\t-0.519\t0.853\t0.029\t0.517\t-0.981\t-1.405\t0.107\t-0.319\t0.626\t-0.080\n2\t0.976\t-2.327\t0.400\t-1.885\t0.306\t-0.150\t2.014\t-1.239\t-0.341\t0.315\t-1.519\t-0.928\t1.438\t-0.060\t-0.873\t0.291\t-0.373\t-0.301\t1.514\t0.309\t0.994\t0.311\t-0.641\t0.043\t1.293\t-0.916\t-0.227\t0.128\n2\t0.590\t-0.656\t-1.808\t0.038\t0.937\t-0.771\t1.704\t-0.602\t2.007\t-0.102\t1.528\t-0.106\t-0.097\t-0.463\t-0.271\t-0.527\t-0.247\t1.329\t0.641\t-1.626\t-0.173\t1.457\t0.926\t-0.801\t-0.457\t0.628\t-0.694\t-1.184\n3\t1.113\t-0.709\t-1.504\t1.121\t0.260\t-0.758\t-0.939\t0.362\t1.093\t0.911\t-0.334\t-2.430\t-1.264\t-0.850\t0.872\t-0.850\t0.161\t0.629\t-0.287\t1.476\t1.221\t-1.516\t-1.016\t-0.160\t-1.957\t0.876\t0.540\t0.289\n2\t-1.141\t1.679\t-0.149\t0.913\t-1.412\t-0.283\t1.302\t-1.565\t2.275\t0.203\t-0.819\t0.352\t1.109\t-0.826\t-0.682\t0.249\t0.463\t-0.198\t0.402\t0.854\t0.173\t-0.691\t-0.021\t1.756\t0.333\t1.240\t-0.514\t0.263\n4\t1.344\t0.447\t-0.053\t-0.721\t1.110\t0.952\t0.796\t-1.327\t-0.151\t-0.436\t-0.071\t0.738\t2.223\t-0.891\t-1.797\t0.074\t0.936\t0.441\t-0.532\t-1.939\t1.842\t-1.919\t2.296\t0.253\t-2.028\t-0.723\t1.423\t-1.477\n1\t-1.926\t-1.031\t1.108\t0.769\t0.211\t0.801\t0.075\t-1.709\t-1.112\t0.490\t-1.743\t-0.804\t-0.713\t0.255\t-0.203\t0.186\t0.477\t0.358\t0.862\t0.166\t-0.991\t-1.248\t0.402\t-0.108\t-0.016\t-0.662\t-0.070\t-1.522\n0\t-1.046\t0.473\t0.291\t0.958\t-0.791\t0.034\t-1.365\t0.701\t0.519\t0.496\t0.002\t0.415\t1.604\t-0.285\t0.139\t-1.086\t-0.868\t0.098\t1.104\t-0.256\t-0.236\t-0.407\t-0.535\t0.295\t-1.497\t1.281\t-0.665\t0.722\n2\t0.690\t-1.642\t-0.828\t0.012\t1.248\t0.736\t-0.459\t-0.582\t-0.138\t1.442\t-1.196\t-1.187\t-0.080\t0.429\t-0.921\t0.793\t0.801\t-0.049\t-0.447\t0.168\t-2.127\t-1.882\t0.410\t-1.487\t-0.751\t0.20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34\t-1.588\t-0.129\t0.851\t1.569\t-0.811\t-0.982\t-0.213\t3.206\t1.318\t1.342\t-0.631\t0.293\t0.509\t0.461\t-1.351\t-1.767\t1.056\n2\t-0.240\t-0.109\t0.616\t-1.351\t0.196\t2.535\t-0.358\t-1.603\t0.411\t-0.901\t-0.107\t1.088\t0.558\t-0.489\t0.838\t-1.668\t0.197\t-0.384\t-2.360\t-1.211\t-0.015\t0.747\t0.022\t-0.296\t0.810\t0.274\t0.997\t0.574\n1\t0.400\t0.567\t0.252\t0.214\t0.142\t1.494\t0.844\t-0.691\t-1.112\t-0.794\t0.428\t-1.104\t-0.653\t-1.343\t0.932\t0.663\t0.879\t-0.475\t-1.759\t-0.835\t-1.347\t-1.358\t1.137\t-0.247\t0.289\t1.103\t-1.020\t0.614\n3\t1.195\t-1.523\t-0.559\t0.377\t1.566\t-0.066\t-0.555\t1.881\t-1.448\t-2.199\t0.440\t-0.502\t-1.021\t0.708\t0.244\t-0.564\t-1.280\t0.872\t0.650\t-0.099\t1.847\t-1.070\t-1.526\t-0.692\t-0.046\t0.243\t-0.241\t0.352\n2\t-0.954\t0.651\t-0.342\t1.831\t-0.105\t0.976\t-0.213\t1.626\t-0.864\t0.239\t0.056\t1.696\t-0.571\t0.718\t1.156\t-1.093\t0.405\t-1.805\t0.678\t-0.218\t1.138\t0.873\t-1.071\t1.743\t-0.369\t-1.261\t-1.000\t0.549\n4\t-0.385\t0.213\t0.764\t1.196\t0.409\t0.092\t1.319\t-1.103\t-1.922\t1.256\t-0.449\t1.337\t1.920\t1.854\t-3.012\t0.801\t-0.509\t-0.652\t0.372\t-0.627\t0.749\t1.321\t-0.202\t0.359\t-0.458\t0.468\t1.417\t0.410\n2\t1.065\t-0.984\t0.924\t0.679\t0.756\t-0.320\t-1.531\t-0.854\t-0.904\t-2.262\t-0.528\t0.755\t-0.505\t-0.741\t0.401\t0.041\t-0.171\t-1.849\t0.801\t-0.244\t-0.793\t1.085\t1.369\t-0.574\t0.827\t0.727\t0.624\t1.248\n1\t-0.782\t-0.461\t-1.169\t-1.812\t0.718\t0.223\t0.010\t-1.400\t1.640\t-0.503\t0.534\t-1.455\t-0.191\t0.291\t0.327\t0.155\t0.495\t-1.131\t0.197\t0.114\t0.232\t0.072\t-1.273\t-1.318\t-2.214\t0.818\t0.828\t0.844\n0\t-0.891\t-0.865\t1.143\t-0.259\t0.764\t0.386\t0.200\t-0.383\t0.496\t-0.426\t0.156\t1.106\t1.317\t-0.436\t0.372\t0.584\t0.237\t0.764\t0.775\t1.838\t2.156\t-0.165\t1.660\t0.439\t-0.177\t0.607\t-0.233\t-0.043\n2\t1.158\t-0.010\t-0.786\t1.109\t0.090\t0.790\t0.291\t1.257\t0.368\t0.483\t0.416\t0.533\t0.564\t-0.209\t-0.417\t0.165\t0.014\t-2.678\t1.528\t-0.479\t-0.350\t-0.997\t-1.212\t0.174\t-2.053\t1.555\t0.086\t1.005\n3\t-0.506\t-0.870\t0.756\t-0.696\t0.236\t-0.168\t-0.530\t-1.298\t1.301\t-1.005\t-0.862\t1.339\t-0.102\t0.320\t1.514\t1.490\t-0.549\t1.152\t-1.603\t-1.758\t0.044\t-0.084\t0.648\t0.543\t0.692\t1.602\t1.963\t0.878\n1\t0.539\t-1.441\t0.159\t-0.420\t0.667\t0.112\t0.873\t-0.441\t1.229\t-0.905\t0.103\t1.077\t-0.213\t0.706\t1.079\t0.491\t0.664\t-0.814\t0.052\t-0.086\t-0.605\t-0.120\t0.259\t1.102\t0.866\t1.547\t-0.235\t-3.096\n2\t-0.139\t-1.201\t1.231\t-0.799\t0.786\t-0.876\t0.704\t-1.609\t1.409\t1.135\t-0.553\t-0.826\t0.458\t-1.526\t0.035\t-0.808\t-0.254\t-0.644\t-0.798\t1.705\t0.781\t-1.480\t-0.876\t0.857\t0.969\t-1.385\t0.699\t0.265\n4\t1.496\t0.018\t1.331\t-1.103\t-0.073\t-1.168\t0.196\t0.328\t1.131\t0.039\t0.464\t-3.056\t-1.643\t1.503\t0.873\t-1.916\t-2.246\t-0.207\t-0.844\t-1.407\t0.339\t1.608\t0.193\t-0.654\t0.495\t1.390\t0.319\t-0.842\n4\t-1.906\t-0.298\t0.476\t-1.877\t-0.753\t1.996\t0.170\t-0.574\t0.210\t-0.576\t1.713\t-0.991\t-0.523\t-1.492\t-0.295\t-0.175\t1.196\t-1.017\t0.484\t0.087\t-1.893\t-0.725\t-0.765\t1.683\t0.546\t0.145\t-4.295\t-1.908\n4\t-1.416\t-1.501\t-0.327\t0.293\t-0.225\t0.439\t-0.377\t-2.059\t-0.191\t-1.521\t0.003\t-1.253\t0.517\t1.355\t-1.729\t-2.042\t-0.300\t1.129\t0.156\t-0.427\t-1.076\t0.723\t1.537\t-1.869\t0.294\t2.545\t-0.606\t-0.191\n0\t-1.224\t1.497\t-1.005\t-0.353\t-0.556\t0.043\t-0.651\t-0.580\t-1.215\t0.131\t-1.338\t-0.295\t-0.999\t-1.075\t-0.748\t-0.861\t0.441\t-0.558\t1.313\t-0.684\t-0.181\t-0.541\t-0.123\t-0.026\t-0.050\t0.637\t0.058\t0.245\n4\t0.883\t-2.263\t0.681\t1.430\t2.001\t0.136\t-0.861\t0.439\t-0.859\t0.130\t1.131\t-2.071\t-0.606\t0.514\t-2.050\t-0.917\t2.921\t0.490\t0.270\t0.534\t0.066\t2.542\t1.189\t-0.372\t-0.745\t0.463\t-1.062\t0.570\n3\t1.671\t-0.901\t0.506\t-0.087\t1.258\t0.406\t-0.235\t-0.894\t0.798\t-1.873\t0.646\t-1.099\t1.252\t0.161\t0.644\t0.082\t-1.752\t-0.861\t0.672\t0.167\t1.181\t0.403\t-0.967\t-0.796\t0.103\t-2.314\t1.673\t0.442\n3\t1.316\t-0.199\t-2.061\t0.430\t1.275\t0.925\t-0.663\t0.087\t-0.533\t-0.819\t0.955\t-1.198\t-0.894\t-1.104\t-1.490\t0.146\t1.162\t-0.185\t-1.506\t0.840\t1.686\t-0.646\t-0.479\t0.516\t1.622\t-0.520\t-1.332\t0.631\n1\t0.523\t-0.653\t-1.019\t-0.815\t-0.455\t-0.354\t2.166\t1.055\t1.025\t1.169\t-0.999\t-1.222\t-1.107\t1.335\t-0.547\t-1.251\t-0.431\t-0.239\t0.268\t0.251\t-0.828\t0.686\t-0.639\t-0.156\t0.687\t-0.642\t-0.569\t0.592\n3\t1.191\t-0.819\t1.092\t-0.548\t-1.336\t0.724\t1.291\t2.588\t-1.200\t0.699\t-0.410\t0.783\t-1.470\t-1.263\t-0.776\t0.079\t-0.641\t0.659\t1.027\t0.600\t1.619\t0.416\t-1.217\t1.363\t-0.605\t0.910\t1.574\t0.457\n3\t0.925\t-0.315\t0.876\t-2.484\t0.036\t0.810\t-1.237\t-0.038\t1.100\t-0.184\t-0.523\t-0.911\t-0.244\t-0.031\t0.245\t2.382\t-0.048\t-0.673\t1.446\t-0.722\t-0.832\t-1.451\t-0.860\t-2.314\t-0.357\t-1.464\t-1.056\t-0.360\n1\t0.121\t-0.585\t0.014\t-0.444\t-0.406\t-0.015\t-0.713\t-0.239\t0.281\t1.575\t0.332\t-0.015\t0.887\t0.660\t0.816\t1.446\t-0.121\t0.375\t0.635\t0.901\t-2.021\t-0.718\t1.529\t1.219\t-0.091\t-0.170\t1.733\t1.459\n1\t2.018\t-1.188\t1.191\t1.071\t0.854\t1.292\t0.281\t-0.583\t1.095\t1.133\t1.105\t0.045\t-0.716\t0.191\t0.942\t1.024\t-0.430\t0.315\t-1.309\t0.985\t-0.783\t0.785\t-1.149\t1.031\t0.322\t-0.781\t0.691\t-0.590\n3\t1.321\t-1.614\t1.492\t-0.254\t-1.149\t-0.247\t-0.723\t0.441\t-0.648\t-1.912\t-0.221\t-1.357\t0.204\t-1.187\t-0.488\t0.183\t-0.212\t0.250\t0.680\t-1.158\t-0.064\t-3.330\t0.247\t-0.301\t-0.983\t0.316\t1.173\t-0.476\n2\t-0.054\t1.370\t-2.002\t-1.315\t1.810\t-0.398\t0.296\t1.253\t0.343\t0.913\t0.616\t0.983\t0.493\t0.731\t-0.580\t0.523\t-0.040\t-0.087\t-1.679\t-0.422\t-0.134\t1.556\t-0.279\t0.283\t1.375\t-1.238\t-0.383\t0.880\n4\t1.100\t-0.322\t-1.347\t-1.861\t-0.231\t-1.299\t-0.041\t-1.364\t-1.317\t-1.119\t-0.398\t1.264\t1.363\t-0.079\t-2.913\t0.003\t0.200\t3.006\t1.094\t-0.930\t-2.209\t1.027\t-0.613\t-1.223\t-0.096\t0.202\t1.681\t1.572\n0\t-0.078\t1.547\t2.552\t0.073\t0.375\t-0.940\t-0.171\t0.179\t0.698\t-0.205\t0.746\t-0.375\t1.009\t0.415\t0.370\t-0.460\t-0.084\t-0.374\t0.869\t-1.355\t-0.706\t1.207\t0.188\t1.223\t0.830\t0.933\t0.421\t-0.658\n4\t1.459\t-0.600\t0.107\t1.594\t-0.129\t-1.005\t0.046\t0.686\t-1.240\t-0.466\t-0.042\t0.130\t-0.762\t-1.439\t-0.581\t0.167\t1.014\t-1.969\t-0.133\t1.647\t2.745\t-2.197\t0.513\t-0.473\t-0.625\t0.779\t-0.395\t1.707\n2\t-1.449\t0.298\t-1.801\t-1.701\t0.533\t-0.925\t-0.424\t0.498\t-0.549\t0.415\t1.199\t-0.841\t-0.557\t0.635\t-0.435\t0.629\t0.515\t-0.329\t1.328\t-1.199\t0.611\t-2.115\t0.094\t0.061\t-1.205\t1.534\t-1.096\t-0.338\n0\t-0.940\t-0.312\t0.317\t0.098\t-1.310\t0.389\t0.341\t1.070\t-1.111\t1.049\t-0.798\t-1.434\t0.593\t0.616\t0.875\t-0.812\t-0.789\t-0.554\t-0.145\t0.844\t-1.032\t0.375\t0.283\t-0.454\t0.767\t1.199\t1.108\t-0.162\n0\t-0.264\t-0.544\t-0.160\t-0.551\t-0.130\t0.016\t-0.898\t0.631\t-0.370\t-0.842\t-0.352\t0.242\t0.083\t0.422\t0.898\t1.305\t0.285\t-1.069\t-0.269\t-1.004\t1.697\t1.987\t0.650\t0.858\t0.251\t0.366\t-1.597\t-0.074\n2\t1.368\t0.126\t0.851\t1.224\t0.338\t-0.592\t0.976\t0.001\t0.686\t-2.154\t0.447\t-0.639\t1.653\t0.623\t0.240\t0.486\t-0.998\t1.623\t1.991\t0.393\t1.590\t-0.569\t-0.797\t0.041\t0.435\t-0.394\t0.538\t0.306\n1\t1.051\t0.197\t0.329\t-0.273\t-1.175\t1.882\t0.624\t-1.368\t0.264\t-1.087\t-1.362\t-0.241\t-0.112\t-0.724\t0.046\t-0.243\t-0.906\t-0.610\t-1.810\t0.333\t0.695\t-0.211\t-2.130\t0.300\t-1.312\t-0.310\t-0.078\t-0.166\n0\t-0.702\t0.639\t-1.250\t0.554\t-0.300\t0.424\t0.211\t0.536\t-0.460\t-0.166\t0.918\t-0.351\t-1.397\t0.213\t-0.251\t-0.478\t0.449\t0.471\t1.193\t0.844\t0.764\t-0.333\t-1.018\t-0.591\t-1.376\t-0.030\t-1.769\t1.267\n2\t-0.253\t0.850\t-1.289\t-0.341\t-0.196\t1.395\t1.175\t0.392\t-0.994\t0.057\t1.880\t-0.149\t0.129\t0.186\t-0.850\t-0.809\t-0.810\t-0.298\t0.442\t0.170\t0.507\t2.160\t2.595\t0.094\t0.923\t0.659\t1.077\t1.042\n3\t0.374\t-1.960\t0.782\t0.004\t0.196\t-0.100\t-0.882\t-1.739\t1.521\t-0.998\t0.290\t-2.092\t0.141\t-0.240\t1.619\t-0.848\t0.729\t1.209\t-1.133\t-1.442\t1.684\t0.025\t-0.110\t1.233\t0.580\t0.835\t0.577\t-0.509\n3\t-0.406\t0.089\t0.547\t-1.102\t0.359\t0.035\t-0.304\t1.173\t-0.904\t0.275\t1.455\t-0.707\t-0.297\t2.345\t0.474\t-0.506\t0.803\t-0.568\t-1.565\t1.372\t-1.103\t2.452\t-0.489\t2.133\t-1.369\t1.025\t0.500\t0.838\n1\t0.376\t-1.867\t-0.832\t-0.876\t-0.742\t1.213\t-0.149\t-0.427\t1.122\t0.495\t-1.741\t-0.617\t0.322\t-0.934\t2.030\t0.113\t-0.753\t-1.123\t0.928\t-0.648\t0.380\t-1.152\t0.070\t-0.636\t-0.315\t-0.796\t1.026\t-0.491\n0\t1.117\t-0.764\t0.759\t0.859\t-0.011\t1.498\t1.643\t-0.565\t0.470\t0.532\t0.897\t0.538\t0.953\t0.344\t2.373\t0.040\t-0.896\t0.886\t-0.258\t-0.479\t0.004\t0.419\t-0.621\t0.780\t-0.444\t0.834\t0.068\t0.423\n2\t-0.269\t-1.230\t-0.185\t-0.109\t-0.372\t0.178\t-2.336\t0.882\t1.773\t-0.181\t0.079\t-0.304\t0.510\t0.837\t-1.137\t-0.478\t-0.887\t1.382\t-0.005\t-0.302\t0.650\t-0.378\t-0.641\t-1.576\t1.273\t0.401\t-0.463\t2.284\n0\t1.486\t0.527\t-0.242\t0.033\t1.037\t1.126\t-0.842\t0.363\t-0.008\t-0.066\t1.330\t-0.238\t-0.576\t-0.815\t-0.175\t-0.735\t-0.865\t-0.890\t-0.831\t-0.761\t0.399\t-1.960\t-0.702\t-0.100\t1.149\t-0.246\t-0.153\t-0.088\n3\t0.817\t-0.889\t0.257\t-0.767\t0.864\t-1.617\t2.312\t0.373\t0.506\t-0.888\t0.567\t-0.429\t-1.543\t0.781\t-0.191\t-1.754\t2.433\t-1.173\t0.044\t-0.321\t-1.127\t0.306\t-1.624\t0.586\t-0.598\t-1.087\t-0.588\t-0.015\n4\t2.204\t-1.678\t-0.820\t0.807\t2.628\t-0.301\t-2.825\t-1.643\t1.478\t-1.546\t-2.861\t-0.936\t0.333\t0.257\t1.700\t-0.530\t-0.043\t-0.528\t-0.684\t0.229\t-0.260\t-0.565\t-1.585\t-1.425\t1.549\t0.998\t-0.179\t0.595\n1\t-1.282\t0.836\t-0.448\t-1.280\t-0.492\t0.062\t0.240\t-0.749\t-0.216\t-1.393\t-0.708\t-1.362\t-0.079\t-0.838\t2.075\t0.383\t-1.626\t-0.746\t0.702\t0.245\t-1.571\t-0.787\t0.643\t0.371\t0.576\t-0.280\t-0.857\t-1.276\n4\t1.455\t0.819\t-0.037\t0.319\t2.454\t0.243\t0.466\t-0.398\t-0.751\t0.814\t0.928\t1.100\t0.751\t1.981\t-1.925\t-0.238\t0.811\t-0.644\t1.271\t-3.281\t1.026\t-0.818\t-0.969\t-2.072\t-0.364\t1.036\t0.842\t0.163\n2\t-1.347\t0.019\t0.186\t-0.176\t-1.202\t-0.545\t-0.502\t-2.070\t0.712\t-1.309\t2.066\t0.435\t-0.907\t1.061\t0.072\t-0.776\t1.308\t0.332\t-1.068\t1.571\t-0.525\t0.022\t0.613\t-0.200\t0.609\t-0.235\t-1.333\t0.107\n2\t-0.365\t-0.741\t-1.115\t-0.972\t-0.988\t-0.216\t-1.259\t-0.347\t0.617\t-0.690\t0.773\t-0.550\t0.237\t0.161\t0.998\t-0.017\t1.106\t-0.610\t0.538\t1.087\t2.375\t0.928\t-0.141\t1.158\t-1.270\t1.425\t0.143\t-1.688\n4\t-1.150\t-1.217\t-0.951\t0.095\t0.549\t-0.466\t1.355\t0.423\t0.368\t0.935\t0.977\t0.524\t-1.340\t2.442\t-0.764\t1.539\t-2.184\t-0.461\t-0.193\t1.079\t0.612\t0.665\t1.991\t1.872\t0.995\t-0.482\t-0.066\t-1.092\n4\t0.840\t-0.749\t0.909\t0.846\t-1.471\t0.949\t0.724\t1.435\t-1.085\t-0.775\t-0.296\t0.006\t-3.295\t0.779\t-1.259\t-0.786\t-0.718\t-0.976\t0.600\t-2.322\t-0.301\t0.082\t0.907\t1.005\t1.195\t-0.307\t-1.032\t-0.298\n4\t0.995\t-1.642\t-1.595\t-0.189\t1.367\t-1.820\t0.044\t-1.007\t-2.303\t1.276\t0.757\t1.434\t-1.354\t-1.109\t-0.086\t0.310\t0.286\t-1.322\t0.744\t-1.266\t0.275\t-0.430\t1.173\t1.931\t0.326\t1.134\t-1.807\t-0.062\n0\t-1.030\t0.147\t0.593\t0.630\t-0.059\t-0.241\t0.452\t0.267\t-0.259\t-1.562\t-0.468\t-1.147\t-0.667\t0.033\t0.359\t1.780\t-0.537\t0.775\t0.306\t1.296\t0.576\t-1.008\t-1.723\t0.311\t-0.528\t-0.980\t-1.268\t0.161\n3\t1.623\t0.610\t-0.101\t1.177\t1.036\t-1.376\t0.084\t-0.415\t0.328\t-0.506\t1.135\t-0.618\t-1.023\t-0.056\t-2.331\t-0.546\t-0.333\t3.019\t-0.136\t2.144\t0.289\t0.136\t1.160\t-0.739\t-0.748\t-0.137\t-0.715\t-0.396\n3\t-0.531\t0.476\t-1.761\t0.505\t0.260\t0.024\t0.258\t0.282\t-0.915\t0.432\t1.726\t1.440\t1.104\t-0.386\t-0.143\t-1.939\t-2.921\t1.034\t0.059\t-0.941\t0.172\t-0.832\t-0.504\t1.685\t-0.108\t-0.076\t0.291\t-0.688\n1\t0.833\t-0.687\t-0.941\t-0.200\t0.265\t0.600\t0.445\t0.979\t0.116\t-1.048\t-1.355\t0.758\t1.006\t1.971\t2.424\t0.972\t0.367\t-0.420\t-0.874\t0.067\t-0.105\t1.905\t0.586\t-0.230\t-0.882\t0.534\t0.138\t-0.451\n1\t0.333\t0.081\t1.393\t2.474\t-1.263\t0.147\t0.146\t-0.014\t-1.085\t-0.917\t0.240\t-0.945\t0.159\t-0.246\t0.142\t-0.953\t0.157\t-0.881\t0.161\t1.339\t0.261\t0.322\t0.817\t-1.404\t1.719\t-1.056\t0.880\t0.747\n3\t1.107\t1.022\t-1.023\t-0.538\t-0.900\t0.511\t0.108\t1.437\t-0.096\t-0.303\t0.215\t0.655\t0.401\t0.589\t-0.337\t0.151\t-2.272\t0.638\t-2.077\t0.507\t-0.276\t0.367\t0.803\t0.455\t1.304\t0.508\t-1.221\t2.920\n3\t1.389\t2.216\t0.361\t-0.322\t-1.231\t-0.400\t0.197\t-0.075\t-0.687\t-0.897\t-0.442\t-0.313\t1.184\t0.195\t-0.783\t1.026\t-0.092\t1.025\t-0.456\t0.946\t1.441\t0.952\t-0.117\t-1.418\t2.609\t-1.486\t-0.510\t1.964\n2\t-0.158\t0.209\t1.727\t1.076\t2.373\t-1.297\t0.130\t-0.142\t-0.126\t-0.575\t-0.444\t-0.398\t0.733\t0.329\t-0.676\t0.939\t-1.554\t0.813\t0.497\t-1.010\t0.219\t-1.057\t0.456\t-1.595\t0.469\t-2.102\t0.470\t-1.208\n0\t-0.067\t-0.187\t-1.072\t0.879\t-0.092\t-0.134\t-0.825\t-0.370\t-1.022\t0.864\t1.033\t1.680\t0.434\t-0.339\t-0.469\t0.491\t0.201\t-0.174\t-0.262\t1.171\t-0.432\t1.347\t0.182\t-0.368\t-0.785\t-0.155\t0.503\t-0.377\n0\t0.180\t0.612\t-0.121\t1.665\t-1.485\t-0.740\t-0.635\t0.009\t-0.646\t-1.241\t-0.356\t1.015\t0.754\t-0.695\t-0.373\t-0.778\t-1.261\t0.427\t-1.281\t0.663\t1.819\t0.376\t0.623\t0.594\t-0.489\t0.370\t0.312\t-0.626\n4\t-1.149\t1.483\t1.821\t0.900\t0.438\t0.890\t-0.337\t-1.328\t-0.740\t2.288\t-1.164\t0.959\t-0.111\t-0.357\t1.689\t0.093\t0.933\t2.399\t-0.209\t0.474\t0.705\t-0.322\t0.492\t1.128\t0.783\t0.117\t-2.883\t-1.446\n3\t-1.696\t0.455\t-2.432\t0.209\t0.163\t-1.535\t0.065\t-0.293\t-0.154\t-0.144\t-0.589\t0.076\t0.866\t-0.239\t-0.050\t0.649\t2.270\t-0.193\t-1.702\t-0.897\t0.602\t1.513\t-0.946\t-0.043\t0.019\t-0.769\t1.535\t1.164\n3\t1.994\t0.158\t0.108\t0.021\t-0.395\t0.314\t0.775\t-0.332\t-2.491\t0.466\t2.133\t-0.376\t-1.471\t0.677\t1.744\t0.529\t1.725\t0.794\t0.707\t-0.518\t0.285\t-0.029\t-0.064\t-0.254\t0.226\t-1.296\t0.812\t-0.878\n3\t-0.285\t0.336\t0.697\t-1.102\t1.001\t-1.164\t-0.575\t0.676\t-1.307\t-2.007\t0.581\t0.377\t-0.321\t-0.712\t0.484\t2.463\t1.026\t0.925\t0.316\t-1.470\t-0.762\t0.181\t-0.193\t0.939\t0.704\t1.456\t-1.050\t2.366\n4\t1.465\t0.452\t0.378\t0.970\t-0.235\t1.378\t-0.222\t-3.150\t1.608\t-2.949\t-0.267\t0.753\t-2.350\t-0.145\t-0.163\t0.918\t0.483\t-1.340\t-0.649\t-1.072\t-1.857\t-0.651\t1.483\t0.445\t0.426\t-1.210\t-1.950\t0.568\n1\t-0.122\t-1.575\t0.012\t-0.393\t0.355\t-1.131\t0.757\t-0.581\t0.616\t1.848\t-1.686\t-1.087\t-0.764\t0.109\t0.174\t-0.677\t0.926\t0.853\t1.156\t0.810\t1.248\t-0.644\t-0.701\t-1.065\t0.491\t0.219\t0.772\t-0.360\n3\t-0.639\t-0.570\t-0.416\t1.993\t-0.260\t1.577\t-0.350\t0.063\t-0.239\t-0.298\t-0.140\t-0.139\t0.745\t-1.774\t2.418\t0.843\t-1.722\t-0.362\t0.284\t-1.031\t-2.120\t0.115\t0.731\t-0.176\t1.374\t0.462\t-1.321\t-1.446\n3\t-0.266\t-0.834\t1.097\t-0.861\t1.573\t0.954\t1.179\t-0.031\t-1.135\t-1.700\t0.839\t-0.097\t-0.448\t0.656\t2.634\t0.039\t1.107\t-0.156\t0.966\t0.101\t0.907\t0.621\t-0.642\t-1.385\t-2.253\t0.415\t1.351\t0.589\n0\t0.553\t0.383\t-0.176\t-0.391\t-0.447\t1.220\t0.438\t-0.823\t-0.326\t0.002\t-0.036\t-0.233\t-0.071\t0.184\t0.899\t-1.264\t0.179\t0.108\t1.273\t0.589\t0.997\t0.633\t-0.407\t-0.181\t0.557\t1.59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09\t0.445\t0.851\n3\t-0.413\t0.724\t-0.829\t1.201\t1.612\t1.377\t0.208\t-0.711\t0.563\t-0.340\t0.300\t0.586\t-0.482\t0.570\t0.456\t-1.272\t0.421\t-2.764\t1.951\t0.681\t-0.339\t-0.429\t0.888\t-0.467\t-0.843\t-0.333\t-2.076\t-1.324\n2\t-0.547\t0.014\t2.156\t0.101\t0.479\t-0.168\t-1.759\t-0.636\t-0.543\t0.580\t0.384\t0.318\t0.897\t-1.155\t2.049\t-1.679\t-1.325\t0.373\t-1.088\t0.050\t-0.725\t0.507\t-0.038\t-0.577\t-0.026\t2.025\t-0.085\t-1.394\n3\t1.898\t-1.069\t-0.176\t-1.462\t-0.154\t-1.859\t-0.343\t-1.972\t1.904\t-1.813\t-0.414\t1.019\t0.760\t1.199\t0.499\t0.969\t0.276\t-0.692\t-1.019\t1.078\t-0.426\t0.482\t-0.627\t-0.732\t-0.164\t0.611\t-0.148\t-0.733\n4\t-1.698\t0.258\t0.058\t-1.305\t-0.336\t-0.323\t0.753\t-0.808\t1.297\t-0.313\t-0.362\t-2.020\t-3.237\t-0.438\t-1.667\t-1.132\t-0.297\t1.113\t-0.265\t-0.319\t-0.585\t0.253\t2.389\t0.505\t-1.191\t0.254\t-0.808\t0.884\n3\t0.669\t-1.448\t-0.204\t0.716\t-2.934\t1.335\t0.354\t0.717\t-0.413\t-1.723\t0.534\t-0.083\t0.032\t1.088\t0.762\t1.921\t0.154\t-0.691\t-0.188\t0.416\t1.016\t-1.467\t0.009\t1.610\t-0.240\t0.931\t0.493\t0.956\n4\t-0.889\t-1.620\t-0.260\t-0.316\t1.549\t-1.116\t-1.900\t-1.475\t-0.030\t-0.990\t-1.285\t-0.295\t-0.529\t0.821\t0.817\t-0.473\t-0.168\t2.112\t2.629\t2.627\t0.165\t-0.320\t-1.315\t1.007\t0.408\t-0.636\t0.361\t-0.557\n2\t0.042\t-0.789\t-2.279\t1.107\t-1.037\t2.010\t-0.619\t1.269\t0.130\t1.583\t0.379\t0.480\t-1.156\t0.760\t0.929\t0.174\t1.100\t0.411\t1.215\t0.119\t1.110\t0.264\t2.058\t-0.336\t-0.093\t-0.663\t-0.387\t-0.518\n0\t-0.042\t0.486\t0.441\t-0.256\t-0.054\t-0.083\t0.683\t-0.695\t-2.434\t-0.343\t1.688\t0.801\t-1.569\t0.675\t0.208\t-0.375\t-1.078\t-0.814\t-0.560\t1.835\t0.640\t-0.710\t-0.542\t-0.037\t-0.433\t0.016\t-0.146\t0.097\n0\t-1.123\t0.959\t1.126\t-0.207\t0.771\t-0.691\t0.403\t0.503\t0.002\t-1.151\t0.140\t0.437\t-0.974\t0.991\t-0.554\t-0.234\t-1.200\t0.529\t2.652\t-0.364\t-0.735\t-0.352\t0.116\t0.486\t1.062\t0.829\t-0.455\t0.467\n2\t0.436\t0.584\t1.078\t-1.489\t1.083\t-0.216\t-0.691\t0.820\t-1.488\t0.939\t-0.157\t-0.100\t-2.244\t0.900\t0.248\t-0.467\t-0.927\t0.782\t0.348\t0.005\t-0.988\t0.303\t0.243\t1.316\t-1.197\t-0.716\t1.944\t0.192\n2\t-1.380\t-1.471\t0.250\t-1.185\t0.131\t1.082\t-0.204\t0.696\t0.457\t0.352\t1.794\t-0.695\t-1.102\t1.798\t-0.427\t-0.230\t0.021\t1.634\t-0.184\t-1.851\t-0.231\t1.348\t-0.310\t0.875\t1.369\t-1.170\t-0.634\t-0.226\n3\t2.632\t0.301\t0.676\t1.700\t-0.433\t0.118\t1.896\t-0.146\t0.055\t0.982\t-1.061\t0.029\t-0.066\t1.565\t1.027\t0.087\t-1.289\t0.678\t-0.463\t1.552\t1.034\t-0.088\t0.277\t-1.312\t1.884\t0.012\t-0.511\t0.002\n4\t2.344\t1.198\t-2.102\t1.357\t1.600\t1.195\t0.751\t1.551\t0.883\t0.579\t0.134\t-1.072\t-1.373\t0.773\t0.755\t-0.043\t-0.857\t-1.130\t-0.362\t0.702\t2.238\t-0.025\t0.288\t1.126\t-0.064\t0.369\t0.822\t1.102\n3\t-0.058\t-1.194\t-0.678\t-0.742\t1.261\t0.610\t1.018\t0.858\t1.238\t-1.441\t0.342\t-0.191\t0.992\t-0.833\t0.455\t0.145\t1.091\t1.187\t-2.148\t0.578\t1.111\t-2.185\t-1.067\t1.303\t-0.496\t0.673\t0.138\t1.224\n4\t1.835\t-2.729\t-2.690\t0.767\t0.236\t0.672\t-0.526\t-2.142\t-0.832\t1.560\t-0.452\t-0.443\t-2.629\t-1.804\t1.110\t-0.259\t0.991\t-0.754\t-1.468\t-0.261\t-0.573\t-0.833\t-0.807\t-0.135\t-0.492\t-0.932\t1.805\t0.458\n4\t1.571\t2.193\t0.293\t-0.280\t-0.303\t0.375\t-1.014\t-2.410\t-0.400\t0.041\t0.556\t0.397\t0.141\t1.169\t-0.873\t-0.672\t2.109\t-3.184\t1.119\t0.563\t-0.001\t0.896\t0.718\t-1.459\t-0.974\t-0.481\t-2.121\t-0.009\n4\t1.169\t-0.054\t-0.447\t1.523\t-0.059\t1.181\t2.459\t0.871\t-1.437\t-0.859\t-0.899\t0.469\t-2.014\t0.008\t-0.721\t-2.239\t-0.051\t-1.443\t-0.878\t0.778\t-0.211\t1.131\t0.834\t1.136\t1.258\t-0.375\t-0.257\t-1.034\n1\t-0.047\t-0.651\t-0.196\t0.680\t-0.419\t1.183\t-0.266\t0.545\t0.827\t-0.623\t1.643\t0.105\t-1.318\t-0.911\t-0.819\t-0.329\t-0.409\t-1.589\t0.044\t1.265\t0.570\t0.916\t-1.046\t-0.828\t-0.986\t0.507\t-1.767\t1.447\n0\t1.526\t0.843\t-0.063\t-0.091\t1.084\t-1.640\t-0.627\t-1.822\t-0.210\t-0.403\t-0.539\t0.357\t-0.818\t-0.622\t0.544\t-0.928\t-0.535\t-0.099\t0.478\t0.821\t-0.143\t1.467\t-0.492\t0.127\t0.767\t-1.105\t1.424\t-0.663\n1\t-0.615\t0.980\t-0.423\t-0.087\t-1.415\t0.274\t-0.611\t-0.477\t-0.840\t-0.124\t-2.006\t0.682\t-0.039\t-0.467\t-1.548\t0.433\t-0.634\t1.306\t0.486\t1.535\t0.137\t1.078\t0.463\t-0.451\t0.187\t-0.834\t2.069\t-0.101\n1\t0.051\t-0.914\t-0.726\t-1.160\t-0.894\t0.352\t-0.833\t-0.904\t-0.338\t-1.185\t-0.818\t-0.962\t-0.679\t2.594\t0.033\t0.853\t-0.080\t-1.481\t0.659\t-0.350\t-0.399\t0.088\t0.218\t1.386\t-0.152\t-1.259\t-0.031\t1.115\n1\t0.284\t1.664\t1.165\t-0.530\t-1.264\t0.838\t-0.653\t-0.336\t-1.060\t0.679\t-0.646\t-0.035\t-0.020\t0.184\t0.662\t-0.243\t-0.741\t-1.860\t0.103\t-0.157\t-0.710\t0.063\t0.856\t1.112\t-1.535\t-0.361\t0.961\t1.596\n2\t-0.889\t-1.123\t-0.604\t0.620\t-1.371\t0.287\t-0.961\t-0.781\t0.401\t2.173\t-0.911\t0.720\t-0.313\t-0.475\t1.071\t-0.844\t-1.154\t0.460\t1.595\t0.750\t-1.407\t-0.615\t0.980\t1.581\t0.229\t-0.579\t1.646\t-0.075\n3\t1.281\t0.805\t-1.431\t1.534\t-2.741\t2.151\t-0.287\t-0.190\t-0.060\t0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44\t-1.620\n1\t-1.579\t0.204\t-0.183\t1.678\t0.928\t-0.630\t0.373\t0.168\t0.454\t-1.019\t0.360\t-0.228\t0.282\t-0.724\t-0.585\t1.174\t-1.052\t-0.978\t-0.052\t-0.772\t-0.885\t0.105\t-1.505\t-1.008\t2.054\t0.459\t-0.747\t0.418\n4\t-0.842\t-1.604\t1.632\t-0.289\t2.710\t0.773\t1.829\t0.993\t0.159\t-0.598\t-0.977\t1.120\t0.265\t0.617\t0.815\t0.356\t-0.472\t1.013\t-0.198\t0.091\t0.717\t-0.059\t-1.818\t1.041\t1.255\t-1.831\t-1.043\t2.024\n4\t2.250\t1.167\t1.155\t1.471\t-2.203\t0.104\t-1.506\t-1.084\t-0.169\t1.512\t-0.311\t-0.570\t-0.118\t-0.048\t-1.541\t1.215\t-0.177\t-0.831\t0.891\t-0.984\t0.702\t0.021\t-1.505\t0.574\t-1.505\t-0.153\t-1.465\t0.783\n2\t-2.578\t-1.181\t-0.285\t0.575\t0.202\t0.673\t0.120\t-0.903\t0.540\t-0.040\t0.538\t0.038\t0.992\t1.050\t-0.585\t-0.324\t-1.302\t2.340\t-0.452\t-0.438\t-0.507\t-0.903\t1.107\t0.118\t-0.183\t-1.579\t0.385\t-0.863\n1\t0.604\t-0.613\t0.476\t-0.956\t0.317\t2.211\t-0.371\t-1.334\t0.897\t0.286\t0.458\t1.272\t-0.711\t-0.681\t0.545\t1.040\t0.237\t-0.071\t0.323\t0.734\t-1.448\t-0.990\t-0.089\t0.690\t2.172\t-0.113\t0.245\t-0.665\n1\t1.358\t1.630\t0.679\t0.239\t0.008\t-0.014\t1.863\t-1.102\t1.048\t-1.103\t1.179\t0.037\t-0.078\t0.340\t1.402\t1.032\t0.411\t-0.529\t-0.654\t0.285\t-0.779\t-0.231\t-1.542\t0.213\t0.002\t-0.701\t-0.941\t-0.227\n2\t0.767\t0.702\t-1.868\t1.123\t0.420\t0.804\t1.830\t0.794\t-1.183\t0.653\t-1.504\t-0.063\t-0.503\t0.010\t-1.510\t-1.394\t-0.449\t-0.223\t-0.426\t1.450\t0.424\t0.396\t-0.382\t-1.392\t-0.004\t1.807\t0.224\t1.113\n3\t-0.953\t-0.465\t-0.780\t-0.190\t-1.049\t-1.483\t-0.441\t-0.270\t1.116\t0.066\t-1.172\t-0.732\t0.713\t-0.424\t-1.093\t0.082\t-0.790\t0.478\t0.363\t-1.147\t1.785\t-0.123\t-2.664\t0.381\t-2.598\t1.040\t0.057\t-0.146\n2\t1.358\t0.059\t-1.950\t-1.325\t0.993\t-0.301\t1.855\t-1.029\t1.060\t1.011\t-1.567\t-0.581\t1.313\t1.302\t-0.108\t-0.034\t-1.136\t1.304\t0.615\t-1.440\t-0.052\t1.034\t-0.149\t0.600\t-0.383\t0.045\t0.436\t0.286\n1\t-0.252\t-1.157\t-2.444\t1.902\t0.307\t-1.107\t-1.261\t-0.279\t0.808\t0.631\t0.682\t-0.887\t-0.179\t0.127\t-0.057\t0.546\t1.574\t0.210\t-0.585\t-1.453\t0.176\t-0.660\t0.429\t0.365\t1.564\t0.214\t0.456\t-0.655\n2\t-0.076\t0.576\t-0.446\t1.066\t-0.426\t-0.678\t-1.807\t0.647\t-0.904\t-1.832\t0.280\t1.340\t0.860\t-0.743\t0.603\t-0.816\t0.747\t-0.872\t0.189\t0.840\t-0.163\t-0.624\t-2.676\t0.055\t0.678\t1.184\t1.336\t-0.167\n0\t0.945\t0.071\t-0.247\t0.033\t-1.049\t-0.095\t-0.923\t-0.644\t0.172\t-0.277\t1.265\t-0.245\t0.549\t-0.599\t0.561\t-0.758\t-0.547\t1.344\t0.250\t-1.742\t1.476\t-0.495\t-0.187\t0.615\t0.213\t-0.713\t-1.367\t-1.568\n4\t-0.776\t-1.441\t0.670\t-1.326\t0.907\t0.468\t-0.075\t0.940\t3.103\t0.478\t-1.120\t1.489\t0.186\t2.111\t-0.506\t1.282\t-0.000\t2.657\t-0.282\t3.002\t0.440\t0.847\t1.700\t-0.788\t0.969\t-0.293\t0.151\t0.083\n4\t-0.607\t2.602\t0.502\t1.849\t0.544\t0.402\t-0.626\t0.978\t0.025\t0.080\t-0.261\t-0.101\t0.025\t0.123\t-0.051\t-1.557\t0.542\t-0.123\t-3.317\t-1.588\t-0.473\t1.279\t0.608\t-0.687\t-0.191\t1.105\t-0.616\t-1.086\n3\t-0.759\t2.015\t-0.680\t0.865\t-1.491\t-1.494\t0.409\t0.704\t-0.886\t1.094\t0.395\t0.215\t0.364\t0.172\t-1.026\t1.634\t0.809\t-0.123\t1.068\t1.564\t0.963\t0.461\t-0.696\t-0.662\t-1.902\t0.242\t-0.059\t-2.313\n0\t-0.017\t-0.279\t-1.461\t0.977\t-0.431\t1.067\t0.464\t0.243\t0.197\t-1.009\t0.912\t0.282\t-0.353\t0.853\t0.162\t-0.440\t-0.385\t-0.802\t0.250\t-1.253\t-0.279\t0.273\t-0.306\t0.306\t0.662\t-1.719\t-0.151\t-1.663\n4\t2.020\t0.854\t-0.456\t-1.453\t-1.728\t1.012\t-0.372\t-0.103\t-0.064\t-1.737\t2.140\t-1.862\t-3.418\t0.521\t0.180\t-0.839\t-0.137\t0.076\t0.106\t1.941\t-0.006\t-0.575\t0.490\t0.179\t1.186\t1.777\t-0.976\t0.541\n4\t0.994\t0.529\t-0.414\t0.533\t-0.607\t0.345\t1.607\t-1.993\t-0.697\t-1.138\t2.337\t0.571\t-1.153\t-0.025\t-0.315\t-0.820\t-0.903\t-2.189\t-0.941\t-1.501\t1.699\t-0.580\t-0.806\t0.505\t-0.377\t-1.799\t-0.697\t-0.232\n1\t-0.537\t0.385\t1.712\t0.101\t-1.035\t0.405\t0.104\t0.033\t-1.339\t0.396\t-0.469\t-0.478\t0.900\t0.581\t-1.137\t1.503\t1.048\t-0.843\t-2.187\t0.356\t0.267\t-0.931\t1.823\t-0.109\t-0.035\t0.340\t1.226\t0.289\n3\t0.942\t0.380\t-1.539\t-0.792\t0.147\t-0.616\t-0.998\t2.144\t-1.000\t1.207\t0.413\t0.608\t1.138\t-0.689\t-0.300\t0.473\t-1.849\t1.650\t0.116\t-0.942\t1.094\t0.092\t-0.721\t0.946\t-2.326\t0.420\t1.310\t-0.833\n2\t0.757\t0.612\t-1.017\t-0.244\t-0.039\t-0.134\t0.334\t1.431\t1.082\t-1.312\t0.622\t1.329\t0.387\t1.091\t2.012\t1.024\t0.249\t1.045\t0.145\t0.024\t-0.351\t1.563\t-0.818\t1.532\t0.500\t-1.399\t0.368\t-2.100\n1\t-0.645\t-0.374\t-0.494\t-0.708\t-1.220\t-0.074\t1.133\t0.085\t-0.298\t0.806\t-0.492\t0.193\t-1.556\t-0.004\t-0.333\t-1.862\t-0.448\t-2.349\t-0.590\t0.494\t-0.907\t-0.711\t-1.520\t0.289\t-0.443\t-0.041\t1.149\t-1.000\n3\t0.383\t0.547\t0.497\t0.200\t-1.012\t2.841\t-0.192\t0.408\t-0.006\t1.433\t-0.570\t-0.281\t0.649\t0.127\t0.147\t0.145\t1.092\t0.601\t0.185\t-0.320\t1.646\t0.689\t-0.236\t1.421\t-2.104\t-0.194\t-1.182\t-1.891\n2\t0.668\t0.971\t-0.649\t-0.678\t-1.267\t-0.084\t-0.516\t-0.904\t-1.730\t-0.173\t-0.316\t0.354\t-1.054\t-1.422\t-0.143\t1.208\t2.031\t-0.422\t-0.507\t-1.895\t0.405\t-0.681\t-0.622\t-0.251\t1.006\t0.225\t0.633\t1.681\n3\t1.273\t-0.804\t-1.198\t-0.966\t0.468\t-1.080\t-2.225\t-0.339\t0.161\t-0.450\t-1.131\t1.166\t0.810\t0.340\t1.301\t-0.901\t-0.211\t1.170\t0.161\t1.701\t-1.750\t-1.818\t-0.496\t0.583\t0.711\t-0.239\t1.031\t0.416\n4\t0.341\t0.661\t0.005\t-0.039\t-0.522\t-0.914\t0.683\t-0.861\t-0.972\t0.181\t-0.955\t-0.039\t1.431\t0.028\t-1.400\t-0.005\t-1.660\t-2.504\t-1.162\t1.451\t-0.737\t1.755\t0.910\t1.687\t-2.333\t0.848\t-0.265\t0.866\n3\t1.344\t1.680\t-0.503\t0.780\t1.430\t0.401\t1.285\t-0.036\t0.394\t-0.622\t-0.165\t0.434\t-0.697\t-0.224\t0.761\t2.053\t-2.049\t1.977\t-0.260\t0.965\t1.161\t1.564\t-0.728\t-0.225\t-0.054\t-0.357\t1.319\t-0.077\n3\t0.901\t2.344\t1.365\t-1.426\t-0.312\t0.360\t-0.064\t1.313\t-0.020\t-0.070\t1.085\t-1.409\t0.489\t-0.632\t0.776\t-0.366\t0.513\t-0.546\t-1.980\t0.361\t-1.299\t1.497\t0.564\t0.725\t-0.283\t-0.019\t-2.366\t0.393\n4\t-1.023\t0.498\t1.905\t-0.844\t-1.809\t-2.126\t1.316\t-1.663\t1.880\t-1.039\t0.140\t0.862\t0.142\t-0.150\t-0.779\t0.560\t0.743\t0.227\t-0.104\t-0.148\t-2.282\t0.448\t0.976\t-0.546\t0.949\t-0.840\t0.998\t-1.509\n3\t1.183\t-0.026\t-0.505\t0.172\t0.618\t-1.906\t0.522\t-0.940\t-1.188\t0.539\t1.339\t0.216\t-1.355\t-0.671\t-0.418\t0.196\t-0.072\t0.495\t1.000\t0.345\t0.898\t-0.270\t3.296\t0.553\t0.684\t1.758\t0.088\t-0.646\n3\t-1.325\t-0.868\t-2.070\t1.542\t0.187\t-0.990\t-0.060\t-2.081\t-0.951\t0.543\t1.560\t-0.489\t1.379\t0.245\t0.357\t-1.404\t0.041\t1.236\t-0.352\t0.493\t-0.140\t-0.754\t0.707\t-2.008\t0.661\t0.586\t0.186\t-1.297\n3\t-1.255\t0.664\t-0.221\t0.599\t-2.736\t-0.760\t0.869\t-0.060\t2.142\t0.311\t0.883\t0.693\t-0.684\t-0.279\t0.675\t0.441\t-1.741\t1.004\t0.154\t-1.912\t-0.623\t0.676\t-0.622\t-0.554\t0.741\t-0.691\t0.674\t-1.212\n4\t-0.842\t-0.424\t0.734\t-1.146\t-0.466\t0.597\t0.875\t-0.193\t-0.411\t-1.322\t0.526\t1.319\t2.565\t0.386\t-0.051\t-0.920\t-1.879\t0.177\t2.350\t1.271\t1.117\t0.246\t0.360\t-2.424\t0.763\t-0.579\t-0.702\t-0.323\n1\t-0.462\t0.289\t0.304\t0.091\t-1.374\t0.038\t0.630\t-1.639\t-0.735\t-0.915\t-0.172\t0.770\t-0.214\t1.450\t0.319\t0.075\t0.039\t-1.795\t-0.107\t0.410\t1.861\t-0.941\t1.279\t0.614\t-0.591\t-0.601\t1.325\t0.533\n1\t0.182\t0.698\t0.191\t-0.643\t1.274\t0.655\t-0.446\t-0.318\t0.492\t-1.170\t0.701\t-0.532\t-0.756\t-1.945\t-0.103\t-0.956\t-0.362\t-0.028\t-1.337\t-2.049\t-1.273\t-1.650\t0.137\t0.175\t0.578\t1.480\t-0.063\t0.412\n3\t-1.466\t-2.257\t1.139\t-0.121\t0.578\t0.889\t-1.297\t0.899\t-2.092\t0.340\t0.809\t0.151\t-1.294\t-0.003\t1.627\t0.551\t-0.595\t-0.021\t0.965\t1.571\t-1.763\t1.145\t1.006\t-0.932\t0.502\t-0.253\t-0.884\t-0.134\n3\t-0.679\t0.236\t0.899\t-0.819\t-2.424\t1.490\t-1.135\t-2.560\t-1.037\t0.773\t-0.808\t0.359\t1.086\t-1.370\t-0.092\t0.220\t-0.259\t-1.914\t-0.619\t-0.395\t-1.493\t0.110\t0.944\t-0.323\t0.824\t-0.226\t-0.478\t-1.444\n4\t-0.465\t-0.106\t2.644\t-1.503\t0.254\t0.468\t1.086\t0.098\t0.308\t-0.392\t0.269\t-0.343\t0.622\t-0.370\t0.377\t-0.029\t1.126\t-0.051\t-1.773\t1.262\t-0.906\t-0.654\t-0.596\t1.374\t-2.136\t3.138\t1.056\t0.223\n1\t-1.908\t-0.041\t0.665\t0.685\t-0.710\t-1.023\t-1.092\t0.466\t1.007\t0.142\t-1.070\t0.180\t-0.952\t-0.737\t-0.369\t-0.799\t-1.130\t1.549\t-1.888\t-0.592\t-0.216\t1.228\t0.295\t-0.282\t-0.741\t1.147\t-0.807\t0.211\n3\t0.646\t-1.263\t-1.939\t0.260\t-1.597\t0.085\t-0.198\t-1.501\t1.892\t-0.676\t0.978\t1.292\t-0.760\t2.078\t-0.858\t-0.112\t0.295\t-1.412\t-0.178\t0.646\t0.340\t1.318\t-1.187\t-0.244\t-0.521\t-0.910\t-0.302\t0.219\n2\t-0.140\t-0.383\t-0.580\t-0.488\t2.274\t1.040\t-0.590\t0.193\t-0.000\t-0.137\t1.013\t-0.001\t-0.658\t1.613\t-0.329\t-0.123\t0.118\t0.200\t-0.660\t-0.164\t1.113\t0.862\t-1.947\t-1.595\t-0.958\t-1.841\t-1.296\t0.324\n1\t-0.183\t0.412\t-1.320\t0.533\t0.165\t-0.073\t0.139\t-0.211\t0.257\t0.976\t1.400\t-0.528\t0.303\t-0.948\t2.422\t0.813\t-0.910\t1.490\t-1.616\t-0.857\t-0.605\t-0.228\t0.605\t-0.889\t-0.459\t-0.155\t0.653\t-0.345\n1\t-0.881\t0.281\t-0.060\t1.947\t0.917\t0.285\t-0.267\t1.676\t-0.030\t0.332\t0.042\t-0.404\t0.037\t-1.962\t-1.548\t0.350\t1.568\t0.348\t-0.720\t-1.443\t0.785\t-0.567\t0.643\t0.390\t0.478\t0.034\t0.384\t-0.119\n2\t0.302\t-1.269\t-0.545\t0.743\t-1.823\t-1.411\t-0.373\t0.927\t-0.217\t0.461\t0.005\t-0.141\t-0.654\t0.840\t-1.254\t-0.234\t1.146\t-0.764\t-1.556\t-2.117\t0.666\t0.734\t-0.344\t-1.271\t-0.146\t1.178\t-1.269\t1.058\n0\t-0.854\t-0.247\t0.042\t1.809\t1.435\t-0.388\t2.009\t0.951\t0.471\t-0.281\t-0.577\t0.543\t0.384\t-0.391\t1.114\t0.163\t0.418\t1.073\t0.827\t-1.620\t0.762\t0.028\t-0.447\t0.052\t-0.306\t0.127\t-0.021\t0.688\n3\t-0.797\t1.321\t0.150\t0.562\t-0.249\t0.201\t-0.891\t-1.178\t0.015\t0.570\t-1.946\t-0.499\t1.842\t0.479\t1.538\t-0.634\t-0.458\t0.967\t0.624\t-0.094\t-1.263\t1.157\t2.510\t-1.572\t1.404\t-0.468\t-0.333\t0.270\n3\t1.495\t-1.098\t-0.149\t0.563\t1.287\t-1.404\t1.779\t-2.162\t-0.032\t0.188\t-1.384\t0.585\t0.039\t-0.232\t-0.422\t0.291\t0.027\t0.678\t-1.031\t-0.565\t1.605\t-2.200\t0.305\t-1.446\t-0.2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454\t-0.462\t-0.788\t-1.060\t0.086\t-0.742\t1.378\t1.626\t0.475\t0.244\t0.019\n2\t1.449\t-1.749\t-0.908\t0.731\t2.055\t-0.864\t1.012\t-1.150\t-0.389\t0.010\t-0.691\t-0.419\t-1.364\t0.674\t-0.898\t-0.664\t-0.782\t-0.729\t0.145\t0.063\t2.519\t-0.245\t-0.100\t1.337\t0.225\t-0.099\t-0.134\t0.194\n1\t0.940\t-2.233\t-1.196\t1.959\t-1.227\t-0.763\t1.251\t0.050\t0.059\t1.591\t-0.575\t-0.536\t0.761\t0.160\t1.028\t-0.645\t0.199\t-0.073\t-0.391\t-1.282\t-0.421\t-0.539\t1.253\t0.567\t0.444\t-0.182\t-0.892\t0.223\n0\t-0.052\t-0.993\t0.281\t0.962\t-1.569\t0.906\t0.082\t-0.455\t0.606\t-0.173\t-0.398\t-1.513\t-0.172\t0.703\t-0.710\t0.369\t-1.321\t-0.941\t-0.753\t-1.626\t-0.187\t0.682\t0.552\t-0.082\t-0.231\t-0.791\t1.981\t-0.535\n2\t-0.735\t0.559\t1.636\t-0.723\t-1.069\t-0.206\t-1.652\t1.140\t-0.017\t-0.932\t0.578\t-0.914\t-1.925\t0.035\t0.176\t-1.092\t-1.095\t-1.129\t1.428\t1.593\t-0.511\t-0.376\t-1.102\t0.650\t0.297\t-0.936\t1.001\t1.372\n4\t-0.465\t-1.019\t-0.669\t-0.289\t0.233\t0.894\t-1.242\t-1.707\t0.838\t-0.321\t-2.836\t-1.117\t-0.932\t-0.934\t0.105\t-0.731\t1.266\t1.336\t0.422\t-1.755\t0.432\t-2.181\t-0.948\t0.842\t0.514\t1.108\t0.273\t-0.699\n4\t0.905\t1.968\t0.673\t-1.548\t-0.689\t-0.608\t-1.023\t1.624\t-2.393\t0.352\t0.028\t-0.694\t-0.828\t-1.919\t1.456\t-0.127\t-1.576\t-0.590\t1.219\t-0.433\t0.628\t0.641\t0.168\t-2.036\t-0.270\t0.915\t-2.773\t0.085\n0\t1.148\t0.259\t-0.834\t1.449\t0.878\t1.287\t0.501\t0.793\t0.086\t-0.445\t0.409\t0.957\t0.459\t2.178\t0.469\t-1.318\t0.385\t0.715\t1.056\t-0.430\t-0.751\t0.666\t-0.084\t0.299\t0.737\t0.321\t0.798\t0.056\n0\t-0.475\t-0.524\t0.671\t-1.276\t0.004\t0.079\t0.924\t-0.855\t-1.307\t0.475\t-1.395\t-0.523\t-0.239\t0.616\t-1.542\t-0.296\t0.026\t-0.861\t-0.358\t0.889\t-0.507\t-0.364\t0.886\t-0.218\t1.338\t-1.638\t-0.273\t0.776\n1\t0.946\t0.338\t0.474\t-1.904\t-0.629\t-0.413\t-1.371\t-0.550\t0.218\t-1.342\t-0.455\t-0.807\t0.608\t-1.538\t0.354\t-0.174\t1.502\t1.222\t-0.993\t-0.233\t-0.091\t-1.769\t-0.869\t0.114\t-0.853\t-0.180\t-0.102\t-0.321\n0\t-0.470\t0.965\t1.085\t0.628\t-0.493\t-0.367\t-0.260\t-1.627\t-0.066\t-0.322\t-0.252\t-0.739\t-0.213\t1.111\t0.742\t2.036\t-0.335\t-0.691\t-1.067\t0.405\t0.086\t1.005\t-1.587\t-0.711\t-0.593\t-0.106\t-0.385\t0.471\n1\t-1.193\t-1.833\t1.018\t-1.296\t-0.179\t0.753\t-0.615\t-0.825\t0.226\t1.118\t-1.169\t-0.150\t-1.732\t-0.393\t0.034\t-0.855\t-0.457\t-1.408\t0.077\t1.056\t-1.097\t-0.623\t0.503\t1.212\t-1.493\t-0.169\t0.283\t-0.102\n2\t1.289\t0.327\t-1.361\t-1.006\t1.161\t2.888\t1.140\t-0.990\t0.194\t0.967\t-0.949\t-1.472\t0.991\t-0.034\t0.483\t0.803\t-0.087\t-0.857\t0.521\t0.652\t-0.675\t0.891\t0.911\t-0.146\t0.108\t-0.008\t1.505\t-0.870\n0\t-1.263\t-0.277\t0.413\t0.457\t-1.977\t0.575\t0.413\t-0.424\t-1.268\t-0.408\t1.611\t0.597\t0.973\t-0.989\t-0.533\t-1.297\t-0.665\t1.358\t-0.449\t-0.689\t0.239\t-0.575\t0.093\t-0.569\t0.191\t-0.380\t-1.118\t1.054\n2\t-0.462\t0.868\t-0.247\t0.024\t-0.078\t-0.892\t-0.171\t-0.458\t-0.692\t-1.058\t-0.101\t0.618\t1.121\t-1.394\t1.606\t0.293\t-1.127\t-0.404\t-1.877\t-1.622\t1.721\t-0.699\t0.537\t-0.111\t0.018\t0.368\t-2.084\t1.319\n1\t-0.246\t-0.282\t0.284\t3.150\t-1.515\t-1.879\t-0.536\t-0.719\t-0.738\t-0.012\t-1.170\t-0.595\t-0.295\t-0.271\t-0.141\t-0.150\t-0.807\t-0.255\t-0.005\t0.960\t-1.481\t0.024\t0.165\t0.184\t0.231\t-1.313\t0.161\t0.106\n4\t-0.422\t-1.204\t2.211\t0.057\t0.512\t0.059\t1.150\t-0.459\t-0.615\t-2.068\t2.339\t-0.560\t0.548\t-0.505\t1.790\t0.803\t1.830\t0.092\t1.099\t-0.058\t0.961\t1.434\t-0.004\t0.235\t-0.721\t0.504\t0.633\t-1.926\n3\t-1.148\t-1.727\t0.991\t-0.437\t0.070\t0.655\t-0.719\t0.137\t0.648\t1.643\t-0.511\t-0.463\t0.563\t0.209\t-0.137\t1.593\t-1.682\t-1.422\t-1.671\t1.202\t0.515\t-1.371\t-0.123\t-0.028\t-2.209\t0.188\t-1.178\t0.707\n3\t-0.194\t2.463\t-0.307\t0.587\t-1.930\t0.432\t1.142\t-0.528\t-2.510\t-0.039\t0.017\t0.724\t2.011\t-0.380\t-0.856\t-1.167\t0.198\t0.479\t-0.546\t0.779\t0.465\t0.043\t-0.902\t-0.482\t-2.430\t0.236\t0.026\t0.188\n3\t0.382\t-0.868\t1.067\t1.936\t0.003\t1.204\t0.299\t1.697\t-0.468\t-0.356\t1.246\t-1.756\t-0.194\t2.232\t-0.591\t0.186\t-0.912\t-0.338\t-0.322\t1.512\t-0.719\t1.319\t-0.931\t-0.219\t-0.013\t-0.414\t0.838\t1.713\n4\t1.779\t-0.052\t-1.333\t1.585\t-2.370\t-2.202\t-0.006\t-0.822\t0.056\t-0.262\t-0.182\t0.594\t0.089\t0.574\t0.717\t0.738\t-0.905\t1.590\t-0.879\t-0.119\t-0.399\t1.549\t-1.745\t-1.433\t0.346\t-0.268\t0.740\t1.308\n1\t1.305\t1.667\t0.311\t0.807\t0.620\t-0.651\t0.118\t0.360\t1.371\t-0.152\t-1.179\t-0.147\t-0.184\t-0.751\t1.014\t1.092\t-2.498\t1.275\t0.301\t0.106\t0.502\t-1.743\t-0.476\t0.683\t0.540\t-0.340\t0.055\t0.137\n0\t1.144\t0.796\t-0.696\t0.008\t0.376\t0.313\t-0.499\t1.501\t-0.234\t-0.204\t0.964\t0.722\t0.900\t1.881\t-0.677\t0.140\t-0.810\t-0.535\t-0.802\t-0.086\t0.803\t-0.361\t0.145\t-0.895\t-0.747\t-1.377\t-0.354\t-0.162\n0\t-0.613\t0.274\t0.285\t-0.977\t2.174\t-0.314\t0.460\t-0.720\t1.251\t-1.523\t0.965\t0.155\t0.632\t-0.034\t-0.064\t-0.208\t-0.540\t0.012\t-1.083\t0.788\t0.478\t-0.057\t-0.397\t-0.621\t-0.115\t-2.087\t0.160\t0.201\n2\t0.165\t1.132\t-0.168\t0.150\t-0.094\t0.803\t-0.668\t-2.196\t0.471\t0.988\t0.446\t-0.640\t-1.135\t0.494\t0.141\t-0.126\t1.822\t-0.947\t1.775\t0.032\t-1.836\t-0.179\t0.959\t-0.365\t-1.475\t-0.165\t-0.857\t0.492\n3\t-1.377\t0.812\t-1.724\t-2.557\t-1.262\t1.299\t-0.327\t0.717\t0.563\t-0.406\t-0.264\t0.622\t1.403\t-0.112\t-1.016\t-0.493\t1.354\t-0.792\t0.857\t-1.443\t1.189\t-0.026\t1.271\t-1.491\t-0.827\t-0.009\t-0.985\t-0.403\n3\t-2.090\t-1.266\t-1.462\t-0.902\t0.740\t0.284\t-0.758\t0.648\t-0.008\t-0.480\t0.330\t0.227\t-0.469\t-2.142\t-0.859\t1.087\t-2.031\t0.249\t0.478\t-0.404\t-1.200\t1.899\t0.955\t-0.655\t-1.635\t0.760\t-0.358\t0.867\n0\t1.341\t0.092\t0.135\t-1.447\t-0.309\t-0.241\t-0.829\t0.022\t-0.429\t0.080\t0.122\t-0.956\t-0.399\t0.077\t1.184\t0.257\t-0.662\t0.399\t1.708\t0.917\t0.548\t0.726\t1.440\t1.076\t0.567\t1.069\t-1.082\t-1.502\n3\t-0.712\t0.430\t1.412\t1.186\t-0.433\t1.255\t1.172\t-0.189\t-0.009\t1.542\t1.058\t1.225\t2.585\t0.547\t0.326\t1.358\t1.988\t-0.611\t-0.202\t-1.019\t0.343\t0.889\t-0.949\t1.134\t-0.453\t-0.535\t1.130\t-0.625\n2\t0.865\t-0.440\t0.404\t-1.547\t0.152\t1.113\t-1.760\t0.069\t-0.815\t0.072\t-1.201\t-1.450\t-0.604\t-0.745\t-1.553\t1.209\t-0.627\t1.060\t-1.161\t0.979\t-0.671\t1.949\t0.175\t1.173\t-0.491\t-1.217\t-0.657\t-0.437\n2\t1.691\t1.335\t-2.001\t-0.770\t0.943\t-0.022\t-0.018\t-1.568\t-0.745\t-1.358\t-1.653\t-1.258\t-0.928\t-0.761\t-0.842\t1.020\t-0.907\t1.426\t-0.484\t0.439\t0.471\t-0.898\t-0.328\t-0.503\t0.372\t-0.129\t-0.748\t0.119\n4\t0.494\t-1.676\t1.176\t2.124\t0.824\t-1.003\t-1.989\t1.364\t-0.147\t-0.391\t1.165\t-0.590\t0.762\t0.024\t0.247\t1.629\t-0.489\t-0.580\t-0.202\t-1.203\t0.936\t0.804\t-0.009\t-0.924\t-2.745\t-0.834\t-0.231\t-1.899\n4\t0.142\t0.334\t1.438\t0.722\t-0.786\t-1.092\t0.277\t0.707\t2.196\t-1.342\t0.872\t-0.500\t1.757\t-0.369\t1.178\t2.514\t0.489\t-0.751\t-0.599\t-0.952\t-0.660\t-0.712\t2.415\t-0.776\t0.147\t0.434\t0.400\t1.821\n1\t-0.369\t-0.598\t0.234\t1.347\t-0.332\t-0.688\t1.053\t-2.114\t0.027\t0.055\t1.335\t1.687\t0.643\t1.678\t1.613\t-0.443\t0.074\t-1.168\t-1.160\t-0.549\t0.254\t0.279\t-0.924\t-0.402\t0.137\t-0.378\t-0.402\t0.644\n2\t2.358\t0.192\t-0.528\t-0.708\t-0.083\t0.117\t0.059\t-1.582\t-2.264\t-0.969\t0.059\t0.063\t0.455\t0.363\t-0.500\t-1.021\t0.521\t0.226\t-1.093\t0.625\t0.957\t0.789\t0.964\t-0.751\t0.852\t-0.158\t2.045\t0.894\n4\t-0.138\t2.219\t-1.543\t-1.489\t-2.084\t-0.300\t0.040\t-1.649\t0.930\t1.529\t0.483\t0.321\t-3.041\t-0.289\t-0.130\t1.132\t-0.701\t1.103\t0.884\t0.750\t0.228\t-2.386\t0.322\t1.599\t0.805\t1.188\t-1.295\t-0.946\n4\t1.583\t0.241\t-0.850\t1.854\t0.533\t0.858\t-2.380\t1.644\t0.492\t0.065\t-0.181\t0.107\t-1.057\t0.319\t-1.794\t2.319\t-1.309\t0.787\t0.544\t-0.485\t-0.030\t0.044\t0.147\t0.053\t1.190\t-2.158\t1.339\t-1.454\n0\t-1.066\t-0.199\t-1.010\t-0.570\t0.975\t-0.045\t0.829\t-0.211\t0.735\t0.818\t0.713\t0.951\t0.488\t-0.428\t-1.416\t-0.761\t0.392\t1.113\t0.489\t-0.312\t-0.426\t0.035\t0.607\t0.036\t-0.500\t-0.849\t-0.716\t-0.695\n3\t-1.039\t0.040\t0.956\t-1.134\t0.227\t-0.590\t0.701\t-0.798\t0.772\t-0.158\t-0.937\t0.064\t-1.938\t-0.559\t0.337\t-1.772\t-2.571\t-2.339\t1.327\t-0.077\t-0.532\t0.945\t-0.404\t0.268\t0.026\t-0.471\t0.294\t0.773\n2\t1.164\t0.188\t0.939\t-0.531\t0.499\t-1.919\t-0.455\t1.177\t-0.562\t-0.223\t1.237\t0.354\t-1.517\t1.041\t1.229\t-0.997\t1.310\t0.221\t0.078\t-1.545\t0.904\t0.618\t0.792\t0.606\t0.182\t-0.567\t1.126\t-1.971\n1\t-0.967\t-0.020\t-1.061\t-0.184\t0.843\t-1.278\t-0.255\t1.495\t0.040\t0.769\t-0.269\t-1.209\t-1.726\t-0.450\t2.169\t-0.150\t-0.579\t0.212\t-0.647\t0.030\t0.245\t-1.374\t1.374\t0.074\t1.511\t-0.670\t-0.755\t-0.535\n0\t0.597\t-1.238\t-0.121\t0.244\t-0.259\t0.309\t-1.747\t-0.007\t1.516\t-0.365\t0.835\t-0.406\t-0.521\t-0.650\t-1.466\t0.332\t-0.185\t-1.078\t-0.685\t-1.763\t0.329\t-0.452\t-0.305\t0.556\t0.988\t1.206\t0.509\t-0.427\n2\t1.919\t0.188\t-0.390\t-1.252\t1.275\t-1.347\t2.079\t0.890\t-0.580\t0.419\t-0.984\t1.033\t-0.855\t-1.300\t-1.231\t0.405\t0.100\t0.116\t-0.630\t-0.287\t0.201\t1.323\t-0.335\t0.397\t1.533\t0.622\t-0.497\t-0.040\n0\t1.762\t-0.932\t-0.411\t-0.570\t0.155\t-1.821\t0.622\t0.465\t0.865\t0.845\t0.454\t-0.306\t0.059\t-0.652\t0.194\t-0.790\t0.137\t-0.440\t-0.818\t1.050\t0.793\t0.057\t-1.743\t-0.879\t-0.077\t0.473\t0.490\t0.232\n0\t1.627\t1.238\t0.096\t0.507\t1.024\t1.694\t-0.875\t-0.381\t1.598\t0.075\t0.127\t-0.066\t0.531\t0.187\t-0.709\t-0.611\t0.518\t-0.129\t0.681\t0.573\t0.105\t0.663\t0.289\t0.217\t-1.378\t-0.337\t-0.622\t0.177\n3\t1.025\t0.581\t-0.434\t0.956\t0.739\t-0.361\t1.485\t2.445\t-0.355\t0.160\t0.833\t-0.320\t-0.248\t0.697\t1.493\t-1.783\t-0.035\t0.349\t-0.233\t1.447\t0.256\t-0.815\t0.824\t-1.672\t-0.692\t-1.427\t-2.133\t0.208\n1\t0.833\t-0.765\t-0.486\t0.924\t-0.122\t0.511\t0.121\t0.732\t-0.248\t-1.185\t1.106\t-0.600\t0.488\t0.430\t-0.082\t0.122\t0.066\t-2.353\t0.139\t0.366\t1.093\t-1.380\t0.508\t-1.234\t1.348\t-0.466\t0.727\t1.434\n2\t0.514\t1.055\t-1.850\t0.356\t-1.278\t-0.256\t-0.031\t-0.347\t-0.806\t0.841\t0.568\t1.228\t-0.316\t-0.119\t2.620\t-0.344\t-1.553\t0.866\t0.967\t1.149\t1.076\t-0.897\t0.682\t0.501\t0.696\t-0.118\t1.046\t0.508\n2\t-0.999\t-0.973\t-1.146\t-0.254\t-0.233\t-1.044\t0.173\t-0.358\t1.605\t1.655\t0.374\t0.156\t-0.744\t-0.030\t-0.573\t1.342\t-1.179\t-2.491\t0.254\t0.397\t1.617\t-0.593\t0.561\t0.130\t1.029\t0.736\t0.768\t-0.915\n4\t0.286\t0.344\t-0.405\t1.895\t0.863\t-1.690\t1.943\t-1.186\t0.527\t-0.507\t2.561\t0.240\t-0.645\t0.757\t-0.891\t-0.334\t1.987\t-0.632\t1.687\t-0.552\t0.467\t0.084\t-0.223\t1.885\t-1.361\t-0.226\t-1.062\t-1.179\n1\t-0.540\t-0.492\t-0.769\t-0.760\t-2.001\t-0.224\t-1.956\t0.751\t-0.560\t-0.119\t-0.373\t-0.244\t1.263\t-1.351\t-1.623\t0.119\t-0.496\t0.360\t0.717\t-1.106\t0.603\t-1.022\t0.075\t-1.237\t0.132\t-0.021\t-1.103\t-1.358\n4\t2.572\t-2.249\t0.609\t-0.557\t-0.586\t-0.620\t-0.909\t1.685\t0.842\t0.023\t1.351\t-1.513\t-1.331\t-2.178\t-0.699\t0.162\t0.103\t0.149\t-1.411\t0.803\t0.240\t0.733\t-1.893\t0.311\t-1.188\t-0.397\t0.534\t0.091\n4\t-0.377\t-2.338\t-0.144\t-0.768\t0.080\t-0.659\t2.603\t-0.028\t-0.643\t1.299\t-0.214\t-0.545\t0.300\t1.144\t0.201\t-0.114\t-0.489\t-1.009\t-2.165\t-0.195\t-1.338\t-0.205\t-1.613\t2.447\t2.412\t0.148\t0.688\t-1.406\n2\t0.222\t2.543\t-1.416\t-0.237\t-1.370\t-0.122\t-0.127\t-0.458\t0.054\t-0.409\t-0.110\t-0.756\t1.443\t-1.109\t0.212\t-1.193\t-0.835\t-0.728\t-0.132\t0.175\t-1.107\t-0.414\t-0.991\t-1.035\t1.134\t-1.392\t0.075\t1.698\n2\t-0.346\t0.423\t-0.767\t0.067\t-0.277\t-1.049\t0.735\t-0.939\t0.267\t-0.373\t0.317\t0.039\t-0.594\t-0.038\t0.074\t1.263\t1.847\t-0.986\t-0.669\t-2.322\t-1.167\t-0.937\t-2.123\t-1.608\t-0.286\t-0.520\t-0.447\t-0.870\n1\t0.392\t-0.487\t1.691\t0.189\t-0.707\t0.022\t1.428\t-1.479\t0.270\t0.074\t-1.540\t0.767\t0.256\t0.879\t1.138\t-0.056\t-0.556\t-1.425\t-0.644\t0.011\t-0.964\t0.583\t-0.903\t-1.763\t-0.578\t0.184\t-1.259\t-0.888\n0\t1.026\t0.441\t-0.326\t-0.268\t0.844\t-0.590\t-0.628\t-0.744\t0.788\t0.637\t-1.141\t0.152\t0.291\t1.185\t-0.407\t-0.525\t0.523\t-0.531\t0.665\t0.232\t-1.732\t-0.253\t0.211\t0.708\t-0.502\t-0.821\t-0.854\t0.191\n3\t0.310\t-1.033\t1.311\t0.019\t-0.343\t-1.069\t0.057\t-0.944\t-0.085\t-0.304\t1.101\t-1.442\t-0.891\t0.595\t-0.583\t-0.855\t0.062\t-0.397\t-1.468\t-2.277\t1.416\t0.618\t1.539\t-1.620\t2.100\t-0.135\t-1.856\t0.727\n3\t-0.183\t-0.667\t0.053\t-0.168\t2.474\t0.360\t-0.799\t-1.035\t0.968\t-1.141\t0.709\t1.515\t0.734\t0.954\t0.421\t0.504\t-1.235\t1.815\t1.930\t-1.019\t0.195\t0.893\t0.699\t1.691\t0.795\t0.451\t0.044\t0.181\n1\t0.176\t-0.662\t-0.413\t0.395\t1.674\t0.603\t-1.221\t-0.780\t-1.136\t0.419\t0.801\t-1.766\t-0.072\t-0.565\t-0.576\t-0.278\t-0.312\t-0.772\t0.359\t-0.787\t-0.350\t-0.465\t1.264\t-0.387\t1.977\t0.576\t-1.580\t-1.080\n4\t0.750\t-0.736\t0.734\t1.231\t0.236\t1.004\t-1.015\t-1.159\t0.781\t2.472\t0.091\t0.885\t-2.937\t0.612\t-0.491\t2.002\t0.775\t0.084\t-0.546\t1.283\t1.307\t-0.807\t1.172\t1.016\t0.276\t0.829\t0.567\t-1.038\n1\t0.808\t0.098\t-1.443\t-0.358\t-0.022\t-0.805\t-0.386\t0.317\t0.347\t0.498\t0.402\t-0.937\t-1.589\t0.291\t-0.196\t0.024\t0.082\t-0.492\t0.334\t-1.483\t2.299\t0.221\t-0.065\t-0.856\t0.420\t-1.683\t-0.191\t1.654\n1\t-1.622\t-0.619\t1.141\t0.951\t-1.271\t-0.291\t-1.333\t0.699\t0.401\t-1.429\t0.604\t0.879\t0.762\t-0.239\t0.596\t0.963\t-2.136\t0.639\t-1.870\t-0.497\t-0.235\t0.577\t-0.091\t0.701\t-0.498\t0.676\t0.492\t0.234\n2\t0.743\t0.598\t0.158\t0.944\t1.072\t-0.952\t1.533\t0.864\t-0.876\t-0.649\t-1.243\t0.628\t-0.177\t-2.493\t-0.554\t-0.381\t0.106\t-1.539\t0.117\t1.501\t0.187\t0.589\t1.563\t-0.095\t-0.127\t0.345\t-0.885\t-0.695\n4\t-2.612\t-1.536\t0.752\t-1.518\t-0.604\t0.259\t0.505\t0.511\t0.677\t-1.063\t0.508\t-0.473\t-2.678\t1.642\t-0.447\t0.001\t-0.806\t0.524\t-0.418\t-0.762\t2.520\t-2.824\t-0.530\t0.192\t-1.200\t-0.758\t0.240\t-2.546\n0\t-0.596\t0.369\t0.240\t0.059\t0.534\t-1.276\t-2.027\t1.190\t-0.131\t0.416\t-0.868\t1.262\t0.350\t-0.180\t0.286\t-1.742\t0.373\t1.487\t0.315\t0.186\t-0.317\t1.083\t1.231\t-0.432\t0.359\t0.232\t0.402\t-0.773\n4\t0.183\t-0.749\t-0.059\t0.991\t0.761\t-0.198\t0.319\t1.235\t1.445\t1.227\t0.557\t-1.024\t-2.077\t0.140\t2.615\t-0.287\t-1.776\t-1.118\t1.364\t-1.542\t-0.713\t1.661\t1.454\t-0.558\t1.700\t0.518\t1.142\t-0.772\n1\t-0.857\t0.147\t0.043\t0.504\t1.389\t-0.407\t0.932\t0.142\t1.024\t1.232\t-0.757\t1.979\t0.181\t-1.147\t-1.139\t1.473\t1.314\t1.097\t-0.541\t-0.374\t-1.576\t-0.292\t0.171\t1.500\t-0.275\t-0.334\t0.289\t0.851\n3\t-1.518\t-2.995\t-0.043\t1.407\t0.124\t0.081\t-0.061\t0.449\t-1.482\t-0.463\t0.818\t0.881\t-0.379\t1.216\t-0.929\t-0.373\t1.845\t1.311\t1.048\t-1.726\t-0.285\t-1.340\t0.767\t0.226\t-0.705\t-0.587\t-0.048\t0.173\n0\t0.160\t-1.098\t-1.135\t-0.216\t-0.331\t-0.535\t0.751\t1.436\t-0.175\t-1.178\t0.493\t-0.278\t-0.182\t-0.397\t-0.533\t-1.426\t-0.188\t-0.287\t-0.773\t-1.568\t0.736\t-1.333\t0.032\t0.465\t-1.584\t-0.554\t0.856\t-0.463\n2\t0.696\t-0.286\t-1.462\t-0.174\t-0.518\t-0.530\t-0.750\t-0.390\t-0.245\t1.928\t-1.114\t1.145\t1.231\t-2.279\t-0.819\t-0.109\t0.144\t-1.513\t-0.210\t0.422\t-1.496\t-0.060\t-0.916\t-0.209\t-0.815\t-0.391\t-0.396\t1.672\n1\t-0.225\t1.154\t-0.974\t0.239\t-2.029\t0.090\t0.349\t-0.208\t1.130\t-0.401\t0.524\t0.639\t-1.069\t1.406\t0.462\t-0.027\t0.623\t-1.154\t-0.689\t0.506\t-1.150\t-0.011\t2.501\t0.978\t0.006\t-1.266\t0.712\t0.121\n1\t-0.970\t0.204\t0.670\t1.004\t-0.243\t0.166\t1.045\t0.721\t0.471\t-0.136\t-0.419\t0.169\t-0.379\t-1.118\t1.211\t-0.931\t0.999\t-0.715\t0.391\t-1.713\t0.567\t0.173\t0.639\t-0.989\t2.376\t-1.936\t0.119\t-0.799\n4\t0.888\t-0.885\t-0.829\t1.479\t1.376\t-0.244\t0.806\t0.096\t-2.973\t2.167\t-0.176\t0.958\t1.926\t0.176\t0.987\t-0.646\t0.594\t0.509\t-1.130\t0.638\t-1.271\t0.176\t1.204\t0.621\t0.434\t-1.438\t-0.029\t-1.777\n3\t0.282\t2.590\t0.167\t1.150\t-0.759\t-1.052\t0.971\t-0.135\t-0.299\t0.055\t-0.179\t0.217\t1.195\t0.956\t0.868\t0.109\t1.250\t1.914\t0.935\t1.604\t-0.869\t0.911\t-0.262\t-2.216\t1.120\t-0.275\t1.356\t0.539\n4\t0.956\t-1.143\t-1.264\t2.742\t-1.943\t-1.191\t0.804\t-1.627\t0.736\t1.227\t1.346\t0.067\t-1.173\t-0.914\t1.265\t-1.866\t0.823\t-1.608\t-0.095\t-0.623\t-0.443\t1.341\t-0.539\t1.043\t-0.720\t0.262\t-0.127\t-1.373\n1\t0.030\t0.680\t-0.075\t1.134\t0.261\t1.012\t-0.721\t-1.228\t-0.340\t-1.346\t0.409\t-2.017\t-0.453\t-0.286\t-1.047\t-0.306\t0.004\t1.269\t-1.041\t0.357\t0.556\t-0.021\t-0.498\t-1.139\t1.899\t0.073\t-0.316\t2.122\n2\t-0.107\t1.153\t-0.177\t0.375\t-0.425\t-2.294\t-0.597\t1.462\t0.335\t0.894\t0.930\t0.779\t1.595\t-0.318\t1.485\t0.671\t0.445\t-2.056\t0.402\t0.918\t0.233\t1.251\t1.296\t-0.279\t0.109\t-0.265\t-0.422\t0.099\n1\t0.548\t-1.232\t0.401\t0.282\t0.513\t1.590\t-0.176\t2.048\t0.407\t-0.290\t2.399\t0.830\t-0.455\t0.084\t0.635\t-0.151\t0.134\t-0.817\t-1.685\t0.106\t0.140\t0.527\t-0.576\t-1.341\t-0.965\t1.056\t-0.667\t-0.442\n3\t-0.545\t1.291\t0.148\t-0.879\t0.917\t-1.264\t1.284\t0.991\t1.390\t-0.158\t-0.496\t0.084\t-0.084\t-0.653\t-0.554\t-0.366\t-1.170\t0.678\t-0.052\t-0.308\t-1.110\t-1.160\t1.936\t-0.627\t0.611\t0.163\t-2.775\t1.792\n3\t-0.788\t1.215\t-1.317\t1.386\t-0.550\t1.928\t0.969\t-0.282\t-0.558\t-1.055\t0.539\t1.714\t-0.764\t-1.294\t1.021\t0.541\t0.740\t-0.260\t1.380\t-1.857\t0.577\t0.423\t0.151\t-1.776\t0.641\t1.357\t-0.305\t0.345\n1\t1.275\t0.858\t0.852\t-0.785\t2.210\t1.551\t0.425\t-0.650\t-0.913\t0.435\t0.267\t-0.235\t-1.051\t0.359\t-0.164\t-1.486\t0.011\t0.938\t0.354\t-0.325\t1.707\t-0.723\t0.375\t-0.516\t0.079\t-0.424\t1.272\t0.313\n2\t-0.332\t0.263\t0.016\t0.787\t-1.201\t1.724\t1.345\t0.935\t1.265\t-1.553\t0.699\t0.740\t0.967\t0.847\t0.445\t0.404\t1.192\t-0.623\t0.367\t-0.553\t-0.152\t-2.672\t-0.871\t-0.436\t-0.423\t-0.333\t-1.185\t-0.332\n2\t0.207\t-0.239\t1.478\t-1.482\t-0.751\t1.520\t0.131\t0.885\t2.051\t-0.694\t-0.099\t-0.272\t-1.955\t-0.310\t0.881\t0.541\t0.619\t-0.376\t-0.656\t1.269\t-0.497\t0.294\t0.163\t1.462\t0.971\t-1.295\t-1.188\t0.579\n2\t0.172\t0.420\t-1.067\t1.041\t0.842\t0.916\t-2.317\t-0.661\t0.994\t-0.821\t0.517\t-0.149\t0.764\t1.810\t-1.032\t0.682\t0.223\t-0.496\t0.795\t0.380\t-0.413\t0.160\t-0.935\t1.107\t-0.615\t-0.955\t1.313\t-1.650\n2\t-0.448\t-1.153\t0.446\t1.150\t0.537\t-2.454\t-1.115\t0.047\t-0.244\t-0.327\t-0.983\t-1.406\t2.008\t0.027\t0.642\t0.006\t0.046\t-0.062\t-1.120\t-1.514\t0.386\t0.188\t0.938\t0.735\t-0.809\t1.133\t0.765\t0.691\n4\t-1.223\t1.770\t-0.205\t0.516\t1.583\t-0.152\t-0.396\t-0.208\t3.224\t0.853\t0.533\t1.619\t0.294\t0.194\t0.816\t0.610\t-0.491\t-0.473\t-0.955\t-0.281\t2.419\t0.705\t0.689\t0.743\t-1.238\t0.654\t-0.481\t-0.426\n4\t2.036\t-0.417\t-1.041\t0.583\t-0.144\t-1.415\t2.231\t-0.352\t-0.378\t0.160\t1.730\t-1.680\t0.276\t2.087\t0.129\t-1.235\t-0.618\t0.207\t1.331\t-0.339\t0.827\t-1.435\t-1.444\t-0.483\t1.438\t0.069\t0.290\t0.278\n1\t0.088\t1.695\t1.391\t0.050\t1.237\t-0.783\t-0.192\t-0.536\t-0.158\t-2.004\t1.799\t-0.305\t0.787\t0.245\t-0.272\t-1.277\t-0.914\t-0.236\t-0.475\t-0.974\t-0.687\t0.461\t0.877\t-1.166\t-0.880\t0.215\t-0.163\t-0.366\n3\t1.873\t1.080\t-0.447\t1.281\t0.068\t0.853\t0.485\t-0.846\t-0.644\t1.030\t-0.335\t-0.404\t-0.955\t0.424\t2.063\t-1.068\t0.024\t1.412\t-0.080\t0.452\t-1.062\t0.428\t-0.187\t0.986\t1.187\t2.590\t0.580\t0.326\n2\t1.187\t0.757\t-0.809\t-0.005\t-1.701\t0.811\t1.541\t-0.340\t-0.006\t0.422\t-0.227\t-2.067\t1.078\t1.206\t0.686\t1.397\t-0.132\t0.443\t-0.700\t-0.875\t-1.302\t0.345\t0.088\t1.099\t-1.741\t0.264\t1.726\t0.118\n1\t0.732\t1.035\t1.150\t0.114\t-0.291\t-0.121\t-0.342\t1.448\t-0.758\t1.134\t-0.482\t1.105\t0.727\t-0.039\t0.259\t-1.110\t0.577\t0.947\t1.338\t-0.325\t2.200\t-0.318\t-0.450\t-0.167\t-0.158\t-1.434\t-1.131\t-1.222\n1\t0.108\t-0.751\t0.192\t1.109\t0.204\t0.154\t0.157\t-0.746\t0.449\t1.042\t1.352\t0.430\t0.015\t1.877\t0.120\t-0.484\t-0.689\t-0.804\t-0.503\t1.927\t-0.757\t1.527\t-0.205\t1.042\t-1.079\t-0.018\t-1.616\t0.022\n1\t-1.301\t0.438\t-0.731\t0.880\t-1.441\t-0.977\t-0.367\t0.789\t-0.862\t0.423\t1.096\t0.153\t-0.160\t0.668\t-0.801\t-0.739\t-1.279\t1.590\t0.584\t-1.346\t-0.632\t0.192\t-0.041\t0.412\t-0.576\t1.073\t-1.039\t1.297\n1\t-0.727\t0.569\t0.110\t0.274\t0.150\t-1.352\t0.225\t-1.403\t0.138\t0.312\t0.542\t1.096\t-0.407\t0.098\t2.272\t0.019\t0.231\t-1.396\t-0.952\t0.241\t-1.872\t-0.306\t1.857\t-0.289\t-0.328\t0.824\t-0.446\t0.925\n4\t-1.254\t-0.335\t0.308\t1.080\t0.025\t-0.550\t0.629\t-1.400\t-0.151\t0.508\t0.187\t-1.195\t-0.104\t-4.374\t0.184\t-1.430\t-1.360\t0.082\t0.853\t-1.359\t-1.169\t0.267\t-0.165\t-0.259\t0.853\t-0.928\t-0.562\t-0.808\n0\t-1.239\t-0.164\t-0.931\t-0.921\t-0.517\t-0.714\t1.138\t0.586\t-0.002\t-0.342\t0.124\t0.449\t-1.515\t0.139\t0.245\t-0.286\t-1.398\t-0.960\t0.730\t0.067\t0.207\t-0.673\t1.082\t-0.321\t-1.075\t0.287\t-1.310\t0.189\n1\t-0.891\t0.930\t-1.191\t1.424\t1.092\t0.519\t-0.967\t-0.098\t-0.274\t0.088\t0.310\t-0.531\t-0.539\t0.724\t2.322\t-1.221\t0.751\t0.529\t0.469\t0.238\t-0.549\t0.144\t-0.295\t0.554\t0.459\t-2.410\t0.183\t0.232\n1\t-1.724\t-0.535\t1.107\t-1.032\t0.762\t-0.547\t-0.584\t-0.191\t0.312\t-0.436\t-0.525\t1.381\t0.125\t-1.401\t-2.107\t1.444\t0.924\t0.179\t0.145\t-0.010\t0.097\t0.212\t1.044\t0.947\t0.197\t0.215\t0.841\t0.584\n2\t-0.774\t0.450\t-1.978\t0.499\t0.861\t-1.155\t0.252\t0.758\t-0.285\t0.880\t-0.699\t0.287\t0.060\t0.129\t1.102\t0.093\t-0.450\t1.758\t1.703\t1.976\t1.183\t0.728\t1.354\t-0.586\t-0.799\t-1.458\t-0.699\t-0.212\n4\t-0.922\t-0.065\t-1.907\t0.630\t-0.866\t0.034\t0.161\t-0.292\t-2.572\t-0.316\t-2.500\t1.564\t-0.312\t-0.908\t-2.097\t0.589\t0.422\t-0.194\t-0.068\t-0.834\t-0.791\t1.212\t-0.388\t0.342\t-0.394\t-0.189\t2.096\t1.356\n1\t0.099\t0.691\t-0.458\t0.693\t2.299\t-0.818\t-0.887\t0.584\t0.079\t0.184\t2.372\t0.526\t-0.199\t-0.621\t-0.842\t-0.737\t0.018\t-0.889\t1.114\t-0.946\t-0.229\t1.386\t-0.659\t0.367\t0.023\t-1.416\t0.272\t-0.071\n3\t0.754\t0.889\t1.368\t0.242\t0.280\t-0.364\t-0.929\t-0.975\t-0.144\t0.344\t-1.455\t2.046\t-0.696\t-0.857\t-0.028\t-2.370\t0.362\t-0.990\t-1.922\t0.143\t0.142\t1.945\t0.180\t0.172\t0.041\t-2.578\t0.077\t-0.048\n3\t1.299\t1.236\t0.874\t-0.676\t-0.844\t-0.725\t2.632\t0.164\t0.921\t0.475\t-0.339\t1.078\t-1.040\t-0.814\t-0.848\t0.972\t1.194\t-0.193\t-1.059\t-1.855\t-0.943\t-0.969\t-0.203\t0.937\t-0.202\t-1.262\t-1.258\t0.374\n1\t0.066\t1.127\t-1.382\t1.854\t0.334\t-0.680\t-0.571\t0.194\t-0.122\t0.632\t-0.521\t-0.550\t0.260\t-0.675\t-0.288\t0.917\t-1.685\t0.215\t0.648\t1.199\t0.099\t-0.103\t-1.204\t-0.447\t0.998\t-1.020\t-2.059\t1.241\n3\t-1.992\t0.131\t-0.405\t0.272\t-0.604\t1.199\t-0.001\t0.240\t2.817\t-0.436\t-0.033\t-0.709\t0.782\t-0.248\t-0.154\t1.051\t-0.259\t1.451\t0.218\t0.545\t1.138\t0.976\t-1.237\t2.504\t0.527\t-0.615\t-0.302\t0.015\n1\t-0.363\t-0.912\t-0.243\t-0.843\t1.001\t-0.553\t1.311\t0.669\t-1.002\t-1.063\t-0.221\t-0.732\t-0.739\t0.840\t0.274\t1.230\t-1.072\t-1.833\t-0.731\t1.077\t-0.627\t1.637\t-0.533\t0.584\t-0.026\t-1.960\t-0.660\t0.480\n3\t0.750\t-0.274\t-1.721\t-0.982\t-0.430\t1.368\t0.901\t1.624\t1.654\t-0.535\t-0.188\t-0.411\t-0.672\t-1.841\t-0.015\t-1.286\t-0.034\t2.116\t-0.509\t0.195\t0.907\t0.280\t0.292\t-0.362\t-0.232\t-0.960\t0.846\t-1.741\n2\t-0.175\t-1.413\t-0.834\t0.470\t-0.533\t1.609\t-0.694\t-0.112\t-0.635\t0.918\t-1.494\t-2.062\t-0.538\t-0.216\t-1.695\t0.429\t0.708\t-0.354\t-0.682\t1.462\t-1.113\t0.531\t-0.478\t0.183\t0.789\t0.320\t-1.811\t0.914\n2\t0.571\t0.715\t-0.108\t0.526\t-2.148\t-0.722\t0.704\t0.722\t-0.543\t-1.308\t-0.071\t-0.434\t0.941\t0.703\t1.549\t1.110\t-0.274\t0.349\t2.034\t-0.400\t0.175\t-0.004\t0.017\t-0.874\t1.308\t-0.917\t1.858\t1.639\n1\t-0.188\t-0.104\t-0.214\t0.970\t0.419\t0.221\t-1.271\t-0.379\t0.899\t0.038\t0.660\t1.767\t1.039\t0.093\t-0.511\t-2.554\t-0.168\t-0.326\t0.390\t-0.154\t-0.320\t-1.779\t-0.940\t0.477\t1.716\t-0.098\t0.328\t0.952\n4\t-0.089\t2.082\t1.450\t-1.116\t0.553\t0.033\t-0.195\t-1.229\t2.054\t1.790\t-0.851\t1.369\t1.013\t-0.142\t1.252\t-0.188\t0.288\t-1.404\t-1.164\t0.679\t0.872\t-0.476\t1.194\t-1.674\t-0.961\t1.352\t0.518\t-0.439\n4\t0.621\t0.353\t-2.494\t1.214\t0.497\t0.096\t1.646\t-0.795\t0.918\t-0.277\t-0.565\t-0.690\t0.672\t0.911\t1.468\t-1.793\t-0.089\t-0.893\t-0.333\t0.369\t-2.472\t0.621\t-2.289\t0.620\t-1.046\t-0.510\t0.606\t-0.591\n2\t1.802\t-0.570\t-1.062\t0.075\t1.699\t0.275\t1.116\t0.639\t1.173\t-0.639\t1.499\t0.622\t-1.373\t-0.198\t0.174\t0.109\t-0.069\t0.255\t0.255\t-0.535\t0.414\t0.785\t1.777\t2.143\t-0.356\t-0.448\t-0.327\t-1.177\n4\t1.797\t-0.047\t0.440\t-0.108\t-0.103\t-0.490\t-0.560\t1.696\t-0.554\t-1.541\t0.125\t1.018\t1.243\t-1.194\t1.113\t2.059\t-0.684\t-0.174\t-1.232\t1.854\t-1.492\t-0.400\t0.785\t-0.684\t-0.989\t-0.052\t2.390\t-0.256\n0\t0.806\t-0.444\t0.519\t0.489\t0.947\t-0.540\t-0.017\t0.353\t-0.532\t-0.694\t0.952\t-0.478\t0.430\t-1.694\t0.477\t0.118\t-0.568\t1.484\t0.240\t-0.524\t-0.401\t-0.724\t-0.381\t-0.429\t-0.931\t-0.215\t-0.096\t-0.855\n2\t-0.541\t-1.888\t0.645\t0.752\t0.773\t0.537\t0.747\t1.100\t0.576\t-0.280\t2.164\t-0.289\t0.469\t-0.213\t0.243\t0.169\t-1.591\t-0.903\t-0.762\t0.747\t-1.014\t-1.778\t0.236\t-0.758\t0.020\t1.059\t0.400\t1.560\n0\t-0.842\t-1.267\t0.826\t0.322\t0.399\t-0.370\t-0.279\t-0.371\t-0.081\t0.835\t0.336\t0.500\t0.500\t0.884\t0.288\t0.359\t1.189\t-0.665\t-0.856\t-1.186\t0.183\t-1.854\t0.462\t-0.109\t1.135\t-0.672\t-0.203\t1.122\n3\t0.155\t-2.221\t-0.197\t-0.896\t0.223\t1.140\t-0.438\t0.139\t-0.493\t0.962\t-0.252\t1.045\t0.220\t1.583\t0.508\t0.082\t0.947\t1.722\t1.581\t2.351\t-0.094\t0.280\t-0.304\t-0.896\t0.588\t-2.468\t0.396\t1.378\n2\t0.934\t0.503\t2.306\t-1.016\t0.823\t-1.312\t0.157\t0.977\t-2.072\t-0.205\t-0.563\t-0.702\t0.385\t1.121\t-0.133\t0.205\t0.114\t0.002\t-1.089\t1.108\t0.295\t0.395\t-0.807\t-0.459\t-0.176\t-1.534\t-1.688\t-0.601\n1\t-1.149\t2.071\t1.242\t-0.243\t-1.280\t0.459\t-1.624\t0.814\t-0.466\t0.126\t-0.291\t0.305\t0.162\t0.575\t0.523\t-1.660\t0.217\t-0.269\t0.005\t-0.237\t-0.841\t-1.589\t1.028\t-0.876\t-0.360\t0.498\t-0.854\t-0.416\n3\t-0.727\t-1.679\t-0.400\t-2.085\t-1.890\t-0.303\t-0.785\t-0.138\t-0.812\t-0.795\t1.199\t-0.917\t0.238\t-0.080\t0.155\t-1.097\t0.149\t0.637\t-2.576\t-0.825\t-0.959\t-1.211\t-1.167\t1.359\t0.012\t-1.361\t0.298\t1.243\n2\t1.630\t-0.323\t-0.043\t-1.609\t0.023\t-1.468\t-0.036\t-0.898\t-0.274\t-0.736\t0.327\t0.881\t-1.103\t-0.544\t1.625\t-0.419\t0.872\t-0.404\t-1.304\t-1.561\t-0.132\t1.157\t-0.422\t-0.744\t-1.265\t1.250\t-1.832\t0.562\n3\t-1.040\t1.583\t-2.297\t-0.105\t-0.130\t0.326\t0.347\t-1.677\t-0.346\t-0.597\t1.170\t-0.280\t0.133\t0.726\t-1.890\t-2.089\t-0.135\t0.456\t0.300\t-1.124\t0.333\t-0.609\t-0.397\t1.309\t0.771\t1.005\t-0.889\t1.753\n0\t0.259\t-1.066\t0.595\t-0.613\t-0.325\t0.238\t0.103\t0.024\t-0.751\t-0.300\t0.002\t-1.375\t-0.463\t0.388\t-1.277\t1.157\t1.270\t-1.245\t1.358\t-0.144\t0.419\t-0.497\t0.923\t0.406\t-0.814\t-1.165\t-1.460\t0.188\n3\t-0.512\t0.350\t0.368\t0.300\t0.303\t-2.205\t-0.006\t-1.501\t-0.758\t0.066\t0.261\t-0.119\t-0.879\t0.095\t1.990\t0.667\t1.343\t-0.382\t-0.810\t0.812\t2.494\t-0.739\t-0.528\t1.354\t-1.217\t-0.155\t1.606\t0.139\n3\t0.724\t-1.234\t-0.490\t-2.538\t0.200\t-0.825\t-0.146\t1.158\t-0.792\t1.172\t-0.899\t0.218\t-0.089\t0.843\t-0.046\t-1.034\t1.664\t1.899\t-1.213\t0.734\t-0.145\t-0.727\t-1.294\t0.901\t0.813\t0.682\t0.704\t-1.443\n2\t-1.865\t-0.082\t-0.372\t0.101\t0.238\t-0.312\t0.919\t1.165\t-1.259\t-1.345\t1.656\t-0.394\t-1.640\t-0.164\t0.395\t0.624\t0.539\t-0.049\t-1.371\t-0.007\t0.650\t1.120\t0.383\t1.291\t-1.076\t0.066\t-0.262\t1.843\n0\t-0.445\t0.303\t-1.511\t0.103\t0.302\t-0.248\t0.040\t1.739\t-0.289\t0.161\t0.157\t0.655\t-0.046\t-0.188\t0.990\t-0.234\t-0.414\t0.226\t-0.039\t-0.105\t0.783\t-0.637\t-0.875\t0.477\t0.652\t0.115\t-1.103\t2.259\n3\t-0.535\t0.982\t-0.186\t-0.548\t1.184\t2.359\t0.454\t0.833\t1.522\t-0.359\t0.030\t-1.246\t0.613\t-0.980\t1.053\t-0.641\t0.325\t-0.206\t1.603\t1.914\t0.599\t-0.449\t1.155\t1.294\t-1.506\t0.532\t0.599\t-0.369\n3\t-0.948\t0.952\t-1.939\t1.288\t-1.085\t-0.397\t-0.895\t-0.999\t0.403\t0.742\t0.521\t-2.041\t0.655\t-0.978\t1.335\t1.391\t-0.814\t-2.460\t-0.137\t-0.305\t0.602\t0.305\t0.657\t-0.398\t1.381\t0.570\t-0.927\t0.899\n0\t0.696\t-0.360\t0.251\t0.066\t0.096\t0.508\t0.196\t-0.407\t2.642\t-1.332\t0.322\t0.646\t1.256\t-0.134\t0.685\t-0.316\t1.009\t0.313\t0.812\t0.339\t-0.275\t-1.310\t-1.146\t-0.634\t-0.171\t0.624\t0.008\t0.622\n4\t-1.078\t0.177\t-0.719\t0.607\t0.300\t-0.360\t-0.335\t-2.522\t-0.393\t0.425\t1.121\t-1.047\t2.050\t0.485\t2.815\t-1.761\t0.162\t0.532\t0.456\t-0.914\t-0.194\t-1.500\t-1.136\t-0.490\t0.222\t0.314\t1.005\t-1.555\n3\t-1.049\t0.586\t-0.232\t1.159\t-0.284\t2.351\t0.029\t1.242\t-0.433\t-1.616\t-0.816\t0.481\t1.589\t-0.375\t-1.391\t0.661\t-1.971\t1.596\t-0.669\t0.952\t-0.273\t-0.706\t-0.314\t0.871\t1.138\t0.418\t0.573\t0.919\n2\t-0.341\t1.022\t0.733\t1.378\t-0.991\t-0.343\t0.759\t0.448\t1.532\t0.421\t1.848\t0.540\t1.062\t-0.324\t0.014\t0.911\t1.613\t0.729\t-0.083\t1.377\t-2.192\t-1.422\t0.682\t0.014\t0.606\t0.064\t0.571\t-0.814\n4\t0.201\t0.976\t-1.192\t-2.613\t-1.427\t-0.515\t-1.222\t-1.872\t-1.114\t1.159\t1.451\t-1.454\t0.506\t0.428\t-0.977\t2.286\t-0.169\t0.058\t-1.818\t0.575\t-1.851\t0.168\t-0.560\t-0.695\t-0.709\t1.383\t0.935\t0.836\n4\t1.667\t-1.058\t-1.125\t2.999\t1.039\t-0.907\t0.815\t0.446\t0.913\t-0.215\t-1.015\t-0.327\t0.190\t-0.007\t0.011\t-0.450\t0.077\t-0.572\t-0.757\t1.240\t1.808\t-0.380\t0.070\t0.003\t1.556\t0.470\t0.371\t-3.133\n0\t0.275\t-0.038\t1.240\t1.484\t-0.918\t-0.406\t-0.477\t1.098\t0.408\t-0.729\t-0.076\t-1.470\t0.616\t-0.918\t1.103\t0.826\t-0.163\t-0.288\t-0.890\t-0.260\t-1.631\t1.298\t1.353\t-0.043\t0.464\t-0.457\t0.487\t-0.631\n1\t-1.169\t-0.237\t1.127\t-0.707\t1.177\t-0.896\t-0.228\t-0.605\t1.593\t0.059\t-1.209\t-0.775\t0.984\t-1.335\t0.459\t-0.427\t0.013\t1.909\t0.061\t2.138\t-1.098\t-0.003\t-0.917\t-0.155\t0.064\t-0.399\t-0.699\t-0.468\n4\t1.181\t-0.627\t0.045\t0.051\t-0.502\t-1.372\t0.323\t-0.061\t0.500\t-0.534\t1.221\t-0.877\t1.712\t-1.748\t0.435\t0.475\t-0.796\t0.424\t1.280\t-2.217\t0.503\t-2.493\t-0.966\t1.629\t-0.582\t-1.174\t-0.198\t2.068\n0\t0.142\t0.771\t0.547\t0.020\t0.384\t-0.593\t-0.048\t1.341\t0.011\t-0.217\t0.210\t-1.166\t0.221\t1.696\t-0.809\t0.267\t-0.023\t-0.049\t-0.067\t0.714\t-1.098\t0.689\t0.069\t-1.763\t0.380\t0.114\t-0.872\t0.161\n3\t2.042\t0.719\t-0.035\t-0.894\t0.280\t2.115\t-0.075\t-0.668\t-0.802\t-1.430\t-1.008\t-0.998\t-0.754\t0.822\t-0.420\t-0.070\t-1.906\t-0.931\t0.549\t-0.878\t-0.677\t-1.143\t1.404\t2.149\t1.460\t-0.394\t0.778\t-0.332\n1\t1.569\t0.607\t0.692\t-0.487\t-0.001\t0.627\t0.153\t1.602\t-0.204\t-0.505\t0.634\t-1.265\t0.924\t-1.301\t0.639\t-0.648\t0.477\t0.907\t0.153\t-1.200\t-0.897\t-0.729\t-1.895\t1.127\t0.110\t-1.136\t1.456\t-0.701\n0\t-0.026\t0.222\t0.287\t-1.164\t-0.141\t0.545\t0.851\t-0.225\t1.474\t0.595\t0.844\t0.401\t1.042\t0.536\t-1.063\t-0.111\t-0.009\t1.084\t-0.163\t0.386\t-0.904\t0.448\t0.697\t-0.162\t0.942\t1.689\t-1.196\t-1.078\n4\t-0.968\t-0.215\t1.896\t1.519\t0.586\t0.209\t0.553\t-0.073\t-0.696\t-0.808\t2.184\t-0.585\t1.046\t-2.155\t-0.521\t-0.113\t-0.548\t-0.770\t0.939\t0.347\t-0.546\t-0.634\t-1.356\t-1.108\t-1.066\t-0.448\t3.067\t-0.548\n1\t-0.188\t-1.106\t1.341\t-0.014\t-0.748\t1.143\t1.257\t-1.887\t1.107\t0.158\t0.971\t-0.068\t0.379\t-0.304\t0.992\t-0.671\t1.026\t0.179\t0.757\t-1.009\t0.423\t0.533\t1.854\t0.698\t-0.588\t-0.630\t0.761\t-0.540\n2\t0.053\t0.059\t0.700\t-1.535\t-0.265\t0.499\t-0.767\t-0.152\t-1.355\t-1.690\t-1.898\t0.118\t-0.628\t0.170\t1.370\t-0.309\t0.406\t-1.335\t-1.130\t-2.374\t-1.462\t-0.849\t-0.404\t-0.666\t-0.322\t0.343\t-0.792\t-0.202\n3\t-0.050\t-0.074\t-0.485\t0.105\t-0.071\t-2.404\t0.026\t-0.138\t0.347\t-2.007\t0.512\t0.029\t0.495\t-1.629\t-1.235\t-1.875\t0.146\t-0.345\t-0.144\t-1.392\t-1.210\t-2.496\t-1.346\t-0.650\t-0.598\t-0.068\t0.762\t-0.689\n3\t2.998\t-0.048\t-0.018\t1.660\t-0.226\t-0.238\t0.503\t0.563\t0.041\t-0.338\t-0.913\t-0.216\t0.713\t-0.009\t2.362\t-0.552\t-1.539\t-0.378\t-0.291\t1.099\t-0.027\t0.396\t0.990\t1.944\t-1.438\t0.847\t-0.137\t-0.049\n2\t0.461\t0.421\t0.919\t-0.064\t0.104\t0.581\t-1.992\t0.192\t-0.731\t-0.439\t-0.487\t0.129\t0.960\t0.636\t1.101\t-0.342\t2.084\t1.510\t0.168\t-0.495\t-0.477\t-1.667\t-0.274\t-0.264\t0.791\t-1.805\t0.933\t1.871\n4\t-1.056\t2.050\t-1.904\t-1.458\t1.024\t0.505\t-0.663\t1.889\t-0.208\t-0.283\t0.335\t0.596\t0.575\t-0.044\t0.585\t-1.788\t-2.603\t0.073\t-0.893\t-0.433\t-0.297\t-0.023\t1.495\t-1.006\t-0.557\t-0.462\t-1.506\t-0.203\n4\t-0.987\t0.794\t0.081\t1.095\t-1.302\t0.024\t-0.028\t1.543\t0.288\t-2.358\t-0.556\t-1.024\t2.016\t0.050\t0.543\t2.127\t1.224\t0.408\t0.333\t0.381\t0.652\t-2.014\t0.090\t0.739\t-1.304\t-1.471\t-0.314\t1.235\n4\t0.554\t-1.007\t2.574\t3.260\t-0.618\t-0.131\t0.791\t0.956\t-0.503\t0.730\t-0.163\t-1.145\t-0.311\t0.303\t0.415\t-0.818\t-0.275\t0.836\t0.595\t-0.010\t-0.074\t1.207\t1.182\t-0.430\t-2.911\t-1.669\t-1.472\t-0.123\n3\t-0.821\t0.239\t1.521\t-0.970\t-0.005\t0.496\t-1.568\t2.022\t-0.826\t-0.526\t-0.169\t-0.982\t0.477\t1.352\t0.621\t0.457\t0.800\t-0.514\t-0.636\t1.618\t0.758\t-1.446\t1.704\t1.439\t0.693\t1.290\t1.511\t-0.057\n2\t1.435\t-0.735\t-0.779\t-2.208\t0.627\t1.529\t0.503\t-0.532\t-1.865\t0.472\t0.323\t-0.635\t-0.141\t0.649\t0.760\t0.612\t1.331\t0.253\t0.666\t-0.373\t-0.983\t0.509\t0.437\t-0.518\t0.979\t-0.239\t-1.946\t0.143\n0\t-0.720\t0.098\t0.846\t-0.950\t0.062\t0.664\t0.929\t1.142\t-0.869\t0.288\t-1.315\t-0.724\t-0.174\t1.144\t0.528\t0.040\t0.449\t0.521\t0.369\t-0.575\t-0.957\t-0.223\t-0.794\t0.621\t1.529\t0.564\t-1.810\t-0.633\n0\t0.865\t0.791\t1.334\t0.082\t0.537\t0.116\t0.825\t-0.507\t1.380\t0.592\t1.712\t-0.562\t0.711\t1.131\t1.012\t0.383\t0.134\t-0.808\t0.299\t-0.780\t0.492\t-1.041\t-0.094\t0.919\t0.053\t0.575\t0.552\t0.234\n3\t0.879\t0.189\t-0.250\t1.147\t0.778\t0.178\t-2.165\t-0.321\t0.906\t-0.626\t0.304\t0.651\t1.258\t0.222\t-0.922\t1.237\t-0.241\t0.515\t-0.639\t-1.299\t-1.249\t1.421\t-0.040\t0.727\t2.062\t0.936\t1.899\t-1.052\n2\t-1.520\t-0.989\t1.211\t-0.913\t0.049\t-0.978\t1.884\t1.534\t-0.547\t0.908\t-0.519\t1.226\t0.175\t-0.868\t0.928\t0.570\t-0.410\t-1.072\t-0.162\t0.003\t-0.785\t-1.066\t-0.233\t-1.993\t0.203\t-0.309\t-1.568\t-0.804\n3\t0.148\t-0.755\t1.515\t-1.200\t1.030\t-0.323\t-0.399\t0.094\t0.704\t-1.079\t0.003\t-1.114\t1.495\t0.381\t-0.070\t-3.402\t-0.289\t-0.693\t0.688\t-0.196\t1.246\t-1.573\t-0.256\t0.283\t1.020\t0.972\t0.714\t-0.907\n0\t-0.504\t0.299\t-0.251\t-0.133\t-0.322\t0.471\t0.082\t0.395\t-0.018\t-1.604\t0.031\t0.064\t0.243\t-0.594\t-1.195\t0.625\t-0.274\t0.171\t-0.281\t-0.006\t-0.449\t-0.968\t0.474\t-0.603\t0.164\t0.410\t-0.587\t-1.204\n3\t1.280\t1.541\t-0.856\t0.111\t0.674\t1.516\t0.718\t0.291\t0.667\t0.674\t-0.928\t0.193\t1.459\t-0.207\t0.129\t-0.243\t-2.314\t0.021\t0.302\t-0.512\t-1.155\t-0.881\t0.764\t1.973\t1.382\t-1.344\t0.855\t-0.663\n4\t-0.023\t1.279\t1.083\t-1.076\t-0.674\t0.958\t-2.713\t-0.149\t-1.431\t-0.477\t-0.299\t-0.358\t-0.552\t-1.097\t2.292\t1.867\t-1.162\t1.582\t-0.399\t0.129\t-0.063\t-0.660\t0.984\t0.126\t-0.477\t-1.305\t-0.894\t-1.641\n2\t0.083\t0.597\t-2.021\t-0.827\t0.519\t-1.615\t0.549\t-0.725\t-1.034\t-0.729\t1.528\t0.333\t-0.910\t0.775\t0.830\t-0.381\t0.959\t1.007\t0.851\t-0.674\t-1.179\t-1.693\t-0.656\t-0.368\t-0.286\t-0.047\t1.733\t-0.340\n1\t-1.513\t1.724\t0.882\t0.037\t-1.189\t0.627\t-1.015\t-0.605\t-0.368\t-0.158\t0.608\t-0.279\t0.400\t-1.844\t1.289\t1.305\t0.450\t0.424\t-0.134\t0.315\t-0.672\t-0.174\t0.149\t-1.628\t1.446\t0.225\t0.667\t-0.052\n2\t-0.321\t-1.362\t-0.684\t-0.009\t0.061\t1.926\t0.081\t1.179\t-0.435\t-0.312\t-1.329\t-0.742\t0.527\t0.008\t0.943\t0.181\t-0.352\t2.148\t-0.094\t1.163\t-0.401\t0.710\t0.928\t0.208\t-2.354\t0.018\t1.230\t0.527\n0\t0.799\t-1.251\t1.454\t1.547\t-0.069\t1.125\t0.130\t0.518\t1.791\t0.660\t-0.641\t-0.152\t-0.412\t-0.504\t0.221\t-0.234\t0.565\t0.460\t0.517\t-0.174\t0.471\t0.019\t0.220\t-0.266\t-0.615\t-0.540\t1.155\t1.636\n2\t1.503\t0.679\t-0.591\t1.431\t0.222\t1.339\t-0.181\t-1.836\t-2.191\t0.532\t-0.527\t-1.824\t0.510\t0.808\t0.093\t-0.146\t0.773\t-0.174\t-0.349\t-1.156\t-1.348\t0.029\t-0.797\t1.694\t0.078\t-1.081\t0.258\t-0.125\n3\t-2.211\t0.463\t1.915\t-0.358\t-0.701\t0.181\t0.710\t0.547\t-0.659\t1.922\t-1.526\t0.073\t0.874\t0.233\t-0.255\t-0.873\t0.432\t1.179\t-0.116\t0.313\t1.159\t1.361\t1.376\t-1.234\t1.158\t-0.013\t0.870\t2.116\n1\t-0.270\t-0.194\t-0.407\t0.399\t0.055\t-2.037\t0.110\t0.374\t-0.041\t1.647\t0.348\t0.024\t0.909\t2.266\t-0.143\t1.054\t1.839\t-0.231\t-1.173\t0.438\t1.347\t-0.285\t1.096\t-0.859\t-0.247\t-0.092\t-0.139\t-0.860\n0\t1.653\t0.076\t-0.893\t0.802\t-0.658\t-0.674\t-0.876\t0.650\t1.028\t-0.314\t1.221\t-0.401\t-0.119\t-0.938\t-0.514\t1.035\t-0.532\t-0.821\t-0.947\t-1.835\t-0.976\t-1.451\t-0.688\t-0.242\t-0.288\t0.201\t0.417\t-1.040\n4\t1.835\t-0.955\t1.168\t-1.226\t-0.963\t0.680\t1.554\t0.651\t0.357\t-1.361\t-2.128\t-0.432\t0.155\t-0.052\t2.211\t-1.542\t-0.305\t1.670\t0.651\t-1.094\t-2.215\t-0.393\t-1.012\t-0.445\t2.287\t-1.013\t-0.647\t0.914\n2\t2.025\t0.741\t-0.590\t0.288\t-1.160\t-0.956\t0.540\t0.583\t0.368\t-1.194\t2.350\t0.571\t-0.117\t0.083\t-0.610\t-0.861\t2.472\t-0.848\t-0.661\t-0.330\t-0.711\t0.012\t-0.427\t-0.128\t-0.322\t-0.232\t-1.539\t-0.312\n3\t-1.194\t1.671\t0.436\t-0.254\t0.165\t0.474\t-1.429\t0.158\t-0.450\t0.507\t-0.740\t-0.605\t1.663\t1.231\t-3.296\t1.157\t-0.074\t-0.708\t-1.007\t-0.949\t-0.680\t-1.127\t-1.355\t-1.040\t0.323\t0.050\t0.556\t-0.066\n3\t0.919\t-1.539\t0.480\t-0.751\t1.218\t-0.526\t-1.761\t-0.040\t-0.840\t0.811\t0.330\t-0.629\t-1.003\t-1.098\t-0.586\t1.364\t-0.144\t0.947\t-1.481\t-1.921\t-0.945\t0.657\t-0.289\t0.890\t1.264\t-0.957\t0.834\t1.388\n3\t0.429\t-2.404\t1.164\t-0.824\t1.293\t-1.202\t-1.260\t-0.446\t0.398\t1.155\t-0.177\t-0.648\t1.236\t-0.261\t-0.211\t0.926\t0.612\t0.700\t0.978\t-1.083\t0.259\t-0.796\t-0.741\t0.943\t1.105\t2.446\t0.087\t-1.332\n0\t-0.185\t-0.461\t-0.343\t-2.298\t0.283\t-1.209\t0.630\t-0.090\t0.105\t0.700\t-0.927\t0.369\t0.268\t-0.185\t-0.928\t0.595\t0.703\t0.114\t-1.070\t-0.527\t-0.060\t0.688\t-1.583\t-0.564\t-0.403\t-0.083\t-1.301\t-0.218\n2\t-1.370\t-0.007\t1.191\t1.762\t-0.130\t0.042\t2.211\t1.135\t-0.617\t-0.619\t0.957\t-1.011\t1.088\t0.334\t0.518\t-0.257\t-0.630\t1.462\t-0.247\t-0.411\t-0.080\t0.193\t-1.031\t-0.565\t-0.618\t0.845\t-0.886\t-1.756\n4\t1.486\t0.387\t1.023\t0.296\t-1.878\t0.454\t-0.193\t1.229\t1.652\t1.804\t-0.172\t-0.472\t-0.320\t-0.117\t0.166\t-0.921\t0.021\t-2.033\t2.151\t-0.230\t-0.426\t0.545\t0.839\t-2.029\t-0.686\t1.422\t0.874\t-1.171\n2\t-1.687\t1.927\t0.333\t0.226\t-1.645\t1.012\t1.246\t-0.229\t-0.436\t-0.376\t1.727\t-0.098\t0.165\t0.950\t-2.439\t-0.082\t-0.282\t0.152\t0.807\t0.974\t-0.236\t-0.145\t0.024\t0.758\t1.148\t-1.405\t0.375\t-0.200\n4\t-0.355\t1.056\t-0.184\t0.002\t-0.269\t-0.253\t-0.540\t-1.273\t-1.863\t1.043\t1.020\t0.094\t0.377\t-0.133\t0.645\t3.008\t-3.486\t0.173\t1.257\t0.861\t1.190\t0.490\t-0.232\t-0.206\t-0.244\t0.857\t-0.986\t-0.971\n1\t-0.048\t-0.268\t1.491\t-0.541\t-0.181\t0.311\t-1.083\t0.802\t0.550\t-0.541\t1.165\t-1.173\t-0.370\t1.494\t-0.132\t-1.483\t0.243\t-0.281\t0.524\t1.097\t-0.067\t-2.372\t0.559\t-1.010\t0.510\t0.866\t1.063\t0.849\n2\t-0.323\t0.707\t1.267\t-0.386\t1.119\t2.221\t0.066\t0.785\t-0.590\t0.019\t-0.177\t0.797\t1.205\t-0.110\t1.534\t1.530\t0.847\t-0.516\t-0.612\t-0.812\t0.949\t-1.212\t0.677\t-1.081\t0.824\t0.017\t1.334\t1.141\n2\t1.134\t2.481\t-1.914\t1.289\t0.642\t-0.742\t-0.363\t0.882\t0.051\t-0.049\t-0.687\t-0.759\t-0.966\t-0.510\t-0.129\t0.354\t-0.823\t0.756\t-0.908\t0.769\t-0.866\t2.017\t0.648\t0.841\t-0.172\t0.607\t-1.252\t-0.976\n2\t0.387\t0.421\t-0.800\t0.907\t-0.316\t-0.646\t-0.168\t-1.686\t-1.770\t0.583\t-0.663\t-1.223\t-1.626\t-1.271\t-0.769\t-1.993\t0.657\t0.415\t-0.797\t1.306\t-0.713\t-0.038\t1.331\t-0.347\t0.800\t0.696\t-0.487\t-0.278\n4\t1.700\t0.563\t-0.730\t0.887\t-0.452\t-0.768\t-2.144\t0.102\t-0.630\t0.096\t-2.299\t0.170\t-2.431\t1.088\t0.522\t-0.279\t0.563\t-0.978\t0.072\t0.795\t-3.094\t-0.447\t-0.023\t-0.196\t1.457\t-1.124\t-0.621\t-1.916\n4\t0.406\t-0.604\t0.433\t-0.531\t-2.603\t0.767\t0.632\t-0.600\t-0.088\t2.489\t0.713\t-0.617\t0.351\t0.583\t-0.320\t-0.167\t-1.742\t-0.158\t-1.285\t-1.193\t-0.433\t0.471\t-1.971\t-0.338\t-1.460\t2.126\t-1.470\t-0.093\n2\t-0.145\t-1.171\t0.775\t0.163\t0.872\t-0.571\t0.406\t-0.091\t-0.422\t0.350\t0.686\t-0.605\t1.578\t-0.248\t2.187\t-0.747\t-0.340\t-2.084\t-0.004\t1.207\t-1.009\t0.645\t-0.656\t0.312\t0.869\t2.230\t1.220\t-0.301\n2\t-0.061\t-0.204\t-0.431\t0.686\t-1.333\t0.013\t-0.211\t0.038\t-1.732\t-0.52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1.229\t1.611\t1.389\n2\t-0.658\t0.648\t-1.432\t-0.119\t-0.304\t-0.571\t-0.163\t0.650\t0.311\t-0.054\t0.218\t1.580\t1.299\t-1.192\t-0.501\t-1.021\t1.425\t-0.259\t-0.636\t-1.588\t0.159\t1.611\t1.238\t-1.489\t0.452\t-0.546\t-0.507\t1.710\n1\t1.005\t0.723\t0.583\t0.810\t-0.070\t0.890\t0.559\t-0.119\t-0.808\t-0.387\t0.628\t-0.985\t1.833\t-0.579\t-0.822\t-0.329\t1.118\t-1.242\t1.462\t1.044\t1.798\t0.608\t0.294\t-1.703\t0.145\t-1.276\t0.939\t-0.143\n0\t1.085\t-0.299\t1.298\t1.233\t-0.587\t0.569\t-1.313\t-1.054\t0.112\t0.509\t1.613\t-0.163\t0.176\t0.098\t1.794\t1.342\t0.385\t0.794\t0.350\t1.075\t0.862\t-0.302\t0.099\t0.162\t0.152\t-0.508\t0.337\t-0.509\n4\t-0.210\t0.407\t-2.131\t0.736\t-0.294\t-0.968\t-0.658\t0.329\t-0.689\t-0.381\t1.727\t-0.856\t-1.635\t-0.415\t-0.868\t1.159\t-0.820\t-1.216\t0.975\t-0.493\t1.432\t-0.540\t-1.848\t-1.196\t-2.565\t-2.722\t-0.015\t0.666\n3\t-1.229\t-0.626\t0.791\t-0.306\t-0.979\t-1.424\t-0.408\t0.082\t0.765\t1.054\t-0.329\t0.439\t0.303\t-0.012\t1.488\t-3.159\t-1.927\t-0.327\t0.412\t1.849\t-0.030\t0.416\t-0.020\t0.878\t-0.715\t-0.071\t-1.223\t-0.596\n3\t-1.550\t0.849\t0.404\t-1.026\t-0.669\t-0.088\t-2.025\t2.272\t-0.980\t0.031\t1.671\t-0.184\t0.322\t-0.957\t-0.548\t-0.925\t1.280\t-0.667\t-0.743\t-0.861\t0.382\t0.581\t-1.998\t0.068\t0.301\t1.405\t-1.224\t-1.121\n1\t0.298\t-0.072\t0.176\t1.841\t-0.812\t0.032\t-0.985\t-0.329\t0.574\t0.363\t0.073\t-1.497\t0.808\t1.312\t-1.577\t0.601\t-1.213\t0.991\t0.840\t1.362\t-0.513\t-0.608\t1.888\t-0.172\t-0.489\t-0.782\t-1.453\t-0.367\n4\t-1.276\t0.448\t1.048\t-0.582\t-1.586\t-0.441\t0.188\t0.592\t-0.521\t-2.035\t-1.276\t0.360\t1.991\t-0.022\t1.313\t-1.057\t-0.648\t-2.340\t0.370\t-0.894\t0.599\t-0.502\t2.022\t1.793\t-2.303\t0.591\t-0.743\t0.647\n3\t1.962\t-0.940\t-0.606\t0.428\t-0.701\t-0.351\t0.143\t0.908\t0.157\t1.254\t-1.434\t0.355\t-1.510\t-2.088\t-0.251\t0.339\t0.136\t-0.382\t-0.182\t-0.261\t-0.874\t-1.776\t-0.406\t-0.095\t0.471\t-0.125\t2.865\t-0.459\n4\t-0.858\t-0.802\t-0.845\t-0.736\t0.631\t-0.547\t0.909\t1.130\t0.538\t-1.426\t-0.788\t0.677\t1.629\t-1.845\t0.846\t-0.918\t-1.720\t0.708\t-1.367\t-1.398\t2.469\t-1.686\t-1.197\t-0.073\t-0.722\t0.301\t0.470\t-1.009\n3\t0.269\t-0.133\t0.812\t1.034\t-1.295\t0.098\t-0.902\t0.199\t-1.387\t-0.061\t0.283\t-2.288\t-0.268\t-1.497\t0.830\t-1.942\t-0.353\t1.314\t-1.047\t-0.734\t-0.075\t0.053\t-2.324\t-0.262\t0.694\t2.151\t-0.762\t-0.296\n0\t-0.719\t0.195\t-0.180\t-0.402\t-1.658\t-0.515\t-1.110\t0.552\t0.991\t0.741\t0.342\t-0.853\t0.205\t0.793\t-0.586\t0.119\t-0.145\t1.662\t0.852\t0.609\t-1.717\t0.173\t-0.155\t-0.164\t-1.563\t-1.088\t-0.068\t1.114\n2\t-0.272\t-0.834\t-0.107\t-0.694\t1.637\t1.323\t0.724\t0.031\t1.139\t0.099\t0.161\t-1.728\t0.261\t-1.291\t-0.403\t0.169\t-0.899\t0.479\t1.052\t-0.884\t0.117\t-1.246\t-1.167\t0.310\t0.713\t-1.509\t2.049\t-0.497\n2\t0.018\t-1.435\t0.031\t0.469\t1.409\t1.819\t-1.192\t-0.117\t-0.104\t-0.713\t0.099\t-0.080\t0.614\t1.357\t0.132\t-0.490\t-1.026\t2.042\t0.907\t0.400\t-0.166\t1.612\t-0.160\t-1.712\t0.265\t-1.228\t0.269\t1.344\n4\t-1.140\t-1.821\t0.722\t-1.306\t0.968\t-2.185\t0.149\t-0.320\t1.677\t0.226\t0.613\t-0.431\t-0.179\t1.052\t0.016\t-0.919\t1.140\t1.686\t1.503\t1.290\t-0.283\t-0.981\t-1.186\t-1.307\t-0.094\t1.771\t-1.654\t0.017\n3\t0.863\t-1.975\t0.514\t0.281\t0.267\t-1.163\t-1.704\t2.244\t0.133\t1.936\t0.903\t0.112\t0.232\t1.080\t1.193\t0.476\t1.093\t1.140\t0.330\t-0.604\t0.497\t-0.748\t-0.380\t0.398\t-0.079\t1.316\t-0.660\t-2.128\n0\t1.012\t-0.806\t-0.031\t-0.136\t0.342\t0.472\t-0.942\t0.644\t0.267\t0.112\t-0.276\t0.774\t1.149\t0.744\t-1.291\t1.625\t0.619\t-0.396\t0.556\t-0.186\t0.491\t1.463\t-0.067\t1.048\t-0.268\t1.029\t0.444\t0.339\n1\t-1.665\t0.327\t0.981\t-1.657\t0.904\t1.348\t0.992\t0.311\t-1.376\t1.696\t-0.251\t-0.529\t0.828\t-0.203\t-0.800\t-0.360\t-0.136\t1.370\t0.216\t1.141\t-0.476\t-0.905\t1.037\t-0.259\t-0.118\t1.032\t-0.863\t0.364\n2\t-0.144\t-0.982\t-0.220\t0.543\t0.685\t0.240\t0.244\t-1.525\t1.213\t0.304\t1.312\t0.507\t0.954\t-1.077\t2.924\t0.251\t0.920\t0.575\t-0.754\t-0.817\t0.031\t-0.993\t1.770\t0.385\t1.296\t0.702\t-0.999\t0.458\n3\t-0.889\t0.160\t0.330\t-0.252\t-1.712\t-0.398\t1.519\t2.296\t2.807\t-0.008\t1.018\t-0.102\t0.204\t0.659\t0.630\t0.306\t0.352\t0.298\t1.126\t-0.818\t-0.493\t-0.461\t0.380\t0.768\t-0.742\t-2.065\t1.734\t0.081\n2\t0.882\t0.495\t1.947\t-0.351\t-0.002\t0.165\t0.754\t-1.970\t-0.733\t-1.528\t-0.840\t0.912\t0.668\t0.037\t1.113\t0.404\t0.748\t-0.387\t-1.660\t2.058\t-0.730\t-1.239\t-0.164\t-0.596\t0.348\t-0.724\t-0.375\t-1.089\n0\t-0.675\t1.449\t-1.155\t0.478\t-0.492\t0.410\t0.050\t-0.283\t0.389\t-0.493\t-1.784\t-0.928\t-1.048\t-1.604\t0.369\t-0.173\t0.354\t0.521\t0.756\t0.407\t-0.980\t0.580\t0.222\t2.190\t0.008\t0.383\t-0.095\t0.049\n2\t-0.968\t-1.876\t1.320\t-1.848\t-0.072\t1.017\t1.682\t-0.899\t0.159\t-0.122\t-0.727\t1.244\t0.570\t-1.191\t-0.199\t-0.378\t0.435\t1.907\t0.336\t-0.280\t-0.097\t0.403\t-1.732\t1.112\t0.168\t-0.124\t-0.409\t-0.068\n1\t0.632\t0.841\t0.932\t0.214\t0.446\t2.257\t2.407\t0.047\t-1.768\t0.129\t0.116\t-1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0.011\t-1.410\t-0.970\t-1.424\t0.798\t0.569\t-0.437\t-0.507\t0.971\t-0.005\t-0.867\t1.073\t-0.141\t0.677\t0.072\n0\t-0.118\t-0.233\t-0.841\t0.319\t1.137\t-0.499\t-0.391\t-0.522\t-1.151\t0.134\t0.170\t-0.041\t1.839\t0.400\t-1.116\t0.632\t-0.339\t-0.329\t-0.173\t-0.285\t0.270\t2.115\t-0.368\t0.089\t0.234\t-0.513\t-0.401\t0.994\n4\t1.470\t-0.073\t-1.397\t-1.999\t1.668\t0.479\t0.544\t1.490\t-1.652\t-0.359\t-0.236\t0.742\t-0.693\t-1.201\t-1.210\t1.149\t-0.743\t-0.552\t0.634\t-0.699\t-1.705\t-1.582\t-0.057\t0.497\t-0.202\t0.430\t-1.613\t-1.545\n0\t-0.765\t-0.625\t1.128\t0.441\t-0.957\t-1.951\t-0.914\t0.157\t-0.632\t-0.710\t-0.589\t-0.073\t0.785\t-0.000\t0.062\t-0.225\t-0.499\t0.894\t0.742\t2.045\t0.611\t-0.498\t-0.905\t0.953\t-0.815\t0.810\t-0.760\t0.349\n4\t-1.999\t0.004\t-0.063\t-3.035\t0.933\t0.446\t-0.415\t-1.286\t-0.300\t1.709\t1.511\t-0.385\t1.740\t1.854\t0.242\t0.931\t0.380\t-0.833\t-1.085\t0.546\t1.569\t0.618\t1.003\t1.457\t0.258\t-0.548\t-1.670\t-0.127\n4\t1.880\t-1.481\t1.848\t0.679\t0.365\t1.724\t0.583\t0.975\t0.194\t-0.626\t-1.043\t2.109\t1.525\t-0.125\t-0.620\t1.607\t0.948\t1.082\t1.325\t1.205\t1.963\t0.851\t-1.319\t-0.335\t0.505\t0.337\t0.810\t0.044\n3\t0.393\t0.719\t-0.133\t-1.036\t-1.601\t-0.763\t0.225\t-1.383\t1.074\t-1.051\t-0.113\t1.476\t-0.182\t-1.092\t-2.510\t0.406\t0.310\t0.576\t0.113\t-0.536\t-0.786\t0.042\t0.990\t-2.959\t0.939\t-0.290\t-0.167\t0.695\n2\t-0.514\t0.336\t0.271\t1.265\t1.580\t0.904\t1.264\t-0.020\t-0.699\t-0.031\t-0.132\t-0.200\t-0.010\t-0.171\t-0.458\t-0.131\t0.802\t-0.865\t1.345\t1.082\t0.087\t-2.383\t-2.178\t0.112\t0.678\t1.773\t-0.405\t-0.514\n3\t-0.528\t-0.847\t1.660\t-1.208\t1.198\t-0.415\t0.751\t0.779\t-0.353\t-0.003\t-1.355\t0.039\t1.250\t-0.196\t-0.081\t0.781\t-0.500\t0.130\t1.386\t-0.037\t-0.266\t-0.710\t2.965\t0.604\t2.499\t0.179\t0.326\t0.265\n3\t0.290\t0.193\t-0.873\t0.623\t-1.098\t0.794\t0.885\t-0.064\t-1.464\t0.802\t-0.584\t0.274\t0.691\t-0.810\t2.146\t-0.044\t-0.671\t0.999\t1.550\t-2.067\t-1.381\t-0.776\t2.113\t-0.479\t1.521\t0.220\t-1.450\t0.881\n1\t1.010\t-0.734\t-0.358\t-0.106\t-0.178\t-0.401\t1.204\t-1.134\t0.989\t-0.763\t-1.832\t0.505\t-0.835\t-1.154\t0.800\t-1.683\t-0.671\t0.688\t-1.094\t-1.320\t0.052\t-0.589\t0.156\t-0.459\t-1.051\t0.508\t0.856\t-0.701\n3\t0.385\t0.100\t2.009\t1.129\t0.122\t0.723\t-0.249\t-1.604\t-1.022\t-0.461\t-0.525\t-0.723\t-0.678\t1.306\t1.833\t1.312\t-2.157\t1.078\t0.737\t-0.037\t-0.497\t-0.305\t0.990\t0.386\t1.220\t0.815\t1.441\t0.634\n0\t-0.372\t-0.074\t0.357\t-0.130\t0.196\t-1.351\t1.016\t0.633\t0.241\t-0.813\t-0.737\t-0.212\t1.125\t-0.984\t-1.309\t-0.851\t0.754\t0.303\t0.686\t-0.745\t-0.002\t0.833\t-0.726\t1.659\t0.788\t-0.306\t1.515\t-0.450\n2\t-0.627\t-0.164\t-2.072\t-0.088\t-0.809\t-0.271\t1.185\t1.525\t-2.141\t-1.106\t0.809\t-0.586\t-0.035\t-0.328\t0.573\t-1.510\t1.333\t-0.655\t-0.701\t-1.002\t1.198\t-1.522\t0.832\t0.192\t-0.080\t-0.376\t-0.172\t-0.069\n1\t0.628\t0.450\t-0.693\t0.236\t1.418\t-1.612\t-0.255\t-0.386\t0.303\t-1.052\t-0.878\t0.576\t-0.431\t0.731\t0.385\t0.878\t-1.006\t1.381\t0.341\t0.978\t-1.390\t-0.144\t0.046\t0.474\t1.257\t-0.331\t-1.411\t-1.982\n0\t0.114\t-0.109\t0.827\t-0.104\t-1.758\t-0.251\t-0.972\t0.080\t1.434\t-1.739\t-0.800\t-0.553\t-0.382\t0.740\t-0.174\t0.416\t-0.626\t0.230\t0.778\t-0.732\t0.781\t0.052\t1.045\t-0.201\t0.381\t-0.041\t-0.096\t0.259\n2\t0.563\t-0.070\t-1.109\t0.009\t0.879\t-1.447\t1.747\t0.573\t0.593\t-0.001\t1.747\t0.962\t-0.368\t0.757\t-0.733\t-0.280\t1.467\t0.388\t0.662\t1.170\t0.548\t-0.811\t1.661\t0.516\t0.587\t0.056\t1.111\t1.915\n0\t1.009\t0.163\t0.760\t-0.334\t0.056\t-0.722\t0.386\t-0.186\t0.606\t0.782\t-0.353\t0.514\t-0.696\t1.796\t1.412\t0.295\t-0.759\t0.245\t-1.213\t0.409\t-2.131\t-1.078\t0.368\t1.393\t0.367\t-0.319\t-0.964\t-0.512\n0\t-1.223\t1.760\t-0.113\t1.227\t-0.293\t0.285\t-0.330\t0.837\t0.550\t0.908\t-0.396\t-1.441\t0.474\t-0.579\t1.034\t0.668\t1.107\t-0.399\t0.206\t-0.454\t-0.990\t1.697\t0.273\t0.890\t-0.336\t-0.680\t-0.388\t0.688\n4\t-2.102\t-0.879\t0.105\t0.842\t1.526\t-0.637\t2.338\t-0.049\t-0.224\t0.562\t0.311\t0.255\t1.996\t0.247\t-0.984\t-0.840\t1.473\t0.684\t-1.556\t-1.606\t0.025\t0.616\t-0.678\t-1.680\t-1.125\t1.646\t0.497\t1.000\n0\t-0.659\t0.615\t0.714\t-1.312\t-0.384\t0.778\t0.989\t0.065\t0.365\t0.253\t0.954\t-1.109\t0.268\t-0.889\t1.088\t-1.058\t0.408\t-0.884\t0.752\t0.439\t0.240\t1.961\t-0.703\t0.926\t1.172\t0.926\t-0.677\t-0.235\n0\t1.710\t-0.204\t-1.273\t-0.615\t1.071\t0.555\t0.047\t-0.169\t0.004\t0.156\t-0.194\t0.102\t-0.301\t-0.856\t-0.264\t0.330\t-0.728\t0.421\t0.830\t0.341\t-0.562\t0.096\t0.153\t0.199\t1.490\t-1.158\t0.207\t-0.400\n4\t1.466\t-2.120\t0.541\t0.766\t0.131\t-0.281\t-1.252\t0.272\t2.100\t1.458\t0.098\t-0.937\t0.893\t0.456\t-2.031\t-1.435\t2.356\t-1.156\t-0.154\t0.814\t-0.066\t-0.539\t1.016\t0.539\t0.458\t-1.004\t0.002\t1.210\n4\t1.272\t0.007\t-0.324\t1.241\t-0.982\t1.697\t-0.386\t-0.050\t-0.533\t0.035\t-0.911\t-0.372\t0.154\t-0.106\t-2.489\t2.474\t1.582\t-1.165\t-0.622\t-0.172\t0.665\t0.472\t-1.099\t2.342\t-0.397\t1.805\t-0.604\t1.057\n1\t0.187\t0.587\t0.445\t-0.050\t-0.425\t0.412\t-0.118\t1.805\t0.586\t-0.923\t-0.478\t0.048\t1.299\t1.411\t-0.396\t-0.934\t-1.061\t0.252\t2.304\t0.539\t-1.594\t-0.726\t-0.624\t-0.822\t-0.388\t0.369\t0.682\t1.092\n3\t-0.891\t0.415\t-1.573\t1.211\t1.411\t-0.159\t-0.052\t0.522\t0.275\t0.687\t-0.731\t1.140\t1.037\t0.862\t-1.009\t-1.264\t0.140\t0.759\t-0.411\t-0.474\t2.006\t-0.084\t-0.574\t-2.141\t1.152\t1.541\t-1.457\t-1.242\n3\t0.056\t-0.109\t0.795\t0.676\t0.173\t0.773\t0.820\t-1.173\t-1.149\t-1.291\t-1.514\t0.811\t0.813\t0.448\t1.662\t-0.308\t-0.690\t0.123\t1.142\t-0.591\t1.942\t-0.072\t0.513\t0.163\t-0.397\t0.910\t-3.475\t-0.339\n1\t0.986\t-1.100\t-1.142\t0.580\t-1.111\t1.018\t0.645\t-0.778\t0.980\t-1.939\t0.472\t-1.246\t-0.523\t-0.459\t1.281\t-0.501\t-0.079\t-0.438\t-0.983\t-1.030\t0.309\t-0.026\t0.763\t-0.242\t-0.210\t0.766\t-0.372\t-1.440\n3\t-2.060\t-1.360\t0.478\t-0.944\t0.261\t0.031\t-1.648\t0.657\t0.299\t0.039\t-0.648\t0.390\t-1.294\t-1.008\t0.321\t1.030\t-0.477\t0.457\t0.205\t0.414\t-0.692\t-0.149\t-0.038\t1.498\t-1.104\t-1.404\t2.999\t0.309\n4\t-1.532\t0.184\t-0.029\t2.252\t-1.957\t0.922\t0.433\t0.766\t-0.447\t0.319\t0.145\t3.536\t-0.309\t-0.785\t2.001\t0.344\t0.514\t1.127\t0.077\t-0.419\t0.132\t-0.841\t-1.452\t-0.843\t-2.362\t-0.364\t-0.500\t0.604\n4\t-0.027\t0.888\t-1.548\t0.068\t0.788\t0.691\t0.963\t-1.682\t-0.116\t-0.028\t1.467\t2.745\t1.438\t0.896\t0.448\t0.033\t1.264\t0.200\t-0.473\t-0.062\t-0.698\t-1.858\t-1.711\t0.043\t-2.049\t-1.155\t0.468\t-0.750\n0\t-0.109\t0.168\t-0.754\t-1.325\t0.463\t-0.356\t0.575\t-0.671\t1.416\t1.001\t0.297\t-0.213\t0.852\t0.393\t0.239\t1.043\t1.427\t-1.252\t-1.050\t-0.780\t-1.502\t-0.063\t0.600\t-0.021\t-0.534\t1.126\t0.141\t0.343\n1\t-1.124\t-0.297\t0.748\t0.860\t-0.772\t2.143\t-0.131\t0.088\t0.978\t1.552\t0.062\t1.173\t-0.456\t1.668\t0.039\t0.314\t-0.347\t0.216\t-0.730\t-0.062\t0.017\t-1.056\t-0.571\t-2.100\t-1.241\t-0.014\t0.959\t-0.357\n4\t-0.110\t-0.869\t0.196\t-1.113\t-0.093\t3.125\t-0.190\t-0.609\t0.111\t-3.029\t-0.396\t-0.065\t0.196\t1.060\t0.395\t-1.147\t-1.882\t1.004\t-0.613\t0.290\t1.502\t0.275\t-1.550\t0.327\t-0.245\t-1.402\t0.967\t0.346\n2\t0.460\t-0.148\t1.716\t-0.738\t-0.333\t0.339\t2.826\t-0.926\t-1.134\t0.226\t1.490\t0.903\t-0.673\t-1.149\t-0.655\t0.104\t0.314\t-0.242\t-0.267\t-0.275\t0.605\t0.349\t0.622\t0.342\t-1.870\t0.011\t-0.670\t1.582\n2\t-1.162\t-0.306\t1.844\t-1.203\t2.573\t1.407\t-1.250\t1.197\t-1.077\t-0.805\t0.321\t1.063\t-0.058\t-0.157\t-0.635\t0.167\t0.146\t0.286\t0.976\t-0.834\t0.579\t0.135\t0.335\t1.754\t-0.117\t-0.352\t-0.668\t-0.553\n0\t1.233\t-0.797\t1.183\t0.912\t-0.582\t-0.853\t-0.118\t0.095\t-0.334\t-1.762\t0.591\t-0.176\t-1.147\t-1.579\t0.532\t0.155\t-1.117\t-0.399\t0.315\t-0.213\t0.907\t-0.658\t1.280\t-0.390\t1.207\t-0.501\t0.031\t-0.979\n1\t-1.537\t1.585\t-1.216\t0.160\t-0.835\t-0.388\t-0.980\t1.676\t-0.140\t0.226\t-1.072\t-1.415\t-0.444\t0.521\t-0.121\t0.975\t0.124\t0.006\t0.535\t0.100\t0.944\t0.555\t0.911\t0.716\t1.360\t0.829\t1.748\t-0.541\n0\t-0.055\t0.286\t0.521\t0.645\t0.556\t0.090\t-0.197\t-0.151\t-0.195\t1.134\t0.594\t-2.940\t0.656\t0.195\t-0.019\t-0.389\t1.124\t0.948\t-0.773\t0.407\t-0.972\t-1.380\t-0.627\t0.862\t0.953\t0.513\t0.725\t0.516\n4\t-0.981\t0.951\t0.463\t0.377\t0.781\t-0.425\t-0.379\t0.810\t-1.345\t-2.143\t-0.433\t0.931\t0.256\t0.857\t-2.016\t0.315\t0.654\t-1.351\t0.862\t-0.112\t3.071\t-2.177\t1.369\t-0.394\t-1.617\t-0.527\t0.277\t1.037\n3\t-1.248\t-1.358\t-0.862\t0.430\t1.909\t-0.292\t-0.825\t0.584\t-1.375\t-1.040\t0.123\t1.617\t0.880\t2.371\t-0.587\t0.279\t-2.282\t-0.019\t-0.269\t1.137\t-0.143\t0.451\t-0.630\t1.213\t0.507\t0.696\t0.024\t-0.572\n3\t-1.047\t1.350\t0.949\t0.674\t-1.686\t-0.324\t-0.305\t-0.005\t0.374\t-1.495\t0.970\t0.808\t1.461\t-0.641\t0.400\t1.223\t-0.608\t-0.430\t-1.657\t-0.903\t-2.768\t-0.683\t0.115\t-1.316\t0.843\t-0.338\t-0.054\t1.084\n1\t0.320\t0.864\t-0.367\t-0.381\t1.019\t1.903\t0.979\t0.072\t-1.591\t0.416\t0.212\t-1.109\t-0.426\t0.932\t-1.028\t0.271\t-1.587\t-0.498\t0.361\t1.250\t-1.876\t-1.062\t-1.145\t-0.959\t-0.918\t-0.172\t-0.562\t-0.110\n1\t-0.551\t-1.014\t0.367\t-0.488\t0.030\t0.317\t1.242\t0.787\t-0.681\t-1.145\t1.707\t-1.583\t-0.453\t0.645\t1.639\t-0.190\t0.027\t-0.875\t0.127\t-1.150\t-0.750\t0.764\t-0.434\t0.917\t-0.154\t-1.369\t1.168\t-1.315\n2\t0.686\t-1.804\t0.980\t-1.994\t0.811\t0.169\t0.713\t-1.061\t-1.752\t0.640\t-1.525\t0.306\t1.277\t-0.136\t-0.054\t-0.599\t1.073\t-0.177\t0.477\t-1.849\t-0.523\t-0.284\t-0.636\t-0.346\t-0.066\t0.011\t-1.035\t0.888\n0\t-0.441\t-0.690\t1.473\t0.548\t0.644\t-0.392\t-0.186\t-0.249\t0.337\t0.642\t-0.030\t-0.793\t0.761\t0.033\t-1.441\t-2.135\t0.298\t-0.829\t0.431\t-0.776\t0.133\t-0.378\t1.752\t0.950\t1.347\t0.024\t0.822\t0.092\n1\t-0.736\t-0.522\t0.216\t-0.093\t-0.162\t-0.258\t-0.877\t-0.461\t2.434\t0.699\t0.020\t-1.500\t-0.990\t-0.042\t1.360\t0.229\t0.216\t1.110\t-0.971\t1.406\t-0.383\t1.158\t1.670\t-0.907\t0.000\t0.789\t-0.287\t-0.259\n0\t1.101\t-0.465\t-0.081\t0.080\t-1.098\t0.442\t1.023\t-0.144\t0.340\t1.399\t-0.405\t0.360\t-0.570\t-0.597\t0.342\t0.241\t0.555\t0.076\t0.465\t1.666\t1.060\t0.338\t0.505\t-0.299\t-0.159\t0.492\t0.088\t0.793\n1\t-0.227\t0.208\t-1.252\t0.299\t0.758\t0.276\t-0.780\t-1.613\t0.095\t-0.572\t1.620\t1.419\t-1.867\t-0.264\t-0.758\t-1.187\t-0.169\t-0.790\t-0.913\t-1.018\t1.327\t-0.198\t-0.062\t1.261\t-0.316\t0.320\t-0.724\t-0.497\n3\t0.008\t-0.488\t-0.205\t-0.358\t-0.634\t-0.018\t-0.228\t0.691\t2.239\t-1.638\t-1.491\t0.064\t-0.287\t-0.315\t1.427\t-0.870\t1.052\t1.620\t0.503\t-0.545\t0.653\t1.047\t1.048\t0.480\t-2.548\t0.602\t1.160\t1.001\n0\t-1.364\t0.102\t-1.176\t-0.675\t1.155\t0.323\t-1.621\t0.106\t0.411\t0.917\t0.612\t0.501\t0.052\t0.464\t0.917\t-0.137\t-0.741\t-0.143\t-0.416\t-0.127\t-1.976\t1.046\t0.223\t1.120\t-0.989\t0.494\t-0.202\t0.117\n2\t-0.907\t-1.915\t-0.531\t-0.183\t-0.110\t-1.831\t2.480\t2.403\t0.445\t0.025\t-0.565\t0.579\t-0.117\t1.252\t0.914\t-0.851\t-0.688\t0.970\t0.695\t-1.113\t-0.457\t-0.154\t-0.037\t-0.072\t0.899\t0.146\t0.081\t-0.757\n3\t0.418\t1.500\t-1.373\t0.660\t1.127\t-1.487\t-0.181\t-1.065\t0.431\t-0.358\t-0.130\t-0.411\t0.720\t0.229\t0.231\t0.161\t0.572\t-0.262\t1.701\t-1.235\t-1.726\t-1.809\t-2.080\t0.673\t0.551\t1.208\t-1.057\t0.745\n4\t2.764\t-0.670\t-0.394\t1.420\t-1.576\t0.245\t-2.415\t-1.380\t-1.190\t-0.877\t-1.247\t0.201\t1.400\t2.039\t-1.029\t0.728\t1.209\t0.121\t0.708\t0.460\t0.459\t-0.737\t-1.994\t-0.349\t-0.767\t-0.278\t0.961\t1.436\n2\t-0.224\t0.669\t-0.067\t0.719\t-1.079\t0.260\t0.900\t-1.474\t2.514\t1.191\t0.001\t-0.828\t-0.175\t0.084\t0.441\t1.501\t-0.476\t0.563\t-1.292\t-0.414\t0.377\t0.691\t-0.753\t-0.441\t1.461\t1.162\t0.208\t-2.237\n4\t0.049\t-0.856\t0.053\t0.168\t0.708\t-1.888\t-0.696\t0.482\t0.779\t3.270\t1.378\t-0.689\t1.774\t0.580\t1.366\t0.467\t1.437\t1.083\t0.552\t-0.438\t-0.442\t-0.950\t-0.074\t1.107\t0.581\t-0.775\t0.968\t-1.176\n0\t-1.532\t0.591\t0.263\t-0.140\t0.055\t-0.564\t-0.937\t0.559\t0.917\t0.074\t0.205\t0.167\t1.814\t-1.302\t0.297\t-0.256\t-0.500\t1.109\t0.438\t-0.155\t0.850\t-0.741\t1.495\t0.302\t0.759\t-0.696\t-0.543\t0.305\n2\t0.224\t1.260\t1.044\t0.010\t-1.219\t-1.221\t1.214\t0.116\t-0.663\t-0.941\t0.575\t1.788\t-0.241\t0.986\t-1.118\t0.768\t1.534\t-0.735\t0.817\t0.217\t-0.050\t-1.459\t-1.203\t0.395\t-0.147\t0.320\t1.390\t-1.435\n3\t-0.242\t0.932\t-0.614\t0.833\t-1.940\t0.876\t1.441\t1.837\t-0.586\t-1.425\t2.518\t-0.547\t0.321\t-0.096\t0.919\t1.399\t1.150\t0.589\t-0.468\t-0.408\t-0.640\t0.736\t-0.925\t1.097\t-0.203\t0.517\t0.511\t-0.151\n1\t0.764\t-0.443\t1.149\t2.127\t0.018\t1.271\t2.106\t0.736\t-0.283\t0.543\t-0.913\t-0.655\t-0.395\t0.037\t0.289\t-0.962\t1.685\t-0.102\t-0.433\t1.602\t0.847\t0.413\t0.412\t0.085\t0.006\t0.223\t0.826\t-0.687\n2\t2.130\t-0.927\t1.052\t0.599\t-1.179\t1.707\t-0.732\t-0.881\t0.354\t0.711\t-1.519\t-0.774\t1.058\t-0.015\t0.017\t-0.674\t-0.054\t0.843\t-0.547\t0.684\t0.705\t-0.117\t-0.925\t-0.732\t1.649\t0.647\t1.373\t0.623\n1\t-0.750\t-1.283\t-0.250\t-2.366\t0.492\t-0.557\t-0.410\t0.587\t0.372\t-0.630\t0.452\t1.549\t-1.012\t0.754\t-1.113\t1.175\t-0.712\t-0.750\t1.149\t0.722\t-0.679\t0.127\t-0.710\t-1.593\t0.189\t-0.356\t-0.946\t1.305\n4\t1.167\t0.882\t-1.579\t1.208\t0.358\t-0.894\t-0.672\t0.582\t-2.512\t-0.125\t-0.655\t-0.757\t-0.457\t-1.479\t1.052\t-2.128\t0.169\t-0.848\t1.984\t0.023\t-2.123\t0.547\t-1.093\t0.985\t0.618\t1.473\t0.187\t0.819\n1\t-1.967\t-1.470\t1.362\t0.016\t0.501\t0.536\t0.056\t-0.075\t1.045\t0.146\t1.399\t0.621\t-0.618\t-0.497\t-0.727\t1.218\t-0.405\t-0.798\t0.320\t-0.097\t0.295\t1.890\t0.913\t0.027\t-1.408\t-0.500\t0.711\t-1.171\n2\t-0.735\t-0.767\t-0.091\t1.187\t-1.207\t-0.149\t-0.801\t-0.380\t0.759\t-0.071\t0.468\t-0.041\t-1.587\t-2.470\t-0.709\t1.343\t1.234\t-0.503\t0.682\t0.809\t1.104\t0.214\t-0.826\t2.204\t0.563\t0.027\t0.042\t0.052\n3\t-0.842\t-0.220\t-0.025\t-1.989\t1.729\t1.653\t0.145\t0.250\t0.278\t-0.909\t0.539\t-0.790\t-1.865\t-0.059\t-0.159\t1.661\t-0.656\t-0.211\t-0.350\t-0.583\t-0.864\t-0.687\t0.244\t-0.829\t2.282\t-0.150\t2.412\t-0.592\n2\t0.397\t-0.747\t-0.281\t-1.403\t-0.108\t1.112\t0.685\t1.402\t-0.303\t1.346\t-0.193\t1.472\t-0.022\t0.707\t1.475\t-0.193\t0.218\t1.289\t-0.133\t-1.321\t2.143\t-0.470\t0.159\t1.156\t-0.608\t-1.008\t0.254\t-1.793\n3\t-1.403\t0.080\t-0.459\t-0.269\t-0.310\t-0.179\t-0.736\t-2.534\t1.958\t1.727\t0.731\t-0.391\t0.786\t-1.747\t0.925\t-0.525\t-1.328\t1.161\t0.507\t-0.485\t-0.049\t-0.929\t0.521\t0.475\t0.371\t-2.147\t0.238\t-1.122\n4\t0.279\t1.188\t-1.202\t-1.927\t-0.024\t-2.838\t-0.575\t0.033\t0.395\t-1.298\t2.243\t0.224\t-0.991\t0.914\t-0.604\t1.014\t1.473\t-0.424\t0.078\t1.019\t0.213\t0.867\t-0.486\t2.636\t-0.678\t0.218\t-0.828\t-0.491\n1\t1.864\t0.415\t0.921\t-0.354\t0.215\t-1.202\t-0.640\t-0.899\t0.297\t1.707\t0.903\t1.812\t0.794\t0.420\t-0.922\t0.323\t-0.474\t0.832\t-1.359\t-0.108\t0.017\t1.787\t-1.023\t1.168\t0.823\t-0.256\t0.410\t-0.250\n3\t0.102\t-1.867\t1.333\t-1.161\t1.789\t-0.433\t1.301\t0.954\t-0.451\t0.691\t-0.320\t-0.231\t0.743\t-1.555\t-0.591\t0.458\t0.909\t0.172\t-0.674\t0.475\t-0.331\t2.203\t0.183\t-1.651\t-0.487\t-0.898\t-1.388\t-1.191\n4\t2.282\t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1.104\t0.025\t0.992\t-0.008\t0.631\t1.719\t0.547\t-0.732\t0.266\t0.101\t-0.360\t-0.798\t2.233\t-0.264\t-2.340\t-0.635\t-0.769\t-0.248\t-1.499\t-0.548\t0.252\t-1.340\n3\t0.052\t-1.061\t-0.717\t-1.285\t0.978\t-2.106\t1.154\t1.386\t-0.302\t-2.603\t-0.361\t-0.064\t-1.011\t-0.515\t1.530\t0.665\t-0.925\t-1.598\t-0.327\t-0.213\t0.496\t-0.535\t0.511\t1.935\t0.816\t-0.048\t-0.183\t-0.357\n0\t-0.680\t0.128\t-0.994\t0.767\t0.857\t0.691\t0.977\t0.945\t0.324\t1.343\t0.483\t-0.584\t-0.061\t0.892\t0.470\t-0.526\t0.050\t-0.766\t0.818\t1.339\t-1.572\t0.414\t-1.068\t-1.389\t1.304\t1.128\t0.216\t-0.503\n0\t-0.347\t0.227\t-0.437\t0.031\t0.884\t0.845\t-0.023\t0.903\t-1.019\t-1.386\t-1.504\t-0.038\t-0.174\t0.142\t-0.064\t-0.686\t-0.214\t-1.282\t1.332\t0.077\t-1.558\t0.838\t0.420\t-0.017\t-0.710\t-0.832\t-1.950\t0.591\n1\t1.451\t-0.395\t-0.556\t0.697\t-0.686\t0.274\t0.042\t-0.409\t-0.016\t1.763\t0.605\t1.093\t-0.919\t1.096\t0.639\t-1.500\t0.540\t-0.513\t-1.546\t0.130\t0.539\t0.152\t-0.109\t-0.331\t2.467\t-1.183\t0.873\t0.015\n4\t-1.310\t-0.954\t-0.562\t-0.273\t0.987\t-0.767\t-2.450\t-0.867\t-0.458\t1.343\t-0.817\t-3.314\t-0.953\t0.015\t-2.343\t0.583\t0.455\t0.016\t0.500\t-0.467\t-0.663\t-0.628\t-0.323\t2.013\t-0.758\t0.762\t-0.973\t-0.023\n1\t0.501\t0.251\t-1.640\t0.712\t0.771\t-0.090\t0.268\t-0.730\t-0.260\t0.208\t1.037\t0.458\t-0.399\t-0.321\t0.123\t-0.233\t-0.019\t-2.653\t-0.873\t1.734\t0.439\t0.521\t0.452\t0.358\t0.147\t1.185\t0.512\t-1.729\n2\t-0.318\t-1.249\t-0.473\t-1.606\t-0.660\t-0.576\t-0.054\t-0.417\t-0.351\t0.492\t-0.269\t-1.234\t-0.264\t0.732\t0.896\t-0.247\t2.187\t-0.847\t-0.009\t-1.654\t-1.221\t1.729\t0.224\t0.844\t-0.093\t0.132\t1.693\t1.042\n4\t-0.725\t0.707\t-1.380\t0.625\t1.781\t0.567\t0.638\t0.870\t1.710\t1.210\t0.440\t0.904\t-0.886\t0.776\t-1.006\t-0.367\t-3.083\t0.401\t-0.644\t1.111\t0.390\t1.124\t-1.084\t1.038\t2.106\t0.764\t0.790\t0.170\n4\t-0.171\t-0.066\t-1.709\t0.392\t-0.089\t1.610\t-0.940\t-1.808\t0.618\t0.347\t-1.008\t-1.530\t1.537\t1.414\t-2.487\t-0.458\t1.664\t-0.476\t-0.491\t-0.283\t-1.121\t-1.705\t1.053\t-1.843\t0.247\t0.887\t-1.451\t-0.924\n1\t0.201\t-0.711\t0.966\t-2.437\t0.358\t-0.501\t-0.788\t-0.756\t-0.811\t-0.545\t-0.839\t-0.249\t0.026\t-1.153\t-1.011\t-0.298\t0.507\t-1.340\t-1.127\t0.287\t-0.284\t-0.846\t-0.825\t-0.799\t-1.046\t1.106\t-0.716\t-0.582\n0\t0.644\t0.650\t-1.042\t-0.163\t-1.067\t-1.015\t-0.189\t-0.062\t-1.509\t0.447\t-0.874\t0.583\t-0.878\t1.543\t-0.984\t-0.675\t-0.481\t0.486\t1.092\t-1.183\t-0.566\t0.306\t-0.346\t-1.023\t-0.314\t0.977\t-0.043\t0.006\n3\t-0.155\t-0.809\t-0.833\t0.739\t0.831\t-2.646\t0.940\t-0.411\t-0.632\t-0.327\t-1.262\t-0.168\t1.915\t-1.548\t0.618\t0.975\t0.582\t1.534\t0.982\t0.671\t1.038\t0.992\t-0.349\t0.631\t-0.273\t-1.334\t1.837\t-0.553\n0\t-0.806\t-0.372\t0.407\t1.034\t-0.382\t-0.596\t0.983\t0.613\t0.149\t1.181\t1.947\t0.366\t-0.590\t0.497\t-0.820\t1.242\t-0.702\t-0.193\t-0.838\t0.182\t0.217\t0.743\t0.120\t0.046\t-0.574\t-0.286\t-1.393\t1.394\n2\t0.278\t-0.443\t1.039\t-0.615\t-1.596\t0.939\t1.324\t-0.822\t1.961\t0.583\t0.434\t-0.517\t1.017\t-1.524\t-0.340\t-0.733\t-1.309\t0.863\t0.341\t0.805\t-0.278\t0.985\t0.077\t-0.472\t-0.639\t-1.983\t1.164\t1.051\n4\t-2.384\t1.430\t0.339\t1.586\t2.361\t1.029\t-0.634\t-0.057\t0.147\t0.936\t0.297\t0.227\t0.422\t0.411\t-1.443\t0.588\t1.049\t-0.392\t0.950\t2.266\t-1.608\t-0.145\t-0.545\t-0.667\t0.699\t-0.397\t-1.808\t0.483\n2\t-1.055\t0.798\t0.212\t1.224\t-0.910\t-0.445\t-0.783\t0.567\t0.791\t0.814\t0.241\t-0.018\t1.204\t-1.062\t-1.230\t0.847\t-1.645\t1.797\t0.082\t2.086\t-0.746\t-0.511\t0.508\t-1.468\t-0.564\t-0.727\t-0.757\t0.627\n2\t0.502\t-0.478\t0.592\t0.869\t0.241\t0.244\t1.797\t0.781\t-0.803\t-0.319\t1.652\t2.665\t-0.571\t0.547\t0.394\t0.474\t-0.807\t0.581\t-0.798\t-1.757\t0.841\t1.837\t-0.400\t0.015\t-0.909\t0.189\t-0.125\t-0.853\n4\t1.553\t-0.703\t-1.282\t0.128\t-0.860\t1.482\t1.702\t0.040\t0.470\t0.039\t-1.880\t1.759\t-1.160\t-0.579\t1.253\t-1.652\t-0.053\t-0.665\t0.398\t-0.174\t1.200\t1.081\t1.301\t1.014\t0.923\t-1.312\t2.258\t-0.726\n3\t0.693\t-1.139\t2.464\t0.556\t-0.057\t-0.252\t0.788\t-0.411\t-0.370\t1.453\t-0.472\t0.136\t-0.760\t0.259\t0.581\t1.683\t-0.937\t-1.448\t-0.267\t-0.617\t-0.015\t0.522\t2.265\t-0.451\t-2.210\t-0.238\t0.351\t-0.970\n2\t0.128\t1.331\t-0.019\t-1.076\t-0.204\t0.567\t-1.110\t0.759\t-1.353\t1.291\t-0.181\t-0.960\t-0.125\t-0.935\t-0.075\t-0.610\t-0.259\t-2.353\t-0.228\t0.429\t0.143\t0.117\t-1.860\t-0.069\t2.069\t0.392\t1.165\t0.796\n2\t0.225\t-0.102\t-1.046\t1.226\t0.331\t-0.048\t-0.107\t0.926\t-0.863\t1.423\t2.190\t-0.311\t1.289\t-0.591\t-1.139\t-0.589\t0.405\t-0.246\t0.397\t0.253\t-0.435\t-1.234\t-0.039\t-1.383\t0.062\t0.327\t-2.337\t1.261\n2\t-0.852\t-0.775\t-0.093\t1.317\t-0.174\t-0.963\t0.515\t2.516\t0.073\t0.610\t1.196\t-0.344\t-0.143\t1.404\t-0.478\t-0.372\t-1.115\t-1.281\t0.896\t1.165\t-0.902\t-0.246\t0.776\t-1.096\t1.752\t0.878\t-0.730\t-0.243\n3\t0.035\t-1.318\t0.925\t1.887\t0.256\t1.202\t-1.657\t0.514\t-1.629\t-0.262\t0.436\t-1.069\t-0.529\t-0.681\t0.782\t-0.312\t-0.311\t0.564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133\t0.059\t0.084\t1.372\t0.676\t-0.761\t-0.222\t0.852\t1.888\t-0.375\t0.395\n4\t-1.490\t-1.324\t1.102\t1.449\t0.033\t-4.466\t-0.744\t0.335\t-0.899\t0.185\t1.899\t1.426\t0.809\t-0.064\t-0.495\t0.758\t0.025\t0.060\t-1.551\t-0.715\t0.752\t-1.101\t0.266\t0.413\t0.471\t0.560\t0.628\t-0.624\n0\t0.689\t-0.684\t-0.274\t-0.014\t0.150\t0.098\t0.436\t0.844\t0.393\t0.454\t-1.582\t0.050\t0.480\t0.729\t-0.692\t-0.659\t-0.574\t0.536\t1.034\t-0.407\t0.908\t0.123\t-0.124\t-0.211\t0.747\t0.326\t2.276\t-0.852\n0\t0.369\t-0.076\t-0.390\t0.520\t0.708\t-0.073\t0.383\t-0.190\t-1.489\t-1.516\t0.576\t-0.248\t0.696\t-1.254\t1.230\t-0.194\t0.208\t1.230\t-0.394\t0.670\t0.486\t0.119\t0.931\t-0.316\t0.944\t2.249\t0.998\t0.147\n1\t-0.306\t0.058\t1.467\t-0.776\t-0.473\t0.422\t0.451\t-1.845\t-0.607\t-0.522\t1.264\t0.126\t-1.632\t1.114\t0.860\t-0.990\t0.656\t-0.195\t0.668\t1.808\t0.134\t-1.644\t-0.904\t-0.385\t-0.084\t-0.800\t-0.090\t-1.300\n2\t0.234\t0.672\t1.959\t-2.017\t0.540\t0.044\t-0.177\t-2.363\t0.580\t-0.125\t-0.834\t0.042\t-0.293\t-1.742\t0.088\t0.580\t-0.549\t0.262\t0.108\t1.045\t-0.104\t0.801\t-0.313\t1.967\t-0.437\t-0.685\t-0.100\t-0.728\n2\t0.787\t0.967\t-0.005\t-0.358\t0.087\t-0.572\t-0.573\t-0.408\t0.228\t1.436\t1.299\t-0.319\t-0.815\t-0.733\t-1.479\t0.728\t-1.394\t-2.258\t0.729\t0.685\t-1.081\t0.303\t-1.319\t-0.097\t0.359\t0.694\t0.396\t-1.894\n3\t-1.790\t-0.564\t0.159\t-3.066\t-1.189\t0.035\t-0.727\t-0.027\t2.007\t-0.480\t-0.257\t-0.930\t1.813\t-0.425\t0.839\t-0.378\t0.141\t0.867\t-0.923\t0.068\t-0.014\t-0.718\t0.073\t2.046\t0.903\t-0.712\t0.767\t0.710\n4\t-0.357\t1.143\t-0.472\t-0.474\t-1.073\t1.489\t-0.127\t0.408\t0.693\t-1.117\t-0.527\t-1.406\t0.752\t-1.225\t-0.900\t-1.030\t1.408\t-1.098\t-0.225\t-0.776\t-0.221\t-1.358\t-0.747\t-3.028\t0.210\t-1.778\t-0.089\t-2.060\n4\t0.322\t-0.020\t0.964\t3.181\t1.689\t0.259\t0.773\t-0.235\t-2.168\t1.535\t-0.693\t-1.321\t1.069\t2.392\t0.490\t-0.230\t1.057\t0.346\t-1.300\t-1.019\t-0.488\t0.381\t0.801\t-0.381\t1.040\t1.436\t-0.156\t-1.410\n3\t1.071\t-0.055\t-0.469\t1.532\t-1.030\t-0.878\t-1.477\t1.301\t-1.832\t-0.149\t-0.454\t0.033\t1.202\t-0.399\t-0.361\t-1.356\t0.478\t-0.042\t0.066\t1.276\t0.264\t-1.389\t-0.737\t2.423\t0.905\t-0.604\t-1.041\t-1.672\n2\t-0.648\t1.178\t1.698\t0.042\t0.404\t-1.474\t0.303\t-0.259\t1.359\t-0.227\t-0.821\t-1.343\t0.181\t-0.543\t1.217\t0.633\t-0.101\t0.627\t1.743\t-1.160\t-0.635\t1.942\t-0.754\t0.662\t1.187\t-0.016\t0.689\t-0.294\n4\t-0.647\t0.042\t0.351\t0.223\t-0.188\t-0.569\t-0.895\t-0.543\t-0.236\t-0.855\t-2.534\t-0.819\t0.360\t-3.111\t0.625\t-1.223\t-0.373\t-1.270\t0.393\t-0.853\t0.034\t-1.580\t1.735\t-1.003\t-1.283\t-0.456\t1.209\t0.354\n4\t-1.221\t0.482\t-1.265\t-1.184\t-1.198\t0.164\t-1.814\t2.088\t-0.376\t-2.116\t-1.061\t0.599\t1.348\t0.163\t0.086\t-1.530\t1.489\t0.556\t0.321\t0.017\t0.531\t-0.800\t0.939\t0.803\t-0.098\t-0.272\t-2.158\t-1.732\n4\t0.049\t0.263\t1.325\t0.421\t0.434\t0.956\t-0.049\t0.258\t1.965\t-0.636\t-0.570\t-0.859\t0.316\t0.376\t-1.917\t0.964\t-1.082\t0.450\t0.127\t-2.833\t-2.367\t-1.122\t1.178\t-1.874\t1.112\t-1.766\t-2.186\t0.297\n1\t-1.453\t0.252\t1.068\t-1.139\t0.829\t0.225\t-0.454\t-1.200\t0.242\t0.494\t-0.317\t-0.981\t0.762\t1.185\t0.954\t1.589\t-0.652\t-1.415\t0.411\t-0.403\t0.988\t-1.037\t-0.964\t0.068\t0.175\t0.675\t-0.098\t-1.917\n2\t-0.566\t0.649\t0.533\t1.941\t-0.669\t-2.142\t-0.227\t-1.650\t0.632\t-0.294\t-0.746\t0.837\t0.890\t1.103\t0.635\t-1.268\t-1.320\t0.832\t-0.271\t1.386\t-0.177\t0.688\t-0.687\t0.164\t-0.276\t-0.746\t0.214\t1.992\n3\t0.977\t0.754\t-0.316\t-0.518\t1.730\t0.027\t-1.134\t-0.585\t-1.962\t-1.629\t-0.530\t-0.796\t-0.687\t0.770\t0.874\t-1.165\t1.892\t0.101\t0.008\t-0.792\t-0.300\t2.029\t-1.081\t0.267\t-0.542\t-1.810\t-1.536\t0.611\n3\t-0.538\t0.810\t-1.197\t-0.747\t0.351\t-0.291\t0.036\t0.531\t-0.038\t0.088\t1.847\t-1.996\t-0.485\t-0.189\t0.944\t0.759\t-2.503\t1.119\t0.106\t0.727\t-0.540\t0.613\t0.409\t-0.113\t1.629\t2.308\t-0.045\t-1.622\n4\t0.849\t1.548\t-1.853\t-0.865\t-0.062\t0.912\t-0.136\t0.297\t-0.867\t0.446\t0.173\t-1.958\t-1.684\t0.388\t-0.919\t0.623\t1.983\t1.250\t0.807\t2.397\t0.618\t-0.167\t-0.452\t0.423\t-0.666\t1.438\t0.584\t1.477\n3\t-1.703\t0.576\t0.204\t0.394\t0.767\t-0.418\t-0.197\t0.812\t0.446\t2.047\t0.294\t0.257\t0.881\t2.066\t0.715\t-0.287\t-0.661\t0.162\t-1.100\t0.646\t0.298\t0.518\t-1.778\t1.347\t-1.121\t-1.259\t-2.018\t-0.605\n0\t-0.333\t0.856\t1.204\t0.151\t0.139\t-0.068\t-0.008\t-0.188\t-1.485\t0.022\t0.984\t1.292\t0.551\t1.194\t0.772\t0.431\t0.644\t-1.708\t-0.329\t-0.936\t-0.129\t-0.751\t0.388\t-0.183\t-0.571\t-0.285\t-0.142\t-0.324\n0\t-0.694\t-1.334\t-0.487\t-1.079\t-0.395\t0.542\t0.037\t-0.480\t-1.791\t0.832\t-0.352\t0.108\t-0.912\t-0.409\t-0.580\t-1.334\t-0.908\t-0.015\t0.299\t-0.684\t-1.203\t-0.356\t0.780\t-0.939\t0.013\t-0.660\t1.129\t-0.507\n3\t-0.038\t0.410\t0.792\t1.753\t1.124\t0.617\t-0.538\t1.443\t-1.083\t1.095\t1.885\t1.400\t-0.389\t-1.645\t-0.749\t0.046\t-0.279\t0.241\t-1.813\t0.794\t-0.623\t-0.001\t-0.478\t0.749\t-0.566\t-1.714\t0.560\t-1.591\n2\t-0.251\t1.527\t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189\t1.643\t-0.901\t0.638\t-0.329\t0.603\t-0.544\t-0.163\t0.041\t-1.002\t0.741\t-0.513\t-0.229\t-0.994\t-2.562\t-0.191\t2.413\t0.785\t-0.019\t-0.263\t0.022\t0.547\t-1.181\t1.114\n2\t0.152\t-1.228\t-1.070\t1.224\t0.223\t-1.054\t-0.093\t0.492\t-0.527\t0.207\t1.033\t1.746\t-1.801\t0.907\t0.498\t0.326\t-0.879\t0.436\t-0.520\t0.431\t0.454\t1.263\t0.808\t-1.909\t0.048\t1.319\t-1.477\t-0.121\n0\t0.176\t-0.283\t-0.348\t0.641\t0.315\t-0.685\t0.849\t-1.187\t1.169\t-0.473\t-0.760\t1.035\t-0.425\t-1.462\t-0.090\t1.187\t0.139\t1.081\t-0.430\t0.213\t0.853\t-1.052\t0.483\t-0.324\t0.173\t0.922\t1.879\t-0.850\n3\t-0.789\t1.168\t-0.407\t-0.068\t0.398\t1.190\t1.402\t-1.012\t0.809\t0.205\t0.343\t-1.093\t1.180\t0.522\t-0.768\t-1.735\t-0.237\t0.547\t-1.363\t-2.530\t-0.073\t-0.660\t-1.320\t-0.615\t0.111\t-1.119\t2.009\t0.483\n3\t0.217\t-0.067\t0.596\t-0.811\t-1.116\t-0.373\t0.833\t1.610\t0.032\t0.903\t0.344\t0.536\t-0.400\t1.066\t-0.373\t-2.116\t-0.402\t-1.625\t2.208\t1.034\t0.094\t-0.500\t1.368\t-0.743\t2.233\t-0.592\t0.077\t0.844\n2\t0.059\t-0.762\t0.720\t-0.618\t1.185\t-0.332\t-0.353\t-0.542\t-0.664\t0.674\t1.611\t-0.014\t-0.103\t-0.110\t2.391\t-1.503\t0.600\t-0.657\t1.473\t1.215\t1.239\t-0.027\t-0.143\t-1.036\t0.508\t-0.461\t-1.320\t-1.305\n4\t0.112\t-1.231\t1.697\t0.676\t-0.922\t2.455\t-0.188\t0.241\t-0.287\t1.888\t0.352\t-0.639\t-2.673\t0.655\t1.118\t-0.618\t1.649\t0.496\t-0.847\t-0.706\t-0.740\t-1.405\t0.892\t-1.350\t-0.109\t0.012\t-0.198\t-1.164\n4\t0.829\t-1.931\t1.993\t-0.338\t1.140\t0.960\t0.040\t-0.781\t0.209\t2.124\t-2.291\t1.099\t-0.512\t-2.798\t-1.838\t-1.001\t-1.174\t-0.196\t0.155\t0.745\t0.909\t-0.549\t1.567\t0.773\t1.763\t0.631\t-0.668\t-0.696\n3\t-2.074\t-0.181\t-1.295\t0.991\t-0.628\t-1.132\t2.078\t0.547\t0.878\t-0.417\t-0.913\t0.529\t0.029\t-1.778\t0.891\t-0.407\t-0.898\t0.022\t0.518\t-0.054\t1.689\t-0.178\t-1.285\t-0.104\t-1.698\t0.775\t-1.093\t0.051\n2\t2.175\t0.580\t0.005\t0.102\t1.233\t-1.149\t-0.111\t0.033\t0.373\t-0.730\t-1.990\t0.059\t2.366\t0.427\t-1.166\t-0.495\t-0.268\t1.580\t-0.132\t1.097\t0.198\t0.082\t-1.003\t0.796\t-0.543\t1.126\t0.028\t-0.094\n1\t-0.914\t-2.048\t-1.041\t1.403\t-0.295\t0.274\t-0.274\t0.562\t0.071\t-0.069\t0.614\t-0.466\t0.372\t-0.175\t-0.107\t-0.819\t-0.216\t-0.541\t1.130\t-1.684\t0.889\t-1.744\t0.537\t-0.838\t0.566\t-0.121\t-1.432\t1.359\n1\t1.085\t-1.104\t0.756\t-0.441\t1.488\t0.853\t-0.861\t0.386\t-0.172\t0.084\t-0.040\t1.688\t-0.589\t0.386\t-0.500\t0.051\t0.873\t-0.554\t0.546\t0.200\t0.062\t0.333\t1.494\t-1.614\t1.547\t1.423\t-0.566\t-0.698\n3\t0.404\t-2.413\t1.056\t-1.510\t0.231\t-0.775\t2.143\t1.440\t-0.742\t-0.127\t0.245\t0.863\t0.029\t-1.025\t-0.718\t-0.004\t1.234\t0.428\t-0.773\t0.648\t-0.387\t0.890\t2.527\t-0.359\t0.802\t-0.745\t-0.112\t0.030\n0\t-0.112\t-0.889\t-0.480\t0.095\t0.490\t-0.823\t-0.640\t0.263\t-0.083\t0.146\t-2.614\t-0.295\t0.853\t-0.616\t0.487\t-1.209\t-1.563\t0.418\t-0.054\t-0.336\t0.846\t-0.312\t-0.633\t-0.741\t-0.952\t0.467\t-0.312\t1.143\n3\t0.505\t0.627\t-1.240\t-1.503\t-0.746\t-0.662\t0.753\t-0.615\t-0.856\t-0.612\t1.119\t0.013\t-0.179\t-0.110\t1.292\t-0.749\t1.316\t0.210\t-0.160\t2.501\t-1.056\t-0.354\t-1.232\t0.952\t1.689\t1.670\t1.224\t-0.198\n4\t1.098\t0.743\t-2.316\t-1.302\t-1.799\t0.429\t1.091\t-0.055\t0.126\t-0.980\t1.099\t-0.770\t1.096\t-0.605\t1.623\t-1.411\t-1.051\t1.175\t1.151\t-0.271\t-1.358\t-2.499\t1.397\t0.418\t0.159\t-0.693\t-0.386\t0.865\n2\t0.386\t-0.006\t0.457\t-1.402\t-0.428\t1.177\t-1.347\t1.033\t-0.275\t-1.406\t0.333\t1.497\t-0.508\t0.829\t0.667\t-0.191\t0.938\t0.594\t-1.523\t-1.201\t-0.169\t1.045\t-0.263\t2.006\t0.954\t-0.518\t-1.721\t-0.077\n4\t1.979\t0.176\t1.196\t0.215\t-0.107\t1.369\t-0.875\t-1.851\t0.401\t1.299\t0.546\t-0.062\t-1.102\t0.425\t0.276\t1.632\t0.087\t-2.646\t-0.857\t2.438\t-0.641\t-0.640\t-1.769\t0.019\t0.008\t-0.515\t-0.307\t0.411\n4\t0.290\t2.075\t0.871\t-0.326\t1.201\t-0.408\t-2.038\t-1.008\t-1.871\t-0.352\t0.018\t1.676\t0.327\t-0.219\t0.829\t-2.211\t0.236\t0.771\t-1.479\t1.144\t0.338\t-0.415\t0.633\t2.271\t0.182\t0.248\t-0.459\t-0.850\n1\t0.101\t-0.029\t-0.825\t-0.922\t1.402\t1.362\t0.249\t-0.655\t0.563\t-0.030\t-0.969\t0.439\t2.155\t-0.885\t0.662\t0.601\t1.680\t-1.592\t-0.836\t-0.158\t1.274\t-0.912\t-0.711\t-0.158\t-1.064\t-0.222\t-0.473\t-1.164\n0\t-0.302\t-1.484\t1.278\t-0.061\t-0.193\t0.897\t0.422\t-1.129\t-0.071\t-0.335\t-1.745\t-0.690\t-0.184\t-0.115\t-1.482\t1.152\t-0.102\t0.038\t0.401\t-0.788\t0.456\t-0.367\t-0.897\t0.573\t1.492\t0.725\t0.923\t-0.521\n1\t0.101\t-0.890\t0.902\t-0.044\t0.387\t-1.187\t-0.751\t0.290\t2.094\t0.542\t1.706\t2.161\t-0.202\t1.284\t0.020\t-0.718\t1.292\t-0.003\t-0.215\t1.083\t-0.521\t-0.475\t0.746\t0.659\t0.350\t0.660\t1.389\t-0.679\n2\t-0.400\t-1.840\t-0.711\t0.165\t-1.217\t-1.658\t0.736\t0.181\t0.376\t0.992\t-0.848\t-0.047\t-1.627\t0.421\t1.515\t-0.477\t-0.637\t0.924\t-0.905\t0.100\t-0.441\t-0.017\t-1.234\t-1.868\t0.163\t-1.516\t0.361\t0.717\n0\t-0.154\t-0.101\t1.205\t0.395\t-0.148\t-0.471\t-0.591\t0.211\t-0.273\t-0.398\t0.285\t1.044\t0.646\t-0.371\t1.618\t-0.626\t-0.520\t0.942\t1.071\t-1.164\t-0.141\t-0.185\t-0.429\t-0.070\t0.420\t-2.105\t0.131\t-0.336\n1\t-0.057\t-0.635\t-1.381\t0.060\t0.804\t-2.269\t-0.568\t-0.210\t1.211\t0.362\t0.830\t0.039\t0.047\t-0.185\t-0.311\t-1.763\t-0.161\t0.754\t-0.631\t-0.752\t1.279\t0.855\t0.552\t-1.537\t0.795\t-1.119\t-1.169\t0.282\n4\t-0.954\t0.541\t0.671\t-0.334\t0.061\t-0.577\t-1.095\t-0.497\t0.074\t-1.475\t-0.424\t0.088\t1.195\t-0.313\t-1.246\t-0.210\t-0.567\t0.552\t-0.338\t-2.711\t3.692\t0.142\t0.089\t-1.541\t-1.465\t-0.092\t0.336\t0.702\n2\t-0.152\t-0.567\t0.764\t-0.045\t-1.319\t-1.931\t-1.129\t2.393\t0.723\t0.189\t-0.182\t-0.247\t1.167\t-0.491\t0.577\t-0.348\t1.940\t0.742\t1.246\t-0.584\t1.058\t-0.313\t0.110\t1.635\t-1.043\t0.203\t0.438\t0.520\n1\t-1.772\t0.509\t-0.627\t-0.856\t-1.667\t-0.313\t1.299\t0.176\t-0.119\t-0.294\t1.447\t-0.210\t-0.933\t-0.243\t0.130\t-0.519\t-2.208\t-0.301\t0.653\t-0.528\t-0.520\t0.867\t0.752\t-0.323\t-0.474\t-1.531\t0.200\t-0.354\n0\t0.789\t-1.029\t-1.090\t0.741\t-0.222\t-0.465\t1.059\t-0.259\t-0.202\t0.556\t0.909\t1.099\t1.024\t-0.131\t0.186\t0.130\t-0.967\t0.051\t-0.507\t-0.925\t0.308\t-0.252\t-0.876\t0.024\t-2.095\t0.963\t-0.770\t0.388\n3\t-0.079\t-0.509\t-2.247\t2.056\t0.028\t0.853\t0.318\t-1.580\t0.269\t-0.265\t0.470\t1.216\t0.516\t-1.182\t-0.401\t-1.025\t-0.282\t-0.516\t0.076\t-1.213\t-0.963\t0.496\t1.346\t1.469\t0.124\t-0.319\t-2.246\t-0.307\n1\t0.126\t0.507\t-0.401\t1.300\t-0.399\t-1.959\t-0.391\t-0.967\t-0.281\t-0.259\t1.197\t0.695\t0.263\t-0.003\t-1.059\t-2.181\t0.113\t0.795\t-0.108\t-0.535\t0.465\t-0.083\t1.686\t1.233\t0.960\t0.758\t-1.290\t0.663\n0\t-0.578\t0.656\t-1.260\t2.005\t-1.379\t0.329\t-0.652\t-0.714\t0.779\t1.321\t-0.280\t0.665\t0.632\t-0.900\t0.503\t-1.223\t0.973\t0.002\t0.565\t-0.655\t-0.186\t0.482\t-0.777\t-0.241\t0.701\t-0.034\t0.033\t-0.925\n0\t-1.496\t-0.918\t1.323\t-0.169\t0.080\t0.759\t0.696\t0.279\t-0.209\t-0.313\t0.858\t0.554\t-0.257\t1.082\t-0.129\t-0.852\t-0.759\t-0.979\t-0.272\t1.234\t-0.805\t0.188\t-0.657\t-0.260\t-1.510\t-0.221\t-0.194\t0.540\n1\t-0.245\t-0.250\t0.073\t-1.181\t0.893\t1.715\t-0.022\t0.316\t-2.115\t-0.435\t-0.696\t-0.858\t-0.658\t-0.558\t-1.455\t-0.487\t-1.042\t0.788\t0.330\t0.332\t0.977\t-0.826\t0.270\t1.070\t-0.896\t-2.029\t-0.691\t0.957\n3\t-1.619\t-0.633\t-1.578\t-0.228\t0.673\t-0.654\t-0.403\t-0.275\t0.141\t0.516\t0.807\t2.256\t0.123\t0.736\t-1.588\t-0.379\t-0.270\t-1.017\t-0.536\t-0.107\t-0.505\t2.602\t0.512\t2.001\t1.493\t-0.639\t0.038\t-0.435\n2\t-1.297\t0.906\t-0.628\t-0.842\t1.023\t-0.883\t0.377\t-0.489\t-2.357\t0.478\t0.126\t-1.910\t-0.699\t-1.807\t0.732\t1.388\t0.849\t0.989\t-0.518\t0.547\t-0.009\t0.185\t0.957\t1.175\t-0.390\t-1.408\t-0.187\t-0.219\n3\t0.020\t-0.667\t-0.106\t-0.971\t0.490\t0.428\t-1.332\t-2.508\t0.872\t0.236\t-0.206\t1.575\t0.287\t0.789\t-0.627\t-1.495\t0.374\t-1.056\t-2.217\t-1.162\t0.564\t-0.438\t-1.360\t-0.735\t-0.676\t1.317\t0.676\t0.390\n0\t0.476\t-1.000\t1.020\t0.141\t-0.357\t-0.783\t-0.655\t-0.257\t-1.027\t0.038\t-0.165\t0.687\t-0.149\t-1.821\t-0.310\t-1.129\t-0.597\t-0.944\t2.090\t-0.340\t0.718\t-0.306\t-0.227\t-0.475\t-0.696\t-0.675\t0.374\t-0.060\n4\t1.631\t-0.971\t-0.742\t-2.817\t-0.644\t-3.084\t0.294\t0.776\t0.442\t-1.521\t-0.066\t0.441\t-0.388\t-1.293\t0.947\t-1.209\t0.473\t0.020\t1.477\t2.302\t-0.568\t-1.179\t0.142\t0.035\t0.613\t-0.759\t-0.756\t-0.094\n1\t-1.219\t0.909\t0.339\t-0.013\t1.883\t-0.726\t-0.848\t1.431\t0.577\t0.642\t-0.553\t-0.539\t-1.136\t0.372\t1.468\t-1.201\t0.761\t0.409\t0.131\t0.683\t-1.070\t0.567\t0.123\t0.268\t-0.127\t1.807\t-0.177\t-0.797\n0\t0.927\t0.522\t-0.385\t0.976\t0.979\t-0.650\t-0.283\t-0.688\t-0.093\t-1.072\t-0.535\t-1.455\t-0.062\t0.373\t-0.858\t0.179\t-1.337\t-1.030\t0.552\t-0.174\t1.216\t-0.042\t0.128\t0.416\t0.124\t0.030\t-0.330\t-2.179\n2\t-1.493\t-0.139\t-0.432\t0.946\t0.675\t-0.233\t-1.047\t1.038\t-0.068\t-1.618\t-1.532\t0.438\t-1.276\t1.860\t-0.937\t-0.000\t0.159\t-0.729\t-2.240\t0.191\t-0.753\t0.117\t-0.122\t0.263\t1.562\t-0.546\t-0.469\t-1.666\n2\t0.651\t-2.390\t0.443\t2.592\t-0.271\t-0.063\t-0.655\t0.059\t1.346\t-1.012\t-0.399\t-1.151\t0.246\t-0.327\t-0.863\t-0.357\t-0.766\t1.034\t-0.793\t0.514\t-0.753\t1.677\t0.525\t0.100\t-0.187\t-0.939\t1.363\t-0.250\n3\t1.181\t-0.947\t-1.957\t-0.111\t0.907\t-0.648\t-0.547\t-1.313\t-1.955\t0.873\t2.214\t1.324\t0.392\t-0.588\t1.205\t0.511\t-1.096\t-1.498\t-1.218\t-0.259\t0.504\t0.618\t-0.686\t0.001\t0.872\t0.268\t-0.727\t-0.956\n4\t0.260\t0.005\t1.902\t-0.453\t0.762\t-0.574\t-0.611\t-2.266\t-2.003\t-1.262\t2.238\t-0.750\t0.311\t0.048\t-1.129\t0.311\t-1.013\t-1.957\t0.076\t1.204\t-0.078\t-0.348\t-2.748\t-1.216\t-0.340\t0.089\t-0.459\t-0.411\n2\t2.834\t1.116\t-0.499\t0.544\t-0.393\t1.015\t-0.814\t1.209\t0.920\t0.643\t2.071\t1.006\t1.022\t1.126\t0.714\t0.464\t-0.677\t-0.664\t0.432\t0.609\t0.018\t-0.482\t-0.136\t-0.053\t0.275\t-0.222\t1.166\t-0.111\n4\t-1.702\t0.174\t1.738\t0.971\t0.376\t1.283\t-0.668\t0.518\t-0.113\t-0.133\t0.191\t1.175\t1.567\t-0.267\t-1.089\t-0.820\t1.948\t1.481\t-1.091\t1.661\t-0.712\t0.665\t-0.712\t2.163\t-0.570\t-1.050\t-0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t-0.597\t2.480\t-1.513\t0.914\t-0.855\t0.782\t1.184\t0.195\t1.076\t3.318\t-1.350\t1.073\t1.173\t-1.433\t0.369\t-0.151\t0.653\t0.238\t0.858\t-0.869\n2\t-2.333\t-1.771\t-1.126\t-0.592\t-0.007\t-0.518\t-0.047\t-0.496\t-0.005\t0.931\t-0.015\t0.541\t0.714\t-0.158\t-1.791\t0.253\t-0.674\t0.862\t1.366\t-0.258\t0.164\t0.668\t-0.813\t0.009\t-1.548\t1.044\t1.686\t0.100\n2\t-0.061\t-0.565\t0.881\t-1.895\t-1.672\t-0.547\t-1.274\t1.086\t-0.443\t0.877\t1.305\t0.061\t0.327\t1.227\t0.999\t-2.071\t-0.412\t0.513\t0.216\t-0.993\t-1.708\t0.611\t-0.843\t-0.134\t0.145\t-0.163\t-0.932\t-0.083\n2\t0.182\t0.657\t-0.124\t0.085\t0.012\t-0.415\t0.568\t0.847\t-0.044\t-0.492\t-1.083\t0.355\t-1.004\t0.159\t0.131\t1.067\t1.387\t-3.038\t-0.202\t-2.101\t1.162\t-0.023\t1.152\t0.258\t-1.350\t-0.968\t-0.134\t0.570\n1\t1.662\t-0.870\t-1.225\t-0.617\t1.894\t0.161\t-0.105\t0.341\t-0.793\t-1.523\t-0.136\t0.448\t0.849\t0.587\t-1.044\t-1.040\t-0.376\t-0.723\t-0.081\t-1.817\t-0.659\t1.084\t-0.023\t0.124\t1.239\t-0.077\t0.652\t0.557\n1\t0.630\t-1.214\t0.565\t0.920\t1.000\t-0.146\t0.937\t0.491\t0.068\t0.916\t1.744\t0.923\t-0.074\t-0.488\t0.527\t0.154\t1.123\t-0.951\t1.037\t-1.396\t-0.175\t0.583\t-0.112\t0.439\t1.386\t-0.567\t-1.404\t-1.135\n3\t0.450\t0.625\t-0.018\t-1.807\t0.974\t0.650\t-1.372\t-0.087\t-0.302\t1.134\t0.652\t-1.181\t2.047\t-0.004\t0.734\t-0.151\t-0.844\t-0.501\t-1.829\t-1.937\t-0.262\t-0.664\t-0.288\t-0.919\t2.284\t0.344\t0.050\t1.331\n0\t-0.030\t0.525\t-0.336\t0.269\t-1.511\t-0.688\t-0.677\t-0.374\t-0.927\t-0.534\t-1.514\t0.549\t-0.083\t-1.705\t-0.835\t0.337\t-0.543\t-0.697\t-0.075\t0.344\t-1.436\t0.550\t-0.800\t-0.018\t-0.035\t-0.527\t-1.024\t-0.286\n0\t-0.413\t1.117\t0.345\t-0.207\t0.212\t-0.018\t1.596\t0.245\t-0.002\t-0.238\t0.843\t0.708\t-0.274\t-1.156\t-0.902\t-0.184\t1.349\t-0.250\t-0.594\t-1.761\t1.514\t0.569\t0.796\t0.962\t-0.165\t0.085\t1.489\t0.180\n3\t0.640\t-1.408\t-1.617\t0.931\t-0.703\t2.389\t-1.159\t1.216\t1.480\t-0.590\t0.607\t-0.182\t1.005\t-0.618\t0.423\t-0.599\t2.152\t0.954\t-0.149\t1.258\t-0.530\t0.271\t0.012\t0.485\t0.392\t-0.917\t1.080\t-0.771\n4\t-1.351\t-1.753\t-0.436\t-1.709\t-1.127\t0.374\t0.268\t-1.321\t0.150\t1.391\t0.150\t-0.710\t-1.551\t-0.045\t-1.084\t-1.042\t-2.570\t0.390\t-1.536\t0.775\t0.241\t-0.345\t0.492\t1.921\t-1.209\t-0.434\t-2.267\t-0.999\n3\t-0.752\t-0.848\t-0.134\t-0.665\t0.044\t1.792\t1.231\t1.721\t-1.796\t1.841\t-0.399\t0.483\t2.321\t0.146\t-1.520\t-1.755\t-0.001\t-1.153\t-0.263\t0.126\t0.460\t-0.195\t1.441\t-0.061\t0.110\t-0.220\t0.197\t0.400\n3\t-1.590\t1.430\t-1.385\t0.473\t-1.319\t0.298\t0.665\t-0.557\t-1.462\t-0.763\t-0.679\t-0.702\t-3.193\t-1.314\t-0.746\t-0.113\t-0.806\t0.375\t-0.683\t0.506\t1.153\t0.288\t0.628\t-0.736\t0.798\t-1.220\t-0.184\t0.415\n1\t-0.501\t-0.622\t0.417\t0.278\t0.261\t1.170\t-2.581\t1.678\t1.034\t-0.303\t-0.264\t-1.363\t-0.264\t0.410\t0.183\t0.821\t-1.031\t-0.591\t0.150\t2.183\t0.512\t-0.349\t0.894\t0.842\t0.804\t-0.506\t0.531\t-0.065\n3\t0.109\t0.726\t0.481\t0.224\t-0.790\t0.471\t1.882\t1.345\t1.593\t-0.511\t-0.990\t-0.126\t0.056\t1.094\t-1.692\t1.530\t-0.158\t-0.427\t-1.012\t-1.655\t0.823\t0.073\t-1.290\t-1.295\t-0.336\t1.669\t-0.260\t-1.503\n1\t1.527\t-0.538\t0.223\t1.417\t0.267\t1.351\t-0.597\t-0.220\t1.103\t1.192\t0.939\t0.874\t0.211\t-1.131\t-0.615\t-0.898\t1.050\t-1.014\t-2.490\t0.233\t0.104\t0.621\t0.932\t0.010\t-0.391\t-0.041\t0.294\t-0.459\n0\t-0.408\t-1.592\t-1.873\t-0.216\t-0.557\t0.503\t0.525\t0.198\t0.483\t0.618\t0.415\t-0.573\t-0.818\t-0.259\t-0.395\t0.180\t1.000\t-0.330\t0.183\t1.973\t0.329\t0.505\t1.658\t-0.203\t0.645\t-0.855\t-0.360\t-0.356\n3\t0.504\t0.686\t-1.023\t-1.522\t0.503\t-1.273\t-1.414\t-1.539\t1.837\t-0.732\t-0.638\t0.757\t-0.418\t-1.361\t2.314\t0.919\t0.638\t-0.744\t0.454\t-0.906\t-0.530\t0.255\t-0.792\t1.313\t0.613\t0.464\t-1.311\t0.662\n1\t-1.160\t-0.457\t0.417\t0.921\t-0.092\t-1.979\t0.127\t0.294\t0.037\t0.486\t-0.057\t-0.293\t0.624\t0.283\t0.270\t1.031\t0.615\t1.267\t0.138\t2.011\t0.744\t-0.454\t-1.719\t0.125\t1.419\t0.401\t0.807\t-1.676\n2\t-1.335\t1.483\t1.056\t0.998\t0.792\t-0.062\t1.630\t-0.766\t0.721\t0.317\t-1.152\t-0.669\t0.243\t1.006\t1.683\t0.181\t-1.317\t1.410\t-0.827\t-0.171\t0.562\t0.485\t-0.552\t0.196\t-0.150\t1.869\t1.399\t-0.965\n3\t0.165\t0.087\t0.917\t-0.142\t-1.074\t1.039\t-1.290\t-1.236\t-1.106\t-0.646\t-0.086\t0.509\t0.196\t0.770\t0.702\t1.967\t-0.277\t0.654\t1.979\t0.814\t-0.595\t-0.880\t0.996\t0.569\t-0.190\t2.805\t-1.011\t-1.532\n1\t0.005\t1.043\t1.129\t-0.451\t-0.695\t0.535\t0.035\t-0.128\t-1.446\t1.114\t-1.401\t-0.260\t-0.719\t-1.292\t2.563\t-0.357\t-0.019\t1.684\t0.391\t-0.125\t-0.265\t-0.239\t1.238\t0.787\t-0.601\t-0.673\t1.305\t-0.382\n2\t0.562\t0.529\t-1.242\t-0.022\t0.316\t0.306\t1.412\t0.408\t0.069\t0.862\t0.910\t0.064\t1.316\t0.183\t-0.510\t0.931\t-0.708\t1.453\t-0.474\t-0.993\t-0.765\t0.779\t-0.647\t-0.768\t-0.362\t-2.652\t1.255\t-1.449\n4\t1.870\t-0.952\t0.084\t-0.633\t-0.708\t0.333\t0.946\t0.665\t-0.810\t1.227\t0.477\t-2.407\t0.772\t1.160\t0.143\t0.575\t1.895\t-2.561\t0.591\t-0.913\t1.146\t-0.306\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1.433\t0.430\t0.656\t0.508\t-1.540\t0.408\n1\t1.465\t1.485\t1.097\t0.574\t-0.493\t0.313\t-0.427\t0.996\t-0.504\t1.483\t0.014\t-0.154\t-1.619\t-1.228\t1.462\t-0.709\t1.219\t-0.498\t-0.103\t-0.594\t0.821\t0.368\t0.337\t0.432\t-0.398\t0.284\t-1.869\t-0.820\n2\t0.619\t1.128\t-0.136\t0.928\t-0.405\t-0.062\t2.086\t-0.371\t0.540\t-0.389\t0.280\t0.696\t-0.901\t-0.705\t0.568\t0.162\t1.172\t0.270\t1.860\t0.697\t1.879\t1.690\t-0.642\t1.480\t0.940\t0.870\t1.185\t-0.309\n4\t0.338\t-0.034\t-0.532\t-0.270\t1.379\t-2.314\t-1.239\t-0.725\t-1.078\t1.510\t1.258\t0.592\t-0.638\t1.335\t-1.588\t2.142\t-0.685\t-0.262\t-0.429\t-1.073\t1.925\t0.789\t1.215\t-1.224\t0.181\t0.521\t0.271\t-1.160\n3\t-0.072\t-1.414\t-0.792\t-0.547\t-0.823\t-0.937\t0.933\t0.832\t0.930\t-1.188\t0.252\t0.308\t-0.812\t-0.423\t-0.799\t-0.791\t-1.882\t-1.532\t0.082\t-0.439\t1.737\t1.473\t1.129\t1.946\t0.604\t-1.184\t-0.439\t0.728\n1\t-1.376\t1.241\t-0.281\t0.647\t0.538\t0.465\t-1.200\t-0.587\t1.055\t0.508\t-1.081\t0.376\t-1.046\t-1.265\t-0.621\t-0.096\t-1.202\t0.891\t-0.709\t-0.032\t0.448\t1.846\t0.593\t-0.305\t-0.987\t-1.252\t-0.982\t-0.149\n4\t-0.307\t1.518\t1.007\t-2.237\t-0.986\t-0.087\t1.256\t0.244\t-2.537\t-1.173\t0.720\t0.204\t0.135\t1.112\t-0.346\t0.699\t1.642\t0.224\t-0.894\t-0.427\t-0.903\t-2.241\t-0.791\t0.636\t0.009\t1.359\t0.144\t0.884\n4\t-0.635\t-0.963\t-2.385\t-0.181\t1.519\t0.730\t0.228\t-0.734\t0.997\t-0.119\t1.575\t-0.981\t-0.750\t-0.847\t-1.482\t-0.937\t0.103\t-0.712\t-3.856\t1.074\t-1.684\t0.589\t0.031\t2.117\t0.997\t-0.505\t-0.702\t1.503\n1\t-0.261\t-1.709\t-0.438\t-0.393\t0.113\t0.669\t-0.549\t1.058\t0.003\t-2.127\t-0.045\t-0.624\t0.133\t1.047\t-1.172\t-0.166\t1.638\t-0.387\t-0.638\t1.868\t0.501\t1.423\t-0.680\t-0.424\t-0.997\t0.513\t-0.687\t-0.895\n4\t-1.499\t-0.965\t-0.493\t0.475\t-0.768\t0.411\t-0.589\t-0.195\t1.081\t-1.048\t-0.997\t1.590\t-0.130\t-0.686\t-0.295\t0.283\t2.267\t-2.126\t1.038\t-1.407\t-1.292\t0.389\t-0.908\t0.611\t-1.388\t-0.606\t-1.876\t2.408\n2\t1.558\t-2.389\t-0.713\t0.587\t0.058\t-0.051\t-0.356\t-0.916\t0.709\t0.011\t0.801\t-1.565\t0.749\t-1.094\t-2.298\t-1.024\t0.788\t-0.476\t-1.536\t0.330\t0.180\t0.778\t-0.142\t0.218\t-0.450\t-0.410\t0.843\t0.493\n0\t-0.357\t-0.540\t-1.081\t-2.298\t0.428\t-0.037\t0.038\t-0.176\t-0.110\t0.028\t0.069\t1.940\t0.180\t0.259\t-0.880\t0.710\t-1.202\t0.346\t1.330\t0.876\t-0.465\t-0.571\t1.384\t-0.136\t0.463\t0.690\t0.038\t-0.412\n3\t0.113\t0.837\t0.482\t0.443\t-0.814\t0.192\t1.036\t-0.155\t1.744\t-1.136\t0.329\t-0.231\t-1.583\t0.175\t-0.125\t-1.142\t-1.628\t0.193\t0.011\t0.069\t1.026\t0.196\t-0.715\t2.568\t1.822\t-0.202\t0.154\t-2.182\n4\t-0.246\t0.087\t-1.515\t0.318\t0.382\t-0.504\t0.607\t0.873\t1.400\t-1.020\t0.669\t1.338\t-0.351\t1.395\t-0.726\t0.070\t-1.727\t-1.867\t-1.553\t1.419\t-0.776\t-0.773\t0.408\t-0.744\t1.205\t1.864\t-2.222\t0.520\n2\t-0.956\t0.540\t0.498\t0.317\t0.141\t-0.276\t-1.660\t1.740\t0.731\t0.463\t0.513\t1.382\t-1.233\t-1.304\t-1.906\t-0.401\t0.129\t-0.013\t-1.642\t-1.142\t1.813\t-0.262\t-0.333\t0.929\t1.118\t0.296\t0.384\t-1.206\n3\t-0.288\t0.245\t-0.270\t-1.543\t1.594\t0.104\t1.431\t-0.260\t-0.880\t0.269\t-0.216\t-1.178\t1.470\t-0.622\t-1.840\t1.328\t-0.504\t-0.608\t-1.765\t1.741\t-1.289\t0.058\t0.913\t0.422\t-0.060\t-1.702\t-0.810\t0.188\n3\t1.156\t0.110\t-0.264\t1.518\t-1.465\t0.449\t0.652\t0.277\t-0.546\t2.191\t0.724\t-0.925\t-0.635\t-1.820\t-0.160\t2.067\t-1.417\t0.310\t-0.349\t0.320\t0.371\t0.178\t-1.430\t-0.837\t-0.408\t0.115\t1.355\t2.166\n1\t0.961\t-0.009\t1.356\t-0.774\t-1.401\t0.723\t1.170\t0.105\t1.822\t0.508\t0.398\t-0.430\t-1.303\t-0.267\t1.581\t0.191\t-0.391\t-0.905\t0.539\t-1.125\t0.357\t-0.461\t-0.261\t0.642\t-0.950\t0.007\t-1.527\t-0.113\n4\t0.647\t2.138\t0.712\t-0.788\t-1.082\t-0.953\t2.003\t-0.327\t-0.663\t-0.158\t-0.317\t1.568\t-1.014\t1.181\t0.122\t1.207\t1.583\t0.085\t-0.315\t0.066\t-0.095\t-1.657\t1.552\t0.246\t-1.163\t-2.068\t-2.328\t0.770\n0\t0.060\t-0.308\t-1.041\t0.711\t0.091\t-0.962\t-0.519\t0.159\t0.941\t-0.650\t1.300\t0.418\t-0.100\t-0.743\t0.982\t1.013\t0.662\t0.326\t-0.748\t-0.475\t-0.444\t-1.324\t0.392\t-0.078\t0.201\t1.301\t-1.775\t-0.158\n3\t0.189\t-0.731\t1.299\t1.024\t0.346\t-1.395\t1.339\t-1.897\t-1.116\t0.811\t-1.495\t1.378\t1.927\t0.637\t-0.738\t-1.298\t-1.523\t0.301\t0.103\t0.202\t1.400\t0.784\t0.304\t0.096\t-1.020\t0.497\t0.239\t1.085\n4\t-0.279\t-0.479\t3.298\t-1.068\t-1.457\t1.725\t-0.124\t-0.872\t0.822\t-1.389\t1.443\t-0.477\t-0.745\t-1.534\t-1.115\t0.645\t0.207\t-0.616\t-0.627\t-0.011\t-1.562\t-0.384\t-0.432\t-1.240\t0.586\t0.058\t-0.860\t-1.654\n2\t-0.995\t-0.000\t-0.732\t-0.558\t-2.152\t-1.461\t-1.431\t-0.378\t-0.366\t0.351\t0.320\t0.211\t1.322\t-1.096\t-0.669\t1.265\t-0.359\t-0.028\t0.130\t-1.473\t-0.784\t-0.852\t1.366\t-1.311\t-1.061\t0.093\t-1.172\t-0.092\n4\t-3.328\t-0.055\t-0.516\t0.031\t1.068\t-1.072\t0.070\t0.477\t-0.310\t-1.309\t-1.204\t-0.035\t-0.620\t-1.928\t-0.798\t-1.301\t0.748\t-1.484\t1.420\t2.160\t-0.741\t-0.614\t0.944\t1.084\t-0.492\t-0.440\t-0.432\t-0.852\n4\t-0.066\t-2.697\t-0.302\t-0.033\t0.219\t-1.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212\t0.431\t-1.476\t1.401\t-1.194\t0.775\t0.118\t-0.189\t-0.270\n4\t0.586\t0.993\t-0.165\t0.198\t0.918\t0.366\t0.708\t-0.681\t0.858\t-0.585\t2.086\t0.195\t1.105\t-0.442\t-0.214\t-0.264\t-1.760\t-2.637\t-0.288\t-1.888\t0.286\t-0.159\t-2.818\t-0.532\t-0.025\t1.411\t-0.457\t-1.642\n2\t0.089\t0.519\t0.601\t-0.691\t1.242\t-2.032\t-1.704\t-0.715\t0.336\t0.133\t0.073\t-0.071\t-0.321\t-1.275\t-1.377\t-1.284\t-1.093\t-1.415\t-1.436\t-0.274\t1.056\t0.440\t-0.926\t-0.690\t1.897\t-0.499\t-1.087\t-0.288\n4\t-1.422\t1.313\t-0.352\t0.302\t-1.697\t0.675\t-2.313\t1.580\t-0.456\t0.102\t-0.243\t-0.007\t-0.350\t0.410\t2.437\t-0.149\t1.982\t1.060\t0.422\t1.054\t1.891\t-1.906\t-0.174\t1.659\t-0.176\t-0.350\t-0.477\t-0.231\n1\t-0.881\t0.090\t1.164\t0.926\t0.034\t-0.362\t0.948\t0.675\t-0.618\t-0.795\t-2.072\t0.154\t-0.083\t1.136\t0.359\t1.334\t-0.697\t1.138\t-0.529\t0.778\t-1.459\t0.235\t-1.985\t-0.267\t0.440\t-0.521\t-1.050\t0.846\n0\t0.151\t-0.340\t0.645\t-0.717\t-0.109\t0.923\t0.513\t-2.110\t-0.241\t1.235\t-0.285\t-0.115\t-0.718\t0.158\t-0.403\t0.933\t0.595\t1.245\t0.665\t0.113\t0.453\t-0.846\t1.209\t0.751\t-0.664\t-0.856\t-0.084\t0.674\n4\t-0.600\t-0.065\t-0.880\t-1.273\t0.169\t-1.033\t0.220\t2.539\t-0.054\t0.267\t-0.019\t-1.988\t-0.655\t-2.486\t0.639\t-0.841\t-0.427\t-0.388\t-0.166\t1.243\t-1.575\t-0.352\t-2.031\t0.658\t-1.633\t0.608\t0.175\t-0.704\n1\t-0.089\t1.246\t-0.314\t2.156\t-1.271\t-1.749\t0.234\t-0.567\t-0.753\t0.021\t-1.232\t0.545\t0.196\t-0.358\t0.211\t1.912\t-0.246\t-0.491\t0.672\t-0.663\t0.481\t0.821\t1.400\t-0.321\t-1.320\t-0.257\t0.177\t-0.618\n4\t1.772\t2.106\t-1.215\t0.712\t-0.078\t0.550\t2.819\t1.137\t1.584\t-0.885\t-2.100\t-0.294\t2.059\t1.384\t-0.342\t0.610\t0.300\t-1.268\t0.345\t1.403\t0.785\t-1.349\t-1.834\t1.641\t0.702\t1.349\t1.944\t-1.144\n1\t1.195\t-1.404\t0.085\t0.645\t0.067\t-0.266\t0.159\t0.819\t1.476\t-1.452\t-0.470\t1.487\t1.566\t0.856\t-1.001\t-0.834\t0.027\t0.363\t0.618\t-1.360\t0.592\t0.154\t0.095\t0.553\t0.173\t-0.837\t1.269\t-1.294\n3\t-1.836\t-0.266\t1.718\t-0.776\t2.271\t0.544\t0.517\t0.239\t1.046\t1.101\t-0.019\t-0.430\t-1.425\t0.814\t0.066\t-0.844\t-0.580\t-0.068\t-0.981\t0.049\t-1.620\t-0.532\t-1.162\t0.200\t-0.978\t-0.559\t2.260\t0.865\n4\t1.143\t0.565\t1.202\t-1.115\t-1.057\t2.059\t1.147\t-0.815\t0.868\t-0.466\t-1.699\t-2.100\t-0.022\t1.561\t1.521\t1.260\t0.830\t0.317\t1.152\t-2.011\t0.990\t-1.121\t-0.913\t0.696\t0.929\t-0.610\t0.646\t0.563\n3\t0.161\t-0.300\t0.707\t-1.966\t-1.483\t-0.896\t0.499\t1.027\t0.166\t-1.518\t0.114\t-0.708\t0.674\t0.955\t1.219\t0.274\t-0.246\t-1.948\t-0.754\t-1.546\t-2.330\t0.238\t-0.460\t0.632\t0.012\t-1.360\t0.828\t0.021\n3\t-0.645\t-0.729\t-0.524\t-1.366\t-0.785\t1.949\t0.736\t0.926\t1.177\t1.834\t-1.771\t-1.230\t-0.804\t-0.470\t-0.301\t-0.447\t0.219\t0.035\t-0.723\t-2.176\t-0.993\t-0.549\t-0.247\t-1.130\t-0.491\t-1.131\t-0.126\t-1.073\n1\t0.273\t-0.501\t-1.585\t0.500\t-0.675\t1.474\t-1.434\t1.437\t1.510\t1.274\t0.660\t-1.621\t0.332\t-0.418\t-0.706\t-0.721\t0.256\t0.825\t-0.150\t-0.567\t-0.368\t-0.601\t-0.138\t-0.559\t-1.379\t-1.612\t0.253\t0.496\n1\t0.493\t1.348\t0.999\t0.957\t-0.587\t0.968\t0.002\t1.475\t1.653\t0.890\t0.235\t0.363\t0.230\t-0.797\t-0.954\t0.648\t-0.679\t0.262\t-0.369\t-0.957\t-0.688\t2.240\t-0.683\t-0.657\t-0.902\t0.214\t-0.259\t-1.709\n3\t0.462\t0.349\t-2.069\t0.034\t1.417\t1.496\t0.682\t1.270\t-0.153\t0.570\t0.164\t0.669\t1.845\t1.002\t-0.342\t1.580\t0.248\t0.652\t-0.236\t0.737\t0.399\t1.132\t-0.502\t0.951\t-2.439\t-0.616\t-1.345\t0.337\n4\t0.247\t0.164\t-0.651\t-0.610\t1.051\t1.447\t0.161\t1.274\t-0.778\t0.534\t-0.255\t0.024\t-0.240\t-0.215\t-2.614\t-1.289\t-1.762\t-0.481\t-1.063\t-1.735\t-1.023\t-0.216\t2.976\t-0.147\t-0.375\t-1.396\t-1.093\t0.867\n0\t-0.131\t1.606\t1.650\t1.552\t0.403\t-0.946\t-1.483\t0.595\t0.669\t0.262\t-1.329\t-0.798\t0.083\t-0.613\t-0.776\t-0.967\t-0.135\t-0.207\t-0.827\t0.010\t0.249\t0.353\t-0.957\t-0.307\t0.016\t0.125\t-1.335\t-0.341\n1\t1.892\t0.382\t-0.707\t-0.826\t-0.490\t-1.539\t1.483\t1.484\t0.927\t0.064\t-1.038\t-0.563\t0.515\t-0.388\t-0.002\t-1.288\t-0.984\t-0.554\t1.112\t0.226\t-0.750\t-0.356\t1.399\t0.718\t-1.000\t0.154\t0.140\t0.765\n0\t0.001\t0.029\t0.208\t-1.236\t0.625\t-1.255\t-0.940\t-1.049\t-0.175\t-0.352\t-0.001\t-0.905\t1.005\t0.834\t-0.174\t-0.478\t-0.579\t-0.310\t-1.400\t-0.960\t0.860\t0.302\t1.634\t-0.299\t-0.362\t-0.408\t-0.319\t-0.660\n0\t-0.436\t-0.637\t0.527\t-1.543\t0.907\t-0.402\t0.989\t1.288\t0.640\t-0.812\t-1.251\t-1.434\t1.103\t-0.135\t-0.599\t-0.132\t1.081\t0.779\t0.439\t0.638\t0.180\t0.302\t0.221\t-0.000\t0.753\t0.361\t-0.881\t0.529\n3\t0.755\t-1.278\t-0.938\t1.289\t-0.984\t-1.341\t-1.570\t-0.669\t0.706\t0.253\t-1.266\t-0.287\t0.626\t-0.859\t-0.177\t1.727\t0.067\t1.612\t-0.784\t-0.424\t1.258\t0.060\t-1.227\t2.033\t0.279\t1.878\t-0.676\t-0.174\n3\t-0.432\t0.011\t-0.424\t1.130\t1.946\t-0.592\t-1.241\t0.907\t0.706\t1.282\t0.216\t-2.062\t-1.188\t1.823\t-0.057\t-0.871\t-1.902\t0.480\t0.065\t-1.232\t-0.936\t-0.797\t0.451\t1.696\t-0.349\t1.635\t-0.015\t-0.820\n2\t-1.219\t-0.021\t-0.906\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t-0.091\t-0.071\t0.503\t-0.476\t-1.169\t0.880\t-1.861\t-1.267\t0.818\t-0.859\t0.113\t1.228\t-0.800\t-1.153\t-0.044\t-0.305\t0.065\t-0.070\t0.577\t-0.667\t0.070\t-0.689\t-1.822\t-1.285\t0.335\t-0.723\t-0.320\n0\t0.539\t-0.322\t0.172\t-0.588\t0.013\t-1.168\t-0.791\t-0.282\t-0.944\t-0.505\t-0.164\t0.402\t0.949\t-0.464\t1.335\t0.939\t0.504\t0.154\t0.319\t1.187\t1.224\t1.335\t0.041\t0.555\t1.344\t-0.624\t-0.318\t0.016\n3\t-0.718\t1.412\t-0.921\t0.249\t0.074\t0.612\t-1.459\t-0.985\t1.148\t-1.223\t-1.670\t0.562\t0.085\t-0.736\t-0.120\t0.069\t0.113\t2.657\t1.244\t0.602\t-0.896\t0.950\t-1.313\t-0.155\t-0.187\t-0.340\t-2.256\t-0.982\n1\t-0.318\t1.173\t0.846\t-1.224\t0.524\t0.325\t1.111\t1.068\t0.103\t-0.722\t-0.311\t0.440\t0.167\t0.542\t1.003\t0.247\t-0.065\t-0.651\t-2.658\t-0.302\t0.008\t-0.913\t0.672\t-0.723\t-0.034\t-0.357\t1.656\t1.533\n0\t0.413\t-0.798\t1.179\t0.133\t0.195\t-0.048\t-0.573\t0.580\t0.228\t2.061\t-0.742\t1.404\t-0.017\t-0.073\t-0.874\t0.378\t-0.180\t0.482\t-0.923\t0.213\t0.022\t-0.446\t0.108\t-0.006\t0.548\t0.877\t0.501\t0.158\n3\t-0.215\t-0.123\t1.915\t0.187\t0.714\t1.155\t-0.259\t-0.643\t0.189\t-0.774\t-0.509\t-2.067\t1.021\t-0.887\t1.222\t-0.957\t-1.363\t-1.734\t-2.226\t1.555\t0.773\t0.168\t1.385\t-0.911\t-1.007\t0.153\t-0.556\t1.239\n3\t0.637\t0.377\t0.925\t-0.746\t-1.839\t0.066\t-0.446\t-0.028\t-0.797\t2.006\t-1.743\t-0.462\t-1.510\t2.149\t-0.545\t1.665\t1.493\t-0.199\t1.013\t0.752\t0.765\t-0.224\t0.202\t1.573\t-0.274\t0.085\t-0.414\t-1.169\n1\t0.602\t-0.358\t-0.127\t-1.258\t-0.526\t1.168\t-2.279\t-0.074\t-0.095\t-0.574\t-1.808\t-0.173\t0.752\t0.594\t-0.132\t1.310\t0.354\t-1.907\t0.416\t1.252\t0.018\t-0.525\t0.440\t-0.643\t-1.177\t-0.775\t-0.206\t0.384\n0\t-0.456\t0.812\t0.990\t0.711\t-0.271\t0.461\t-0.773\t0.753\t-0.018\t-0.475\t-0.729\t0.678\t-0.137\t-0.490\t-0.493\t-1.243\t0.167\t0.287\t-1.482\t-1.076\t-1.648\t1.205\t0.618\t1.104\t1.620\t0.439\t-0.060\t0.195\n1\t0.497\t-0.138\t0.648\t1.523\t-0.234\t-0.234\t1.579\t0.767\t-0.469\t0.543\t-0.463\t-0.466\t0.242\t-1.913\t-1.725\t-0.562\t-1.013\t0.314\t-0.908\t-1.412\t1.466\t-0.226\t0.068\t-1.425\t-0.544\t0.111\t-1.151\t0.376\n2\t0.224\t-1.069\t-0.321\t-1.904\t0.086\t1.330\t-1.796\t0.129\t0.668\t0.816\t-0.746\t-1.102\t-0.587\t1.191\t-1.710\t-1.216\t-0.459\t-0.976\t-0.042\t1.415\t-1.204\t-0.146\t-0.008\t-0.335\t-1.578\t-0.638\t-0.635\t0.634\n3\t2.363\t-0.025\t1.213\t-2.514\t-0.265\t-1.461\t-0.861\t0.051\t-0.335\t1.314\t1.769\t-0.967\t-0.488\t-1.031\t1.156\t0.159\t0.032\t0.312\t-0.311\t-0.856\t0.051\t-0.267\t0.347\t-1.632\t0.186\t-0.689\t0.151\t0.905\n0\t-0.188\t-1.247\t0.076\t-1.818\t0.361\t0.576\t-1.634\t-0.295\t0.113\t-0.637\t0.390\t-1.756\t-0.589\t0.933\t-0.648\t0.918\t0.704\t0.178\t-0.200\t1.296\t-1.169\t-0.155\t-0.013\t-0.752\t0.598\t-0.247\t1.467\t0.087\n4\t-1.000\t1.317\t2.207\t-1.687\t-1.085\t0.773\t0.513\t0.777\t-0.574\t1.988\t0.118\t-1.217\t-0.253\t-1.204\t1.591\t-0.660\t0.492\t-0.471\t-0.615\t-1.910\t0.598\t1.299\t-0.916\t-1.545\t0.803\t-1.646\t1.220\t-0.904\n3\t-0.433\t0.392\t-0.926\t-1.209\t1.243\t0.727\t0.066\t1.665\t-0.268\t1.323\t0.176\t-1.025\t1.848\t0.991\t-0.121\t0.727\t-0.404\t0.829\t-2.391\t-1.020\t0.979\t-0.250\t0.208\t-1.347\t1.848\t1.191\t-0.201\t-0.405\n2\t-0.730\t-1.166\t-0.465\t0.202\t1.586\t0.457\t0.778\t-1.134\t0.029\t1.694\t1.027\t1.996\t1.336\t0.857\t0.014\t-0.246\t0.992\t-0.970\t0.057\t-0.086\t1.176\t-0.801\t0.846\t2.060\t0.641\t-1.011\t-0.562\t0.527\n4\t1.773\t-1.165\t-0.090\t-0.078\t0.174\t2.147\t-0.449\t-1.158\t-0.680\t0.350\t-0.168\t0.687\t2.259\t-0.063\t1.250\t0.504\t0.085\t0.886\t-0.891\t0.376\t2.682\t-2.371\t1.462\t-1.226\t-0.154\t-0.069\t-1.646\t-0.705\n4\t-0.611\t-0.832\t-0.492\t2.177\t-0.399\t0.907\t-0.376\t0.261\t0.792\t-0.572\t-0.641\t1.659\t0.985\t0.791\t0.787\t0.460\t2.018\t0.044\t0.577\t-1.451\t-0.825\t-1.201\t1.274\t-2.553\t-0.402\t-2.994\t-0.831\t-0.491\n3\t1.599\t0.412\t0.037\t0.764\t-0.250\t-0.183\t0.756\t-0.205\t1.699\t-0.486\t-0.492\t-1.221\t0.096\t1.943\t1.669\t0.495\t0.181\t-1.493\t0.628\t-0.678\t0.741\t0.950\t3.191\t-0.268\t0.635\t0.657\t-0.249\t0.660\n2\t0.341\t-0.203\t0.701\t-0.554\t-0.966\t-0.861\t-1.053\t0.232\t0.084\t-2.350\t-0.257\t0.862\t1.546\t0.997\t-1.402\t0.224\t-0.148\t1.329\t-1.307\t0.388\t1.283\t-1.898\t-0.357\t-1.103\t0.657\t0.286\t0.853\t-0.800\n0\t0.570\t1.451\t-0.216\t-0.735\t-0.107\t0.904\t1.129\t-1.375\t0.115\t-1.434\t0.884\t0.422\t-0.921\t1.534\t0.335\t0.225\t-0.179\t-0.008\t-0.381\t-0.741\t1.229\t-0.493\t-0.526\t-0.129\t1.425\t1.131\t-0.395\t1.227\n4\t1.814\t-1.198\t0.339\t1.951\t0.395\t-0.144\t-0.072\t0.527\t2.014\t-0.548\t0.402\t1.603\t0.883\t1.901\t-1.505\t-0.431\t-1.028\t0.236\t0.580\t-0.611\t-1.148\t-1.505\t-1.020\t1.433\t-1.025\t1.743\t0.997\t-2.328\n1\t1.092\t1.083\t-1.089\t-0.324\t0.394\t-0.605\t0.498\t-1.348\t0.261\t1.027\t0.054\t0.176\t0.844\t-0.444\t0.622\t-0.031\t-0.908\t0.902\t1.401\t1.159\t0.902\t0.511\t0.363\t-0.094\t-0.663\t-2.066\t-1.567\t0.015\n4\t-0.699\t0.820\t0.832\t1.175\t-2.020\t0.296\t-0.258\t0.988\t1.476\t-1.309\t0.016\t0.833\t-0.338\t1.746\t-0.401\t-0.300\t-1.073\t0.154\t-3.018\t-0.248\t1.269\t-1.397\t-1.618\t1.116\t1.252\t-1.356\t1.733\t0.432\n2\t-0.304\t0.849\t-0.870\t0.096\t-0.854\t-0.418\t-1.514\t2.318\t0.306\t-0.326\t1.291\t-0.090\t0.459\t1.562\t1.474\t-1.144\t0.013\t1.277\t-1.081\t-0.258\t-0.294\t0.501\t0.803\t1.150\t0.083\t-1.041\t-0.355\t-1.058\n3\t-0.319\t-0.235\t-0.934\t-0.286\t1.912\t1.101\t1.017\t0.149\t0.467\t0.335\t0.711\t-0.673\t-2.788\t0.922\t0.325\t-0.919\t-0.183\t1.577\t1.181\t1.700\t0.280\t-2.075\t1.044\t0.480\t0.066\t0.286\t1.324\t0.522\n4\t1.270\t-1.094\t0.979\t-1.340\t0.948\t-0.851\t-0.314\t-0.645\t1.961\t0.994\t-0.411\t-0.825\t1.314\t-3.398\t0.479\t-0.959\t2.254\t2.731\t0.031\t-0.365\t0.068\t-0.622\t-0.794\t0.235\t0.624\t0.133\t0.290\t-0.633\n1\t0.746\t0.119\t0.330\t-0.051\t1.634\t0.180\t0.272\t-0.287\t-1.408\t1.598\t-0.389\t0.580\t1.202\t-0.142\t1.513\t-0.629\t-1.952\t-0.762\t0.341\t0.090\t0.510\t-0.477\t-2.387\t0.227\t0.366\t0.338\t-0.312\t0.155\n1\t-1.446\t0.245\t0.203\t0.426\t-0.875\t-1.026\t-0.285\t0.908\t-1.018\t0.151\t-0.512\t-1.524\t-1.281\t0.910\t-0.604\t1.244\t-0.037\t-1.368\t0.477\t-0.801\t-0.377\t1.133\t-1.290\t-0.450\t-1.084\t-1.190\t-0.473\t-0.858\n4\t0.646\t0.387\t-1.503\t1.828\t-0.693\t-1.332\t0.673\t-0.302\t0.771\t-0.887\t0.143\t-0.113\t1.022\t-0.375\t0.803\t-1.208\t1.232\t1.421\t0.733\t-0.016\t2.055\t-0.472\t1.185\t1.373\t-1.243\t0.715\t0.719\t-2.495\n2\t0.502\t1.347\t-0.027\t0.805\t0.333\t-0.750\t-0.566\t-0.311\t-0.247\t1.166\t1.489\t0.867\t1.478\t0.278\t-2.671\t0.004\t-0.775\t0.671\t-0.142\t0.313\t0.051\t-0.551\t-0.839\t-0.488\t0.439\t1.375\t0.438\t2.549\n4\t-1.231\t0.371\t-2.281\t1.416\t2.439\t1.144\t0.120\t-0.401\t-1.020\t-1.274\t-0.633\t0.544\t-0.532\t-0.199\t0.262\t0.961\t1.875\t-1.625\t1.972\t1.372\t0.783\t-0.328\t-0.434\t1.055\t-0.359\t-1.961\t-0.148\t-0.611\n3\t-1.186\t-0.325\t0.661\t0.510\t0.262\t0.933\t-0.362\t0.179\t-3.376\t-0.450\t0.764\t-0.390\t-0.466\t-1.041\t1.003\t0.548\t0.825\t-1.726\t-0.160\t-0.280\t-0.281\t-0.616\t-0.346\t0.850\t-1.913\t1.777\t0.484\t-0.042\n0\t-0.615\t-0.402\t-0.541\t0.024\t0.158\t0.230\t0.886\t0.733\t-1.502\t0.150\t-0.612\t0.820\t1.296\t0.842\t1.906\t0.753\t0.999\t-0.794\t-0.025\t0.690\t0.289\t-0.914\t-0.924\t-1.810\t0.372\t-1.030\t0.672\t0.724\n4\t0.963\t-0.549\t-0.188\t1.710\t0.172\t0.898\t-0.097\t1.323\t1.597\t1.877\t1.110\t-1.270\t-0.917\t0.806\t-2.651\t0.013\t-0.414\t0.566\t-0.689\t1.121\t1.422\t-1.392\t-1.660\t-1.235\t-1.034\t-2.276\t1.110\t0.935\n2\t-0.270\t-1.390\t1.352\t2.099\t-0.041\t2.159\t0.091\t-0.380\t-0.630\t1.637\t-0.780\t0.735\t-0.559\t-0.460\t1.030\t-0.380\t-0.405\t1.133\t1.597\t0.645\t-0.655\t-0.511\t0.080\t0.321\t-0.988\t0.723\t0.086\t-1.716\n4\t0.080\t-0.973\t-0.270\t-0.037\t-0.074\t1.743\t0.468\t-1.235\t-1.871\t0.397\t0.597\t2.094\t1.046\t0.307\t-0.028\t0.427\t1.116\t-0.374\t0.012\t-2.105\t-2.059\t0.545\t-0.594\t0.291\t1.622\t0.885\t-0.697\t-2.435\n3\t1.532\t0.186\t-0.823\t-1.533\t0.123\t-0.783\t0.155\t0.291\t0.717\t-1.172\t-0.887\t-0.314\t0.261\t0.202\t2.578\t-2.259\t1.110\t1.059\t-0.915\t-0.472\t1.762\t-0.622\t-0.606\t0.120\t0.512\t0.998\t1.041\t0.782\n0\t1.982\t-0.585\t0.454\t0.212\t0.156\t-0.837\t0.102\t0.251\t0.096\t1.020\t-0.699\t-0.562\t-0.659\t-0.672\t0.823\t1.645\t0.166\t0.074\t-1.615\t0.872\t-0.627\t0.762\t0.421\t0.604\t-0.916\t-0.685\t0.555\t-1.775\n1\t0.144\t-0.482\t0.592\t1.196\t1.738\t-0.431\t0.047\t-0.332\t0.336\t0.022\t-0.757\t1.387\t-0.902\t-0.073\t0.504\t-0.745\t-0.848\t1.093\t0.950\t1.922\t0.766\t-0.963\t-1.456\t-1.576\t-1.329\t0.699\t0.609\t-0.651\n2\t0.516\t-0.965\t0.394\t-0.074\t-0.361\t-1.247\t0.132\t0.039\t-0.840\t-1.597\t-1.644\t-0.254\t0.306\t1.850\t-0.045\t-1.625\t-0.207\t1.251\t-0.683\t1.119\t0.176\t0.647\t1.859\t-1.837\t0.897\t-0.095\t-0.949\t-0.101\n4\t-2.292\t-1.492\t-1.099\t-0.248\t-1.719\t0.461\t1.585\t1.094\t0.584\t0.897\t1.341\t0.577\t-1.244\t1.283\t0.307\t-0.468\t0.459\t-0.358\t-0.475\t1.826\t-1.902\t-0.173\t-0.901\t-0.885\t-1.365\t-0.173\t0.808\t0.519\n2\t1.612\t0.169\t-0.170\t1.793\t-0.920\t-0.249\t-0.535\t-2.226\t-0.160\t0.831\t-2.171\t1.392\t0.449\t-1.564\t-0.703\t0.264\t0.328\t0.036\t-1.286\t-0.798\t-0.680\t0.314\t-0.492\t0.474\t-0.971\t-0.227\t-0.091\t-0.921\n4\t0.142\t-0.795\t-0.378\t-2.450\t1.434\t1.337\t0.108\t0.319\t-1.268\t-1.195\t-1.697\t-0.161\t1.061\t-0.383\t0.348\t0.979\t-1.624\t1.846\t1.157\t0.514\t0.924\t0.971\t0.293\t1.428\t-0.453\t0.462\t1.322\t-2.691\n0\t-0.616\t-0.962\t-1.585\t-0.664\t-0.954\t-0.294\t0.869\t-0.682\t0.836\t0.929\t-0.097\t0.494\t0.833\t-0.358\t-0.212\t0.366\t0.458\t-0.057\t0.753\t-0.182\t0.548\t0.384\t-0.630\t1.370\t0.938\t-1.048\t0.669\t-0.319\n0\t0.700\t-0.389\t1.299\t-0.087\t1.250\t1.220\t-0.237\t-0.030\t-0.330\t0.543\t0.359\t-0.028\t-0.042\t0.026\t1.199\t0.786\t1.284\t-0.311\t-0.734\t-1.057\t-0.227\t0.110\t0.193\t-0.362\t-0.239\t-0.432\t1.700\t0.242\n2\t-1.220\t-0.380\t-1.755\t-0.121\t-0.827\t-1.168\t-0.025\t1.882\t0.795\t-0.252\t-0.049\t1.878\t1.259\t-0.311\t0.501\t-0.999\t0.025\t0.764\t1.020\t-0.640\t1.002\t0.148\t-0.289\t1.909\t-0.360\t-1.108\t-0.672\t0.726\n4\t-0.276\t0.650\t2.828\t1.970\t-0.455\t1.124\t-0.219\t-1.788\t-0.868\t-0.280\t0.255\t-0.585\t0.877\t-1.072\t0.811\t-0.711\t-0.844\t-1.232\t-0.198\t-0.217\t1.796\t-0.160\t2.115\t-0.321\t0.128\t-1.004\t-0.225\t-2.270\n2\t-1.733\t0.441\t-0.756\t-1.285\t0.019\t0.973\t-0.574\t0.497\t0.682\t0.792\t-0.732\t-1.121\t-0.985\t0.791\t0.667\t-0.957\t2.133\t-1.076\t0.999\t1.520\t0.110\t-0.770\t-0.748\t-0.262\t1.081\t-1.200\t-1.839\t0.362\n3\t-0.864\t0.013\t0.027\t-0.621\t-0.371\t-0.231\t0.810\t-0.148\t0.825\t0.586\t0.572\t1.388\t-1.119\t-0.473\t1.763\t-0.257\t-2.815\t-0.203\t0.135\t-2.617\t1.334\t-0.714\t0.329\t-0.226\t-1.288\t-1.071\t-0.935\t1.086\n2\t0.327\t-0.464\t-0.996\t2.213\t-0.078\t-0.210\t1.413\t1.887\t1.446\t-0.390\t0.347\t0.585\t2.237\t0.420\t0.465\t-1.013\t-1.255\t1.354\t0.346\t0.447\t1.074\t0.852\t0.722\t0.636\t0.139\t0.209\t0.916\t-0.938\n1\t0.581\t-0.382\t-1.073\t-0.181\t-0.563\t0.454\t2.167\t0.442\t0.063\t1.109\t0.125\t-0.342\t-1.201\t-1.155\t-0.303\t-1.117\t0.609\t0.086\t-0.676\t1.286\t0.357\t0.732\t0.540\t-0.531\t-0.146\t1.808\t-1.583\t0.793\n1\t-0.261\t-0.740\t0.285\t0.997\t-0.360\t0.261\t-0.935\t-1.567\t0.402\t0.880\t-1.914\t-0.091\t0.432\t0.289\t-0.590\t0.048\t0.926\t-0.860\t1.555\t-1.388\t0.898\t0.054\t-0.515\t-1.842\t1.190\t-0.934\t-0.030\t0.471\n4\t-1.664\t-1.636\t-0.983\t-0.537\t0.099\t0.606\t-0.968\t0.538\t2.325\t-0.628\t0.090\t0.613\t1.230\t-2.032\t-0.063\t2.103\t-2.451\t-0.336\t0.746\t-0.654\t1.377\t-0.540\t0.986\t0.909\t-0.326\t0.772\t-0.745\t1.447\n0\t0.346\t0.774\t-0.393\t0.236\t1.220\t-0.173\t-0.105\t0.006\t-0.441\t-0.352\t-0.769\t0.429\t-0.072\t0.599\t1.022\t-1.051\t-0.837\t0.692\t-0.254\t0.081\t0.084\t-0.948\t0.506\t0.249\t-1.008\t-0.165\t-0.109\t0.748\n2\t-0.363\t0.550\t-1.540\t1.272\t-1.116\t-1.362\t1.218\t-0.003\t-0.306\t1.568\t0.731\t-0.450\t-0.134\t1.074\t-0.964\t1.443\t-0.443\t-0.871\t1.087\t0.735\t-1.777\t-0.107\t-1.105\t0.798\t1.720\t0.538\t-0.760\t0.890\n3\t-1.918\t0.663\t0.452\t-0.285\t0.463\t-1.114\t-1.334\t0.553\t-0.334\t1.493\t-0.363\t-1.023\t2.641\t-1.424\t0.150\t0.107\t0.714\t-0.337\t-0.274\t-0.149\t1.801\t0.865\t-0.853\t-0.529\t-0.131\t-0.896\t-1.607\t-0.681\n1\t0.772\t1.086\t0.548\t1.180\t0.569\t-0.416\t-2.103\t-1.156\t-0.600\t0.487\t-1.275\t-1.387\t-0.443\t-0.329\t0.076\t1.246\t-0.531\t-0.175\t0.852\t-0.939\t1.132\t-1.016\t-0.455\t-0.744\t1.538\t0.089\t-0.209\t-1.212\n4\t-1.186\t-0.582\t-0.719\t0.061\t0.567\t-0.274\t-0.179\t0.886\t1.780\t-1.566\t-0.220\t-0.750\t0.556\t0.010\t-0.588\t0.448\t-0.167\t1.441\t0.470\t2.608\t-0.361\t1.825\t0.425\t-1.210\t0.179\t-2.196\t2.129\t0.353\n2\t0.072\t-1.201\t1.861\t-1.021\t-1.222\t-0.084\t0.466\t-0.899\t0.696\t-0.493\t2.445\t0.333\t-1.085\t-0.704\t0.493\t-0.812\t-0.076\t1.970\t0.640\t0.595\t1.984\t-0.759\t-0.017\t-0.303\t-0.626\t-0.255\t-0.456\t-0.658\n3\t-0.672\t-0.216\t0.960\t-2.072\t-1.503\t-0.036\t-0.003\t-1.572\t-1.848\t-1.501\t0.289\t0.773\t0.181\t-1.818\t-1.284\t-0.214\t-0.015\t-0.589\t-0.375\t0.292\t2.012\t1.022\t-0.368\t-0.173\t-0.116\t-0.576\t1.270\t1.244\n3\t-0.929\t-0.504\t-0.073\t1.889\t0.238\t-0.841\t0.221\t-0.352\t0.325\t0.581\t1.209\t-0.024\t1.639\t0.908\t-0.707\t2.210\t0.327\t-0.835\t1.653\t2.078\t-0.033\t-0.504\t-0.172\t0.715\t1.278\t0.570\t0.102\t1.498\n4\t-0.202\t2.422\t0.460\t1.696\t-0.254\t1.365\t2.073\t-0.109\t-0.539\t-1.318\t-0.309\t0.082\t2.031\t-1.295\t0.060\t-1.814\t-0.427\t0.311\t0.513\t0.589\t0.397\t0.580\t0.046\t-1.048\t-1.852\t-0.547\t-0.135\t1.254\n0\t-1.588\t-0.497\t-0.326\t-0.228\t0.297\t-1.348\t1.025\t0.887\t0.674\t-0.208\t-0.738\t-0.325\t0.061\t0.666\t-0.081\t0.360\t-0.434\t0.286\t-0.249\t0.268\t-1.767\t0.487\t0.540\t0.529\t-0.987\t-0.710\t-0.869\t0.330\n4\t-0.457\t-1.355\t0.069\t-0.818\t-1.055\t-0.720\t2.485\t0.136\t0.362\t-1.222\t-1.844\t-0.819\t1.438\t0.334\t0.599\t0.739\t-0.591\t1.388\t-0.633\t1.406\t0.442\t-1.789\t-0.748\t-1.785\t1.163\t-0.025\t-2.228\t0.376\n3\t0.174\t-0.877\t1.983\t0.025\t0.881\t-0.009\t0.997\t-2.101\t-0.705\t-0.522\t-0.530\t0.519\t0.587\t-0.074\t-0.049\t2.441\t0.325\t-2.225\t0.770\t0.559\t1.159\t0.598\t-0.905\t0.890\t0.099\t1.046\t1.548\t-0.122\n0\t0.958\t-0.267\t-0.892\t0.726\t0.375\t-0.313\t-0.360\t-0.915\t0.725\t-0.360\t-0.770\t-1.286\t0.392\t-0.509\t-2.013\t-1.203\t-0.234\t-0.206\t-0.244\t-0.383\t-1.476\t-0.875\t1.184\t1.036\t0.578\t0.490\t0.905\t-1.121\n3\t-0.517\t-0.854\t-0.355\t0.152\t-0.440\t-0.211\t1.391\t0.887\t-0.789\t0.354\t1.485\t-0.443\t-0.953\t-0.929\t-1.201\t0.337\t-0.719\t0.434\t0.593\t0.291\t1.519\t-0.592\t-1.075\t0.388\t-2.913\t-0.771\t-0.637\t-2.030\n4\t1.665\t0.757\t-0.776\t-1.253\t-0.498\t-0.253\t0.682\t-2.302\t-0.965\t1.800\t0.016\t0.062\t-0.564\t0.789\t-1.804\t-0.425\t-2.081\t-0.020\t-0.446\t-1.630\t1.011\t0.690\t1.030\t-1.507\t-1.004\t1.202\t0.811\t1.404\n3\t0.737\t-0.587\t-1.058\t-0.544\t1.954\t0.282\t2.126\t-0.120\t-0.259\t-0.074\t-0.092\t-0.774\t-1.149\t0.501\t1.055\t2.445\t-0.434\t-0.353\t-0.771\t1.126\t0.815\t-0.877\t-0.902\t0.953\t0.383\t0.758\t-0.883\t-1.878\n1\t-1.243\t0.357\t-0.797\t0.032\t0.248\t0.980\t0.892\t-0.003\t-1.136\t0.205\t0.118\t-0.542\t-1.916\t0.048\t0.052\t0.656\t0.324\t-0.757\t-0.514\t1.360\t-0.294\t-0.083\t-0.409\t0.713\t-1.336\t-0.313\t2.669\t0.201\n1\t-2.217\t0.491\t0.384\t1.934\t-0.435\t1.636\t0.043\t1.524\t0.338\t-0.561\t1.422\t-0.351\t-0.360\t0.266\t0.029\t0.552\t0.255\t0.568\t0.553\t-1.232\t-1.981\t0.279\t-0.691\t-0.805\t-0.272\t0.267\t-0.125\t0.725\n3\t-0.539\t1.515\t-0.125\t2.071\t-1.741\t-0.522\t-1.547\t-1.209\t-0.828\t0.315\t0.267\t-0.679\t-2.228\t1.478\t-0.286\t-1.116\t0.275\t0.204\t-0.323\t0.149\t-1.155\t0.760\t-0.997\t-0.606\t0.649\t1.204\t1.586\t1.056\n1\t-0.701\t0.097\t-0.572\t0.129\t-0.181\t1.024\t-1.821\t-0.112\t0.411\t-2.546\t-1.026\t1.191\t-0.069\t0.469\t-0.835\t-1.160\t-0.019\t1.421\t-0.432\t-0.058\t0.383\t0.926\t0.724\t0.952\t-1.326\t0.126\t-0.138\t-0.554\n3\t0.420\t0.758\t-1.137\t1.163\t-0.149\t1.183\t0.124\t0.214\t-0.288\t-0.332\t0.410\t-0.322\t-1.671\t-0.124\t-2.587\t-1.124\t-1.000\t0.578\t2.551\t-0.450\t0.323\t1.238\t0.240\t-1.667\t-0.655\t0.496\t0.575\t2.071\n0\t-0.149\t-0.146\t-1.156\t-0.268\t0.243\t1.105\t-0.620\t-0.190\t-0.949\t0.225\t0.784\t0.630\t-1.170\t0.147\t-0.677\t1.011\t-0.161\t-1.520\t-0.220\t0.458\t0.244\t-0.512\t-0.585\t-0.988\t-0.395\t-0.856\t0.970\t-0.901\n0\t0.649\t-0.009\t0.458\t-0.044\t0.915\t0.731\t0.679\t0.308\t0.487\t0.898\t2.350\t1.022\t-1.303\t-0.087\t-0.575\t-0.012\t-0.117\t-0.149\t0.491\t-0.062\t0.819\t-0.315\t-0.832\t-0.063\t-0.523\t1.009\t0.756\t1.174\n3\t-0.628\t-2.053\t0.361\t0.072\t0.556\t-0.227\t-0.430\t2.008\t-1.472\t0.734\t-0.575\t-0.336\t-0.317\t-0.803\t0.571\t1.391\t0.470\t0.103\t-0.986\t0.517\t2.156\t-0.381\t-0.376\t-0.561\t-0.489\t-2.745\t0.405\t-0.846\n3\t1.097\t-1.425\t1.825\t0.142\t-0.541\t0.027\t0.116\t0.433\t0.214\t2.029\t-0.547\t-0.030\t-2.056\t2.859\t0.111\t-0.052\t0.823\t0.712\t-1.011\t1.042\t-0.622\t-1.511\t-0.687\t-0.048\t-0.040\t-0.909\t-0.437\t0.048\n0\t-0.624\t-0.185\t-0.605\t-2.206\t0.897\t1.275\t0.652\t-1.138\t-0.201\t-0.007\t0.599\t0.668\t-0.734\t0.082\t0.457\t1.456\t0.705\t0.789\t0.084\t1.410\t0.410\t-0.861\t1.403\t0.698\t-0.443\t-0.479\t0.297\t0.463\n0\t0.096\t0.092\t-0.508\t-0.707\t0.941\t0.960\t0.114\t-1.592\t0.065\t-0.408\t-0.064\t0.126\t-0.113\t0.986\t-0.665\t0.977\t-0.351\t0.805\t0.903\t0.419\t-0.587\t-1.819\t0.474\t-0.425\t0.095\t0.972\t-0.696\t0.132\n1\t-0.335\t-0.019\t0.796\t-0.261\t-0.820\t-0.114\t0.307\t-1.045\t-0.303\t-0.054\t0.019\t1.266\t0.563\t0.487\t-1.109\t-0.492\t0.271\t-0.070\t0.859\t0.658\t-2.060\t0.243\t0.482\t-2.067\t0.348\t2.755\t-0.510\t-0.474\n4\t1.600\t0.960\t0.568\t0.233\t0.215\t0.076\t2.055\t-1.267\t-2.765\t0.443\t1.403\t0.186\t-1.980\t0.746\t-1.065\t-1.284\t-0.978\t-1.609\t0.203\t-0.648\t-1.330\t2.380\t-0.531\t0.052\t1.554\t0.677\t1.908\t-0.696\n4\t-1.699\t-0.396\t0.974\t-1.420\t-2.715\t-0.070\t-1.403\t-1.727\t-0.106\t1.532\t-0.590\t-1.640\t-2.376\t0.188\t0.211\t-0.157\t0.945\t0.824\t-1.143\t1.101\t0.080\t-0.386\t-1.212\t-0.915\t-1.115\t1.366\t-0.444\t0.049\n3\t0.781\t0.550\t0.349\t0.155\t2.131\t-0.666\t-2.133\t0.601\t-0.950\t-1.927\t0.088\t-0.355\t-0.874\t-0.893\t0.450\t1.113\t-2.097\t0.872\t1.335\t-0.146\t-1.482\t-0.904\t-1.506\t-1.114\t0.088\t-0.294\t0.584\t-0.039\n1\t-1.182\t-0.394\t0.403\t-0.495\t-0.545\t1.945\t-1.273\t-0.325\t0.525\t0.889\t-1.251\t0.419\t-0.396\t1.449\t0.372\t0.241\t1.667\t-0.002\t-0.804\t-1.327\t-0.685\t1.170\t-0.850\t0.072\t-0.533\t0.278\t-0.311\t-0.776\n2\t-0.698\t0.065\t0.181\t-0.016\t-0.464\t-0.666\t-0.422\t-0.746\t1.121\t-1.025\t0.946\t-0.564\t-0.416\t0.361\t0.757\t-0.273\t0.152\t2.261\t0.272\t1.358\t-1.399\t-1.737\t0.280\t0.653\t-1.913\t1.284\t0.956\t-0.727\n2\t0.258\t-0.741\t-0.021\t-1.040\t1.024\t-0.837\t0.017\t0.595\t-0.080\t-0.423\t1.398\t1.016\t1.953\t-0.886\t-0.651\t0.298\t1.110\t-0.728\t2.084\t-0.267\t0.395\t1.193\t-0.484\t-1.186\t1.836\t0.480\t0.952\t0.158\n3\t0.350\t-1.512\t-1.225\t0.753\t0.153\t-0.317\t-1.394\t0.369\t0.087\t0.723\t-1.737\t0.620\t-0.542\t0.291\t-0.063\t-0.851\t-0.412\t-0.877\t-0.525\t-0.909\t0.402\t-0.860\t0.558\t3.266\t-0.257\t-1.582\t2.104\t0.349\n0\t0.386\t0.543\t0.156\t0.207\t-0.111\t0.047\t-0.255\t-0.517\t-0.770\t0.424\t0.140\t-0.825\t1.210\t-0.125\t1.489\t0.435\t-2.140\t-0.134\t-1.449\t-0.459\t-0.240\t1.069\t0.430\t-0.143\t-0.211\t-1.050\t-0.101\t0.416\n3\t1.022\t-0.275\t1.194\t-0.037\t0.556\t-0.666\t0.028\t-2.632\t1.905\t0.563\t-0.283\t-0.449\t-1.507\t-1.077\t0.377\t-0.004\t0.744\t-1.067\t1.330\t-0.693\t-1.038\t-0.514\t1.528\t0.796\t-1.001\t-0.768\t-2.344\t0.480\n4\t-0.528\t1.529\t-0.054\t0.465\t-2.384\t-0.912\t0.230\t1.313\t1.368\t0.388\t1.544\t1.959\t-0.110\t0.750\t-1.328\t1.543\t0.887\t0.714\t-0.000\t1.061\t0.999\t-0.927\t0.207\t0.416\t-0.305\t1.244\t1.957\t1.208\n0\t1.670\t0.808\t0.421\t0.800\t-0.006\t0.739\t-1.127\t0.432\t0.647\t-0.117\t-0.654\t-0.043\t0.015\t0.172\t-0.231\t1.770\t-1.121\t-0.294\t0.714\t-0.876\t-0.133\t-0.192\t1.199\t0.952\t-0.042\t-0.600\t-0.312\t0.670\n2\t0.584\t-0.707\t1.001\t-1.156\t-0.855\t-0.801\t1.328\t-0.179\t-0.629\t0.164\t0.542\t1.589\t-1.497\t1.263\t-2.067\t0.906\t1.163\t1.215\t0.569\t-1.065\t0.747\t-1.068\t-0.939\t-0.456\t-0.366\t0.241\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\n3\t-0.834\t-1.328\t2.887\t0.542\t1.242\t-0.158\t-0.406\t-0.331\t-0.516\t-0.160\t-0.086\t1.432\t0.464\t0.687\t1.441\t-1.579\t-0.409\t1.071\t-0.833\t-1.496\t-1.212\t0.940\t1.341\t-0.155\t0.376\t-0.053\t0.200\t-0.862\n0\t1.487\t-0.275\t-1.314\t-0.546\t-0.874\t-0.576\t-0.594\t-0.634\t-0.308\t-0.065\t0.764\t-0.245\t0.326\t0.008\t0.541\t0.047\t-0.647\t-0.918\t0.210\t1.273\t-0.229\t-0.701\t-0.256\t-0.561\t-0.667\t-1.444\t-0.244\t0.236\n3\t0.384\t1.319\t0.212\t0.699\t0.352\t1.270\t0.352\t0.991\t2.020\t-0.804\t-0.733\t1.245\t0.248\t0.273\t0.660\t0.239\t-1.587\t0.259\t-1.094\t0.542\t0.468\t0.323\t-0.121\t2.097\t2.247\t-1.789\t-0.474\t1.721\n2\t-0.022\t-1.152\t-0.562\t-1.799\t1.231\t-0.973\t-0.756\t1.169\t0.146\t-0.127\t-0.385\t0.142\t-1.306\t1.774\t-1.208\t-0.228\t-1.468\t1.677\t-1.258\t-0.893\t0.773\t-0.347\t-1.809\t0.077\t0.653\t-0.124\t-0.849\t-0.326\n0\t-0.346\t-0.209\t-0.050\t-0.743\t-0.066\t0.296\t0.045\t-1.045\t-0.263\t0.069\t-0.431\t0.206\t-0.509\t0.173\t0.671\t-0.351\t0.372\t1.192\t-0.164\t0.637\t-0.561\t0.251\t-0.339\t-1.883\t1.462\t-0.358\t0.480\t-0.236\n3\t1.661\t0.614\t-1.459\t0.029\t0.487\t1.136\t-0.475\t1.028\t-0.308\t-0.714\t-1.230\t-0.899\t0.154\t-0.015\t-0.493\t0.023\t0.804\t2.128\t-0.147\t1.696\t0.059\t-0.668\t1.474\t-0.663\t-0.129\t-0.399\t2.920\t-0.628\n4\t1.978\t-1.167\t-0.298\t-0.255\t0.916\t1.129\t0.108\t-3.190\t-0.889\t-1.198\t1.159\t-0.921\t1.683\t0.408\t1.135\t2.256\t-0.944\t-1.620\t0.187\t0.954\t-0.665\t0.036\t1.217\t1.118\t-0.582\t0.271\t-0.936\t-1.450\n2\t-0.419\t-0.499\t0.320\t-0.183\t0.319\t1.563\t-0.478\t-0.305\t0.174\t-0.228\t-1.914\t-0.580\t0.687\t1.438\t-1.155\t-1.159\t0.030\t-2.076\t-0.445\t-1.736\t1.369\t-0.769\t1.653\t1.114\t-0.646\t-1.072\t-0.194\t-0.105\n3\t1.865\t1.474\t2.413\t0.230\t0.806\t-0.251\t0.663\t0.137\t-1.663\t-1.504\t1.809\t0.155\t1.420\t-0.567\t-0.892\t-0.086\t0.785\t1.392\t0.186\t0.654\t-0.144\t1.456\t1.471\t-0.785\t0.383\t-0.996\t0.816\t-0.394\n4\t0.016\t1.506\t0.546\t-0.416\t-0.906\t-1.157\t-0.446\t0.374\t-2.110\t-0.135\t0.802\t-1.035\t-0.178\t-0.992\t-2.708\t-0.800\t-1.064\t0.775\t0.481\t-1.042\t1.293\t0.725\t-0.256\t0.330\t-0.945\t-2.462\t0.091\t-1.364\n4\t2.521\t-0.878\t-0.017\t1.891\t0.839\t-1.005\t1.672\t0.781\t1.554\t-1.217\t-1.131\t-0.139\t0.545\t-1.128\t1.026\t2.294\t-0.603\t-0.652\t1.789\t-0.535\t-1.354\t1.485\t0.747\t1.505\t0.035\t0.111\t-0.268\t-0.358\n0\t-0.512\t0.105\t-1.435\t-0.264\t-0.140\t-1.785\t-0.197\t0.762\t-0.391\t-0.718\t-0.030\t0.988\t0.341\t0.910\t-0.339\t0.023\t0.587\t0.145\t-1.245\t-0.524\t-1.644\t0.315\t0.779\t0.731\t-0.550\t-0.852\t0.206\t-0.165\n0\t-0.773\t1.445\t1.121\t1.828\t0.018\t-0.382\t-0.968\t0.950\t-1.110\t0.302\t-0.388\t0.845\t-0.192\t0.705\t-0.069\t-0.180\t0.583\t1.314\t0.636\t1.069\t-1.397\t0.705\t0.597\t0.168\t-0.657\t0.462\t1.337\t-0.143\n2\t-0.782\t-0.917\t-1.423\t0.347\t-0.554\t-0.475\t-0.226\t-0.319\t0.723\t0.417\t1.051\t1.339\t0.416\t-1.760\t-0.323\t-0.693\t-0.281\t-0.800\t2.670\t-2.171\t0.330\t0.032\t1.035\t0.739\t0.165\t0.941\t-0.917\t-0.080\n3\t1.683\t-0.407\t-0.317\t0.023\t-0.399\t-0.178\t1.028\t-0.066\t0.290\t2.033\t1.464\t-1.047\t1.744\t1.377\t1.494\t-0.625\t0.212\t-0.392\t0.121\t1.752\t0.140\t-1.720\t-0.373\t-0.655\t1.790\t-0.813\t-0.407\t-0.373\n4\t-0.356\t0.362\t0.054\t0.763\t-1.518\t1.652\t0.525\t1.360\t0.874\t-0.012\t-1.284\t-0.564\t1.590\t1.385\t-0.620\t-2.818\t-0.213\t-0.288\t-0.250\t0.529\t0.876\t0.990\t0.446\t-1.801\t-0.341\t2.304\t0.374\t0.368\n1\t-1.054\t-1.610\t1.883\t-0.684\t1.413\t-0.221\t-1.131\t0.509\t-0.391\t-0.207\t0.145\t-0.812\t-1.621\t-0.051\t0.964\t-0.902\t0.154\t1.506\t-0.090\t0.086\t0.055\t-0.682\t-0.993\t-0.989\t0.368\t0.758\t0.805\t-0.379\n2\t-1.408\t-1.148\t-1.242\t-0.333\t-0.016\t-1.563\t0.050\t-0.804\t-0.067\t0.932\t-0.261\t0.207\t0.099\t0.136\t-0.970\t0.404\t1.486\t-0.698\t-0.958\t1.977\t-1.640\t-0.450\t-0.430\t0.978\t-0.225\t-0.158\t2.359\t0.376\n1\t0.015\t0.753\t2.545\t0.964\t0.248\t1.424\t-1.421\t-0.405\t0.067\t-1.039\t-0.541\t-1.656\t-1.279\t-0.245\t-0.941\t0.421\t0.268\t0.357\t-0.504\t1.082\t0.501\t0.225\t0.233\t-0.208\t1.319\t1.142\t-0.365\t0.165\n2\t-0.497\t-0.163\t-0.135\t0.166\t0.361\t-0.450\t-0.239\t0.114\t-2.957\t0.553\t-0.954\t-0.289\t-0.677\t0.748\t1.025\t-0.112\t-0.099\t-0.039\t-0.016\t1.001\t3.010\t-0.172\t0.568\t-0.847\t-0.755\t-1.020\t1.339\t0.704\n0\t-0.649\t-0.846\t-0.361\t-0.318\t0.096\t-0.308\t-1.254\t0.209\t-0.636\t-0.335\t0.971\t0.351\t-0.608\t0.543\t0.016\t-0.505\t-1.413\t0.376\t0.061\t-1.914\t0.651\t-0.045\t0.775\t-0.203\t0.672\t-0.251\t0.989\t-0.309\n0\t2.086\t0.472\t-1.363\t0.758\t-0.127\t0.509\t0.227\t1.386\t0.460\t0.007\t-0.062\t-0.383\t0.320\t0.658\t0.779\t0.205\t-0.680\t0.506\t0.789\t0.727\t0.032\t-0.910\t0.881\t-0.209\t1.293\t1.446\t-0.043\t-0.534\n4\t-0.956\t2.318\t-1.407\t-1.298\t1.314\t0.713\t-1.026\t0.607\t1.468\t-0.030\t2.599\t-1.724\t0.694\t1.167\t0.891\t-0.204\t-0.290\t0.444\t0.816\t0.602\t1.040\t-1.019\t-0.228\t-0.940\t-0.386\t1.015\t0.108\t2.607\n3\t-0.067\t-0.938\t-0.594\t-0.687\t0.326\t0.316\t0.012\t0.941\t-0.273\t0.476\t-1.260\t1.938\t0.975\t-0.788\t0.893\t0.845\t-1.682\t1.016\t-1.057\t0.608\t1.773\t0.361\t3.058\t0.814\t-0.111\t-0.155\t0.399\t-0.043\n2\t-2.484\t0.283\t0.048\t-0.017\t-0.323\t-2.014\t-0.736\t-0.307\t0.551\t1.534\t0.354\t-1.388\t0.622\t2.116\t0.207\t-1.172\t0.820\t-0.325\t0.154\t-0.256\t0.294\t0.986\t0.089\t-0.001\t-0.078\t1.460\t0.487\t-0.251\n4\t-0.182\t0.124\t0.897\t-0.763\t-1.453\t1.243\t-0.038\t-1.289\t0.745\t0.535\t1.822\t-1.233\t-0.684\t0.363\t1.474\t1.550\t0.967\t-0.705\t-0.696\t0.310\t-2.116\t1.618\t0.693\t1.785\t-0.986\t-0.862\t1.595\t-0.258\n4\t0.283\t-0.110\t-2.279\t1.996\t0.176\t0.001\t1.310\t0.989\t-0.298\t-1.953\t0.564\t-0.963\t1.239\t-0.776\t-0.452\t-1.343\t-1.262\t-0.109\t-0.202\t-0.476\t2.053\t0.604\t0.010\t-2.757\t0.826\t2.995\t-0.188\t-0.811\n4\t0.283\t-1.823\t2.127\t0.685\t0.587\t1.124\t-1.078\t0.727\t-0.353\t-1.476\t0.957\t-2.496\t0.502\t0.287\t2.216\t-1.125\t-0.140\t-0.002\t0.545\t0.755\t-0.116\t0.419\t0.569\t0.801\t0.286\t-0.300\t2.424\t-0.634\n0\t0.390\t-2.281\t1.726\t-0.802\t0.299\t-0.003\t0.199\t-0.270\t0.943\t0.357\t-0.716\t-1.393\t-0.008\t0.585\t-0.723\t0.645\t0.124\t-0.792\t1.200\t0.425\t0.429\t-0.894\t1.056\t-0.618\t-0.000\t-0.522\t-0.417\t0.074\n4\t1.296\t0.738\t1.567\t3.137\t1.431\t0.926\t-1.312\t-1.186\t0.360\t-1.698\t0.615\t-2.135\t1.152\t-0.322\t0.127\t-0.748\t-1.899\t1.235\t1.336\t-0.351\t0.662\t-0.391\t0.288\t0.759\t0.555\t1.341\t0.519\t0.028\n1\t0.716\t-0.696\t-0.188\t-1.594\t-0.008\t-0.865\t-1.327\t0.113\t-1.073\t-0.653\t1.932\t-0.981\t-0.306\t-1.038\t1.406\t0.220\t1.135\t0.429\t0.218\t0.571\t-1.780\t-1.144\t0.489\t0.313\t-1.098\t0.100\t-0.267\t1.011\n0\t0.163\t-1.128\t-0.130\t0.942\t-0.547\t0.890\t0.232\t0.455\t-0.781\t1.287\t-0.215\t-0.466\t0.849\t0.095\t-0.180\t1.098\t0.181\t0.484\t-0.070\t-0.848\t-0.177\t-1.093\t1.007\t-0.784\t1.275\t-0.167\t-0.426\t0.993\n3\t0.835\t-0.449\t0.508\t-0.572\t-0.134\t-1.208\t2.472\t2.720\t0.729\t0.541\t0.010\t-0.976\t-0.655\t0.494\t0.974\t-0.452\t0.934\t-0.794\t0.265\t0.710\t2.089\t0.818\t-0.669\t-0.058\t-1.076\t0.980\t-0.070\t-0.406\n1\t-0.023\t0.549\t0.305\t0.372\t2.114\t-0.159\t0.186\t-0.567\t1.097\t1.527\t0.237\t0.389\t1.380\t-0.391\t0.314\t-1.611\t0.135\t-0.205\t1.202\t-0.171\t-0.315\t1.607\t-0.199\t0.642\t1.421\t-0.583\t0.864\t-0.742\n2\t0.309\t-1.014\t-1.314\t1.299\t1.433\t-0.289\t-0.284\t0.392\t-1.186\t-0.231\t0.781\t-0.928\t0.798\t0.897\t0.530\t2.087\t1.693\t-0.938\t-0.239\t0.361\t-0.770\t0.741\t1.384\t-0.960\t-0.540\t-1.032\t-0.642\t-0.459\n1\t-0.357\t-0.198\t-0.651\t0.857\t-0.436\t0.080\t-0.707\t-1.095\t-0.114\t0.677\t1.037\t0.715\t-0.944\t0.055\t-0.084\t0.727\t0.509\t-0.625\t0.348\t0.571\t0.329\t-0.037\t-0.657\t-0.402\t2.164\t-1.184\t1.882\t1.875\n4\t-0.278\t-1.712\t-1.091\t-0.478\t-0.344\t0.148\t-0.939\t1.086\t-0.645\t1.173\t-0.166\t2.689\t-1.566\t-0.215\t1.865\t-0.654\t-1.134\t0.291\t-0.112\t1.405\t1.023\t0.646\t-0.709\t-1.783\t-1.406\t-0.338\t1.038\t-1.123\n1\t0.670\t-0.780\t1.167\t0.160\t1.569\t-0.060\t0.948\t-1.639\t1.282\t0.689\t-0.645\t0.865\t-1.267\t-0.497\t-0.851\t0.920\t1.248\t-0.449\t-1.271\t-0.977\t0.092\t0.029\t-0.340\t-0.830\t1.628\t-0.620\t-0.833\t1.147\n2\t0.336\t-0.238\t-0.109\t1.323\t0.037\t-1.085\t-1.525\t0.215\t-1.041\t-0.756\t0.874\t0.212\t-0.598\t0.323\t-1.352\t2.773\t-1.125\t-0.743\t0.348\t0.932\t-1.109\t0.127\t-0.490\t-1.495\t-0.969\t-0.558\t0.100\t-0.581\n0\t0.799\t-0.441\t-1.354\t-0.550\t-0.305\t-0.330\t-0.672\t0.034\t-0.090\t-0.788\t1.057\t1.123\t0.015\t0.947\t0.641\t-0.602\t0.434\t-0.881\t-0.939\t0.609\t0.513\t0.450\t0.142\t-1.713\t-1.498\t0.375\t0.735\t-0.711\n4\t-0.278\t-0.572\t0.171\t0.808\t-0.495\t0.128\t3.381\t0.476\t0.966\t-0.958\t-1.357\t1.465\t1.179\t1.714\t-0.080\t1.254\t-2.554\t0.541\t1.033\t-0.688\t1.056\t-0.180\t-0.080\t-0.259\t1.581\t-0.454\t-0.577\t-0.857\n1\t0.473\t-0.339\t0.487\t1.031\t-1.163\t-0.996\t0.706\t0.790\t-0.326\t0.603\t-0.383\t-0.021\t-0.022\t0.119\t1.389\t2.480\t-1.367\t-0.567\t-0.694\t1.379\t1.190\t0.891\t-0.394\t0.213\t-1.588\t0.730\t0.391\t-0.463\n4\t-2.479\t0.106\t-0.560\t-0.221\t-0.824\t1.359\t-1.883\t0.935\t-1.571\t-0.743\t0.365\t1.950\t0.779\t-2.582\t0.348\t-0.808\t2.398\t0.311\t0.953\t-0.872\t-0.934\t-0.050\t-0.751\t0.329\t-0.447\t-0.027\t0.968\t0.031\n1\t-0.197\t0.697\t0.144\t-0.323\t1.580\t0.727\t-1.679\t-0.152\t-0.153\t0.459\t-0.810\t-0.628\t-1.803\t0.508\t-1.154\t-0.294\t1.126\t0.344\t-1.512\t1.045\t-1.521\t0.611\t0.341\t1.364\t-1.596\t0.546\t0.262\t-0.215\n4\t-0.775\t2.524\t-0.762\t0.466\t-1.687\t-1.202\t-1.326\t1.343\t-0.260\t-0.540\t1.843\t-1.605\t1.688\t-0.207\t1.209\t-1.010\t0.965\t0.233\t-2.095\t-2.371\t1.612\t-2.380\t0.348\t-1.017\t-1.126\t1.505\t-0.167\t-0.804\n4\t0.778\t1.523\t0.396\t-1.637\t-0.419\t-0.125\t0.581\t-0.891\t-1.596\t-0.026\t-0.087\t-0.627\t-1.037\t-1.500\t-0.580\t0.972\t2.392\t1.354\t0.669\t-0.699\t0.304\t0.989\t0.253\t1.034\t0.653\t2.730\t1.196\t-2.094\n0\t0.123\t0.701\t1.602\t1.142\t0.413\t0.579\t0.393\t0.481\t0.438\t0.342\t0.115\t0.530\t0.229\t-0.221\t-0.282\t0.897\t-0.200\t-0.463\t-0.517\t-0.517\t-1.483\t-1.527\t-0.648\t-0.914\t1.140\t0.200\t-1.437\t-0.632\n2\t-1.435\t1.521\t-0.365\t0.820\t0.714\t1.608\t-0.991\t0.323\t0.700\t-1.247\t-0.384\t0.198\t-0.438\t-2.189\t-1.720\t2.163\t1.204\t0.643\t-0.120\t-0.661\t0.404\t-0.941\t1.011\t0.552\t0.092\t-0.267\t-0.282\t0.326\n1\t0.396\t-1.338\t1.046\t-1.555\t1.224\t0.944\t-0.785\t-0.534\t-1.896\t-1.761\t-0.299\t0.191\t1.040\t-0.282\t-0.135\t-1.257\t-0.147\t0.097\t0.587\t-0.047\t0.939\t0.021\t-0.135\t0.800\t0.807\t-0.322\t1.801\t0.262\n3\t1.862\t-0.405\t2.388\t-1.788\t2.000\t0.852\t-1.123\t-0.243\t1.010\t-0.307\t0.600\t-1.725\t0.239\t-0.479\t-1.188\t-0.394\t-0.520\t-0.276\t0.631\t-0.764\t-0.778\t-0.600\t-0.588\t1.287\t0.515\t-0.050\t-1.452\t0.778\n2\t0.705\t1.097\t0.512\t-0.103\t1.207\t0.500\t1.877\t0.383\t0.255\t0.908\t1.533\t0.059\t-0.163\t0.483\t0.338\t0.610\t-1.051\t-1.185\t-1.368\t-1.401\t0.851\t2.154\t-1.020\t-0.875\t1.457\t0.749\t-0.587\t-0.849\n3\t1.391\t-0.518\t0.407\t2.656\t0.997\t0.196\t-2.268\t1.859\t1.343\t-0.516\t0.203\t-0.546\t0.135\t0.592\t0.973\t-0.369\t1.206\t-1.283\t-0.233\t-0.376\t-1.116\t0.477\t-0.678\t-0.312\t-0.617\t-0.112\t-0.556\t-1.168\n2\t-0.193\t0.039\t0.140\t-0.698\t0.826\t0.259\t-1.373\t-1.124\t0.707\t-0.073\t1.086\t1.234\t0.738\t0.942\t-0.015\t-0.487\t0.282\t-2.504\t0.803\t-0.342\t1.479\t-0.160\t1.478\t-1.221\t-1.335\t0.457\t0.900\t1.001\n3\t0.045\t0.943\t0.362\t0.701\t2.226\t-0.119\t1.321\t0.585\t-0.229\t0.425\t-0.822\t0.510\t1.220\t-0.232\t-1.359\t-0.983\t-0.566\t0.676\t0.684\t-0.126\t0.163\t-0.400\t0.899\t-0.054\t-3.484\t-0.980\t0.728\t0.155\n4\t0.008\t-1.629\t1.865\t-0.237\t-0.756\t1.912\t0.643\t-0.762\t-0.563\t0.208\t2.452\t-0.811\t0.721\t-0.300\t0.957\t-0.304\t-0.156\t-1.631\t-0.149\t2.632\t-1.116\t-0.217\t0.026\t1.012\t0.040\t1.275\t-0.841\t-0.005\n0\t-1.111\t-0.215\t1.363\t0.484\t1.068\t-0.919\t-0.199\t-0.258\t-0.501\t-1.043\t0.974\t-2.209\t0.274\t0.541\t-0.002\t1.393\t0.029\t0.176\t-0.465\t-0.556\t-1.308\t-0.535\t0.451\t-0.055\t0.026\t-0.960\t-0.084\t-0.092\n4\t-0.974\t0.270\t-3.599\t-0.911\t1.284\t-0.256\t-0.235\t0.575\t-0.344\t0.437\t-1.232\t-0.941\t1.639\t-0.124\t2.092\t0.045\t-0.731\t-1.315\t1.251\t0.232\t-0.677\t0.840\t1.155\t-0.574\t2.420\t0.053\t-0.123\t2.101\n2\t-0.888\t0.385\t0.890\t0.512\t1.355\t-0.935\t1.389\t-2.053\t-2.031\t-1.463\t0.480\t-1.509\t-0.638\t-0.423\t0.746\t1.278\t0.902\t-1.431\t0.179\t-0.370\t-0.164\t-0.066\t-0.464\t-0.726\t-0.448\t-0.691\t1.016\t-0.064\n1\t-0.467\t-0.360\t-1.153\t0.732\t-0.751\t0.114\t-0.507\t-0.878\t-1.458\t-0.623\t-0.232\t0.118\t-0.442\t0.052\t0.245\t0.765\t1.149\t1.051\t-0.993\t0.094\t1.532\t-0.494\t1.327\t0.324\t-1.439\t1.972\t0.833\t0.404\n0\t-1.065\t-0.305\t-0.610\t-0.187\t0.057\t0.530\t-0.070\t0.487\t0.064\t-1.975\t-0.939\t-0.144\t-1.210\t0.600\t1.531\t1.219\t-0.213\t1.491\t0.149\t-0.337\t-0.613\t-0.302\t-0.388\t0.170\t0.161\t0.003\t0.437\t1.191\n1\t-0.790\t-0.524\t1.236\t-1.861\t-0.481\t-1.286\t0.053\t0.644\t0.211\t-0.923\t-0.740\t-0.427\t0.095\t1.786\t-0.516\t0.949\t-0.529\t-1.609\t1.447\t0.273\t0.966\t0.365\t1.103\t0.676\t0.720\t-0.572\t0.263\t0.551\n1\t-0.597\t-0.013\t0.166\t0.259\t0.588\t-1.138\t0.357\t0.315\t0.329\t0.425\t-0.743\t0.214\t0.233\t-1.692\t1.413\t-0.886\t0.230\t-2.111\t1.083\t-0.598\t-0.415\t-0.988\t-0.495\t0.125\t1.555\t0.878\t0.846\t-1.196\n0\t-0.780\t0.171\t0.809\t0.581\t0.011\t-0.334\t-0.581\t0.229\t0.078\t-0.111\t0.859\t0.761\t0.170\t-0.402\t-1.402\t-0.508\t-0.798\t-0.206\t-0.354\t-0.806\t0.496\t-1.799\t-0.202\t-0.710\t1.368\t-0.056\t-0.364\t-0.389\n2\t0.374\t-0.049\t1.121\t-1.099\t0.664\t1.319\t-1.349\t-0.740\t1.645\t1.081\t-0.819\t-1.215\t0.283\t0.330\t-0.059\t2.333\t0.647\t-0.840\t1.058\t0.058\t-0.677\t0.901\t1.476\t0.216\t-0.977\t0.113\t-0.956\t0.227\n0\t-0.819\t0.266\t0.268\t0.842\t-0.266\t-0.014\t2.665\t0.039\t-0.829\t0.531\t0.155\t-0.876\t0.419\t1.015\t-0.667\t1.262\t-0.642\t-0.818\t0.133\t0.733\t0.844\t0.315\t-1.133\t-0.886\t-0.335\t0.544\t-0.392\t0.985\n2\t0.066\t0.571\t0.600\t1.685\t0.250\t-1.241\t-1.065\t1.256\t1.611\t-0.035\t0.382\t0.093\t-0.298\t-1.491\t0.477\t1.782\t0.170\t-0.302\t-0.432\t0.497\t1.151\t0.533\t-1.870\t1.742\t0.833\t-0.180\t-0.530\t-0.447\n3\t-2.729\t0.005\t-0.460\t-1.063\t-0.553\t-0.574\t-0.048\t-1.367\t0.316\t1.366\t0.281\t0.809\t1.095\t1.236\t1.870\t-0.499\t-0.718\t0.783\t0.708\t0.776\t1.889\t0.011\t0.426\t-0.980\t2.135\t-0.337\t-0.308\t-0.347\n1\t1.134\t-1.038\t-0.593\t-1.063\t-0.163\t0.057\t-1.170\t-0.799\t-0.158\t1.074\t-0.941\t0.799\t-0.781\t0.476\t1.214\t1.144\t0.021\t0.046\t-1.697\t1.198\t-0.829\t-0.502\t0.542\t-1.393\t0.106\t1.715\t1.133\t-0.408\n1\t-0.760\t-0.741\t0.771\t0.786\t2.306\t0.353\t-1.290\t-0.237\t-1.222\t-0.275\t0.626\t0.239\t-0.750\t-1.627\t-0.951\t-0.661\t0.705\t-0.617\t0.923\t-0.760\t0.835\t0.245\t0.316\t0.818\t-0.623\t0.554\t1.879\t-1.124\n4\t-0.515\t-1.744\t-1.052\t0.240\t-1.245\t-0.849\t1.005\t0.577\t-0.502\t2.387\t0.364\t-0.778\t1.979\t0.852\t2.865\t1.468\t0.806\t-0.679\t1.828\t-1.974\t-2.067\t2.443\t-1.108\t-1.138\t-1.222\t-0.683\t0.891\t0.142\n0\t-0.066\t-1.269\t-1.037\t0.738\t0.116\t-0.776\t0.120\t-0.607\t-0.784\t1.164\t0.783\t-0.021\t0.14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t-0.904\t-1.155\n2\t-2.399\t0.664\t0.966\t-0.328\t-1.157\t1.522\t-0.146\t-0.344\t0.449\t2.736\t0.484\t0.178\t0.070\t1.654\t0.527\t0.864\t0.304\t0.536\t-0.278\t0.169\t-0.686\t-0.508\t0.490\t-0.704\t0.131\t-0.368\t-1.124\t0.228\n2\t-1.508\t-0.742\t0.756\t2.587\t-1.976\t-0.201\t0.412\t-0.616\t0.850\t-0.867\t0.154\t-0.522\t1.219\t-0.676\t-0.352\t0.946\t-1.028\t0.069\t-1.823\t-1.088\t0.382\t-0.991\t-0.545\t0.493\t-0.579\t0.823\t0.216\t0.436\n4\t0.915\t-0.404\t-1.246\t-0.777\t-3.532\t-1.265\t0.687\t-0.333\t0.065\t0.774\t1.321\t1.105\t1.015\t-1.192\t-1.493\t-1.823\t0.158\t1.033\t0.123\t0.231\t1.792\t0.460\t-0.445\t-2.015\t1.266\t-0.201\t0.557\t-1.894\n1\t0.698\t-1.403\t0.778\t0.193\t-0.425\t0.211\t2.064\t-1.241\t0.026\t-0.384\t0.126\t0.219\t1.079\t-1.148\t1.380\t0.023\t-1.820\t-0.622\t0.287\t0.507\t-0.161\t-0.453\t0.861\t0.742\t-0.368\t-0.829\t-1.296\t-0.176\n3\t1.557\t0.187\t0.626\t1.370\t-0.641\t1.111\t0.275\t0.914\t0.186\t1.296\t0.224\t-1.484\t1.452\t-0.948\t1.253\t-0.090\t1.176\t1.135\t0.430\t0.509\t0.423\t-2.014\t0.118\t-1.116\t0.171\t-1.143\t-1.185\t1.605\n3\t-0.554\t0.187\t0.519\t0.542\t-0.497\t0.616\t1.027\t1.090\t0.045\t-1.920\t-0.159\t-1.399\t0.488\t-0.571\t-0.260\t-0.565\t1.616\t0.875\t-0.148\t1.169\t0.774\t0.969\t-2.432\t2.555\t1.227\t-0.136\t0.921\t-0.135\n1\t-0.624\t1.031\t-0.653\t-0.191\t-0.769\t-0.140\t-1.128\t1.298\t0.975\t-1.338\t1.035\t1.403\t0.577\t1.204\t0.096\t0.642\t0.488\t-0.928\t0.865\t0.365\t-1.060\t-1.629\t0.268\t-0.146\t-1.250\t1.217\t0.461\t0.462\n0\t-0.334\t-1.718\t-1.511\t0.593\t-0.019\t0.184\t1.005\t-0.374\t0.750\t-0.626\t0.092\t0.184\t-1.068\t-0.006\t0.854\t0.511\t1.456\t-0.468\t1.512\t0.219\t-0.476\t0.163\t-0.718\t0.311\t-1.642\t1.241\t-0.643\t0.036\n3\t-2.316\t0.623\t-0.304\t-0.218\t-0.315\t0.232\t-0.753\t1.476\t-1.476\t0.445\t0.642\t-0.760\t-1.024\t1.017\t-0.991\t-0.607\t0.887\t0.035\t-1.797\t1.747\t-0.530\t-0.403\t0.256\t0.625\t1.961\t-1.660\t0.826\t0.743\n2\t-0.079\t0.226\t-0.010\t0.681\t-0.705\t-0.207\t-1.608\t0.890\t-0.733\t-0.129\t0.169\t-1.755\t1.068\t1.429\t-0.762\t-0.120\t1.754\t0.336\t-0.319\t-0.506\t-1.825\t0.588\t2.033\t0.814\t0.534\t-0.966\t-0.932\t1.395\n4\t0.815\t0.559\t-0.500\t1.784\t-1.460\t0.313\t0.847\t1.235\t0.122\t0.766\t0.004\t1.668\t0.323\t1.160\t-1.156\t-0.114\t0.539\t0.531\t-0.568\t-1.861\t-1.760\t-0.947\t2.787\t-3.036\t-0.168\t-0.294\t0.042\t-0.082\n4\t-1.601\t-0.150\t0.124\t-0.573\t0.028\t-1.965\t-1.296\t1.270\t0.243\t-0.379\t0.980\t1.387\t1.810\t-1.887\t1.383\t-1.161\t0.302\t0.611\t1.609\t0.469\t0.279\t-0.838\t-1.688\t-0.864\t1.407\t0.033\t1.661\t-0.717\n1\t0.149\t-1.222\t0.306\t-1.992\t-0.863\t0.110\t1.194\t1.431\t-0.237\t0.156\t0.984\t-1.329\t0.405\t-0.828\t-0.172\t-0.459\t0.435\t0.550\t0.891\t0.348\t0.172\t1.854\t-1.524\t1.696\t-0.290\t0.571\t-0.326\t-0.703\n0\t0.027\t0.045\t0.444\t-1.562\t0.113\t-0.808\t-0.366\t-1.277\t-1.311\t0.325\t-0.135\t1.644\t-0.381\t0.749\t1.181\t0.179\t0.245\t1.230\t1.332\t0.605\t1.395\t0.197\t-1.084\t0.316\t-0.666\t1.252\t-0.269\t0.900\n0\t-1.307\t-1.260\t-0.554\t0.118\t0.846\t-1.582\t-0.254\t1.852\t0.721\t0.772\t1.245\t0.241\t-0.902\t0.049\t-0.526\t-0.544\t-0.609\t1.216\t1.035\t0.146\t-0.398\t0.663\t0.766\t0.019\t0.280\t-0.889\t-0.693\t-0.233\n2\t-0.698\t1.709\t-1.733\t-0.211\t1.300\t-1.293\t-1.078\t-0.781\t0.705\t-0.986\t-0.408\t-1.772\t0.162\t0.241\t0.969\t-0.077\t1.416\t-1.802\t-0.510\t0.469\t-0.709\t-0.894\t-1.074\t0.213\t0.534\t0.431\t-0.176\t0.200\n0\t1.150\t-1.020\t-0.995\t0.348\t0.530\t0.191\t1.083\t0.003\t0.124\t1.262\t-0.832\t0.310\t0.365\t-0.630\t0.013\t-0.026\t-0.622\t-1.303\t-0.842\t0.008\t-0.465\t-1.386\t1.674\t0.068\t-0.065\t-1.472\t0.997\t1.559\n0\t-0.065\t-0.234\t-1.211\t-1.279\t-0.335\t0.027\t-0.427\t-0.394\t0.654\t-0.454\t-1.450\t0.616\t1.086\t0.555\t-0.419\t-0.155\t-0.386\t0.439\t1.187\t0.398\t-0.033\t0.061\t0.404\t1.819\t0.781\t-0.969\t-0.965\t1.799\n1\t0.091\t1.392\t-0.806\t0.132\t0.521\t-0.692\t-1.251\t-0.019\t-1.068\t-1.544\t1.521\t-0.600\t0.475\t0.274\t-1.227\t0.250\t0.318\t0.142\t0.023\t0.524\t0.450\t0.611\t-1.214\t-1.742\t-0.552\t-0.107\t0.786\t1.964\n0\t-0.539\t-0.599\t1.182\t0.910\t0.698\t-0.391\t0.342\t1.148\t1.450\t-0.781\t-2.079\t-1.143\t-0.518\t0.263\t0.525\t-0.066\t0.371\t0.653\t-0.602\t-0.417\t0.552\t0.406\t-1.315\t0.767\t0.123\t-1.041\t-0.217\t-0.226\n4\t2.627\t-1.343\t-2.268\t-0.775\t-0.531\t-0.958\t-2.034\t-0.106\t0.367\t1.214\t1.777\t-0.492\t-1.086\t0.280\t0.874\t-1.248\t1.183\t1.392\t-0.531\t-0.241\t-0.250\t-0.377\t-1.459\t1.508\t0.771\t1.005\t-0.147\t0.349\n3\t-0.693\t-1.541\t0.366\t1.595\t0.077\t-1.974\t-0.774\t0.992\t0.214\t-1.327\t2.542\t-0.573\t-0.236\t-0.326\t0.880\t-0.231\t-1.867\t-1.859\t-0.589\t0.204\t-0.517\t0.606\t1.439\t-1.253\t0.204\t-0.503\t-0.771\t-0.539\n4\t0.405\t-0.593\t0.776\t-1.141\t-0.305\t0.742\t0.849\t-0.276\t0.472\t0.368\t-2.310\t0.148\t-1.132\t0.802\t1.851\t0.825\t2.351\t-0.319\t-0.367\t0.530\t1.384\t1.757\t1.671\t0.767\t1.421\t0.246\t1.817\t-1.574\n2\t-0.037\t-1.738\t0.018\t1.211\t-0.600\t0.412\t0.852\t-0.910\t-0.451\t-1.018\t-0.822\t-1.288\t-0.900\t1.419\t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t-0.662\t-1.067\t0.421\t-0.426\t0.832\t0.134\t1.378\t-2.787\t0.248\t1.598\t0.642\n2\t-0.238\t-1.661\t0.365\t-1.044\t0.840\t-1.719\t-0.350\t-1.257\t-0.401\t0.217\t1.694\t0.589\t1.971\t-0.713\t0.028\t0.989\t0.250\t-0.490\t-1.343\t-1.361\t0.235\t-0.181\t-0.314\t-1.072\t-0.807\t0.174\t-1.590\t-1.094\n1\t-0.522\t-1.458\t-0.065\t-0.641\t0.995\t0.955\t-0.245\t-0.036\t-0.371\t-0.531\t-1.070\t0.068\t-0.290\t1.490\t-0.353\t2.406\t1.807\t-0.720\t-0.075\t-1.553\t-0.420\t0.374\t0.206\t-0.808\t-0.076\t0.501\t-0.128\t1.349\n3\t-3.203\t1.243\t0.920\t-1.836\t0.929\t1.058\t0.562\t-1.559\t0.683\t1.007\t-0.572\t0.441\t-0.353\t0.885\t-0.314\t-0.580\t-0.039\t-1.342\t-0.139\t0.362\t0.870\t0.249\t-1.433\t1.352\t1.436\t0.113\t-0.752\t-0.532\n1\t-0.722\t1.132\t-1.131\t-0.242\t1.561\t-0.487\t-0.744\t-1.412\t-0.429\t0.565\t-0.529\t0.026\t-0.527\t0.890\t0.974\t-0.221\t1.484\t0.157\t0.167\t-1.653\t-0.268\t-0.029\t0.318\t-0.679\t-0.868\t1.404\t0.736\t1.625\n1\t-0.335\t0.689\t-1.227\t0.199\t0.714\t1.371\t-0.136\t0.711\t-0.793\t-0.052\t0.475\t0.843\t-0.394\t0.862\t-0.984\t0.113\t-1.367\t-0.634\t-0.339\t1.753\t1.629\t-0.960\t-0.808\t-0.908\t-0.107\t-0.781\t-1.716\t-1.649\n4\t3.098\t-0.258\t0.510\t-0.649\t0.295\t-0.238\t-0.411\t1.819\t1.177\t0.137\t-2.304\t-0.361\t2.703\t-0.231\t1.103\t1.442\t0.219\t-0.842\t-2.435\t0.684\t-0.815\t0.176\t-1.250\t-0.265\t0.034\t0.269\t-1.115\t1.197\n2\t0.548\t-0.431\t-0.388\t2.390\t0.810\t0.257\t-0.280\t-0.344\t0.237\t-0.965\t-1.511\t0.664\t1.532\t1.560\t0.653\t-0.402\t1.426\t1.578\t0.293\t1.254\t-1.984\t0.402\t0.401\t-0.530\t-0.338\t1.047\t0.370\t0.647\n4\t2.075\t1.747\t1.532\t1.911\t-1.260\t-0.525\t-0.911\t3.495\t0.244\t-1.414\t-1.102\t0.107\t-1.363\t1.869\t0.791\t0.313\t-1.054\t-1.321\t0.349\t0.137\t-1.283\t-0.054\t-0.051\t0.738\t2.106\t-2.080\t0.801\t-0.696\n0\t-0.261\t0.009\t0.303\t-0.589\t0.193\t-0.144\t-0.094\t0.181\t-1.314\t0.040\t0.163\t0.129\t-0.216\t1.138\t-0.354\t0.227\t1.297\t-0.079\t0.214\t-0.096\t1.237\t-0.258\t1.283\t-0.104\t-0.859\t0.679\t-0.305\t0.621\n1\t-0.643\t0.598\t-0.783\t-0.893\t-0.068\t-1.226\t1.602\t1.927\t-0.454\t-1.387\t0.337\t-0.482\t1.175\t-0.787\t0.545\t0.114\t-0.676\t0.240\t0.879\t0.087\t0.097\t-0.873\t1.144\t1.045\t-0.411\t-0.124\t1.414\t-1.134\n0\t0.439\t1.268\t-0.024\t-1.422\t0.843\t0.755\t-1.942\t-0.004\t-1.590\t-0.665\t-0.903\t0.809\t0.117\t-1.286\t0.880\t-1.434\t0.330\t-0.814\t-0.366\t0.504\t0.521\t1.337\t-0.082\t0.306\t-0.438\t0.182\t0.439\t-0.098\n1\t0.774\t-0.980\t0.812\t0.608\t1.439\t0.174\t0.306\t-0.923\t-0.001\t0.611\t0.706\t1.713\t0.461\t-0.093\t0.557\t-0.557\t1.325\t-0.694\t-0.405\t-1.075\t0.083\t0.645\t1.653\t-0.057\t-0.224\t-2.669\t0.223\t0.028\n0\t-0.657\t-1.356\t-0.648\t-0.330\t-2.369\t-0.197\t0.924\t0.567\t-0.496\t0.047\t-0.556\t0.149\t1.540\t0.968\t0.521\t-0.493\t0.750\t-1.687\t-0.252\t0.584\t0.740\t0.670\t0.295\t-0.196\t0.538\t-0.953\t-0.845\t0.062\n1\t-0.828\t-0.248\t1.112\t-1.390\t-0.078\t-0.741\t-0.827\t-0.279\t1.141\t1.086\t-0.059\t1.002\t0.740\t-0.025\t-0.094\t0.287\t0.791\t0.280\t-0.014\t1.969\t-1.048\t-0.456\t-0.483\t-0.210\t0.613\t1.878\t-1.207\t1.083\n1\t-0.964\t-0.957\t0.344\t-0.049\t0.033\t-0.758\t-0.230\t-0.924\t0.890\t1.035\t-1.846\t-0.930\t-1.497\t-0.650\t-0.083\t-1.450\t-0.922\t-1.004\t0.207\t0.069\t-0.722\t0.177\t-0.547\t-0.272\t1.673\t1.340\t-1.300\t0.830\n2\t1.512\t-0.037\t-2.741\t0.388\t-0.198\t-1.829\t-1.679\t0.975\t-0.732\t-0.907\t-0.036\t-0.644\t0.100\t-0.203\t-1.200\t-0.511\t0.443\t0.042\t-0.082\t1.937\t0.731\t0.852\t-0.166\t0.220\t-0.431\t0.778\t-0.110\t0.222\n3\t0.871\t1.609\t-1.005\t-0.211\t-0.600\t0.139\t0.268\t-0.239\t-0.044\t-0.094\t2.203\t-0.455\t2.870\t0.484\t0.118\t1.696\t0.736\t0.557\t-1.015\t-0.488\t-0.896\t-1.543\t0.105\t1.203\t-0.159\t-0.216\t-0.522\t-1.668\n2\t-0.564\t-1.494\t1.255\t-0.498\t-1.268\t-0.168\t0.068\t-1.746\t0.332\t-1.047\t1.711\t0.524\t-0.400\t-0.745\t-0.105\t0.613\t-0.025\t-0.217\t0.058\t-0.604\t-0.343\t-2.086\t0.974\t0.299\t-0.773\t1.899\t0.087\t-1.036\n1\t-0.941\t-1.101\t0.760\t-0.409\t-0.847\t-0.019\t-0.899\t2.204\t-0.774\t0.190\t0.518\t0.948\t-1.078\t0.454\t-1.642\t0.309\t-0.058\t0.752\t0.260\t-0.111\t-0.035\t-1.455\t-0.814\t1.521\t0.205\t0.682\t-0.617\t0.871\n3\t0.052\t-0.983\t-2.196\t0.927\t-1.252\t1.954\t-0.815\t1.516\t0.192\t1.068\t2.017\t-0.531\t1.116\t-0.722\t1.254\t0.140\t0.775\t0.731\t0.298\t0.866\t-0.884\t-1.202\t0.524\t0.500\t0.682\t0.725\t0.941\t0.300\n3\t-1.070\t-0.114\t-0.832\t-0.735\t0.020\t0.833\t0.652\t0.340\t-0.562\t0.091\t-1.173\t-0.611\t-0.175\t-0.714\t0.317\t0.524\t1.439\t0.309\t0.546\t-0.222\t2.870\t2.042\t0.848\t-1.948\t1.948\t-1.243\t1.143\t-0.821\n4\t-1.090\t0.652\t0.173\t-2.119\t0.662\t2.128\t-2.601\t-1.236\t-2.849\t-0.075\t0.990\t0.106\t1.392\t0.831\t1.702\t1.160\t-0.403\t0.067\t-0.869\t-0.231\t0.062\t-0.085\t1.342\t-0.330\t-0.932\t-0.870\t0.655\t-1.403\n0\t0.027\t-0.675\t-0.097\t-0.526\t-0.888\t-0.254\t0.068\t-0.166\t-0.265\t0.226\t0.531\t-1.144\t-0.252\t-1.974\t-0.162\t0.151\t0.570\t0.154\t0.548\t0.065\t-0.676\t-1.116\t-0.263\t-0.766\t-0.120\t-0.492\t0.419\t0.049\n4\t-0.821\t-1.363\t-0.876\t-0.821\t-0.185\t1.083\t-0.036\t-0.742\t-0.518\t0.272\t-0.117\t-0.465\t0.127\t-1.341\t-0.902\t-0.284\t1.752\t-2.346\t3.063\t1.621\t0.137\t-2.499\t2.861\t-0.850\t0.032\t0.250\t0.090\t0.450\n1\t0.194\t0.121\t0.958\t0.465\t-0.725\t0.570\t0.917\t-0.787\t0.198\t-1.068\t1.452\t1.012\t-0.433\t-0.502\t-0.042\t-0.048\t-0.206\t-0.042\t-1.062\t-1.092\t-1.046\t0.210\t1.076\t2.183\t1.599\t-0.802\t1.200\t0.152\n3\t-1.715\t-0.300\t0.178\t-0.266\t-0.231\t-0.203\t-1.193\t0.818\t-1.330\t-1.215\t1.688\t-0.016\t-2.159\t2.036\t0.972\t-0.523\t0.748\t-1.067\t-0.894\t-0.646\t0.704\t1.230\t-0.101\t0.417\t0.298\t-1.477\t-2.189\t0.280\n0\t-0.182\t0.441\t2.258\t0.659\t-0.997\t-0.831\t-0.681\t-0.391\t0.226\t-0.944\t-0.219\t-0.199\t0.338\t-0.407\t-0.766\t-0.704\t0.392\t-0.725\t0.699\t0.366\t-1.089\t1.239\t0.080\t-0.666\t1.830\t-0.012\t0.358\t0.016\n1\t-1.263\t-0.485\t-0.330\t1.555\t0.925\t1.148\t0.820\t0.946\t-0.688\t-0.199\t-0.990\t-0.210\t0.684\t0.135\t1.311\t-1.141\t0.500\t-1.248\t0.094\t0.740\t-0.215\t0.551\t-0.960\t-1.664\t0.557\t1.744\t-0.996\t1.222\n1\t0.806\t-0.984\t0.323\t0.096\t-0.935\t0.163\t-0.772\t-1.553\t1.060\t-0.832\t-0.109\t1.298\t-1.745\t1.589\t-1.031\t-0.534\t0.828\t-0.296\t-0.373\t1.357\t0.508\t0.449\t-0.067\t0.095\t-1.537\t-0.049\t1.333\t-1.375\n4\t2.414\t-0.637\t0.544\t0.927\t0.101\t1.386\t1.995\t-1.110\t0.116\t0.916\t-1.633\t1.064\t1.707\t1.284\t0.438\t1.539\t0.357\t-1.320\t-0.522\t-0.256\t-0.064\t-0.094\t-0.338\t0.219\t1.504\t0.214\t0.629\t-2.194\n4\t-0.901\t0.021\t0.660\t-1.924\t0.519\t-1.427\t-0.986\t0.514\t-1.128\t-1.090\t2.072\t-1.523\t1.384\t0.597\t0.767\t2.173\t-0.313\t1.250\t-2.389\t1.336\t-0.274\t0.092\t-0.331\t-0.232\t-1.917\t1.260\t0.070\t-2.111\n2\t0.128\t-0.782\t0.451\t1.160\t0.930\t0.121\t0.078\t-0.009\t2.125\t-0.582\t0.079\t-0.181\t-0.783\t-0.380\t0.028\t-1.185\t1.649\t0.726\t1.092\t-0.161\t-0.313\t-0.214\t-0.696\t1.300\t1.397\t0.550\t1.698\t2.396\n2\t0.083\t0.536\t-0.669\t0.452\t-1.776\t0.718\t-0.871\t0.567\t0.482\t2.686\t0.889\t1.211\t0.277\t0.158\t-1.907\t1.388\t1.952\t-0.012\t-0.700\t-0.595\t-0.191\t-0.796\t0.251\t0.948\t-0.136\t0.461\t0.968\t-0.377\n4\t1.653\t0.488\t-0.979\t1.507\t-1.147\t0.614\t2.418\t-0.131\t-0.288\t0.168\t2.225\t1.476\t-0.443\t0.305\t0.923\t-0.429\t-1.132\t1.084\t0.510\t0.319\t1.107\t-1.145\t-0.232\t2.638\t0.568\t0.628\t0.106\t-1.841\n3\t-0.643\t0.445\t-1.529\t1.062\t-0.644\t-2.459\t-0.360\t-0.514\t0.499\t-1.153\t0.349\t-0.681\t-0.070\t1.066\t1.099\t1.276\t-0.762\t0.297\t0.340\t1.252\t-0.328\t-1.489\t0.849\t1.341\t0.317\t-1.093\t2.166\t0.120\n3\t-0.671\t0.961\t-0.440\t-0.883\t0.044\t1.493\t0.809\t-1.900\t-0.156\t-1.508\t1.626\t0.940\t1.533\t0.526\t1.134\t-1.177\t0.176\t-0.721\t-1.648\t-0.814\t1.143\t1.017\t0.016\t-0.796\t0.398\t0.070\t1.200\t1.365\n4\t-0.519\t-0.482\t0.476\t-0.567\t1.003\t-2.387\t-0.618\t0.633\t-0.026\t-1.414\t0.201\t0.902\t-1.675\t1.315\t-0.739\t-0.248\t-1.274\t0.060\t3.732\t-0.311\t0.053\t-0.836\t-1.172\t0.148\t-0.554\t0.250\t-0.769\t0.113\n3\t-0.545\t0.960\t1.199\t3.055\t-0.305\t-0.278\t-0.330\t0.174\t-0.645\t1.212\t1.008\t-0.257\t-0.711\t-0.436\t-0.469\t0.494\t0.193\t0.822\t1.487\t0.299\t0.593\t2.456\t0.571\t-0.490\t-0.068\t-1.666\t0.545\t-0.550\n4\t0.642\t-0.155\t-0.580\t-1.044\t-0.612\t1.759\t-0.648\t0.312\t1.028\t0.007\t-1.932\t-2.461\t0.643\t0.376\t1.549\t2.217\t-0.584\t-0.115\t-0.874\t-1.780\t0.379\t1.695\t-0.225\t1.049\t-0.804\t-1.239\t0.520\t0.119\n3\t-1.686\t-1.237\t-2.261\t0.317\t0.835\t-1.122\t-0.774\t1.199\t-0.530\t0.080\t0.606\t-1.135\t-1.685\t-1.027\t-0.940\t0.034\t0.992\t-1.825\t-1.424\t-0.630\t0.047\t1.685\t-0.273\t0.159\t0.388\t-0.709\t0.392\t0.162\n4\t-0.577\t-0.246\t-0.343\t-1.689\t1.229\t-0.013\t-0.190\t-0.087\t-0.014\t-0.379\t-1.597\t0.345\t0.121\t0.263\t-1.035\t0.343\t-2.812\t2.185\t0.827\t-0.721\t2.339\t-0.251\t-1.652\t-1.722\t-1.204\t0.847\t0.248\t1.433\n4\t-1.961\t0.601\t1.172\t1.825\t0.884\t-0.081\t-1.133\t0.481\t0.142\t-0.237\t0.224\t-0.120\t1.384\t-0.964\t1.259\t-0.606\t1.897\t0.426\t2.275\t-0.464\t-0.286\t0.467\t1.655\t2.276\t-0.289\t0.599\t0.104\t-1.210\n0\t-1.146\t-1.719\t-0.690\t0.269\t-0.525\t-0.763\t-0.186\t0.649\t1.015\t-0.032\t1.070\t-0.631\t0.209\t-0.386\t-0.132\t-0.616\t0.162\t1.050\t-0.870\t0.167\t-0.812\t-0.508\t1.674\t-0.031\t0.618\t-0.478\t-0.351\t-1.781\n0\t1.062\t1.376\t-1.542\t-0.300\t1.570\t-0.605\t0.133\t0.935\t0.785\t0.550\t-0.355\t0.677\t-1.196\t1.741\t0.593\t-0.263\t-0.098\t-1.362\t0.333\t-0.218\t1.231\t0.248\t-0.141\t1.250\t0.062\t0.210\t-0.242\t-0.466\n0\t-0.652\t0.308\t0.113\t1.210\t-1.152\t-0.009\t-1.002\t0.110\t-0.181\t0.272\t-0.409\t0.762\t-0.715\t-1.015\t0.225\t1.854\t-1.521\t-1.145\t0.232\t1.127\t0.306\t-0.608\t0.420\t0.516\t-0.680\t0.825\t-0.035\t-1.750\n4\t0.469\t0.467\t2.181\t-0.370\t-1.350\t0.791\t-0.246\t-1.301\t1.519\t1.066\t1.377\t-0.491\t-0.461\t1.076\t-0.559\t-0.290\t0.526\t1.666\t-2.373\t-0.356\t-0.982\t0.937\t-1.295\t0.467\t-2.063\t0.796\t0.889\t0.074\n3\t-1.219\t0.917\t-1.198\t1.485\t-0.313\t-1.807\t-0.226\t1.071\t-2.442\t0.055\t1.364\t-0.256\t1.215\t-1.215\t-0.873\t-1.268\t1.281\t0.427\t-1.283\t0.098\t0.368\t0.275\t-0.056\t-0.554\t0.378\t-1.065\t-0.623\t1.630\n0\t-0.085\t1.834\t-0.223\t0.924\t0.443\t1.982\t1.173\t1.116\t-1.116\t0.217\t-0.917\t0.594\t0.572\t0.843\t1.401\t0.583\t-1.032\t0.251\t-0.279\t0.419\t-0.393\t0.035\t0.811\t-0.239\t0.679\t0.913\t0.155\t-0.778\n3\t-1.276\t0.414\t1.747\t-3.225\t-0.972\t2.186\t0.834\t-0.112\t-0.532\t0.586\t0.979\t-1.269\t1.492\t0.398\t0.938\t1.024\t0.312\t0.445\t0.518\t-0.357\t0.648\t-0.260\t0.001\t1.564\t-0.036\t0.414\t-0.175\t-0.366\n1\t-0.627\t-0.018\t0.646\t0.801\t-0.161\t1.574\t0.833\t0.288\t0.691\t-1.004\t1.141\t-1.299\t0.436\t-0.082\t-1.433\t-1.222\t-0.031\t-0.100\t-0.751\t-0.390\t-1.470\t-1.442\t-0.316\t-1.831\t-0.217\t0.453\t-0.202\t0.482\n0\t-0.285\t-0.593\t-0.976\t0.491\t0.186\t-0.010\t-0.130\t0.907\t1.559\t-0.169\t0.487\t-0.489\t-0.275\t0.027\t-1.008\t-0.309\t-0.159\t0.857\t1.050\t0.214\t0.358\t-0.043\t-0.137\t0.033\t-1.720\t-0.321\t1.051\t-0.794\n4\t0.372\t-1.031\t0.128\t-0.906\t1.708\t-1.229\t0.559\t-2.036\t-0.916\t1.272\t0.677\t-1.003\t0.154\t-0.176\t-1.131\t0.179\t-2.151\t-0.508\t2.014\t-0.700\t-0.325\t0.754\t0.286\t-1.905\t-1.107\t0.209\t-1.484\t1.552\n2\t-0.571\t-0.164\t0.864\t0.771\t0.474\t1.029\t0.409\t1.448\t-1.711\t-0.060\t-0.102\t-0.219\t0.497\t0.214\t0.825\t-1.184\t2.292\t-0.391\t0.287\t-0.206\t0.081\t-0.535\t0.206\t0.580\t-2.402\t-0.329\t-0.367\t2.321\n0\t-0.587\t-1.035\t0.342\t1.274\t0.778\t0.102\t0.668\t-0.019\t-0.278\t-1.014\t-0.329\t0.304\t-0.417\t-1.143\t0.115\t0.304\t-0.369\t-0.022\t-2.531\t0.742\t0.288\t1.415\t-0.213\t0.734\t1.178\t0.025\t0.344\t0.143\n4\t-0.277\t-2.882\t-0.442\t-0.868\t-0.855\t3.121\t-1.389\t0.396\t-0.796\t-0.912\t-1.347\t1.128\t1.204\t-0.620\t0.912\t-1.036\t-0.401\t0.679\t-1.001\t-0.739\t0.025\t0.037\t-1.078\t2.233\t-0.844\t0.953\t0.596\t-0.805\n3\t-0.433\t-1.527\t1.500\t1.406\t0.433\t-1.751\t0.954\t-1.161\t-1.026\t-0.146\t0.676\t-0.532\t-0.931\t0.016\t0.363\t-1.867\t-0.770\t1.580\t0.434\t1.681\t0.626\t0.833\t2.036\t-0.386\t-0.473\t0.708\t-0.399\t-1.517\n4\t0.092\t-0.188\t1.107\t1.306\t-1.777\t0.476\t-1.307\t1.758\t0.440\t-0.823\t-0.241\t-0.289\t0.525\t1.042\t-1.148\t1.839\t1.606\t-0.875\t0.073\t1.711\t1.233\t-0.337\t0.814\t2.997\t0.105\t-0.633\t0.172\t0.247\n0\t-0.031\t0.225\t1.383\t0.473\t0.201\t-0.958\t0.671\t0.330\t1.284\t0.047\t-1.816\t-1.700\t0.736\t-0.570\t0.437\t0.114\t-0.167\t-0.096\t-0.865\t-0.016\t-0.584\t-0.372\t-0.324\t-0.227\t0.922\t-0.130\t1.024\t1.538\n4\t1.023\t-0.203\t0.911\t-2.477\t1.111\t-1.290\t-1.073\t-0.705\t1.084\t-0.889\t-0.656\t-0.962\t0.255\t0.744\t1.704\t2.400\t0.650\t0.366\t-1.217\t1.909\t0.215\t-0.289\t0.388\t-0.636\t-2.338\t0.572\t1.342\t0.557\n4\t1.171\t-0.712\t-0.525\t1.021\t-2.642\t-0.586\t-1.098\t0.294\t0.557\t0.573\t1.508\t0.554\t0.249\t2.433\t-2.128\t2.201\t1.742\t-0.148\t-0.707\t-0.658\t-0.289\t1.196\t-2.467\t2.010\t-0.001\t1.177\t-1.230\t0.724\n2\t-0.203\t-0.404\t0.857\t0.194\t2.257\t0.161\t-0.551\t2.671\t0.414\t0.990\t0.592\t0.623\t0.536\t-1.102\t0.368\t-1.325\t-0.174\t-0.726\t0.357\t0.334\t0.481\t0.287\t-0.607\t-0.261\t0.920\t1.068\t-1.147\t-1.531\n1\t1.636\t-1.863\t0.358\t-0.099\t-0.183\t0.552\t-0.769\t0.755\t0.866\t0.956\t0.702\t-0.795\t-0.284\t-0.953\t-0.419\t-0.413\t0.719\t-0.051\t-1.705\t0.322\t-0.291\t0.870\t0.505\t-0.610\t0.232\t1.548\t-1.791\t0.618\n0\t1.204\t-0.514\t0.132\t1.565\t0.396\t-0.088\t1.462\t-0.470\t-1.696\t-0.779\t1.136\t0.521\t0.068\t1.061\t0.167\t-0.222\t0.835\t-0.382\t0.490\t-0.795\t-1.055\t-0.866\t-0.026\t-0.648\t-0.620\t-0.153\t-0.072\t-1.551\n4\t0.005\t-0.237\t0.849\t1.744\t-0.011\t0.503\t-0.851\t-0.808\t-0.501\t0.396\t1.647\t-1.577\t-0.473\t1.247\t1.512\t1.124\t-0.025\t0.819\t-0.986\t0.414\t-1.336\t-0.184\t-1.385\t2.068\t-0.009\t1.235\t-2.701\t-0.033\n1\t-0.573\t1.412\t-1.522\t-0.994\t-0.316\t0.704\t0.046\t0.059\t2.861\t0.115\t-0.761\t1.065\t0.548\t0.567\t-0.180\t-0.574\t-0.330\t-0.892\t0.760\t0.312\t0.601\t-0.883\t-0.149\t1.344\t-1.199\t0.140\t-0.205\t-0.007\n1\t-1.553\t-1.404\t-0.084\t2.254\t-0.202\t-0.183\t-1.442\t0.946\t0.953\t0.789\t-0.629\t-0.707\t-1.293\t0.084\t-1.080\t-1.177\t-0.424\t-0.489\t-0.538\t0.466\t0.079\t-0.288\t0.183\t-0.992\t0.105\t0.151\t-1.503\t0.996\n2\t-0.552\t-0.510\t-0.315\t-0.877\t-1.620\t1.180\t-0.515\t-2.120\t-0.286\t0.445\t-1.596\t0.619\t0.315\t-0.870\t0.286\t1.626\t0.056\t-0.499\t-0.823\t1.814\t-0.209\t-0.238\t0.880\t0.572\t-1.378\t0.204\t-0.489\t1.178\n1\t0.845\t-1.684\t0.310\t1.225\t1.034\t0.873\t0.831\t-0.990\t0.269\t1.917\t-0.559\t0.827\t0.411\t-1.928\t-0.322\t0.277\t0.060\t1.501\t-0.550\t0.975\t0.583\t-0.089\t0.076\t0.881\t-0.379\t-0.660\t0.209\t-0.609\n4\t1.485\t0.394\t0.820\t1.230\t1.027\t-0.856\t-0.311\t0.468\t0.203\t0.753\t0.644\t0.247\t-2.604\t1.173\t0.839\t2.069\t-0.477\t-1.843\t-0.180\t0.457\t-0.442\t1.372\t-0.660\t-0.101\t-0.246\t2.774\t-0.906\t0.584\n2\t-0.972\t0.956\t-0.822\t1.405\t-0.715\t-1.503\t0.525\t-0.447\t-1.581\t0.255\t-1.306\t-1.989\t0.677\t1.489\t-0.395\t1.212\t0.802\t0.667\t-0.170\t-0.596\t0.344\t0.648\t-1.261\t-0.324\t-0.189\t-0.282\t-0.675\t-1.333\n3\t-0.782\t0.460\t1.568\t1.446\t-2.130\t0.437\t0.031\t0.015\t0.039\t-0.284\t-2.424\t0.583\t0.822\t0.507\t-0.175\t-1.431\t0.160\t-0.239\t2.339\t-0.512\t1.534\t-0.188\t-1.365\t-0.269\t0.013\t-0.439\t-0.291\t0.301\n0\t-0.208\t0.462\t-1.411\t0.722\t-0.997\t0.180\t-1.172\t0.366\t-1.395\t-0.564\t-0.550\t0.511\t1.442\t-0.463\t-0.350\t-0.723\t1.561\t-0.281\t0.466\t0.376\t-0.811\t0.545\t1.239\t-0.447\t0.959\t0.283\t-1.189\t1.548\n3\t-0.636\t-1.617\t-0.814\t1.935\t-0.704\t-2.284\t-1.188\t0.954\t0.877\t-0.474\t0.598\t0.540\t-0.426\t1.213\t-0.453\t0.005\t-1.183\t0.277\t0.923\t-0.342\t-0.299\t-2.258\t0.975\t-0.557\t-0.771\t-0.618\t-1.608\t-0.260\n1\t0.344\t0.475\t0.971\t-0.937\t0.832\t-0.438\t-0.461\t0.069\t-0.495\t-0.620\t-0.673\t-0.176\t-1.334\t-0.745\t0.428\t-1.000\t0.398\t1.668\t1.663\t-1.702\t0.228\t-1.028\t-1.164\t0.038\t1.056\t0.954\t-1.605\t0.288\n3\t2.626\t-0.093\t-0.684\t0.707\t-0.051\t-1.086\t0.483\t-0.853\t1.576\t0.495\t-0.277\t0.109\t0.903\t0.186\t2.122\t1.136\t0.488\t-0.937\t-0.147\t0.479\t-0.823\t1.293\t-0.974\t-0.637\t-0.562\t0.744\t-0.805\t-1.800\n4\t1.313\t0.542\t-0.169\t2.071\t-1.684\t-0.548\t0.156\t-0.993\t-1.434\t1.017\t1.600\t-0.616\t2.277\t-2.398\t0.115\t-1.129\t-0.024\t0.070\t-0.098\t-1.271\t0.127\t0.005\t0.221\t-1.463\t1.366\t-0.702\t0.361\t0.513\n1\t-1.515\t-0.983\t-0.147\t0.157\t0.258\t0.759\t1.418\t0.673\t-1.655\t-0.131\t-0.982\t-0.837\t0.239\t0.011\t0.912\t-0.637\t0.802\t-0.458\t-0.856\t0.829\t1.997\t0.073\t-0.929\t0.594\t-0.245\t0.481\t0.166\t1.495\n4\t0.160\t0.915\t2.951\t2.475\t-0.183\t0.294\t-1.054\t0.799\t0.389\t-0.774\t1.187\t-0.266\t-0.521\t0.399\t-0.465\t-0.032\t1.746\t-0.615\t0.675\t-0.583\t-0.973\t-0.082\t2.684\t0.782\t0.462\t-0.525\t-0.385\t-1.483\n4\t0.202\t-1.044\t-0.493\t-1.867\t-1.142\t-1.615\t-0.920\t-2.183\t-0.490\t0.036\t0.289\t1.148\t-1.025\t-1.024\t-0.864\t0.489\t0.267\t-0.458\t1.589\t-0.114\t-0.486\t-0.397\t-0.152\t-0.681\t-3.159\t0.418\t0.699\t1.304\n3\t-1.821\t0.270\t-1.912\t-0.069\t-1.368\t1.987\t0.911\t0.106\t1.264\t-0.846\t0.543\t0.200\t0.264\t1.272\t0.732\t0.289\t-1.655\t-0.960\t-0.123\t0.093\t-1.130\t2.412\t1.516\t0.602\t0.072\t-0.212\t-0.952\t0.077\n1\t0.887\t1.901\t0.246\t-0.839\t-0.067\t2.179\t-1.172\t1.176\t-1.014\t0.029\t1.046\t0.290\t0.823\t1.570\t0.073\t-0.869\t0.560\t-0.041\t-0.231\t0.198\t-1.188\t0.129\t0.053\t-0.040\t0.384\t-1.152\t-1.088\t1.255\n0\t0.379\t-1.210\t-1.839\t1.170\t0.356\t0.151\t0.451\t0.382\t-0.163\t0.533\t-0.197\t0.007\t-0.439\t-1.412\t0.786\t0.559\t-0.429\t-0.165\t-1.392\t-0.050\t-1.454\t-0.429\t-0.860\t-0.193\t0.338\t0.982\t0.426\t-1.415\n3\t-2.212\t1.533\t-1.424\t-0.267\t-0.429\t0.589\t-1.598\t0.462\t2.024\t-1.363\t0.190\t-0.662\t0.426\t0.019\t-0.641\t0.488\t1.804\t-0.191\t0.720\t-1.293\t-0.956\t0.472\t1.484\t0.356\t-0.313\t-0.001\t-1.250\t0.605\n3\t-0.866\t0.806\t0.447\t0.770\t0.635\t-0.887\t1.001\t-0.298\t0.929\t-0.125\t0.313\t0.343\t0.813\t-1.635\t-1.576\t0.672\t0.356\t-0.776\t0.364\t-1.408\t3.264\t1.312\t0.622\t0.076\t-0.619\t-1.310\t-0.114\t-0.860\n3\t-1.531\t0.895\t-1.388\t-0.438\t-0.759\t0.186\t0.899\t-0.604\t1.836\t0.020\t-0.200\t-0.736\t-0.065\t0.926\t1.891\t0.198\t-1.074\t-0.170\t-2.772\t0.909\t0.336\t0.345\t-1.083\t-1.789\t-0.679\t-0.853\t-0.490\t0.852\n2\t-0.769\t-0.800\t0.123\t-0.007\t-0.498\t-1.957\t-1.103\t0.336\t-0.339\t-0.299\t-1.254\t1.260\t-2.482\t1.566\t0.432\t-0.590\t-0.355\t-0.217\t0.575\t-0.159\t1.354\t-1.233\t0.725\t0.870\t-1.033\t0.222\t0.020\t0.479\n1\t1.180\t-1.248\t0.500\t1.434\t0.510\t0.478\t-0.472\t0.220\t1.236\t0.328\t-1.248\t-0.456\t1.209\t0.325\t0.119\t-0.554\t1.189\t1.662\t1.263\t1.443\t-0.359\t-0.500\t-0.156\t0.039\t1.431\t1.166\t0.538\t-1.431\n1\t0.393\t-0.774\t-0.199\t-0.029\t-0.802\t-0.034\t-0.358\t0.080\t-2.069\t-0.046\t-0.599\t-0.859\t0.451\t-0.400\t-1.061\t1.419\t0.500\t-0.057\t1.466\t-0.645\t0.304\t1.095\t0.156\t2.524\t0.295\t0.330\t1.271\t0.521\n2\t-1.422\t0.538\t1.651\t-0.315\t1.082\t0.082\t-1.392\t0.120\t-0.185\t0.896\t-0.363\t0.499\t-1.056\t0.429\t0.024\t-0.128\t0.031\t-0.292\t0.142\t-0.197\t-0.973\t-1.296\t-0.494\t-0.317\t-3.005\t0.019\t2.203\t0.690\n2\t-1.242\t-0.207\t-1.656\t-1.062\t1.619\t-1.101\t-1.174\t0.670\t0.863\t-1.666\t0.249\t-0.548\t0.331\t-0.303\t-1.366\t-0.481\t0.626\t1.944\t0.390\t-0.391\t-1.180\t-0.158\t-0.378\t0.306\t-0.060\t0.480\t-0.360\t1.709\n4\t-0.693\t-0.332\t1.312\t0.008\t0.751\t-1.678\t-0.542\t0.762\t-0.139\t-0.206\t-0.415\t0.866\t0.018\t-1.323\t-0.718\t2.250\t-0.090\t-2.040\t0.268\t0.733\t1.296\t-0.282\t2.074\t0.501\t2.187\t-0.108\t1.168\t2.015\n2\t0.019\t0.166\t0.261\t1.306\t0.157\t1.388\t-1.750\t-2.184\t0.127\t0.327\t-0.534\t-0.010\t1.075\t0.670\t-0.330\t0.198\t0.759\t-1.230\t-0.805\t0.923\t-1.730\t-1.090\t-1.213\t0.918\t-1.085\t0.792\t-0.353\t1.779\n0\t-0.056\t0.027\t-0.260\t0.679\t-0.574\t-1.054\t-0.911\t1.247\t-0.319\t0.902\t-0.430\t-0.801\t0.523\t0.778\t-0.651\t1.013\t0.293\t-0.480\t0.108\t-0.792\t-0.827\t1.572\t1.577\t0.730\t0.063\t-0.166\t0.486\t-0.135\n4\t2.035\t0.735\t-1.195\t0.855\t-0.205\t-0.014\t-1.641\t0.076\t-0.757\t-0.776\t2.752\t-1.696\t2.788\t-0.260\t0.365\t0.61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1.203\t-2.123\t-1.056\t0.764\t0.741\t0.985\t0.838\t1.087\t0.406\t1.803\t0.294\t1.999\t-0.626\t-0.652\n3\t-1.316\t-0.326\t0.086\t0.612\t-1.098\t-0.360\t-0.732\t-0.709\t0.428\t0.594\t1.142\t0.024\t-1.527\t0.898\t-3.062\t-0.273\t1.100\t-0.713\t-2.180\t0.657\t0.166\t1.204\t0.309\t-0.388\t0.141\t1.835\t-0.289\t0.730\n0\t2.698\t0.367\t0.773\t-0.121\t-0.460\t0.950\t0.316\t-1.161\t0.711\t0.254\t1.338\t0.178\t-0.525\t-0.228\t0.063\t-0.660\t-1.192\t-1.292\t-1.214\t0.166\t0.253\t0.428\t-0.051\t0.321\t0.184\t0.910\t-0.018\t0.442\n2\t1.800\t-0.549\t-0.852\t0.296\t0.716\t0.912\t-0.962\t0.435\t-0.686\t-1.489\t-0.929\t-0.751\t0.522\t1.152\t-1.473\t0.352\t-1.177\t0.629\t-0.623\t-1.051\t0.800\t-0.587\t-0.316\t-2.220\t0.947\t0.821\t0.005\t-1.528\n2\t-2.010\t-0.297\t-0.934\t-1.424\t0.182\t0.312\t-0.502\t-0.121\t-0.277\t-0.122\t0.953\t-0.421\t-0.137\t-0.300\t-0.177\t-0.505\t0.360\t-2.414\t1.839\t0.707\t-1.661\t0.637\t0.989\t0.354\t0.772\t-0.439\t1.665\t0.738\n3\t-0.658\t0.347\t1.588\t0.024\t0.266\t-0.682\t-0.564\t1.121\t-1.455\t-0.762\t-0.239\t0.109\t-0.090\t0.791\t2.956\t-1.057\t0.273\t-0.672\t0.929\t1.841\t-0.056\t1.004\t-0.967\t1.267\t1.725\t-0.131\t0.671\t-1.247\n4\t0.617\t-1.211\t-0.437\t-0.302\t-1.517\t0.754\t1.129\t0.453\t1.874\t0.348\t0.021\t-0.552\t0.276\t-2.695\t1.763\t0.392\t1.134\t1.146\t0.736\t-1.021\t-3.361\t2.183\t0.018\t-0.338\t-0.264\t-0.462\t-0.015\t-0.075\n4\t0.174\t-1.041\t-1.800\t-0.538\t1.525\t0.556\t1.484\t-0.034\t-0.038\t-2.694\t-1.914\t0.461\t0.191\t-2.267\t0.132\t1.005\t0.314\t-0.533\t-2.490\t0.405\t-0.890\t0.511\t0.698\t0.921\t0.381\t-0.346\t0.586\t-1.014\n2\t-0.923\t0.043\t-0.525\t-0.391\t-0.429\t1.105\t-0.945\t-0.711\t0.187\t1.889\t1.467\t0.527\t-0.858\t0.427\t1.032\t1.341\t1.389\t1.458\t0.893\t0.501\t0.675\t1.538\t0.691\t-0.729\t0.812\t-1.128\t0.031\t-1.119\n4\t-1.024\t2.383\t0.636\t-0.490\t-1.868\t0.874\t0.419\t-0.925\t0.109\t-1.400\t-1.367\t-0.297\t-0.223\t1.493\t0.003\t-0.456\t-0.733\t0.268\t-2.498\t0.111\t1.728\t1.002\t2.103\t-0.698\t0.460\t-0.165\t1.219\t-0.098\n4\t-0.356\t0.769\t1.008\t-0.138\t-1.188\t0.641\t0.698\t-2.051\t0.731\t-0.198\t-0.687\t0.073\t-0.936\t0.590\t-0.424\t3.247\t1.709\t-0.861\t1.251\t-0.643\t-1.252\t-1.309\t1.148\t-1.307\t0.555\t-1.107\t0.194\t-0.847\n1\t-0.443\t-0.502\t0.777\t-0.761\t-1.019\t-0.315\t-1.315\t1.095\t-0.683\t0.249\t0.154\t0.206\t-0.493\t-0.807\t0.484\t-1.022\t0.179\t-0.952\t0.073\t0.543\t-1.974\t-0.872\t-0.134\t1.096\t-0.399\t-1.536\t-1.085\t-1.780\n0\t0.556\t0.890\t-0.274\t-1.439\t-1.212\t0.286\t0.987\t1.117\t0.168\t0.967\t1.912\t1.190\t0.922\t-0.807\t0.963\t0.461\t0.651\t1.261\t0.359\t-0.103\t-1.070\t-0.297\t0.213\t0.278\t0.244\t0.960\t0.865\t-0.062\n2\t-0.759\t0.873\t0.417\t2.052\t-0.598\t1.019\t1.858\t1.118\t1.136\t0.121\t0.231\t-0.921\t-2.672\t0.204\t-0.960\t-0.425\t-0.826\t0.324\t-0.200\t1.257\t-0.723\t0.181\t-0.191\t-0.294\t-0.077\t1.520\t0.843\t-0.456\n4\t0.719\t-0.861\t-0.208\t-0.213\t-0.777\t-2.295\t0.694\t0.796\t0.543\t1.193\t0.247\t1.284\t-0.094\t1.188\t-0.224\t1.079\t0.134\t0.797\t-1.017\t1.726\t1.840\t-0.211\t-2.191\t-0.221\t-2.659\t-0.169\t0.231\t-0.130\n0\t0.760\t-0.653\t0.574\t0.115\t0.411\t-0.684\t-0.693\t-1.833\t-0.457\t-0.510\t-0.667\t-0.115\t0.064\t0.500\t0.566\t0.650\t0.692\t0.275\t0.055\t0.929\t0.693\t-1.482\t-0.173\t-1.803\t-1.823\t-0.509\t-0.128\t-0.424\n2\t-0.156\t-0.898\t-0.959\t-0.953\t-0.041\t1.587\t1.269\t0.540\t-0.912\t-1.020\t-0.289\t0.409\t0.558\t0.240\t-0.093\t0.214\t-0.551\t-1.442\t-0.289\t0.183\t-0.128\t-1.525\t-0.212\t-0.613\t1.336\t0.562\t-3.012\t0.051\n2\t-0.567\t-0.348\t-0.599\t0.045\t-1.136\t0.763\t2.037\t-0.598\t-1.593\t-0.102\t0.315\t0.453\t1.603\t0.816\t-1.812\t-1.236\t0.406\t-0.454\t1.515\t-0.937\t0.961\t-0.649\t0.262\t-0.828\t0.559\t1.073\t0.253\t1.909\n0\t-0.478\t-0.632\t-1.509\t-0.941\t-1.615\t-0.999\t0.384\t0.563\t-1.175\t-1.392\t-0.715\t0.739\t-0.689\t-0.042\t0.281\t-0.415\t-0.596\t-0.457\t0.144\t0.338\t1.838\t-0.742\t0.135\t1.244\t-0.037\t0.451\t0.496\t0.190\n2\t-0.132\t-1.235\t-0.880\t-0.435\t0.367\t0.105\t-1.246\t1.347\t-0.743\t0.441\t-0.947\t2.482\t1.325\t-0.774\t0.088\t-1.029\t-0.356\t0.423\t0.845\t-1.238\t-0.749\t0.942\t1.324\t-0.773\t0.957\t-1.236\t-0.298\t-1.144\n2\t0.780\t0.030\t-1.716\t0.615\t-1.340\t-1.231\t-0.317\t-0.285\t-1.753\t-0.148\t-0.089\t-0.128\t-1.688\t0.144\t0.620\t-1.347\t-1.076\t0.787\t1.580\t-0.166\t-0.450\t0.198\t1.356\t-0.850\t-0.484\t-0.510\t-1.417\t0.853\n0\t-1.077\t0.051\t-0.518\t0.999\t1.028\t-0.015\t-0.836\t-0.204\t-0.369\t1.582\t-0.334\t0.228\t0.646\t1.452\t-0.353\t-1.468\t0.123\t1.134\t0.383\t-0.549\t-0.337\t-0.858\t-0.228\t-0.870\t0.390\t0.500\t0.085\t0.877\n2\t0.246\t0.032\t-2.036\t-0.652\t0.527\t-1.006\t1.470\t1.825\t-0.639\t0.082\t1.922\t0.461\t0.518\t-1.010\t0.545\t-0.615\t0.798\t-0.425\t-0.153\t-0.732\t-1.593\t0.268\t-0.590\t-0.223\t0.972\t-1.177\t0.098\t2.184\n4\t-2.425\t-0.348\t0.408\t-2.238\t-0.770\t-0.542\t-1.733\t-0.721\t1.301\t0.425\t1.674\t-0.681\t-0.399\t0.271\t-0.269\t0.303\t0.789\t0.571\t-1.656\t-1.245\t0.271\t-1.858\t-0.756\t-1.150\t-1.508\t-1.579\t-0.611\t0.562\n0\t-1.166\t-0.032\t0.674\t1.369\t0.980\t-0.394\t-0.377\t-1.273\t0.537\t-0.446\t-0.389\t1.179\t0.277\t-0.546\t-1.556\t0.213\t-1.338\t-1.235\t-0.061\t0.493\t-0.238\t0.173\t1.360\t-0.112\t-0.088\t0.364\t1.731\t1.183\n4\t-0.324\t-0.689\t0.837\t1.252\t-0.100\t-2.658\t1.912\t0.998\t1.812\t-0.150\t0.336\t-0.532\t-2.149\t-0.411\t-0.898\t-0.092\t-1.586\t0.639\t0.343\t-2.127\t-2.438\t-0.851\t0.817\t2.135\t-1.032\t-0.560\t-1.272\t-0.215\n1\t0.008\t-0.177\t-0.155\t-0.477\t1.267\t1.399\t1.176\t1.614\t0.625\t0.236\t-0.285\t0.155\t0.073\t1.316\t0.565\t1.551\t-0.928\t2.060\t-0.231\t1.659\t-0.342\t0.518\t-0.143\t0.287\t0.381\t-0.244\t-0.947\t0.609\n0\t0.857\t0.398\t0.395\t-0.426\t0.450\t-0.262\t0.556\t-1.905\t-0.434\t1.722\t-0.767\t-0.604\t-0.448\t-1.215\t-2.208\t-0.231\t0.136\t-0.053\t-0.145\t0.043\t1.023\t-0.190\t-0.574\t-0.281\t0.579\t1.412\t-0.628\t0.099\n1\t0.077\t-0.415\t-0.115\t1.101\t-0.832\t-0.162\t-0.084\t1.387\t0.728\t-0.642\t-1.135\t-0.166\t-1.279\t0.709\t1.545\t0.032\t0.215\t0.905\t-0.124\t0.868\t-0.972\t1.613\t0.922\t1.023\t1.132\t0.166\t0.239\t-1.691\n1\t-1.908\t-0.794\t1.193\t0.700\t-0.161\t0.070\t-1.117\t-1.072\t-0.695\t-0.315\t-0.539\t0.137\t0.052\t0.210\t-1.928\t2.561\t0.291\t-0.361\t-0.403\t-0.004\t-0.473\t-0.769\t-0.083\t0.851\t-0.092\t0.181\t0.984\t0.812\n4\t-0.222\t1.135\t0.215\t1.693\t0.248\t2.191\t1.227\t-0.087\t0.175\t-0.840\t0.160\t-0.983\t1.330\t-0.344\t0.356\t-2.281\t-0.267\t-0.372\t0.921\t0.522\t1.018\t0.443\t1.231\t0.419\t-1.376\t-0.090\t-0.039\t-3.265\n4\t0.041\t0.625\t1.540\t-0.309\t-0.425\t-0.227\t0.366\t-1.242\t-1.796\t0.325\t-1.351\t1.142\t-2.209\t-0.785\t-1.352\t-0.673\t-0.880\t1.125\t2.809\t-1.226\t-0.693\t-0.145\t-1.060\t0.559\t-1.386\t0.610\t0.089\t0.755\n0\t-0.276\t-0.558\t-1.030\t0.366\t-0.288\t-1.310\t0.298\t-0.876\t0.509\t1.337\t1.211\t-0.283\t-0.301\t0.041\t-0.136\t1.775\t0.225\t-0.406\t-1.765\t-1.136\t-0.610\t0.904\t-0.899\t-0.788\t0.406\t-1.589\t0.284\t-0.020\n4\t-1.580\t-1.422\t-1.430\t3.140\t-0.180\t-0.407\t-1.324\t-0.450\t1.557\t1.152\t-1.818\t-0.546\t1.864\t-0.037\t-1.320\t-0.419\t0.192\t2.159\t-0.762\t0.342\t-1.124\t-0.224\t0.642\t-1.252\t0.602\t1.943\t0.320\t1.107\n4\t-0.246\t-0.273\t-2.697\t-0.054\t-0.231\t0.696\t1.849\t1.127\t-0.269\t-1.107\t2.573\t0.059\t0.014\t-0.024\t0.198\t-0.144\t-0.574\t-0.547\t-0.033\t-0.543\t-0.713\t0.106\t-0.255\t1.504\t-2.651\t1.092\t1.246\t-2.073\n2\t-0.722\t2.502\t-1.762\t-0.093\t0.325\t-0.074\t-1.489\t-0.711\t0.322\t0.929\t1.549\t-0.092\t0.868\t1.028\t1.177\t-0.092\t0.337\t0.411\t0.407\t1.146\t-0.099\t0.419\t0.297\t-1.247\t-0.087\t0.779\t0.328\t-1.423\n0\t0.703\t0.565\t-0.756\t-0.936\t-0.800\t0.482\t-0.125\t0.692\t-1.607\t0.082\t-0.218\t-0.414\t0.289\t0.816\t-0.576\t-0.015\t-0.654\t-0.567\t0.997\t0.438\t-2.088\t0.759\t0.014\t-0.436\t0.200\t-0.133\t-0.348\t0.513\n1\t-0.448\t-0.556\t0.453\t-1.276\t-1.007\t1.096\t0.265\t0.172\t0.890\t0.040\t-1.192\t0.495\t0.564\t-1.519\t0.306\t0.651\t0.696\t0.837\t0.412\t0.997\t-0.998\t1.547\t-1.888\t0.859\t-0.955\t1.246\t0.721\t-0.551\n1\t-1.693\t-0.098\t-0.989\t-1.104\t0.180\t1.392\t0.918\t-1.571\t-0.990\t0.941\t-0.982\t-0.225\t0.550\t-0.968\t0.105\t-1.334\t-0.601\t0.320\t-1.593\t0.440\t-0.020\t0.552\t0.224\t1.364\t0.125\t-0.429\t0.122\t0.543\n0\t-0.547\t1.704\t0.483\t0.212\t0.195\t-0.712\t0.042\t1.409\t-1.145\t1.044\t0.580\t-0.769\t0.573\t0.258\t0.005\t1.698\t0.125\t0.113\t0.639\t1.778\t0.269\t-0.629\t-1.002\t0.648\t-0.055\t-0.997\t-1.482\t0.384\n4\t-0.508\t-0.004\t-0.775\t-1.656\t-0.447\t-0.227\t0.751\t-0.206\t0.390\t-0.724\t-0.018\t-1.731\t-0.628\t-2.722\t-1.191\t-1.274\t0.615\t0.352\t1.068\t-1.320\t0.822\t-0.577\t1.794\t-0.179\t1.605\t2.876\t0.423\t1.782\n0\t1.327\t0.647\t-0.965\t-0.068\t0.790\t-1.123\t-0.180\t0.910\t-1.495\t0.332\t-1.755\t-0.944\t-0.505\t0.514\t-0.701\t0.126\t-0.687\t-0.531\t1.251\t-1.150\t0.759\t-0.001\t-0.576\t-1.218\t-1.146\t-0.739\t-0.150\t0.604\n1\t-0.804\t-0.465\t-0.236\t1.006\t-0.379\t-0.333\t-0.292\t-0.048\t1.698\t-0.379\t0.561\t0.350\t0.062\t-0.958\t0.524\t-1.926\t-1.021\t-0.542\t-0.225\t0.084\t1.391\t0.630\t0.169\t-0.336\t-0.325\t-1.135\t-2.107\t-1.275\n3\t-1.385\t-1.930\t-1.089\t-2.178\t0.092\t-0.460\t-0.383\t0.510\t0.874\t0.238\t-0.223\t-0.702\t-0.351\t0.207\t0.925\t-1.390\t-1.128\t-0.006\t-2.111\t0.285\t1.056\t2.358\t0.082\t0.859\t0.022\t1.620\t-0.423\t0.329\n0\t0.885\t-0.597\t0.793\t1.457\t-0.075\t-0.960\t-0.283\t-0.063\t0.987\t-0.870\t0.669\t0.587\t0.503\t1.235\t0.292\t-0.532\t-0.035\t0.071\t-0.476\t-1.435\t0.132\t0.257\t-0.037\t0.054\t-0.893\t-2.395\t-0.254\t-1.335\n2\t1.048\t-0.418\t1.009\t-0.424\t-0.016\t1.001\t-0.025\t0.680\t-0.570\t-2.534\t0.726\t0.206\t-1.540\t1.063\t0.104\t-0.187\t0.050\t0.576\t-0.655\t-1.030\t-0.935\t0.922\t-2.125\t0.299\t-1.099\t0.867\t-0.748\t-1.258\n3\t2.464\t-0.359\t-0.753\t0.896\t0.406\t-0.214\t-1.890\t-1.758\t-0.712\t0.761\t0.452\t-0.451\t1.728\t-1.670\t-0.020\t-0.800\t-0.151\t-2.016\t-1.083\t0.524\t1.256\t-0.138\t-0.873\t-0.423\t-0.780\t-0.036\t-0.280\t-0.338\n4\t1.381\t1.671\t0.511\t-2.230\t-1.167\t-0.678\t-1.385\t-1.651\t-0.038\t-1.166\t-0.795\t0.275\t-0.870\t0.066\t-0.275\t1.074\t-0.646\t-1.355\t-0.316\t2.150\t1.536\t1.647\t0.420\t0.190\t-1.006\t0.459\t-1.358\t-0.192\n2\t-0.804\t-0.591\t0.032\t1.572\t-0.295\t-0.899\t-2.019\t-0.761\t-0.868\t-1.192\t0.070\t-0.263\t1.426\t0.215\t1.009\t-0.171\t-0.878\t0.619\t-1.129\t1.157\t-0.748\t0.740\t0.376\t-0.971\t1.211\t1.920\t-0.749\t-0.655\n3\t0.216\t0.084\t0.093\t0.336\t0.751\t-0.889\t1.037\t-0.617\t-1.015\t-0.754\t-0.272\t0.124\t3.708\t-0.048\t0.806\t1.106\t-1.390\t0.877\t1.980\t-1.371\t0.931\t-0.257\t0.019\t0.880\t-0.316\t-0.467\t-0.797\t1.432\n2\t0.090\t0.961\t0.821\t0.337\t0.153\t-1.091\t1.106\t-1.800\t-0.342\t-0.902\t0.605\t1.721\t-0.353\t1.634\t-0.503\t-0.615\t-0.675\t1.222\t1.174\t-0.327\t0.615\t-0.551\t1.084\t-0.489\t0.923\t0.784\t0.463\t-2.185\n3\t0.993\t2.671\t-0.621\t-0.182\t-1.129\t-0.233\t1.370\t-0.972\t0.990\t1.507\t-1.946\t-0.679\t1.519\t0.507\t-0.941\t-0.444\t0.337\t0.997\t2.024\t-0.152\t0.735\t-0.196\t0.270\t0.870\t-0.784\t0.698\t-1.413\t0.551\n3\t-1.564\t0.138\t-0.881\t1.026\t0.297\t2.099\t1.710\t0.008\t0.473\t1.587\t-0.252\t-1.023\t-0.796\t-0.807\t-0.625\t-1.459\t1.243\t-0.040\t-0.851\t-0.856\t0.483\t-1.095\t2.193\t-0.548\t0.045\t-1.642\t-0.808\t0.582\n1\t1.827\t0.635\t1.145\t-0.934\t0.385\t0.046\t-0.530\t-0.392\t-2.011\t1.206\t-1.691\t0.097\t0.638\t1.251\t1.655\t0.651\t0.339\t1.559\t-0.408\t-0.626\t0.056\t-0.817\t-0.764\t0.450\t-0.059\t-0.604\t0.083\t0.387\n4\t-0.506\t-0.180\t0.894\t-0.766\t-0.265\t0.366\t-1.942\t-2.385\t0.346\t0.492\t2.064\t0.158\t0.820\t-0.195\t0.297\t1.667\t-2.124\t0.414\t2.136\t0.282\t1.651\t-0.629\t-0.032\t0.034\t-2.218\t-1.168\t-1.097\t0.822\n2\t0.152\t0.559\t0.360\t0.258\t-2.162\t-0.120\t-0.082\t0.545\t1.387\t-0.259\t-0.345\t0.131\t0.022\t0.432\t0.529\t-0.176\t0.499\t-0.219\t-1.450\t-1.156\t-2.335\t1.663\t1.285\t1.035\t-0.216\t0.977\t0.898\t-1.454\n0\t0.622\t-0.971\t0.033\t0.403\t0.005\t-0.161\t0.037\t-0.105\t0.564\t1.177\t-0.287\t0.205\t0.881\t1.070\t-0.886\t-0.811\t0.324\t-0.925\t-0.130\t-0.049\t0.983\t-0.557\t1.259\t0.217\t1.373\t-0.991\t-0.053\t-2.419\n2\t-1.196\t0.600\t0.499\t-0.585\t0.943\t1.085\t0.604\t-0.612\t1.702\t-0.685\t-0.258\t-0.019\t0.416\t-0.346\t-0.268\t-1.518\t-2.054\t0.796\t0.910\t2.606\t0.192\t-1.051\t-1.329\t-0.755\t0.404\t-0.871\t0.841\t0.579\n2\t-0.049\t0.265\t-0.357\t-1.012\t-0.764\t-1.244\t0.203\t0.587\t-1.365\t0.301\t1.140\t1.109\t2.072\t-0.937\t-0.342\t-0.382\t0.246\t0.439\t-1.859\t0.802\t0.082\t0.803\t0.998\t-1.462\t0.231\t-1.811\t1.372\t0.930\n0\t-0.931\t0.984\t0.086\t-0.371\t1.038\t0.226\t0.092\t-0.607\t0.700\t0.747\t0.285\t0.097\t1.878\t-0.845\t-0.328\t-0.859\t0.176\t-0.990\t-0.312\t0.842\t-1.062\t0.838\t-1.134\t0.180\t0.405\t1.054\t0.903\t-0.862\n3\t0.839\t-1.167\t1.237\t1.550\t-0.913\t-0.914\t0.411\t-1.187\t-0.493\t1.103\t0.738\t1.980\t0.171\t-0.611\t0.051\t-0.151\t-0.137\t-0.024\t-0.932\t0.282\t-1.409\t-0.717\t-0.119\t-2.260\t0.769\t-2.053\t0.670\t-1.293\n3\t1.703\t-0.189\t-1.568\t1.053\t0.617\t-0.176\t2.611\t0.942\t-0.941\t-1.698\t-0.424\t2.073\t-0.367\t-0.082\t-1.162\t-0.492\t-0.099\t0.176\t-0.166\t-0.193\t-0.745\t0.720\t1.116\t1.368\t-1.100\t-1.465\t-0.045\t0.932\n2\t1.722\t1.422\t-0.965\t1.130\t0.391\t-0.655\t-0.212\t-0.710\t0.037\t-0.730\t-1.028\t0.479\t0.629\t-0.188\t1.368\t-0.260\t-0.303\t0.660\t-0.132\t0.085\t-2.520\t0.017\t-1.827\t-1.221\t-1.335\t-1.043\t-0.549\t-0.486\n2\t0.001\t-0.048\t-1.837\t0.399\t-1.436\t2.305\t1.142\t1.847\t-0.579\t-1.002\t1.073\t0.691\t0.737\t1.132\t1.019\t0.355\t0.185\t-0.726\t-0.646\t0.618\t-0.010\t0.574\t1.778\t0.766\t-0.198\t-0.933\t-0.348\t-0.965\n3\t-0.847\t-1.489\t0.586\t1.514\t0.574\t0.646\t-0.525\t0.488\t-0.751\t-0.575\t-0.038\t0.042\t1.792\t0.867\t0.116\t1.709\t2.403\t0.020\t0.383\t-1.239\t0.165\t-0.678\t-1.886\t0.990\t0.106\t-0.869\t-1.827\t-0.609\n4\t-0.775\t1.368\t1.967\t-0.039\t1.250\t-1.056\t1.075\t1.047\t0.863\t-0.016\t-2.954\t-0.642\t-0.626\t-0.978\t0.122\t-0.419\t-0.825\t-1.059\t-0.716\t1.121\t-0.661\t-0.515\t-0.457\t0.339\t0.456\t-3.045\t1.022\t1.436\n1\t0.223\t0.321\t-1.337\t-1.026\t0.866\t-0.431\t-0.219\t-0.231\t0.220\t-0.620\t-0.181\t1.809\t-0.760\t0.495\t0.383\t-0.850\t0.776\t-0.462\t-0.431\t0.106\t0.845\t2.241\t0.071\t1.270\t1.382\t1.295\t0.509\t-0.183\n4\t1.314\t-0.417\t-0.212\t2.290\t-1.041\t0.741\t-1.567\t-0.866\t0.737\t-1.415\t0.876\t-0.221\t-0.975\t-0.107\t-0.586\t-0.712\t-0.495\t-1.393\t-1.763\t-2.127\t0.503\t0.432\t-0.794\t-0.130\t1.385\t-0.626\t-0.267\t1.939\n0\t0.626\t0.656\t0.163\t0.163\t1.135\t0.814\t-0.589\t0.493\t-0.619\t-0.313\t0.568\t-0.634\t0.148\t0.380\t-0.339\t0.720\t-0.439\t0.943\t0.131\t0.173\t-0.899\t-0.235\t0.195\t1.024\t-0.322\t1.021\t-0.756\t0.334\n3\t0.328\t1.703\t1.252\t0.066\t-0.675\t1.128\t-0.849\t0.893\t-1.435\t1.966\t-1.068\t-0.990\t-1.907\t-0.397\t0.436\t-0.598\t-0.889\t-1.449\t0.280\t0.695\t0.164\t0.692\t-1.215\t-0.633\t-0.200\t1.278\t1.272\t0.793\n4\t-0.929\t1.072\t-2.408\t0.118\t-0.558\t-1.372\t1.305\t-2.147\t1.384\t-1.533\t1.053\t-0.779\t-1.954\t-0.453\t0.564\t-0.898\t0.260\t0.690\t1.493\t0.063\t1.166\t0.849\t-0.092\t1.147\t0.8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48\t-1.152\t-0.001\t-0.533\n1\t0.263\t0.651\t-0.197\t-0.360\t-0.271\t1.829\t-0.657\t-0.785\t-1.840\t-0.476\t-0.255\t1.051\t1.898\t-0.159\t-1.741\t-0.632\t0.914\t-0.348\t0.831\t-0.299\t0.730\t1.862\t-0.083\t-0.276\t-0.088\t-0.482\t-0.312\t-0.289\n3\t1.074\t0.512\t0.384\t0.538\t-1.417\t0.977\t0.452\t0.690\t-0.026\t0.235\t1.079\t-1.590\t-2.360\t1.072\t-0.060\t-1.989\t-0.598\t0.410\t-0.022\t0.014\t0.439\t-0.773\t-1.376\t0.820\t1.379\t0.084\t-2.294\t-0.202\n3\t-0.101\t2.437\t1.234\t1.022\t0.531\t-0.692\t0.261\t0.583\t0.199\t0.095\t-0.397\t0.273\t-0.526\t0.703\t-2.016\t1.359\t-1.312\t-1.067\t0.409\t0.540\t2.078\t0.296\t-0.040\t0.191\t1.590\t2.424\t-0.185\t0.543\n2\t0.990\t-0.477\t0.235\t-0.199\t1.374\t0.126\t0.721\t-0.805\t-0.144\t0.533\t-1.592\t-0.392\t-0.702\t-1.385\t-1.962\t0.311\t-0.767\t-2.071\t0.712\t-0.613\t-0.614\t1.204\t-1.061\t0.230\t0.532\t0.674\t-2.088\t0.895\n3\t-1.372\t-1.614\t1.471\t-0.209\t-0.669\t1.040\t-0.606\t1.826\t0.678\t-0.488\t2.157\t-0.606\t0.742\t0.299\t1.302\t1.562\t0.032\t-0.753\t0.460\t-0.678\t2.013\t0.137\t-0.365\t0.185\t-1.347\t-0.972\t1.200\t-0.657\n3\t-1.546\t0.011\t0.226\t-1.203\t-0.198\t-0.781\t0.258\t0.478\t0.423\t-0.759\t0.629\t-0.655\t-0.399\t2.255\t-0.238\t-1.597\t1.632\t-0.092\t-1.440\t-2.568\t-1.000\t0.479\t0.589\t-1.125\t1.172\t0.139\t-0.319\t-0.903\n4\t1.677\t-1.156\t-0.587\t-1.797\t1.706\t0.381\t-0.328\t-1.725\t1.363\t-0.761\t0.893\t-1.569\t0.029\t-1.084\t1.138\t-1.014\t-0.984\t-0.326\t-1.251\t0.767\t0.658\t-0.240\t0.078\t2.156\t-0.550\t-1.997\t0.571\t-1.265\n3\t-1.391\t-1.199\t1.323\t1.635\t-2.215\t0.470\t1.382\t-0.342\t1.519\t0.767\t0.451\t-0.245\t0.092\t-0.447\t0.296\t0.253\t-0.434\t-1.947\t-0.195\t-1.018\t-1.121\t-0.690\t0.827\t-1.111\t-0.887\t0.707\t-0.766\t-1.127\n4\t-0.648\t-1.457\t0.929\t-0.284\t-0.149\t-0.908\t-0.890\t0.120\t-2.024\t-1.879\t0.440\t0.321\t-0.911\t-0.372\t-0.620\t-1.318\t-0.275\t-0.334\t1.070\t-1.243\t2.606\t-1.143\t0.499\t0.050\t2.538\t0.782\t0.666\t1.252\n3\t-0.241\t-0.351\t-1.124\t0.387\t-0.529\t0.953\t1.143\t-0.145\t0.692\t-2.417\t-0.090\t-0.312\t1.130\t-0.448\t0.936\t0.832\t-2.142\t0.317\t1.678\t0.690\t-0.083\t-1.138\t-0.915\t0.891\t-2.598\t0.517\t0.133\t-0.178\n3\t0.278\t-0.888\t-0.757\t0.222\t0.182\t0.616\t2.308\t1.254\t-0.893\t0.062\t-0.290\t0.265\t-0.995\t-1.440\t-1.349\t-2.475\t-0.734\t2.119\t-0.463\t0.303\t-1.093\t-0.806\t-1.000\t-0.624\t-0.025\t1.093\t1.361\t0.663\n0\t-0.737\t0.852\t-1.172\t-0.172\t1.166\t0.909\t1.216\t-0.591\t0.784\t-1.063\t-0.276\t-0.182\t-0.576\t-0.362\t-0.493\t-0.982\t0.856\t-0.182\t0.201\t0.164\t0.055\t0.093\t-0.305\t1.418\t0.761\t0.018\t-0.447\t-0.119\n0\t-1.037\t1.552\t0.748\t1.787\t0.526\t-0.177\t-0.298\t-0.419\t-0.163\t-0.125\t0.681\t-1.237\t-0.611\t-1.505\t0.435\t0.737\t0.716\t-0.865\t-0.901\t-0.734\t-1.115\t0.573\t-0.773\t0.327\t1.103\t-0.222\t-0.142\t-0.779\n3\t0.523\t-0.867\t-1.065\t-0.209\t0.897\t0.111\t2.326\t-0.458\t-0.770\t1.244\t1.146\t-2.078\t-0.946\t-0.890\t-1.001\t-0.307\t1.359\t-0.555\t1.709\t0.524\t1.096\t-0.132\t1.563\t0.533\t-0.366\t1.211\t0.106\t1.105\n0\t2.249\t2.044\t0.690\t0.565\t1.082\t-0.584\t-0.570\t0.673\t-0.856\t0.593\t0.157\t-0.856\t0.026\t1.305\t0.082\t0.087\t0.329\t0.459\t-0.331\t0.350\t-1.049\t-0.799\t-0.671\t0.470\t0.160\t-0.134\t0.691\t0.379\n2\t-0.852\t-0.760\t0.573\t0.780\t1.278\t-0.858\t0.815\t1.335\t-0.394\t-0.002\t2.413\t-1.077\t-1.111\t0.682\t0.927\t-0.553\t1.202\t0.579\t0.297\t1.000\t0.151\t0.423\t-1.293\t0.576\t-1.664\t-1.365\t-0.077\t-0.603\n0\t-0.014\t-0.820\t-0.600\t-0.277\t0.605\t0.711\t0.528\t1.079\t-0.705\t0.113\t0.752\t-0.347\t0.025\t0.054\t0.480\t1.320\t0.113\t0.696\t-0.255\t1.850\t0.664\t-0.909\t-0.461\t-0.320\t-0.108\t1.182\t0.656\t-1.433\n0\t-0.725\t1.471\t-0.225\t-0.843\t-0.352\t-1.099\t0.785\t-1.603\t1.390\t-0.936\t0.837\t0.024\t0.271\t-0.590\t0.353\t0.135\t-1.458\t-0.963\t-0.541\t-0.755\t1.253\t0.007\t-0.779\t-0.491\t-0.504\t-0.288\t-1.075\t-0.814\n0\t0.570\t-0.473\t0.532\t0.544\t-0.648\t-0.476\t1.880\t0.122\t-2.597\t-0.737\t0.602\t-0.049\t-0.000\t0.710\t-0.800\t0.151\t-0.183\t0.228\t-0.364\t-0.326\t0.460\t-0.928\t-0.589\t1.592\t0.521\t0.875\t0.050\t0.148\n4\t-0.791\t-1.105\t1.369\t1.557\t0.454\t0.807\t-1.254\t0.659\t0.649\t0.439\t1.647\t-2.280\t-0.179\t1.482\t-1.112\t0.650\t-0.486\t0.088\t-1.057\t2.263\t-1.147\t-0.130\t-0.259\t1.074\t0.070\t-1.577\t-0.643\t-2.162\n3\t-2.020\t0.432\t-0.955\t1.326\t1.490\t-0.300\t-1.743\t-0.616\t-2.022\t1.335\t0.019\t-1.081\t-1.247\t-0.665\t0.647\t-0.765\t1.294\t0.178\t0.612\t-0.477\t1.880\t0.765\t-0.405\t0.291\t-1.858\t-0.494\t-0.343\t0.902\n3\t0.044\t0.219\t0.834\t0.688\t-1.153\t-0.314\t1.848\t-0.851\t-0.111\t-0.104\t0.318\t1.429\t-0.372\t1.594\t0.432\t0.090\t-0.172\t0.661\t0.439\t-1.191\t-1.173\t1.341\t1.619\t-2.788\t-0.472\t1.009\t-1.183\t0.311\n4\t-0.432\t0.110\t-2.584\t0.193\t0.098\t1.856\t2.335\t-0.280\t0.987\t1.344\t0.096\t0.248\t-0.375\t-1.310\t1.112\t-0.978\t-0.256\t1.054\t-1.382\t-0.299\t1.424\t-1.419\t-0.436\t-1.280\t0.978\t1.940\t1.205\t0.367\n2\t-0.983\t-1.387\t0.398\t0.546\t0.406\t-0.284\t1.520\t-0.152\t1.666\t0.791\t-1.452\t-0.508\t-0.543\t-0.500\t0.537\t0.248\t-0.563\t-1.735\t0.607\t-1.500\t-1.093\t0.974\t0.812\t2.390\t-0.344\t0.609\t-0.337\t-0.034\n4\t2.236\t0.089\t-1.328\t-1.142\t0.747\t-2.095\t-0.514\t0.156\t2.404\t2.256\t0.049\t-0.938\t1.253\t-0.287\t0.277\t0.649\t1.278\t0.167\t0.749\t-0.597\t-0.907\t-0.247\t-0.758\t0.854\t2.365\t-0.125\t-0.051\t1.752\n0\t-0.936\t0.651\t-0.307\t0.664\t0.586\t-0.243\t1.711\t-0.665\t1.349\t1.175\t1.771\t0.174\t-0.789\t2.077\t-0.335\t-0.814\t-0.190\t-0.982\t0.526\t-0.606\t-0.287\t0.968\t0.624\t0.016\t0.171\t0.012\t-0.431\t-0.003\n3\t-1.457\t0.190\t1.341\t-0.369\t1.486\t0.241\t-0.420\t-0.242\t-0.410\t-0.197\t1.155\t-1.500\t-0.970\t-0.556\t-0.173\t-0.650\t0.081\t0.244\t0.562\t1.133\t0.717\t1.710\t1.240\t2.485\t1.516\t-1.447\t0.526\t1.152\n0\t-1.106\t-0.962\t0.047\t0.881\t0.539\t0.189\t1.147\t0.128\t-0.527\t-0.770\t0.552\t-1.101\t0.349\t-0.428\t-0.838\t-0.855\t0.965\t-0.858\t-0.279\t-0.469\t1.059\t1.538\t0.909\t-0.376\t0.342\t-0.910\t0.210\t-1.757\n3\t0.017\t-0.621\t1.534\t1.018\t-0.783\t-0.893\t-0.406\t0.980\t-2.591\t1.310\t-0.358\t-1.224\t0.142\t0.281\t0.871\t1.147\t0.346\t0.506\t0.064\t0.665\t-0.610\t-0.392\t-1.567\t1.651\t-0.631\t1.076\t1.259\t-1.546\n4\t-0.088\t0.797\t-1.884\t-0.813\t1.149\t2.104\t-0.455\t-0.190\t0.379\t-0.390\t-0.138\t-2.574\t-1.360\t0.772\t0.679\t-0.056\t-1.166\t0.616\t-1.881\t0.455\t-1.084\t-0.650\t-0.557\t1.705\t-2.433\t1.835\t1.866\t0.870\n4\t0.892\t0.778\t3.083\t1.401\t-0.346\t0.447\t0.304\t2.383\t0.926\t-0.202\t-0.134\t-0.426\t-0.354\t1.710\t-0.034\t1.481\t0.387\t-0.990\t-0.952\t-0.462\t1.072\t0.353\t-1.775\t-0.583\t0.080\t0.192\t-1.503\t-0.891\n1\t1.783\t1.570\t2.484\t-0.238\t-0.103\t-0.394\t0.450\t-1.167\t-0.255\t0.812\t-0.807\t-0.259\t0.519\t-0.189\t-0.575\t0.676\t-0.016\t0.205\t-0.686\t-0.595\t0.715\t1.460\t-0.508\t0.723\t0.565\t0.788\t-0.086\t0.955\n2\t1.586\t-1.084\t-1.760\t0.593\t1.259\t-0.180\t-2.264\t-1.491\t0.436\t-0.010\t1.284\t0.073\t-0.741\t0.309\t1.016\t1.351\t-0.111\t0.067\t0.779\t-0.749\t0.737\t-0.232\t-0.076\t0.003\t0.130\t1.914\t-0.469\t-0.417\n1\t-0.279\t-0.168\t-0.323\t-1.290\t0.491\t-0.732\t-0.856\t-0.250\t1.075\t-0.117\t-1.022\t0.117\t1.278\t0.819\t-1.792\t0.210\t1.247\t0.135\t-1.562\t-1.599\t-0.898\t-1.390\t-1.207\t-1.417\t-0.226\t-0.531\t-0.155\t0.568\n4\t1.092\t-0.976\t-0.728\t0.666\t-1.452\t-0.532\t2.168\t0.189\t-0.284\t-1.724\t-2.407\t1.063\t-1.163\t0.045\t1.274\t-0.498\t-0.460\t0.696\t1.355\t0.847\t1.470\t-2.308\t0.421\t1.284\t0.450\t-0.144\t-1.225\t-0.734\n1\t0.484\t-0.396\t-1.123\t-1.523\t-0.161\t0.919\t-1.051\t0.566\t0.193\t0.145\t2.652\t0.274\t0.218\t-0.582\t-0.659\t-1.102\t-1.315\t-0.243\t0.519\t-1.867\t-0.137\t-0.792\t0.778\t-0.393\t0.837\t-0.589\t0.474\t0.000\n3\t1.625\t-1.362\t1.692\t-0.260\t0.035\t-1.087\t-0.771\t-0.503\t-1.046\t-0.209\t-0.407\t1.851\t-1.166\t0.103\t0.248\t-1.070\t0.687\t-0.398\t-2.492\t0.660\t-1.117\t-1.048\t0.688\t-0.347\t0.918\t1.210\t-0.645\t0.793\n4\t1.185\t0.898\t-0.694\t0.342\t-1.277\t0.911\t-0.074\t-0.409\t-1.224\t-2.478\t-1.039\t1.113\t-0.403\t-0.169\t0.656\t1.806\t1.325\t-0.972\t-1.943\t2.360\t2.269\t0.188\t-1.270\t0.963\t-0.108\t-0.113\t0.894\t0.334\n0\t-0.585\t-0.119\t0.269\t1.226\t0.710\t1.110\t1.773\t-0.585\t0.711\t0.751\t0.256\t0.893\t0.088\t0.671\t-1.416\t0.002\t0.708\t1.028\t-0.861\t-0.082\t-1.063\t-0.388\t0.316\t1.035\t0.904\t0.223\t-0.651\t-0.139\n2\t0.456\t-0.595\t0.477\t-1.757\t-1.285\t0.475\t-0.630\t-0.323\t1.836\t0.138\t0.902\t-0.759\t-1.341\t-0.081\t-1.490\t-0.699\t0.238\t-0.111\t0.335\t0.600\t-1.196\t-0.942\t-2.179\t-0.371\t0.737\t1.491\t0.190\t1.373\n0\t-0.795\t1.580\t-0.493\t0.254\t0.592\t-0.709\t-2.043\t-0.217\t-0.203\t-0.760\t-0.771\t-0.403\t-1.191\t0.053\t0.414\t-0.015\t-2.150\t-1.228\t-0.060\t0.592\t-0.294\t1.376\t0.484\t0.231\t0.219\t0.070\t-0.171\t0.091\n3\t2.688\t0.492\t1.301\t0.225\t-0.047\t-0.670\t-0.787\t0.213\t-1.009\t-0.554\t1.169\t0.904\t-0.254\t-1.008\t1.891\t0.374\t0.213\t-1.011\t-0.869\t0.859\t0.546\t1.386\t-0.246\t-1.620\t-0.743\t0.004\t0.068\t1.774\n0\t-0.182\t0.142\t0.161\t1.264\t0.438\t1.177\t1.507\t-1.611\t1.277\t0.653\t0.488\t0.990\t-0.265\t0.603\t-1.058\t-1.412\t0.532\t-0.069\t-0.003\t-0.798\t0.391\t-0.426\t-0.047\t-0.487\t-0.802\t-0.207\t1.186\t-0.473\n1\t1.072\t-0.275\t0.335\t-1.168\t0.562\t0.845\t0.266\t0.902\t-0.007\t-0.101\t-0.660\t0.556\t-0.837\t0.414\t0.303\t1.344\t0.584\t-1.757\t-0.664\t-0.689\t-0.732\t0.967\t-2.203\t1.424\t0.999\t0.186\t0.349\t0.075\n1\t-0.839\t0.363\t2.461\t-2.197\t-0.501\t0.797\t1.262\t-1.382\t-0.638\t-0.580\t-0.653\t-0.116\t-1.429\t-0.255\t-0.936\t-0.069\t-0.705\t-0.169\t-1.036\t0.953\t-0.326\t0.014\t-0.146\t0.007\t0.002\t0.805\t-0.286\t-0.720\n1\t0.496\t-0.833\t-0.400\t1.891\t-0.406\t-0.799\t1.171\t-1.051\t-0.693\t0.577\t0.938\t1.220\t-0.747\t0.596\t-0.466\t-0.606\t1.203\t-0.550\t0.278\t0.156\t-1.071\t0.966\t0.560\t-1.986\t0.277\t-0.773\t1.097\t-1.445\n1\t0.105\t-0.031\t-0.148\t-0.009\t-1.101\t-0.528\t-1.276\t-0.771\t-0.654\t-0.607\t-0.369\t0.276\t-0.136\t-0.396\t-0.889\t-0.879\t0.313\t-0.345\t-0.164\t1.431\t-1.165\t-0.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24\n0\t-0.594\t-0.008\t0.208\t-0.007\t-0.415\t0.031\t-1.021\t-0.359\t-2.228\t0.638\t0.714\t0.094\t0.833\t-0.008\t0.007\t-0.028\t0.038\t0.145\t-0.978\t-0.699\t0.085\t0.998\t0.634\t-0.309\t2.497\t0.677\t0.296\t1.713\n2\t1.291\t-1.191\t2.162\t-1.767\t0.727\t0.141\t0.111\t0.033\t-0.240\t0.897\t-0.987\t0.465\t-0.565\t0.361\t-0.482\t0.561\t-1.146\t-0.282\t-0.110\t2.571\t0.448\t-0.109\t0.466\t-0.611\t1.207\t0.457\t0.779\t0.229\n2\t-1.186\t0.895\t-2.815\t-0.174\t-2.517\t-0.212\t0.497\t-0.235\t0.259\t-0.565\t0.462\t0.483\t-0.650\t0.894\t0.102\t0.016\t0.359\t0.485\t0.114\t0.678\t1.460\t-0.330\t-1.017\t1.238\t-0.455\t0.929\t1.533\t0.930\n4\t0.066\t-0.357\t0.693\t-1.102\t-0.895\t-1.451\t1.235\t-0.721\t0.404\t-0.126\t2.898\t0.442\t-1.862\t1.175\t-0.387\t-0.201\t-0.673\t0.196\t0.183\t-0.645\t-0.508\t0.101\t-2.343\t-1.441\t1.670\t0.328\t-0.525\t-1.696\n1\t1.231\t0.104\t2.035\t0.822\t0.683\t-0.803\t0.627\t0.217\t0.520\t-0.685\t0.128\t-0.817\t-2.147\t0.998\t-1.502\t0.107\t0.333\t0.327\t-0.283\t0.271\t0.471\t0.735\t1.733\t0.234\t0.064\t-0.907\t0.436\t0.667\n4\t1.922\t-0.168\t-0.723\t-3.250\t-1.100\t-0.312\t0.895\t1.385\t0.091\t-0.413\t-0.530\t0.358\t-0.787\t0.861\t1.163\t1.222\t-1.459\t0.346\t-0.212\t-1.762\t-0.069\t-0.390\t-0.464\t1.606\t-1.529\t-0.072\t0.183\t-2.646\n3\t0.849\t-1.715\t-1.296\t0.536\t-0.342\t0.219\t0.414\t-1.672\t1.742\t-0.106\t-0.070\t-0.191\t0.554\t0.755\t-2.923\t0.802\t-1.271\t-0.531\t-1.079\t-0.559\t-0.361\t-0.230\t-1.667\t0.006\t0.459\t-0.095\t-0.581\t-0.949\n2\t1.551\t-0.768\t2.033\t0.831\t-0.096\t-1.425\t0.630\t-0.245\t0.341\t-1.330\t-0.442\t0.895\t-1.948\t-1.061\t-1.582\t0.273\t0.901\t-0.562\t0.135\t2.085\t0.302\t-0.196\t0.285\t0.033\t0.506\t0.350\t0.119\t0.555\n2\t-0.477\t-0.321\t1.003\t1.159\t-1.807\t-1.852\t1.447\t0.957\t1.844\t-0.257\t-0.076\t-0.567\t0.184\t0.233\t-1.553\t1.145\t0.985\t0.082\t0.639\t-0.620\t0.490\t-0.418\t0.095\t0.913\t0.481\t0.713\t-1.571\t-0.254\n0\t0.507\t0.253\t0.279\t-1.382\t-0.640\t-0.938\t0.399\t0.434\t-0.043\t0.968\t1.491\t0.353\t0.671\t-0.542\t-0.725\t1.010\t-0.875\t0.697\t1.719\t0.653\t-0.081\t-1.340\t0.163\t1.009\t-0.179\t1.021\t0.365\t0.291\n1\t-0.457\t0.028\t0.509\t-0.007\t1.680\t-1.593\t1.516\t-1.371\t0.851\t-0.582\t-0.063\t-1.113\t0.319\t1.019\t-0.284\t-0.600\t0.041\t0.056\t0.375\t-0.582\t-0.468\t1.488\t0.312\t2.524\t-0.375\t0.480\t0.331\t-0.593\n2\t-1.066\t-2.235\t-0.145\t0.163\t1.814\t1.861\t-0.663\t0.808\t-1.151\t0.073\t-0.130\t-0.876\t-0.120\t0.843\t1.096\t-0.145\t-0.087\t-1.839\t0.274\t-0.265\t-0.090\t-0.481\t-0.949\t-0.873\t-0.677\t-1.327\t0.291\t0.560\n3\t0.965\t-1.363\t-0.207\t-0.816\t1.794\t0.399\t0.850\t0.293\t0.761\t1.081\t0.038\t-0.531\t-0.935\t1.759\t0.821\t-2.223\t-0.245\t0.416\t1.672\t-1.103\t0.426\t-0.400\t-1.197\t1.345\t-0.964\t0.971\t-1.595\t-0.490\n2\t-0.332\t-0.879\t2.848\t-0.804\t1.039\t-0.565\t0.243\t0.008\t-0.165\t-0.197\t0.247\t1.376\t-1.131\t-1.768\t-0.830\t0.128\t-0.296\t1.542\t1.420\t-0.455\t1.545\t0.421\t-0.729\t-0.120\t-0.193\t-0.736\t-0.634\t0.451\n2\t-0.052\t-0.082\t0.454\t2.743\t0.105\t-0.889\t0.328\t-0.670\t-0.549\t0.193\t1.821\t-0.288\t0.302\t-0.020\t0.671\t-2.285\t-0.282\t-0.127\t0.251\t0.770\t1.370\t0.102\t1.537\t0.807\t1.142\t0.427\t0.339\t1.382\n3\t-1.036\t-0.245\t-1.387\t0.588\t1.979\t0.527\t-1.955\t0.105\t-0.015\t-0.530\t-2.045\t0.479\t0.948\t-0.884\t-0.894\t-0.317\t1.743\t-0.283\t-0.766\t-0.399\t0.579\t-2.023\t-0.232\t-0.348\t-0.108\t-0.696\t-1.331\t0.932\n3\t-0.759\t0.826\t-1.316\t0.066\t-1.930\t-1.259\t-0.423\t1.820\t-0.165\t0.735\t1.509\t-0.222\t1.723\t-0.039\t-1.027\t1.102\t0.628\t-0.298\t-0.956\t1.008\t-1.109\t-2.168\t1.164\t0.883\t0.681\t-0.280\t0.685\t-0.754\n3\t-0.901\t-0.964\t1.080\t0.943\t0.487\t1.072\t-0.879\t0.349\t-0.125\t0.847\t-0.115\t-0.385\t0.541\t0.876\t-0.668\t-0.061\t-1.058\t-2.498\t0.200\t0.609\t0.262\t-1.950\t-0.351\t-2.218\t1.370\t-1.086\t1.255\t0.946\n0\t-0.003\t-0.798\t2.625\t-0.169\t1.294\t-1.569\t-0.530\t0.097\t-0.908\t-0.422\t-0.218\t-0.234\t1.855\t0.378\t0.480\t0.540\t0.613\t-0.083\t-0.321\t0.167\t1.157\t1.126\t-0.240\t0.768\t-0.195\t0.034\t-0.474\t-0.182\n2\t0.390\t2.425\t0.402\t-0.387\t0.176\t-0.950\t-0.958\t1.250\t0.304\t1.119\t-1.625\t-0.976\t0.298\t-0.824\t-1.424\t-1.079\t-0.321\t0.413\t1.715\t-1.053\t-1.001\t-1.451\t0.573\t0.125\t-0.965\t-0.831\t-0.124\t-0.067\n1\t0.486\t1.809\t-0.922\t0.360\t0.619\t0.765\t0.181\t-0.839\t1.056\t-0.940\t1.118\t0.512\t-0.585\t-0.560\t-1.273\t0.184\t-1.867\t-1.318\t1.177\t-0.087\t1.076\t-0.794\t0.625\t-0.186\t-0.984\t-0.625\t-0.533\t0.855\n3\t0.491\t-0.702\t-1.033\t0.207\t-0.477\t-0.740\t1.257\t-0.184\t-0.415\t0.760\t-0.119\t0.560\t-1.990\t-2.082\t-0.966\t-0.195\t1.153\t-1.514\t-1.345\t1.046\t-0.953\t-1.076\t0.951\t-2.891\t0.397\t1.102\t-0.078\t-0.248\n1\t1.614\t-0.136\t0.494\t1.715\t-0.936\t-1.037\t-1.244\t0.755\t-1.013\t0.560\t1.360\t-0.662\t-0.762\t0.670\t0.048\t-2.363\t1.532\t0.552\t-0.380\t-0.421\t0.700\t-0.045\t0.057\t0.151\t-0.819\t-0.361\t-0.188\t-0.761\n3\t-0.758\t1.171\t0.070\t-0.625\t0.385\t0.367\t0.270\t1.378\t-0.583\t0.357\t-1.737\t-1.534\t-0.097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038\n1\t-0.071\t1.206\t-0.377\t2.022\t-0.172\t1.301\t0.886\t0.503\t1.354\t1.293\t-0.204\t-0.156\t0.457\t1.634\t0.564\t1.583\t0.373\t-0.668\t-1.399\t0.636\t-0.539\t0.742\t0.200\t-0.446\t-0.935\t-0.483\t-1.086\t-0.949\n4\t-1.345\t2.034\t-0.356\t-0.406\t0.781\t2.264\t-0.481\t0.598\t-0.990\t-0.042\t1.745\t1.487\t-0.444\t0.910\t0.176\t-0.078\t-1.780\t-0.421\t-1.041\t0.310\t-0.808\t-2.242\t0.346\t0.585\t-0.740\t-2.315\t-0.313\t-0.847\n4\t2.406\t-0.594\t0.374\t1.067\t1.565\t-0.251\t-1.798\t-0.308\t0.355\t2.643\t-1.388\t-0.055\t-0.679\t0.915\t-0.617\t-1.532\t-2.058\t0.259\t0.078\t0.521\t0.135\t0.306\t0.463\t-0.833\t-0.000\t0.508\t-0.953\t-1.760\n4\t-0.445\t-1.681\t-0.651\t-1.677\t-1.091\t-2.230\t2.609\t0.635\t0.148\t1.945\t1.105\t1.771\t0.276\t0.359\t0.338\t-0.592\t-0.391\t1.821\t-0.724\t2.416\t2.381\t-0.217\t0.925\t1.067\t1.280\t-0.969\t-0.625\t0.330\n3\t-0.068\t-1.181\t1.190\t-0.569\t0.265\t1.698\t-0.227\t-2.377\t-1.946\t-0.886\t-0.703\t-0.350\t0.290\t1.896\t0.129\t0.625\t0.357\t-1.177\t-0.077\t-1.512\t-0.255\t-0.011\t-0.022\t0.752\t1.757\t-1.034\t-0.801\t-0.804\n0\t-0.247\t1.270\t-0.041\t1.197\t-0.683\t-0.343\t-0.360\t0.494\t0.939\t-0.126\t0.868\t0.268\t-1.464\t0.897\t-1.342\t-1.970\t-0.579\t-1.135\t0.391\t0.552\t0.701\t-0.158\t-0.630\t-0.611\t-0.089\t0.252\t1.040\t-1.568\n1\t0.366\t0.864\t1.262\t-1.446\t0.915\t1.129\t-0.591\t-0.231\t-0.775\t0.311\t0.391\t0.196\t-0.766\t1.372\t-0.582\t0.553\t0.788\t-1.712\t0.335\t1.254\t-0.014\t0.899\t-0.678\t1.054\t-1.137\t-0.022\t1.235\t1.180\n0\t-0.284\t-0.662\t0.259\t0.229\t-0.141\t-1.501\t0.871\t0.222\t-0.708\t0.199\t-0.513\t-1.024\t1.035\t-1.254\t0.903\t0.092\t-0.328\t-0.031\t-0.391\t0.066\t-1.984\t0.785\t1.896\t-0.320\t-0.303\t-0.107\t0.058\t-0.830\n3\t-0.153\t1.139\t-0.070\t-1.033\t-1.269\t0.617\t0.335\t1.285\t1.529\t0.447\t-0.719\t0.555\t1.064\t-1.366\t-0.356\t-2.818\t0.544\t1.554\t0.760\t-1.664\t-1.070\t0.126\t0.639\t1.449\t0.353\t-1.387\t-0.292\t-0.394\n0\t-1.006\t-0.240\t0.853\t0.384\t-0.639\t0.601\t-0.741\t-0.271\t-0.953\t0.122\t0.748\t0.842\t0.209\t2.409\t0.391\t-0.178\t-0.290\t1.034\t0.365\t-0.447\t-0.519\t-0.188\t-0.408\t0.308\t-0.413\t0.451\t1.257\t-1.454\n2\t0.592\t1.000\t-0.499\t-0.468\t0.297\t-0.053\t1.842\t-0.863\t-0.231\t0.813\t-1.347\t1.224\t-1.605\t-2.539\t0.946\t-0.308\t-0.429\t0.020\t-0.005\t-1.955\t0.103\t0.638\t0.840\t-0.120\t-0.228\t-0.164\t-0.596\t-0.476\n3\t-0.119\t-1.687\t0.001\t0.853\t0.136\t-1.156\t1.585\t-1.190\t-0.773\t-1.125\t-1.036\t0.045\t0.611\t-0.587\t0.564\t0.769\t-0.329\t-1.514\t1.514\t-0.671\t-2.210\t-0.389\t0.584\t0.734\t-1.137\t-1.849\t0.306\t0.517\n2\t0.249\t-1.562\t-0.626\t-0.417\t-0.149\t-0.427\t-0.028\t0.478\t0.076\t-0.200\t-0.261\t1.417\t-0.386\t0.779\t0.022\t-1.865\t2.320\t1.232\t2.264\t0.884\t0.055\t0.575\t-0.477\t-0.851\t2.095\t-0.285\t0.139\t0.471\n0\t-1.666\t-1.132\t0.701\t0.333\t-0.941\t-0.760\t2.065\t0.050\t-0.100\t-0.550\t-1.051\t-0.510\t0.126\t0.373\t-1.133\t-0.033\t-0.321\t-0.116\t-0.540\t-0.827\t-1.423\t0.194\t-0.960\t-0.407\t-0.664\t1.103\t-0.037\t0.696\n4\t1.401\t0.220\t0.807\t0.746\t-2.218\t-0.012\t-0.262\t-1.133\t0.460\t1.717\t-2.673\t-0.534\t-1.246\t0.072\t-0.326\t-0.606\t0.690\t1.636\t2.661\t-0.740\t-1.821\t0.559\t1.610\t0.814\t1.606\t-0.225\t-0.610\t0.115\n3\t0.922\t0.641\t-0.963\t0.039\t-0.169\t0.466\t-1.372\t0.372\t0.118\t0.548\t2.244\t-1.311\t-0.303\t0.319\t1.994\t-0.185\t1.408\t-0.088\t1.437\t-0.937\t-2.386\t-0.193\t-0.463\t-0.691\t-0.768\t0.130\t-0.266\t1.323\n4\t-0.369\t-1.758\t1.977\t1.733\t1.046\t-0.130\t1.352\t-0.537\t-1.318\t-0.926\t1.312\t-1.143\t-1.213\t-0.469\t-1.149\t0.231\t1.478\t2.033\t0.183\t0.182\t-0.078\t-0.049\t-0.560\t-1.200\t1.980\t-0.892\t1.447\t0.598\n0\t-0.895\t1.095\t-0.568\t0.285\t-2.370\t0.691\t-0.874\t-0.608\t0.949\t0.187\t-0.138\t0.120\t0.583\t-0.111\t0.169\t-1.333\t-0.900\t-0.803\t-1.372\t0.268\t0.924\t0.301\t0.585\t0.827\t-0.283\t-0.425\t1.212\t-1.266\n1\t0.608\t1.914\t-0.376\t0.213\t-0.951\t-0.124\t1.041\t-0.319\t0.760\t0.005\t0.688\t-0.855\t-0.992\t-0.726\t0.013\t-0.018\t-0.914\t0.723\t1.006\t-0.352\t1.704\t0.712\t1.284\t-0.062\t-1.478\t0.866\t0.463\t-1.411\n1\t1.207\t1.614\t1.063\t-0.067\t-1.930\t0.085\t0.412\t0.238\t0.477\t-0.812\t-0.484\t-2.394\t0.890\t-0.898\t-0.249\t0.253\t-0.561\t-1.073\t0.256\t-1.783\t0.767\t0.660\t-0.550\t-0.494\t-0.988\t-0.382\t-0.201\t-0.318\n0\t-1.942\t-0.403\t-0.136\t0.048\t-1.321\t-0.763\t-0.623\t-0.536\t-0.519\t-0.133\t0.310\t1.774\t0.871\t1.317\t-0.481\t-1.004\t0.801\t-0.016\t0.730\t-0.882\t0.649\t-0.175\t-0.461\t-0.367\t0.490\t0.774\t-0.366\t0.344\n2\t1.590\t0.574\t1.400\t-1.342\t-1.366\t-0.149\t0.503\t1.796\t0.706\t-0.243\t-1.026\t1.230\t-0.965\t1.628\t-0.284\t1.591\t0.679\t-0.137\t-0.519\t-0.341\t0.428\t0.077\t-0.594\t-0.166\t0.079\t-2.129\t0.458\t-0.980\n3\t0.060\t2.463\t-0.192\t0.302\t-0.035\t-1.169\t1.143\t0.752\t0.791\t-0.909\t1.403\t-1.402\t0.587\t2.190\t-0.991\t-0.566\t0.100\t-0.503\t-1.551\t0.069\t-1.062\t0.474\t-0.919\t1.550\t-0.783\t-0.322\t0.814\t-1.231\n4\t1.144\t-0.575\t0.810\t-1.321\t-3.199\t-0.133\t-0.610\t-0.882\t-0.999\t-0.708\t-0.987\t-0.897\t-1.359\t-2.129\t-2.209\t2.461\t-0.063\t1.525\t-0.178\t-0.144\t0.307\t0.640\t-1.247\t-0.380\t0.305\t-1.934\t1.297\t1.063\n4\t-0.608\t-1.870\t0.762\t0.053\t-0.220\t-0.428\t-0.159\t0.408\t-1.190\t-0.736\t-1.018\t-2.571\t1.781\t0.679\t-0.812\t0.239\t-1.165\t0.501\t2.402\t0.953\t-1.981\t-1.535\t-1.487\t-0.419\t-0.066\t1.411\t-1.357\t0.022\n3\t0.963\t-0.508\t1.578\t-0.096\t-0.231\t-0.024\t0.011\t-0.666\t0.394\t-1.368\t0.022\t1.458\t0.149\t0.510\t-1.030\t0.666\t1.812\t1.589\t-0.046\t1.551\t-0.909\t1.515\t2.759\t0.060\t0.775\t-0.940\t-0.559\t-0.327\n1\t-0.867\t0.574\t-2.293\t-0.119\t0.666\t0.190\t-0.693\t-2.586\t0.342\t0.944\t-0.132\t0.844\t0.217\t-0.019\t0.466\t0.015\t1.257\t0.641\t0.055\t1.808\t-0.529\t0.493\t0.537\t1.032\t0.360\t-0.216\t-0.041\t0.484\n1\t0.539\t-0.756\t1.831\t-0.256\t-1.180\t0.406\t-0.097\t-0.548\t-1.230\t1.290\t1.082\t-1.620\t0.970\t-0.008\t-1.217\t-0.310\t0.612\t-1.312\t-0.228\t-1.544\t-0.130\t-0.388\t1.027\t0.363\t-0.506\t-0.511\t1.638\t0.052\n3\t-0.605\t1.204\t0.164\t-0.117\t-0.583\t-1.657\t-0.175\t-1.475\t1.268\t2.779\t1.195\t-0.031\t0.316\t0.421\t-0.894\t0.930\t-0.241\t-0.393\t-0.234\t0.540\t1.139\t2.299\t-0.445\t-0.135\t-0.315\t-1.413\t-0.412\t0.988\n0\t1.696\t-1.521\t-0.918\t-0.861\t-1.752\t-0.779\t0.666\t1.371\t0.388\t1.886\t-0.825\t0.792\t0.667\t0.193\t-0.102\t-0.273\t0.830\t0.478\t0.333\t0.539\t-0.067\t0.791\t-0.686\t0.110\t-0.035\t-0.205\t0.080\t-0.584\n1\t1.239\t0.560\t0.220\t-0.988\t-0.208\t-0.361\t0.330\t-1.414\t-0.537\t0.404\t-0.702\t-0.916\t-0.791\t-0.272\t-2.381\t-0.746\t0.953\t0.324\t0.420\t-1.828\t-0.414\t-0.116\t0.464\t1.111\t0.955\t-0.585\t-0.562\t1.412\n0\t1.445\t0.580\t-0.383\t-0.394\t-0.018\t1.006\t-0.526\t-0.281\t0.618\t1.064\t-0.232\t-0.459\t-0.519\t0.836\t0.215\t-0.742\t1.822\t0.593\t0.199\t-0.002\t-0.522\t-1.507\t0.932\t-0.554\t-0.503\t-0.180\t-0.499\t1.201\n1\t-0.377\t-2.313\t-0.544\t0.003\t-0.755\t0.247\t0.107\t-2.194\t-0.710\t1.323\t0.535\t0.629\t0.974\t-0.245\t-0.563\t0.845\t1.114\t1.234\t-0.639\t0.657\t-0.115\t0.707\t-0.092\t-0.026\t0.799\t-0.329\t-0.373\t1.062\n2\t-0.134\t-0.878\t0.522\t-2.190\t-0.193\t-1.006\t-0.403\t-0.759\t1.705\t0.211\t-0.125\t0.782\t1.693\t0.339\t-0.111\t1.482\t-0.077\t-0.011\t-0.364\t1.483\t-0.437\t0.321\t1.859\t1.325\t0.406\t0.606\t0.394\t-1.520\n2\t-1.132\t-0.543\t1.594\t-0.243\t0.723\t0.350\t-0.058\t-1.974\t0.837\t-0.743\t-1.656\t-1.847\t0.589\t0.806\t-2.285\t-0.522\t0.364\t0.155\t-0.514\t-0.084\t0.947\t0.437\t0.647\t-0.437\t-1.272\t0.243\t0.800\t-0.678\n1\t-0.779\t0.203\t-0.458\t0.186\t1.509\t0.606\t0.509\t-0.430\t-1.484\t-0.208\t1.207\t-1.527\t-1.055\t0.126\t-0.985\t-0.793\t-0.424\t-1.366\t-0.974\t0.703\t-1.307\t-1.598\t-0.630\t0.732\t-0.197\t-0.465\t0.044\t-1.746\n2\t-0.315\t0.888\t0.609\t0.297\t0.378\t-1.141\t0.547\t-0.457\t2.021\t-0.380\t2.226\t-0.161\t-0.095\t-0.294\t-1.258\t1.326\t0.097\t0.627\t1.615\t-0.131\t-0.093\t0.145\t-1.283\t-1.829\t-1.504\t1.241\t-0.326\t0.784\n2\t-0.778\t-0.858\t0.149\t1.019\t2.063\t0.095\t0.062\t-0.526\t0.553\t0.500\t-0.668\t0.403\t-1.494\t0.875\t0.024\t-0.817\t-0.125\t0.183\t1.583\t1.499\t0.093\t0.966\t-2.447\t0.592\t0.269\t-0.111\t-1.354\t1.027\n0\t-0.294\t-0.316\t-0.809\t0.371\t0.524\t0.423\t-0.243\t-0.432\t-1.226\t-0.092\t0.729\t-0.144\t0.111\t0.234\t-0.133\t-0.682\t-0.025\t0.270\t0.552\t2.125\t0.106\t0.445\t-0.703\t0.099\t-1.020\t-1.393\t-1.063\t0.011\n3\t-0.294\t-0.604\t2.471\t0.120\t-0.954\t-0.437\t-0.725\t-0.443\t0.693\t2.076\t-0.060\t-1.231\t-1.641\t0.523\t0.370\t-0.342\t-0.745\t-1.709\t-0.012\t0.108\t-1.527\t-1.092\t0.633\t-1.239\t-0.799\t0.640\t1.654\t0.500\n4\t2.518\t1.197\t0.547\t-2.137\t0.392\t1.582\t-0.427\t2.191\t0.544\t0.295\t-0.991\t1.504\t-0.831\t-0.564\t-1.881\t0.896\t0.588\t0.010\t0.740\t0.646\t-0.366\t-0.785\t-1.715\t-0.178\t-1.308\t0.163\t-0.160\t-0.402\n1\t0.082\t0.140\t0.636\t0.421\t-1.047\t0.509\t-0.246\t-0.556\t0.970\t-0.934\t-0.271\t-1.066\t-0.402\t2.393\t-0.765\t-0.151\t1.392\t0.439\t-0.139\t-0.693\t0.674\t-0.324\t2.175\t-0.283\t1.690\t0.878\t0.869\t-0.158\n4\t0.591\t1.809\t-0.073\t-0.062\t-1.056\t1.278\t1.957\t0.907\t1.423\t-2.737\t2.340\t-0.104\t2.356\t1.732\t0.772\t-0.343\t0.158\t-1.910\t0.931\t-2.555\t-0.299\t1.543\t0.721\t-1.530\t-1.340\t-0.597\t0.078\t0.877\n3\t-1.715\t-0.714\t-0.736\t-0.114\t-1.857\t0.792\t-1.035\t0.631\t1.328\t2.383\t-1.672\t0.866\t-0.331\t0.480\t-0.374\t-0.856\t0.055\t0.383\t1.652\t-1.463\t-0.106\t-1.172\t-0.542\t0.512\t-0.338\t0.308\t1.521\t-0.646\n0\t-0.396\t0.405\t-0.698\t-1.053\t-0.549\t1.243\t-1.393\t-0.642\t1.211\t0.494\t0.179\t-0.648\t0.031\t-0.538\t-0.859\t-1.080\t0.784\t-0.060\t0.315\t0.152\t0.441\t-0.255\t-0.482\t-0.815\t-1.414\t-1.122\t-0.365\t-1.603\n1\t-0.190\t-0.519\t0.681\t-0.325\t0.143\t0.165\t0.493\t-0.476\t1.205\t1.037\t1.878\t-0.902\t0.183\t-1.586\t-1.123\t0.745\t-0.117\t-0.966\t0.778\t0.336\t-2.393\t1.105\t0.061\t-0.546\t-1.344\t0.462\t-0.482\t-0.148\n2\t1.533\t-0.080\t-0.115\t0.448\t2.000\t-0.688\t-1.716\t-0.010\t1.397\t0.585\t-0.515\t0.007\t-0.983\t-1.783\t-0.284\t-0.996\t-0.313\t-1.488\t1.722\t0.948\t-0.533\t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18\t0.828\t-0.522\t0.557\t-0.840\t-0.376\t-0.606\t-0.383\t0.224\t-0.274\t0.625\t-1.160\t-0.725\t0.339\t0.899\t0.087\t0.744\t-0.891\t-0.486\t0.572\t-0.363\n2\t-2.007\t0.453\t-0.664\t-0.574\t-0.483\t-0.597\t-1.498\t-2.048\t0.900\t-0.221\t0.528\t0.529\t-0.594\t-1.365\t0.779\t-0.941\t0.113\t1.052\t-0.940\t1.695\t0.792\t-0.514\t-1.331\t-0.325\t-0.942\t-0.249\t-0.133\t-1.379\n3\t0.288\t0.051\t-0.392\t1.001\t0.924\t-0.560\t-1.228\t0.179\t1.186\t-1.262\t0.470\t-0.821\t-1.017\t0.931\t0.650\t-0.152\t0.789\t0.684\t-1.486\t0.084\t1.413\t0.474\t-2.616\t-1.071\t0.996\t-0.557\t1.121\t2.046\n1\t-0.606\t1.010\t0.058\t-0.146\t-0.008\t1.573\t1.563\t-0.037\t1.984\t0.526\t-0.070\t0.480\t-0.113\t-0.986\t0.608\t0.894\t-0.983\t-0.532\t-0.744\t1.062\t-0.803\t1.720\t0.279\t0.300\t0.202\t-0.671\t-0.900\t-1.167\n3\t-0.864\t-1.006\t-0.811\t-1.462\t0.656\t0.522\t0.190\t0.710\t0.432\t0.588\t1.824\t0.982\t0.176\t-0.934\t-1.596\t-1.759\t0.946\t-0.558\t0.079\t-0.953\t2.206\t-1.796\t0.139\t-0.359\t0.018\t-0.321\t-0.891\t-1.118\n1\t-0.273\t0.418\t-1.732\t-1.604\t0.320\t0.152\t-0.218\t0.541\t0.546\t-0.180\t-0.196\t-0.659\t-0.317\t0.095\t0.277\t-2.122\t-0.273\t0.415\t-0.261\t1.512\t-0.267\t-0.003\t0.668\t-0.331\t2.335\t-0.297\t-1.584\t-1.349\n3\t-0.210\t-0.590\t0.823\t1.261\t-0.533\t-1.133\t0.694\t-1.449\t-1.306\t-1.063\t-0.415\t-1.336\t-1.939\t-1.135\t0.642\t-1.272\t-0.340\t1.947\t-0.049\t0.343\t-0.946\t2.163\t-0.541\t-0.228\t1.317\t-0.182\t0.048\t-0.282\n0\t-0.754\t1.722\t-0.277\t-0.869\t0.160\t1.591\t-1.113\t0.121\t-0.196\t0.313\t-0.916\t0.428\t0.335\t0.824\t0.439\t0.914\t0.412\t0.954\t0.753\t0.480\t0.585\t0.345\t-0.670\t0.107\t-0.546\t0.521\t-2.299\t-0.314\n4\t1.258\t-0.172\t-0.509\t0.932\t-1.147\t0.253\t1.060\t0.430\t-0.567\t-1.001\t1.187\t-0.176\t-0.901\t1.139\t0.801\t-1.052\t2.514\t-0.541\t0.784\t1.987\t0.888\t0.259\t-1.432\t2.041\t0.865\t-1.415\t1.545\t0.061\n4\t0.258\t-1.755\t-1.327\t0.404\t-0.049\t1.341\t0.647\t1.546\t1.357\t0.258\t1.206\t-1.068\t0.621\t-1.053\t1.685\t-0.017\t-0.075\t-0.897\t-3.227\t-0.050\t-1.209\t2.147\t-0.023\t1.046\t1.250\t1.555\t1.453\t0.991\n1\t-0.207\t-0.181\t0.514\t-0.771\t-1.175\t-1.784\t1.232\t-0.372\t0.570\t-0.485\t-1.162\t0.652\t0.328\t-0.237\t0.302\t-0.960\t1.061\t1.292\t0.759\t-0.372\t0.059\t1.569\t1.281\t1.994\t-0.781\t-0.870\t0.134\t0.407\n2\t0.378\t-1.789\t-1.495\t-0.773\t0.851\t0.659\t1.085\t-1.510\t0.507\t-0.560\t-0.808\t-0.537\t-0.116\t0.661\t1.123\t0.521\t0.633\t1.823\t0.697\t-0.214\t0.581\t1.708\t-0.870\t-0.431\t1.450\t-0.290\t0.764\t1.289\n2\t1.385\t-0.550\t1.106\t0.123\t0.783\t-0.872\t0.763\t-0.639\t0.231\t-0.029\t1.192\t0.832\t-1.766\t-0.810\t-0.636\t0.692\t-0.495\t-1.089\t-1.365\t0.727\t-2.074\t1.055\t-1.287\t-0.448\t-1.169\t-0.919\t-0.223\t-0.137\n3\t1.384\t0.477\t1.344\t0.377\t2.426\t0.275\t0.514\t-0.720\t0.956\t-0.135\t0.702\t1.445\t0.063\t0.695\t1.412\t-0.988\t0.143\t-1.626\t-0.142\t0.940\t-1.836\t-0.853\t-1.261\t0.847\t0.500\t1.165\t0.953\t-0.537\n4\t1.795\t0.269\t0.182\t0.204\t2.090\t0.300\t-0.193\t1.799\t-0.130\t-1.692\t-1.527\t-0.192\t0.292\t0.822\t-0.918\t-0.225\t-0.810\t1.114\t1.282\t1.958\t0.352\t1.865\t-0.693\t0.216\t-1.061\t0.573\t0.285\t-2.454\n0\t-0.384\t0.737\t-0.578\t1.653\t-1.269\t-0.517\t-0.514\t0.533\t-0.484\t-0.400\t1.214\t1.094\t0.349\t-1.062\t0.412\t0.826\t-0.123\t-0.777\t-0.521\t0.270\t1.370\t-0.902\t0.426\t1.041\t0.453\t0.991\t-0.541\t-0.187\n4\t2.320\t0.146\t0.165\t-0.784\t-2.298\t-0.247\t-2.074\t0.786\t0.343\t-0.833\t0.810\t1.165\t0.014\t-0.232\t-0.071\t1.789\t0.077\t0.579\t1.943\t-0.973\t-2.933\t0.096\t0.691\t-0.853\t0.511\t1.307\t-1.757\t-1.015\n3\t0.354\t0.868\t1.010\t1.279\t0.355\t0.493\t-1.722\t-0.615\t-1.629\t-1.349\t-1.232\t0.332\t-1.156\t-1.728\t0.557\t-0.706\t-0.350\t-1.009\t-0.586\t-0.100\t0.694\t-0.161\t1.281\t-1.101\t0.576\t1.553\t-0.984\t-1.615\n1\t0.991\t2.015\t-0.547\t0.361\t0.568\t0.368\t0.566\t-0.904\t0.915\t1.314\t-0.716\t-0.972\t-0.080\t0.317\t-0.021\t0.052\t-0.289\t0.380\t-1.847\t-0.798\t-0.277\t0.530\t1.074\t1.295\t-0.977\t-1.554\t-1.353\t-1.236\n1\t0.441\t0.119\t-0.606\t0.916\t-1.212\t0.316\t-0.436\t0.121\t0.486\t-1.146\t-1.671\t-0.932\t0.879\t0.373\t-0.403\t1.968\t1.285\t1.043\t-0.724\t1.032\t0.341\t-0.722\t-0.104\t-1.038\t-0.713\t-1.144\t0.957\t-0.071\n0\t-1.026\t0.748\t0.559\t0.382\t-0.587\t0.765\t-0.371\t1.302\t-0.747\t-0.525\t-0.151\t0.847\t-0.035\t0.038\t-0.392\t0.268\t-0.071\t-0.784\t0.127\t0.151\t2.350\t-0.611\t-0.839\t0.279\t-0.993\t-1.073\t-1.437\t-0.775\n2\t1.207\t-0.445\t-0.566\t1.897\t-1.037\t-0.057\t0.219\t1.215\t-0.080\t-1.090\t0.428\t-0.875\t-1.473\t-1.057\t1.060\t0.841\t1.712\t-1.703\t-0.620\t0.581\t0.137\t-0.825\t-1.001\t0.573\t-0.523\t0.888\t-0.745\t-0.225\n2\t-0.525\t0.761\t-1.506\t-0.418\t-1.771\t-0.704\t0.642\t0.505\t0.051\t0.170\t-1.886\t-0.373\t-0.681\t-0.659\t0.600\t-0.861\t-0.605\t-2.087\t-0.294\t-1.088\t0.079\t-0.951\t0.105\t-0.145\t1.672\t0.497\t-1.840\t-1.265\n0\t-0.788\t0.446\t-0.791\t0.391\t-0.593\t-0.558\t1.370\t-1.070\t0.003\t1.112\t0.615\t0.999\t-0.667\t-0.471\t-0.159\t0.650\t0.318\t1.992\t-0.251\t0.643\t0.234\t-0.101\t1.002\t0.677\t0.284\t0.757\t0.531\t-1.623\n3\t1.093\t0.307\t0.071\t-3.339\t-0.817\t-1.521\t0.627\t0.095\t-1.072\t0.304\t-1.423\t-1.044\t-0.710\t0.540\t1.399\t-0.586\t1.322\t0.047\t0.236\t-0.064\t-0.197\t0.439\t0.740\t-0.885\t-0.189\t1.669\t0.177\t0.287\n0\t0.461\t-1.294\t-0.122\t-2.050\t0.596\t0.620\t0.136\t0.353\t-0.455\t0.318\t0.611\t0.342\t0.415\t1.308\t-0.144\t-0.524\t-0.356\t-0.964\t-0.759\t0.462\t-0.655\t1.227\t-2.098\t-0.181\t-0.049\t0.159\t-0.421\t-1.313\n1\t1.906\t1.623\t-1.146\t-0.040\t-0.116\t0.115\t0.182\t1.095\t-1.710\t0.797\t0.594\t0.600\t-0.597\t0.607\t-0.513\t0.265\t-0.160\t-0.741\t-1.649\t0.858\t-0.470\t-0.215\t0.450\t-1.241\t0.618\t0.275\t-1.326\t-0.630\n2\t1.447\t0.197\t1.032\t-1.486\t0.267\t0.890\t0.082\t1.065\t-0.517\t1.409\t2.299\t-0.363\t-0.446\t1.453\t1.580\t-0.523\t-0.420\t-0.282\t-1.344\t-0.919\t-1.004\t-0.768\t-0.035\t0.234\t1.551\t-0.998\t0.984\t-0.214\n4\t0.510\t-0.324\t0.115\t0.084\t0.231\t0.649\t-0.224\t1.163\t-0.306\t-2.773\t0.887\t-1.622\t1.891\t2.013\t-1.260\t-1.092\t1.825\t0.503\t0.168\t2.657\t-0.229\t1.186\t1.020\t0.056\t0.252\t-1.706\t0.136\t0.144\n0\t-0.092\t-1.082\t-0.403\t0.085\t0.910\t-0.184\t-1.209\t0.057\t0.113\t0.186\t0.920\t1.019\t-0.594\t-0.616\t-1.040\t0.699\t1.449\t-0.315\t-0.241\t1.467\t0.506\t0.436\t-1.191\t0.679\t-0.929\t-1.134\t-0.347\t-0.586\n3\t-1.610\t0.753\t-1.082\t-0.339\t1.466\t-1.409\t-1.533\t0.269\t1.149\t-1.574\t0.408\t0.581\t-2.059\t0.799\t0.877\t-0.312\t1.252\t1.136\t0.103\t-0.451\t-0.973\t-0.038\t0.570\t-0.324\t-2.198\t1.269\t0.190\t-0.380\n3\t-2.555\t-0.093\t0.887\t-0.686\t-0.280\t1.064\t-1.015\t0.554\t-1.997\t1.178\t-0.259\t0.053\t0.543\t-2.411\t-1.331\t0.019\t-1.003\t-1.366\t0.736\t-0.820\t-0.071\t-0.664\t-0.425\t0.309\t-0.953\t0.303\t-0.848\t0.085\n3\t2.081\t0.261\t0.348\t-1.145\t0.410\t0.658\t1.110\t0.630\t-0.127\t-0.169\t-0.072\t0.107\t1.268\t1.073\t0.753\t2.029\t-2.656\t-0.460\t-0.011\t-0.672\t-0.930\t-1.717\t-0.311\t0.894\t0.774\t-0.752\t0.220\t1.358\n4\t0.113\t1.256\t0.312\t-0.242\t0.210\t-0.492\t0.360\t0.359\t-1.239\t0.773\t2.233\t0.546\t-2.030\t-0.427\t-1.279\t-0.292\t0.686\t-0.718\t1.885\t-2.881\t-0.248\t0.379\t-1.219\t-1.088\t0.756\t1.928\t2.046\t-0.347\n0\t-0.030\t0.297\t-1.064\t-0.321\t-1.645\t0.596\t0.034\t-0.785\t0.172\t0.491\t-0.509\t-1.397\t-0.668\t0.088\t-0.752\t0.700\t1.257\t0.215\t1.314\t-0.449\t0.779\t0.620\t0.982\t1.312\t-1.462\t-0.054\t-1.227\t-0.740\n1\t1.681\t0.397\t-0.809\t-1.189\t0.966\t0.897\t-0.507\t-1.259\t0.386\t0.594\t-0.373\t-1.103\t0.025\t0.462\t-0.487\t0.262\t0.361\t1.145\t-0.421\t-1.139\t-0.067\t1.688\t-1.763\t-2.313\t-0.108\t-0.379\t-0.208\t-0.246\n2\t-0.413\t1.992\t0.018\t-1.680\t-1.578\t0.881\t0.853\t-0.117\t-0.350\t0.777\t-0.586\t-0.662\t-0.934\t1.011\t0.952\t1.073\t1.754\t1.383\t0.021\t0.197\t-1.554\t-0.389\t-0.010\t-0.335\t0.926\t-0.123\t-1.104\t-0.127\n1\t-0.243\t-1.890\t-0.455\t-1.040\t-0.608\t-0.137\t0.958\t0.208\t0.308\t-1.096\t-0.881\t1.799\t-0.735\t1.656\t-0.243\t0.002\t1.070\t1.272\t-0.111\t-0.388\t-0.056\t0.338\t0.891\t0.004\t0.619\t1.638\t0.381\t0.312\n3\t0.257\t0.709\t1.354\t-0.095\t2.101\t-1.759\t0.419\t-0.799\t-1.907\t-0.202\t-1.457\t-0.161\t0.035\t1.119\t-1.508\t0.908\t0.098\t1.029\t-0.432\t-1.332\t-1.217\t-1.292\t0.478\t1.477\t-0.565\t0.629\t-1.523\t0.180\n0\t-0.293\t-0.147\t0.561\t-0.056\t-0.792\t-1.247\t0.040\t0.267\t-0.812\t0.497\t1.818\t0.209\t0.188\t-0.386\t1.933\t-0.358\t-0.196\t0.380\t-0.747\t-0.033\t1.021\t0.645\t-1.218\t-0.142\t-0.729\t1.488\t0.556\t-1.150\n0\t-0.035\t-1.760\t-1.202\t0.305\t-0.371\t0.621\t-0.336\t0.987\t-1.147\t0.588\t-1.268\t0.308\t0.640\t-1.063\t0.378\t0.564\t0.500\t-0.746\t-0.364\t1.709\t-0.885\t0.140\t-0.207\t0.338\t1.404\t-0.592\t0.365\t0.709\n2\t0.649\t0.269\t-0.887\t0.204\t0.139\t-0.561\t-0.863\t0.154\t-1.397\t-1.068\t-1.283\t-1.212\t-0.479\t1.062\t0.618\t1.123\t-0.611\t-2.011\t-0.597\t1.128\t-0.394\t-0.022\t-0.215\t0.697\t0.830\t-1.549\t-2.430\t0.655\n3\t0.137\t0.343\t-0.070\t0.117\t1.555\t1.040\t-0.047\t0.635\t0.865\t0.327\t-0.759\t-1.841\t1.824\t0.038\t0.476\t-0.879\t-0.230\t-0.795\t-1.002\t0.330\t0.335\t0.124\t0.100\t0.532\t-2.720\t0.791\t2.263\t-1.475\n3\t-0.611\t-1.481\t0.147\t-0.461\t0.642\t-1.627\t-1.175\t-0.513\t-1.068\t-2.203\t-0.218\t0.447\t-0.210\t1.585\t-0.075\t0.519\t0.072\t0.338\t0.384\t0.952\t-0.480\t-0.157\t-0.085\t-0.139\t-0.824\t-3.310\t-1.318\t0.682\n0\t-0.221\t0.451\t-0.711\t-1.408\t0.583\t0.378\t-0.046\t0.557\t0.172\t-0.765\t0.059\t0.334\t-1.625\t0.261\t0.322\t1.112\t-0.275\t-0.504\t-0.814\t-0.681\t0.211\t0.225\t-0.700\t1.173\t0.827\t0.399\t0.941\t0.235\n1\t0.102\t0.365\t0.777\t0.923\t0.032\t-1.354\t-1.122\t-0.673\t0.527\t-0.882\t-0.749\t-0.041\t0.554\t0.050\t-0.291\t-0.847\t-1.165\t0.782\t-1.465\t-1.665\t0.201\t0.598\t-0.814\t-1.118\t1.134\t-1.462\t-1.071\t0.488\n3\t0.389\t-0.654\t-0.619\t-0.965\t-0.040\t-2.208\t0.037\t-0.420\t-1.041\t0.785\t0.461\t-0.945\t0.718\t0.343\t-0.288\t-2.366\t-0.054\t0.482\t-1.091\t0.512\t-1.512\t1.339\t-1.571\t-1.712\t-0.376\t-0.635\t0.336\t-2.076\n1\t0.950\t0.535\t-1.708\t-1.673\t0.554\t-0.031\t1.243\t-1.063\t-0.467\t0.568\t-0.575\t-1.486\t-0.760\t-0.974\t-0.273\t0.489\t-0.153\t0.234\t1.769\t-0.337\t-1.238\t0.899\t-0.383\t-0.610\t0.786\t-0.927\t0.019\t-0.650\n2\t0.051\t1.090\t-2.723\t-0.486\t-0.913\t0.702\t-0.740\t0.976\t0.787\t-0.607\t0.239\t-0.214\t0.116\t0.750\t-0.326\t-1.656\t0.596\t1.072\t-1.700\t-0.037\t-0.932\t1.127\t1.182\t-0.810\t-1.316\t0.245\t-1.109\t0.858\n2\t-1.572\t0.535\t-1.222\t-1.022\t-1.101\t0.222\t0.679\t-0.301\t-0.039\t-1.575\t0.330\t-0.498\t-1.076\t-2.533\t-1.034\t-0.263\t0.551\t-1.033\t-0.347\t1.494\t1.155\t-0.543\t-0.493\t1.848\t0.165\t-0.342\t-0.102\t-0.909\n0\t1.213\t1.283\t-0.466\t0.296\t0.076\t0.660\t-0.289\t0.093\t0.072\t0.532\t0.656\t0.094\t0.124\t0.226\t1.647\t-1.730\t-0.970\t0.674\t0.270\t0.263\t0.667\t0.214\t-0.559\t0.629\t0.021\t-0.448\t0.690\t0.651\n3\t1.249\t-0.630\t-1.288\t1.142\t-0.423\t-0.578\t-1.880\t-0.514\t-0.699\t-1.803\t-0.585\t1.185\t0.342\t-1.637\t0.390\t0.727\t0.104\t-0.035\t-0.793\t0.418\t0.585\t0.281\t1.745\t-0.205\t-1.303\t1.674\t-1.890\t-0.399\n4\t1.847\t-0.032\t1.528\t-0.030\t-0.384\t-0.680\t-0.360\t0.539\t-0.046\t-0.460\t-1.169\t-1.464\t-1.280\t-0.731\t3.465\t1.130\t1.373\t0.070\t-1.055\t-1.649\t0.469\t-0.128\t-0.155\t-0.778\t-0.285\t-1.728\t-0.790\t-0.325\n4\t-1.046\t-0.282\t-0.254\t0.109\t-0.074\t0.301\t0.617\t-1.570\t-0.349\t-0.788\t-1.734\t-0.985\t1.021\t-0.254\t0.514\t0.432\t-0.905\t0.538\t-1.103\t0.725\t-0.595\t-1.334\t0.140\t-0.706\t0.629\t0.711\t-2.987\t3.444\n4\t-1.172\t-1.451\t-0.205\t-0.495\t-0.639\t-0.917\t-0.189\t1.636\t-1.611\t0.841\t1.534\t-0.626\t0.376\t-0.122\t-1.719\t1.219\t0.035\t-0.521\t-1.522\t0.521\t0.152\t0.606\t0.555\t-0.813\t-3.420\t-0.110\t-0.177\t0.164\n3\t-0.811\t0.367\t-0.740\t0.865\t0.151\t0.298\t-0.783\t0.707\t2.085\t-1.008\t-0.340\t0.232\t0.786\t-0.560\t-2.833\t1.359\t0.761\t-1.743\t-0.608\t1.568\t1.448\t0.939\t-0.176\t0.083\t-0.131\t1.128\t-0.876\t-0.806\n1\t0.946\t1.344\t0.603\t-0.295\t0.189\t2.329\t0.985\t0.072\t-0.151\t-0.598\t-1.187\t1.708\t0.478\t-1.269\t-0.125\t1.270\t-0.447\t-0.814\t0.357\t0.658\t-0.446\t-0.600\t0.461\t-1.125\t-0.538\t-0.120\t0.739\t-0.428\n1\t1.670\t0.154\t-1.602\t-1.785\t-0.544\t-0.134\t1.222\t-0.408\t-1.413\t-0.431\t-1.156\t-0.009\t-0.514\t0.012\t0.668\t0.342\t0.568\t1.603\t1.122\t-0.921\t0.091\t0.391\t-0.120\t-1.116\t0.655\t-0.338\t0.992\t-0.304\n2\t-1.846\t0.767\t-1.181\t-1.201\t0.087\t0.338\t0.364\t-0.059\t-2.071\t-0.138\t-0.607\t-2.373\t-1.077\t0.805\t-0.461\t0.953\t-0.413\t-0.190\t1.246\t0.361\t0.029\t0.053\t-0.712\t0.957\t0.654\t1.473\t0.163\t0.209\n2\t-0.229\t0.563\t2.244\t-0.037\t1.427\t-0.848\t0.675\t0.873\t-0.507\t0.354\t-1.087\t-0.659\t0.567\t0.518\t-0.274\t-0.191\t2.135\t0.676\t-0.636\t-1.178\t1.480\t-0.379\t0.718\t-0.340\t1.291\t1.295\t0.728\t0.034\n1\t0.166\t-0.248\t0.643\t1.192\t-0.001\t-1.390\t-1.132\t1.416\t-0.782\t0.950\t-1.342\t-1.255\t0.435\t-0.722\t1.321\t-0.023\t-0.898\t-1.256\t1.865\t-0.193\t-0.090\t-0.219\t0.214\t0.227\t0.031\t-1.735\t0.736\t-0.255\n3\t-0.142\t-0.390\t1.267\t-0.091\t-1.215\t-1.100\t0.247\t3.003\t-0.976\t-0.315\t2.405\t-0.484\t0.455\t1.876\t-0.951\t-0.309\t-0.484\t-1.518\t1.569\t-0.585\t-0.876\t-0.444\t0.929\t0.113\t-0.792\t0.409\t0.813\t-0.024\n1\t-0.551\t0.475\t-0.392\t0.200\t1.531\t0.456\t-0.051\t-0.221\t1.321\t0.391\t0.758\t-0.580\t-1.329\t1.283\t1.066\t-1.058\t0.837\t1.369\t-0.389\t-0.045\t1.914\t1.262\t-0.733\t-0.642\t-0.231\t0.262\t0.530\t-1.251\n4\t-0.867\t0.349\t3.286\t0.695\t2.736\t0.569\t-0.630\t0.348\t1.781\t1.830\t-0.575\t0.253\t-1.429\t0.825\t1.553\t0.040\t-0.667\t0.360\t0.417\t0.081\t0.179\t-0.174\t0.996\t0.736\t-0.983\t-1.011\t-0.254\t0.737\n3\t0.961\t0.596\t0.811\t-1.204\t-1.220\t1.133\t0.826\t-0.258\t0.853\t-1.203\t-1.244\t-1.617\t0.361\t0.217\t-0.522\t-0.780\t2.415\t-0.259\t1.388\t-0.146\t-0.501\t1.417\t0.790\t-0.024\t1.134\t2.520\t-0.732\t0.472\n2\t-0.023\t-0.627\t0.232\t1.630\t0.832\t0.291\t-0.700\t1.491\t0.783\t-0.209\t1.242\t1.450\t-1.301\t-1.559\t-0.125\t0.714\t0.162\t1.283\t1.421\t-0.953\t-0.494\t-0.694\t-0.573\t-0.132\t-0.440\t-0.096\t-1.343\t2.044\n1\t-0.333\t-0.608\t-0.961\t-0.995\t0.030\t0.613\t-0.149\t0.582\t1.147\t0.548\t0.910\t-1.090\t-0.556\t-1.536\t-0.373\t-2.310\t0.642\t0.701\t-1.120\t0.150\t0.823\t0.314\t-1.288\t-0.115\t0.798\t-1.413\t-0.840\t0.334\n1\t-0.615\t0.128\t-0.963\t0.788\t-1.057\t0.578\t-0.628\t2.425\t0.573\t0.949\t-0.417\t0.880\t-0.004\t-0.170\t0.489\t-1.300\t-0.508\t-0.690\t1.994\t1.426\t-0.705\t0.735\t-0.372\t0.746\t1.006\t-0.232\t-0.773\t0.168\n1\t0.890\t0.421\t0.121\t0.662\t-0.546\t-1.414\t0.723\t1.238\t-1.118\t0.822\t1.681\t-0.002\t-0.818\t0.123\t-0.551\t-0.617\t0.433\t-1.604\t-1.444\t0.019\t-0.022\t0.344\t1.875\t0.105\t0.598\t-0.976\t0.768\t-0.526\n3\t-0.890\t-0.713\t0.436\t0.939\t-0.487\t0.544\t1.582\t-1.259\t0.356\t0.831\t-0.095\t1.238\t2.199\t0.343\t-0.234\t0.977\t0.842\t-2.519\t-0.003\t-0.395\t0.397\t-0.194\t-0.085\t-0.863\t0.735\t1.655\t-0.848\t-1.397\n3\t0.517\t-0.966\t1.656\t2.291\t-0.353\t-0.575\t1.795\t-1.215\t0.478\t2.753\t0.358\t0.162\t0.117\t0.815\t-1.443\t0.935\t-1.214\t-1.050\t-0.849\t-0.203\t-1.050\t-0.180\t0.160\t1.262\t-0.799\t0.706\t-0.620\t-0.221\n0\t0.577\t-0.590\t0.608\t0.773\t-0.265\t-0.561\t0.184\t-0.811\t0.615\t0.911\t0.076\t-0.631\t0.167\t-1.288\t0.127\t1.474\t0.421\t-1.249\t0.689\t-0.933\t-0.655\t-0.813\t-0.504\t-0.428\t-0.721\t-0.491\t0.511\t0.197\n4\t0.017\t-0.365\t-1.104\t-0.958\t-0.991\t-0.287\t1.952\t0.078\t1.183\t0.507\t1.114\t-2.800\t-0.304\t1.307\t-0.572\t1.762\t1.484\t-0.469\t-0.503\t-1.014\t-0.630\t-1.233\t0.429\t-1.456\t0.604\t0.628\t-1.661\t1.550\n2\t1.634\t-0.412\t0.911\t-1.497\t-0.773\t-1.085\t-1.244\t-0.516\t0.367\t1.533\t-0.202\t0.082\t0.339\t-0.728\t-0.024\t2.088\t0.388\t2.087\t0.024\t-0.620\t1.044\t1.264\t-0.497\t0.364\t-0.064\t-1.381\t-1.323\t-0.205\n2\t-0.283\t-0.127\t-1.825\t0.042\t-0.487\t0.633\t-0.898\t0.683\t0.543\t0.160\t1.254\t-1.885\t0.444\t-0.045\t-0.074\t-0.982\t-1.420\t0.892\t0.788\t1.799\t1.963\t-1.548\t-0.155\t0.251\t0.962\t1.427\t-1.029\t-0.541\n3\t1.995\t-0.744\t0.344\t1.257\t1.484\t-0.268\t-2.025\t-0.384\t1.744\t-0.763\t0.291\t-0.307\t-0.072\t1.312\t0.254\t2.042\t0.976\t0.131\t-0.094\t0.332\t-1.233\t-1.058\t0.757\t0.920\t-1.323\t0.860\t-0.176\t-0.831\n1\t-0.805\t-0.869\t1.174\t-1.774\t0.040\t-0.726\t0.963\t-0.530\t-0.977\t-0.852\t1.487\t0.359\t1.637\t-0.218\t0.240\t-1.491\t-0.084\t0.087\t1.563\t0.627\t-0.971\t-0.661\t-1.232\t-0.072\t0.465\t1.017\t0.082\t-0.969\n0\t1.011\t0.618\t-0.508\t-1.087\t0.116\t-1.060\t1.024\t-1.124\t0.548\t-0.081\t-0.561\t-0.103\t1.080\t-1.192\t-0.412\t-0.752\t-0.039\t-0.685\t-1.768\t-1.055\t1.638\t0.247\t0.212\t-0.283\t-0.052\t-0.430\t0.527\t0.101\n4\t-0.089\t-1.530\t-0.270\t-0.498\t-0.392\t1.145\t0.049\t1.425\t1.066\t-1.421\t0.659\t-0.800\t-0.325\t0.514\t-0.430\t1.473\t1.674\t-0.090\t-0.299\t0.538\t-1.857\t-0.991\t-0.974\t-1.639\t-1.174\t2.129\t-1.205\t2.039\n1\t-0.554\t-0.543\t-0.912\t0.349\t-0.931\t-0.575\t-1.735\t1.074\t-0.418\t0.342\t1.336\t1.187\t-0.395\t-0.524\t-0.112\t-0.861\t0.912\t-0.133\t-0.766\t0.655\t0.371\t-1.416\t0.454\t2.113\t0.141\t0.719\t1.430\t1.007\n0\t0.150\t0.986\t-0.114\t-1.345\t1.416\t-1.431\t0.829\t0.466\t0.979\t-0.401\t1.260\t0.920\t-0.370\t0.321\t1.216\t-1.509\t1.348\t-0.233\t-0.232\t0.858\t0.322\t-0.096\t-0.916\t-0.556\t0.436\t0.961\t-0.541\t0.024\n4\t-1.286\t1.938\t1.474\t2.058\t-0.065\t-0.979\t-0.097\t-0.584\t1.646\t-0.261\t3.261\t0.659\t1.403\t-1.616\t2.047\t-0.021\t-0.269\t-0.984\t-1.329\t0.367\t-1.030\t1.270\t-0.314\t-0.168\t-1.057\t0.681\t-0.359\t-0.696\n4\t-0.068\t-1.301\t-1.880\t-1.674\t-0.364\t1.629\t0.451\t-0.033\t-0.524\t-0.271\t1.188\t2.823\t-0.203\t-0.090\t-0.403\t-0.338\t-0.169\t0.485\t-1.107\t0.994\t0.255\t1.082\t-0.666\t2.648\t1.813\t-0.136\t-0.536\t-0.982\n2\t0.341\t-0.130\t-0.354\t0.082\t0.058\t-0.817\t0.744\t-0.795\t-0.303\t0.848\t1.085\t0.944\t-1.694\t-0.188\t-1.160\t-1.046\t-0.640\t-0.290\t-0.556\t-2.641\t0.256\t-0.093\t1.232\t-0.089\t-1.890\t-1.362\t-1.700\t-0.270\n1\t-1.247\t-1.631\t-0.741\t0.808\t1.580\t0.917\t1.057\t-0.081\t-1.742\t-0.455\t-0.259\t-0.497\t0.055\t0.441\t0.853\t-0.278\t-1.904\t-0.483\t0.044\t1.223\t-0.568\t0.513\t0.290\t0.044\t0.931\t-0.661\t-0.575\t-0.012\n1\t0.407\t0.337\t-0.988\t0.524\t1.341\t1.285\t1.462\t1.770\t1.677\t-1.278\t-0.482\t0.171\t0.194\t-0.966\t-0.409\t0.729\t0.972\t-0.528\t-0.248\t0.683\t-0.160\t-1.849\t-0.165\t-0.608\t-0.168\t1.556\t0.579\t-0.582\n4\t-1.432\t1.074\t1.207\t-0.331\t-1.934\t1.361\t1.188\t-0.117\t1.069\t-0.041\t0.217\t-1.626\t1.222\t-1.753\t1.721\t1.823\t-2.555\t1.557\t-0.239\t0.422\t-1.411\t-0.323\t-1.181\t-0.789\t1.222\t0.999\t0.820\t-0.539\n0\t-0.103\t0.015\t-0.857\t0.191\t-1.566\t-1.011\t-1.040\t-0.571\t0.289\t0.174\t0.273\t0.809\t0.181\t-1.007\t0.030\t-0.933\t-0.335\t0.398\t-0.169\t0.643\t0.008\t-1.514\t-0.395\t0.635\t0.457\t-1.072\t-0.460\t0.207\n4\t-0.780\t-1.172\t1.162\t-1.188\t-1.585\t-0.670\t0.742\t0.236\t1.092\t-1.034\t0.926\t-0.207\t-1.858\t1.373\t0.089\t1.433\t1.225\t1.051\t2.548\t-1.118\t2.105\t-0.233\t0.920\t0.125\t0.931\t-0.098\t1.597\t0.807\n1\t0.298\t-0.642\t-0.025\t1.502\t-0.145\t-0.660\t1.074\t-0.923\t0.818\t1.410\t-1.570\t-0.298\t0.455\t0.547\t0.699\t0.336\t-0.317\t-0.695\t0.808\t-0.097\t-0.038\t-0.444\t0.661\t2.052\t0.226\t-0.230\t1.087\t1.746\n4\t-0.987\t0.829\t-0.853\t-0.075\t2.210\t-0.883\t-2.020\t1.367\t-1.400\t-0.812\t0.729\t0.689\t-2.374\t-0.343\t0.852\t2.029\t-1.239\t1.068\t-0.662\t1.042\t-1.040\t-0.230\t0.387\t0.954\t0.570\t-0.429\t-0.502\t1.779\n3\t0.516\t-0.614\t1.473\t-0.298\t-0.412\t-1.832\t-0.468\t0.980\t1.505\t-1.859\t-1.121\t0.511\t-0.395\t2.405\t0.602\t1.149\t1.148\t-0.002\t-0.743\t2.386\t-0.566\t-0.587\t0.295\t-0.322\t-0.933\t0.219\t0.238\t0.197\n0\t0.300\t-0.283\t-1.060\t-0.239\t-1.022\t-1.086\t1.206\t-0.420\t-0.741\t1.618\t0.815\t-0.372\t0.428\t-0.236\t-0.693\t0.475\t-0.324\t0.075\t1.021\t0.911\t-0.550\t0.486\t-1.128\t0.450\t0.148\t0.379\t-0.297\t2.084\n3\t-1.463\t-1.200\t-1.319\t0.563\t0.099\t0.208\t0.144\t1.275\t-0.742\t0.703\t-1.095\t0.004\t-0.352\t-0.990\t2.448\t0.657\t-1.560\t-1.238\t0.989\t1.447\t-0.375\t0.179\t1.683\t-1.745\t-0.689\t0.093\t0.962\t-0.058\n3\t0.805\t-0.91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63\t0.812\t0.416\t-0.524\t1.347\t-1.274\t-0.021\t-0.829\t1.044\t-1.181\t-0.919\t2.201\n3\t0.929\t0.142\t-0.015\t1.558\t-1.147\t1.083\t-1.807\t-1.208\t0.021\t1.624\t0.099\t-0.166\t-0.649\t1.224\t0.433\t0.007\t1.696\t-1.396\t1.594\t-1.366\t-0.648\t0.830\t0.725\t0.593\t-0.217\t1.138\t0.748\t1.382\n2\t0.027\t-0.184\t0.140\t-0.106\t-0.269\t1.884\t-1.471\t2.287\t-0.879\t-0.385\t-0.394\t1.467\t0.488\t0.706\t1.492\t-0.924\t0.213\t1.975\t-0.986\t1.096\t-0.462\t-0.098\t0.618\t-0.794\t0.585\t0.500\t0.592\t-0.066\n1\t-0.830\t-0.655\t0.781\t-1.428\t0.451\t-0.724\t-1.859\t-0.980\t0.028\t1.395\t0.913\t-0.294\t0.972\t-0.357\t-1.467\t0.021\t-1.506\t0.791\t0.806\t-0.880\t0.146\t0.136\t0.872\t-1.245\t0.599\t-1.317\t0.777\t1.063\n2\t1.560\t-0.766\t-0.896\t-2.445\t0.364\t0.973\t0.298\t-0.435\t0.191\t0.534\t0.040\t-0.779\t0.474\t0.114\t-0.894\t1.503\t-0.483\t-0.927\t-1.685\t1.385\t1.721\t-0.030\t0.569\t0.418\t-1.365\t0.821\t0.365\t-0.362\n0\t1.054\t0.010\t-0.413\t0.936\t-0.745\t-1.526\t0.624\t1.306\t-0.357\t-0.749\t1.207\t0.513\t-0.273\t0.252\t-0.988\t0.330\t-1.975\t-0.861\t-0.161\t0.159\t-0.398\t0.971\t0.178\t-0.475\t-0.209\t0.642\t0.387\t-0.313\n4\t0.596\t0.694\t-0.458\t1.130\t-1.400\t1.601\t-1.662\t0.537\t0.245\t0.824\t-1.763\t-0.274\t1.049\t-1.088\t1.434\t-1.992\t-1.707\t-0.866\t0.639\t1.409\t-0.675\t0.222\t1.252\t0.611\t2.132\t-0.644\t1.282\t-0.757\n3\t0.882\t-0.896\t0.730\t0.293\t1.774\t2.194\t0.796\t-0.260\t0.491\t0.581\t0.527\t0.420\t0.534\t1.050\t2.198\t-1.809\t1.300\t0.142\t-0.790\t-1.351\t0.306\t0.036\t-1.079\t-0.031\t1.037\t1.552\t0.127\t0.124\n1\t0.447\t-0.571\t0.104\t-0.749\t0.467\t-1.605\t0.339\t-0.302\t-0.728\t-1.070\t-0.811\t2.089\t-0.378\t-0.097\t-0.002\t0.338\t-0.152\t0.184\t1.132\t0.693\t2.610\t0.214\t-0.800\t0.245\t-0.990\t0.989\t-0.049\t0.418\n2\t-1.003\t-0.540\t0.545\t-0.069\t-0.721\t1.003\t0.140\t2.055\t0.659\t-1.135\t0.116\t-1.204\t2.005\t-0.989\t1.114\t0.954\t-0.683\t0.328\t0.256\t0.833\t0.177\t-0.668\t0.561\t0.564\t-0.931\t-2.305\t0.592\t0.373\n1\t-0.997\t-0.639\t0.496\t-0.545\t0.513\t0.803\t0.587\t1.161\t0.988\t0.885\t-0.263\t-2.123\t-1.293\t-0.183\t0.454\t0.980\t-0.654\t0.693\t0.034\t0.184\t0.594\t0.905\t-0.673\t-0.348\t1.817\t-1.768\t1.422\t-0.538\n0\t-1.545\t0.636\t-0.207\t0.794\t1.372\t-1.328\t0.011\t1.170\t0.093\t0.158\t-0.922\t-0.300\t0.446\t0.946\t-0.854\t0.533\t-0.022\t0.368\t-0.825\t0.368\t0.824\t0.362\t-1.239\t1.279\t-0.581\t-0.284\t0.229\t0.297\n4\t-0.593\t-0.403\t0.321\t0.657\t0.527\t0.032\t-0.306\t0.332\t-3.201\t1.869\t-0.186\t0.182\t2.099\t-0.741\t2.332\t0.740\t0.447\t0.644\t-0.841\t-1.649\t-1.180\t1.087\t-0.936\t-1.654\t-0.079\t0.575\t0.039\t0.606\n2\t-0.283\t0.619\t-1.545\t0.363\t1.185\t0.581\t0.276\t-1.443\t-0.108\t-1.320\t-0.077\t0.029\t-0.770\t1.302\t-1.387\t2.422\t0.720\t0.829\t0.084\t-1.609\t-0.688\t-0.358\t-0.201\t-0.740\t1.613\t-0.989\t-0.320\t-1.019\n4\t2.155\t-1.635\t-1.439\t-0.532\t1.395\t0.195\t1.094\t-0.187\t0.251\t0.756\t-0.350\t0.041\t0.610\t2.495\t-0.533\t-0.501\t0.692\t-1.064\t1.215\t-0.003\t-0.379\t0.103\t2.482\t-0.798\t-1.663\t1.181\t-0.262\t-0.798\n4\t0.081\t2.341\t0.352\t-0.214\t-0.899\t-1.443\t-0.991\t-0.880\t0.395\t-0.526\t0.187\t1.282\t-0.088\t2.079\t-1.176\t-0.151\t-0.539\t0.937\t-2.240\t-1.443\t-0.107\t1.551\t0.226\t-1.352\t-1.197\t1.673\t-0.621\t-0.943\n3\t2.100\t1.532\t0.474\t-1.516\t0.400\t0.129\t0.525\t1.359\t-0.417\t-0.469\t1.032\t1.164\t0.191\t1.399\t-1.936\t0.440\t0.285\t1.315\t-1.057\t1.126\t-0.607\t-0.163\t-0.787\t-1.349\t0.728\t-0.440\t-0.863\t-0.880\n2\t1.237\t-0.254\t-0.458\t0.475\t-1.063\t1.022\t-1.056\t-1.050\t-0.910\t0.760\t-0.617\t-1.348\t-0.606\t-0.032\t-1.181\t-1.152\t2.111\t-0.861\t-0.323\t-0.367\t-0.421\t0.621\t0.948\t-0.306\t-1.236\t1.274\t-0.095\t1.730\n0\t0.408\t-0.542\t-0.177\t-0.187\t1.483\t-0.651\t0.593\t-0.021\t-0.990\t1.415\t-0.724\t-1.046\t0.337\t0.874\t-0.983\t1.197\t-0.712\t-1.639\t-0.730\t-0.509\t1.137\t0.907\t-0.583\t-0.879\t0.214\t0.266\t0.119\t-0.915\n2\t-0.063\t-0.388\t0.874\t-1.713\t-1.376\t-0.551\t0.497\t-0.390\t0.629\t-0.189\t0.022\t0.195\t-2.180\t0.498\t-0.305\t0.578\t1.749\t1.291\t-0.106\t0.630\t-0.772\t-1.365\t-1.013\t0.756\t-0.621\t-0.007\t-0.074\t2.463\n3\t-0.787\t2.933\t-0.473\t0.729\t1.909\t-1.343\t0.611\t0.023\t1.795\t-0.909\t-0.752\t-0.050\t-0.494\t-0.459\t2.028\t0.127\t-0.785\t-0.390\t0.405\t0.337\t-0.577\t-1.628\t-0.051\t-0.078\t-0.646\t1.280\t0.575\t0.135\n0\t-0.109\t0.211\t0.627\t-0.424\t0.520\t-0.242\t0.239\t0.083\t0.391\t0.363\t-0.002\t-0.824\t1.369\t-0.455\t1.174\t-1.177\t-1.105\t1.097\t1.746\t0.584\t-1.411\t1.138\t-0.934\t-1.103\t0.155\t-0.497\t0.813\t0.514\n3\t-0.667\t0.874\t2.840\t0.356\t0.862\t0.694\t-1.422\t0.586\t0.306\t-0.260\t-0.884\t0.694\t-0.972\t2.134\t0.114\t-1.223\t-0.514\t-0.278\t2.412\t-0.885\t-0.116\t-0.538\t0.499\t-0.522\t-1.139\t-0.195\t-0.299\t-0.592\n4\t1.080\t-1.530\t0.045\t-0.489\t-1.328\t0.089\t0.666\t0.311\t2.529\t2.082\t0.092\t-0.059\t-0.114\t1.197\t-1.643\t0.188\t-2.029\t0.283\t0.160\t1.574\t0.326\t0.671\t1.334\t0.537\t-0.230\t-1.320\t-0.671\t1.781\n4\t-0.375\t1.065\t0.034\t-1.998\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173\t-0.825\t0.307\t-0.576\t-1.470\t-1.467\t-1.607\t-0.091\t0.911\t-0.271\t-0.573\t-1.973\t-0.765\t-0.457\t-1.485\t-0.195\t0.929\t-1.022\t-1.848\t-1.259\t-2.103\n4\t-3.335\t-0.646\t-1.413\t0.400\t-0.289\t0.015\t-2.079\t-0.310\t1.115\t1.409\t0.719\t-1.335\t1.259\t0.724\t-0.117\t-2.025\t0.954\t-0.592\t0.346\t0.068\t1.538\t-0.125\t-0.015\t1.579\t-0.658\t0.814\t-0.199\t-1.003\n0\t-0.527\t1.051\t0.068\t0.311\t-0.468\t1.558\t-0.449\t0.155\t0.882\t-0.356\t0.338\t0.614\t0.346\t0.368\t1.163\t0.714\t0.087\t-0.265\t1.348\t0.742\t1.484\t-0.222\t1.240\t-1.128\t0.441\t0.842\t-0.053\t2.081\n2\t-0.159\t1.117\t0.705\t-0.289\t-0.076\t-0.092\t-2.078\t-1.787\t-0.420\t-0.723\t1.385\t-0.807\t-1.230\t1.047\t-2.128\t1.377\t-1.122\t-0.440\t-0.051\t-0.481\t0.333\t-0.587\t1.074\t0.079\t0.801\t-0.549\t-0.173\t0.462\n2\t1.456\t0.055\t0.582\t0.443\t0.587\t-0.919\t1.280\t0.484\t0.513\t0.471\t-2.364\t0.106\t0.085\t0.265\t1.016\t0.914\t0.988\t-0.967\t0.125\t-1.564\t0.084\t-1.120\t-0.264\t0.806\t2.135\t-0.959\t0.254\t-0.047\n0\t0.740\t-0.591\t0.644\t0.826\t-0.213\t0.214\t1.315\t-0.225\t-0.570\t0.409\t-0.211\t0.120\t1.031\t-1.155\t0.575\t-0.619\t-0.327\t0.048\t-0.119\t-1.676\t1.382\t1.134\t-0.146\t0.934\t-0.398\t-0.982\t0.054\t-1.169\n4\t-1.602\t0.148\t-1.437\t2.466\t-1.032\t-0.075\t1.506\t-0.366\t-0.359\t-2.587\t-1.310\t0.562\t1.673\t-0.891\t0.197\t2.708\t0.567\t0.976\t-0.766\t1.552\t-0.692\t-0.868\t1.479\t-0.637\t-0.004\t1.626\t-0.914\t-0.237\n4\t-1.308\t-0.144\t-0.563\t-1.222\t0.790\t0.847\t-0.970\t-0.216\t0.044\t-1.815\t-1.397\t1.286\t-1.065\t1.258\t-1.345\t0.659\t-0.311\t-0.171\t1.856\t-1.254\t0.595\t0.553\t3.355\t-1.077\t-0.626\t0.816\t-0.200\t-0.463\n4\t0.244\t-1.090\t-0.986\t-0.970\t0.111\t-0.626\t0.494\t-2.855\t-0.857\t-1.126\t-0.256\t-0.538\t0.798\t-1.859\t0.045\t0.558\t1.857\t-0.482\t0.324\t-1.804\t-2.392\t0.845\t-0.006\t1.179\t-1.373\t0.520\t1.869\t0.420\n1\t1.130\t0.869\t0.125\t0.739\t0.573\t0.226\t-0.593\t-0.910\t-0.351\t0.264\t0.091\t-0.756\t2.130\t0.886\t-0.292\t0.186\t-0.514\t2.603\t-0.752\t-0.250\t-0.527\t0.034\t-0.309\t0.592\t-0.573\t-0.064\t0.018\t1.615\n1\t-0.102\t0.911\t-1.744\t-0.368\t0.093\t1.186\t0.843\t0.892\t-2.239\t1.188\t0.779\t0.640\t0.379\t0.425\t0.395\t0.393\t-0.290\t1.052\t0.052\t-0.478\t0.080\t-0.496\t0.329\t0.432\t-0.721\t0.514\t-2.149\t0.873\n4\t0.640\t1.388\t-1.616\t0.786\t-0.981\t0.445\t-0.244\t-0.689\t1.174\t-1.677\t1.813\t0.631\t0.169\t-0.149\t-0.371\t0.468\t-1.394\t-3.073\t0.086\t-0.746\t-0.916\t-1.576\t1.204\t1.821\t-0.052\t-0.156\t0.423\t0.363\n1\t0.140\t-0.681\t-0.730\t0.308\t-0.282\t1.978\t1.782\t-0.162\t-1.541\t-0.291\t1.048\t-0.887\t1.605\t0.581\t-1.171\t-0.660\t-0.519\t-0.806\t-1.145\t-0.575\t-0.381\t1.299\t0.439\t0.498\t0.350\t-0.796\t1.147\t-0.434\n2\t-1.150\t0.525\t-1.666\t-1.154\t-0.403\t0.675\t0.333\t0.416\t0.230\t1.683\t1.649\t-1.101\t1.173\t1.138\t0.397\t-0.708\t-0.152\t0.051\t-1.314\t-0.541\t0.409\t-1.089\t-0.414\t-2.247\t0.371\t-1.314\t0.474\t0.235\n4\t0.112\t-1.009\t-2.023\t0.316\t-0.794\t1.095\t0.014\t-0.700\t1.798\t0.576\t-3.655\t-0.486\t-1.384\t-0.800\t-0.145\t0.354\t0.261\t1.078\t0.637\t0.575\t0.201\t1.287\t-0.316\t-0.290\t-1.906\t1.056\t-0.077\t2.934\n4\t-1.467\t-2.022\t-2.454\t0.340\t-1.052\t0.498\t1.180\t0.590\t-0.227\t1.848\t-0.461\t-0.903\t0.334\t0.773\t-2.585\t1.111\t-1.625\t-2.052\t-0.344\t-1.063\t0.416\t0.356\t0.308\t1.224\t-0.315\t0.367\t-1.669\t0.961\n1\t-0.476\t0.602\t-0.589\t-2.749\t0.997\t-0.346\t-0.225\t0.698\t0.216\t0.362\t-0.786\t1.991\t0.588\t1.395\t-0.589\t-0.294\t-0.462\t-0.718\t-0.772\t1.074\t-0.413\t0.535\t-1.132\t-0.791\t1.406\t0.638\t-0.063\t-0.523\n3\t0.488\t0.251\t0.985\t0.498\t0.830\t3.439\t-1.594\t1.342\t1.479\t-1.098\t-0.799\t0.502\t-1.250\t-0.614\t0.239\t-0.684\t-0.228\t-0.867\t-0.942\t0.472\t-0.101\t0.123\t0.704\t0.184\t-0.335\t-0.880\t0.449\t1.994\n4\t-0.838\t-1.070\t0.411\t0.148\t-0.741\t2.958\t1.587\t0.018\t-0.692\t0.642\t-0.830\t-0.461\t-0.467\t-0.395\t0.750\t0.329\t-0.502\t-1.139\t-0.838\t-1.092\t-0.671\t2.342\t-2.050\t0.391\t1.602\t2.309\t-1.359\t-1.060\n0\t-0.320\t-1.298\t1.743\t0.999\t0.255\t0.815\t0.753\t-0.993\t0.637\t1.270\t-0.678\t-1.534\t-1.162\t-0.096\t-1.220\t-0.172\t-0.655\t-0.001\t-0.510\t-0.036\t0.603\t-0.958\t0.281\t-0.852\t-0.006\t-0.082\t0.356\t0.185\n2\t-1.054\t-0.648\t-0.313\t-0.185\t-2.438\t-0.753\t-0.554\t1.891\t0.227\t-0.798\t-0.439\t0.425\t-0.378\t-0.585\t-0.553\t0.480\t-1.382\t-1.087\t-0.928\t-1.104\t-0.824\t0.897\t1.036\t0.272\t0.215\t-0.670\t-2.081\t0.144\n3\t-0.366\t-0.458\t0.955\t0.153\t-0.653\t0.491\t0.768\t-0.240\t-0.212\t1.715\t-0.821\t-0.540\t-0.463\t1.699\t-1.977\t0.822\t-0.029\t1.813\t0.356\t-2.151\t1.611\t-0.236\t-2.041\t-0.405\t0.036\t-1.049\t-1.531\t0.297\n1\t-0.667\t0.409\t0.347\t-0.646\t0.840\t0.636\t0.611\t1.529\t0.616\t-0.121\t-0.084\t-1.738\t-0.779\t1.293\t1.202\t0.457\t0.118\t-1.107\t-1.467\t0.129\t0.376\t-1.018\t-0.107\t1.273\t0.387\t0.097\t-0.726\t1.867\n3\t0.288\t-0.115\t-1.091\t-1.181\t0.583\t0.659\t1.190\t-0.169\t-0.471\t-1.831\t-0.013\t-1.395\t-0.574\t-0.336\t-1.027\t1.381\t2.484\t1.461\t1.420\t0.475\t0.957\t0.803\t-0.412\t1.226\t-0.567\t1.180\t0.867\t-0.181\n3\t1.227\t-0.900\t0.683\t-0.624\t-2.269\t0.932\t0.772\t1.485\t-1.463\t0.164\t-0.223\t1.144\t-1.389\t-0.253\t0.094\t0.742\t0.415\t-0.032\t-0.311\t-0.089\t0.752\t-0.826\t-0.234\t-0.234\t-2.086\t-1.956\t-1.491\t-0.213\n0\t0.491\t-0.126\t0.420\t-0.837\t-0.604\t-0.734\t-0.565\t1.287\t0.726\t-1.077\t0.437\t-0.391\t-0.682\t-1.486\t0.972\t-1.364\t-0.052\t-0.946\t0.776\t-0.334\t-0.134\t-0.101\t0.303\t0.013\t-0.043\t-0.518\t-1.575\t-1.119\n2\t-0.316\t1.946\t0.839\t-1.289\t-1.518\t1.871\t0.366\t0.986\t1.648\t-0.735\t0.454\t0.343\t-0.768\t-0.742\t0.548\t-1.308\t-0.535\t0.398\t0.057\t1.000\t0.078\t0.469\t-0.653\t0.051\t0.513\t-0.186\t-1.679\t1.098\n2\t-0.010\t-0.555\t0.570\t2.541\t-1.295\t1.338\t-0.411\t-0.683\t0.818\t-0.256\t-1.286\t-0.024\t0.137\t-0.040\t0.086\t-0.396\t0.922\t0.266\t-1.657\t-1.771\t1.562\t0.832\t0.263\t-0.503\t0.389\t0.995\t0.194\t0.654\n" }, { "path": "examples/multiclass_classification/predict.conf", "content": "task = predict\n\ndata = multiclass.test\n\ninput_model= LightGBM_model.txt\n" }, { "path": "examples/multiclass_classification/train.conf", "content": "# task type, support train and predict\ntask = train\n\n# boosting type, support gbdt for now, alias: boosting, boost\nboosting_type = gbdt\n\n# application type, support following application\n# regression , regression task\n# binary , binary classification task\n# lambdarank , LambdaRank task\n# multiclass\n# alias: application, app\nobjective = multiclass\n\n# eval metrics, support multi metric, delimited by ',' , support following metrics\n# l1\n# l2 , default metric for regression\n# ndcg , default metric for lambdarank\n# auc\n# binary_logloss , default metric for binary\n# binary_error\n# multi_logloss\n# multi_error\n# auc_mu\nmetric = multi_logloss,auc_mu\n\n# AUC-mu weights; the matrix of loss weights below is passed in parameter auc_mu_weights as a list\n# 0 1 2 3 4\n# 5 0 6 7 8\n# 9 10 0 11 12\n# 13 14 15 0 16\n# 17 18 19 20 0\nauc_mu_weights = 0,1,2,3,4,5,0,6,7,8,9,10,0,11,12,13,14,15,0,16,17,18,19,20,0\n\n# number of class, for multiclass classification\nnum_class = 5\n\n# frequency for metric output\nmetric_freq = 1\n\n# true if need output metric for training data, alias: tranining_metric, train_metric\nis_training_metric = true\n\n# column in data to use as label\nlabel_column = 0\n\n# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.\nmax_bin = 255\n\n# training data\n# if existing weight file, should name to \"regression.train.weight\"\n# alias: train_data, train\ndata = multiclass.train\n\n# valid data\nvalid_data = multiclass.test\n\n# round for early stopping\nearly_stopping = 10\n\n# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds\nnum_trees = 100\n\n# shrinkage rate , alias: shrinkage_rate\nlearning_rate = 0.05\n\n# number of leaves for one tree, alias: num_leaf\nnum_leaves = 31\n" }, { "path": "examples/parallel_learning/README.md", "content": "Distributed Learning Example\n============================\n\n\nHere is an example for LightGBM to perform distributed learning for 2 machines.\n\n1. Edit [mlist.txt](./mlist.txt): write the ip of these 2 machines that you want to run application on.\n\n ```\n machine1_ip 12400\n machine2_ip 12400\n ```\n\n2. Copy this folder and executable file to these 2 machines that you want to run application on.\n\n3. Run command in this folder on both 2 machines:\n\n ```\"./lightgbm\" config=train.conf```\n\nThis distributed learning example is based on socket. LightGBM also supports distributed learning based on MPI.\n\nFor more details about the usage of distributed learning, please refer to [this](https://github.com/lightgbm-org/LightGBM/blob/master/docs/Parallel-Learning-Guide.rst).\n" }, { "path": "examples/parallel_learning/binary.test", "content": 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0.806\t1.270\t-1.358\t0.720\t0.140\t0.842\t0.000\t0.410\t0.097\t-0.101\t0.000\t0.310\t1.538\t1.343\t0.000\t0.576\t1.156\t-1.669\t3.102\t0.868\t0.746\t0.984\t0.866\t0.484\t0.709\t0.735\n1\t0.758\t0.682\t-0.444\t0.938\t-1.632\t1.179\t-0.084\t0.624\t0.000\t0.880\t0.535\t0.412\t0.000\t1.320\t0.587\t1.483\t2.548\t2.797\t0.726\t-1.090\t1.551\t0.934\t0.869\t1.024\t0.698\t1.089\t0.735\t0.650\n1\t1.095\t-0.733\t0.372\t0.939\t0.191\t1.186\t0.014\t-1.148\t2.173\t0.896\t-0.208\t1.243\t0.000\t0.647\t-2.172\t-0.622\t0.000\t0.612\t0.423\t1.064\t3.102\t1.923\t1.222\t1.002\t1.687\t0.853\t1.124\t1.103\n1\t0.433\t0.852\t-1.542\t1.366\t0.726\t1.085\t-0.534\t-1.507\t0.000\t0.814\t-0.632\t-1.026\t2.215\t1.652\t-1.256\t-0.104\t0.000\t1.415\t0.489\t0.883\t3.102\t0.897\t0.972\t0.990\t0.604\t1.151\t0.947\t1.158\n0\t0.416\t-1.352\t0.839\t0.854\t1.269\t0.978\t-0.442\t-1.513\t2.173\t0.898\t2.369\t0.484\t0.000\t0.764\t0.045\t-0.462\t2.548\t1.698\t-0.947\t-0.406\t0.000\t4.819\t2.601\t0.983\t0.871\t0.915\t1.898\t1.486\n0\t0.535\t1.359\t1.126\t1.322\t-1.572\t1.203\t1.087\t-1.301\t1.087\t1.973\t-0.763\t0.410\t2.215\t1.053\t-0.945\t-0.083\t0.000\t0.593\t-2.137\t-0.134\t0.000\t0.789\t0.844\t0.992\t0.815\t3.348\t1.654\t1.385\n0\t1.719\t0.561\t1.517\t0.784\t-1.006\t0.942\t0.932\t0.153\t0.000\t0.345\t0.832\t1.035\t0.000\t0.795\t0.857\t-1.473\t2.548\t0.811\t0.179\t-0.663\t3.102\t0.866\t0.917\t1.229\t0.758\t0.469\t0.615\t0.697\n1\t1.040\t-0.803\t1.637\t0.264\t-0.814\t1.819\t0.055\t0.353\t0.000\t1.541\t-0.934\t-0.811\t2.215\t1.701\t-0.112\t-1.387\t0.000\t1.762\t-0.267\t1.163\t3.102\t1.301\t1.135\t0.979\t0.914\t1.534\t1.142\t1.018\n" }, { "path": "examples/parallel_learning/predict.conf", "content": "task = predict\n\ndata = binary.test\n\ninput_model= LightGBM_model.txt\n" }, { "path": "examples/parallel_learning/train.conf", "content": "# task type, support train and predict\ntask = train\n\n# boosting type, support gbdt for now, alias: boosting, boost\nboosting_type = gbdt\n\n# application type, support following application\n# regression , regression task\n# binary , binary classification task\n# lambdarank , LambdaRank task\n# alias: application, app\nobjective = binary\n\n# eval metrics, support multi metric, delimited by ',' , support following metrics\n# l1\n# l2 , default metric for regression\n# ndcg , default metric for lambdarank\n# auc\n# binary_logloss , default metric for binary\n# binary_error\nmetric = binary_logloss,auc\n\n# frequency for metric output\nmetric_freq = 1\n\n# true if need output metric for training data, alias: tranining_metric, train_metric\nis_training_metric = true\n\n# column in data to use as label\nlabel_column = 0\n\n# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.\nmax_bin = 255\n\n# training data\n# if existing weight file, should name to \"binary.train.weight\"\n# alias: train_data, train\ndata = binary.train\n\n# validation data, support multi validation data, separated by ','\n# if existing weight file, should name to \"binary.test.weight\"\n# alias: valid, test, test_data,\nvalid_data = binary.test\n\n# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds\nnum_trees = 100\n\n# shrinkage rate , alias: shrinkage_rate\nlearning_rate = 0.1\n\n# number of leaves for one tree, alias: num_leaf\nnum_leaves = 63\n\n# type of tree learner, support following types:\n# serial , single machine version\n# feature , use feature parallel to train\n# data , use data parallel to train\n# voting , use voting based parallel to train\n# alias: tree\ntree_learner = feature\n\n# number of threads for multi-threading. One thread will use each CPU. The default is the CPU count.\n# num_threads = 8\n\n# feature sub-sample, will random select 80% feature to train on each iteration\n# alias: sub_feature\nfeature_fraction = 0.8\n\n# Support bagging (data sub-sample), will perform bagging every 5 iterations\nbagging_freq = 5\n\n# Bagging fraction, will random select 80% data on bagging\n# alias: sub_row\nbagging_fraction = 0.8\n\n# minimal number data for one leaf, use this to deal with over-fit\n# alias : min_data_per_leaf, min_data\nmin_data_in_leaf = 50\n\n# minimal sum Hessians for one leaf, use this to deal with over-fit\nmin_sum_hessian_in_leaf = 5.0\n\n# save memory and faster speed for sparse feature, alias: is_sparse\nis_enable_sparse = true\n\n# when data is bigger than memory size, set this to true. otherwise set false will have faster speed\n# alias: two_round_loading, two_round\nuse_two_round_loading = false\n\n# true if need to save data to binary file and application will auto load data from binary file next time\n# alias: is_save_binary, save_binary\nis_save_binary_file = false\n\n# output model file\noutput_model = LightGBM_model.txt\n\n# support continuous train from trained gbdt model\n# input_model= trained_model.txt\n\n# output prediction file for predict task\n# output_result= prediction.txt\n\n\n# number of machines in parallel training, alias: num_machine\nnum_machines = 2\n\n# local listening port in parallel training, alias: local_port\nlocal_listen_port = 12400\n\n# machines list file for parallel training, alias: mlist\nmachine_list_file = mlist.txt\n" }, { "path": "examples/python-guide/README.md", "content": "Python-package Examples\n=======================\n\nHere is an example for LightGBM to use Python-package.\n\nYou should install LightGBM [Python-package](https://github.com/lightgbm-org/LightGBM/tree/master/python-package) first.\n\nYou also need scikit-learn, pandas, matplotlib (only for plot example), and scipy (only for logistic regression example) to run the examples, but they are not required for the package itself. You can install them with pip:\n\n```\npip install scikit-learn pandas matplotlib scipy -U\n```\n\nNow you can run examples in this folder, for example:\n\n```\npython simple_example.py\n```\n\nExamples include:\n\n- [`dask/`](./dask): examples using Dask for distributed training\n- [simple_example.py](https://github.com/lightgbm-org/LightGBM/blob/master/examples/python-guide/simple_example.py)\n - Construct Dataset\n - Basic train and predict\n - Eval during training\n - Early stopping\n - Save model to file\n- [sklearn_example.py](https://github.com/lightgbm-org/LightGBM/blob/master/examples/python-guide/sklearn_example.py)\n - Create data for learning with sklearn interface\n - Basic train and predict with sklearn interface\n - Feature importances with sklearn interface\n - Self-defined eval metric with sklearn interface\n - Find best parameters for the model with sklearn's GridSearchCV\n- [advanced_example.py](https://github.com/lightgbm-org/LightGBM/blob/master/examples/python-guide/advanced_example.py)\n - Construct Dataset\n - Set feature names\n - Directly use categorical features without one-hot encoding\n - Save model to file\n - Dump model to JSON format\n - Get feature names\n - Get feature importances\n - Load model to predict\n - Dump and load model with pickle\n - Load model file to continue training\n - Change learning rates during training\n - Change any parameters during training\n - Self-defined objective function\n - Self-defined eval metric\n - Callback function\n- [logistic_regression.py](https://github.com/lightgbm-org/LightGBM/blob/master/examples/python-guide/logistic_regression.py)\n - Use objective `xentropy` or `binary`\n - Use `xentropy` with binary labels or probability labels\n - Use `binary` only with binary labels\n - Compare speed of `xentropy` versus `binary`\n- [plot_example.py](https://github.com/lightgbm-org/LightGBM/blob/master/examples/python-guide/plot_example.py)\n - Construct Dataset\n - Train and record eval results for further plotting\n - Plot metrics recorded during training\n - Plot feature importances\n - Plot split value histogram\n - Plot one specified tree\n - Plot one specified tree with Graphviz\n- [dataset_from_multi_hdf5.py](https://github.com/lightgbm-org/LightGBM/blob/master/examples/python-guide/dataset_from_multi_hdf5.py)\n - Construct Dataset from multiple HDF5 files\n - Avoid loading all data into memory\n" }, { "path": "examples/python-guide/advanced_example.py", "content": "# coding: utf-8\nimport copy\nimport json\nimport pickle\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import roc_auc_score\n\nimport lightgbm as lgb\n\nprint(\"Loading data...\")\n# load or create your dataset\nbinary_example_dir = Path(__file__).absolute().parents[1] / \"binary_classification\"\ndf_train = pd.read_csv(str(binary_example_dir / \"binary.train\"), header=None, sep=\"\\t\")\ndf_test = pd.read_csv(str(binary_example_dir / \"binary.test\"), header=None, sep=\"\\t\")\nW_train = pd.read_csv(str(binary_example_dir / \"binary.train.weight\"), header=None)[0]\nW_test = pd.read_csv(str(binary_example_dir / \"binary.test.weight\"), header=None)[0]\n\ny_train = df_train[0]\ny_test = df_test[0]\nX_train = df_train.drop(0, axis=1)\nX_test = df_test.drop(0, axis=1)\n\nnum_train, num_feature = X_train.shape\n\n# generate feature names\nfeature_name = [f\"feature_{col}\" for col in range(num_feature)]\n\n# create dataset for lightgbm\n# if you want to re-use data, remember to set free_raw_data=False\nlgb_train = lgb.Dataset(\n X_train, y_train, weight=W_train, feature_name=feature_name, categorical_feature=[21], free_raw_data=False\n)\nlgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train, weight=W_test, free_raw_data=False)\n\n# specify your configurations as a dict\nparams = {\n \"boosting_type\": \"gbdt\",\n \"objective\": \"binary\",\n \"metric\": \"binary_logloss\",\n \"num_leaves\": 31,\n \"learning_rate\": 0.05,\n \"feature_fraction\": 0.9,\n \"bagging_fraction\": 0.8,\n \"bagging_freq\": 5,\n \"verbose\": 0,\n}\n\nprint(\"Starting training...\")\n# feature_name and categorical_feature\ngbm = lgb.train(\n params,\n lgb_train,\n num_boost_round=10,\n valid_sets=lgb_train, # eval training data\n)\n\nprint(\"Finished first 10 rounds...\")\n# check feature name\nprint(f\"7th feature name is: {lgb_train.feature_name[6]}\")\n\nprint(\"Saving model...\")\n# save model to file\ngbm.save_model(\"model.txt\")\n\nprint(\"Dumping model to JSON...\")\n# dump model to JSON (and save to file)\nmodel_json = gbm.dump_model()\n\nwith open(\"model.json\", \"w+\") as f:\n json.dump(model_json, f, indent=4)\n\n# feature names\nprint(f\"Feature names: {gbm.feature_name()}\")\n\n# feature importances\nprint(f\"Feature importances: {list(gbm.feature_importance())}\")\n\nprint(\"Loading model to predict...\")\n# load model to predict\nbst = lgb.Booster(model_file=\"model.txt\")\n# can only predict with the best iteration (or the saving iteration)\ny_pred = bst.predict(X_test)\n# eval with loaded model\nauc_loaded_model = roc_auc_score(y_test, y_pred)\nprint(f\"The ROC AUC of loaded model's prediction is: {auc_loaded_model}\")\n\nprint(\"Dumping and loading model with pickle...\")\n# dump model with pickle\nwith open(\"model.pkl\", \"wb\") as fout:\n pickle.dump(gbm, fout)\n# load model with pickle to predict\nwith open(\"model.pkl\", \"rb\") as fin:\n pkl_bst = pickle.load(fin)\n# can predict with any iteration when loaded in pickle way\ny_pred = pkl_bst.predict(X_test, num_iteration=7)\n# eval with loaded model\nauc_pickled_model = roc_auc_score(y_test, y_pred)\nprint(f\"The ROC AUC of pickled model's prediction is: {auc_pickled_model}\")\n\n# continue training\n# init_model accepts:\n# 1. model file name\n# 2. Booster()\ngbm = lgb.train(params, lgb_train, num_boost_round=10, init_model=\"model.txt\", valid_sets=lgb_eval)\n\nprint(\"Finished 10 - 20 rounds with model file...\")\n\n# decay learning rates\n# reset_parameter callback accepts:\n# 1. list with length = num_boost_round\n# 2. function(curr_iter)\ngbm = lgb.train(\n params,\n lgb_train,\n num_boost_round=10,\n init_model=gbm,\n valid_sets=lgb_eval,\n callbacks=[lgb.reset_parameter(learning_rate=lambda iter: 0.05 * (0.99**iter))],\n)\n\nprint(\"Finished 20 - 30 rounds with decay learning rates...\")\n\n# change other parameters during training\ngbm = lgb.train(\n params,\n lgb_train,\n num_boost_round=10,\n init_model=gbm,\n valid_sets=lgb_eval,\n callbacks=[lgb.reset_parameter(bagging_fraction=[0.7] * 5 + [0.6] * 5)],\n)\n\nprint(\"Finished 30 - 40 rounds with changing bagging_fraction...\")\n\n\n# self-defined objective function\n# f(preds: array, train_data: Dataset) -> grad: array, hess: array\n# log likelihood loss\ndef loglikelihood(preds, train_data):\n labels = train_data.get_label()\n preds = 1.0 / (1.0 + np.exp(-preds))\n grad = preds - labels\n hess = preds * (1.0 - preds)\n return grad, hess\n\n\n# self-defined eval metric\n# f(preds: array, train_data: Dataset) -> name: str, eval_result: float, is_higher_better: bool\n# binary error\n# NOTE: when you do customized loss function, the default prediction value is margin\n# This may make built-in evaluation metric calculate wrong results\n# For example, we are doing log likelihood loss, the prediction is score before logistic transformation\n# Keep this in mind when you use the customization\ndef binary_error(preds, train_data):\n labels = train_data.get_label()\n preds = 1.0 / (1.0 + np.exp(-preds))\n return \"error\", np.mean(labels != (preds > 0.5)), False\n\n\n# Pass custom objective function through params\nparams_custom_obj = copy.deepcopy(params)\nparams_custom_obj[\"objective\"] = loglikelihood\n\ngbm = lgb.train(\n params_custom_obj, lgb_train, num_boost_round=10, init_model=gbm, feval=binary_error, valid_sets=lgb_eval\n)\n\nprint(\"Finished 40 - 50 rounds with self-defined objective function and eval metric...\")\n\n\n# another self-defined eval metric\n# f(preds: array, train_data: Dataset) -> name: str, eval_result: float, is_higher_better: bool\n# accuracy\n# NOTE: when you do customized loss function, the default prediction value is margin\n# This may make built-in evaluation metric calculate wrong results\n# For example, we are doing log likelihood loss, the prediction is score before logistic transformation\n# Keep this in mind when you use the customization\ndef accuracy(preds, train_data):\n labels = train_data.get_label()\n preds = 1.0 / (1.0 + np.exp(-preds))\n return \"accuracy\", np.mean(labels == (preds > 0.5)), True\n\n\n# Pass custom objective function through params\nparams_custom_obj = copy.deepcopy(params)\nparams_custom_obj[\"objective\"] = loglikelihood\n\ngbm = lgb.train(\n params_custom_obj,\n lgb_train,\n num_boost_round=10,\n init_model=gbm,\n feval=[binary_error, accuracy],\n valid_sets=lgb_eval,\n)\n\nprint(\"Finished 50 - 60 rounds with self-defined objective function and multiple self-defined eval metrics...\")\n\nprint(\"Starting a new training job...\")\n\n\n# callback\ndef reset_metrics():\n def callback(env):\n lgb_eval_new = lgb.Dataset(X_test, y_test, reference=lgb_train)\n if env.iteration - env.begin_iteration == 5:\n print(\"Add a new valid dataset at iteration 5...\")\n env.model.add_valid(lgb_eval_new, \"new_valid\")\n\n callback.before_iteration = True\n callback.order = 0\n return callback\n\n\ngbm = lgb.train(params, lgb_train, num_boost_round=10, valid_sets=lgb_train, callbacks=[reset_metrics()])\n\nprint(\"Finished first 10 rounds with callback function...\")\n" }, { "path": "examples/python-guide/dask/README.md", "content": "Dask Examples\n=============\n\nThis directory contains examples of machine learning workflows with LightGBM and [Dask](https://dask.org/).\n\nBefore running this code, see [the installation instructions for the Dask-package](https://github.com/lightgbm-org/LightGBM/tree/master/python-package#install-dask-package).\n\nAfter installing the package and its dependencies, any of the examples here can be run with a command like this:\n\n```shell\npython binary-classification.py\n```\n\nThe examples listed below contain minimal code showing how to train LightGBM models using Dask.\n\n**Training**\n\n* [binary-classification.py](./binary-classification.py)\n* [multiclass-classification.py](./multiclass-classification.py)\n* [ranking.py](./ranking.py)\n* [regression.py](./regression.py)\n\n**Prediction**\n\n* [prediction.py](./prediction.py)\n" }, { "path": "examples/python-guide/dask/binary-classification.py", "content": "import dask.array as da\nfrom distributed import Client, LocalCluster\nfrom sklearn.datasets import make_blobs\n\nimport lightgbm as lgb\n\nif __name__ == \"__main__\":\n print(\"loading data\")\n\n X, y = make_blobs(n_samples=1000, n_features=50, centers=2)\n\n print(\"initializing a Dask cluster\")\n\n cluster = LocalCluster()\n client = Client(cluster)\n\n print(\"created a Dask LocalCluster\")\n\n print(\"distributing training data on the Dask cluster\")\n\n dX = da.from_array(X, chunks=(100, 50))\n dy = da.from_array(y, chunks=(100,))\n\n print(\"beginning training\")\n\n dask_model = lgb.DaskLGBMClassifier(n_estimators=10)\n dask_model.fit(dX, dy)\n assert dask_model.fitted_\n\n print(\"done training\")\n" }, { "path": "examples/python-guide/dask/multiclass-classification.py", "content": "import dask.array as da\nfrom distributed import Client, LocalCluster\nfrom sklearn.datasets import make_blobs\n\nimport lightgbm as lgb\n\nif __name__ == \"__main__\":\n print(\"loading data\")\n\n X, y = make_blobs(n_samples=1000, n_features=50, centers=3)\n\n print(\"initializing a Dask cluster\")\n\n cluster = LocalCluster(n_workers=2)\n client = Client(cluster)\n\n print(\"created a Dask LocalCluster\")\n\n print(\"distributing training data on the Dask cluster\")\n\n dX = da.from_array(X, chunks=(100, 50))\n dy = da.from_array(y, chunks=(100,))\n\n print(\"beginning training\")\n\n dask_model = lgb.DaskLGBMClassifier(n_estimators=10)\n dask_model.fit(dX, dy)\n assert dask_model.fitted_\n\n print(\"done training\")\n" }, { "path": "examples/python-guide/dask/prediction.py", "content": "import dask.array as da\nfrom distributed import Client, LocalCluster\nfrom sklearn.datasets import make_regression\nfrom sklearn.metrics import mean_squared_error\n\nimport lightgbm as lgb\n\nif __name__ == \"__main__\":\n print(\"loading data\")\n\n X, y = make_regression(n_samples=1000, n_features=50)\n\n print(\"initializing a Dask cluster\")\n\n cluster = LocalCluster(n_workers=2)\n client = Client(cluster)\n\n print(\"created a Dask LocalCluster\")\n\n print(\"distributing training data on the Dask cluster\")\n\n dX = da.from_array(X, chunks=(100, 50))\n dy = da.from_array(y, chunks=(100,))\n\n print(\"beginning training\")\n\n dask_model = lgb.DaskLGBMRegressor(n_estimators=10)\n dask_model.fit(dX, dy)\n assert dask_model.fitted_\n\n print(\"done training\")\n\n print(\"predicting on the training data\")\n\n preds = dask_model.predict(dX)\n\n # the code below uses sklearn.metrics, but this requires pulling all of the\n # predictions and target values back from workers to the client\n #\n # for larger datasets, consider the metrics from dask-ml instead\n # https://ml.dask.org/modules/api.html#dask-ml-metrics-metrics\n print(\"computing MSE\")\n\n preds_local = preds.compute()\n actuals_local = dy.compute()\n mse = mean_squared_error(actuals_local, preds_local)\n\n print(f\"MSE: {mse}\")\n" }, { "path": "examples/python-guide/dask/ranking.py", "content": "from pathlib import Path\n\nimport dask.array as da\nimport numpy as np\nfrom distributed import Client, LocalCluster\nfrom sklearn.datasets import load_svmlight_file\n\nimport lightgbm as lgb\n\nif __name__ == \"__main__\":\n print(\"loading data\")\n\n rank_example_dir = Path(__file__).absolute().parents[2] / \"lambdarank\"\n X, y = load_svmlight_file(str(rank_example_dir / \"rank.train\"))\n group = np.loadtxt(str(rank_example_dir / \"rank.train.query\"))\n\n print(\"initializing a Dask cluster\")\n\n cluster = LocalCluster(n_workers=2)\n client = Client(cluster)\n\n print(\"created a Dask LocalCluster\")\n\n print(\"distributing training data on the Dask cluster\")\n\n # split training data into two partitions\n rows_in_part1 = int(np.sum(group[:100]))\n rows_in_part2 = X.shape[0] - rows_in_part1\n num_features = X.shape[1]\n\n # make this array dense because we're splitting across\n # a sparse boundary to partition the data\n X = X.toarray()\n\n dX = da.from_array(x=X, chunks=[(rows_in_part1, rows_in_part2), (num_features,)])\n dy = da.from_array(\n x=y,\n chunks=[\n (rows_in_part1, rows_in_part2),\n ],\n )\n dg = da.from_array(x=group, chunks=[(100, group.size - 100)])\n\n print(\"beginning training\")\n\n dask_model = lgb.DaskLGBMRanker(n_estimators=10)\n dask_model.fit(dX, dy, group=dg)\n assert dask_model.fitted_\n\n print(\"done training\")\n" }, { "path": "examples/python-guide/dask/regression.py", "content": "import dask.array as da\nfrom distributed import Client, LocalCluster\nfrom sklearn.datasets import make_regression\n\nimport lightgbm as lgb\n\nif __name__ == \"__main__\":\n print(\"loading data\")\n\n X, y = make_regression(n_samples=1000, n_features=50)\n\n print(\"initializing a Dask cluster\")\n\n cluster = LocalCluster(n_workers=2)\n client = Client(cluster)\n\n print(\"created a Dask LocalCluster\")\n\n print(\"distributing training data on the Dask cluster\")\n\n dX = da.from_array(X, chunks=(100, 50))\n dy = da.from_array(y, chunks=(100,))\n\n print(\"beginning training\")\n\n dask_model = lgb.DaskLGBMRegressor(n_estimators=10)\n dask_model.fit(dX, dy)\n assert dask_model.fitted_\n\n print(\"done training\")\n" }, { "path": "examples/python-guide/dataset_from_multi_hdf5.py", "content": "from pathlib import Path\n\nimport h5py\nimport numpy as np\nimport pandas as pd\n\nimport lightgbm as lgb\n\n\nclass HDFSequence(lgb.Sequence):\n def __init__(self, hdf_dataset, batch_size):\n \"\"\"\n Construct a sequence object from HDF5 with required interface.\n\n Parameters\n ----------\n hdf_dataset : h5py.Dataset\n Dataset in HDF5 file.\n batch_size : int\n Size of a batch. When reading data to construct lightgbm Dataset, each read reads batch_size rows.\n \"\"\"\n # We can also open HDF5 file once and get access to\n self.data = hdf_dataset\n self.batch_size = batch_size\n\n def __getitem__(self, idx):\n return self.data[idx]\n\n def __len__(self):\n return len(self.data)\n\n\ndef create_dataset_from_multiple_hdf(input_flist, batch_size):\n data = []\n ylist = []\n for f in input_flist:\n f = h5py.File(f, \"r\")\n data.append(HDFSequence(f[\"X\"], batch_size))\n ylist.append(f[\"Y\"][:])\n\n params = {\n \"bin_construct_sample_cnt\": 200000,\n \"max_bin\": 255,\n }\n y = np.concatenate(ylist)\n dataset = lgb.Dataset(data, label=y, params=params)\n # With binary dataset created, we can use either Python API or cmdline version to train.\n #\n # Note: in order to create exactly the same dataset with the one created in simple_example.py, we need\n # to modify simple_example.py to pass numpy array instead of pandas DataFrame to Dataset constructor.\n # The reason is that DataFrame column names will be used in Dataset. For a DataFrame with Int64Index\n # as columns, Dataset will use column names like [\"0\", \"1\", \"2\", ...]. While for numpy array, column names\n # are using the default one assigned in C++ code (dataset_loader.cpp), like [\"Column_0\", \"Column_1\", ...].\n dataset.save_binary(\"regression.train.from_hdf.bin\")\n\n\ndef save2hdf(input_data, fname, batch_size):\n \"\"\"Store numpy array to HDF5 file.\n\n Please note chunk size settings in the implementation for I/O performance optimization.\n \"\"\"\n with h5py.File(fname, \"w\") as f:\n for name, data in input_data.items():\n nrow, ncol = data.shape\n if ncol == 1:\n # Y has a single column and we read it in single shot. So store it as an 1-d array.\n chunk = (nrow,)\n data = data.values.flatten()\n else:\n # We use random access for data sampling when creating LightGBM Dataset from Sequence.\n # When accessing any element in a HDF5 chunk, it's read entirely.\n # To save I/O for sampling, we should keep number of total chunks much larger than sample count.\n # Here we are just creating a chunk size that matches with batch_size.\n #\n # Also note that the data is stored in row major order to avoid extra copy when passing to\n # lightgbm Dataset.\n chunk = (batch_size, ncol)\n f.create_dataset(name, data=data, chunks=chunk, compression=\"lzf\")\n\n\ndef generate_hdf(input_fname, output_basename, batch_size):\n # Save to 2 HDF5 files for demonstration.\n df = pd.read_csv(input_fname, header=None, sep=\"\\t\")\n\n mid = len(df) // 2\n df1 = df.iloc[:mid]\n df2 = df.iloc[mid:]\n\n # We can store multiple datasets inside a single HDF5 file.\n # Separating X and Y for choosing best chunk size for data loading.\n fname1 = f\"{output_basename}1.h5\"\n fname2 = f\"{output_basename}2.h5\"\n save2hdf({\"Y\": df1.iloc[:, :1], \"X\": df1.iloc[:, 1:]}, fname1, batch_size)\n save2hdf({\"Y\": df2.iloc[:, :1], \"X\": df2.iloc[:, 1:]}, fname2, batch_size)\n\n return [fname1, fname2]\n\n\ndef main():\n batch_size = 64\n output_basename = \"regression\"\n hdf_files = generate_hdf(\n str(Path(__file__).absolute().parents[1] / \"regression\" / \"regression.train\"), output_basename, batch_size\n )\n\n create_dataset_from_multiple_hdf(hdf_files, batch_size=batch_size)\n\n\nif __name__ == \"__main__\":\n main()\n" }, { "path": "examples/python-guide/logistic_regression.py", "content": "# coding: utf-8\n\"\"\"Comparison of `binary` and `xentropy` objectives.\n\nBLUF: The `xentropy` objective does logistic regression and generalizes\nto the case where labels are probabilistic (i.e. numbers between 0 and 1).\n\nDetails: Both `binary` and `xentropy` minimize the log loss and use\n`boost_from_average = TRUE` by default. Possibly the only difference\nbetween them with default settings is that `binary` may achieve a slight\nspeed improvement by assuming that the labels are binary instead of\nprobabilistic.\n\"\"\"\n\nimport time\n\nimport numpy as np\nimport pandas as pd\nfrom scipy.special import expit\n\nimport lightgbm as lgb\n\n#################\n# Simulate some binary data with a single categorical and\n# single continuous predictor\nrng = np.random.default_rng(seed=0)\nN = 1000\nX = pd.DataFrame({\"continuous\": range(N), \"categorical\": np.repeat([0, 1, 2, 3, 4], N / 5)})\nCATEGORICAL_EFFECTS = [-1, -1, -2, -2, 2]\nLINEAR_TERM = np.array(\n [-0.5 + 0.01 * X[\"continuous\"][k] + CATEGORICAL_EFFECTS[X[\"categorical\"][k]] for k in range(X.shape[0])]\n) + rng.normal(loc=0, scale=1, size=X.shape[0])\nTRUE_PROB = expit(LINEAR_TERM)\nY = rng.binomial(n=1, p=TRUE_PROB, size=N)\nDATA = {\n \"X\": X,\n \"probability_labels\": TRUE_PROB,\n \"binary_labels\": Y,\n \"lgb_with_binary_labels\": lgb.Dataset(X, Y),\n \"lgb_with_probability_labels\": lgb.Dataset(X, TRUE_PROB),\n}\n\n\n#################\n# Set up a couple of utilities for our experiments\ndef log_loss(preds, labels):\n \"\"\"Logarithmic loss with non-necessarily-binary labels.\"\"\"\n log_likelihood = np.sum(labels * np.log(preds)) / len(preds)\n return -log_likelihood\n\n\ndef experiment(objective, label_type, data):\n \"\"\"Measure performance of an objective.\n\n Parameters\n ----------\n objective : {'binary', 'xentropy'}\n Objective function.\n label_type : {'binary', 'probability'}\n Type of the label.\n data : dict\n Data for training.\n\n Returns\n -------\n result : dict\n Experiment summary stats.\n \"\"\"\n nrounds = 5\n lgb_data = data[f\"lgb_with_{label_type}_labels\"]\n params = {\"objective\": objective, \"feature_fraction\": 1, \"bagging_fraction\": 1, \"verbose\": -1, \"seed\": 123}\n time_zero = time.time()\n gbm = lgb.train(params, lgb_data, num_boost_round=nrounds)\n y_fitted = gbm.predict(data[\"X\"])\n y_true = data[f\"{label_type}_labels\"]\n duration = time.time() - time_zero\n return {\"time\": duration, \"correlation\": np.corrcoef(y_fitted, y_true)[0, 1], \"logloss\": log_loss(y_fitted, y_true)}\n\n\n#################\n# Observe the behavior of `binary` and `xentropy` objectives\nprint(\"Performance of `binary` objective with binary labels:\")\nprint(experiment(\"binary\", label_type=\"binary\", data=DATA))\n\nprint(\"Performance of `xentropy` objective with binary labels:\")\nprint(experiment(\"xentropy\", label_type=\"binary\", data=DATA))\n\nprint(\"Performance of `xentropy` objective with probability labels:\")\nprint(experiment(\"xentropy\", label_type=\"probability\", data=DATA))\n\n# Trying this throws an error on non-binary values of y:\n# experiment('binary', label_type='probability', DATA)\n\n# The speed of `binary` is not drastically different than\n# `xentropy`. `xentropy` runs faster than `binary` in many cases, although\n# there are reasons to suspect that `binary` should run faster when the\n# label is an integer instead of a float\nK = 10\nA = [experiment(\"binary\", label_type=\"binary\", data=DATA)[\"time\"] for k in range(K)]\nB = [experiment(\"xentropy\", label_type=\"binary\", data=DATA)[\"time\"] for k in range(K)]\nprint(f\"Best `binary` time: {min(A)}\")\nprint(f\"Best `xentropy` time: {min(B)}\")\n" }, { "path": "examples/python-guide/notebooks/interactive_plot_example.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Load libraries\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2018-10-25T22:13:39.152526Z\",\n \"start_time\": \"2018-10-25T22:13:37.503680Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"from pathlib import Path\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"import lightgbm as lgb\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"try:\\n\",\n \" # To enable interactive mode you should install ipywidgets\\n\",\n \" # https://github.com/jupyter-widgets/ipywidgets\\n\",\n \" from ipywidgets import SelectMultiple, interact\\n\",\n \"\\n\",\n \" INTERACTIVE = True\\n\",\n \"except ImportError:\\n\",\n \" INTERACTIVE = False\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Load data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2018-10-25T22:13:39.238695Z\",\n \"start_time\": \"2018-10-25T22:13:39.160165Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"regression_example_dir = Path().absolute().parents[1] / \\\"regression\\\"\\n\",\n \"df_train = pd.read_csv(str(regression_example_dir / \\\"regression.train\\\"), header=None, sep=\\\"\\\\t\\\")\\n\",\n \"df_test = pd.read_csv(str(regression_example_dir / \\\"regression.test\\\"), header=None, sep=\\\"\\\\t\\\")\\n\",\n \"\\n\",\n \"y_train = df_train[0]\\n\",\n \"y_test = df_test[0]\\n\",\n \"X_train = df_train.drop(0, axis=1)\\n\",\n \"X_test = df_test.drop(0, axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Create Dataset object for LightGBM\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"lgb_train = lgb.Dataset(\\n\",\n \" X_train,\\n\",\n \" y_train,\\n\",\n \" feature_name=[f\\\"f{i + 1}\\\" for i in range(X_train.shape[-1])],\\n\",\n \" categorical_feature=[21],\\n\",\n \")\\n\",\n \"lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Configuration dictionary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2018-10-25T22:13:39.243104Z\",\n \"start_time\": \"2018-10-25T22:13:39.240578Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"params = {\\\"num_leaves\\\": 5, \\\"metric\\\": [\\\"l1\\\", \\\"l2\\\"], \\\"verbose\\\": -1}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2018-10-25T22:13:39.336630Z\",\n \"start_time\": \"2018-10-25T22:13:39.246006Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10]\\ttraining's l1: 0.457448\\ttraining's l2: 0.217995\\tvalid_1's l1: 0.456464\\tvalid_1's l2: 0.21641\\n\",\n \"[20]\\ttraining's l1: 0.436869\\ttraining's l2: 0.205099\\tvalid_1's l1: 0.434057\\tvalid_1's l2: 0.201616\\n\",\n \"[30]\\ttraining's l1: 0.421302\\ttraining's l2: 0.197421\\tvalid_1's l1: 0.417019\\tvalid_1's l2: 0.192514\\n\",\n \"[40]\\ttraining's l1: 0.411107\\ttraining's l2: 0.192856\\tvalid_1's l1: 0.406303\\tvalid_1's l2: 0.187258\\n\",\n \"[50]\\ttraining's l1: 0.403695\\ttraining's l2: 0.189593\\tvalid_1's l1: 0.398997\\tvalid_1's l2: 0.183688\\n\",\n \"[60]\\ttraining's l1: 0.398704\\ttraining's l2: 0.187043\\tvalid_1's l1: 0.393977\\tvalid_1's l2: 0.181009\\n\",\n \"[70]\\ttraining's l1: 0.394876\\ttraining's l2: 0.184982\\tvalid_1's l1: 0.389805\\tvalid_1's l2: 0.178803\\n\",\n \"[80]\\ttraining's l1: 0.391147\\ttraining's l2: 0.1828\\tvalid_1's l1: 0.386476\\tvalid_1's l2: 0.176799\\n\",\n \"[90]\\ttraining's l1: 0.388101\\ttraining's l2: 0.180817\\tvalid_1's l1: 0.384404\\tvalid_1's l2: 0.175775\\n\",\n \"[100]\\ttraining's l1: 0.385174\\ttraining's l2: 0.179171\\tvalid_1's l1: 0.382929\\tvalid_1's l2: 0.175321\\n\"\n ]\n }\n ],\n \"source\": [\n \"evals_result = {} # to record eval results for plotting\\n\",\n \"gbm = lgb.train(\\n\",\n \" params,\\n\",\n \" lgb_train,\\n\",\n \" num_boost_round=100,\\n\",\n \" valid_sets=[lgb_train, lgb_test],\\n\",\n \" callbacks=[lgb.log_evaluation(10), lgb.record_evaluation(evals_result)],\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Plot metrics recorded during training\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2018-10-25T22:13:39.809203Z\",\n \"start_time\": \"2018-10-25T22:13:39.338985Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def render_metric(metric_name):\\n\",\n \" lgb.plot_metric(evals_result, metric=metric_name, figsize=(10, 5))\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"if INTERACTIVE:\\n\",\n \" # create widget to switch between metrics\\n\",\n \" interact(render_metric, metric_name=params[\\\"metric\\\"])\\n\",\n \"else:\\n\",\n \" render_metric(params[\\\"metric\\\"][0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Plot feature importances\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2018-10-25T22:13:39.958548Z\",\n \"start_time\": \"2018-10-25T22:13:39.811530Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"def render_plot_importance(importance_type, max_features=10, ignore_zero=True, precision=3):\\n\",\n \" lgb.plot_importance(\\n\",\n \" gbm,\\n\",\n \" importance_type=importance_type,\\n\",\n \" max_num_features=max_features,\\n\",\n \" ignore_zero=ignore_zero,\\n\",\n \" figsize=(12, 8),\\n\",\n \" precision=precision,\\n\",\n \" )\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"if INTERACTIVE:\\n\",\n \" # create widget for interactive feature importance plot\\n\",\n \" interact(\\n\",\n \" render_plot_importance,\\n\",\n \" importance_type=[\\\"split\\\", \\\"gain\\\"],\\n\",\n \" max_features=(1, X_train.shape[-1]),\\n\",\n \" precision=(0, 10),\\n\",\n \" )\\n\",\n \"else:\\n\",\n \" render_plot_importance(importance_type=\\\"split\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Plot split value histogram\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def render_histogram(feature):\\n\",\n \" lgb.plot_split_value_histogram(gbm, feature=feature, bins=\\\"auto\\\", figsize=(10, 5))\\n\",\n \" plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"if INTERACTIVE:\\n\",\n \" # create widget for interactive split value histogram\\n\",\n \" interact(render_histogram, feature=gbm.feature_name())\\n\",\n \"else:\\n\",\n \" render_histogram(feature=\\\"f26\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2018-10-25T22:13:40.027803Z\",\n \"start_time\": \"2018-10-25T22:13:39.960713Z\"\n }\n },\n \"source\": [\n \"## Plot trees\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def render_tree(tree_index, show_info, precision=3):\\n\",\n \" show_info = None if \\\"None\\\" in show_info else show_info\\n\",\n \" return lgb.create_tree_digraph(gbm, tree_index=tree_index, show_info=show_info, precision=precision)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"if INTERACTIVE:\\n\",\n \" # create widget to switch between trees and control info in nodes\\n\",\n \" interact(\\n\",\n \" render_tree,\\n\",\n \" tree_index=(0, gbm.num_trees() - 1),\\n\",\n \" show_info=SelectMultiple( # allow multiple values to be selected\\n\",\n \" options=[\\n\",\n \" \\\"None\\\",\\n\",\n \" \\\"split_gain\\\",\\n\",\n \" \\\"internal_value\\\",\\n\",\n \" \\\"internal_count\\\",\\n\",\n \" \\\"internal_weight\\\",\\n\",\n \" \\\"leaf_count\\\",\\n\",\n \" \\\"leaf_weight\\\",\\n\",\n \" \\\"data_percentage\\\",\\n\",\n \" ],\\n\",\n \" value=[\\\"None\\\"],\\n\",\n \" ),\\n\",\n \" precision=(0, 10),\\n\",\n \" )\\n\",\n \" tree = None\\n\",\n \"else:\\n\",\n \" tree = render_tree(53, [\\\"None\\\"])\\n\",\n \"tree\"\n ]\n }\n ],\n \"metadata\": {\n \"hide_input\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.7\"\n },\n \"varInspector\": {\n \"cols\": {\n \"lenName\": 16,\n \"lenType\": 16,\n \"lenVar\": 40\n },\n \"kernels_config\": {\n \"python\": {\n \"delete_cmd_postfix\": \"\",\n \"delete_cmd_prefix\": \"del \",\n \"library\": \"var_list.py\",\n \"varRefreshCmd\": \"print(var_dic_list())\"\n },\n \"r\": {\n \"delete_cmd_postfix\": \") \",\n \"delete_cmd_prefix\": \"rm(\",\n \"library\": \"var_list.r\",\n \"varRefreshCmd\": \"cat(var_dic_list()) \"\n }\n },\n \"types_to_exclude\": [\n \"module\",\n \"function\",\n \"builtin_function_or_method\",\n \"instance\",\n \"_Feature\"\n ],\n \"window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "examples/python-guide/plot_example.py", "content": "# coding: utf-8\nfrom pathlib import Path\n\nimport pandas as pd\n\nimport lightgbm as lgb\n\nif lgb.compat.MATPLOTLIB_INSTALLED:\n import matplotlib.pyplot as plt\nelse:\n raise ImportError(\"You need to install matplotlib and restart your session for plot_example.py.\")\n\nprint(\"Loading data...\")\n# load or create your dataset\nregression_example_dir = Path(__file__).absolute().parents[1] / \"regression\"\ndf_train = pd.read_csv(str(regression_example_dir / \"regression.train\"), header=None, sep=\"\\t\")\ndf_test = pd.read_csv(str(regression_example_dir / \"regression.test\"), header=None, sep=\"\\t\")\n\ny_train = df_train[0]\ny_test = df_test[0]\nX_train = df_train.drop(0, axis=1)\nX_test = df_test.drop(0, axis=1)\n\n# create dataset for lightgbm\nlgb_train = lgb.Dataset(\n X_train,\n y_train,\n feature_name=[f\"f{i + 1}\" for i in range(X_train.shape[-1])],\n categorical_feature=[21],\n)\nlgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train)\n\n# specify your configurations as a dict\nparams = {\"num_leaves\": 5, \"metric\": (\"l1\", \"l2\"), \"verbose\": 0}\n\nevals_result = {} # to record eval results for plotting\n\nprint(\"Starting training...\")\n# train\ngbm = lgb.train(\n params,\n lgb_train,\n num_boost_round=100,\n valid_sets=[lgb_train, lgb_test],\n callbacks=[lgb.log_evaluation(10), lgb.record_evaluation(evals_result)],\n)\n\nprint(\"Plotting metrics recorded during training...\")\nax = lgb.plot_metric(evals_result, metric=\"l1\")\nplt.show()\n\nprint(\"Plotting feature importances...\")\nax = lgb.plot_importance(gbm, max_num_features=10)\nplt.show()\n\nprint(\"Plotting split value histogram...\")\nax = lgb.plot_split_value_histogram(gbm, feature=\"f26\", bins=\"auto\")\nplt.show()\n\nprint(\"Plotting 54th tree...\") # one tree use categorical feature to split\nax = lgb.plot_tree(gbm, tree_index=53, figsize=(15, 15), show_info=[\"split_gain\"])\nplt.show()\n\nprint(\"Plotting 54th tree with graphviz...\")\ngraph = lgb.create_tree_digraph(gbm, tree_index=53, name=\"Tree54\")\ngraph.render(view=True)\n" }, { "path": "examples/python-guide/simple_example.py", "content": "# coding: utf-8\nfrom pathlib import Path\n\nimport pandas as pd\nfrom sklearn.metrics import mean_squared_error\n\nimport lightgbm as lgb\n\nprint(\"Loading data...\")\n# load or create your dataset\nregression_example_dir = Path(__file__).absolute().parents[1] / \"regression\"\ndf_train = pd.read_csv(str(regression_example_dir / \"regression.train\"), header=None, sep=\"\\t\")\ndf_test = pd.read_csv(str(regression_example_dir / \"regression.test\"), header=None, sep=\"\\t\")\n\ny_train = df_train[0]\ny_test = df_test[0]\nX_train = df_train.drop(0, axis=1)\nX_test = df_test.drop(0, axis=1)\n\n# create dataset for lightgbm\nlgb_train = lgb.Dataset(X_train, y_train)\nlgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)\n\n# specify your configurations as a dict\nparams = {\n \"boosting_type\": \"gbdt\",\n \"objective\": \"regression\",\n \"metric\": {\"l2\", \"l1\"},\n \"num_leaves\": 31,\n \"learning_rate\": 0.05,\n \"feature_fraction\": 0.9,\n \"bagging_fraction\": 0.8,\n \"bagging_freq\": 5,\n \"verbose\": 0,\n}\n\nprint(\"Starting training...\")\n# train\ngbm = lgb.train(\n params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, callbacks=[lgb.early_stopping(stopping_rounds=5)]\n)\n\nprint(\"Saving model...\")\n# save model to file\ngbm.save_model(\"model.txt\")\n\nprint(\"Starting predicting...\")\n# predict\ny_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)\n# eval\nrmse_test = mean_squared_error(y_test, y_pred) ** 0.5\nprint(f\"The RMSE of prediction is: {rmse_test}\")\n" }, { "path": "examples/python-guide/sklearn_example.py", "content": "# coding: utf-8\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import GridSearchCV\n\nimport lightgbm as lgb\n\nprint(\"Loading data...\")\n# load or create your dataset\nregression_example_dir = Path(__file__).absolute().parents[1] / \"regression\"\ndf_train = pd.read_csv(str(regression_example_dir / \"regression.train\"), header=None, sep=\"\\t\")\ndf_test = pd.read_csv(str(regression_example_dir / \"regression.test\"), header=None, sep=\"\\t\")\n\ny_train = df_train[0]\ny_test = df_test[0]\nX_train = df_train.drop(0, axis=1)\nX_test = df_test.drop(0, axis=1)\n\nprint(\"Starting training...\")\n# train\ngbm = lgb.LGBMRegressor(num_leaves=31, learning_rate=0.05, n_estimators=20)\ngbm.fit(X_train, y_train, eval_X=(X_test,), eval_y=(y_test,), eval_metric=\"l1\", callbacks=[lgb.early_stopping(5)])\n\nprint(\"Starting predicting...\")\n# predict\ny_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)\n# eval\nrmse_test = mean_squared_error(y_test, y_pred) ** 0.5\nprint(f\"The RMSE of prediction is: {rmse_test}\")\n\n# feature importances\nprint(f\"Feature importances: {list(gbm.feature_importances_)}\")\n\n\n# self-defined eval metric\n# f(y_true: array, y_pred: array) -> name: str, eval_result: float, is_higher_better: bool\n# Root Mean Squared Logarithmic Error (RMSLE)\ndef rmsle(y_true, y_pred):\n return \"RMSLE\", np.sqrt(np.mean(np.power(np.log1p(y_pred) - np.log1p(y_true), 2))), False\n\n\nprint(\"Starting training with custom eval function...\")\n# train\ngbm.fit(X_train, y_train, eval_X=(X_test,), eval_y=(y_test,), eval_metric=rmsle, callbacks=[lgb.early_stopping(5)])\n\n\n# another self-defined eval metric\n# f(y_true: array, y_pred: array) -> name: str, eval_result: float, is_higher_better: bool\n# Relative Absolute Error (RAE)\ndef rae(y_true, y_pred):\n return \"RAE\", np.sum(np.abs(y_pred - y_true)) / np.sum(np.abs(np.mean(y_true) - y_true)), False\n\n\nprint(\"Starting training with multiple custom eval functions...\")\n# train\ngbm.fit(\n X_train, y_train, eval_X=(X_test,), eval_y=(y_test,), eval_metric=[rmsle, rae], callbacks=[lgb.early_stopping(5)]\n)\n\nprint(\"Starting predicting...\")\n# predict\ny_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_)\n# eval\nrmsle_test = rmsle(y_test, y_pred)[1]\nrae_test = rae(y_test, y_pred)[1]\nprint(f\"The RMSLE of prediction is: {rmsle_test}\")\nprint(f\"The RAE of prediction is: {rae_test}\")\n\n# other scikit-learn modules\nestimator = lgb.LGBMRegressor(num_leaves=31)\n\nparam_grid = {\"learning_rate\": [0.01, 0.1, 1], \"n_estimators\": [20, 40]}\n\ngbm = GridSearchCV(estimator, param_grid, cv=3)\ngbm.fit(X_train, y_train)\n\nprint(f\"Best parameters found by grid search are: {gbm.best_params_}\")\n" }, { "path": "examples/regression/README.md", "content": "Regression Example\n==================\n\nHere is an example for LightGBM to run regression task.\n\n***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html)\nfor the following commands to work. The `lightgbm` binary must be built and available at the root of this project.***\n\nTraining\n--------\n\nRun the following command in this folder:\n\n```bash\n\"../../lightgbm\" config=train.conf\n```\n\nPrediction\n----------\n\nYou should finish training first.\n\nRun the following command in this folder:\n\n```bash\n\"../../lightgbm\" config=predict.conf\n```\n" }, { "path": "examples/regression/forced_bins.json", "content": "[\n {\n \"feature\": 0,\n \"bin_upper_bound\": [\n 0.3,\n 0.35,\n 0.4\n ]\n },\n {\n \"feature\": 1,\n \"bin_upper_bound\": [\n -0.1,\n -0.15,\n -0.2\n ]\n }\n]\n" }, { "path": "examples/regression/forced_bins2.json", "content": "[\n {\n \"feature\": 0,\n \"bin_upper_bound\": [\n 0.19,\n 0.39,\n 0.59,\n 0.79\n ]\n }\n]\n" }, { "path": "examples/regression/predict.conf", "content": "task = predict\n\ndata = regression.test\n\ninput_model= LightGBM_model.txt\n" }, { "path": "examples/regression/regression.test", "content": 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}, { "path": "examples/regression/train.conf", "content": "# task type, support train and predict\ntask = train\n\n# boosting type, support gbdt for now, alias: boosting, boost\nboosting_type = gbdt\n\n# application type, support following application\n# regression , regression task\n# binary , binary classification task\n# lambdarank , lambdarank task\n# alias: application, app\nobjective = regression\n\n# eval metrics, support multi metric, delimited by ',' , support following metrics\n# l1\n# l2 , default metric for regression\n# ndcg , default metric for lambdarank\n# auc\n# binary_logloss , default metric for binary\n# binary_error\nmetric = l2\n\n# frequency for metric output\nmetric_freq = 1\n\n# true if need output metric for training data, alias: tranining_metric, train_metric\nis_training_metric = true\n\n# column in data to use as label\nlabel_column = 0\n\n# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.\nmax_bin = 255\n\n# forced bin thresholds\n# forcedbins_filename = forced_bins.json\n\n# training data\n# if existing weight file, should name to \"regression.train.weight\"\n# alias: train_data, train\ndata = regression.train\n\n# validation data, support multi validation data, separated by ','\n# if existing weight file, should name to \"regression.test.weight\"\n# alias: valid, test, test_data,\nvalid_data = regression.test\n\n# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds\nnum_trees = 100\n\n# shrinkage rate , alias: shrinkage_rate\nlearning_rate = 0.05\n\n# number of leaves for one tree, alias: num_leaf\nnum_leaves = 31\n\n# type of tree learner, support following types:\n# serial , single machine version\n# feature , use feature parallel to train\n# data , use data parallel to train\n# voting , use voting based parallel to train\n# alias: tree\ntree_learner = serial\n\n# number of threads for multi-threading. One thread will use one CPU, default is set to #cpu.\n# num_threads = 8\n\n# feature sub-sample, will random select 80% feature to train on each iteration\n# alias: sub_feature\nfeature_fraction = 0.9\n\n# Support bagging (data sub-sample), will perform bagging every 5 iterations\nbagging_freq = 5\n\n# Bagging fraction, will random select 80% data on bagging\n# alias: sub_row\nbagging_fraction = 0.8\n\n# minimal number data for one leaf, use this to deal with over-fit\n# alias : min_data_per_leaf, min_data\nmin_data_in_leaf = 100\n\n# minimal sum hessians for one leaf, use this to deal with over-fit\nmin_sum_hessian_in_leaf = 5.0\n\n# save memory and faster speed for sparse feature, alias: is_sparse\nis_enable_sparse = true\n\n# when data is bigger than memory size, set this to true. otherwise set false will have faster speed\n# alias: two_round_loading, two_round\nuse_two_round_loading = false\n\n# true if need to save data to binary file and application will auto load data from binary file next time\n# alias: is_save_binary, save_binary\nis_save_binary_file = false\n\n# output model file\noutput_model = LightGBM_model.txt\n\n# support continuous train from trained gbdt model\n# input_model= trained_model.txt\n\n# output prediction file for predict task\n# output_result= prediction.txt\n\n\n# number of machines in distributed training, alias: num_machine\nnum_machines = 1\n\n# local listening port in distributed training, alias: local_port\nlocal_listen_port = 12400\n\n# machines list file for distributed training, alias: mlist\nmachine_list_file = mlist.txt\n" }, { "path": "examples/xendcg/README.md", "content": "XE_NDCG Ranking Example\n=======================\n\nHere is an example for LightGBM to train a ranking model with the [XE_NDCG loss](https://arxiv.org/abs/1911.09798).\n\n***You must follow the [installation instructions](https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html)\nfor the following commands to work. The `lightgbm` binary must be built and available at the root of this project.***\n\nTraining\n--------\n\nRun the following command in this folder:\n\n```bash\n\"../../lightgbm\" config=train.conf\n```\n\nPrediction\n----------\n\nYou should finish training first.\n\nRun the following command in this folder:\n\n```bash\n\"../../lightgbm\" config=predict.conf\n```\n\nData Format\n-----------\n\nTo learn more about the query format used in this example, check out the\n[query data format](https://lightgbm.readthedocs.io/en/latest/Parameters.html#query-data).\n" }, { "path": "examples/xendcg/predict.conf", "content": "task = predict\n\ndata = rank.test\n\ninput_model= LightGBM_model.txt\n" }, { "path": "examples/xendcg/rank.test", "content": "2 1:0.74 6:0.87 8:0.75 9:0.80 11:0.88 12:0.37 17:0.66 20:0.97 21:0.30 27:0.15 28:0.95 30:0.54 32:0.80 34:0.21 36:0.62 37:0.40 39:0.43 41:0.88 43:0.90 60:0.87 66:0.47 69:0.47 70:0.62 74:0.97 77:0.96 78:0.96 81:0.84 83:0.85 85:0.98 91:0.48 96:0.86 98:0.73 100:0.91 101:0.85 104:0.95 106:0.81 108:0.36 111:0.94 114:0.86 117:0.86 120:0.77 122:0.57 123:0.35 124:0.66 126:0.54 127:0.68 129:0.81 133:0.67 135:0.78 140:0.45 144:0.85 145:0.67 146:0.95 147:0.96 149:0.97 150:0.89 151:0.94 152:0.97 153:0.80 154:0.89 155:0.67 158:0.92 159:0.82 161:0.88 162:0.78 164:0.70 165:0.84 167:0.47 169:0.86 172:0.86 173:0.25 176:0.73 177:0.38 179:0.24 181:0.89 186:0.96 187:0.39 189:0.90 192:0.59 195:0.92 201:0.74 202:0.59 206:0.81 208:0.64 212:0.39 215:0.72 216:0.48 222:0.76 232:0.97 235:0.42 238:0.83 241:0.12 242:0.51 243:0.59 245:0.94 247:0.67 248:0.84 253:0.90 255:0.79 256:0.58 259:0.21 260:0.77 261:0.94 265:0.74 266:0.80 267:0.65 268:0.64 271:0.21 276:0.88 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.99 300:0.70\n3 1:0.74 6:0.81 8:0.60 9:0.80 11:0.88 12:0.37 17:0.47 20:0.99 21:0.30 27:0.15 28:0.95 30:0.17 34:0.80 36:1.00 37:0.81 39:0.43 41:0.95 43:0.77 55:0.71 60:0.97 66:0.34 69:0.80 70:0.68 74:0.79 78:0.99 81:0.84 83:0.84 85:0.80 91:0.38 96:0.91 98:0.43 100:0.97 101:0.73 104:0.98 108:0.64 111:0.98 114:0.86 117:0.86 120:0.84 122:0.67 123:0.62 124:0.77 126:0.54 127:0.37 129:0.59 133:0.76 135:0.60 138:0.98 140:0.45 144:0.85 145:0.74 146:0.67 147:0.72 149:0.66 150:0.89 151:0.97 152:0.93 153:0.89 154:0.69 155:0.67 158:0.92 159:0.64 161:0.88 162:0.65 163:0.81 164:0.82 165:0.84 167:0.53 169:0.96 172:0.37 173:0.71 176:0.73 177:0.38 179:0.67 181:0.89 186:0.98 187:0.87 189:0.83 192:0.59 201:0.74 202:0.77 208:0.64 212:0.19 215:0.72 216:0.54 222:0.76 232:0.95 235:0.42 238:0.90 241:0.46 242:0.63 243:0.50 245:0.57 247:0.47 248:0.90 253:0.92 255:0.79 256:0.81 259:0.21 260:0.84 261:0.96 265:0.45 266:0.75 267:0.65 268:0.79 271:0.21 276:0.47 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.43\n2 1:0.74 6:0.80 8:0.60 11:0.88 12:0.37 17:0.61 20:0.91 27:0.34 28:0.95 30:0.21 32:0.78 34:0.63 36:0.93 37:0.36 39:0.43 43:0.87 60:0.87 66:0.29 69:0.54 70:0.87 74:0.85 77:0.89 78:0.90 81:0.98 83:0.84 85:0.90 91:0.25 98:0.38 100:0.84 101:0.78 104:0.80 108:0.84 111:0.88 114:0.98 123:0.83 124:0.79 126:0.54 127:0.34 129:0.81 133:0.76 135:0.98 144:0.85 145:0.99 146:0.62 147:0.67 149:0.79 150:0.99 152:0.85 154:0.85 155:0.67 158:0.92 159:0.64 161:0.88 163:0.90 165:0.92 167:0.47 169:0.81 172:0.45 173:0.39 176:0.73 177:0.38 178:0.55 179:0.50 181:0.89 186:0.90 187:0.21 189:0.83 192:0.59 195:0.87 201:0.74 208:0.64 212:0.27 215:0.96 216:0.84 222:0.76 232:0.85 235:0.05 238:0.81 241:0.12 242:0.18 243:0.37 245:0.69 247:0.67 253:0.78 259:0.21 261:0.88 265:0.40 266:0.75 267:0.28 271:0.21 276:0.59 279:0.86 282:0.86 283:0.82 290:0.98 297:0.36 300:0.70\n0 1:0.74 6:0.84 7:0.81 8:0.66 9:0.80 11:0.88 12:0.37 17:0.84 20:0.92 21:0.05 27:0.27 28:0.95 30:0.73 32:0.80 34:0.29 36:0.72 37:0.35 39:0.43 41:0.82 43:0.96 60:0.87 66:0.27 69:0.10 70:0.40 74:0.96 77:0.70 78:0.86 81:0.95 83:0.85 85:0.98 91:0.15 96:0.77 98:0.51 100:0.83 101:0.85 104:0.79 106:0.81 108:0.90 111:0.84 114:0.95 117:0.86 120:0.65 121:0.99 122:0.57 123:0.09 124:0.71 126:0.54 127:0.71 129:0.81 133:0.67 135:0.63 140:0.45 144:0.85 145:0.70 146:0.80 147:0.83 149:0.97 150:0.98 151:0.77 152:0.91 153:0.48 154:0.96 155:0.67 158:0.92 159:0.78 161:0.88 162:0.86 164:0.57 165:0.90 167:0.48 169:0.81 172:0.73 173:0.10 176:0.73 177:0.38 179:0.33 181:0.89 186:0.86 187:0.21 189:0.86 192:0.59 195:0.90 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.73 215:0.91 216:0.70 220:0.62 222:0.76 230:0.87 232:0.85 233:0.76 235:0.12 238:0.82 241:0.21 242:0.57 243:0.46 245:0.91 247:0.55 248:0.71 253:0.85 255:0.79 256:0.45 259:0.21 260:0.66 261:0.87 265:0.44 266:0.80 267:0.84 268:0.46 271:0.21 276:0.76 279:0.86 282:0.88 283:0.82 285:0.62 290:0.95 297:0.36 299:0.88 300:0.55\n2 1:0.74 6:0.91 7:0.81 8:0.83 9:0.80 11:0.88 12:0.37 17:0.43 20:0.83 21:0.22 27:0.42 28:0.95 30:0.76 32:0.80 34:0.30 36:0.83 37:0.55 39:0.43 41:0.82 43:0.86 60:0.99 66:0.93 69:0.60 70:0.33 74:0.96 77:0.89 78:0.91 81:0.88 83:0.88 85:0.97 91:0.35 96:0.73 98:0.76 100:0.81 101:0.86 104:0.92 106:0.81 108:0.45 111:0.88 114:0.90 117:0.86 120:0.60 121:0.90 122:0.57 123:0.44 126:0.54 127:0.25 129:0.05 135:0.78 140:0.45 144:0.85 145:0.74 146:0.79 147:0.82 149:0.97 150:0.93 151:0.63 152:0.79 153:0.48 154:0.83 155:0.67 158:0.92 159:0.35 161:0.88 162:0.86 164:0.57 165:0.86 167:0.48 169:0.79 172:0.82 173:0.60 176:0.73 177:0.38 179:0.27 181:0.89 186:0.91 187:0.39 189:0.92 192:0.59 195:0.69 201:0.74 202:0.36 204:0.89 206:0.81 208:0.64 212:0.92 215:0.79 216:0.66 220:0.82 222:0.76 230:0.87 232:0.94 233:0.76 235:0.31 238:0.82 241:0.21 242:0.63 243:0.94 247:0.39 248:0.60 253:0.81 255:0.79 256:0.45 259:0.21 260:0.60 261:0.84 265:0.76 266:0.27 267:0.65 268:0.46 271:0.21 276:0.72 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.97 300:0.43\n1 1:0.74 11:0.88 12:0.37 17:0.62 20:0.80 21:0.40 27:0.33 28:0.95 30:0.70 32:0.80 34:0.63 36:0.91 37:0.78 39:0.43 43:0.86 66:0.46 69:0.60 70:0.43 74:0.94 77:0.89 78:0.85 81:0.87 83:0.76 85:0.98 91:0.09 98:0.55 100:0.73 101:0.84 104:0.84 106:0.81 108:0.46 111:0.83 114:0.82 117:0.86 122:0.57 123:0.44 124:0.58 126:0.54 127:0.54 129:0.59 133:0.47 135:0.51 144:0.85 145:0.99 146:0.84 147:0.87 149:0.96 150:0.92 152:0.77 154:0.83 155:0.67 159:0.78 161:0.88 165:0.86 167:0.56 169:0.72 172:0.77 173:0.40 176:0.73 177:0.38 178:0.55 179:0.32 181:0.89 186:0.86 187:0.39 192:0.59 195:0.92 201:0.74 206:0.81 212:0.50 215:0.67 216:0.62 222:0.76 232:0.89 235:0.68 241:0.56 242:0.58 243:0.59 245:0.93 247:0.47 253:0.76 259:0.21 261:0.79 265:0.56 266:0.75 267:0.65 271:0.21 276:0.76 282:0.88 290:0.82 297:0.36 299:0.88 300:0.43\n2 1:0.74 6:0.86 8:0.70 9:0.80 11:0.88 12:0.37 17:0.15 20:0.93 21:0.76 27:0.15 28:0.95 30:0.45 34:0.63 36:0.70 37:0.88 39:0.43 41:0.82 43:0.83 55:0.84 66:0.24 69:0.69 70:0.73 74:0.95 78:0.98 81:0.50 83:0.77 85:0.91 91:0.06 96:0.82 98:0.52 100:0.92 101:0.75 108:0.52 111:0.95 114:0.66 120:0.72 122:0.67 123:0.83 124:0.74 126:0.54 127:0.42 129:0.81 133:0.67 135:0.83 140:0.90 144:0.85 146:0.89 147:0.91 149:0.88 150:0.65 151:0.90 152:0.90 153:0.69 154:0.78 155:0.67 159:0.85 161:0.88 162:0.48 163:0.75 164:0.62 165:0.65 167:0.51 169:0.90 172:0.70 173:0.40 176:0.73 177:0.38 178:0.50 179:0.24 181:0.89 186:0.97 187:0.95 189:0.88 191:0.70 192:0.59 201:0.74 202:0.67 208:0.64 212:0.16 215:0.46 216:0.68 222:0.76 232:0.99 235:0.97 238:0.83 241:0.39 242:0.76 243:0.44 245:0.93 247:0.47 248:0.79 253:0.87 255:0.79 256:0.45 259:0.21 260:0.73 261:0.94 265:0.49 266:0.80 267:0.91 268:0.55 271:0.21 276:0.84 285:0.62 290:0.66 297:0.36 300:0.70\n0 1:0.74 6:0.86 7:0.81 8:0.68 9:0.80 11:0.88 12:0.37 17:0.73 20:0.95 21:0.30 27:0.25 28:0.95 30:0.57 34:0.37 36:0.55 37:0.55 39:0.43 41:0.88 43:0.85 60:0.97 66:0.93 69:0.61 70:0.64 74:0.94 78:0.95 81:0.84 83:0.87 85:0.95 91:0.38 96:0.82 98:0.88 100:0.88 101:0.85 108:0.47 111:0.92 114:0.86 120:0.72 121:0.97 122:0.57 123:0.45 126:0.54 127:0.41 129:0.05 135:0.65 140:0.45 144:0.85 146:0.85 147:0.88 149:0.94 150:0.89 151:0.87 152:0.88 153:0.48 154:0.82 155:0.67 158:0.87 159:0.42 161:0.88 162:0.86 164:0.67 165:0.84 167:0.47 169:0.86 172:0.81 173:0.64 176:0.73 177:0.38 179:0.40 181:0.89 186:0.94 187:0.39 189:0.87 192:0.59 201:0.74 202:0.50 204:0.89 208:0.64 212:0.60 215:0.72 216:0.38 222:0.76 230:0.87 233:0.76 235:0.42 238:0.82 241:0.15 242:0.44 243:0.93 247:0.67 248:0.79 253:0.88 255:0.79 256:0.58 259:0.21 260:0.73 261:0.92 265:0.88 266:0.54 267:0.23 268:0.59 271:0.21 276:0.70 279:0.86 283:0.71 285:0.62 290:0.86 297:0.36 300:0.55\n2 1:0.74 6:0.86 8:0.77 9:0.80 11:0.88 12:0.37 17:0.13 20:0.80 21:0.74 27:0.15 28:0.95 30:0.53 34:0.55 36:0.43 37:0.88 39:0.43 41:0.90 43:0.83 55:0.84 66:0.15 69:0.68 70:0.76 74:0.95 78:0.95 81:0.53 83:0.77 85:0.91 91:0.10 96:0.82 98:0.49 100:0.93 101:0.74 108:0.85 111:0.93 114:0.67 120:0.72 122:0.67 123:0.92 124:0.87 126:0.54 127:0.42 129:0.89 133:0.87 135:0.85 140:0.80 144:0.85 146:0.38 147:0.45 149:0.88 150:0.67 151:0.87 152:0.90 153:0.48 154:0.78 155:0.67 159:0.88 161:0.88 162:0.49 163:0.75 164:0.72 165:0.65 167:0.49 169:0.90 172:0.70 173:0.36 176:0.73 177:0.38 178:0.42 179:0.23 181:0.89 186:0.95 187:0.95 189:0.92 192:0.59 201:0.74 202:0.76 208:0.64 212:0.15 215:0.46 216:0.68 220:0.87 222:0.76 232:0.99 235:0.95 238:0.85 241:0.32 242:0.74 243:0.16 245:0.88 247:0.60 248:0.79 253:0.87 255:0.79 256:0.64 259:0.21 260:0.73 261:0.94 265:0.49 266:0.87 267:0.84 268:0.65 271:0.21 276:0.85 285:0.62 290:0.67 297:0.36 300:0.70\n1 1:0.74 11:0.88 12:0.37 17:0.43 21:0.54 27:0.45 28:0.95 30:0.95 34:0.81 36:0.19 37:0.50 39:0.43 43:0.82 66:0.54 69:0.69 74:0.43 81:0.70 83:0.74 85:0.72 91:0.12 98:0.09 101:0.72 108:0.52 114:0.77 123:0.50 126:0.54 127:0.06 129:0.05 135:0.60 144:0.85 145:0.49 146:0.13 147:0.18 149:0.46 150:0.80 154:0.78 155:0.67 159:0.08 161:0.88 165:0.79 167:0.47 172:0.10 173:0.67 176:0.73 177:0.38 178:0.55 179:0.08 181:0.89 187:0.68 192:0.59 201:0.74 215:0.58 216:0.77 222:0.76 235:0.68 241:0.15 243:0.63 253:0.59 259:0.21 265:0.09 267:0.28 271:0.21 290:0.77 297:0.36 300:0.08\n2 1:0.74 6:0.87 8:0.72 9:0.80 11:0.88 12:0.37 17:0.66 20:0.93 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64:0.77 66:0.35 69:0.25 70:0.81 71:0.57 74:0.61 75:0.96 76:0.85 79:0.87 81:0.71 83:0.69 86:0.84 91:0.35 96:0.69 98:0.62 99:0.95 101:0.65 102:0.95 107:0.95 108:0.75 110:0.94 114:0.80 120:0.56 122:0.95 123:0.73 124:0.71 125:0.97 126:0.54 127:0.74 128:0.95 129:0.59 133:0.67 135:0.56 137:0.87 139:0.79 140:0.45 145:0.49 146:0.34 147:0.40 149:0.39 150:0.56 151:0.52 153:0.48 154:0.83 155:0.55 159:0.52 160:0.96 162:0.86 164:0.57 165:0.81 168:0.95 170:0.82 172:0.54 173:0.28 174:0.94 176:0.73 177:0.88 179:0.58 187:0.21 192:0.57 195:0.70 196:0.89 197:0.96 201:0.64 202:0.36 205:0.95 208:0.64 212:0.49 215:0.63 216:0.09 220:0.93 222:0.35 226:0.96 230:0.71 233:0.76 235:0.74 236:0.97 239:0.97 241:0.24 242:0.22 243:0.37 245:0.77 247:0.76 248:0.52 253:0.61 254:0.86 255:0.70 256:0.45 259:0.21 260:0.56 265:0.63 266:0.71 267:0.36 268:0.46 271:0.38 276:0.62 284:0.89 285:0.62 289:0.95 290:0.80 291:0.97 297:0.36 300:0.70\n3 1:0.66 10:0.87 11:0.52 12:0.86 17:0.43 18:0.64 21:0.13 27:0.72 29:0.86 30:0.68 34:0.90 36:0.62 37:0.92 39:0.51 43:0.68 45:0.73 46:0.96 55:0.71 64:0.77 66:0.79 69:0.52 70:0.31 71:0.57 74:0.41 81:0.70 83:0.69 86:0.70 91:0.27 98:0.80 99:0.95 101:0.65 107:0.92 108:0.40 110:0.92 114:0.92 122:0.41 123:0.38 125:0.88 126:0.51 127:0.28 129:0.05 135:0.37 139:0.67 146:0.55 147:0.61 149:0.47 150:0.58 154:0.58 155:0.50 159:0.20 160:0.88 165:0.81 170:0.82 172:0.41 173:0.73 174:0.83 176:0.70 177:0.88 178:0.55 179:0.80 187:0.39 192:0.50 197:0.88 201:0.63 208:0.61 212:0.42 215:0.84 216:0.24 222:0.35 235:0.31 239:0.87 241:0.41 242:0.19 243:0.80 244:0.61 247:0.55 253:0.63 259:0.21 265:0.80 266:0.27 267:0.52 271:0.38 276:0.32 289:0.95 290:0.92 297:0.36 300:0.25\n2 1:0.66 9:0.74 10:0.89 11:0.52 12:0.86 17:0.73 18:0.69 21:0.13 27:0.72 29:0.86 30:0.72 31:0.92 34:0.37 36:0.40 39:0.51 43:0.69 45:0.83 46:0.96 64:0.77 66:0.60 69:0.75 70:0.37 71:0.57 74:0.50 79:0.91 81:0.70 83:0.69 86:0.75 91:0.83 96:0.77 98:0.79 99:0.95 101:0.65 107:0.94 108:0.59 110:0.94 114:0.92 120:0.65 122:0.65 123:0.56 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30:0.13 32:0.63 34:0.74 36:0.31 37:0.35 39:0.54 43:0.42 45:0.87 55:0.59 66:0.26 69:0.79 70:0.95 71:0.92 74:0.52 76:0.94 77:0.70 81:0.73 83:0.60 86:0.72 91:0.27 96:0.73 97:0.89 98:0.69 99:0.83 101:0.87 108:0.63 114:0.70 122:0.77 123:0.85 124:0.64 126:0.44 127:0.92 129:0.05 131:0.83 133:0.60 135:0.65 139:0.70 140:0.45 144:0.57 145:0.39 146:0.81 147:0.05 149:0.66 150:0.69 151:0.53 153:0.72 154:0.63 155:0.58 157:0.61 158:0.72 159:0.76 162:0.66 165:0.70 166:0.88 172:0.88 173:0.69 176:0.55 177:0.05 179:0.25 182:0.61 187:0.21 190:0.89 191:0.82 192:0.59 195:0.86 201:0.68 202:0.61 204:0.85 208:0.54 212:0.86 215:0.78 216:0.55 220:0.74 222:0.76 227:0.84 229:0.61 230:0.64 231:0.89 232:0.72 233:0.76 235:0.12 241:0.64 242:0.51 243:0.06 245:0.94 246:0.61 247:0.82 248:0.52 253:0.52 254:0.84 256:0.58 257:0.84 259:0.21 265:0.58 266:0.80 267:0.36 268:0.62 276:0.88 277:0.69 279:0.82 282:0.71 283:0.78 285:0.47 287:0.61 290:0.70 297:0.36 300:0.94\n1 1:0.74 7:0.63 11:0.64 12:0.86 17:0.59 18:0.70 21:0.05 22:0.93 27:0.72 29:0.62 30:0.33 34:0.92 36:0.76 39:0.54 43:0.79 47:0.93 60:0.87 64:0.77 66:0.79 69:0.74 70:0.49 71:0.92 74:0.55 76:0.94 81:0.83 83:0.91 85:0.72 86:0.74 91:0.35 98:0.60 101:0.87 108:0.57 114:0.89 117:0.86 122:0.57 123:0.55 126:0.54 127:0.18 129:0.05 131:0.61 135:0.56 139:0.80 144:0.57 145:0.74 146:0.40 147:0.47 149:0.55 150:0.89 154:0.72 155:0.67 157:0.87 158:0.92 159:0.15 165:0.93 166:0.93 172:0.41 173:0.93 176:0.73 177:0.70 178:0.55 179:0.59 182:0.98 187:0.68 192:0.87 201:0.93 208:0.64 212:0.42 215:0.78 216:0.08 219:0.95 222:0.76 227:0.61 229:0.61 230:0.63 231:0.89 232:0.99 233:0.76 235:0.22 241:0.12 242:0.19 243:0.80 246:0.93 247:0.55 253:0.60 259:0.21 262:0.93 265:0.61 266:0.38 267:0.52 276:0.31 279:0.86 283:0.82 287:0.61 290:0.89 292:0.95 297:0.36 300:0.33\n2 1:0.74 7:0.64 8:0.63 11:0.92 12:0.86 17:0.94 18:0.99 21:0.13 27:0.66 29:0.62 30:0.40 34:0.89 36:0.53 37:0.55 39:0.54 43:0.74 44:0.85 45:0.92 48:0.99 55:0.42 64:0.77 66:0.74 69:0.10 70:0.73 71:0.92 74:0.92 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219:0.92 222:0.48 223:0.96 224:0.98 227:0.96 229:0.92 230:0.63 231:0.78 232:0.72 233:0.73 234:0.98 235:0.52 238:0.95 241:0.95 242:0.63 243:0.77 246:0.96 247:0.35 248:0.97 253:0.96 254:0.90 255:0.95 256:0.93 259:0.21 260:0.95 261:0.94 265:0.75 266:0.12 267:0.79 268:0.93 271:0.64 274:1.00 276:0.27 279:0.82 281:0.91 282:0.88 284:0.99 285:0.62 287:0.97 290:0.65 292:0.91 297:0.36 300:0.25\n2 6:0.98 7:0.63 8:0.97 9:0.80 11:0.64 12:0.86 17:0.09 18:0.80 20:0.99 21:0.59 25:0.88 26:0.96 27:0.08 28:0.73 29:0.77 30:0.13 31:1.00 32:0.62 34:0.52 36:0.64 37:0.80 39:0.66 41:0.99 43:0.62 45:0.90 55:0.59 58:1.00 60:0.97 66:0.32 69:0.51 70:0.96 74:0.49 78:0.92 79:1.00 81:0.26 83:0.91 86:0.86 91:0.99 96:0.99 97:0.94 98:0.55 100:0.98 101:0.52 104:0.58 108:0.94 111:0.96 120:0.99 123:0.94 124:0.92 126:0.26 127:0.93 129:0.81 131:0.90 133:0.92 135:0.65 137:1.00 138:0.99 139:0.88 140:0.45 141:0.98 144:0.76 145:0.89 146:0.55 147:0.61 149:0.60 150:0.25 151:1.00 152:1.00 153:0.99 154:0.80 155:0.67 157:0.61 158:0.92 159:0.88 161:0.58 162:0.79 164:0.97 166:0.75 167:0.95 169:0.98 172:0.77 173:0.23 177:0.70 179:0.30 181:0.64 182:0.85 186:0.92 189:0.99 191:0.98 192:0.87 195:0.96 196:1.00 199:1.00 202:0.98 208:0.64 212:0.21 215:0.39 216:0.82 219:0.95 222:0.48 223:0.96 224:0.99 227:0.96 229:0.92 230:0.63 231:0.78 233:0.73 234:0.99 235:0.74 238:0.98 241:0.85 242:0.63 243:0.23 245:0.83 246:0.61 247:0.60 248:0.99 253:0.99 254:0.92 255:0.95 256:0.99 259:0.21 260:0.98 261:0.98 265:0.56 266:0.92 267:0.65 268:0.99 271:0.64 274:1.00 276:0.85 279:0.86 283:0.82 284:1.00 285:0.62 287:0.97 292:0.95 297:0.36 300:0.94\n2 9:0.80 11:0.81 12:0.86 17:0.07 18:0.88 21:0.30 25:0.88 26:0.96 27:0.08 28:0.73 29:0.77 30:0.74 31:0.95 34:0.50 36:0.86 37:0.80 39:0.66 41:0.82 43:0.72 45:0.87 58:0.98 66:0.87 69:0.17 70:0.44 74:0.44 79:0.94 81:0.31 83:0.91 86:0.91 91:0.31 96:0.84 98:0.94 101:0.52 104:0.58 108:0.14 120:0.81 122:0.80 123:0.13 126:0.26 127:0.62 129:0.05 131:0.90 135:0.61 137:0.95 139:0.88 140:0.80 141:0.98 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154:0.77 155:0.67 157:0.81 158:0.91 159:0.50 161:0.58 165:0.93 166:0.84 167:0.61 172:0.61 173:0.60 176:0.73 177:0.70 178:0.55 179:0.58 181:0.64 182:0.84 187:0.68 192:0.87 199:0.98 201:0.93 208:0.64 212:0.30 215:0.37 216:0.77 219:0.95 222:0.48 223:0.96 224:0.93 227:0.61 229:0.61 230:0.64 231:0.76 233:0.76 234:0.91 235:0.88 241:0.15 242:0.57 243:0.29 245:0.71 246:0.96 247:0.67 253:0.41 259:0.21 265:0.69 266:0.71 267:0.28 271:0.64 274:0.91 276:0.54 279:0.86 283:0.78 287:0.61 290:0.56 292:0.91 297:0.36 300:0.55\n2 1:0.69 6:0.93 8:0.95 9:0.80 11:0.64 12:0.86 17:0.24 18:0.90 20:0.74 21:0.13 26:0.96 27:0.09 28:0.73 29:0.77 30:0.67 31:0.96 34:0.92 36:0.89 37:0.86 39:0.66 41:0.88 43:0.71 44:0.90 45:0.91 58:0.98 64:0.77 66:0.47 69:0.32 70:0.63 74:0.35 77:0.70 78:0.72 79:0.95 81:0.74 83:0.91 86:0.96 91:0.38 96:0.86 97:0.89 98:0.44 99:0.83 100:0.73 101:0.52 108:0.25 111:0.72 114:0.64 120:0.75 122:0.80 123:0.24 124:0.66 126:0.44 127:0.77 129:0.59 131:0.90 133:0.66 135:0.54 137:0.96 139:0.96 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8:0.98 9:0.73 10:0.85 12:0.20 17:0.45 18:0.63 21:0.64 23:1.00 27:0.12 28:0.83 29:0.62 30:0.60 31:0.89 32:0.64 34:0.34 36:0.62 37:0.80 39:0.60 43:0.08 55:0.45 62:0.98 66:0.34 69:0.79 70:0.46 71:0.88 74:0.39 75:0.96 76:0.85 77:0.70 79:0.89 81:0.39 83:0.60 86:0.62 91:0.80 96:0.86 98:0.21 104:0.89 108:0.95 114:0.69 120:0.77 123:0.94 124:0.72 126:0.43 127:0.69 128:0.99 129:0.59 133:0.66 135:0.61 137:0.89 139:0.61 140:0.87 143:1.00 145:0.76 146:0.28 147:0.34 149:0.08 150:0.39 151:0.64 153:0.83 154:0.20 155:0.57 159:0.87 161:0.87 162:0.63 164:0.85 167:0.66 168:0.99 170:0.87 172:0.37 173:0.55 174:0.86 175:0.91 176:0.63 177:0.70 179:0.76 181:0.91 187:0.68 190:0.95 191:0.70 192:0.87 193:0.95 195:0.96 196:0.87 197:0.85 201:0.59 202:0.88 205:1.00 208:0.54 212:0.19 215:0.53 216:0.75 220:0.91 222:0.76 226:0.96 230:0.68 232:0.75 233:0.66 235:0.88 236:0.94 239:0.88 241:0.67 242:0.12 243:0.36 244:0.93 245:0.62 247:0.82 248:0.68 253:0.81 254:0.80 255:0.73 256:0.89 257:0.84 259:0.21 260:0.77 264:0.95 265:0.23 266:0.63 267:0.23 268:0.89 271:0.43 276:0.23 277:0.87 282:0.73 283:0.67 284:0.88 285:0.62 290:0.69 297:0.36 300:0.43\n3 1:0.56 7:0.69 8:0.96 9:0.74 10:0.84 11:0.45 12:0.20 17:0.13 18:0.58 21:0.64 23:1.00 27:0.56 28:0.83 29:0.62 30:0.45 31:0.98 32:0.65 34:0.71 36:0.44 39:0.60 43:0.57 45:0.73 55:0.52 62:0.99 66:0.36 69:0.98 70:0.74 71:0.88 74:0.44 75:0.97 77:0.70 79:0.98 81:0.45 83:0.91 86:0.60 91:0.94 96:0.98 97:0.97 98:0.20 99:0.83 101:0.46 104:0.79 108:0.96 114:0.69 120:0.93 123:0.96 124:0.70 126:0.43 127:0.97 128:1.00 129:0.05 133:0.60 135:0.39 137:0.98 138:0.99 139:0.68 140:1.00 143:1.00 145:0.47 146:0.48 147:0.25 149:0.28 150:0.38 151:0.76 153:0.91 154:0.45 155:0.65 159:0.90 161:0.87 162:0.54 164:0.95 165:0.63 167:0.74 168:1.00 170:0.87 172:0.37 173:0.99 174:0.86 175:0.91 176:0.63 177:0.38 179:0.82 181:0.91 187:0.98 190:0.89 192:0.99 195:0.97 196:0.92 197:0.86 201:0.54 202:0.98 204:0.79 205:1.00 208:0.64 212:0.22 215:0.53 216:0.72 219:0.91 220:0.74 222:0.76 226:0.98 230:0.71 233:0.66 235:0.84 236:0.94 239:0.88 241:0.74 242:0.22 243:0.31 244:0.93 245:0.60 247:0.76 248:0.77 253:0.96 255:0.78 256:0.98 257:0.93 259:0.21 260:0.93 264:0.95 265:0.21 266:0.75 267:0.99 268:0.98 271:0.43 276:0.29 277:0.87 279:0.79 282:0.74 284:0.99 285:0.62 290:0.69 292:0.95 294:0.95 295:0.99 297:0.36 300:0.55\n2 1:0.65 6:0.96 7:0.75 8:0.84 9:0.75 10:0.97 11:0.49 12:0.20 17:0.57 18:0.74 20:0.94 21:0.05 23:0.99 27:0.37 28:0.83 29:0.62 30:0.45 32:0.67 33:0.97 34:0.81 36:0.43 37:0.94 39:0.60 43:0.65 45:0.85 56:0.97 62:1.00 66:0.86 69:0.45 70:0.66 71:0.88 74:0.77 75:0.98 76:0.94 78:0.89 80:1.00 81:0.72 82:0.99 83:0.72 86:0.73 88:0.99 91:0.67 96:0.91 97:1.00 98:0.50 99:0.95 100:0.94 101:0.52 102:0.96 104:0.95 106:0.81 108:0.35 111:0.90 114:0.95 117:0.86 120:0.84 122:0.90 123:0.33 124:0.37 126:0.53 127:0.82 128:0.99 129:0.05 133:0.36 135:0.72 139:0.73 140:0.45 143:1.00 145:0.59 146:0.70 147:0.74 149:0.64 150:0.59 151:0.84 152:0.88 153:0.72 154:0.59 155:0.58 158:0.81 159:0.70 161:0.87 162:0.72 164:0.79 165:0.70 167:0.54 168:0.99 169:0.92 170:0.87 172:0.78 173:0.34 174:0.90 176:0.70 177:0.96 179:0.53 181:0.91 186:0.89 187:0.68 189:0.97 190:0.77 192:0.98 193:0.99 195:0.83 197:0.93 201:0.66 202:0.79 204:0.85 205:0.99 206:0.81 208:0.64 212:0.83 215:0.91 216:0.25 219:0.91 220:0.62 222:0.76 226:0.96 230:0.77 232:0.96 233:0.76 235:0.31 236:0.95 238:0.93 239:0.97 241:0.46 242:0.18 243:0.86 245:0.60 247:0.92 248:0.87 253:0.92 254:1.00 255:0.94 256:0.82 259:0.21 260:0.84 261:0.92 264:0.95 265:0.52 266:0.47 267:0.79 268:0.82 271:0.43 275:0.95 276:0.45 279:0.82 283:0.67 285:0.61 290:0.95 291:0.97 292:0.88 294:0.99 295:0.98 297:0.36 300:0.70\n3 1:0.58 7:0.66 8:0.98 9:0.73 10:0.85 12:0.20 17:0.45 18:0.64 21:0.64 23:1.00 27:0.12 28:0.83 29:0.62 30:0.45 31:0.89 32:0.64 34:0.31 36:0.62 37:0.80 39:0.60 43:0.08 55:0.45 62:0.98 66:0.34 69:0.97 70:0.65 71:0.88 74:0.39 75:0.96 76:0.85 77:0.70 79:0.89 81:0.39 83:0.60 86:0.63 91:0.78 96:0.86 98:0.23 104:0.89 108:0.95 114:0.69 120:0.77 123:0.94 124:0.72 126:0.43 127:0.74 128:0.99 129:0.05 133:0.65 135:0.54 137:0.89 139:0.61 140:0.87 143:1.00 145:0.76 146:0.53 147:0.34 149:0.08 150:0.39 151:0.64 153:0.83 154:0.19 155:0.57 159:0.87 161:0.87 162:0.64 164:0.85 167:0.66 168:0.99 170:0.87 172:0.41 173:0.98 174:0.86 175:0.75 176:0.63 177:0.70 179:0.73 181:0.91 187:0.68 190:0.95 191:0.70 192:0.87 193:0.95 195:0.96 196:0.87 197:0.85 201:0.59 202:0.88 205:1.00 208:0.54 212:0.27 215:0.53 216:0.75 220:0.91 222:0.76 226:0.96 230:0.68 232:0.75 233:0.66 235:0.88 236:0.94 239:0.88 241:0.67 242:0.12 243:0.33 244:0.93 245:0.66 247:0.86 248:0.68 253:0.81 254:0.80 255:0.73 256:0.89 257:0.84 259:0.21 260:0.77 264:0.95 265:0.25 266:0.63 267:0.23 268:0.89 271:0.43 276:0.27 277:0.69 282:0.73 283:0.67 284:0.88 285:0.62 290:0.69 297:0.36 300:0.55\n3 1:0.56 6:0.99 7:0.66 8:0.97 9:0.73 10:0.87 12:0.20 17:0.43 18:0.71 20:0.98 21:0.68 23:1.00 27:0.12 28:0.83 29:0.62 30:0.45 31:0.90 32:0.66 34:0.34 36:0.65 37:0.80 39:0.60 43:0.08 55:0.71 62:0.99 66:0.18 69:0.80 70:0.54 71:0.88 74:0.49 75:0.97 76:0.85 77:0.70 78:0.94 79:0.90 81:0.36 83:0.54 86:0.66 91:0.91 96:0.97 97:0.97 98:0.26 100:0.99 102:0.96 108:0.92 111:0.98 114:0.67 120:0.68 122:0.98 123:0.91 124:0.86 126:0.43 127:0.60 128:1.00 129:0.05 133:0.84 135:0.58 137:0.90 139:0.64 140:0.92 143:1.00 145:0.92 146:0.26 147:0.32 149:0.15 150:0.36 151:0.94 152:0.94 153:0.97 154:0.19 155:0.55 158:0.77 159:0.79 161:0.87 162:0.61 163:0.75 164:0.80 167:0.74 168:1.00 169:0.97 170:0.87 172:0.32 173:0.65 174:0.89 175:0.91 176:0.63 177:0.70 179:0.64 181:0.91 186:0.94 187:0.39 189:0.99 190:0.89 192:0.86 193:0.95 195:0.92 196:0.88 197:0.90 201:0.56 202:0.92 205:1.00 208:0.54 212:0.21 215:0.49 216:0.75 222:0.76 226:0.98 230:0.68 232:0.75 233:0.66 235:0.88 236:0.94 238:0.97 239:0.92 241:0.75 242:0.21 243:0.24 244:0.93 245:0.64 247:0.84 248:0.84 253:0.95 254:0.80 255:0.77 256:0.92 257:0.84 259:0.21 260:0.68 261:0.97 264:0.95 265:0.28 266:0.80 267:0.69 268:0.93 271:0.43 276:0.50 277:0.95 279:0.78 282:0.75 284:0.94 285:0.62 290:0.67 294:0.95 295:0.99 297:0.36 300:0.43\n2 6:0.95 7:0.69 8:0.89 9:0.74 10:0.98 11:0.54 12:0.20 17:0.47 18:0.87 20:0.91 21:0.54 23:0.99 27:0.21 28:0.83 29:0.62 30:0.44 33:0.97 34:0.68 36:0.77 37:0.74 39:0.60 43:0.33 45:0.88 55:0.52 62:0.99 66:0.51 69:0.19 70:0.76 71:0.88 74:0.58 75:0.97 76:0.85 78:0.82 81:0.33 83:0.60 86:0.85 91:0.49 96:0.85 97:0.94 98:0.52 100:0.93 101:0.87 104:0.84 106:0.81 108:0.93 111:0.87 120:0.74 122:0.98 123:0.93 124:0.69 126:0.31 127:0.90 128:0.99 129:0.05 133:0.74 135:0.18 139:0.79 140:0.97 143:1.00 146:0.90 147:0.92 149:0.41 150:0.34 151:0.80 152:0.97 153:0.46 154:0.52 155:0.58 159:0.90 161:0.87 162:0.56 164:0.75 167:0.51 168:0.99 169:0.86 170:0.87 172:0.94 173:0.09 174:0.99 177:0.96 179:0.16 181:0.91 186:0.83 187:0.39 190:0.77 192:0.98 193:0.96 197:0.98 201:0.54 202:0.76 205:0.99 206:0.81 208:0.54 212:0.87 215:0.58 216:0.95 220:0.74 222:0.76 226:0.98 230:0.68 232:0.93 233:0.65 235:0.68 236:0.92 239:0.98 241:0.37 242:0.12 243:0.43 244:0.61 245:0.97 247:1.00 248:0.75 253:0.82 254:0.90 255:0.78 256:0.79 259:0.21 260:0.74 261:0.90 264:0.95 265:0.53 266:0.87 267:0.73 268:0.75 271:0.43 275:0.95 276:0.92 277:0.69 279:0.77 283:0.66 285:0.61 291:0.97 294:0.97 295:0.99 297:0.36 300:0.98\n3 1:0.56 8:0.89 9:0.73 10:0.87 11:0.45 12:0.20 17:0.13 18:0.61 20:0.81 21:0.64 23:1.00 27:0.12 28:0.83 29:0.62 30:0.30 31:0.95 34:0.80 36:0.42 37:0.78 39:0.60 43:0.27 45:0.77 56:0.97 62:0.99 66:0.26 69:0.83 70:0.75 71:0.88 74:0.30 75:0.97 78:0.72 79:0.94 81:0.38 83:0.60 86:0.66 91:0.85 96:0.93 97:0.97 98:0.19 99:0.83 100:0.73 101:0.46 108:0.67 111:0.72 120:0.75 122:0.57 123:0.93 124:0.72 126:0.31 127:0.60 128:1.00 129:0.05 133:0.66 135:0.24 137:0.95 139:0.70 140:0.94 143:1.00 145:0.49 146:0.24 147:0.30 149:0.17 150:0.36 151:0.91 152:0.78 153:0.86 154:0.27 155:0.54 159:0.80 161:0.87 162:0.57 164:0.73 165:0.63 167:0.63 168:1.00 169:0.72 170:0.87 172:0.27 173:0.68 174:0.86 175:0.91 177:0.70 178:0.42 179:0.80 181:0.91 186:0.73 187:0.68 190:0.89 192:0.85 196:0.91 197:0.87 201:0.54 202:0.93 205:1.00 208:0.54 212:0.39 216:0.81 219:0.91 220:0.99 222:0.76 226:0.98 232:0.83 235:0.80 236:0.92 239:0.89 241:0.64 242:0.13 243:0.45 244:0.83 245:0.58 247:0.76 248:0.91 253:0.90 254:0.86 255:0.77 256:0.94 257:0.84 259:0.21 260:0.76 261:0.80 264:0.95 265:0.14 266:0.54 267:0.76 268:0.93 271:0.43 275:0.94 276:0.30 277:0.95 279:0.79 284:0.95 285:0.62 292:0.95 294:0.97 295:0.99 300:0.55\n2 8:0.92 9:0.74 10:0.86 11:0.45 12:0.20 17:0.12 18:0.63 21:0.68 23:0.99 27:0.05 28:0.83 29:0.62 30:0.15 31:0.87 34:0.24 36:0.43 37:0.78 39:0.60 43:0.14 45:0.66 55:0.45 62:0.98 66:0.33 69:0.96 70:0.84 71:0.88 74:0.37 75:0.97 79:0.87 81:0.25 83:0.60 86:0.62 91:0.86 96:0.92 97:0.94 98:0.33 101:0.46 104:0.94 106:0.81 108:0.92 120:0.73 122:0.75 123:0.92 124:0.71 126:0.23 127:0.45 128:1.00 129:0.05 133:0.65 135:0.23 137:0.87 139:0.61 140:0.98 143:1.00 146:0.63 147:0.24 149:0.11 150:0.27 151:0.76 153:0.72 154:0.16 155:0.58 158:0.77 159:0.76 161:0.87 162:0.57 163:0.80 164:0.83 167:0.56 168:1.00 170:0.87 172:0.27 173:0.98 174:0.86 175:0.75 177:0.38 179:0.82 181:0.91 187:0.87 190:0.89 192:0.98 196:0.87 197:0.86 202:0.88 205:0.99 206:0.81 208:0.54 212:0.15 215:0.49 216:0.83 220:0.62 222:0.76 226:0.98 232:0.88 235:0.80 239:0.88 241:0.53 242:0.14 243:0.28 244:0.93 245:0.53 247:0.67 248:0.70 253:0.87 255:0.78 256:0.88 257:0.93 259:0.21 260:0.74 264:0.95 265:0.35 266:0.63 267:0.36 268:0.88 271:0.43 275:0.91 276:0.29 277:0.69 279:0.77 283:0.66 284:0.86 285:0.62 294:0.95 295:0.99 297:0.36 300:0.55\n1 1:0.66 7:0.75 10:0.93 11:0.82 17:0.89 18:0.86 21:0.13 22:0.93 27:0.69 29:0.71 30:0.68 32:0.72 34:1.00 36:0.46 37:0.43 39:0.40 43:0.65 44:0.92 45:0.85 47:0.93 64:0.77 66:0.63 69:0.81 70:0.79 71:0.67 74:0.68 77:0.70 81:0.68 83:0.69 85:0.72 86:0.90 91:0.38 97:0.94 98:0.61 99:0.95 101:0.96 106:0.81 108:0.65 114:0.92 117:0.86 122:0.93 123:0.63 124:0.47 126:0.51 127:0.26 129:0.59 133:0.47 135:0.97 139:0.93 145:0.41 146:0.71 147:0.76 149:0.76 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279:0.86 283:0.82 285:0.62 290:0.64 297:0.36 300:0.43\n4 1:0.74 6:0.98 7:0.81 8:0.98 9:0.80 11:0.57 12:0.01 17:0.01 20:0.99 21:0.47 27:0.09 28:0.96 30:0.85 32:0.80 34:0.66 36:0.86 37:0.78 39:0.83 41:1.00 43:0.92 60:1.00 66:0.35 69:0.40 70:0.41 74:0.90 76:0.85 77:0.70 78:0.92 81:0.57 83:0.91 85:0.96 91:0.89 96:1.00 98:0.44 100:0.99 101:0.82 108:0.85 111:0.99 114:0.80 120:0.99 121:0.97 122:0.43 123:0.84 124:0.83 126:0.54 127:0.62 129:0.93 133:0.84 135:0.89 138:1.00 140:0.45 145:0.67 146:0.37 147:0.44 149:0.91 150:0.74 151:1.00 152:0.99 153:0.99 154:0.91 155:0.67 158:0.92 159:0.79 161:0.48 162:0.66 164:0.98 165:0.80 167:0.79 169:0.98 172:0.54 173:0.21 176:0.73 177:0.38 179:0.56 181:0.92 186:0.92 187:0.68 189:0.99 191:0.92 192:0.59 195:0.91 201:0.74 202:0.97 204:0.89 208:0.64 212:0.42 215:0.63 216:0.89 222:0.76 230:0.87 232:0.88 233:0.76 235:0.60 238:0.99 241:0.71 242:0.63 243:0.33 245:0.68 247:0.30 248:1.00 253:1.00 255:0.79 256:0.98 259:0.21 260:0.99 261:0.99 265:0.46 266:0.63 267:0.84 268:0.98 271:0.42 276:0.61 279:0.86 282:0.88 283:0.82 285:0.62 290:0.80 297:0.36 299:0.88 300:0.43\n3 1:0.74 6:0.85 8:0.73 9:0.80 11:0.57 12:0.01 17:0.08 20:0.85 21:0.05 27:0.14 28:0.96 30:0.76 34:0.56 36:0.19 37:0.67 39:0.83 41:0.97 43:0.86 60:0.99 66:0.31 69:0.57 70:0.41 74:0.86 78:0.76 81:0.80 83:0.90 85:0.97 91:0.73 96:0.97 98:0.42 100:0.79 101:0.84 108:0.85 111:0.75 114:0.95 120:0.94 122:0.43 123:0.84 124:0.87 126:0.54 127:0.47 129:0.95 133:0.87 135:0.98 140:0.45 145:0.86 146:0.34 147:0.41 149:0.92 150:0.88 151:0.98 152:0.89 153:0.92 154:0.84 155:0.67 158:0.92 159:0.73 161:0.48 162:0.77 163:0.81 164:0.88 165:0.90 167:0.59 169:0.77 172:0.59 173:0.39 176:0.73 177:0.38 179:0.42 181:0.92 186:0.76 187:0.39 189:0.88 192:0.59 201:0.74 202:0.81 208:0.64 212:0.30 215:0.91 216:0.93 222:0.76 235:0.12 238:0.87 241:0.12 242:0.63 243:0.21 245:0.73 247:0.39 248:0.97 253:0.97 255:0.79 256:0.86 259:0.21 260:0.94 261:0.79 265:0.44 266:0.75 267:0.84 268:0.86 271:0.42 276:0.70 279:0.86 283:0.82 285:0.62 290:0.95 297:0.36 300:0.43\n1 7:0.81 9:0.80 11:0.57 12:0.01 17:0.36 21:0.13 27:0.43 28:0.96 30:0.85 34:0.19 36:0.98 37:0.84 39:0.83 41:0.97 43:0.85 60:0.95 66:0.46 69:0.62 70:0.44 74:0.90 78:0.91 81:0.56 83:0.89 85:0.96 91:0.78 96:0.97 98:0.29 101:0.82 108:0.47 111:0.97 114:0.74 120:0.93 121:0.90 122:0.43 123:0.45 124:0.67 126:0.32 127:0.65 129:0.81 133:0.67 135:0.85 140:0.45 145:0.39 146:0.53 147:0.59 149:0.93 150:0.42 151:0.98 153:0.93 154:0.81 155:0.67 158:0.92 159:0.77 161:0.48 162:0.81 164:0.89 167:0.78 169:0.97 172:0.59 173:0.44 176:0.56 177:0.38 179:0.56 181:0.92 187:0.21 191:0.77 192:0.59 202:0.83 204:0.89 208:0.64 212:0.76 216:0.50 222:0.76 230:0.87 232:0.74 233:0.76 235:0.12 238:0.98 241:0.70 242:0.63 243:0.58 245:0.75 247:0.30 248:0.96 253:0.98 255:0.79 256:0.88 260:0.94 265:0.31 266:0.71 267:0.36 268:0.87 271:0.42 276:0.44 279:0.86 283:0.82 285:0.62 290:0.74 300:0.70\n3 1:0.74 6:0.96 8:0.94 9:0.80 11:0.57 12:0.01 17:0.32 20:0.99 21:0.30 27:0.34 28:0.96 30:0.76 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285:0.62 290:0.98 297:0.36 299:0.88 300:0.84\n3 1:0.74 6:0.85 7:0.81 8:0.68 9:0.80 11:0.88 12:0.37 17:0.18 20:0.99 21:0.40 27:0.09 28:0.70 30:0.55 34:0.19 36:0.52 37:0.32 39:0.42 41:0.99 43:0.92 55:0.71 60:0.95 66:0.92 69:0.37 70:0.60 74:0.77 78:0.88 81:0.73 83:0.90 85:0.88 91:0.90 96:0.99 98:0.98 100:0.90 101:0.80 104:0.54 108:0.29 111:0.87 114:0.82 120:0.98 121:0.90 123:0.28 126:0.54 127:0.84 129:0.05 135:0.49 138:0.98 140:0.45 144:0.85 146:0.91 147:0.93 149:0.85 150:0.83 151:0.98 152:0.90 153:0.93 154:0.92 155:0.67 158:0.92 159:0.53 161:0.75 162:0.73 164:0.96 165:0.83 167:0.88 169:0.88 172:0.77 173:0.38 176:0.73 177:0.38 179:0.56 181:0.94 186:0.88 189:0.87 191:0.77 192:0.59 201:0.74 202:0.93 204:0.89 208:0.64 212:0.30 215:0.67 216:0.23 222:0.60 230:0.87 232:0.74 233:0.76 235:0.52 238:0.88 241:0.85 242:0.75 243:0.92 247:0.47 248:0.99 253:0.99 255:0.79 256:0.94 259:0.21 260:0.98 261:0.92 265:0.98 266:0.63 267:0.28 268:0.95 271:0.60 276:0.66 279:0.86 283:0.82 285:0.62 290:0.82 297:0.36 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267:0.59 268:0.92 271:0.60 276:0.73 279:0.86 282:0.88 283:0.82 285:0.62 290:0.77 297:0.36 299:0.88 300:0.55\n3 1:0.74 6:0.93 8:0.78 9:0.80 11:0.88 12:0.37 17:0.02 20:0.75 27:0.14 28:0.70 30:0.70 32:0.80 34:0.66 36:0.34 37:0.67 39:0.42 41:0.99 43:0.86 60:0.87 66:0.08 69:0.57 70:0.66 74:0.86 77:0.70 78:0.85 81:0.95 83:0.90 85:0.95 91:0.92 96:0.99 98:0.22 100:0.95 101:0.80 108:0.93 111:0.87 114:0.98 120:0.98 122:0.43 123:0.91 124:0.89 126:0.54 127:0.67 129:0.95 133:0.87 135:0.51 138:0.98 140:0.45 144:0.85 145:0.80 146:0.29 147:0.36 149:0.89 150:0.98 151:1.00 152:0.94 153:0.99 154:0.84 155:0.67 158:0.92 159:0.83 161:0.75 162:0.65 164:0.96 165:0.92 167:0.60 169:0.89 172:0.41 173:0.33 176:0.73 177:0.38 179:0.54 181:0.94 186:0.88 187:0.39 189:0.94 191:0.83 192:0.59 195:0.93 201:0.74 202:0.95 208:0.64 212:0.19 215:0.96 216:0.93 222:0.60 235:0.05 238:0.94 241:0.43 242:0.53 243:0.24 245:0.70 247:0.60 248:1.00 253:1.00 255:0.79 256:0.95 259:0.21 260:0.99 261:0.93 265:0.24 266:0.84 267:0.65 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36:0.25 37:0.41 43:0.34 55:0.59 66:0.69 69:0.62 70:0.25 78:0.90 81:0.58 91:0.89 98:0.84 100:0.87 104:0.58 108:0.47 111:0.88 120:0.90 123:0.45 126:0.32 127:0.34 129:0.05 135:0.44 140:0.45 145:0.70 146:0.53 147:0.59 149:0.29 150:0.43 151:0.64 152:0.79 153:0.78 154:0.25 159:0.19 161:0.57 162:0.85 163:0.90 164:0.91 167:0.78 169:0.84 172:0.21 173:0.86 175:0.75 177:0.05 179:0.96 181:0.51 186:0.90 189:0.84 190:0.77 191:0.87 195:0.54 202:0.84 212:0.61 215:0.58 216:0.21 220:0.74 222:0.25 230:0.81 233:0.76 235:0.74 238:0.84 241:0.79 242:0.82 243:0.73 244:0.61 247:0.12 248:0.85 253:0.20 256:0.90 257:0.65 259:0.21 260:0.90 261:0.83 265:0.84 266:0.17 267:0.23 268:0.89 271:0.97 276:0.20 277:0.69 283:0.71 285:0.50 297:0.36 300:0.18\n1 1:0.74 8:0.58 9:0.80 12:0.69 17:0.90 21:0.30 27:0.72 28:0.73 30:0.76 32:0.74 34:0.37 36:0.80 37:0.24 43:0.34 55:0.59 66:0.77 69:0.55 70:0.29 74:0.61 77:0.70 81:0.73 91:0.85 96:0.84 98:0.84 108:0.42 114:0.86 120:0.77 123:0.41 126:0.54 127:0.34 129:0.05 135:0.49 140:0.90 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30:0.30 32:0.75 34:0.20 36:0.60 37:0.62 43:0.39 55:0.45 66:0.12 69:0.45 70:0.75 81:0.53 91:0.89 98:0.43 104:0.58 108:0.69 120:0.94 123:0.77 124:0.64 126:0.32 127:0.88 129:0.81 133:0.67 135:0.44 140:0.45 145:0.82 146:0.05 147:0.05 149:0.46 150:0.40 151:0.74 154:0.30 159:0.51 161:0.57 162:0.60 164:0.93 167:0.82 172:0.41 173:0.47 175:0.75 177:0.05 179:0.83 181:0.51 190:0.77 191:0.95 195:0.68 202:0.91 212:0.39 215:0.55 216:0.34 220:0.62 222:0.25 230:0.81 233:0.69 235:0.80 241:0.93 242:0.82 243:0.15 244:0.61 245:0.55 247:0.12 248:0.91 253:0.20 256:0.93 257:0.65 259:0.21 260:0.94 265:0.41 266:0.63 267:0.79 268:0.91 271:0.97 276:0.40 277:0.69 283:0.71 285:0.50 297:0.36 300:0.55\n1 12:0.69 17:0.30 21:0.40 27:0.45 28:0.73 30:0.32 32:0.80 34:0.70 36:0.17 37:0.50 43:0.32 55:0.84 66:0.52 69:0.69 70:0.75 81:0.66 91:0.10 98:0.59 108:0.68 123:0.66 124:0.78 126:0.54 127:0.68 129:0.93 133:0.84 135:0.75 145:0.40 146:0.36 147:0.43 149:0.72 150:0.48 154:0.24 159:0.83 161:0.45 167:0.64 172:0.79 173:0.47 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78:0.80 81:0.77 91:0.94 100:0.82 104:0.74 111:0.79 120:0.94 126:0.54 135:0.37 140:0.45 150:0.57 151:0.83 152:0.74 153:0.95 161:0.45 162:0.76 164:0.94 167:0.95 169:0.79 175:0.75 181:0.87 186:0.81 189:0.89 191:0.93 202:0.90 215:0.91 216:0.27 235:0.05 238:0.87 241:0.81 244:0.61 248:0.92 256:0.92 257:0.65 259:0.21 260:0.95 261:0.74 267:0.85 268:0.93 277:0.69 283:0.82 285:0.62 297:0.36\n2 6:0.81 7:0.81 8:0.61 12:0.69 17:0.09 20:0.98 21:0.30 27:0.21 28:0.73 30:0.56 34:0.62 36:0.88 37:0.74 43:0.52 55:0.71 66:0.15 69:0.10 70:0.61 78:0.76 81:0.69 91:0.91 98:0.57 100:0.78 104:0.84 106:0.81 108:0.98 111:0.75 120:0.98 123:0.90 124:0.91 126:0.54 127:0.90 129:0.97 133:0.92 135:0.44 140:0.45 146:0.97 147:0.98 149:0.91 150:0.50 151:0.86 152:0.90 153:0.99 154:0.41 159:0.97 161:0.45 162:0.66 164:0.97 167:0.83 169:0.76 172:0.96 173:0.05 177:0.05 179:0.10 181:0.87 186:0.76 187:0.39 189:0.83 191:0.89 202:0.96 206:0.81 212:0.54 215:0.72 216:0.95 230:0.87 233:0.76 235:0.31 238:0.86 241:0.71 242:0.82 243:0.23 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85:0.88 86:0.94 91:0.37 96:0.77 98:0.34 101:0.87 108:0.90 114:0.54 120:0.65 122:0.86 123:0.90 124:0.90 126:0.54 127:0.73 129:0.81 133:0.89 137:0.92 139:0.94 140:0.45 145:0.40 146:0.70 147:0.74 149:0.80 150:0.69 151:0.75 153:0.72 154:0.86 155:0.67 158:0.91 159:0.88 161:0.85 162:0.86 164:0.69 165:0.93 167:0.60 172:0.51 173:0.27 176:0.73 177:0.70 179:0.42 181:0.60 187:0.21 192:0.87 196:0.95 201:0.93 202:0.58 208:0.64 212:0.35 215:0.36 216:0.10 219:0.95 220:0.62 222:0.35 230:0.69 233:0.76 235:0.98 241:0.32 242:0.71 243:0.36 245:0.77 247:0.67 248:0.70 253:0.70 255:0.95 256:0.64 260:0.66 262:0.93 265:0.36 266:0.94 268:0.66 271:0.31 276:0.74 279:0.86 281:0.91 283:0.78 284:0.94 285:0.62 290:0.54 292:0.91 297:0.36 300:0.84\n1 1:0.74 11:0.57 12:0.37 17:0.90 18:0.88 21:0.22 22:0.93 27:0.72 28:0.87 29:0.56 30:0.88 34:0.79 36:0.28 39:0.79 43:0.93 47:0.93 60:0.95 64:0.77 66:0.85 69:0.33 70:0.29 71:0.62 74:0.70 81:0.67 83:0.91 85:0.85 86:0.93 91:0.37 98:0.63 101:0.87 104:0.95 106:0.81 108:0.26 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267:0.28 276:0.41 283:0.60 297:0.36 300:0.13\n0 8:0.80 12:0.37 17:0.26 20:0.81 21:0.78 27:0.05 34:0.44 36:0.44 37:0.97 78:0.72 91:0.68 100:0.73 111:0.72 120:0.60 135:0.34 140:0.95 151:0.54 152:0.78 153:0.48 162:0.47 164:0.57 169:0.72 175:0.75 178:0.53 186:0.73 187:0.39 191:0.76 202:0.75 216:0.58 220:0.62 235:0.31 241:0.68 244:0.61 248:0.54 256:0.45 257:0.65 259:0.21 260:0.61 261:0.73 267:0.52 268:0.46 277:0.69 285:0.62\n2 7:0.81 12:0.37 17:0.45 20:0.80 21:0.30 27:0.50 30:0.95 32:0.80 34:0.37 36:0.91 37:0.60 43:0.50 55:0.92 66:0.36 69:0.21 78:0.72 81:0.95 91:0.44 98:0.43 100:0.73 104:0.84 106:0.81 108:0.17 111:0.72 120:0.69 123:0.16 124:0.52 126:0.54 127:0.22 129:0.59 133:0.47 135:0.78 140:0.45 145:0.69 146:0.39 147:0.46 149:0.27 150:0.93 151:0.66 152:0.77 153:0.48 154:0.39 159:0.24 162:0.86 163:0.86 164:0.57 169:0.72 172:0.10 173:0.34 177:0.05 179:0.96 186:0.73 187:0.39 195:0.60 202:0.36 206:0.81 212:0.95 215:0.72 216:0.86 230:0.87 233:0.76 235:0.31 241:0.27 242:0.82 243:0.51 245:0.37 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8:0.62 12:0.37 17:0.40 20:0.88 21:0.71 28:0.66 30:0.37 32:0.80 34:0.40 36:0.28 37:0.97 43:0.30 55:0.71 66:0.62 69:0.73 70:0.89 78:0.74 81:0.76 91:0.54 98:0.47 100:0.77 104:0.80 108:0.70 111:0.73 120:0.93 123:0.68 124:0.75 126:0.54 127:0.66 129:0.95 133:0.87 135:0.69 140:0.45 145:0.63 146:0.40 147:0.46 149:0.71 150:0.57 151:0.80 152:0.83 153:0.93 154:0.22 159:0.89 161:0.61 162:0.72 164:0.92 167:0.55 169:0.75 172:0.77 173:0.44 177:0.05 179:0.43 181:0.91 186:0.75 187:0.21 195:0.97 202:0.87 212:0.42 215:0.48 216:0.98 235:0.84 241:0.49 242:0.82 243:0.23 245:0.70 247:0.12 248:0.90 256:0.89 259:0.21 260:0.93 261:0.75 265:0.49 266:0.75 267:0.44 268:0.90 271:0.55 276:0.70 283:0.60 285:0.62 297:0.36 300:0.70\n3 8:0.88 12:0.37 17:0.57 20:0.76 21:0.22 27:0.72 28:0.66 30:0.21 34:0.19 36:0.25 37:0.86 43:0.44 55:0.45 66:0.54 69:0.45 70:0.37 78:0.76 81:0.92 91:0.87 98:0.33 100:0.80 104:0.54 108:0.35 111:0.76 120:0.77 123:0.33 124:0.45 126:0.54 127:0.83 129:0.59 133:0.47 135:0.54 140:0.45 145:0.70 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242:0.82 243:0.16 245:0.48 247:0.12 248:0.79 256:0.85 259:0.21 260:0.86 261:0.82 265:0.84 266:0.54 267:0.36 268:0.86 271:0.55 276:0.41 283:0.60 285:0.62 300:0.33\n1 12:0.37 17:0.36 21:0.30 27:0.45 28:0.66 30:0.68 34:0.71 36:0.30 37:0.50 43:0.33 55:0.71 66:0.68 69:0.68 70:0.43 81:0.91 91:0.22 98:0.53 108:0.52 123:0.49 124:0.41 126:0.54 127:0.37 129:0.59 133:0.47 135:0.69 145:0.52 146:0.64 147:0.69 149:0.42 150:0.83 154:0.24 159:0.48 161:0.61 163:0.75 167:0.49 172:0.37 173:0.67 177:0.05 178:0.55 179:0.85 181:0.91 187:0.68 212:0.42 215:0.72 216:0.77 235:0.31 241:0.21 242:0.82 243:0.72 245:0.45 247:0.12 259:0.21 265:0.55 266:0.27 267:0.28 271:0.55 276:0.28 297:0.36 300:0.33\n3 6:0.85 7:0.81 8:0.78 12:0.37 17:0.73 20:0.76 21:0.74 27:0.43 28:0.66 30:0.21 32:0.80 34:0.59 36:0.62 37:0.69 43:0.49 55:0.45 66:0.54 69:0.38 70:0.37 78:0.89 81:0.73 91:0.76 98:0.49 100:0.95 104:0.58 108:0.59 111:0.91 120:0.85 123:0.56 124:0.45 126:0.54 127:0.75 129:0.59 133:0.47 135:0.42 140:0.45 145:0.98 146:0.05 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216:0.55 220:0.62 235:0.12 238:1.00 241:0.97 242:0.82 243:0.34 245:0.37 247:0.12 248:0.92 256:0.98 259:0.21 260:0.95 261:0.99 265:0.23 266:0.12 267:0.69 268:0.98 271:0.55 276:0.14 283:0.60 285:0.62 297:0.36 300:0.13\n3 8:1.00 12:0.37 17:0.77 20:0.80 21:0.13 27:0.54 28:0.66 30:0.76 34:0.45 36:0.97 37:0.85 43:0.32 55:0.92 66:0.72 69:0.70 70:0.22 78:0.72 81:0.94 91:0.85 98:0.78 100:0.78 104:0.72 108:0.53 111:0.73 120:0.85 123:0.51 126:0.54 127:0.26 129:0.05 135:0.74 140:0.45 146:0.66 147:0.71 149:0.31 150:0.89 151:0.78 152:0.78 153:0.89 154:0.23 159:0.25 161:0.61 162:0.79 163:0.90 164:0.88 167:0.83 169:0.75 172:0.27 173:0.82 177:0.05 179:0.91 181:0.91 186:0.73 191:0.90 202:0.81 212:0.42 215:0.84 216:0.24 220:0.74 235:0.12 241:0.89 242:0.82 243:0.75 247:0.12 248:0.79 256:0.83 259:0.21 260:0.86 261:0.75 265:0.78 266:0.23 267:0.28 268:0.85 271:0.55 276:0.24 283:0.60 285:0.62 297:0.36 300:0.18\n1 7:0.81 8:0.85 12:0.37 17:0.57 20:0.76 27:0.20 28:0.66 30:0.94 32:0.80 34:0.24 36:0.41 37:0.73 43:0.43 55:0.71 66:0.64 69:0.45 70:0.20 78:0.72 81:0.96 91:0.29 98:0.81 100:0.73 104:0.88 106:0.81 108:0.68 111:0.72 123:0.66 124:0.47 126:0.54 127:0.52 129:0.59 133:0.47 135:0.71 145:0.39 146:0.46 147:0.53 149:0.76 150:0.94 152:0.75 154:0.33 159:0.65 161:0.61 167:0.54 169:0.72 172:0.80 173:0.34 177:0.05 178:0.55 179:0.36 181:0.91 186:0.73 187:0.68 195:0.82 206:0.81 212:0.86 215:0.96 216:0.72 230:0.87 233:0.76 235:0.02 241:0.44 242:0.82 243:0.32 245:0.88 247:0.12 259:0.21 261:0.73 265:0.81 266:0.63 267:0.23 271:0.55 276:0.77 283:0.82 297:0.36 300:0.43\n2 7:0.81 8:0.93 12:0.37 17:0.13 21:0.54 27:0.54 28:0.66 30:0.33 32:0.80 34:0.55 36:0.56 37:0.95 43:0.50 55:0.52 66:0.86 69:0.27 70:0.69 81:0.94 91:0.67 98:0.92 104:0.58 108:0.21 120:0.75 123:0.20 126:0.54 127:0.54 129:0.05 135:0.60 140:0.45 145:0.47 146:0.57 147:0.63 149:0.65 150:0.90 151:0.73 153:0.78 154:0.39 159:0.21 161:0.61 162:0.60 164:0.73 167:0.73 172:0.61 173:0.58 177:0.05 179:0.71 181:0.91 187:0.39 191:0.82 195:0.56 202:0.67 212:0.85 215:0.58 216:0.24 230:0.87 233:0.76 235:0.68 241:0.74 242:0.82 243:0.87 247:0.12 248:0.68 256:0.64 259:0.21 260:0.76 265:0.92 266:0.27 267:0.28 268:0.67 271:0.55 276:0.51 283:0.60 285:0.62 297:0.36 300:0.55\n2 7:0.81 12:0.37 17:0.49 20:0.81 21:0.22 27:0.64 28:0.66 30:0.54 32:0.80 34:0.92 36:0.90 37:0.55 43:0.49 55:0.71 66:0.88 69:0.27 70:0.42 78:0.74 81:0.92 91:0.74 98:0.98 100:0.79 104:0.58 108:0.21 111:0.74 120:0.73 123:0.20 126:0.54 127:0.80 129:0.05 135:0.77 140:0.45 145:0.84 146:0.85 147:0.88 149:0.62 150:0.86 151:0.66 152:0.82 153:0.48 154:0.38 159:0.42 161:0.61 162:0.74 164:0.67 167:0.83 169:0.75 172:0.67 173:0.37 177:0.05 179:0.69 181:0.91 186:0.75 187:0.21 195:0.67 202:0.56 212:0.61 215:0.79 216:0.21 230:0.87 233:0.76 235:0.22 241:0.91 242:0.82 243:0.89 247:0.12 248:0.66 256:0.58 259:0.21 260:0.74 261:0.76 265:0.98 266:0.54 267:0.59 268:0.59 271:0.55 276:0.56 285:0.62 297:0.36 300:0.33\n2 7:0.81 8:0.80 12:0.37 17:0.36 20:0.83 21:0.05 25:0.88 27:0.37 28:0.66 30:0.72 32:0.80 34:0.42 36:0.78 37:0.67 43:0.49 55:0.71 66:0.84 69:0.25 70:0.29 78:0.75 81:0.95 91:0.83 98:0.98 100:0.79 104:0.58 108:0.20 111:0.75 120:0.64 123:0.19 126:0.54 127:0.85 129:0.05 135:0.39 140:0.45 145:0.76 146:0.82 147:0.85 149:0.54 150:0.92 151:0.59 152:0.79 153:0.91 154:0.37 159:0.38 161:0.61 162:0.69 163:0.75 164:0.82 167:0.83 169:0.77 172:0.54 173:0.39 177:0.05 179:0.83 181:0.91 186:0.76 191:0.73 195:0.63 202:0.74 212:0.39 215:0.91 216:0.24 220:0.62 230:0.65 233:0.63 235:0.05 241:0.90 242:0.82 243:0.85 247:0.12 248:0.58 256:0.75 259:0.21 260:0.65 261:0.76 265:0.98 266:0.47 267:0.28 268:0.77 271:0.55 276:0.45 283:0.60 285:0.62 297:0.36 300:0.25\n3 6:0.94 7:0.81 8:0.94 12:0.37 17:0.20 20:0.99 21:0.05 27:0.16 28:0.66 30:0.91 32:0.80 34:0.64 36:0.73 37:0.90 43:0.42 55:0.59 66:0.69 69:0.47 70:0.13 78:0.78 81:0.95 91:0.98 98:0.44 100:0.90 104:0.58 108:0.36 111:0.83 120:0.97 123:0.35 126:0.54 127:0.14 129:0.05 135:0.34 140:0.45 145:0.47 146:0.32 147:0.39 149:0.33 150:0.92 151:0.83 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287:0.88 290:0.77 300:0.55\n0 11:0.42 12:0.51 17:0.49 18:0.60 21:0.78 26:0.94 27:0.45 29:0.99 30:0.11 34:0.86 36:0.19 37:0.50 39:0.79 43:0.08 55:0.45 58:0.93 66:0.16 69:0.86 70:0.54 71:0.59 74:0.33 86:0.62 91:0.17 98:0.18 108:0.73 122:0.51 123:0.77 124:0.81 127:0.22 129:0.59 131:0.61 133:0.76 135:0.95 139:0.61 141:0.91 144:0.57 145:0.49 146:0.19 147:0.24 149:0.08 154:0.23 157:0.61 159:0.64 163:0.89 166:0.61 172:0.10 173:0.71 175:0.75 177:0.38 178:0.55 179:0.81 182:0.61 187:0.68 199:0.94 212:0.30 216:0.77 222:0.25 223:0.92 224:0.93 227:0.61 229:0.61 231:0.62 234:0.91 235:0.84 241:0.21 242:0.33 243:0.37 244:0.83 245:0.42 246:0.61 247:0.39 253:0.29 254:0.84 257:0.65 259:0.21 265:0.09 266:0.27 267:0.65 271:0.79 274:0.91 276:0.25 277:0.69 287:0.61 300:0.33\n2 1:0.69 8:0.84 11:0.52 12:0.51 17:0.91 18:0.96 22:0.93 26:0.98 27:0.63 29:0.99 30:0.53 31:0.92 34:0.22 36:0.85 37:0.78 39:0.79 43:0.77 44:0.92 45:0.91 47:0.93 48:0.97 55:0.52 58:0.98 64:0.77 66:0.77 69:0.10 70:0.68 71:0.59 74:0.68 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266:0.71 267:0.82 276:0.50 279:0.86 282:0.88 283:0.82 290:0.98 297:0.36 299:0.97 300:0.43\n3 1:0.74 6:0.87 7:0.81 8:0.77 9:0.80 11:0.57 12:0.51 17:0.79 20:0.98 21:0.64 27:0.37 28:0.99 30:0.43 32:0.80 34:0.83 36:0.29 37:0.36 39:0.39 41:0.90 43:0.89 60:0.87 66:0.23 69:0.52 70:0.73 74:0.98 77:0.70 78:0.98 81:0.70 83:0.81 85:0.96 91:0.85 96:0.88 98:0.71 100:0.97 101:0.85 104:0.58 108:0.40 111:0.97 114:0.72 120:0.79 121:0.90 122:0.57 123:0.69 124:0.55 126:0.54 127:0.47 129:0.59 133:0.47 135:0.61 140:0.45 145:0.72 146:0.61 147:0.66 149:0.96 150:0.75 151:0.97 152:0.96 153:0.88 154:0.87 155:0.67 158:0.92 159:0.52 161:0.88 162:0.73 164:0.81 167:0.83 169:0.94 172:0.76 173:0.49 176:0.73 177:0.38 179:0.35 181:0.47 186:0.98 189:0.90 191:0.76 192:0.59 195:0.72 201:0.74 202:0.74 204:0.89 208:0.64 212:0.77 215:0.53 216:0.15 222:0.48 230:0.83 232:0.79 233:0.66 235:0.84 238:0.89 241:0.85 242:0.28 243:0.61 245:0.90 247:0.60 248:0.86 253:0.92 255:0.79 256:0.75 259:0.21 260:0.80 261:0.95 265:0.52 266:0.54 267:0.65 268:0.78 276:0.75 279:0.86 282:0.88 283:0.82 285:0.62 290:0.72 297:0.36 299:0.88 300:0.55\n0 12:0.51 17:0.18 21:0.78 27:0.45 28:0.99 30:0.09 34:0.87 36:0.17 37:0.50 39:0.39 43:0.30 55:0.45 66:0.44 69:0.83 70:0.99 74:0.80 77:0.70 91:0.37 98:0.21 108:0.68 122:0.57 123:0.66 124:0.76 127:0.51 129:0.05 133:0.74 135:0.90 145:0.52 146:0.38 147:0.40 149:0.25 154:0.64 159:0.74 161:0.88 167:0.52 172:0.37 173:0.72 177:0.05 178:0.55 179:0.78 181:0.47 187:0.68 195:0.89 212:0.52 216:0.77 222:0.48 235:0.52 241:0.30 242:0.42 243:0.25 245:0.52 247:0.47 253:0.57 254:0.74 257:0.65 259:0.21 265:0.23 266:0.54 267:0.52 276:0.39 282:0.84 300:0.84\n3 1:0.74 6:0.86 7:0.81 8:0.71 9:0.80 11:0.57 12:0.51 17:0.95 20:0.87 21:0.30 27:0.34 28:0.99 30:0.28 32:0.80 34:0.75 36:0.33 37:0.38 39:0.39 41:0.90 43:0.78 60:0.87 66:0.36 69:0.77 70:0.88 74:0.85 77:0.98 78:0.98 81:0.89 83:0.86 85:0.94 91:0.33 96:0.85 98:0.32 100:0.95 101:0.77 104:0.87 108:0.89 111:0.96 114:0.68 120:0.76 121:0.90 123:0.88 124:0.92 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290:0.92 297:0.36 300:0.18\n2 1:0.74 6:0.97 7:0.81 8:0.84 9:0.80 11:0.87 12:0.51 17:0.15 18:0.98 20:0.99 21:0.30 22:0.93 27:0.21 28:0.73 29:0.62 30:0.42 31:0.99 34:0.44 36:0.84 37:0.74 39:0.67 41:0.95 43:0.97 45:0.93 47:0.93 60:0.98 64:0.77 66:0.43 69:0.15 70:0.82 71:0.73 74:0.85 78:0.85 79:0.99 81:0.59 83:0.91 85:0.90 86:0.96 91:0.73 96:0.96 97:0.89 98:0.58 100:0.91 101:0.97 104:0.84 106:0.81 108:0.12 111:0.86 114:0.65 117:0.86 120:0.92 121:0.90 122:0.85 123:0.12 124:0.79 126:0.54 127:0.81 129:0.59 131:0.93 133:0.81 135:0.44 137:0.99 139:0.97 140:0.45 144:0.76 146:0.88 147:0.90 149:0.95 150:0.76 151:0.99 152:0.99 153:0.95 154:0.97 155:0.67 157:0.98 158:0.92 159:0.82 161:0.71 162:0.61 164:0.86 165:0.93 166:0.89 167:0.73 169:0.86 172:0.87 173:0.10 176:0.73 177:0.70 179:0.22 181:0.73 182:0.99 186:0.85 187:0.39 189:0.98 192:0.87 196:0.99 201:0.93 202:0.88 204:0.89 206:0.81 208:0.64 212:0.61 215:0.44 216:0.95 219:0.95 222:0.90 227:0.96 229:0.99 230:0.87 231:0.85 232:0.90 233:0.76 235:0.52 238:0.94 241:0.51 242:0.19 243:0.57 245:0.93 246:0.99 247:0.90 248:0.94 253:0.96 255:0.95 256:0.88 259:0.21 260:0.90 261:0.89 262:0.93 265:0.59 266:0.87 267:0.52 268:0.89 271:0.69 276:0.90 279:0.86 281:0.91 283:0.82 284:0.98 285:0.62 287:0.99 290:0.65 292:0.95 297:0.36 300:0.84\n3 1:0.74 6:0.91 9:0.80 11:0.87 12:0.51 17:0.02 18:0.96 20:0.84 21:0.05 27:0.52 28:0.73 29:0.62 30:0.56 31:0.96 34:0.66 36:0.76 37:0.45 39:0.67 41:0.90 43:0.96 45:0.77 64:0.77 66:0.94 69:0.10 70:0.62 71:0.73 74:0.77 78:0.80 79:0.95 81:0.79 83:0.91 85:0.88 86:0.97 91:0.54 96:0.86 98:0.98 100:0.84 101:0.97 108:0.09 111:0.80 114:0.89 120:0.78 122:0.57 123:0.09 126:0.54 127:0.85 129:0.05 131:0.93 135:0.42 137:0.96 139:0.96 140:0.45 144:0.76 146:0.81 147:0.84 149:0.90 150:0.87 151:0.93 152:0.96 153:0.78 154:0.96 155:0.67 157:0.98 159:0.37 161:0.71 162:0.72 164:0.73 165:0.93 166:0.90 167:0.74 169:0.92 172:0.83 173:0.28 176:0.73 177:0.70 179:0.47 181:0.73 182:0.99 186:0.81 187:0.87 192:0.87 196:0.98 201:0.93 202:0.64 208:0.64 212:0.73 215:0.78 216:0.46 222:0.90 227:0.96 229:0.99 231:0.84 235:0.22 241:0.56 242:0.18 243:0.94 246:0.99 247:0.86 248:0.85 253:0.88 255:0.95 256:0.64 259:0.21 260:0.78 261:0.94 265:0.98 266:0.47 267:0.44 268:0.67 271:0.69 276:0.72 284:0.94 285:0.62 287:0.99 290:0.89 297:0.36 300:0.55\n4 1:0.74 6:0.91 8:0.77 9:0.80 11:0.87 12:0.51 17:0.04 18:0.95 20:0.99 21:0.05 27:0.52 28:0.73 29:0.62 30:0.66 31:0.99 34:0.36 36:0.55 37:0.45 39:0.67 41:0.98 43:0.81 45:0.73 60:0.95 64:0.77 66:0.93 69:0.23 70:0.49 71:0.73 74:0.75 78:0.92 79:0.99 81:0.79 83:0.91 85:0.90 86:0.91 91:0.90 96:0.97 98:0.92 100:0.96 101:0.97 108:0.18 111:0.93 114:0.89 120:0.95 122:0.57 123:0.18 126:0.54 127:0.53 129:0.05 131:0.93 135:0.28 137:0.99 139:0.92 140:0.45 144:0.76 146:0.85 147:0.88 149:0.91 150:0.87 151:0.99 152:0.96 153:0.95 154:0.76 155:0.67 157:0.99 158:0.92 159:0.42 161:0.71 162:0.80 164:0.92 165:0.93 166:0.90 167:1.00 169:0.92 172:0.80 173:0.32 176:0.73 177:0.70 179:0.45 181:0.73 182:0.99 186:0.92 189:0.93 191:0.89 192:0.87 196:0.99 201:0.93 202:0.90 208:0.64 212:0.77 215:0.78 216:0.46 219:0.95 220:0.62 222:0.90 227:0.96 229:1.00 231:0.85 235:0.22 238:0.94 241:0.95 242:0.22 243:0.93 246:0.99 247:0.76 248:0.97 253:0.97 255:0.95 256:0.92 259:0.21 260:0.94 261:0.94 265:0.92 266:0.27 267:0.52 268:0.93 271:0.69 276:0.69 279:0.86 283:0.82 284:0.99 285:0.62 287:0.99 290:0.89 292:0.95 297:0.36 300:0.43\n2 1:0.74 6:0.99 8:0.88 9:0.80 11:0.78 12:0.51 17:0.16 18:0.64 20:0.80 21:0.30 27:0.17 28:0.73 29:0.62 30:0.62 31:0.87 34:0.55 36:0.77 37:1.00 39:0.67 41:0.82 43:0.80 64:0.77 66:0.75 69:0.41 70:0.34 71:0.73 74:0.42 78:0.85 79:0.87 81:0.59 83:0.91 85:0.72 86:0.74 91:0.70 96:0.70 98:0.81 100:0.91 101:0.87 108:0.31 111:0.86 114:0.65 120:0.56 123:0.30 126:0.54 127:0.30 129:0.05 131:0.93 135:0.51 137:0.87 139:0.71 140:0.45 144:0.76 146:0.56 147:0.62 149:0.57 150:0.76 151:0.53 152:0.92 153:0.78 154:0.74 155:0.67 157:0.87 159:0.20 161:0.71 162:0.53 164:0.65 165:0.93 166:0.89 167:0.79 169:1.00 172:0.32 173:0.63 176:0.73 177:0.70 179:0.89 181:0.73 182:0.98 186:0.85 187:0.21 191:0.80 192:0.87 196:0.88 201:0.93 202:0.62 208:0.64 212:0.30 215:0.44 216:0.48 220:0.87 222:0.90 227:0.96 229:0.98 231:0.84 235:0.52 241:0.67 242:0.33 243:0.77 246:0.99 247:0.39 248:0.52 253:0.50 255:0.95 256:0.45 259:0.21 260:0.57 261:0.98 265:0.81 266:0.27 267:0.36 268:0.58 271:0.69 276:0.25 284:0.91 285:0.62 287:0.99 290:0.65 297:0.36 300:0.25\n2 1:0.74 6:0.97 7:0.81 8:0.86 9:0.80 11:0.87 12:0.51 17:0.12 18:0.88 20:0.98 21:0.40 22:0.93 27:0.21 28:0.73 29:0.62 30:0.72 31:0.96 34:0.50 36:0.61 37:0.74 39:0.67 41:0.92 43:0.97 45:0.66 47:0.93 48:0.91 60:0.87 64:0.77 66:0.77 69:0.17 70:0.50 71:0.73 74:0.82 78:0.79 79:0.96 81:0.54 83:0.91 85:0.91 86:0.93 91:0.44 96:0.87 98:0.80 100:0.81 101:0.97 104:0.84 106:0.81 108:0.61 111:0.78 114:0.62 117:0.86 120:0.77 121:0.99 122:0.57 123:0.59 124:0.40 126:0.54 127:0.56 129:0.59 131:0.93 133:0.46 135:0.17 137:0.96 139:0.95 140:0.45 144:0.76 146:0.17 147:0.22 149:0.92 150:0.74 151:0.82 152:0.99 153:0.46 154:0.97 155:0.67 157:0.99 158:0.91 159:0.49 161:0.71 162:0.68 164:0.76 165:0.93 166:0.89 167:0.72 169:0.86 172:0.78 173:0.23 176:0.73 177:0.70 179:0.46 181:0.73 182:0.99 186:0.80 187:0.39 192:0.87 196:0.98 201:0.93 202:0.76 204:0.89 206:0.81 208:0.64 212:0.82 215:0.42 216:0.95 219:0.95 220:0.62 222:0.90 227:0.96 229:0.99 230:0.87 231:0.84 232:0.90 233:0.76 235:0.60 241:0.46 242:0.33 243:0.18 245:0.76 246:0.99 247:0.74 248:0.84 253:0.88 255:0.95 256:0.79 259:0.21 260:0.78 261:0.89 262:0.93 265:0.80 266:0.63 267:0.85 268:0.79 271:0.69 276:0.67 279:0.86 281:0.91 283:0.78 284:0.96 285:0.62 287:0.99 290:0.62 292:0.91 297:0.36 300:0.55\n2 1:0.74 8:0.84 9:0.80 11:0.78 12:0.51 17:0.15 18:0.64 20:0.86 21:0.30 25:0.88 27:0.16 28:0.73 29:0.62 30:0.76 31:0.87 34:0.49 36:0.39 37:1.00 39:0.67 41:0.82 43:0.79 64:0.77 66:0.54 69:0.75 70:0.22 71:0.73 74:0.42 78:0.81 79:0.87 81:0.59 83:0.91 85:0.72 86:0.73 91:0.69 96:0.69 98:0.23 100:0.87 101:0.87 104:0.75 108:0.58 111:0.82 114:0.65 120:0.55 122:0.41 123:0.56 124:0.45 126:0.54 127:0.32 129:0.05 131:0.93 133:0.37 135:0.34 137:0.87 139:0.71 140:0.45 144:0.76 146:0.22 147:0.05 149:0.56 150:0.76 151:0.52 152:0.81 153:0.48 154:0.73 155:0.67 157:0.87 159:0.21 161:0.71 162:0.86 164:0.57 165:0.93 166:0.89 167:0.72 169:0.85 172:0.21 173:0.94 176:0.73 177:0.05 179:0.93 181:0.73 182:0.98 186:0.81 187:0.39 191:0.86 192:0.87 196:0.88 201:0.93 202:0.36 208:0.64 212:0.42 215:0.44 216:0.22 220:0.87 222:0.90 227:0.96 229:0.99 231:0.84 235:0.52 241:0.46 242:0.45 243:0.26 245:0.40 246:0.99 247:0.35 248:0.51 253:0.50 255:0.95 256:0.45 257:0.84 259:0.21 260:0.56 261:0.82 265:0.25 266:0.23 267:0.59 268:0.46 271:0.69 276:0.15 284:0.89 285:0.62 287:0.99 290:0.65 297:0.36 300:0.18\n2 1:0.74 8:0.79 9:0.80 11:0.92 12:0.51 17:0.47 18:0.98 20:0.96 21:0.71 27:0.68 28:0.73 29:0.62 30:0.42 31:0.95 34:0.70 36:0.95 37:0.47 39:0.67 41:0.94 43:0.91 45:0.88 60:0.87 64:0.77 66:0.20 69:0.45 70:0.86 71:0.73 74:0.84 78:0.76 79:0.95 81:0.44 83:0.91 85:0.98 86:0.96 91:0.40 96:0.84 98:0.43 100:0.73 101:0.97 104:0.88 106:0.81 108:0.80 111:0.75 114:0.55 120:0.81 122:0.57 123:0.78 124:0.95 126:0.54 127:0.98 129:0.98 131:0.93 133:0.95 135:0.66 137:0.95 139:0.96 140:0.80 144:0.76 146:0.17 147:0.22 149:0.96 150:0.69 151:0.92 152:0.89 153:0.72 154:0.91 155:0.67 157:0.99 158:0.91 159:0.95 161:0.71 162:0.60 164:0.76 165:0.93 166:0.89 167:0.67 169:0.72 172:0.90 173:0.11 176:0.73 177:0.70 178:0.42 179:0.14 181:0.73 182:0.99 186:0.77 187:0.39 192:0.87 196:0.97 201:0.93 202:0.74 206:0.81 208:0.64 212:0.48 215:0.37 216:0.29 219:0.95 222:0.90 227:0.96 229:0.99 231:0.84 235:0.95 241:0.24 242:0.16 243:0.07 245:0.96 246:0.99 247:0.93 248:0.82 253:0.87 255:0.95 256:0.79 259:0.21 260:0.76 261:0.78 265:0.45 266:0.96 267:0.52 268:0.75 271:0.69 276:0.96 279:0.86 283:0.78 284:0.95 285:0.62 287:0.99 290:0.55 292:0.91 297:0.36 300:0.94\n1 1:0.74 11:0.78 12:0.51 17:0.30 18:0.65 21:0.54 27:0.45 28:0.73 29:0.62 30:0.91 34:0.89 36:0.18 37:0.50 39:0.67 43:0.82 64:0.77 66:0.69 69:0.70 70:0.13 71:0.73 74:0.43 81:0.48 83:0.91 85:0.72 86:0.74 91:0.20 98:0.34 101:0.87 108:0.53 114:0.59 122:0.57 123:0.51 126:0.54 127:0.12 129:0.05 131:0.61 135:0.26 139:0.72 144:0.76 145:0.52 146:0.42 147:0.49 149:0.58 150:0.72 154:0.77 155:0.67 157:0.85 159:0.16 161:0.71 163:0.75 165:0.93 166:0.89 167:0.65 172:0.21 173:0.70 176:0.73 177:0.70 178:0.55 179:0.54 181:0.73 182:0.97 187:0.68 192:0.87 201:0.93 208:0.64 212:0.61 215:0.39 216:0.77 222:0.90 227:0.61 229:0.61 231:0.83 235:0.74 241:0.12 242:0.63 243:0.73 246:0.99 247:0.30 253:0.42 259:0.21 265:0.36 266:0.17 267:0.23 271:0.69 276:0.13 287:0.61 290:0.59 297:0.36 300:0.13\n1 1:0.74 11:0.78 12:0.51 17:0.24 18:0.83 21:0.54 27:0.45 28:0.73 29:0.62 30:0.44 34:0.86 36:0.18 37:0.50 39:0.67 43:0.82 55:0.71 64:0.77 66:0.10 69:0.69 70:0.82 71:0.73 74:0.43 81:0.48 83:0.91 85:0.72 86:0.75 91:0.18 98:0.25 101:0.87 108:0.53 114:0.59 123:0.88 124:0.90 126:0.54 127:0.38 129:0.05 131:0.61 133:0.89 135:0.85 139:0.72 144:0.76 145:0.49 146:0.39 147:0.46 149:0.73 150:0.72 154:0.77 155:0.67 157:0.82 159:0.73 161:0.71 165:0.93 166:0.89 167:0.71 172:0.48 173:0.51 176:0.73 177:0.70 178:0.55 179:0.45 181:0.73 182:0.97 187:0.68 192:0.87 201:0.93 208:0.64 212:0.50 215:0.39 216:0.77 222:0.90 227:0.61 229:0.61 231:0.83 235:0.74 241:0.44 242:0.45 243:0.44 245:0.67 246:0.99 247:0.74 253:0.42 259:0.21 265:0.23 266:0.87 267:0.52 271:0.69 276:0.65 277:0.69 287:0.61 290:0.59 297:0.36 300:0.70\n2 9:0.80 11:0.87 12:0.51 17:0.30 18:0.94 20:0.87 21:0.30 27:0.55 28:0.73 29:0.62 30:0.17 31:0.93 32:0.80 34:0.36 36:0.93 37:0.34 39:0.67 41:0.82 43:0.74 44:0.93 45:0.66 55:0.71 60:0.87 64:0.77 66:0.30 69:0.83 70:0.92 71:0.73 74:0.73 77:0.70 78:0.76 79:0.94 81:0.30 83:0.91 85:0.90 86:0.89 91:0.15 96:0.79 98:0.45 100:0.73 101:0.97 108:0.89 111:0.75 120:0.67 122:0.85 123:0.74 124:0.76 126:0.26 127:0.29 129:0.81 131:0.93 133:0.74 135:0.92 137:0.93 139:0.92 140:0.80 144:0.76 145:0.91 146:0.31 147:0.38 149:0.81 150:0.28 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32:0.74 34:0.74 36:0.90 39:0.42 41:0.94 43:0.60 55:0.59 66:0.54 69:0.85 74:0.68 76:0.94 77:0.70 78:0.82 81:0.64 83:0.80 91:0.79 96:0.94 98:0.19 104:0.89 108:0.71 111:0.80 114:0.82 120:0.89 123:0.69 126:0.54 127:0.10 129:0.05 135:0.23 140:0.80 145:0.69 146:0.24 147:0.30 149:0.28 150:0.70 151:0.96 153:0.86 154:0.49 155:0.67 158:0.84 159:0.11 161:0.71 162:0.57 164:0.90 167:0.77 169:0.80 172:0.10 173:0.95 176:0.73 177:0.38 179:0.48 181:0.81 187:0.39 191:0.86 192:0.59 195:0.64 201:0.74 202:0.89 208:0.64 215:0.67 216:0.50 220:0.91 222:0.95 230:0.81 233:0.69 235:0.52 241:0.71 243:0.63 248:0.91 253:0.93 254:0.98 255:0.79 256:0.88 259:0.21 260:0.90 265:0.21 267:0.18 268:0.89 282:0.83 283:0.71 285:0.62 290:0.82 297:0.36 300:0.08\n2 1:0.74 6:0.82 8:0.63 9:0.80 11:0.57 12:0.79 17:0.22 20:0.80 21:0.13 27:0.35 28:0.53 30:0.72 34:0.75 36:0.89 37:0.31 39:0.42 41:0.90 43:0.81 66:0.44 69:0.70 70:0.40 74:0.72 78:0.84 81:0.82 83:0.81 85:0.90 91:0.29 96:0.91 98:0.51 100:0.86 101:0.83 104:0.58 108:0.53 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276:0.33 283:0.71 297:0.36 300:0.43\n3 6:0.92 8:0.88 9:0.80 12:0.20 17:0.49 20:0.99 25:0.88 27:0.26 28:0.48 30:0.76 34:0.34 36:0.53 37:0.60 39:0.39 41:0.88 43:0.42 55:0.52 66:0.80 69:0.21 70:0.21 74:0.87 78:0.89 81:0.80 83:0.76 91:0.72 96:0.97 98:0.92 100:0.93 104:0.58 108:0.17 111:0.90 114:0.80 117:0.86 120:0.87 122:0.67 123:0.16 126:0.32 127:0.54 129:0.05 135:0.61 138:0.98 140:0.95 146:0.48 147:0.54 149:0.51 150:0.60 151:0.77 152:0.99 153:0.95 154:0.32 155:0.67 159:0.17 161:0.54 162:0.84 164:0.87 167:0.87 169:0.88 172:0.45 173:0.62 175:0.75 176:0.56 177:0.05 179:0.86 181:0.73 186:0.89 187:0.21 189:0.96 190:0.77 191:0.93 192:0.59 202:0.85 208:0.64 212:0.95 216:0.83 222:0.25 232:0.81 235:0.12 238:0.94 241:0.76 242:0.82 243:0.82 244:0.61 247:0.12 248:0.85 253:0.98 255:0.79 256:0.89 257:0.65 259:0.21 260:0.87 261:0.92 265:0.92 266:0.12 267:0.44 268:0.90 271:0.74 276:0.37 277:0.69 283:0.71 285:0.62 290:0.80 300:0.18\n1 12:0.20 17:0.34 21:0.47 27:0.45 28:0.48 30:0.20 34:0.77 36:0.21 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255:0.79 256:0.64 259:0.21 260:0.73 261:0.87 265:0.85 266:0.23 267:0.19 268:0.65 271:0.16 276:0.25 285:0.62 290:0.92 297:0.36 300:0.13\n2 7:0.78 8:0.91 9:0.80 11:0.57 12:0.20 17:0.90 21:0.78 27:0.54 28:0.98 30:0.76 34:0.91 36:0.54 37:0.66 39:0.61 43:0.94 55:0.71 60:0.87 66:0.16 69:0.21 70:0.36 74:0.80 83:0.78 85:0.72 91:0.48 96:0.75 98:0.26 101:0.70 104:0.81 108:0.69 120:0.77 122:0.67 123:0.67 124:0.82 127:0.27 129:0.81 133:0.76 135:0.84 140:0.90 145:0.91 146:0.17 147:0.22 149:0.64 151:0.69 153:0.83 154:0.94 155:0.67 158:0.92 159:0.65 161:0.90 162:0.54 163:0.90 164:0.74 167:0.64 172:0.21 173:0.13 177:0.38 179:0.62 181:0.67 187:0.21 190:0.77 191:0.87 192:0.59 202:0.74 208:0.64 212:0.36 216:0.28 220:0.62 222:0.60 230:0.81 233:0.69 235:0.52 241:0.47 242:0.61 243:0.23 245:0.57 247:0.47 248:0.64 253:0.74 255:0.79 256:0.68 259:0.21 260:0.78 265:0.28 266:0.47 267:0.89 268:0.71 271:0.16 276:0.44 279:0.86 283:0.82 285:0.62 300:0.33\n2 6:0.87 7:0.78 8:0.66 11:0.57 12:0.20 17:0.97 20:0.81 21:0.59 27:0.36 28:0.98 30:0.15 32:0.80 34:0.90 36:0.55 37:0.56 39:0.61 43:0.28 55:0.92 66:0.16 69:0.99 70:0.96 74:0.55 77:0.70 78:0.85 81:0.43 85:0.72 91:0.23 98:0.19 100:0.85 101:0.64 104:0.58 108:0.98 111:0.84 122:0.41 123:0.56 124:0.84 126:0.32 127:0.89 129:0.05 133:0.82 135:0.49 145:0.59 146:0.28 147:0.63 149:0.30 150:0.36 152:0.97 154:0.13 159:0.94 161:0.90 167:0.52 169:0.94 172:0.21 173:1.00 177:0.05 178:0.55 179:0.87 181:0.67 186:0.85 187:0.39 195:0.99 212:0.15 215:0.55 216:0.37 222:0.60 230:0.81 232:0.80 233:0.69 235:0.74 241:0.02 242:0.33 243:0.37 245:0.47 247:0.55 253:0.44 257:0.65 259:0.21 261:0.96 265:0.12 266:0.63 267:0.91 271:0.16 276:0.31 277:0.69 282:0.88 297:0.36 299:0.88 300:0.70\n2 11:0.57 12:0.20 17:0.89 21:0.22 27:0.63 28:0.98 30:0.38 32:0.80 34:0.93 36:0.36 37:0.45 39:0.61 43:0.63 55:0.42 66:0.09 69:0.91 70:0.63 74:0.76 77:0.89 81:0.73 85:0.85 91:0.67 98:0.25 101:0.78 104:0.58 106:0.81 108:0.81 122:0.43 123:0.54 124:0.74 126:0.32 127:0.62 129:0.05 133:0.74 135:0.58 145:0.69 146:0.31 147:0.05 149:0.65 150:0.54 154:0.53 159:0.51 161:0.90 167:0.57 172:0.37 173:0.98 177:0.05 178:0.55 179:0.82 181:0.67 187:0.21 195:0.69 202:0.36 206:0.81 212:0.57 215:0.79 216:0.28 222:0.60 232:0.80 235:0.31 241:0.21 242:0.29 243:0.16 245:0.49 247:0.60 253:0.55 257:0.65 259:0.21 265:0.27 266:0.47 267:0.52 271:0.16 276:0.39 277:0.69 282:0.88 283:0.71 297:0.36 299:0.88 300:0.43\n2 1:0.74 6:0.83 7:0.81 8:0.70 9:0.80 11:0.57 12:0.20 17:0.90 20:0.97 21:0.05 27:0.63 28:0.98 30:0.88 32:0.80 34:0.36 36:0.49 37:0.45 39:0.61 41:0.97 43:0.61 55:0.92 60:0.87 66:0.24 69:0.75 70:0.29 74:0.87 76:0.85 77:0.89 78:0.98 81:0.91 83:0.88 85:0.72 91:0.91 96:0.96 98:0.67 100:0.95 101:0.72 108:0.80 111:0.96 114:0.95 120:0.93 121:0.90 122:0.67 123:0.56 124:0.67 126:0.54 127:0.80 129:0.59 133:0.64 135:0.66 140:0.80 145:0.72 146:0.73 147:0.05 149:0.51 150:0.95 151:0.98 152:0.95 153:0.91 154:0.54 155:0.67 158:0.87 159:0.55 161:0.90 162:0.64 163:0.81 164:0.89 165:0.90 167:0.91 169:0.91 172:0.37 173:0.77 176:0.73 177:0.05 178:0.42 179:0.80 181:0.67 186:0.98 189:0.85 191:0.83 192:0.59 195:0.71 201:0.74 202:0.84 204:0.89 208:0.64 212:0.22 215:0.91 216:0.28 220:0.62 222:0.60 230:0.87 232:0.80 233:0.76 235:0.22 238:0.85 241:0.89 242:0.38 243:0.14 245:0.59 247:0.60 248:0.96 253:0.97 255:0.79 256:0.85 257:0.65 259:0.21 260:0.93 261:0.95 265:0.56 266:0.54 267:0.65 268:0.86 271:0.16 276:0.47 279:0.86 282:0.88 283:0.71 285:0.62 290:0.95 297:0.36 299:0.88 300:0.33\n3 7:0.78 8:0.91 9:0.80 11:0.57 12:0.20 17:0.88 21:0.78 27:0.54 28:0.98 30:0.73 34:0.91 36:0.59 37:0.66 39:0.61 43:0.94 55:0.71 60:0.87 66:0.15 69:0.21 70:0.37 74:0.84 83:0.78 85:0.72 91:0.48 96:0.75 98:0.26 101:0.70 104:0.81 108:0.69 120:0.77 122:0.67 123:0.67 124:0.82 127:0.27 129:0.81 133:0.76 135:0.84 140:0.90 145:0.91 146:0.31 147:0.38 149:0.66 151:0.69 153:0.83 154:0.94 155:0.67 158:0.92 159:0.65 161:0.90 162:0.54 163:0.90 164:0.74 167:0.67 172:0.21 173:0.13 177:0.38 179:0.58 181:0.67 187:0.21 190:0.77 191:0.87 192:0.59 202:0.74 208:0.64 212:0.30 216:0.28 220:0.62 222:0.60 230:0.81 233:0.69 235:0.52 241:0.54 242:0.63 243:0.29 245:0.59 247:0.47 248:0.64 253:0.76 255:0.79 256:0.68 259:0.21 260:0.78 265:0.28 266:0.63 267:0.91 268:0.71 271:0.16 276:0.47 279:0.86 283:0.82 285:0.62 300:0.33\n2 6:0.82 7:0.81 8:0.61 12:0.20 17:0.43 20:0.87 21:0.30 27:0.21 28:0.78 30:0.30 34:0.58 36:0.98 37:0.74 43:0.52 55:0.52 66:0.44 69:0.12 70:0.60 78:0.74 81:0.95 91:0.57 98:0.44 100:0.77 106:0.81 108:0.60 111:0.74 120:0.77 123:0.57 124:0.58 126:0.54 127:0.27 129:0.59 133:0.47 135:0.20 140:0.45 146:0.42 147:0.48 149:0.57 150:0.92 151:0.74 152:0.82 153:0.84 154:0.41 159:0.29 161:0.54 162:0.69 164:0.79 167:0.72 169:0.75 172:0.37 173:0.25 177:0.05 179:0.64 181:0.52 186:0.75 187:0.39 189:0.84 202:0.71 206:0.81 212:0.73 215:0.72 216:0.95 220:0.87 230:0.87 233:0.76 235:0.42 238:0.85 241:0.52 242:0.82 243:0.45 245:0.64 247:0.12 248:0.69 256:0.68 259:0.21 260:0.78 261:0.76 265:0.46 266:0.38 267:0.85 268:0.74 271:0.61 276:0.43 283:0.82 285:0.62 297:0.36 300:0.43\n1 6:0.90 7:0.81 8:0.89 12:0.20 17:0.75 20:0.94 21:0.71 25:0.88 27:0.06 28:0.78 30:0.95 32:0.80 34:0.53 36:0.80 37:0.95 43:0.37 55:0.84 66:0.54 69:0.61 78:0.82 81:0.83 91:0.93 98:0.54 100:0.86 104:0.58 108:0.46 111:0.85 120:0.96 123:0.45 126:0.54 127:0.16 129:0.05 135:0.26 140:0.45 145:0.89 146:0.33 147:0.39 149:0.21 150:0.63 151:0.83 152:0.88 153:0.95 154:0.28 159:0.13 161:0.54 162:0.60 164:0.95 167:0.97 169:0.90 172:0.10 173:0.86 177:0.05 179:0.97 181:0.52 186:0.81 189:0.91 191:0.95 195:0.58 202:0.93 215:0.48 216:0.52 220:0.62 230:0.87 233:0.76 235:0.84 238:0.97 241:0.86 243:0.63 248:0.94 256:0.93 260:0.96 261:0.84 265:0.55 267:0.76 268:0.94 271:0.61 283:0.60 285:0.62 297:0.36 300:0.08\n1 12:0.20 17:0.64 21:0.30 25:0.88 27:0.05 28:0.78 34:0.67 36:0.41 37:0.96 81:0.95 91:0.62 104:0.58 126:0.54 132:0.97 135:0.49 150:0.92 161:0.54 167:0.74 175:0.75 178:0.55 181:0.52 187:0.39 215:0.72 216:0.64 235:0.42 241:0.61 244:0.61 257:0.65 267:0.93 271:0.61 277:0.69 297:0.36\n2 6:0.81 8:0.82 12:0.20 17:0.55 20:0.86 21:0.22 25:0.88 27:0.16 28:0.78 30:0.76 34:0.56 36:0.74 37:0.95 43:0.48 55:0.52 66:0.64 69:0.34 70:0.15 78:0.77 81:0.96 91:0.88 98:0.83 100:0.80 104:0.75 108:0.27 111:0.77 120:0.62 123:0.26 126:0.54 127:0.33 129:0.05 135:0.51 140:0.45 146:0.30 147:0.36 149:0.29 150:0.94 151:0.56 152:0.81 153:0.72 154:0.36 159:0.13 161:0.54 162:0.85 164:0.81 167:0.98 169:0.78 172:0.16 173:0.86 177:0.05 179:0.98 181:0.52 186:0.78 189:0.83 191:0.89 202:0.70 212:0.95 215:0.79 216:0.32 220:0.87 235:0.22 238:0.90 241:0.92 242:0.82 243:0.69 247:0.12 248:0.56 256:0.73 259:0.21 260:0.63 261:0.79 265:0.83 266:0.12 267:0.36 268:0.77 271:0.61 276:0.16 283:0.60 285:0.62 297:0.36 300:0.13\n1 6:0.84 7:0.81 8:0.89 12:0.20 17:0.96 20:0.80 21:0.76 27:0.24 28:0.78 30:0.42 32:0.80 34:0.50 36:0.83 37:0.68 43:0.47 55:0.84 66:0.88 69:0.47 70:0.48 78:0.76 81:0.78 91:0.66 98:0.95 100:0.78 104:0.58 108:0.36 111:0.75 120:0.73 123:0.34 126:0.54 127:0.67 129:0.05 135:0.47 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285:0.62 287:0.61\n2 1:0.74 7:0.81 9:0.80 11:0.64 12:0.20 17:0.79 18:0.87 21:0.40 22:0.93 27:0.72 28:0.96 29:0.71 30:0.28 31:0.95 32:0.80 34:0.68 36:0.67 39:0.90 41:0.82 43:0.86 44:0.93 47:0.93 60:0.87 64:0.77 66:0.96 69:0.59 70:0.62 71:0.77 74:0.88 77:0.89 79:0.95 81:0.58 83:0.91 85:0.91 86:0.95 91:0.82 96:0.85 98:0.92 101:0.87 104:0.75 106:0.81 108:0.45 114:0.62 117:0.86 120:0.76 121:0.90 123:0.43 126:0.54 127:0.54 129:0.05 131:0.85 135:0.86 137:0.95 139:0.97 140:0.45 144:0.57 145:0.98 146:0.89 147:0.91 149:0.93 150:0.76 151:0.94 153:0.80 154:0.83 155:0.67 157:0.91 158:0.92 159:0.49 161:0.86 162:0.70 164:0.65 165:0.93 166:0.79 167:0.54 172:0.88 173:0.60 176:0.73 177:0.70 179:0.32 181:0.80 182:0.89 187:0.39 192:0.87 195:0.72 196:0.98 201:0.93 202:0.55 204:0.89 206:0.81 208:0.64 212:0.91 215:0.42 216:0.09 219:0.95 222:0.97 227:0.84 229:0.94 230:0.87 231:0.69 232:0.85 233:0.76 235:0.80 241:0.24 242:0.38 243:0.96 246:0.86 247:0.90 248:0.83 253:0.88 255:0.95 256:0.45 259:0.21 260:0.77 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196:0.88 201:0.68 202:0.36 208:0.54 212:0.85 216:0.13 219:0.92 220:0.97 222:0.97 227:0.82 229:0.61 231:0.69 232:0.98 235:0.80 241:0.24 242:0.15 243:0.95 246:0.61 247:0.92 248:0.46 253:0.36 254:0.92 255:0.74 256:0.45 259:0.21 261:0.84 262:0.93 265:0.88 266:0.27 267:0.79 268:0.46 271:0.94 276:0.77 279:0.82 284:0.87 285:0.56 287:0.61 290:0.55 292:0.91 300:0.55\n2 8:0.77 9:0.76 11:0.75 12:0.20 17:0.09 18:0.68 21:0.47 22:0.93 27:0.16 28:0.96 29:0.71 30:0.10 31:0.95 34:0.79 36:0.28 37:0.80 39:0.90 43:0.21 44:0.87 45:0.77 47:0.93 55:0.45 64:0.77 66:0.15 69:0.54 70:0.96 71:0.77 74:0.37 76:0.85 79:0.97 81:0.26 83:0.60 86:0.77 91:0.74 96:0.77 98:0.29 101:0.52 104:0.58 108:0.94 120:0.57 123:0.40 124:0.88 126:0.26 127:0.65 129:0.05 131:0.85 133:0.86 135:0.95 137:0.95 139:0.78 140:0.98 144:0.57 145:0.42 146:0.64 147:0.69 149:0.43 150:0.26 151:0.58 153:0.77 154:0.64 155:0.58 157:0.83 159:0.85 161:0.86 162:0.54 164:0.53 166:0.61 167:0.62 172:0.63 173:0.27 177:0.70 178:0.42 179:0.32 181:0.80 182:0.75 187:0.21 190:0.89 191:0.86 192:0.51 195:0.95 196:0.94 202:0.88 208:0.54 212:0.72 216:0.72 219:0.89 220:0.91 222:0.97 227:0.84 229:0.61 231:0.69 235:0.52 241:0.51 242:0.73 243:0.36 245:0.85 246:0.61 247:0.76 248:0.57 253:0.44 254:0.98 255:0.74 256:0.86 259:0.21 260:0.54 262:0.93 265:0.23 266:0.87 267:0.99 268:0.86 271:0.94 276:0.78 283:0.78 284:0.97 285:0.62 287:0.61 292:0.91 300:0.94\n2 1:0.74 7:0.63 8:0.60 11:0.75 12:0.20 17:0.94 18:0.68 20:0.86 21:0.22 27:0.57 28:0.96 29:0.71 30:0.09 32:0.78 34:0.91 36:0.73 37:0.64 39:0.90 43:0.63 44:0.93 45:0.66 48:0.91 55:0.45 64:0.77 66:0.26 69:0.54 70:0.91 71:0.77 74:0.57 76:0.85 77:0.70 78:0.91 81:0.58 86:0.71 91:0.69 98:0.69 100:0.84 101:0.52 104:0.82 108:0.41 111:0.88 114:0.71 123:0.64 124:0.71 126:0.54 127:0.63 129:0.59 131:0.61 133:0.64 135:0.65 139:0.81 144:0.57 145:0.52 146:0.45 147:0.52 149:0.48 150:0.70 152:0.81 154:0.53 155:0.67 157:0.78 158:0.78 159:0.51 161:0.86 166:0.79 167:0.49 169:0.82 172:0.57 173:0.54 175:0.75 176:0.73 177:0.38 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123:0.77 124:0.50 126:0.54 127:0.74 129:0.59 131:0.61 132:0.97 133:0.46 135:0.30 139:0.99 144:0.57 145:0.90 146:0.64 147:0.69 149:0.99 150:0.89 152:0.85 154:0.87 155:0.67 157:0.95 158:0.92 159:0.65 161:0.86 165:0.93 166:0.79 167:0.49 169:0.92 172:0.95 173:0.18 176:0.73 177:0.70 178:0.55 179:0.17 181:0.80 182:0.88 186:0.91 187:0.21 189:0.88 191:0.70 192:0.87 195:0.81 201:0.93 206:0.81 208:0.64 212:0.91 215:0.96 216:0.46 219:0.95 222:0.97 227:0.61 229:0.61 230:0.64 231:0.69 232:0.88 233:0.76 235:0.12 238:0.87 241:0.02 242:0.29 243:0.37 245:0.98 246:0.86 247:0.92 253:0.69 261:0.89 262:0.93 265:0.72 266:0.71 267:0.44 271:0.94 276:0.92 279:0.86 281:0.91 282:0.88 283:0.82 287:0.61 290:0.98 292:0.95 297:0.36 299:0.99 300:0.70\n1 7:0.63 8:0.82 11:0.42 12:0.20 17:0.75 18:0.65 21:0.59 27:0.37 28:0.96 29:0.71 30:0.71 32:0.62 34:0.37 36:0.53 37:0.60 39:0.90 43:0.18 55:0.52 66:0.98 69:0.29 70:0.48 71:0.77 74:0.37 81:0.26 86:0.70 91:0.80 98:0.98 104:0.78 108:0.22 120:0.76 123:0.22 126:0.26 127:0.82 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36:0.63 37:0.97 39:0.86 41:0.95 43:0.68 46:0.98 66:0.75 69:0.89 70:0.60 74:0.84 76:0.85 77:0.70 83:0.82 85:0.96 91:0.80 96:0.94 98:0.37 101:0.85 104:0.58 107:0.98 108:0.86 110:0.92 120:0.89 122:0.57 123:0.85 124:0.42 125:0.99 127:0.25 129:0.59 133:0.64 135:0.89 140:0.45 145:0.56 146:0.13 147:0.18 149:0.87 151:0.93 153:0.78 154:0.58 155:0.67 159:0.46 160:0.99 161:0.55 162:0.65 164:0.84 167:0.83 172:0.75 173:0.89 177:0.38 179:0.33 181:0.48 187:0.68 192:0.59 195:0.81 202:0.78 208:0.64 212:0.82 216:0.85 220:0.62 222:0.25 232:0.76 235:0.05 241:0.72 242:0.63 243:0.14 245:0.65 247:0.39 248:0.94 253:0.95 255:0.79 256:0.78 259:0.21 260:0.90 265:0.39 266:0.54 267:0.85 268:0.80 271:0.90 276:0.58 282:0.88 285:0.62 289:0.90 299:0.88 300:0.84\n1 1:0.74 7:0.66 11:0.64 12:0.06 17:0.77 18:0.93 22:0.93 27:0.65 28:0.73 29:0.91 30:0.32 31:0.86 32:0.80 34:0.97 36:0.17 37:0.43 39:0.52 43:0.78 44:0.93 46:0.96 47:0.93 48:0.91 55:0.59 60:0.95 64:0.77 66:0.94 69:0.40 70:0.91 71:0.95 74:0.76 77:0.70 79:0.86 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241:0.80 243:0.40 245:0.36 259:0.21 265:0.09 267:0.44 271:0.71 297:0.36 300:0.08\n0 12:0.37 17:0.47 21:0.59 27:0.45 28:0.76 34:0.85 36:0.22 37:0.50 43:0.32 66:0.36 69:0.70 81:0.91 91:0.20 98:0.06 108:0.53 123:0.51 126:0.54 127:0.05 129:0.05 135:0.58 145:0.50 146:0.05 147:0.05 149:0.13 150:0.83 154:0.23 159:0.05 161:0.50 167:0.72 172:0.05 173:0.51 177:0.05 178:0.55 181:0.63 187:0.68 215:0.55 216:0.77 235:0.68 241:0.51 243:0.51 259:0.21 265:0.06 267:0.73 271:0.71 283:0.60 297:0.36\n2 6:0.98 8:0.94 12:0.37 17:0.20 20:0.75 21:0.13 25:0.88 27:0.03 28:0.76 30:0.95 34:0.31 36:0.30 37:0.95 43:0.27 55:0.99 66:0.20 69:0.79 78:0.94 81:0.98 91:0.86 98:0.07 100:0.96 104:0.58 108:0.62 111:0.95 120:0.89 123:0.60 124:0.61 126:0.54 127:0.41 129:0.59 133:0.47 135:0.42 140:0.87 145:0.76 146:0.05 147:0.05 149:0.15 150:0.97 151:0.75 152:1.00 153:0.84 154:0.20 159:0.53 161:0.50 162:0.69 164:0.85 167:0.86 169:0.96 172:0.05 173:0.77 177:0.05 178:0.48 179:1.00 181:0.63 186:0.93 187:0.21 189:0.98 191:0.98 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147:0.05 149:0.14 150:0.97 151:0.49 153:0.48 154:0.18 159:0.53 161:0.50 162:0.62 164:0.67 167:0.88 172:0.05 173:0.81 177:0.05 178:0.42 179:1.00 181:0.63 187:0.39 202:0.60 206:0.81 215:0.84 216:0.65 220:0.74 235:0.12 241:0.75 243:0.40 245:0.36 248:0.50 256:0.58 259:0.21 260:0.58 265:0.07 267:0.89 268:0.59 271:0.71 285:0.62 297:0.36 300:0.08\n2 6:0.98 8:0.66 12:0.37 17:0.32 20:0.74 21:0.13 25:0.88 27:0.03 28:0.76 30:0.45 34:0.29 36:0.28 37:0.95 43:0.31 55:0.52 66:0.27 69:0.70 70:0.25 78:0.89 81:0.98 91:0.48 98:0.07 100:0.91 104:0.58 108:0.92 111:0.88 120:0.95 123:0.92 124:0.72 126:0.54 127:0.93 129:0.81 133:0.67 135:0.94 140:0.45 145:0.84 146:0.05 147:0.05 149:0.18 150:0.97 151:0.80 152:1.00 153:0.93 154:0.23 159:0.96 161:0.50 162:0.85 163:0.97 164:0.90 167:0.69 169:0.96 172:0.10 173:0.22 177:0.05 179:0.98 181:0.63 186:0.85 187:0.21 189:0.87 191:0.84 202:0.81 212:0.61 215:0.84 216:0.65 235:0.12 238:0.87 241:0.41 242:0.82 243:0.29 245:0.38 247:0.12 248:0.92 256:0.86 259:0.21 260:0.95 261:1.00 265:0.07 266:0.17 267:0.91 268:0.87 271:0.71 276:0.17 285:0.62 297:0.36 300:0.18\n2 7:0.81 8:0.80 12:0.37 17:0.78 20:0.75 21:0.30 27:0.43 28:0.76 30:0.68 32:0.80 34:0.64 36:0.47 37:0.63 43:0.51 55:0.52 66:0.77 69:0.19 70:0.61 78:0.78 81:0.97 91:0.51 98:0.75 100:0.80 106:0.81 108:0.58 111:0.77 120:0.69 123:0.56 124:0.38 126:0.54 127:0.45 129:0.59 133:0.47 135:0.58 140:0.45 145:0.65 146:0.05 147:0.05 149:0.59 150:0.95 151:0.66 152:0.74 153:0.48 154:0.40 159:0.41 161:0.50 162:0.86 164:0.57 167:0.64 169:0.78 172:0.59 173:0.28 177:0.05 179:0.68 181:0.63 186:0.79 187:0.21 195:0.67 202:0.36 206:0.81 212:0.69 215:0.72 216:0.69 230:0.87 233:0.76 235:0.42 241:0.18 242:0.82 243:0.13 245:0.52 247:0.12 248:0.63 256:0.45 259:0.21 260:0.70 261:0.74 265:0.75 266:0.47 267:0.23 268:0.46 271:0.71 276:0.48 283:0.60 285:0.62 297:0.36 300:0.55\n2 6:0.81 7:0.81 8:0.61 12:0.37 17:0.45 20:0.90 21:0.30 27:0.21 28:0.76 30:0.58 34:0.47 36:0.67 37:0.74 43:0.52 55:0.84 66:0.36 69:0.10 70:0.33 78:0.76 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159:0.90 161:0.50 162:0.71 163:0.75 164:0.81 167:0.63 169:0.76 172:0.54 173:0.39 177:0.05 179:0.42 181:0.63 186:0.77 187:0.21 189:0.88 191:0.73 195:0.98 202:0.73 212:0.15 215:0.46 216:0.98 220:0.96 235:0.95 238:0.89 241:0.15 242:0.82 243:0.29 245:0.70 247:0.12 248:0.68 256:0.77 259:0.21 260:0.77 261:0.79 265:0.33 266:0.97 267:0.18 268:0.76 271:0.71 276:0.73 283:0.82 285:0.62 297:0.36 300:0.70\n3 6:0.98 8:0.97 12:0.37 17:0.36 20:0.97 21:0.13 25:0.88 27:0.03 28:0.76 30:0.45 34:0.29 36:0.73 37:0.95 43:0.51 55:0.71 66:0.27 69:0.10 70:0.25 78:0.99 81:0.98 91:0.99 98:0.21 100:0.99 104:0.58 108:0.09 111:1.00 120:0.98 123:0.09 124:0.72 126:0.54 127:0.65 129:0.81 133:0.67 135:0.82 140:0.45 145:0.76 146:0.28 147:0.34 149:0.29 150:0.97 151:0.85 152:1.00 153:0.98 154:0.40 159:0.50 161:0.50 162:0.61 163:0.79 164:0.97 167:0.99 169:0.96 172:0.10 173:0.19 177:0.05 179:0.98 181:0.63 186:0.99 189:0.99 191:1.00 202:0.96 212:0.61 215:0.84 216:0.65 220:0.62 235:0.12 238:1.00 241:0.93 242:0.82 243:0.46 245:0.38 247:0.12 248:0.97 256:0.98 259:0.21 260:0.98 261:1.00 265:0.23 266:0.17 267:0.95 268:0.97 271:0.71 276:0.20 283:0.82 285:0.62 297:0.36 300:0.18\n4 12:0.37 17:0.88 21:0.13 27:0.53 28:0.76 30:0.21 34:0.53 36:0.38 37:0.90 43:0.51 55:0.71 66:0.16 69:0.62 70:0.37 81:0.98 91:0.37 98:0.35 104:0.95 106:0.81 108:0.53 123:0.82 124:0.81 126:0.54 127:0.65 129:0.89 133:0.78 135:0.96 145:0.76 146:0.05 147:0.05 149:0.36 150:0.97 154:0.40 159:0.56 161:0.50 163:0.89 167:0.85 172:0.10 173:0.60 177:0.05 178:0.55 179:0.96 181:0.63 187:0.39 202:0.36 206:0.81 212:0.42 215:0.84 216:0.27 235:0.12 241:0.73 242:0.82 243:0.23 245:0.42 247:0.12 259:0.21 265:0.21 266:0.23 267:0.82 271:0.71 276:0.24 297:0.36 300:0.25\n2 6:0.81 8:0.60 12:0.37 17:0.93 20:0.84 21:0.59 25:0.88 27:0.72 28:0.76 30:0.76 32:0.80 34:0.39 36:0.36 43:0.49 55:0.52 66:0.64 69:0.37 70:0.15 78:0.74 81:0.98 91:0.65 98:0.65 100:0.77 104:0.54 108:0.29 111:0.74 120:0.68 123:0.28 126:0.54 127:0.19 129:0.05 135:0.69 140:0.45 145:0.54 146:0.29 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37:0.28 39:0.31 43:0.79 66:0.75 69:0.19 70:0.54 74:0.59 81:0.95 83:0.79 85:0.72 91:0.53 98:0.85 101:0.75 104:0.74 108:0.15 114:0.95 120:0.65 122:0.41 123:0.15 126:0.54 127:0.35 129:0.05 131:0.96 135:0.60 140:0.45 144:0.57 146:0.60 147:0.65 149:0.43 150:0.97 151:0.61 153:0.48 154:0.84 155:0.67 157:0.87 159:0.22 162:0.86 163:0.81 164:0.55 165:0.90 166:0.99 172:0.32 173:0.45 176:0.73 177:0.38 179:0.91 182:0.98 187:0.21 190:0.77 192:0.59 201:0.74 202:0.36 212:0.61 215:0.91 216:0.15 220:0.62 222:0.76 227:0.61 229:0.61 231:0.99 235:0.12 241:0.27 242:0.33 243:0.77 246:0.61 247:0.39 248:0.59 253:0.72 256:0.45 259:0.21 260:0.65 265:0.85 266:0.23 267:0.28 268:0.46 271:0.26 276:0.26 285:0.50 287:0.61 290:0.95 297:0.36 300:0.33\n2 1:0.74 7:0.76 8:0.61 11:0.42 12:0.69 17:0.66 21:0.13 27:0.72 30:0.95 32:0.78 34:0.81 36:0.72 37:0.26 39:0.31 43:0.86 55:0.52 66:0.54 69:0.10 74:0.64 76:0.94 77:0.98 81:0.90 91:0.81 98:0.46 104:0.58 108:0.09 114:0.92 123:0.09 126:0.54 127:0.14 129:0.05 131:0.61 135:0.21 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276:0.19 282:0.86 287:0.61 290:0.80 297:0.36 300:0.18\n2 1:0.74 7:0.81 9:0.80 11:0.42 12:0.69 17:0.53 21:0.64 27:0.58 30:0.21 32:0.78 34:0.76 36:0.91 37:0.47 39:0.31 43:0.44 55:0.45 66:0.80 69:0.30 70:0.67 74:0.65 76:0.94 77:0.70 81:0.65 91:0.58 96:0.73 98:0.86 104:0.58 108:0.24 114:0.72 120:0.67 123:0.23 126:0.54 127:0.38 129:0.05 131:1.00 135:0.51 140:0.45 144:0.57 145:0.88 146:0.53 147:0.59 149:0.33 150:0.70 151:0.62 153:0.48 154:0.75 155:0.67 157:0.61 159:0.19 162:0.86 164:0.66 166:0.99 172:0.45 173:0.61 176:0.73 177:0.38 179:0.82 182:0.61 187:0.21 190:0.77 192:0.59 195:0.55 201:0.74 202:0.50 204:0.89 208:0.64 212:0.71 215:0.53 216:0.28 220:0.82 222:0.76 227:0.61 229:0.61 230:0.83 231:0.99 233:0.69 235:0.84 241:0.53 242:0.72 243:0.82 246:0.61 247:0.39 248:0.60 253:0.66 254:0.80 255:0.79 256:0.58 259:0.21 260:0.67 265:0.86 266:0.27 267:0.59 268:0.59 271:0.26 276:0.36 282:0.86 285:0.62 287:0.61 290:0.72 297:0.36 300:0.43\n2 7:0.76 8:0.73 9:0.80 11:0.64 12:0.69 17:0.79 21:0.71 27:0.26 30:0.33 32:0.78 34:0.89 36:0.96 37:0.94 39:0.31 41:0.82 43:0.84 55:0.45 66:0.20 69:0.65 70:0.49 74:0.68 77:0.98 81:0.38 83:0.72 85:0.72 91:0.68 96:0.71 98:0.44 101:0.73 104:0.76 108:0.49 120:0.58 123:0.74 124:0.56 126:0.32 127:0.55 129:0.05 131:1.00 133:0.39 135:0.24 140:0.80 144:0.57 145:1.00 146:0.41 147:0.48 149:0.56 150:0.34 151:0.58 153:0.48 154:0.80 155:0.67 157:0.86 159:0.42 162:0.62 164:0.57 166:0.94 172:0.27 173:0.72 177:0.38 178:0.42 179:0.89 182:0.98 187:0.39 191:0.84 192:0.59 195:0.83 202:0.50 208:0.64 212:0.19 215:0.48 216:0.29 220:0.87 222:0.76 227:0.61 229:0.99 230:0.78 231:0.99 233:0.69 235:0.95 241:0.75 242:0.19 243:0.58 245:0.53 246:0.61 247:0.55 248:0.57 253:0.56 255:0.79 256:0.45 259:0.21 260:0.59 265:0.43 266:0.47 267:0.65 268:0.46 271:0.26 276:0.28 277:0.69 282:0.86 285:0.62 287:0.61 297:1.00 300:0.33\n2 7:0.76 8:0.87 11:0.42 12:0.69 17:0.78 21:0.71 27:0.26 30:0.30 32:0.78 34:0.88 36:0.80 37:0.94 39:0.31 43:0.33 55:0.59 66:0.64 69:0.49 70:0.83 74:0.84 77:0.70 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271:0.53 276:0.46 279:0.86 282:0.81 283:0.82 285:0.62 290:0.95 297:0.36 300:0.55\n0 1:0.74 8:0.60 9:0.80 11:0.88 12:0.37 17:0.12 20:0.92 21:0.22 27:0.64 28:0.78 30:0.32 34:0.34 36:0.55 37:0.45 39:0.72 41:0.99 43:0.93 55:0.92 60:1.00 66:0.06 69:0.32 70:0.86 74:0.66 78:0.83 81:0.76 83:0.90 85:0.80 91:0.76 96:0.98 98:0.16 100:0.78 101:0.68 104:0.79 106:0.81 108:0.25 111:0.82 114:0.90 117:0.86 120:0.96 123:0.97 124:0.96 126:0.54 127:0.95 129:0.05 133:0.96 135:0.30 140:0.45 144:0.85 145:0.41 146:0.22 147:0.27 149:0.73 150:0.85 151:0.99 152:0.92 153:0.95 154:0.93 155:0.67 158:0.92 159:0.94 161:0.88 162:0.65 164:0.95 165:0.86 167:0.65 169:0.79 172:0.21 173:0.08 176:0.73 177:0.38 179:0.69 181:0.89 186:0.79 187:0.21 192:0.59 201:0.74 202:0.92 206:0.81 208:0.64 212:0.14 215:0.79 216:0.87 222:0.48 232:0.85 235:0.31 238:0.83 241:0.66 242:0.78 243:0.29 245:0.52 247:0.39 248:0.98 253:0.98 255:0.79 256:0.94 259:0.21 260:0.96 261:0.81 265:0.09 266:0.89 267:0.97 268:0.93 271:0.53 276:0.56 277:0.69 279:0.86 283:0.82 285:0.62 290:0.90 297:0.36 300:0.70\n0 1:0.74 6:0.79 7:0.81 8:0.60 9:0.80 11:0.88 12:0.37 17:0.20 20:0.92 21:0.22 27:0.57 28:0.78 30:0.45 32:0.80 34:0.86 36:0.40 37:0.23 39:0.72 41:0.98 43:0.75 60:0.99 66:0.82 69:0.82 70:0.42 74:0.77 77:0.70 78:0.91 81:0.76 83:0.83 85:0.85 91:0.89 96:0.93 98:0.70 100:0.90 101:0.79 104:0.58 108:0.67 111:0.90 114:0.90 120:0.88 121:0.90 122:0.43 123:0.65 126:0.54 127:0.21 129:0.05 135:0.34 140:0.45 144:0.85 145:0.88 146:0.64 147:0.69 149:0.68 150:0.85 151:0.98 152:0.86 153:0.93 154:0.66 155:0.67 158:0.92 159:0.24 161:0.88 162:0.60 164:0.89 165:0.86 167:0.83 169:0.87 172:0.48 173:0.92 176:0.73 177:0.38 179:0.62 181:0.89 186:0.89 189:0.82 191:0.78 192:0.59 195:0.64 201:0.74 202:0.86 204:0.89 208:0.64 212:0.61 215:0.79 216:0.28 222:0.48 230:0.87 232:0.77 233:0.76 235:0.31 238:0.85 241:0.91 242:0.53 243:0.83 247:0.47 248:0.93 253:0.94 255:0.79 256:0.88 259:0.21 260:0.89 261:0.90 265:0.70 266:0.38 267:0.92 268:0.87 271:0.53 276:0.36 279:0.86 282:0.88 283:0.82 285:0.62 290:0.90 297:0.36 299:0.88 300:0.33\n2 1:0.74 7:0.81 8:0.78 9:0.80 11:0.88 12:0.37 17:0.06 21:0.40 27:0.64 28:0.78 30:0.95 32:0.80 34:0.47 36:0.87 37:0.70 39:0.72 41:0.98 43:0.96 60:0.95 66:0.54 69:0.21 74:0.74 77:0.70 78:0.96 81:0.66 83:0.90 85:0.72 91:0.88 96:0.97 98:0.23 101:0.77 104:0.58 108:0.17 111:0.94 114:0.82 120:0.94 121:0.90 123:0.16 126:0.54 127:0.11 129:0.05 132:0.97 135:0.42 140:0.45 144:0.85 145:0.61 146:0.13 147:0.18 149:0.53 150:0.79 151:0.93 153:0.78 154:0.96 155:0.67 158:0.87 159:0.08 161:0.88 162:0.76 164:0.92 165:0.83 167:0.83 169:0.91 172:0.10 173:0.84 176:0.73 177:0.38 179:0.42 181:0.89 191:0.75 192:0.59 195:0.53 201:0.74 202:0.86 204:0.89 208:0.64 215:0.67 216:0.19 220:0.87 222:0.48 230:0.87 232:0.77 233:0.76 235:0.52 238:0.86 241:0.90 243:0.63 248:0.97 253:0.97 255:0.79 256:0.89 259:0.21 260:0.94 265:0.25 267:0.69 268:0.89 271:0.53 279:0.86 282:0.88 283:0.71 285:0.62 290:0.82 297:0.36 299:0.88 300:0.08\n2 1:0.74 6:0.86 7:0.81 8:0.78 9:0.80 11:0.88 12:0.37 17:0.26 20:0.85 21:0.30 27:0.57 28:0.78 30:0.62 32:0.80 34:0.55 36:0.98 37:0.31 39:0.72 41:0.99 43:0.98 55:0.71 60:0.97 66:0.44 69:0.12 70:0.66 74:0.88 77:0.70 78:0.93 81:0.71 83:0.90 85:0.90 91:0.58 96:0.97 98:0.72 100:0.90 101:0.80 108:0.73 111:0.92 114:0.86 120:0.94 121:0.90 123:0.71 124:0.60 126:0.54 127:0.67 129:0.59 133:0.47 135:0.90 138:0.98 140:0.45 144:0.85 145:0.58 146:0.73 147:0.77 149:0.85 150:0.82 151:0.98 152:0.89 153:0.91 154:0.98 155:0.67 158:0.92 159:0.57 161:0.88 162:0.73 164:0.93 165:0.84 167:0.64 169:0.87 172:0.65 173:0.18 176:0.73 177:0.38 179:0.47 181:0.89 186:0.89 187:0.21 189:0.91 191:0.77 192:0.59 195:0.73 201:0.74 202:0.89 204:0.89 208:0.64 212:0.58 215:0.72 216:0.90 220:0.87 222:0.48 230:0.87 233:0.76 235:0.42 238:0.89 241:0.64 242:0.57 243:0.49 245:0.87 247:0.60 248:0.97 253:0.98 255:0.79 256:0.91 259:0.21 260:0.95 261:0.89 265:0.72 266:0.47 267:0.73 268:0.91 271:0.53 276:0.71 279:0.86 282:0.88 283:0.82 285:0.62 290:0.86 297:0.36 299:0.88 300:0.70\n2 1:0.74 6:0.98 7:0.81 8:0.95 9:0.80 11:0.88 12:0.37 17:0.43 20:0.97 21:0.30 27:0.21 28:0.78 30:0.36 34:0.69 36:0.79 37:0.74 39:0.72 41:0.98 43:0.98 55:0.71 60:0.97 66:0.20 69:0.12 70:0.83 74:0.99 78:0.94 81:0.71 83:0.90 85:0.99 91:0.76 96:0.98 98:0.62 100:0.99 101:0.79 104:0.84 106:0.81 108:0.95 111:0.98 114:0.86 117:0.86 120:0.96 121:0.90 122:0.67 123:0.94 124:0.93 126:0.54 127:0.71 129:0.97 133:0.93 135:0.26 140:0.45 144:0.85 146:0.35 147:0.42 149:0.95 150:0.82 151:0.99 152:0.95 153:0.96 154:0.98 155:0.67 158:0.92 159:0.96 161:0.88 162:0.67 164:0.92 165:0.84 167:0.49 169:0.97 172:0.95 173:0.06 176:0.73 177:0.38 179:0.11 181:0.89 186:0.93 187:0.39 189:0.99 191:0.73 192:0.59 201:0.74 202:0.89 204:0.89 206:0.81 208:0.64 212:0.48 215:0.72 216:0.95 222:0.48 230:0.87 232:0.89 233:0.76 235:0.42 238:0.98 241:0.24 242:0.70 243:0.11 245:0.98 247:0.60 248:0.98 253:0.99 255:0.79 256:0.91 259:0.21 260:0.96 261:0.97 265:0.63 266:0.89 267:0.89 268:0.90 271:0.53 276:0.98 279:0.86 283:0.82 285:0.62 290:0.86 297:0.36 300:0.94\n2 1:0.74 6:0.93 8:0.86 9:0.80 11:0.88 12:0.37 17:0.36 20:0.97 21:0.40 27:0.39 28:0.78 30:0.10 34:0.44 36:1.00 37:0.57 39:0.72 41:0.98 43:0.98 55:0.92 60:0.87 66:0.20 69:0.15 70:0.88 74:0.82 78:0.93 81:0.66 83:0.89 85:0.94 91:0.78 96:0.97 98:0.52 100:0.94 101:0.78 108:0.83 111:0.92 114:0.82 120:0.94 123:0.82 124:0.92 126:0.54 127:0.93 129:0.96 133:0.91 135:0.92 140:0.45 144:0.85 146:0.57 147:0.63 149:0.86 150:0.79 151:0.97 152:0.89 153:0.90 154:0.98 155:0.67 158:0.92 159:0.86 161:0.88 162:0.79 164:0.91 165:0.83 167:0.56 169:0.91 172:0.67 173:0.09 176:0.73 177:0.38 179:0.33 181:0.89 186:0.92 187:0.39 189:0.94 192:0.59 201:0.74 202:0.85 208:0.64 212:0.18 215:0.67 216:0.26 220:0.74 222:0.48 232:0.95 235:0.52 238:0.90 241:0.51 242:0.41 243:0.17 245:0.83 247:0.60 248:0.97 253:0.97 255:0.79 256:0.88 259:0.21 260:0.95 261:0.93 265:0.54 266:0.91 267:0.23 268:0.89 271:0.53 276:0.83 279:0.86 283:0.82 285:0.62 290:0.82 297:0.36 300:0.70\n" }, { "path": "examples/xendcg/rank.train.query", "content": "1\n13\n5\n8\n19\n12\n18\n5\n14\n13\n8\n9\n16\n11\n21\n14\n21\n9\n14\n11\n20\n18\n13\n20\n22\n22\n13\n17\n10\n13\n12\n13\n13\n23\n18\n13\n20\n12\n22\n14\n13\n23\n13\n14\n14\n5\n13\n15\n14\n14\n16\n16\n15\n21\n22\n10\n22\n18\n25\n16\n12\n12\n15\n15\n25\n13\n9\n12\n8\n16\n25\n19\n24\n12\n16\n10\n16\n9\n17\n15\n7\n9\n15\n14\n16\n17\n8\n17\n12\n18\n23\n10\n12\n12\n4\n14\n12\n15\n27\n16\n20\n13\n19\n13\n17\n17\n16\n12\n15\n14\n14\n19\n12\n23\n18\n16\n9\n23\n11\n15\n8\n10\n10\n16\n11\n15\n22\n16\n17\n23\n16\n22\n17\n14\n12\n14\n20\n15\n17\n15\n15\n22\n9\n21\n9\n17\n16\n15\n13\n13\n15\n14\n18\n21\n14\n17\n15\n14\n16\n12\n17\n19\n16\n11\n18\n11\n13\n14\n9\n16\n15\n16\n25\n9\n13\n22\n16\n18\n20\n14\n11\n9\n16\n19\n19\n11\n11\n13\n14\n14\n13\n16\n6\n21\n16\n12\n16\n11\n24\n12\n10\n" }, { "path": "examples/xendcg/train.conf", "content": "# task type, support train and predict\ntask = train\n\n# boosting type, support gbdt for now, alias: boosting, boost\nboosting_type = gbdt\n\n# application type, support following application\n# regression , regression task\n# binary , binary classification task\n# lambdarank , LambdaRank task\n# alias: application, app\nobjective = rank_xendcg\n\n# eval metrics, support multi metric, delimited by ',' , support following metrics\n# l1\n# l2 , default metric for regression\n# ndcg , default metric for lambdarank\n# auc\n# binary_logloss , default metric for binary\n# binary_error\nmetric = ndcg\n\n# evaluation position for ndcg metric, alias : ndcg_at\nndcg_eval_at = 1,3,5\n\n# frequency for metric output\nmetric_freq = 1\n\n# true if need output metric for training data, alias: tranining_metric, train_metric\nis_training_metric = true\n\n# column in data to use as label\nlabel_column = 0\n\n# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.\nmax_bin = 255\n\n# training data\n# if existing weight file, should name to \"rank.train.weight\"\n# if existing query file, should name to \"rank.train.query\"\n# alias: train_data, train\ndata = rank.train\n\n# validation data, support multi validation data, separated by ','\n# if existing weight file, should name to \"rank.test.weight\"\n# if existing query file, should name to \"rank.test.query\"\n# alias: valid, test, test_data,\nvalid_data = rank.test\n\n# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds\nnum_trees = 100\n\n# shrinkage rate , alias: shrinkage_rate\nlearning_rate = 0.1\n\n# number of leaves for one tree, alias: num_leaf\nnum_leaves = 31\n\n# type of tree learner, support following types:\n# serial , single machine version\n# feature , use feature parallel to train\n# data , use data parallel to train\n# voting , use voting based parallel to train\n# alias: tree\ntree_learner = serial\n\n# Set num_threads and objective_seed for stable unit-tests. Comment out otherwise.\nnum_threads = 1\nobjective_seed = 1025\n\n# feature sub-sample, will random select 80% feature to train on each iteration\n# alias: sub_feature\nfeature_fraction = 1.0\n\n# Support bagging (data sub-sample), will perform bagging every 5 iterations\nbagging_freq = 1\n\n# Bagging fraction, will random select 80% data on bagging\n# alias: sub_row\nbagging_fraction = 0.9\n\n# minimal number data for one leaf, use this to deal with over-fit\n# alias : min_data_per_leaf, min_data\nmin_data_in_leaf = 50\n\n# minimal sum Hessians for one leaf, use this to deal with over-fit\nmin_sum_hessian_in_leaf = 5.0\n\n# save memory and faster speed for sparse feature, alias: is_sparse\nis_enable_sparse = true\n\n# when data is bigger than memory size, set this to true. otherwise set false will have faster speed\n# alias: two_round_loading, two_round\nuse_two_round_loading = false\n\n# true if need to save data to binary file and application will auto load data from binary file next time\n# alias: is_save_binary, save_binary\nis_save_binary_file = false\n\n# output model file\noutput_model = LightGBM_model.txt\n\n# support continuous train from trained gbdt model\n# input_model= trained_model.txt\n\n# output prediction file for predict task\n# output_result= prediction.txt\n\n\n# number of machines in distributed training, alias: num_machine\nnum_machines = 1\n\n# local listening port in distributed training, alias: local_port\nlocal_listen_port = 12400\n\n# machines list file for distributed training, alias: mlist\nmachine_list_file = mlist.txt\n" }, { "path": "include/LightGBM/application.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_APPLICATION_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_APPLICATION_H_\n\n#include \n#include \n\n#include \n#include \n\nnamespace LightGBM {\n\nclass DatasetLoader;\nclass Dataset;\nclass Boosting;\nclass ObjectiveFunction;\nclass Metric;\n\n/*!\n* \\brief The main entrance of LightGBM. this application has two tasks:\n* Train and Predict.\n* Train task will train a new model\n* Predict task will predict the scores of test data using existing model,\n* and save the score to disk.\n*/\nclass Application {\n public:\n Application(int argc, char** argv);\n\n /*! \\brief Destructor */\n ~Application();\n\n /*! \\brief To call this function to run application*/\n inline void Run();\n\n private:\n /*! \\brief Load parameters from command line and config file*/\n void LoadParameters(int argc, char** argv);\n\n /*! \\brief Load data, including training data and validation data*/\n void LoadData();\n\n /*! \\brief Initialization before training*/\n void InitTrain();\n\n /*! \\brief Main Training logic */\n void Train();\n\n /*! \\brief Initializations before prediction */\n void InitPredict();\n\n /*! \\brief Main predicting logic */\n void Predict();\n\n /*! \\brief Main Convert model logic */\n void ConvertModel();\n\n /*! \\brief All configs */\n Config config_;\n /*! \\brief Training data */\n std::unique_ptr train_data_;\n /*! \\brief Validation data */\n std::vector> valid_datas_;\n /*! \\brief Metric for training data */\n std::vector> train_metric_;\n /*! \\brief Metrics for validation data */\n std::vector>> valid_metrics_;\n /*! \\brief Boosting object */\n std::unique_ptr boosting_;\n /*! \\brief Training objective function */\n std::unique_ptr objective_fun_;\n};\n\n\ninline void Application::Run() {\n if (config_.task == TaskType::kPredict || config_.task == TaskType::KRefitTree) {\n InitPredict();\n Predict();\n } else if (config_.task == TaskType::kConvertModel) {\n ConvertModel();\n } else {\n InitTrain();\n Train();\n }\n}\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_APPLICATION_H_\n" }, { "path": "include/LightGBM/arrow.h", "content": "/*!\n * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n *\n * Author: Oliver Borchert\n */\n\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_ARROW_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_ARROW_H_\n\n#ifdef __cplusplus\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#endif\n\n/* -------------------------------------- C DATA INTERFACE ------------------------------------- */\n// The C data interface is taken from\n// https://arrow.apache.org/docs/format/CDataInterface.html#structure-definitions\n// and is available under Apache License 2.0 (https://www.apache.org/licenses/LICENSE-2.0).\n\n#ifdef __cplusplus\nextern \"C\" {\n#endif\n\n#define ARROW_FLAG_DICTIONARY_ORDERED 1\n#define ARROW_FLAG_NULLABLE 2\n#define ARROW_FLAG_MAP_KEYS_SORTED 4\n\nstruct ArrowSchema {\n // Array type description\n const char* format;\n const char* name;\n const char* metadata;\n int64_t flags;\n int64_t n_children;\n struct ArrowSchema** children;\n struct ArrowSchema* dictionary;\n\n // Release callback\n void (*release)(struct ArrowSchema*);\n // Opaque producer-specific data\n void* private_data;\n};\n\nstruct ArrowArray {\n // Array data description\n int64_t length;\n int64_t null_count;\n int64_t offset;\n int64_t n_buffers;\n int64_t n_children;\n const void** buffers;\n struct ArrowArray** children;\n struct ArrowArray* dictionary;\n\n // Release callback\n void (*release)(struct ArrowArray*);\n // Opaque producer-specific data\n void* private_data;\n};\n\n#ifdef __cplusplus\n}\n#endif\n\n/* --------------------------------------------------------------------------------------------- */\n/* CHUNKED ARRAY */\n/* --------------------------------------------------------------------------------------------- */\n\n#ifdef __cplusplus\nnamespace LightGBM {\n\n/**\n * @brief Arrow array-like container for a list of Arrow arrays.\n */\nclass ArrowChunkedArray {\n /* List of length `n` for `n` chunks containing the individual Arrow arrays. */\n std::vector chunks_;\n /* Schema for all chunks. */\n const ArrowSchema* schema_;\n /* List of length `n + 1` for `n` chunks containing the offsets for each chunk. */\n std::vector chunk_offsets_;\n /* Indicator whether this chunked array needs to call the arrays' release callbacks.\n NOTE: This is MUST only be set to `true` if this chunked array is not part of a\n `ArrowTable` as children arrays may not be released by the consumer (see below). */\n const bool releases_arrow_;\n\n inline void construct_chunk_offsets() {\n chunk_offsets_.reserve(chunks_.size() + 1);\n chunk_offsets_.emplace_back(0);\n for (size_t k = 0; k < chunks_.size(); ++k) {\n chunk_offsets_.emplace_back(chunks_[k]->length + chunk_offsets_.back());\n }\n }\n\n public:\n /**\n * @brief Construct a new Arrow Chunked Array object.\n *\n * @param chunks A list with the chunks.\n * @param schema The schema for all chunks.\n */\n inline ArrowChunkedArray(std::vector chunks, const ArrowSchema* schema)\n : releases_arrow_(false) {\n chunks_ = chunks;\n schema_ = schema;\n construct_chunk_offsets();\n }\n\n /**\n * @brief Construct a new Arrow Chunked Array object.\n *\n * @param n_chunks The number of chunks.\n * @param chunks A C-style array containing the chunks.\n * @param schema The schema for all chunks.\n */\n inline ArrowChunkedArray(int64_t n_chunks, const struct ArrowArray* chunks,\n const struct ArrowSchema* schema)\n : releases_arrow_(true) {\n chunks_.reserve(n_chunks);\n for (int64_t k = 0; k < n_chunks; ++k) {\n if (chunks[k].length == 0) continue;\n chunks_.push_back(&chunks[k]);\n }\n schema_ = schema;\n construct_chunk_offsets();\n }\n\n ~ArrowChunkedArray() {\n if (!releases_arrow_) {\n return;\n }\n for (size_t i = 0; i < chunks_.size(); ++i) {\n auto chunk = chunks_[i];\n if (chunk->release) {\n chunk->release(const_cast(chunk));\n }\n }\n if (schema_->release) {\n schema_->release(const_cast(schema_));\n }\n }\n\n /**\n * @brief Get the length of the chunked array.\n * This method returns the cumulative length of all chunks.\n * Complexity: O(1)\n *\n * @return int64_t The number of elements in the chunked array.\n */\n inline int64_t get_length() const { return chunk_offsets_.back(); }\n\n /* ----------------------------------------- ITERATOR ---------------------------------------- */\n template \n class Iterator {\n using getter_fn = std::function;\n\n /* Reference to the chunked array that this iterator iterates over. */\n const ArrowChunkedArray& array_;\n /* Function to fetch the value at a certain index from a single chunk. */\n getter_fn get_;\n /* The chunk the iterator currently points to. */\n int64_t ptr_chunk_;\n /* The index inside the current chunk that the iterator points to. */\n int64_t ptr_offset_;\n\n public:\n using iterator_category = std::random_access_iterator_tag;\n using difference_type = int64_t;\n using value_type = T;\n using pointer = value_type*;\n using reference = value_type&;\n\n /**\n * @brief Construct a new Iterator object.\n *\n * @param array Reference to the chunked array to iterator over.\n * @param get Function to fetch the value at a certain index from a single chunk.\n * @param ptr_chunk The index of the chunk to whose first index the iterator points to.\n */\n Iterator(const ArrowChunkedArray& array, getter_fn get, int64_t ptr_chunk);\n\n T operator*() const;\n template \n T operator[](I idx) const;\n\n Iterator& operator++();\n Iterator& operator--();\n Iterator& operator+=(int64_t c);\n\n template \n friend bool operator==(const Iterator& a, const Iterator& b);\n template \n friend bool operator!=(const Iterator& a, const Iterator& b);\n template \n friend int64_t operator-(const Iterator& a, const Iterator& b);\n };\n\n /**\n * @brief Obtain an iterator to the beginning of the chunked array.\n *\n * @tparam T The value type of the iterator. May be any primitive type.\n * @return Iterator The iterator.\n */\n template \n inline Iterator begin() const;\n\n /**\n * @brief Obtain an iterator to the beginning of the chunked array.\n *\n * @tparam T The value type of the iterator. May be any primitive type.\n * @return Iterator The iterator.\n */\n template \n inline Iterator end() const;\n\n template \n friend int64_t operator-(const Iterator& a, const Iterator& b);\n};\n\n/**\n * @brief Arrow container for a list of chunked arrays.\n */\nclass ArrowTable {\n std::vector columns_;\n const int64_t n_chunks_;\n const ArrowArray* chunks_ptr_;\n const ArrowSchema* schema_ptr_;\n\n public:\n /**\n * @brief Construct a new Arrow Table object.\n *\n * @param n_chunks The number of chunks.\n * @param chunks A C-style array containing the chunks.\n * @param schema The schema for all chunks.\n */\n inline ArrowTable(int64_t n_chunks, const ArrowArray* chunks, const ArrowSchema* schema)\n : n_chunks_(n_chunks), chunks_ptr_(chunks), schema_ptr_(schema) {\n columns_.reserve(schema->n_children);\n for (int64_t j = 0; j < schema->n_children; ++j) {\n std::vector children_chunks;\n children_chunks.reserve(n_chunks);\n for (int64_t k = 0; k < n_chunks; ++k) {\n if (chunks[k].length == 0) continue;\n children_chunks.push_back(chunks[k].children[j]);\n }\n columns_.emplace_back(children_chunks, schema->children[j]);\n }\n }\n\n ~ArrowTable() {\n // As consumer of the Arrow array, the Arrow table must release all Arrow arrays it receives\n // as well as the schema. As per the specification, children arrays are released by the\n // producer. See:\n // https://arrow.apache.org/docs/format/CDataInterface.html#release-callback-semantics-for-consumers\n for (int64_t i = 0; i < n_chunks_; ++i) {\n auto chunk = &chunks_ptr_[i];\n if (chunk->release) {\n chunk->release(const_cast(chunk));\n }\n }\n if (schema_ptr_->release) {\n schema_ptr_->release(const_cast(schema_ptr_));\n }\n }\n\n /**\n * @brief Get the number of rows in the table.\n *\n * @return int64_t The number of rows.\n */\n inline int64_t get_num_rows() const { return columns_.front().get_length(); }\n\n /**\n * @brief Get the number of columns of this table.\n *\n * @return int64_t The column count.\n */\n inline int64_t get_num_columns() const { return columns_.size(); }\n\n /**\n * @brief Get the column at a particular index.\n *\n * @param idx The index of the column, must me in the range `[0, num_columns)`.\n * @return const ArrowChunkedArray& The chunked array for the child at the provided index.\n */\n inline const ArrowChunkedArray& get_column(size_t idx) const { return this->columns_[idx]; }\n};\n\n} // namespace LightGBM\n\n#include \"arrow.tpp\"\n#endif /* __cplusplus */\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_ARROW_H_\n" }, { "path": "include/LightGBM/arrow.tpp", "content": "#include \n\n#ifndef ARROW_TPP_\n#define ARROW_TPP_\n\nnamespace LightGBM {\n\n/**\n * @brief Obtain a function to access an index from an Arrow array.\n *\n * @tparam T The return type of the function, must be a primitive type.\n * @param dtype The Arrow format string describing the datatype of the Arrow array.\n * @return std::function The index accessor function.\n */\ntemplate \nstd::function get_index_accessor(const char* dtype);\n\n/* ---------------------------------- ITERATOR INITIALIZATION ---------------------------------- */\n\ntemplate \ninline ArrowChunkedArray::Iterator ArrowChunkedArray::begin() const {\n return ArrowChunkedArray::Iterator(*this, get_index_accessor(schema_->format), 0);\n}\n\ntemplate \ninline ArrowChunkedArray::Iterator ArrowChunkedArray::end() const {\n return ArrowChunkedArray::Iterator(*this, get_index_accessor(schema_->format),\n chunk_offsets_.size() - 1);\n}\n\n/* ---------------------------------- ITERATOR IMPLEMENTATION ---------------------------------- */\n\ntemplate \nArrowChunkedArray::Iterator::Iterator(const ArrowChunkedArray& array, getter_fn get,\n int64_t ptr_chunk)\n : array_(array), get_(get), ptr_chunk_(ptr_chunk) {\n this->ptr_offset_ = 0;\n}\n\ntemplate \nT ArrowChunkedArray::Iterator::operator*() const {\n auto chunk = array_.chunks_[ptr_chunk_];\n return get_(chunk, ptr_offset_);\n}\n\ntemplate \ntemplate \nT ArrowChunkedArray::Iterator::operator[](I idx) const {\n auto it = std::lower_bound(array_.chunk_offsets_.begin(), array_.chunk_offsets_.end(), idx,\n [](int64_t a, int64_t b) { return a <= b; });\n\n auto chunk_idx = std::distance(array_.chunk_offsets_.begin() + 1, it);\n auto chunk = array_.chunks_[chunk_idx];\n\n auto ptr_offset = static_cast(idx) - array_.chunk_offsets_[chunk_idx];\n return get_(chunk, ptr_offset);\n}\n\ntemplate \nArrowChunkedArray::Iterator& ArrowChunkedArray::Iterator::operator++() {\n if (ptr_offset_ + 1 >= array_.chunks_[ptr_chunk_]->length) {\n ptr_offset_ = 0;\n ptr_chunk_++;\n } else {\n ptr_offset_++;\n }\n return *this;\n}\n\ntemplate \nArrowChunkedArray::Iterator& ArrowChunkedArray::Iterator::operator--() {\n if (ptr_offset_ == 0) {\n ptr_chunk_--;\n ptr_offset_ = array_.chunks_[ptr_chunk_]->length - 1;\n } else {\n ptr_chunk_--;\n }\n return *this;\n}\n\ntemplate \nArrowChunkedArray::Iterator& ArrowChunkedArray::Iterator::operator+=(int64_t c) {\n while (ptr_offset_ + c >= array_.chunks_[ptr_chunk_]->length) {\n c -= array_.chunks_[ptr_chunk_]->length - ptr_offset_;\n ptr_offset_ = 0;\n ptr_chunk_++;\n }\n ptr_offset_ += c;\n return *this;\n}\n\ntemplate \nbool operator==(const ArrowChunkedArray::Iterator& a, const ArrowChunkedArray::Iterator& b) {\n return a.ptr_chunk_ == b.ptr_chunk_ && a.ptr_offset_ == b.ptr_offset_;\n}\n\ntemplate \nbool operator!=(const ArrowChunkedArray::Iterator& a, const ArrowChunkedArray::Iterator& b) {\n return a.ptr_chunk_ != b.ptr_chunk_ || a.ptr_offset_ != b.ptr_offset_;\n}\n\ntemplate \nint64_t operator-(const ArrowChunkedArray::Iterator& a,\n const ArrowChunkedArray::Iterator& b) {\n auto full_offset_a = a.array_.chunk_offsets_[a.ptr_chunk_] + a.ptr_offset_;\n auto full_offset_b = b.array_.chunk_offsets_[b.ptr_chunk_] + b.ptr_offset_;\n return full_offset_a - full_offset_b;\n}\n\n/* --------------------------------------- INDEX ACCESSOR -------------------------------------- */\n\n/**\n * @brief The value of \"no value\" for a primitive type.\n *\n * @tparam T The type for which the missing value is defined.\n * @return T The missing value.\n */\ntemplate \ninline T arrow_primitive_missing_value() {\n return 0;\n}\n\ntemplate <>\ninline double arrow_primitive_missing_value() {\n return std::numeric_limits::quiet_NaN();\n}\n\ntemplate <>\ninline float arrow_primitive_missing_value() {\n return std::numeric_limits::quiet_NaN();\n}\n\ntemplate \nstruct ArrayIndexAccessor {\n V operator()(const ArrowArray* array, size_t idx) {\n auto buffer_idx = idx + array->offset;\n\n // For primitive types, buffer at idx 0 provides validity, buffer at idx 1 data, see:\n // https://arrow.apache.org/docs/format/Columnar.html#buffer-listing-for-each-layout\n auto validity = static_cast(array->buffers[0]);\n\n // Take return value from data buffer conditional on the validity of the index:\n // - The structure of validity bitmasks is taken from here:\n // https://arrow.apache.org/docs/format/Columnar.html#validity-bitmaps\n // - If the bitmask is NULL, all indices are valid\n if (validity == nullptr || (validity[buffer_idx / 8] & (1 << (buffer_idx % 8)))) {\n // In case the index is valid, we take it from the data buffer\n auto data = static_cast(array->buffers[1]);\n return static_cast(data[buffer_idx]);\n }\n\n // In case the index is not valid, we return a default value\n return arrow_primitive_missing_value();\n }\n};\n\ntemplate \nstruct ArrayIndexAccessor {\n V operator()(const ArrowArray* array, size_t idx) {\n // Custom implementation for booleans as values are bit-packed:\n // https://arrow.apache.org/docs/cpp/api/datatype.html#_CPPv4N5arrow4Type4type4BOOLE\n auto buffer_idx = idx + array->offset;\n auto validity = static_cast(array->buffers[0]);\n if (validity == nullptr || (validity[buffer_idx / 8] & (1 << (buffer_idx % 8)))) {\n // In case the index is valid, we have to take the appropriate bit from the buffer\n auto data = static_cast(array->buffers[1]);\n auto value = (data[buffer_idx / 8] & (1 << (buffer_idx % 8))) >> (buffer_idx % 8);\n return static_cast(value);\n }\n return arrow_primitive_missing_value();\n }\n};\n\ntemplate \nstd::function get_index_accessor(const char* dtype) {\n // Mapping obtained from:\n // https://arrow.apache.org/docs/format/CDataInterface.html#data-type-description-format-strings\n switch (dtype[0]) {\n case 'c':\n return ArrayIndexAccessor();\n case 'C':\n return ArrayIndexAccessor();\n case 's':\n return ArrayIndexAccessor();\n case 'S':\n return ArrayIndexAccessor();\n case 'i':\n return ArrayIndexAccessor();\n case 'I':\n return ArrayIndexAccessor();\n case 'l':\n return ArrayIndexAccessor();\n case 'L':\n return ArrayIndexAccessor();\n case 'f':\n return ArrayIndexAccessor();\n case 'g':\n return ArrayIndexAccessor();\n case 'b':\n return ArrayIndexAccessor();\n default:\n throw std::invalid_argument(\"unsupported Arrow datatype\");\n }\n}\n\n} // namespace LightGBM\n\n#endif\n" }, { "path": "include/LightGBM/bin.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_BIN_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_BIN_H_\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nenum BinType {\n NumericalBin,\n CategoricalBin\n};\n\nenum MissingType {\n None,\n Zero,\n NaN\n};\n\ntypedef double hist_t;\ntypedef int32_t int_hist_t;\ntypedef uint64_t hist_cnt_t;\n// check at compile time\nstatic_assert(sizeof(hist_t) == sizeof(hist_cnt_t), \"Histogram entry size is not correct\");\n\nconst size_t kHistEntrySize = 2 * sizeof(hist_t);\nconst size_t kInt32HistEntrySize = 2 * sizeof(int_hist_t);\nconst size_t kInt16HistEntrySize = 2 * sizeof(int16_t);\nconst int kHistOffset = 2;\nconst double kSparseThreshold = 0.7;\n\n#define GET_GRAD(hist, i) hist[(i) << 1]\n#define GET_HESS(hist, i) hist[((i) << 1) + 1]\n\ninline static void HistogramSumReducer(const char* src, char* dst, int type_size, comm_size_t len) {\n comm_size_t used_size = 0;\n const hist_t* p1;\n hist_t* p2;\n while (used_size < len) {\n // convert\n p1 = reinterpret_cast(src);\n p2 = reinterpret_cast(dst);\n *p2 += *p1;\n src += type_size;\n dst += type_size;\n used_size += type_size;\n }\n}\n\ninline static void Int32HistogramSumReducer(const char* src, char* dst, int type_size, comm_size_t len) {\n const int64_t* src_ptr = reinterpret_cast(src);\n int64_t* dst_ptr = reinterpret_cast(dst);\n const comm_size_t steps = (len + (type_size * 2) - 1) / (type_size * 2);\n #pragma omp parallel for schedule(static) num_threads(OMP_NUM_THREADS())\n for (comm_size_t i = 0; i < steps; ++i) {\n dst_ptr[i] += src_ptr[i];\n }\n}\n\ninline static void Int16HistogramSumReducer(const char* src, char* dst, int type_size, comm_size_t len) {\n const int32_t* src_ptr = reinterpret_cast(src);\n int32_t* dst_ptr = reinterpret_cast(dst);\n const comm_size_t steps = (len + (type_size * 2) - 1) / (type_size * 2);\n #pragma omp parallel for schedule(static) num_threads(OMP_NUM_THREADS())\n for (comm_size_t i = 0; i < steps; ++i) {\n dst_ptr[i] += src_ptr[i];\n }\n}\n\n/*! \\brief This class used to convert feature values into bin,\n* and store some meta information for bin*/\nclass BinMapper {\n public:\n BinMapper();\n BinMapper(const BinMapper& other);\n explicit BinMapper(const void* memory);\n ~BinMapper();\n\n bool CheckAlign(const BinMapper& other) const {\n if (num_bin_ != other.num_bin_) {\n return false;\n }\n if (missing_type_ != other.missing_type_) {\n return false;\n }\n if (bin_type_ == BinType::NumericalBin) {\n for (int i = 0; i < num_bin_; ++i) {\n if (bin_upper_bound_[i] != other.bin_upper_bound_[i]) {\n return false;\n }\n }\n } else {\n for (int i = 0; i < num_bin_; i++) {\n if (bin_2_categorical_[i] != other.bin_2_categorical_[i]) {\n return false;\n }\n }\n }\n return true;\n }\n\n /*! \\brief Get number of bins */\n inline int num_bin() const { return num_bin_; }\n\n /*! \\brief Missing Type */\n inline MissingType missing_type() const { return missing_type_; }\n\n /*! \\brief True if bin is trivial (contains only one bin) */\n inline bool is_trivial() const { return is_trivial_; }\n\n /*! \\brief Sparsity of this bin ( num_zero_bins / num_data ) */\n inline double sparse_rate() const { return sparse_rate_; }\n\n /*!\n * \\brief Save binary data to file\n * \\param file File want to write\n */\n void SaveBinaryToFile(BinaryWriter* writer) const;\n\n /*!\n * \\brief Mapping bin into feature value\n * \\param bin\n * \\return Feature value of this bin\n */\n inline double BinToValue(uint32_t bin) const {\n if (bin_type_ == BinType::NumericalBin) {\n return bin_upper_bound_[bin];\n } else {\n return bin_2_categorical_[bin];\n }\n }\n\n /*!\n * \\brief Maximum categorical value\n * \\return Maximum categorical value for categorical features, 0 for numerical features\n */\n inline int MaxCatValue() const {\n if (bin_2_categorical_.size() == 0) {\n return 0;\n }\n int max_cat_value = bin_2_categorical_[0];\n for (size_t i = 1; i < bin_2_categorical_.size(); ++i) {\n if (bin_2_categorical_[i] > max_cat_value) {\n max_cat_value = bin_2_categorical_[i];\n }\n }\n return max_cat_value;\n }\n\n /*!\n * \\brief Get sizes in byte of this object\n */\n size_t SizesInByte() const;\n\n /*!\n * \\brief Mapping feature value into bin\n * \\param value\n * \\return bin for this feature value\n */\n inline uint32_t ValueToBin(double value) const;\n\n /*!\n * \\brief Get the default bin when value is 0\n * \\return default bin\n */\n inline uint32_t GetDefaultBin() const {\n return default_bin_;\n }\n\n inline uint32_t GetMostFreqBin() const {\n return most_freq_bin_;\n }\n\n /*!\n * \\brief Construct feature value to bin mapper according feature values\n * \\param values (Sampled) values of this feature, Note: not include zero.\n * \\param num_values number of values.\n * \\param total_sample_cnt number of total sample count, equal with values.size() + num_zeros\n * \\param max_bin The maximal number of bin\n * \\param min_data_in_bin min number of data in one bin\n * \\param min_split_data\n * \\param pre_filter\n * \\param bin_type Type of this bin\n * \\param use_missing True to enable missing value handle\n * \\param zero_as_missing True to use zero as missing value\n * \\param forced_upper_bounds Vector of split points that must be used (if this has size less than max_bin, remaining splits are found by the algorithm)\n */\n void FindBin(double* values, int num_values, size_t total_sample_cnt, int max_bin, int min_data_in_bin, int min_split_data, bool pre_filter, BinType bin_type,\n bool use_missing, bool zero_as_missing, const std::vector& forced_upper_bounds);\n\n /*!\n * \\brief Serializing this object to buffer\n * \\param buffer The destination\n */\n void CopyTo(char* buffer) const;\n\n /*!\n * \\brief Deserializing this object from buffer\n * \\param buffer The source\n */\n void CopyFrom(const char* buffer);\n\n /*!\n * \\brief Get bin types\n */\n inline BinType bin_type() const { return bin_type_; }\n\n /*!\n * \\brief Get bin info\n */\n inline std::string bin_info_string() const {\n if (bin_type_ == BinType::CategoricalBin) {\n return Common::Join(bin_2_categorical_, \":\");\n } else {\n std::stringstream str_buf;\n str_buf << std::setprecision(std::numeric_limits::digits10 + 2);\n str_buf << '[' << min_val_ << ':' << max_val_ << ']';\n return str_buf.str();\n }\n }\n\n private:\n /*! \\brief Number of bins */\n int num_bin_;\n MissingType missing_type_;\n /*! \\brief Store upper bound for each bin */\n std::vector bin_upper_bound_;\n /*! \\brief True if this feature is trivial */\n bool is_trivial_;\n /*! \\brief Sparse rate of this bins( num_bin0/num_data ) */\n double sparse_rate_;\n /*! \\brief Type of this bin */\n BinType bin_type_;\n /*! \\brief Mapper from categorical to bin */\n std::unordered_map categorical_2_bin_;\n /*! \\brief Mapper from bin to categorical */\n std::vector bin_2_categorical_;\n /*! \\brief minimal feature value */\n double min_val_;\n /*! \\brief maximum feature value */\n double max_val_;\n /*! \\brief bin value of feature value 0 */\n uint32_t default_bin_;\n\n uint32_t most_freq_bin_;\n};\n\n/*! \\brief Iterator for one bin column */\nclass BinIterator {\n public:\n /*!\n * \\brief Get bin data on specific row index\n * \\param idx Index of this data\n * \\return Bin data\n */\n virtual uint32_t Get(data_size_t idx) = 0;\n virtual uint32_t RawGet(data_size_t idx) = 0;\n virtual void Reset(data_size_t idx) = 0;\n virtual ~BinIterator() = default;\n};\n\n/*!\n* \\brief Interface for bin data. This class will store bin data for one feature.\n* unlike OrderedBin, this class will store data by original order.\n* Note that it may cause cache misses when construct histogram,\n* but it doesn't need to re-order operation, So it will be faster than OrderedBin for dense feature\n*/\nclass Bin {\n public:\n /*! \\brief virtual destructor */\n virtual ~Bin() {}\n /*!\n * \\brief Initialize for pushing. By default, no action needed.\n * \\param num_thread The number of external threads that will be calling the push APIs\n * \\param omp_max_threads The maximum number of OpenMP threads to allocate for\n */\n virtual void InitStreaming(uint32_t /*num_thread*/, int32_t /*omp_max_threads*/) { }\n /*!\n * \\brief Push one record\n * \\param tid Thread id\n * \\param idx Index of record\n * \\param value bin value of record\n */\n virtual void Push(int tid, data_size_t idx, uint32_t value) = 0;\n\n virtual void CopySubrow(const Bin* full_bin, const data_size_t* used_indices, data_size_t num_used_indices) = 0;\n /*!\n * \\brief Get bin iterator of this bin for specific feature\n * \\param min_bin min_bin of current used feature\n * \\param max_bin max_bin of current used feature\n * \\param most_freq_bin\n * \\return Iterator of this bin\n */\n virtual BinIterator* GetIterator(uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin) const = 0;\n\n /*!\n * \\brief Save binary data to file\n * \\param file File want to write\n */\n virtual void SaveBinaryToFile(BinaryWriter* writer) const = 0;\n\n /*!\n * \\brief Load from memory\n * \\param memory\n * \\param local_used_indices\n */\n virtual void LoadFromMemory(const void* memory,\n const std::vector& local_used_indices) = 0;\n\n /*!\n * \\brief Get sizes in byte of this object\n */\n virtual size_t SizesInByte() const = 0;\n\n /*! \\brief Number of all data */\n virtual data_size_t num_data() const = 0;\n\n /*! \\brief Get data pointer */\n virtual void* get_data() = 0;\n\n virtual void ReSize(data_size_t num_data) = 0;\n\n /*!\n * \\brief Construct histogram of this feature,\n * Note: We use ordered_gradients and ordered_hessians to improve cache hit chance\n * The naive solution is using gradients[data_indices[i]] for data_indices[i] to get gradients,\n which is not cache friendly, since the access of memory is not continuous.\n * ordered_gradients and ordered_hessians are preprocessed, and they are re-ordered by data_indices.\n * Ordered_gradients[i] is aligned with data_indices[i]'s gradients (same for ordered_hessians).\n * \\param data_indices Used data indices in current leaf\n * \\param start start index in data_indices\n * \\param end end index in data_indices\n * \\param ordered_gradients Pointer to gradients, the data_indices[i]-th data's gradient is ordered_gradients[i]\n * \\param ordered_hessians Pointer to hessians, the data_indices[i]-th data's hessian is ordered_hessians[i]\n * \\param out Output Result\n */\n virtual void ConstructHistogram(\n const data_size_t* data_indices, data_size_t start, data_size_t end,\n const score_t* ordered_gradients, const score_t* ordered_hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogram(data_size_t start, data_size_t end,\n const score_t* ordered_gradients, const score_t* ordered_hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt8(\n const data_size_t* data_indices, data_size_t start, data_size_t end,\n const score_t* ordered_gradients, const score_t* ordered_hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt8(data_size_t start, data_size_t end,\n const score_t* ordered_gradients, const score_t* ordered_hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt16(\n const data_size_t* data_indices, data_size_t start, data_size_t end,\n const score_t* ordered_gradients, const score_t* ordered_hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt16(data_size_t start, data_size_t end,\n const score_t* ordered_gradients, const score_t* ordered_hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt32(\n const data_size_t* data_indices, data_size_t start, data_size_t end,\n const score_t* ordered_gradients, const score_t* ordered_hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt32(data_size_t start, data_size_t end,\n const score_t* ordered_gradients, const score_t* ordered_hessians,\n hist_t* out) const = 0;\n\n /*!\n * \\brief Construct histogram of this feature,\n * Note: We use ordered_gradients and ordered_hessians to improve cache hit chance\n * The naive solution is using gradients[data_indices[i]] for data_indices[i] to get gradients,\n which is not cache friendly, since the access of memory is not continuous.\n * ordered_gradients and ordered_hessians are preprocessed, and they are re-ordered by data_indices.\n * Ordered_gradients[i] is aligned with data_indices[i]'s gradients (same for ordered_hessians).\n * \\param data_indices Used data indices in current leaf\n * \\param start start index in data_indices\n * \\param end end index in data_indices\n * \\param ordered_gradients Pointer to gradients, the data_indices[i]-th data's gradient is ordered_gradients[i]\n * \\param out Output Result\n */\n virtual void ConstructHistogram(const data_size_t* data_indices, data_size_t start, data_size_t end,\n const score_t* ordered_gradients, hist_t* out) const = 0;\n\n virtual void ConstructHistogram(data_size_t start, data_size_t end,\n const score_t* ordered_gradients, hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start, data_size_t end,\n const score_t* ordered_gradients, hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt8(data_size_t start, data_size_t end,\n const score_t* ordered_gradients, hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start, data_size_t end,\n const score_t* ordered_gradients, hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt16(data_size_t start, data_size_t end,\n const score_t* ordered_gradients, hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start, data_size_t end,\n const score_t* ordered_gradients, hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt32(data_size_t start, data_size_t end,\n const score_t* ordered_gradients, hist_t* out) const = 0;\n\n virtual data_size_t Split(uint32_t min_bin, uint32_t max_bin,\n uint32_t default_bin, uint32_t most_freq_bin,\n MissingType missing_type, bool default_left,\n uint32_t threshold, const data_size_t* data_indices,\n data_size_t cnt,\n data_size_t* lte_indices,\n data_size_t* gt_indices) const = 0;\n\n virtual data_size_t SplitCategorical(\n uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin,\n const uint32_t* threshold, int num_threshold,\n const data_size_t* data_indices, data_size_t cnt,\n data_size_t* lte_indices, data_size_t* gt_indices) const = 0;\n\n virtual data_size_t Split(uint32_t max_bin, uint32_t default_bin,\n uint32_t most_freq_bin, MissingType missing_type,\n bool default_left, uint32_t threshold,\n const data_size_t* data_indices, data_size_t cnt,\n data_size_t* lte_indices,\n data_size_t* gt_indices) const = 0;\n\n virtual data_size_t SplitCategorical(\n uint32_t max_bin, uint32_t most_freq_bin, const uint32_t* threshold,\n int num_threshold, const data_size_t* data_indices, data_size_t cnt,\n data_size_t* lte_indices, data_size_t* gt_indices) const = 0;\n\n /*!\n * \\brief After pushed all feature data, call this could have better refactor for bin data\n */\n virtual void FinishLoad() = 0;\n\n /*!\n * \\brief Create object for bin data of one feature, used for dense feature\n * \\param num_data Total number of data\n * \\param num_bin Number of bin\n * \\return The bin data object\n */\n static Bin* CreateDenseBin(data_size_t num_data, int num_bin);\n\n /*!\n * \\brief Create object for bin data of one feature, used for sparse feature\n * \\param num_data Total number of data\n * \\param num_bin Number of bin\n * \\return The bin data object\n */\n static Bin* CreateSparseBin(data_size_t num_data, int num_bin);\n\n /*!\n * \\brief Deep copy the bin\n */\n virtual Bin* Clone() = 0;\n\n virtual const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const = 0;\n\n virtual const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const = 0;\n};\n\n\nclass MultiValBin {\n public:\n virtual ~MultiValBin() {}\n\n virtual data_size_t num_data() const = 0;\n\n virtual int32_t num_bin() const = 0;\n\n virtual double num_element_per_row() const = 0;\n\n virtual const std::vector& offsets() const = 0;\n\n virtual void PushOneRow(int tid, data_size_t idx, const std::vector& values) = 0;\n\n virtual void CopySubrow(const MultiValBin* full_bin,\n const data_size_t* used_indices,\n data_size_t num_used_indices) = 0;\n\n virtual MultiValBin* CreateLike(data_size_t num_data, int num_bin,\n int num_feature,\n double estimate_element_per_row,\n const std::vector& offsets) const = 0;\n\n virtual void CopySubcol(const MultiValBin* full_bin,\n const std::vector& used_feature_index,\n const std::vector& lower,\n const std::vector& upper,\n const std::vector& delta) = 0;\n\n virtual void ReSize(data_size_t num_data, int num_bin, int num_feature,\n double estimate_element_per_row, const std::vector& offsets) = 0;\n\n virtual void CopySubrowAndSubcol(\n const MultiValBin* full_bin, const data_size_t* used_indices,\n data_size_t num_used_indices, const std::vector& used_feature_index,\n const std::vector& lower, const std::vector& upper,\n const std::vector& delta) = 0;\n\n virtual void ConstructHistogram(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogram(data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramOrdered(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n const score_t* ordered_hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt32(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt32(data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramOrderedInt32(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n const score_t* ordered_hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt16(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt16(data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramOrderedInt16(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n const score_t* ordered_hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt8(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramInt8(data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* hessians,\n hist_t* out) const = 0;\n\n virtual void ConstructHistogramOrderedInt8(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n const score_t* ordered_hessians,\n hist_t* out) const = 0;\n\n virtual void FinishLoad() = 0;\n\n virtual bool IsSparse() = 0;\n\n static MultiValBin* CreateMultiValBin(data_size_t num_data, int num_bin,\n int num_feature, double sparse_rate, const std::vector& offsets);\n\n static MultiValBin* CreateMultiValDenseBin(data_size_t num_data, int num_bin,\n int num_feature, const std::vector& offsets);\n\n static MultiValBin* CreateMultiValSparseBin(data_size_t num_data, int num_bin, double estimate_element_per_row);\n\n static constexpr double multi_val_bin_sparse_threshold = 0.25f;\n\n virtual MultiValBin* Clone() = 0;\n\n #ifdef USE_CUDA\n virtual const void* GetRowWiseData(uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const = 0;\n #endif // USE_CUDA\n};\n\ninline uint32_t BinMapper::ValueToBin(double value) const {\n if (std::isnan(value)) {\n if (bin_type_ == BinType::CategoricalBin) {\n return 0;\n } else if (missing_type_ == MissingType::NaN) {\n return num_bin_ - 1;\n } else {\n value = 0.0f;\n }\n }\n if (bin_type_ == BinType::NumericalBin) {\n // binary search to find bin\n int l = 0;\n int r = num_bin_ - 1;\n if (missing_type_ == MissingType::NaN) {\n r -= 1;\n }\n while (l < r) {\n int m = (r + l - 1) / 2;\n if (value <= bin_upper_bound_[m]) {\n r = m;\n } else {\n l = m + 1;\n }\n }\n return l;\n } else {\n int int_value = static_cast(value);\n // convert negative value to NaN bin\n if (int_value < 0) {\n return 0;\n }\n if (categorical_2_bin_.count(int_value)) {\n return categorical_2_bin_.at(int_value);\n } else {\n return 0;\n }\n }\n}\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_BIN_H_\n" }, { "path": "include/LightGBM/boosting.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_BOOSTING_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_BOOSTING_H_\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\n/*! \\brief forward declaration */\nclass Dataset;\nclass ObjectiveFunction;\nclass Metric;\nstruct PredictionEarlyStopInstance;\n\n/*!\n* \\brief The interface for Boosting\n*/\nclass LIGHTGBM_EXPORT Boosting {\n public:\n /*! \\brief virtual destructor */\n virtual ~Boosting() {}\n\n /*!\n * \\brief Initialization logic\n * \\param config Configs for boosting\n * \\param train_data Training data\n * \\param objective_function Training objective function\n * \\param training_metrics Training metric\n */\n virtual void Init(\n const Config* config,\n const Dataset* train_data,\n const ObjectiveFunction* objective_function,\n const std::vector& training_metrics) = 0;\n\n /*!\n * \\brief Merge model from other boosting object\n Will insert to the front of current boosting object\n * \\param other\n */\n virtual void MergeFrom(const Boosting* other) = 0;\n\n /*!\n * \\brief Shuffle Existing Models\n */\n virtual void ShuffleModels(int start_iter, int end_iter) = 0;\n\n virtual void ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function,\n const std::vector& training_metrics) = 0;\n\n virtual void ResetConfig(const Config* config) = 0;\n\n\n\n /*!\n * \\brief Add a validation data\n * \\param valid_data Validation data\n * \\param valid_metrics Metric for validation data\n */\n virtual void AddValidDataset(const Dataset* valid_data,\n const std::vector& valid_metrics) = 0;\n\n virtual void Train(int snapshot_freq, const std::string& model_output_path) = 0;\n\n /*!\n * \\brief Update the tree output by new training data\n */\n virtual void RefitTree(const int* tree_leaf_prediction, const size_t nrow, const size_t ncol) = 0;\n\n /*!\n * \\brief Training logic\n * \\param gradients nullptr for using default objective, otherwise use self-defined boosting\n * \\param hessians nullptr for using default objective, otherwise use self-defined boosting\n * \\return True if cannot train anymore\n */\n virtual bool TrainOneIter(const score_t* gradients, const score_t* hessians) = 0;\n\n /*!\n * \\brief Rollback one iteration\n */\n virtual void RollbackOneIter() = 0;\n\n /*!\n * \\brief return current iteration\n */\n virtual int GetCurrentIteration() const = 0;\n\n /*!\n * \\brief Get evaluation result at data_idx data\n * \\param data_idx 0: training data, 1: 1st validation data\n * \\return evaluation result\n */\n virtual std::vector GetEvalAt(int data_idx) const = 0;\n\n /*!\n * \\brief Get current training score\n * \\param out_len length of returned score\n * \\return training score\n */\n virtual const double* GetTrainingScore(int64_t* out_len) = 0;\n\n /*!\n * \\brief Get prediction result at data_idx data\n * \\param data_idx 0: training data, 1: 1st validation data\n * \\return out_len length of returned score\n */\n virtual int64_t GetNumPredictAt(int data_idx) const = 0;\n\n /*!\n * \\brief Get prediction result at data_idx data\n * \\param data_idx 0: training data, 1: 1st validation data\n * \\param result used to store prediction result, should allocate memory before call this function\n * \\param out_len length of returned score\n */\n virtual void GetPredictAt(int data_idx, double* result, int64_t* out_len) = 0;\n\n virtual int NumPredictOneRow(int start_iteration, int num_iteration, bool is_pred_leaf, bool is_pred_contrib) const = 0;\n\n /*!\n * \\brief Prediction for one record, not sigmoid transform\n * \\param feature_values Feature value on this record\n * \\param output Prediction result for this record\n * \\param early_stop Early stopping instance. If nullptr, no early stopping is applied and all models are evaluated.\n */\n virtual void PredictRaw(const double* features, double* output,\n const PredictionEarlyStopInstance* early_stop) const = 0;\n\n virtual void PredictRawByMap(const std::unordered_map& features, double* output,\n const PredictionEarlyStopInstance* early_stop) const = 0;\n\n\n /*!\n * \\brief Prediction for one record, sigmoid transformation will be used if needed\n * \\param feature_values Feature value on this record\n * \\param output Prediction result for this record\n * \\param early_stop Early stopping instance. If nullptr, no early stopping is applied and all models are evaluated.\n */\n virtual void Predict(const double* features, double* output,\n const PredictionEarlyStopInstance* early_stop) const = 0;\n\n virtual void PredictByMap(const std::unordered_map& features, double* output,\n const PredictionEarlyStopInstance* early_stop) const = 0;\n\n\n /*!\n * \\brief Prediction for one record with leaf index\n * \\param feature_values Feature value on this record\n * \\param output Prediction result for this record\n */\n virtual void PredictLeafIndex(\n const double* features, double* output) const = 0;\n\n virtual void PredictLeafIndexByMap(\n const std::unordered_map& features, double* output) const = 0;\n\n /*!\n * \\brief Feature contributions for the model's prediction of one record\n * \\param feature_values Feature value on this record\n * \\param output Prediction result for this record\n */\n virtual void PredictContrib(const double* features, double* output) const = 0;\n\n virtual void PredictContribByMap(const std::unordered_map& features,\n std::vector>* output) const = 0;\n\n /*!\n * \\brief Dump model to json format string\n * \\param start_iteration The model will be saved start from\n * \\param num_iteration Number of iterations that want to dump, -1 means dump all\n * \\param feature_importance_type Type of feature importance, 0: split, 1: gain\n * \\return Json format string of model\n */\n virtual std::string DumpModel(int start_iteration, int num_iteration, int feature_importance_type) const = 0;\n\n /*!\n * \\brief Translate model to if-else statement\n * \\param num_iteration Number of iterations that want to translate, -1 means translate all\n * \\return if-else format codes of model\n */\n virtual std::string ModelToIfElse(int num_iteration) const = 0;\n\n /*!\n * \\brief Translate model to if-else statement\n * \\param num_iteration Number of iterations that want to translate, -1 means translate all\n * \\param filename Filename that want to save to\n * \\return is_finish Is training finished or not\n */\n virtual bool SaveModelToIfElse(int num_iteration, const char* filename) const = 0;\n\n /*!\n * \\brief Save model to file\n * \\param start_iteration The model will be saved start from\n * \\param num_iterations Number of model that want to save, -1 means save all\n * \\param feature_importance_type Type of feature importance, 0: split, 1: gain\n * \\param filename Filename that want to save to\n * \\return true if succeeded\n */\n virtual bool SaveModelToFile(int start_iteration, int num_iterations, int feature_importance_type, const char* filename) const = 0;\n\n /*!\n * \\brief Save model to string\n * \\param start_iteration The model will be saved start from\n * \\param num_iterations Number of model that want to save, -1 means save all\n * \\param feature_importance_type Type of feature importance, 0: split, 1: gain\n * \\return Non-empty string if succeeded\n */\n virtual std::string SaveModelToString(int start_iteration, int num_iterations, int feature_importance_type) const = 0;\n\n /*!\n * \\brief Restore from a serialized string\n * \\param buffer The content of model\n * \\param len The length of buffer\n * \\return true if succeeded\n */\n virtual bool LoadModelFromString(const char* buffer, size_t len) = 0;\n\n /*!\n * \\brief Calculate feature importances\n * \\param num_iteration Number of model that want to use for feature importance, -1 means use all\n * \\param importance_type: 0 for split, 1 for gain\n * \\return vector of feature_importance\n */\n virtual std::vector FeatureImportance(int num_iteration, int importance_type) const = 0;\n\n /*!\n * \\brief Calculate upper bound value\n * \\return max possible value\n */\n virtual double GetUpperBoundValue() const = 0;\n\n /*!\n * \\brief Calculate lower bound value\n * \\return min possible value\n */\n virtual double GetLowerBoundValue() const = 0;\n\n /*!\n * \\brief Get max feature index of this model\n * \\return Max feature index of this model\n */\n virtual int MaxFeatureIdx() const = 0;\n\n /*!\n * \\brief Get feature names of this model\n * \\return Feature names of this model\n */\n virtual std::vector FeatureNames() const = 0;\n\n /*!\n * \\brief Get index of label column\n * \\return index of label column\n */\n virtual int LabelIdx() const = 0;\n\n /*!\n * \\brief Get number of weak sub-models\n * \\return Number of weak sub-models\n */\n virtual int NumberOfTotalModel() const = 0;\n\n /*!\n * \\brief Get number of models per iteration\n * \\return Number of models per iteration\n */\n virtual int NumModelPerIteration() const = 0;\n\n /*!\n * \\brief Get number of classes\n * \\return Number of classes\n */\n virtual int NumberOfClasses() const = 0;\n\n /*! \\brief The prediction should be accurate or not. True will disable early stopping for prediction. */\n virtual bool NeedAccuratePrediction() const = 0;\n\n /*!\n * \\brief Initial work for the prediction\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration number of used iteration\n * \\param is_pred_contrib\n */\n virtual void InitPredict(int start_iteration, int num_iteration, bool is_pred_contrib) = 0;\n\n /*!\n * \\brief Name of submodel\n */\n virtual const char* SubModelName() const = 0;\n\n Boosting() = default;\n /*! \\brief Disable copy */\n Boosting& operator=(const Boosting&) = delete;\n /*! \\brief Disable copy */\n Boosting(const Boosting&) = delete;\n\n static bool LoadFileToBoosting(Boosting* boosting, const char* filename);\n\n /*!\n * \\brief Create boosting object\n * \\param type Type of boosting\n * \\param format Format of model\n * \\param config config for boosting\n * \\param filename name of model file, if existing will continue to train from this model\n * \\param device_type type of device, can be cpu, gpu or cuda\n * \\param num_gpu number of GPUs to use\n * \\return The boosting object\n */\n static Boosting* CreateBoosting(const std::string& type, const char* filename, const std::string& device_type, const int num_gpu);\n\n virtual std::string GetLoadedParam() const = 0;\n\n virtual bool IsLinear() const { return false; }\n\n virtual std::string ParserConfigStr() const = 0;\n};\n\nclass GBDTBase : public Boosting {\n public:\n virtual double GetLeafValue(int tree_idx, int leaf_idx) const = 0;\n virtual void SetLeafValue(int tree_idx, int leaf_idx, double val) = 0;\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_BOOSTING_H_\n" }, { "path": "include/LightGBM/c_api.h", "content": "/*!\n * \\file c_api.h\n * \\copyright Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n * \\note\n * To avoid type conversion on large data, the most of our exposed interface supports both float32 and float64,\n * except the following:\n * 1. gradient and Hessian;\n * 2. current score for training and validation data.\n * .\n * The reason is that they are called frequently, and the type conversion on them may be time-cost.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_C_API_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_C_API_H_\n\n#include \n#include \n\n#ifdef __cplusplus\n#include \n#include \n#include \n#else\n#include \n#include \n#include \n#endif\n\n\ntypedef void* DatasetHandle; /*!< \\brief Handle of dataset. */\ntypedef void* BoosterHandle; /*!< \\brief Handle of booster. */\ntypedef void* FastConfigHandle; /*!< \\brief Handle of FastConfig. */\ntypedef void* ByteBufferHandle; /*!< \\brief Handle of ByteBuffer. */\n\n#define C_API_DTYPE_FLOAT32 (0) /*!< \\brief float32 (single precision float). */\n#define C_API_DTYPE_FLOAT64 (1) /*!< \\brief float64 (double precision float). */\n#define C_API_DTYPE_INT32 (2) /*!< \\brief int32. */\n#define C_API_DTYPE_INT64 (3) /*!< \\brief int64. */\n\n#define C_API_PREDICT_NORMAL (0) /*!< \\brief Normal prediction, with transform (if needed). */\n#define C_API_PREDICT_RAW_SCORE (1) /*!< \\brief Predict raw score. */\n#define C_API_PREDICT_LEAF_INDEX (2) /*!< \\brief Predict leaf index. */\n#define C_API_PREDICT_CONTRIB (3) /*!< \\brief Predict feature contributions (SHAP values). */\n\n#define C_API_MATRIX_TYPE_CSR (0) /*!< \\brief CSR sparse matrix type. */\n#define C_API_MATRIX_TYPE_CSC (1) /*!< \\brief CSC sparse matrix type. */\n\n#define C_API_FEATURE_IMPORTANCE_SPLIT (0) /*!< \\brief Split type of feature importance. */\n#define C_API_FEATURE_IMPORTANCE_GAIN (1) /*!< \\brief Gain type of feature importance. */\n\n/*!\n * \\brief Get string message of the last error.\n * \\return Error information\n */\nLIGHTGBM_C_EXPORT const char* LGBM_GetLastError();\n\n/*!\n * \\brief Dump all parameter names with their aliases to JSON.\n * \\param buffer_len String buffer length, if ``buffer_len < out_len``, you should re-allocate buffer\n * \\param[out] out_len Actual output length\n * \\param[out] out_str JSON format string of parameters, should pre-allocate memory\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DumpParamAliases(int64_t buffer_len,\n int64_t* out_len,\n char* out_str);\n\n/*!\n * \\brief Register a callback function for log redirecting.\n * \\param callback The callback function to register\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_RegisterLogCallback(void (*callback)(const char*));\n\n/*!\n * \\brief Get number of samples based on parameters and total number of rows of data.\n * \\param num_total_row Number of total rows\n * \\param parameters Additional parameters, namely, ``bin_construct_sample_cnt`` is used to calculate returned value\n * \\param[out] out Number of samples. This value is used to pre-allocate memory to hold sample indices when calling ``LGBM_SampleIndices``\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_GetSampleCount(int32_t num_total_row,\n const char* parameters,\n int* out);\n\n/*!\n * \\brief Create sample indices for total number of rows.\n * \\note\n * You should pre-allocate memory for ``out``, you can get its length by ``LGBM_GetSampleCount``.\n * \\param num_total_row Number of total rows\n * \\param parameters Additional parameters, namely, ``bin_construct_sample_cnt`` and ``data_random_seed`` are used to produce the output\n * \\param[out] out Created indices, type is int32_t\n * \\param[out] out_len Number of indices\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_SampleIndices(int32_t num_total_row,\n const char* parameters,\n void* out,\n int32_t* out_len);\n\n/*!\n * \\brief Get a ByteBuffer value at an index.\n * \\param handle Handle of byte buffer to be read\n * \\param index Index of value to return\n * \\param[out] out_val Byte value at index to return\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_ByteBufferGetAt(ByteBufferHandle handle, int32_t index, uint8_t* out_val);\n\n/*!\n * \\brief Free space for byte buffer.\n * \\param handle Handle of byte buffer to be freed\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_ByteBufferFree(ByteBufferHandle handle);\n\n/* --- start Dataset interface */\n\n/*!\n * \\brief Load dataset from file (like LightGBM CLI version does).\n * \\param filename The name of the file\n * \\param parameters Additional parameters\n * \\param reference Used to align bin mapper with other dataset, nullptr means isn't used\n * \\param[out] out A loaded dataset\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromFile(const char* filename,\n const char* parameters,\n const DatasetHandle reference,\n DatasetHandle* out);\n\n/*!\n * \\brief Allocate the space for dataset and bucket feature bins according to sampled data.\n * \\param sample_data Sampled data, grouped by the column\n * \\param sample_indices Indices of sampled data\n * \\param ncol Number of columns\n * \\param num_per_col Size of each sampling column\n * \\param num_sample_row Number of sampled rows\n * \\param num_local_row Total number of rows local to machine\n * \\param num_dist_row Number of total distributed rows\n * \\param parameters Additional parameters\n * \\param[out] out Created dataset\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromSampledColumn(double** sample_data,\n int** sample_indices,\n int32_t ncol,\n const int* num_per_col,\n int32_t num_sample_row,\n int32_t num_local_row,\n int64_t num_dist_row,\n const char* parameters,\n DatasetHandle* out);\n\n/*!\n * \\brief Allocate the space for dataset and bucket feature bins according to reference dataset.\n * \\param reference Used to align bin mapper with other dataset\n * \\param num_total_row Number of total rows\n * \\param[out] out Created dataset\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetCreateByReference(const DatasetHandle reference,\n int64_t num_total_row,\n DatasetHandle* out);\n\n/*!\n * \\brief Initialize the Dataset for streaming.\n * \\param dataset Handle of dataset\n * \\param has_weights Whether the dataset has Metadata weights\n * \\param has_init_scores Whether the dataset has Metadata initial scores\n * \\param has_queries Whether the dataset has Metadata queries/groups\n * \\param nclasses Number of initial score classes\n * \\param nthreads Number of external threads that will use the PushRows APIs\n * \\param omp_max_threads Maximum number of OpenMP threads (-1 for default)\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetInitStreaming(DatasetHandle dataset,\n int32_t has_weights,\n int32_t has_init_scores,\n int32_t has_queries,\n int32_t nclasses,\n int32_t nthreads,\n int32_t omp_max_threads);\n\n/*!\n * \\brief Allocate the space for dataset and bucket feature bins according to serialized reference dataset.\n * \\param ref_buffer A binary representation of the dataset schema (feature groups, bins, etc.)\n * \\param ref_buffer_size The size of the reference array in bytes\n * \\param num_row Number of total rows the dataset will contain\n * \\param num_classes Number of classes (will be used only in case of multiclass and specifying initial scores)\n * \\param parameters Additional parameters\n * \\param[out] out Created dataset\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromSerializedReference(const void* ref_buffer,\n int32_t ref_buffer_size,\n int64_t num_row,\n int32_t num_classes,\n const char* parameters,\n DatasetHandle* out);\n\n/*!\n * \\brief Push data to existing dataset, if ``nrow + start_row == num_total_row``, will call ``dataset->FinishLoad``.\n * \\param dataset Handle of dataset\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param nrow Number of rows\n * \\param ncol Number of columns\n * \\param start_row Row start index\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetPushRows(DatasetHandle dataset,\n const void* data,\n int data_type,\n int32_t nrow,\n int32_t ncol,\n int32_t start_row);\n\n/*!\n * \\brief Push data to existing dataset.\n * The general flow for a streaming scenario is:\n * 1. create Dataset \"schema\" (e.g. ``LGBM_DatasetCreateFromSampledColumn``)\n * 2. init them for thread-safe streaming (``LGBM_DatasetInitStreaming``)\n * 3. push data (``LGBM_DatasetPushRowsWithMetadata`` or ``LGBM_DatasetPushRowsByCSRWithMetadata``)\n * 4. call ``LGBM_DatasetMarkFinished``\n * \\param dataset Handle of dataset\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param nrow Number of rows\n * \\param ncol Number of feature columns\n * \\param start_row Row start index, i.e., the index at which to start inserting data\n * \\param label Pointer to array with nrow labels\n * \\param weight Optional pointer to array with nrow weights\n * \\param init_score Optional pointer to array with nrow*nclasses initial scores, in column format\n * \\param query Optional pointer to array with nrow query values\n * \\param tid The id of the calling thread, from 0...N-1 threads\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetPushRowsWithMetadata(DatasetHandle dataset,\n const void* data,\n int data_type,\n int32_t nrow,\n int32_t ncol,\n int32_t start_row,\n const float* label,\n const float* weight,\n const double* init_score,\n const int32_t* query,\n int32_t tid);\n\n/*!\n * \\brief Push data to existing dataset, if ``nrow + start_row == num_total_row``, will call ``dataset->FinishLoad``.\n * \\param dataset Handle of dataset\n * \\param indptr Pointer to row headers\n * \\param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``\n * \\param indices Pointer to column indices\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param nindptr Number of rows in the matrix + 1\n * \\param nelem Number of nonzero elements in the matrix\n * \\param num_col Number of columns\n * \\param start_row Row start index\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetPushRowsByCSR(DatasetHandle dataset,\n const void* indptr,\n int indptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t nindptr,\n int64_t nelem,\n int64_t num_col,\n int64_t start_row);\n\n/*!\n * \\brief Push CSR data to existing dataset. (See ``LGBM_DatasetPushRowsWithMetadata`` for more details.)\n * \\param dataset Handle of dataset\n * \\param indptr Pointer to row headers\n * \\param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``\n * \\param indices Pointer to column indices\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param nindptr Number of rows in the matrix + 1\n * \\param nelem Number of nonzero elements in the matrix\n * \\param start_row Row start index\n * \\param label Pointer to array with nindptr-1 labels\n * \\param weight Optional pointer to array with nindptr-1 weights\n * \\param init_score Optional pointer to array with (nindptr-1)*nclasses initial scores, in column format\n * \\param query Optional pointer to array with nindptr-1 query values\n * \\param tid The id of the calling thread, from 0...N-1 threads\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetPushRowsByCSRWithMetadata(DatasetHandle dataset,\n const void* indptr,\n int indptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t nindptr,\n int64_t nelem,\n int64_t start_row,\n const float* label,\n const float* weight,\n const double* init_score,\n const int32_t* query,\n int32_t tid);\n\n/*!\n * \\brief Set whether or not the Dataset waits for a manual MarkFinished call or calls FinishLoad on itself automatically.\n * Set to 1 for streaming scenario, and use ``LGBM_DatasetMarkFinished`` to manually finish the Dataset.\n * \\param dataset Handle of dataset\n * \\param wait Whether to wait or not (1 or 0)\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetSetWaitForManualFinish(DatasetHandle dataset, int wait);\n\n/*!\n * \\brief Mark the Dataset as complete by calling ``dataset->FinishLoad``.\n * \\param dataset Handle of dataset\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetMarkFinished(DatasetHandle dataset);\n\n/*!\n * \\brief Create a dataset from CSR format.\n * \\param indptr Pointer to row headers\n * \\param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``\n * \\param indices Pointer to column indices\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param nindptr Number of rows in the matrix + 1\n * \\param nelem Number of nonzero elements in the matrix\n * \\param num_col Number of columns\n * \\param parameters Additional parameters\n * \\param reference Used to align bin mapper with other dataset, nullptr means isn't used\n * \\param[out] out Created dataset\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSR(const void* indptr,\n int indptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t nindptr,\n int64_t nelem,\n int64_t num_col,\n const char* parameters,\n const DatasetHandle reference,\n DatasetHandle* out);\n\n/*!\n * \\brief Create a dataset from CSR format through callbacks.\n * \\param get_row_funptr Pointer to ``std::function>& ret)>``\n * (called for every row and expected to clear and fill ``ret``)\n * \\param num_rows Number of rows\n * \\param num_col Number of columns\n * \\param parameters Additional parameters\n * \\param reference Used to align bin mapper with other dataset, nullptr means isn't used\n * \\param[out] out Created dataset\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSRFunc(void* get_row_funptr,\n int num_rows,\n int64_t num_col,\n const char* parameters,\n const DatasetHandle reference,\n DatasetHandle* out);\n\n/*!\n * \\brief Create a dataset from CSC format.\n * \\param col_ptr Pointer to column headers\n * \\param col_ptr_type Type of ``col_ptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``\n * \\param indices Pointer to row indices\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param ncol_ptr Number of columns in the matrix + 1\n * \\param nelem Number of nonzero elements in the matrix\n * \\param num_row Number of rows\n * \\param parameters Additional parameters\n * \\param reference Used to align bin mapper with other dataset, nullptr means isn't used\n * \\param[out] out Created dataset\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromCSC(const void* col_ptr,\n int col_ptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t ncol_ptr,\n int64_t nelem,\n int64_t num_row,\n const char* parameters,\n const DatasetHandle reference,\n DatasetHandle* out);\n\n/*!\n * \\brief Create dataset from dense matrix.\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param nrow Number of rows\n * \\param ncol Number of columns\n * \\param is_row_major 1 for row-major, 0 for column-major\n * \\param parameters Additional parameters\n * \\param reference Used to align bin mapper with other dataset, nullptr means isn't used\n * \\param[out] out Created dataset\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromMat(const void* data,\n int data_type,\n int32_t nrow,\n int32_t ncol,\n int is_row_major,\n const char* parameters,\n const DatasetHandle reference,\n DatasetHandle* out);\n\n/*!\n * \\brief Create dataset from array of dense matrices.\n * \\param nmat Number of dense matrices\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param nrow Number of rows\n * \\param ncol Number of columns\n * \\param is_row_major Pointer to the data layouts. 1 for row-major, 0 for column-major\n * \\param parameters Additional parameters\n * \\param reference Used to align bin mapper with other dataset, nullptr means isn't used\n * \\param[out] out Created dataset\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromMats(int32_t nmat,\n const void** data,\n int data_type,\n int32_t* nrow,\n int32_t ncol,\n int* is_row_major,\n const char* parameters,\n const DatasetHandle reference,\n DatasetHandle* out);\n\n/*!\n * \\brief Create dataset from Arrow.\n * \\param n_chunks The number of Arrow arrays passed to this function\n * \\param chunks Pointer to the list of Arrow arrays\n * \\param schema Pointer to the schema of all Arrow arrays\n * \\param parameters Additional parameters\n * \\param reference Used to align bin mapper with other dataset, nullptr means isn't used\n * \\param[out] out Created dataset\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetCreateFromArrow(int64_t n_chunks,\n const struct ArrowArray* chunks,\n const struct ArrowSchema* schema,\n const char* parameters,\n const DatasetHandle reference,\n DatasetHandle *out);\n\n/*!\n * \\brief Create subset of a data.\n * \\param handle Handle of full dataset\n * \\param used_row_indices Indices used in subset\n * \\param num_used_row_indices Length of ``used_row_indices``\n * \\param parameters Additional parameters\n * \\param[out] out Subset of data\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetGetSubset(const DatasetHandle handle,\n const int32_t* used_row_indices,\n int32_t num_used_row_indices,\n const char* parameters,\n DatasetHandle* out);\n\n/*!\n * \\brief Save feature names to dataset.\n * \\param handle Handle of dataset\n * \\param feature_names Feature names\n * \\param num_feature_names Number of feature names\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetSetFeatureNames(DatasetHandle handle,\n const char** feature_names,\n int num_feature_names);\n\n/*!\n * \\brief Get feature names of dataset.\n * \\param handle Handle of dataset\n * \\param len Number of ``char*`` pointers stored at ``out_strs``.\n * If smaller than the max size, only this many strings are copied\n * \\param[out] num_feature_names Number of feature names\n * \\param buffer_len Size of pre-allocated strings.\n * Content is copied up to ``buffer_len - 1`` and null-terminated\n * \\param[out] out_buffer_len String sizes required to do the full string copies\n * \\param[out] feature_names Feature names, should pre-allocate memory\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetGetFeatureNames(DatasetHandle handle,\n const int len,\n int* num_feature_names,\n const size_t buffer_len,\n size_t* out_buffer_len,\n char** feature_names);\n\n/*!\n * \\brief Free space for dataset.\n * \\param handle Handle of dataset to be freed\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetFree(DatasetHandle handle);\n\n/*!\n * \\brief Save dataset to binary file.\n * \\param handle Handle of dataset\n * \\param filename The name of the file\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetSaveBinary(DatasetHandle handle,\n const char* filename);\n\n/*!\n * \\brief Create a dataset schema representation as a binary byte array (excluding data).\n * \\param handle Handle of dataset\n * \\param[out] out The output byte array\n * \\param[out] out_len The length of the output byte array (returned for convenience)\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetSerializeReferenceToBinary(DatasetHandle handle,\n ByteBufferHandle* out,\n int32_t* out_len);\n\n/*!\n * \\brief Save dataset to text file, intended for debugging use only.\n * \\param handle Handle of dataset\n * \\param filename The name of the file\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetDumpText(DatasetHandle handle,\n const char* filename);\n\n/*!\n * \\brief Set vector to a content in info.\n * \\note\n * - \\a group only works for ``C_API_DTYPE_INT32``;\n * - \\a label and \\a weight only work for ``C_API_DTYPE_FLOAT32``;\n * - \\a init_score only works for ``C_API_DTYPE_FLOAT64``.\n * \\param handle Handle of dataset\n * \\param field_name Field name, can be \\a label, \\a weight, \\a init_score, \\a group\n * \\param field_data Pointer to data vector\n * \\param num_element Number of elements in ``field_data``\n * \\param type Type of ``field_data`` pointer, can be ``C_API_DTYPE_INT32``, ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetSetField(DatasetHandle handle,\n const char* field_name,\n const void* field_data,\n int num_element,\n int type);\n\n/*!\n * \\brief Set vector to a content in info.\n * \\note\n * - \\a group converts input datatype into ``int32``;\n * - \\a label and \\a weight convert input datatype into ``float32``;\n * - \\a init_score converts input datatype into ``float64``.\n * \\param handle Handle of dataset\n * \\param field_name Field name, can be \\a label, \\a weight, \\a init_score, \\a group\n * \\param n_chunks The number of Arrow arrays passed to this function\n * \\param chunks Pointer to the list of Arrow arrays\n * \\param schema Pointer to the schema of all Arrow arrays\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetSetFieldFromArrow(DatasetHandle handle,\n const char* field_name,\n int64_t n_chunks,\n const struct ArrowArray* chunks,\n const struct ArrowSchema* schema);\n\n/*!\n * \\brief Get info vector from dataset.\n * \\param handle Handle of dataset\n * \\param field_name Field name\n * \\param[out] out_len Used to set result length\n * \\param[out] out_ptr Pointer to the result\n * \\param[out] out_type Type of result pointer, can be ``C_API_DTYPE_INT32``, ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetGetField(DatasetHandle handle,\n const char* field_name,\n int* out_len,\n const void** out_ptr,\n int* out_type);\n\n/*!\n * \\brief Raise errors for attempts to update dataset parameters.\n * \\param old_parameters Current dataset parameters\n * \\param new_parameters New dataset parameters\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetUpdateParamChecking(const char* old_parameters,\n const char* new_parameters);\n\n/*!\n * \\brief Get number of data points.\n * \\param handle Handle of dataset\n * \\param[out] out The address to hold number of data points\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetGetNumData(DatasetHandle handle,\n int* out);\n\n/*!\n * \\brief Get number of features.\n * \\param handle Handle of dataset\n * \\param[out] out The address to hold number of features\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetGetNumFeature(DatasetHandle handle,\n int* out);\n\n/*!\n * \\brief Get number of bins for feature.\n * \\param handle Handle of dataset\n * \\param feature Index of the feature\n * \\param[out] out The address to hold number of bins\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetGetFeatureNumBin(DatasetHandle handle,\n int feature,\n int* out);\n\n/*!\n * \\brief Add features from ``source`` to ``target``.\n * \\param target The handle of the dataset to add features to\n * \\param source The handle of the dataset to take features from\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_DatasetAddFeaturesFrom(DatasetHandle target,\n DatasetHandle source);\n\n/* --- start Booster interfaces */\n\n/*!\n* \\brief Get int representing whether booster is fitting linear trees.\n* \\param handle Handle of booster\n* \\param[out] out The address to hold linear trees indicator\n* \\return 0 when succeed, -1 when failure happens\n*/\nLIGHTGBM_C_EXPORT int LGBM_BoosterGetLinear(BoosterHandle handle, int* out);\n\n/*!\n * \\brief Create a new boosting learner.\n * \\param train_data Training dataset\n * \\param parameters Parameters in format 'key1=value1 key2=value2'\n * \\param[out] out Handle of created booster\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterCreate(const DatasetHandle train_data,\n const char* parameters,\n BoosterHandle* out);\n\n/*!\n * \\brief Load an existing booster from model file.\n * \\param filename Filename of model\n * \\param[out] out_num_iterations Number of iterations of this booster\n * \\param[out] out Handle of created booster\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterCreateFromModelfile(const char* filename,\n int* out_num_iterations,\n BoosterHandle* out);\n\n/*!\n * \\brief Load an existing booster from string.\n * \\param model_str Model string\n * \\param[out] out_num_iterations Number of iterations of this booster\n * \\param[out] out Handle of created booster\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterLoadModelFromString(const char* model_str,\n int* out_num_iterations,\n BoosterHandle* out);\n\n/*!\n * \\brief Get parameters as JSON string.\n * \\param handle Handle of booster\n * \\param buffer_len Allocated space for string\n * \\param[out] out_len Actual size of string\n * \\param[out] out_str JSON string containing parameters\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterGetLoadedParam(BoosterHandle handle,\n int64_t buffer_len,\n int64_t* out_len,\n char* out_str);\n\n\n/*!\n * \\brief Free space for booster.\n * \\param handle Handle of booster to be freed\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterFree(BoosterHandle handle);\n\n/*!\n * \\brief Shuffle models.\n * \\param handle Handle of booster\n * \\param start_iter The first iteration that will be shuffled\n * \\param end_iter The last iteration that will be shuffled\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterShuffleModels(BoosterHandle handle,\n int start_iter,\n int end_iter);\n\n/*!\n * \\brief Merge model from ``other_handle`` into ``handle``.\n * \\param handle Handle of booster, will merge another booster into this one\n * \\param other_handle Other handle of booster\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterMerge(BoosterHandle handle,\n BoosterHandle other_handle);\n\n/*!\n * \\brief Add new validation data to booster.\n * \\param handle Handle of booster\n * \\param valid_data Validation dataset\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterAddValidData(BoosterHandle handle,\n const DatasetHandle valid_data);\n\n/*!\n * \\brief Reset training data for booster.\n * \\param handle Handle of booster\n * \\param train_data Training dataset\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterResetTrainingData(BoosterHandle handle,\n const DatasetHandle train_data);\n\n/*!\n * \\brief Reset config for booster.\n * \\param handle Handle of booster\n * \\param parameters Parameters in format 'key1=value1 key2=value2'\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterResetParameter(BoosterHandle handle,\n const char* parameters);\n\n/*!\n * \\brief Get number of classes.\n * \\param handle Handle of booster\n * \\param[out] out_len Number of classes\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterGetNumClasses(BoosterHandle handle,\n int* out_len);\n\n/*!\n * \\brief Update the model for one iteration.\n * \\param handle Handle of booster\n * \\param[out] produced_empty_tree 1 means the tree(s) produced by this iteration did not have any splits.\n * This usually means that training is \"finished\" (calling this function again will not change the model's predictions).\n * However, that is not always the case.\n * For example, if you have added any randomness (like column sampling by setting ``feature_fraction_bynode < 1.0``),\n * it is possible that another call to this function would produce a non-empty tree.\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterUpdateOneIter(BoosterHandle handle,\n int* produced_empty_tree);\n\n/*!\n * \\brief Refit the tree model using the new data (online learning).\n * \\param handle Handle of booster\n * \\param leaf_preds Pointer to predicted leaf indices\n * \\param nrow Number of rows of ``leaf_preds``\n * \\param ncol Number of columns of ``leaf_preds``\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterRefit(BoosterHandle handle,\n const int32_t* leaf_preds,\n int32_t nrow,\n int32_t ncol);\n\n/*!\n * \\brief Update the model by specifying gradient and Hessian directly\n * (this can be used to support customized loss functions).\n * \\note\n * The length of the arrays referenced by ``grad`` and ``hess`` must be equal to\n * ``num_class * num_train_data``, this is not verified by the library, the caller must ensure this.\n * \\param handle Handle of booster\n * \\param grad The first order derivative (gradient) statistics\n * \\param hess The second order derivative (Hessian) statistics\n * \\param[out] produced_empty_tree 1 means the tree(s) produced by this iteration did not have any splits.\n * This usually means that training is \"finished\" (calling this function again will not change the model's predictions).\n * However, that is not always the case.\n * For example, if you have added any randomness (like column sampling by setting ``feature_fraction_bynode < 1.0``),\n * it is possible that another call to this function would produce a non-empty tree.\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterUpdateOneIterCustom(BoosterHandle handle,\n const float* grad,\n const float* hess,\n int* produced_empty_tree);\n\n/*!\n * \\brief Rollback one iteration.\n * \\param handle Handle of booster\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterRollbackOneIter(BoosterHandle handle);\n\n/*!\n * \\brief Get index of the current boosting iteration.\n * \\param handle Handle of booster\n * \\param[out] out_iteration Index of the current boosting iteration\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterGetCurrentIteration(BoosterHandle handle,\n int* out_iteration);\n\n/*!\n * \\brief Get number of trees per iteration.\n * \\param handle Handle of booster\n * \\param[out] out_tree_per_iteration Number of trees per iteration\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterNumModelPerIteration(BoosterHandle handle,\n int* out_tree_per_iteration);\n\n/*!\n * \\brief Get number of weak sub-models.\n * \\param handle Handle of booster\n * \\param[out] out_models Number of weak sub-models\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterNumberOfTotalModel(BoosterHandle handle,\n int* out_models);\n\n/*!\n * \\brief Get number of evaluation metrics.\n * \\param handle Handle of booster\n * \\param[out] out_len Total number of evaluation metrics\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalCounts(BoosterHandle handle,\n int* out_len);\n\n/*!\n * \\brief Get names of evaluation metrics.\n * \\param handle Handle of booster\n * \\param len Number of ``char*`` pointers stored at ``out_strs``.\n * If smaller than the max size, only this many strings are copied\n * \\param[out] out_len Total number of evaluation metrics\n * \\param buffer_len Size of pre-allocated strings.\n * Content is copied up to ``buffer_len - 1`` and null-terminated\n * \\param[out] out_buffer_len String sizes required to do the full string copies\n * \\param[out] out_strs Names of evaluation metrics, should pre-allocate memory\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterGetEvalNames(BoosterHandle handle,\n const int len,\n int* out_len,\n const size_t buffer_len,\n size_t* out_buffer_len,\n char** out_strs);\n\n/*!\n * \\brief Get names of features.\n * \\param handle Handle of booster\n * \\param len Number of ``char*`` pointers stored at ``out_strs``.\n * If smaller than the max size, only this many strings are copied\n * \\param[out] out_len Total number of features\n * \\param buffer_len Size of pre-allocated strings.\n * Content is copied up to ``buffer_len - 1`` and null-terminated\n * \\param[out] out_buffer_len String sizes required to do the full string copies\n * \\param[out] out_strs Names of features, should pre-allocate memory\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterGetFeatureNames(BoosterHandle handle,\n const int len,\n int* out_len,\n const size_t buffer_len,\n size_t* out_buffer_len,\n char** out_strs);\n\n/*!\n * \\brief Check that the feature names of the data match the ones used to train the booster.\n * \\param handle Handle of booster\n * \\param data_names Array with the feature names in the data\n * \\param data_num_features Number of features in the data\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterValidateFeatureNames(BoosterHandle handle,\n const char** data_names,\n int data_num_features);\n\n/*!\n * \\brief Get number of features.\n * \\param handle Handle of booster\n * \\param[out] out_len Total number of features\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterGetNumFeature(BoosterHandle handle,\n int* out_len);\n\n/*!\n * \\brief Get evaluation for training data and validation data.\n * \\note\n * 1. You should call ``LGBM_BoosterGetEvalNames`` first to get the names of evaluation metrics.\n * 2. You should pre-allocate memory for ``out_results``, you can get its length by ``LGBM_BoosterGetEvalCounts``.\n * \\param handle Handle of booster\n * \\param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on\n * \\param[out] out_len Length of output result\n * \\param[out] out_results Array with evaluation results\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterGetEval(BoosterHandle handle,\n int data_idx,\n int* out_len,\n double* out_results);\n\n/*!\n * \\brief Get number of predictions for training data and validation data\n * (this can be used to support customized evaluation functions).\n * \\param handle Handle of booster\n * \\param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on\n * \\param[out] out_len Number of predictions\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterGetNumPredict(BoosterHandle handle,\n int data_idx,\n int64_t* out_len);\n\n/*!\n * \\brief Get prediction for training data and validation data.\n * \\note\n * You should pre-allocate memory for ``out_result``, its length is equal to ``num_class * num_data``.\n * \\param handle Handle of booster\n * \\param data_idx Index of data, 0: training data, 1: 1st validation data, 2: 2nd validation data and so on\n * \\param[out] out_len Length of output result\n * \\param[out] out_result Pointer to array with predictions\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterGetPredict(BoosterHandle handle,\n int data_idx,\n int64_t* out_len,\n double* out_result);\n\n/*!\n * \\brief Make prediction for file.\n * \\param handle Handle of booster\n * \\param data_filename Filename of file with data\n * \\param data_has_header Whether file has header or not\n * \\param predict_type What should be predicted\n * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);\n * - ``C_API_PREDICT_RAW_SCORE``: raw score;\n * - ``C_API_PREDICT_LEAF_INDEX``: leaf index;\n * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration Number of iterations for prediction, <= 0 means no limit\n * \\param parameter Other parameters for prediction, e.g. early stopping for prediction\n * \\param result_filename Filename of result file in which predictions will be written\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterPredictForFile(BoosterHandle handle,\n const char* data_filename,\n int data_has_header,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n const char* result_filename);\n\n/*!\n * \\brief Get number of predictions.\n * \\param handle Handle of booster\n * \\param num_row Number of rows\n * \\param predict_type What should be predicted\n * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);\n * - ``C_API_PREDICT_RAW_SCORE``: raw score;\n * - ``C_API_PREDICT_LEAF_INDEX``: leaf index;\n * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration Number of iterations for prediction, <= 0 means no limit\n * \\param[out] out_len Length of prediction\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterCalcNumPredict(BoosterHandle handle,\n int num_row,\n int predict_type,\n int start_iteration,\n int num_iteration,\n int64_t* out_len);\n\n/*!\n * \\brief Release FastConfig object.\n *\n * \\param fastConfig Handle to the FastConfig object acquired with a ``*FastInit()`` method.\n * \\return 0 when it succeeds, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_FastConfigFree(FastConfigHandle fastConfig);\n\n/*!\n * \\brief Make prediction for a new dataset in CSR format.\n * \\note\n * You should pre-allocate memory for ``out_result``:\n * - for normal and raw score, its length is equal to ``num_class * num_data``;\n * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;\n * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.\n * \\param handle Handle of booster\n * \\param indptr Pointer to row headers\n * \\param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``\n * \\param indices Pointer to column indices\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param nindptr Number of rows in the matrix + 1\n * \\param nelem Number of nonzero elements in the matrix\n * \\param num_col Number of columns\n * \\param predict_type What should be predicted\n * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);\n * - ``C_API_PREDICT_RAW_SCORE``: raw score;\n * - ``C_API_PREDICT_LEAF_INDEX``: leaf index;\n * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration Number of iterations for prediction, <= 0 means no limit\n * \\param parameter Other parameters for prediction, e.g. early stopping for prediction\n * \\param[out] out_len Length of output result\n * \\param[out] out_result Pointer to array with predictions\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSR(BoosterHandle handle,\n const void* indptr,\n int indptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t nindptr,\n int64_t nelem,\n int64_t num_col,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int64_t* out_len,\n double* out_result);\n\n/*!\n * \\brief Make sparse prediction for a new dataset in CSR or CSC format. Currently only used for feature contributions.\n * \\note\n * The outputs are pre-allocated, as they can vary for each invocation, but the shape should be the same:\n * - for feature contributions, the shape of sparse matrix will be ``num_class * num_data * (num_feature + 1)``.\n * The output indptr_type for the sparse matrix will be the same as the given input indptr_type.\n * Call ``LGBM_BoosterFreePredictSparse`` to deallocate resources.\n * \\param handle Handle of booster\n * \\param indptr Pointer to row headers for CSR or column headers for CSC\n * \\param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``\n * \\param indices Pointer to column indices for CSR or row indices for CSC\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param nindptr Number of entries in ``indptr``\n * \\param nelem Number of nonzero elements in the matrix\n * \\param num_col_or_row Number of columns for CSR or number of rows for CSC\n * \\param predict_type What should be predicted, only feature contributions supported currently\n * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration Number of iterations for prediction, <= 0 means no limit\n * \\param parameter Other parameters for prediction, e.g. early stopping for prediction\n * \\param matrix_type Type of matrix input and output, can be ``C_API_MATRIX_TYPE_CSR`` or ``C_API_MATRIX_TYPE_CSC``\n * \\param[out] out_len Length of output data and output indptr (pointer to an array with two entries where to write them)\n * \\param[out] out_indptr Pointer to output row headers for CSR or column headers for CSC\n * \\param[out] out_indices Pointer to sparse column indices for CSR or row indices for CSC\n * \\param[out] out_data Pointer to sparse data space\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterPredictSparseOutput(BoosterHandle handle,\n const void* indptr,\n int indptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t nindptr,\n int64_t nelem,\n int64_t num_col_or_row,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int matrix_type,\n int64_t* out_len,\n void** out_indptr,\n int32_t** out_indices,\n void** out_data);\n\n/*!\n * \\brief Method corresponding to ``LGBM_BoosterPredictSparseOutput`` to free the allocated data.\n * \\param indptr Pointer to output row headers or column headers to be deallocated\n * \\param indices Pointer to sparse indices to be deallocated\n * \\param data Pointer to sparse data space to be deallocated\n * \\param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterFreePredictSparse(void* indptr, int32_t* indices, void* data, int indptr_type, int data_type);\n\n/*!\n * \\brief Make prediction for a new dataset in CSR format. This method re-uses the internal predictor structure\n * from previous calls and is optimized for single row invocation.\n * \\note\n * You should pre-allocate memory for ``out_result``:\n * - for normal and raw score, its length is equal to ``num_class * num_data``;\n * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;\n * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.\n * \\param handle Handle of booster\n * \\param indptr Pointer to row headers\n * \\param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``\n * \\param indices Pointer to column indices\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param nindptr Number of rows in the matrix + 1\n * \\param nelem Number of nonzero elements in the matrix\n * \\param num_col Number of columns\n * \\param predict_type What should be predicted\n * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);\n * - ``C_API_PREDICT_RAW_SCORE``: raw score;\n * - ``C_API_PREDICT_LEAF_INDEX``: leaf index;\n * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration Number of iterations for prediction, <= 0 means no limit\n * \\param parameter Other parameters for prediction, e.g. early stopping for prediction\n * \\param[out] out_len Length of output result\n * \\param[out] out_result Pointer to array with predictions\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSRSingleRow(BoosterHandle handle,\n const void* indptr,\n int indptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t nindptr,\n int64_t nelem,\n int64_t num_col,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int64_t* out_len,\n double* out_result);\n\n/*!\n * \\brief Initialize and return a ``FastConfigHandle`` for use with ``LGBM_BoosterPredictForCSRSingleRowFast``.\n *\n * Release the ``FastConfig`` by passing its handle to ``LGBM_FastConfigFree`` when no longer needed.\n *\n * \\param handle Booster handle\n * \\param predict_type What should be predicted\n * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);\n * - ``C_API_PREDICT_RAW_SCORE``: raw score;\n * - ``C_API_PREDICT_LEAF_INDEX``: leaf index;\n * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration Number of iterations for prediction, <= 0 means no limit\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param num_col Number of columns\n * \\param parameter Other parameters for prediction, e.g. early stopping for prediction\n * \\param[out] out_fastConfig FastConfig object with which you can call ``LGBM_BoosterPredictForCSRSingleRowFast``\n * \\return 0 when it succeeds, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSRSingleRowFastInit(BoosterHandle handle,\n const int predict_type,\n const int start_iteration,\n const int num_iteration,\n const int data_type,\n const int64_t num_col,\n const char* parameter,\n FastConfigHandle *out_fastConfig);\n\n/*!\n * \\brief Faster variant of ``LGBM_BoosterPredictForCSRSingleRow``.\n *\n * Score single rows after setup with ``LGBM_BoosterPredictForCSRSingleRowFastInit``.\n *\n * By removing the setup steps from this call extra optimizations can be made like\n * initializing the config only once, instead of once per call.\n *\n * \\note\n * Setting up the number of threads is only done once at ``LGBM_BoosterPredictForCSRSingleRowFastInit``\n * instead of at each prediction.\n * If you use a different number of threads in other calls, you need to start the setup process over,\n * or that number of threads will be used for these calls as well.\n *\n * \\note\n * You should pre-allocate memory for ``out_result``:\n * - for normal and raw score, its length is equal to ``num_class * num_data``;\n * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;\n * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.\n *\n * \\param fastConfig_handle FastConfig object handle returned by ``LGBM_BoosterPredictForCSRSingleRowFastInit``\n * \\param indptr Pointer to row headers\n * \\param indptr_type Type of ``indptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``\n * \\param indices Pointer to column indices\n * \\param data Pointer to the data space\n * \\param nindptr Number of rows in the matrix + 1\n * \\param nelem Number of nonzero elements in the matrix\n * \\param[out] out_len Length of output result\n * \\param[out] out_result Pointer to array with predictions\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSRSingleRowFast(FastConfigHandle fastConfig_handle,\n const void* indptr,\n const int indptr_type,\n const int32_t* indices,\n const void* data,\n const int64_t nindptr,\n const int64_t nelem,\n int64_t* out_len,\n double* out_result);\n\n/*!\n * \\brief Make prediction for a new dataset in CSC format.\n * \\note\n * You should pre-allocate memory for ``out_result``:\n * - for normal and raw score, its length is equal to ``num_class * num_data``;\n * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;\n * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.\n * \\param handle Handle of booster\n * \\param col_ptr Pointer to column headers\n * \\param col_ptr_type Type of ``col_ptr``, can be ``C_API_DTYPE_INT32`` or ``C_API_DTYPE_INT64``\n * \\param indices Pointer to row indices\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param ncol_ptr Number of columns in the matrix + 1\n * \\param nelem Number of nonzero elements in the matrix\n * \\param num_row Number of rows\n * \\param predict_type What should be predicted\n * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);\n * - ``C_API_PREDICT_RAW_SCORE``: raw score;\n * - ``C_API_PREDICT_LEAF_INDEX``: leaf index;\n * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration Number of iteration for prediction, <= 0 means no limit\n * \\param parameter Other parameters for prediction, e.g. early stopping for prediction\n * \\param[out] out_len Length of output result\n * \\param[out] out_result Pointer to array with predictions\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterPredictForCSC(BoosterHandle handle,\n const void* col_ptr,\n int col_ptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t ncol_ptr,\n int64_t nelem,\n int64_t num_row,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int64_t* out_len,\n double* out_result);\n\n/*!\n * \\brief Make prediction for a new dataset.\n * \\note\n * You should pre-allocate memory for ``out_result``:\n * - for normal and raw score, its length is equal to ``num_class * num_data``;\n * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;\n * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.\n * \\param handle Handle of booster\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param nrow Number of rows\n * \\param ncol Number of columns\n * \\param is_row_major 1 for row-major, 0 for column-major\n * \\param predict_type What should be predicted\n * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);\n * - ``C_API_PREDICT_RAW_SCORE``: raw score;\n * - ``C_API_PREDICT_LEAF_INDEX``: leaf index;\n * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration Number of iteration for prediction, <= 0 means no limit\n * \\param parameter Other parameters for prediction, e.g. early stopping for prediction\n * \\param[out] out_len Length of output result\n * \\param[out] out_result Pointer to array with predictions\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMat(BoosterHandle handle,\n const void* data,\n int data_type,\n int32_t nrow,\n int32_t ncol,\n int is_row_major,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int64_t* out_len,\n double* out_result);\n\n/*!\n * \\brief Make prediction for a new dataset. This method re-uses the internal predictor structure\n * from previous calls and is optimized for single row invocation.\n * \\note\n * You should pre-allocate memory for ``out_result``:\n * - for normal and raw score, its length is equal to ``num_class * num_data``;\n * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;\n * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.\n * \\param handle Handle of booster\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param ncol Number columns\n * \\param is_row_major 1 for row-major, 0 for column-major\n * \\param predict_type What should be predicted\n * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);\n * - ``C_API_PREDICT_RAW_SCORE``: raw score;\n * - ``C_API_PREDICT_LEAF_INDEX``: leaf index;\n * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration Number of iteration for prediction, <= 0 means no limit\n * \\param parameter Other parameters for prediction, e.g. early stopping for prediction\n * \\param[out] out_len Length of output result\n * \\param[out] out_result Pointer to array with predictions\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMatSingleRow(BoosterHandle handle,\n const void* data,\n int data_type,\n int ncol,\n int is_row_major,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int64_t* out_len,\n double* out_result);\n\n/*!\n * \\brief Initialize and return a ``FastConfigHandle`` for use with ``LGBM_BoosterPredictForMatSingleRowFast``.\n *\n * Release the ``FastConfig`` by passing its handle to ``LGBM_FastConfigFree`` when no longer needed.\n *\n * \\param handle Booster handle\n * \\param predict_type What should be predicted\n * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);\n * - ``C_API_PREDICT_RAW_SCORE``: raw score;\n * - ``C_API_PREDICT_LEAF_INDEX``: leaf index;\n * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration Number of iterations for prediction, <= 0 means no limit\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param ncol Number of columns\n * \\param parameter Other parameters for prediction, e.g. early stopping for prediction\n * \\param[out] out_fastConfig FastConfig object with which you can call ``LGBM_BoosterPredictForMatSingleRowFast``\n * \\return 0 when it succeeds, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMatSingleRowFastInit(BoosterHandle handle,\n const int predict_type,\n const int start_iteration,\n const int num_iteration,\n const int data_type,\n const int32_t ncol,\n const char* parameter,\n FastConfigHandle *out_fastConfig);\n\n/*!\n * \\brief Faster variant of ``LGBM_BoosterPredictForMatSingleRow``.\n *\n * Score a single row after setup with ``LGBM_BoosterPredictForMatSingleRowFastInit``.\n *\n * By removing the setup steps from this call extra optimizations can be made like\n * initializing the config only once, instead of once per call.\n *\n * \\note\n * Setting up the number of threads is only done once at ``LGBM_BoosterPredictForMatSingleRowFastInit``\n * instead of at each prediction.\n * If you use a different number of threads in other calls, you need to start the setup process over,\n * or that number of threads will be used for these calls as well.\n *\n * \\param fastConfig_handle FastConfig object handle returned by ``LGBM_BoosterPredictForMatSingleRowFastInit``\n * \\param data Single-row array data (no other way than row-major form).\n * \\param[out] out_len Length of output result\n * \\param[out] out_result Pointer to array with predictions\n * \\return 0 when it succeeds, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMatSingleRowFast(FastConfigHandle fastConfig_handle,\n const void* data,\n int64_t* out_len,\n double* out_result);\n\n/*!\n * \\brief Make prediction for a new dataset presented in a form of array of pointers to rows.\n * \\note\n * You should pre-allocate memory for ``out_result``:\n * - for normal and raw score, its length is equal to ``num_class * num_data``;\n * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;\n * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.\n * \\param handle Handle of booster\n * \\param data Pointer to the data space\n * \\param data_type Type of ``data`` pointer, can be ``C_API_DTYPE_FLOAT32`` or ``C_API_DTYPE_FLOAT64``\n * \\param nrow Number of rows\n * \\param ncol Number columns\n * \\param predict_type What should be predicted\n * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);\n * - ``C_API_PREDICT_RAW_SCORE``: raw score;\n * - ``C_API_PREDICT_LEAF_INDEX``: leaf index;\n * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration Number of iteration for prediction, <= 0 means no limit\n * \\param parameter Other parameters for prediction, e.g. early stopping for prediction\n * \\param[out] out_len Length of output result\n * \\param[out] out_result Pointer to array with predictions\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterPredictForMats(BoosterHandle handle,\n const void** data,\n int data_type,\n int32_t nrow,\n int32_t ncol,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int64_t* out_len,\n double* out_result);\n\n/*!\n * \\brief Make prediction for a new dataset.\n * \\note\n * You should pre-allocate memory for ``out_result``:\n * - for normal and raw score, its length is equal to ``num_class * num_data``;\n * - for leaf index, its length is equal to ``num_class * num_data * num_iteration``;\n * - for feature contributions, its length is equal to ``num_class * num_data * (num_feature + 1)``.\n * \\param handle Handle of booster\n * \\param n_chunks The number of Arrow arrays passed to this function\n * \\param chunks Pointer to the list of Arrow arrays\n * \\param schema Pointer to the schema of all Arrow arrays\n * \\param predict_type What should be predicted\n * - ``C_API_PREDICT_NORMAL``: normal prediction, with transform (if needed);\n * - ``C_API_PREDICT_RAW_SCORE``: raw score;\n * - ``C_API_PREDICT_LEAF_INDEX``: leaf index;\n * - ``C_API_PREDICT_CONTRIB``: feature contributions (SHAP values)\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration Number of iteration for prediction, <= 0 means no limit\n * \\param parameter Other parameters for prediction, e.g. early stopping for prediction\n * \\param[out] out_len Length of output result\n * \\param[out] out_result Pointer to array with predictions\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterPredictForArrow(BoosterHandle handle,\n int64_t n_chunks,\n const struct ArrowArray* chunks,\n const struct ArrowSchema* schema,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int64_t* out_len,\n double* out_result);\n\n/*!\n * \\brief Save model into file.\n * \\param handle Handle of booster\n * \\param start_iteration Start index of the iteration that should be saved\n * \\param num_iteration Index of the iteration that should be saved, <= 0 means save all\n * \\param feature_importance_type Type of feature importance, can be ``C_API_FEATURE_IMPORTANCE_SPLIT`` or ``C_API_FEATURE_IMPORTANCE_GAIN``\n * \\param filename The name of the file\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterSaveModel(BoosterHandle handle,\n int start_iteration,\n int num_iteration,\n int feature_importance_type,\n const char* filename);\n\n/*!\n * \\brief Save model to string.\n * \\param handle Handle of booster\n * \\param start_iteration Start index of the iteration that should be saved\n * \\param num_iteration Index of the iteration that should be saved, <= 0 means save all\n * \\param feature_importance_type Type of feature importance, can be ``C_API_FEATURE_IMPORTANCE_SPLIT`` or ``C_API_FEATURE_IMPORTANCE_GAIN``\n * \\param buffer_len String buffer length, if ``buffer_len < out_len``, you should re-allocate buffer\n * \\param[out] out_len Actual output length\n * \\param[out] out_str String of model, should pre-allocate memory\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterSaveModelToString(BoosterHandle handle,\n int start_iteration,\n int num_iteration,\n int feature_importance_type,\n int64_t buffer_len,\n int64_t* out_len,\n char* out_str);\n\n/*!\n * \\brief Dump model to JSON.\n * \\param handle Handle of booster\n * \\param start_iteration Start index of the iteration that should be dumped\n * \\param num_iteration Index of the iteration that should be dumped, <= 0 means dump all\n * \\param feature_importance_type Type of feature importance, can be ``C_API_FEATURE_IMPORTANCE_SPLIT`` or ``C_API_FEATURE_IMPORTANCE_GAIN``\n * \\param buffer_len String buffer length, if ``buffer_len < out_len``, you should re-allocate buffer\n * \\param[out] out_len Actual output length\n * \\param[out] out_str JSON format string of model, should pre-allocate memory\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterDumpModel(BoosterHandle handle,\n int start_iteration,\n int num_iteration,\n int feature_importance_type,\n int64_t buffer_len,\n int64_t* out_len,\n char* out_str);\n\n/*!\n * \\brief Get leaf value.\n * \\param handle Handle of booster\n * \\param tree_idx Index of tree\n * \\param leaf_idx Index of leaf\n * \\param[out] out_val Output result from the specified leaf\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterGetLeafValue(BoosterHandle handle,\n int tree_idx,\n int leaf_idx,\n double* out_val);\n\n/*!\n * \\brief Set leaf value.\n * \\param handle Handle of booster\n * \\param tree_idx Index of tree\n * \\param leaf_idx Index of leaf\n * \\param val Leaf value\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterSetLeafValue(BoosterHandle handle,\n int tree_idx,\n int leaf_idx,\n double val);\n\n/*!\n * \\brief Get model feature importance.\n * \\param handle Handle of booster\n * \\param num_iteration Number of iterations for which feature importance is calculated, <= 0 means use all\n * \\param importance_type Method of importance calculation:\n * - ``C_API_FEATURE_IMPORTANCE_SPLIT``: result contains numbers of times the feature is used in a model;\n * - ``C_API_FEATURE_IMPORTANCE_GAIN``: result contains total gains of splits which use the feature\n * \\param[out] out_results Result array with feature importance\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterFeatureImportance(BoosterHandle handle,\n int num_iteration,\n int importance_type,\n double* out_results);\n\n/*!\n * \\brief Get model upper bound value.\n * \\param handle Handle of booster\n * \\param[out] out_results Result pointing to max value\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterGetUpperBoundValue(BoosterHandle handle,\n double* out_results);\n\n/*!\n * \\brief Get model lower bound value.\n * \\param handle Handle of booster\n * \\param[out] out_results Result pointing to min value\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_BoosterGetLowerBoundValue(BoosterHandle handle,\n double* out_results);\n\n/*!\n * \\brief Initialize the network.\n * \\param machines List of machines in format 'ip1:port1,ip2:port2'\n * \\param local_listen_port TCP listen port for local machines\n * \\param listen_time_out Socket time-out in minutes\n * \\param num_machines Total number of machines\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_NetworkInit(const char* machines,\n int local_listen_port,\n int listen_time_out,\n int num_machines);\n\n/*!\n * \\brief Finalize the network.\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_NetworkFree();\n\n/*!\n * \\brief Initialize the network with external collective functions.\n * \\param num_machines Total number of machines\n * \\param rank Rank of local machine\n * \\param reduce_scatter_ext_fun The external reduce-scatter function\n * \\param allgather_ext_fun The external allgather function\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_NetworkInitWithFunctions(int num_machines,\n int rank,\n void* reduce_scatter_ext_fun,\n void* allgather_ext_fun);\n\n/*!\n * \\brief Set maximum number of threads used by LightGBM routines in this process.\n * \\param num_threads maximum number of threads used by LightGBM. -1 means defaulting to omp_get_num_threads().\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_SetMaxThreads(int num_threads);\n\n/*!\n * \\brief Get current maximum number of threads used by LightGBM routines in this process.\n * \\param[out] out current maximum number of threads used by LightGBM. -1 means defaulting to omp_get_num_threads().\n * \\return 0 when succeed, -1 when failure happens\n */\nLIGHTGBM_C_EXPORT int LGBM_GetMaxThreads(int* out);\n\n#if !defined(__cplusplus) && (!defined(__STDC__) || (__STDC_VERSION__ < 199901L))\n/*! \\brief Inline specifier no-op in C using standards before C99. */\n#define INLINE_FUNCTION\n#else\n/*! \\brief Inline specifier. */\n#define INLINE_FUNCTION inline\n#endif\n\n#if !defined(__cplusplus) && (!defined(__STDC__) || (__STDC_VERSION__ < 201112L))\n/*! \\brief Thread local specifier no-op in C using standards before C11. */\n#define THREAD_LOCAL\n#elif !defined(__cplusplus)\n/*! \\brief Thread local specifier. */\n#define THREAD_LOCAL _Thread_local\n#elif defined(_MSC_VER)\n/*! \\brief Thread local specifier. */\n#define THREAD_LOCAL __declspec(thread)\n#else\n/*! \\brief Thread local specifier. */\n#define THREAD_LOCAL thread_local\n#endif\n\n/*!\n * \\brief Handle of error message.\n * \\return Error message\n */\nstatic char* LastErrorMsg() { static THREAD_LOCAL char err_msg[512] = \"Everything is fine\"; return err_msg; }\n\n#ifdef _MSC_VER\n #pragma warning(disable : 4996)\n#endif\n/*!\n * \\brief Set string message of the last error.\n * \\note\n * This will call unsafe ``sprintf`` when compiled using C standards before C99.\n * \\param msg Error message\n */\nINLINE_FUNCTION void LGBM_SetLastError(const char* msg) {\n#if !defined(__cplusplus) && (!defined(__STDC__) || (__STDC_VERSION__ < 199901L))\n sprintf(LastErrorMsg(), \"%s\", msg); /* NOLINT(runtime/printf) */\n#else\n const int err_buf_len = 512;\n snprintf(LastErrorMsg(), err_buf_len, \"%s\", msg);\n#endif\n}\n\n#endif /* LIGHTGBM_INCLUDE_LIGHTGBM_C_API_H_ */\n" }, { "path": "include/LightGBM/config.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n *\n * \\note\n * - desc and descl2 fields must be written in reStructuredText format;\n * - nested sections can be placed only at the bottom of parent's section;\n * - [no-automatically-extract]\n * - do not automatically extract this parameter into a Config property with the same name in Config::GetMembersFromString(). Use if:\n * - specialized extraction logic for this param exists in Config::GetMembersFromString()\n * - [no-save]\n * - this param should not be saved into a model text representation via Config::SaveMembersToString(). Use if:\n * - param is only used by the CLI (especially the \"predict\" and \"convert_model\" tasks)\n * - param is related to LightGBM writing files (e.g. \"output_model\", \"save_binary\")\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CONFIG_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_CONFIG_H_\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\n/*! \\brief Types of tasks */\nenum TaskType {\n kTrain, kPredict, kConvertModel, KRefitTree, kSaveBinary\n};\nconst int kDefaultNumLeaves = 31;\n\nstruct Config {\n public:\n Config() {}\n explicit Config(std::unordered_map parameters_map) {\n Set(parameters_map);\n }\n std::string ToString() const;\n /*!\n * \\brief Get string value by specific name of key\n * \\param params Store the key and value for params\n * \\param name Name of key\n * \\param out Value will assign to out if key exists\n * \\return True if key exists\n */\n inline static bool GetString(\n const std::unordered_map& params,\n const std::string& name, std::string* out);\n\n /*!\n * \\brief Get int value by specific name of key\n * \\param params Store the key and value for params\n * \\param name Name of key\n * \\param out Value will assign to out if key exists\n * \\return True if key exists\n */\n inline static bool GetInt(\n const std::unordered_map& params,\n const std::string& name, int* out);\n\n /*!\n * \\brief Get double value by specific name of key\n * \\param params Store the key and value for params\n * \\param name Name of key\n * \\param out Value will assign to out if key exists\n * \\return True if key exists\n */\n inline static bool GetDouble(\n const std::unordered_map& params,\n const std::string& name, double* out);\n\n /*!\n * \\brief Get bool value by specific name of key\n * \\param params Store the key and value for params\n * \\param name Name of key\n * \\param out Value will assign to out if key exists\n * \\return True if key exists\n */\n inline static bool GetBool(\n const std::unordered_map& params,\n const std::string& name, bool* out);\n\n /*!\n * \\brief Sort aliases by length and then alphabetically\n * \\param x Alias 1\n * \\param y Alias 2\n * \\return true if x has higher priority than y\n */\n inline static bool SortAlias(const std::string& x, const std::string& y);\n\n static void KeepFirstValues(const std::unordered_map>& params, std::unordered_map* out);\n static void KV2Map(std::unordered_map>* params, const char* kv);\n static void SetVerbosity(const std::unordered_map>& params);\n static std::unordered_map Str2Map(const char* parameters);\n\n #ifndef __NVCC__\n #pragma region Parameters\n\n #pragma region Core Parameters\n #endif // __NVCC__\n\n // [no-automatically-extract]\n // [no-save]\n // alias = config_file\n // desc = path of config file\n // desc = **Note**: can be used only in CLI version\n std::string config = \"\";\n\n // [no-automatically-extract]\n // [no-save]\n // type = enum\n // default = train\n // options = train, predict, convert_model, refit\n // alias = task_type\n // desc = ``train``, for training, aliases: ``training``\n // desc = ``predict``, for prediction, aliases: ``prediction``, ``test``\n // desc = ``convert_model``, for converting model file into if-else format, see more information in `Convert Parameters <#convert-parameters>`__\n // desc = ``refit``, for refitting existing models with new data, aliases: ``refit_tree``\n // desc = ``save_binary``, load train (and validation) data then save dataset to binary file. Typical usage: ``save_binary`` first, then run multiple ``train`` tasks in parallel using the saved binary file\n // desc = **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent functions\n TaskType task = TaskType::kTrain;\n\n // [no-automatically-extract]\n // [no-save]\n // type = enum\n // options = regression, regression_l1, huber, fair, poisson, quantile, mape, gamma, tweedie, binary, multiclass, multiclassova, cross_entropy, cross_entropy_lambda, lambdarank, rank_xendcg\n // alias = objective_type, app, application, loss\n // desc = regression application\n // descl2 = ``regression``, L2 loss, aliases: ``regression_l2``, ``l2``, ``mean_squared_error``, ``mse``, ``l2_root``, ``root_mean_squared_error``, ``rmse``\n // descl2 = ``regression_l1``, L1 loss, aliases: ``l1``, ``mean_absolute_error``, ``mae``\n // descl2 = ``huber``, `Huber loss `__\n // descl2 = ``fair``, `Fair loss `__\n // descl2 = ``poisson``, `Poisson regression `__\n // descl2 = ``quantile``, `Quantile regression `__\n // descl2 = ``mape``, `MAPE loss `__, aliases: ``mean_absolute_percentage_error``\n // descl2 = ``gamma``, Gamma regression with log-link. It might be useful, e.g., for modeling insurance claims severity, or for any target that might be `gamma-distributed `__\n // descl2 = ``tweedie``, Tweedie regression with log-link. It might be useful, e.g., for modeling total loss in insurance, or for any target that might be `tweedie-distributed `__\n // desc = binary classification application\n // descl2 = ``binary``, binary `log loss `__ classification (or logistic regression)\n // descl2 = requires labels in {0, 1}; see ``cross-entropy`` application for general probability labels in [0, 1]\n // desc = multi-class classification application\n // descl2 = ``multiclass``, `softmax `__ objective function, aliases: ``softmax``\n // descl2 = ``multiclassova``, `One-vs-All `__ binary objective function, aliases: ``multiclass_ova``, ``ova``, ``ovr``\n // descl2 = ``num_class`` should be set as well\n // desc = cross-entropy application\n // descl2 = ``cross_entropy``, objective function for cross-entropy (with optional linear weights), aliases: ``xentropy``\n // descl2 = ``cross_entropy_lambda``, alternative parameterization of cross-entropy, aliases: ``xentlambda``\n // descl2 = label is anything in interval [0, 1]\n // desc = ranking application\n // descl2 = ``lambdarank``, `lambdarank `__ objective. `label_gain <#label_gain>`__ can be used to set the gain (weight) of ``int`` label and all values in ``label`` must be smaller than number of elements in ``label_gain``\n // descl2 = ``rank_xendcg``, `XE_NDCG_MART `__ ranking objective function, aliases: ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``\n // descl2 = ``rank_xendcg`` is faster than and achieves the similar performance as ``lambdarank``\n // descl2 = label should be ``int`` type, and larger number represents the higher relevance (e.g. 0:bad, 1:fair, 2:good, 3:perfect)\n // desc = custom objective function (gradients and hessians not computed directly by LightGBM)\n // descl2 = ``custom``\n // descl2 = must be passed through parameters explicitly in the C API\n // descl2 = **Note**: cannot be used in CLI version\n std::string objective = \"regression\";\n\n // [no-automatically-extract]\n // [no-save]\n // type = enum\n // alias = boosting_type, boost\n // options = gbdt, rf, dart\n // desc = ``gbdt``, traditional Gradient Boosting Decision Tree, aliases: ``gbrt``\n // desc = ``rf``, Random Forest, aliases: ``random_forest``\n // desc = ``dart``, `Dropouts meet Multiple Additive Regression Trees `__\n // descl2 = **Note**: internally, LightGBM uses ``gbdt`` mode for the first ``1 / learning_rate`` iterations\n std::string boosting = \"gbdt\";\n\n // [no-automatically-extract]\n // type = enum\n // options = bagging, goss\n // desc = ``bagging``, Randomly Bagging Sampling\n // descl2 = **Note**: ``bagging`` is only effective when ``bagging_freq > 0`` and ``bagging_fraction < 1.0``\n // desc = ``goss``, Gradient-based One-Side Sampling\n // desc = *New in version 4.0.0*\n std::string data_sample_strategy = \"bagging\";\n\n // alias = train, train_data, train_data_file, data_filename\n // desc = path of training data, LightGBM will train from this data\n // desc = **Note**: can be used only in CLI version\n std::string data = \"\";\n\n // alias = test, valid_data, valid_data_file, test_data, test_data_file, valid_filenames\n // default = \"\"\n // desc = path(s) of validation/test data, LightGBM will output metrics for these data\n // desc = support multiple validation data, separated by ``,``\n // desc = **Note**: can be used only in CLI version\n std::vector valid;\n\n // alias = num_iteration, n_iter, num_tree, num_trees, num_round, num_rounds, nrounds, num_boost_round, n_estimators, max_iter\n // check = >=0\n // desc = number of boosting iterations\n // desc = **Note**: internally, LightGBM constructs ``num_class * num_iterations`` trees for multi-class classification problems\n int num_iterations = 100;\n\n // alias = shrinkage_rate, eta\n // check = >0.0\n // desc = shrinkage rate\n // desc = in ``dart``, it also affects on normalization weights of dropped trees\n double learning_rate = 0.1;\n\n // default = 31\n // alias = num_leaf, max_leaves, max_leaf, max_leaf_nodes\n // check = >1\n // check = <=131072\n // desc = max number of leaves in one tree\n int num_leaves = kDefaultNumLeaves;\n\n // [no-automatically-extract]\n // [no-save]\n // type = enum\n // options = serial, feature, data, voting\n // alias = tree, tree_type, tree_learner_type\n // desc = ``serial``, single machine tree learner\n // desc = ``feature``, feature parallel tree learner, aliases: ``feature_parallel``\n // desc = ``data``, data parallel tree learner, aliases: ``data_parallel``\n // desc = ``voting``, voting parallel tree learner, aliases: ``voting_parallel``\n // desc = refer to `Distributed Learning Guide <./Parallel-Learning-Guide.rst>`__ to get more details\n std::string tree_learner = \"serial\";\n\n // alias = num_thread, nthread, nthreads, n_jobs\n // desc = used only in ``train``, ``prediction`` and ``refit`` tasks or in correspondent functions of language-specific packages\n // desc = number of threads for LightGBM\n // desc = ``0`` means default number of threads in OpenMP\n // desc = for the best speed, set this to the number of **real CPU cores**, not the number of threads (most CPUs use `hyper-threading `__ to generate 2 threads per CPU core)\n // desc = do not set it too large if your dataset is small (for instance, do not use 64 threads for a dataset with 10,000 rows)\n // desc = be aware a task manager or any similar CPU monitoring tool might report that cores not being fully utilized. **This is normal**\n // desc = for distributed learning, do not use all CPU cores because this will cause poor performance for the network communication\n // desc = **Note**: please **don't** change this during training, especially when running multiple jobs simultaneously by external packages, otherwise it may cause undesirable errors\n int num_threads = 0;\n\n // [no-automatically-extract]\n // [no-save]\n // type = enum\n // options = cpu, gpu, cuda\n // alias = device\n // desc = device for the tree learning\n // desc = ``cpu`` supports all LightGBM functionality and is portable across the widest range of operating systems and hardware\n // desc = ``cuda`` offers faster training than ``gpu`` or ``cpu``, but only works on GPUs supporting CUDA or ROCm\n // desc = ``gpu`` can be faster than ``cpu`` and works on a wider range of GPUs than CUDA\n // desc = **Note**: it is recommended to use the smaller ``max_bin`` (e.g. 63) to get the better speed up\n // desc = **Note**: for the faster speed, GPU uses 32-bit float point to sum up by default, so this may affect the accuracy for some tasks. You can set ``gpu_use_dp=true`` to enable 64-bit float point, but it will slow down the training\n // desc = **Note**: refer to `Installation Guide <./Installation-Guide.rst>`__ to build LightGBM with GPU, CUDA, or ROCm support\n std::string device_type = \"cpu\";\n\n // [no-automatically-extract]\n // alias = random_seed, random_state\n // default = None\n // desc = this seed is used to generate other seeds, e.g. ``data_random_seed``, ``feature_fraction_seed``, etc.\n // desc = by default, this seed is unused in favor of default values of other seeds\n // desc = this seed has lower priority in comparison with other seeds, which means that it will be overridden, if you set other seeds explicitly\n int seed = 0;\n\n // desc = used only with ``cpu`` device type\n // desc = setting this to ``true`` should ensure the stable results when using the same data and the same parameters (and different ``num_threads``)\n // desc = when you use the different seeds, different LightGBM versions, the binaries compiled by different compilers, or in different systems, the results are expected to be different\n // desc = you can `raise issues `__ in LightGBM GitHub repo when you meet the unstable results\n // desc = **Note**: setting this to ``true`` may slow down the training\n // desc = **Note**: to avoid potential instability due to numerical issues, please set ``force_col_wise=true`` or ``force_row_wise=true`` when setting ``deterministic=true``\n bool deterministic = false;\n\n #ifndef __NVCC__\n #pragma endregion\n\n #pragma region Learning Control Parameters\n #endif // __NVCC__\n\n // desc = used only with ``cpu`` device type\n // desc = set this to ``true`` to force col-wise histogram building\n // desc = enabling this is recommended when:\n // descl2 = the number of columns is large, or the total number of bins is large\n // descl2 = ``num_threads`` is large, e.g. ``> 20``\n // descl2 = you want to reduce memory cost\n // desc = **Note**: when both ``force_col_wise`` and ``force_row_wise`` are ``false``, LightGBM will firstly try them both, and then use the faster one. To remove the overhead of testing set the faster one to ``true`` manually\n // desc = **Note**: this parameter cannot be used at the same time with ``force_row_wise``, choose only one of them\n bool force_col_wise = false;\n\n // desc = used only with ``cpu`` device type\n // desc = set this to ``true`` to force row-wise histogram building\n // desc = enabling this is recommended when:\n // descl2 = the number of data points is large, and the total number of bins is relatively small\n // descl2 = ``num_threads`` is relatively small, e.g. ``<= 16``\n // descl2 = you want to use small ``bagging_fraction`` or ``goss`` sample strategy to speed up\n // desc = **Note**: setting this to ``true`` will double the memory cost for Dataset object. If you have not enough memory, you can try setting ``force_col_wise=true``\n // desc = **Note**: when both ``force_col_wise`` and ``force_row_wise`` are ``false``, LightGBM will firstly try them both, and then use the faster one. To remove the overhead of testing set the faster one to ``true`` manually\n // desc = **Note**: this parameter cannot be used at the same time with ``force_col_wise``, choose only one of them\n bool force_row_wise = false;\n\n // alias = hist_pool_size\n // desc = max cache size in MB for historical histogram\n // desc = ``< 0`` means no limit\n double histogram_pool_size = -1.0;\n\n // desc = limit the max depth for tree model. This is used to deal with over-fitting when ``#data`` is small. Tree still grows leaf-wise\n // desc = ``<= 0`` means no limit\n int max_depth = -1;\n\n // alias = min_data_per_leaf, min_data, min_child_samples, min_samples_leaf\n // check = >=0\n // desc = minimal number of data in one leaf. Can be used to deal with over-fitting\n // desc = **Note**: this is an approximation based on the Hessian, so occasionally you may observe splits which produce leaf nodes that have less than this many observations\n int min_data_in_leaf = 20;\n\n // alias = min_sum_hessian_per_leaf, min_sum_hessian, min_hessian, min_child_weight\n // check = >=0.0\n // desc = minimal sum hessian in one leaf. Like ``min_data_in_leaf``, it can be used to deal with over-fitting\n double min_sum_hessian_in_leaf = 1e-3;\n\n // alias = sub_row, subsample, bagging\n // check = >0.0\n // check = <=1.0\n // desc = like ``feature_fraction``, but this will randomly select part of data without resampling\n // desc = can be used to speed up training\n // desc = can be used to deal with over-fitting\n // desc = **Note**: to enable bagging, ``bagging_freq`` should be set to a non zero value as well\n double bagging_fraction = 1.0;\n\n // alias = pos_sub_row, pos_subsample, pos_bagging\n // check = >0.0\n // check = <=1.0\n // desc = used only in ``binary`` application\n // desc = used for imbalanced binary classification problem, will randomly sample ``#pos_samples * pos_bagging_fraction`` positive samples in bagging\n // desc = should be used together with ``neg_bagging_fraction``\n // desc = set this to ``1.0`` to disable\n // desc = **Note**: to enable this, you need to set ``bagging_freq`` and ``neg_bagging_fraction`` as well\n // desc = **Note**: if both ``pos_bagging_fraction`` and ``neg_bagging_fraction`` are set to ``1.0``, balanced bagging is disabled\n // desc = **Note**: if balanced bagging is enabled, ``bagging_fraction`` will be ignored\n double pos_bagging_fraction = 1.0;\n\n // alias = neg_sub_row, neg_subsample, neg_bagging\n // check = >0.0\n // check = <=1.0\n // desc = used only in ``binary`` application\n // desc = used for imbalanced binary classification problem, will randomly sample ``#neg_samples * neg_bagging_fraction`` negative samples in bagging\n // desc = should be used together with ``pos_bagging_fraction``\n // desc = set this to ``1.0`` to disable\n // desc = **Note**: to enable this, you need to set ``bagging_freq`` and ``pos_bagging_fraction`` as well\n // desc = **Note**: if both ``pos_bagging_fraction`` and ``neg_bagging_fraction`` are set to ``1.0``, balanced bagging is disabled\n // desc = **Note**: if balanced bagging is enabled, ``bagging_fraction`` will be ignored\n double neg_bagging_fraction = 1.0;\n\n // alias = subsample_freq\n // desc = frequency for bagging\n // desc = ``0`` means disable bagging; ``k`` means perform bagging at every ``k`` iteration. Every ``k``-th iteration, LightGBM will randomly select ``bagging_fraction * 100%`` of the data to use for the next ``k`` iterations\n // desc = **Note**: bagging is only effective when ``0.0 < bagging_fraction < 1.0``\n int bagging_freq = 0;\n\n // alias = bagging_fraction_seed\n // desc = random seed for bagging\n int bagging_seed = 3;\n\n // desc = whether to do bagging sample by query\n // desc = *New in version 4.6.0*\n bool bagging_by_query = false;\n\n // alias = sub_feature, colsample_bytree\n // check = >0.0\n // check = <=1.0\n // desc = LightGBM will randomly select a subset of features on each iteration (tree) if ``feature_fraction`` is smaller than ``1.0``. For example, if you set it to ``0.8``, LightGBM will select 80% of features before training each tree\n // desc = can be used to speed up training\n // desc = can be used to deal with over-fitting\n double feature_fraction = 1.0;\n\n // alias = sub_feature_bynode, colsample_bynode\n // check = >0.0\n // check = <=1.0\n // desc = LightGBM will randomly select a subset of features on each tree node if ``feature_fraction_bynode`` is smaller than ``1.0``. For example, if you set it to ``0.8``, LightGBM will select 80% of features at each tree node\n // desc = can be used to deal with over-fitting\n // desc = **Note**: unlike ``feature_fraction``, this cannot speed up training\n // desc = **Note**: if both ``feature_fraction`` and ``feature_fraction_bynode`` are smaller than ``1.0``, the final fraction of each node is ``feature_fraction * feature_fraction_bynode``\n double feature_fraction_bynode = 1.0;\n\n // desc = random seed for ``feature_fraction``\n int feature_fraction_seed = 2;\n\n // alias = extra_tree\n // desc = use extremely randomized trees\n // desc = if set to ``true``, when evaluating node splits LightGBM will check only one randomly-chosen threshold for each feature\n // desc = can be used to speed up training\n // desc = can be used to deal with over-fitting\n bool extra_trees = false;\n\n // desc = random seed for selecting thresholds when ``extra_trees`` is true\n int extra_seed = 6;\n\n // alias = early_stopping_rounds, early_stopping, n_iter_no_change\n // desc = will stop training if one metric of one validation data doesn't improve in last ``early_stopping_round`` rounds\n // desc = ``<= 0`` means disable\n // desc = can be used to speed up training\n int early_stopping_round = 0;\n\n // check = >=0.0\n // desc = when early stopping is used (i.e. ``early_stopping_round > 0``), require the early stopping metric to improve by at least this delta to be considered an improvement\n // desc = *New in version 4.4.0*\n double early_stopping_min_delta = 0.0;\n\n // desc = LightGBM allows you to provide multiple evaluation metrics. Set this to ``true``, if you want to use only the first metric for early stopping\n bool first_metric_only = false;\n\n // alias = max_tree_output, max_leaf_output\n // desc = used to limit the max output of tree leaves\n // desc = ``<= 0`` means no constraint\n // desc = the final max output of leaves is ``learning_rate * max_delta_step``\n double max_delta_step = 0.0;\n\n // alias = reg_alpha, l1_regularization\n // check = >=0.0\n // desc = L1 regularization\n double lambda_l1 = 0.0;\n\n // alias = reg_lambda, lambda, l2_regularization\n // check = >=0.0\n // desc = L2 regularization\n double lambda_l2 = 0.0;\n\n // check = >=0.0\n // desc = linear tree regularization, corresponds to the parameter ``lambda`` in Eq. 3 of `Gradient Boosting with Piece-Wise Linear Regression Trees `__\n double linear_lambda = 0.0;\n\n // alias = min_split_gain\n // check = >=0.0\n // desc = the minimal gain to perform split\n // desc = can be used to speed up training\n double min_gain_to_split = 0.0;\n\n // alias = rate_drop\n // check = >=0.0\n // check = <=1.0\n // desc = used only in ``dart``\n // desc = dropout rate: a fraction of previous trees to drop during the dropout\n double drop_rate = 0.1;\n\n // desc = used only in ``dart``\n // desc = max number of dropped trees during one boosting iteration\n // desc = ``<=0`` means no limit\n int max_drop = 50;\n\n // check = >=0.0\n // check = <=1.0\n // desc = used only in ``dart``\n // desc = probability of skipping the dropout procedure during a boosting iteration\n double skip_drop = 0.5;\n\n // desc = used only in ``dart``\n // desc = set this to ``true``, if you want to use XGBoost DART mode\n bool xgboost_dart_mode = false;\n\n // desc = used only in ``dart``\n // desc = set this to ``true``, if you want to use uniform drop\n bool uniform_drop = false;\n\n // desc = used only in ``dart``\n // desc = random seed to choose dropping models\n int drop_seed = 4;\n\n // check = >=0.0\n // check = <=1.0\n // desc = used only in ``goss``\n // desc = the retain ratio of large gradient data\n double top_rate = 0.2;\n\n // check = >=0.0\n // check = <=1.0\n // desc = used only in ``goss``\n // desc = the retain ratio of small gradient data\n double other_rate = 0.1;\n\n // check = >0\n // desc = used for the categorical features\n // desc = minimal number of data per categorical group\n int min_data_per_group = 100;\n\n // check = >0\n // desc = used for the categorical features\n // desc = limit number of split points considered for categorical features. See `the documentation on how LightGBM finds optimal splits for categorical features <./Features.rst#optimal-split-for-categorical-features>`_ for more details\n // desc = can be used to speed up training\n int max_cat_threshold = 32;\n\n // check = >=0.0\n // desc = used for the categorical features\n // desc = L2 regularization in categorical split\n double cat_l2 = 10.0;\n\n // check = >=0.0\n // desc = used for the categorical features\n // desc = this can reduce the effect of noises in categorical features, especially for categories with few data\n double cat_smooth = 10.0;\n\n // check = >0\n // desc = used for the categorical features\n // desc = when number of categories of one feature smaller than or equal to ``max_cat_to_onehot``, one-vs-other split algorithm will be used\n int max_cat_to_onehot = 4;\n\n // alias = topk\n // check = >0\n // desc = used only in ``voting`` tree learner, refer to `Voting parallel <./Parallel-Learning-Guide.rst#choose-appropriate-parallel-algorithm>`__\n // desc = set this to larger value for more accurate result, but it will slow down the training speed\n int top_k = 20;\n\n // type = multi-int\n // alias = mc, monotone_constraint, monotonic_cst\n // default = None\n // desc = used for constraints of monotonic features\n // desc = ``1`` means increasing, ``-1`` means decreasing, ``0`` means non-constraint\n // desc = you need to specify all features in order. For example, ``mc=-1,0,1`` means decreasing for the 1st feature, non-constraint for the 2nd feature and increasing for the 3rd feature\n std::vector monotone_constraints;\n\n // type = enum\n // alias = monotone_constraining_method, mc_method\n // options = basic, intermediate, advanced\n // desc = used only if ``monotone_constraints`` is set\n // desc = monotone constraints method\n // descl2 = ``basic``, the most basic monotone constraints method. It does not slow down the training speed at all, but over-constrains the predictions\n // descl2 = ``intermediate``, a `more advanced method `__, which may slow down the training speed very slightly. However, this method is much less constraining than the basic method and should significantly improve the results\n // descl2 = ``advanced``, an `even more advanced method `__, which may slow down the training speed. However, this method is even less constraining than the intermediate method and should again significantly improve the results\n std::string monotone_constraints_method = \"basic\";\n\n // alias = monotone_splits_penalty, ms_penalty, mc_penalty\n // check = >=0.0\n // desc = used only if ``monotone_constraints`` is set\n // desc = `monotone penalty `__: a penalization parameter X forbids any monotone splits on the first X (rounded down) level(s) of the tree. The penalty applied to monotone splits on a given depth is a continuous, increasing function the penalization parameter\n // desc = if ``0.0`` (the default), no penalization is applied\n double monotone_penalty = 0.0;\n\n // type = multi-double\n // alias = feature_contrib, fc, fp, feature_penalty\n // default = None\n // desc = used to control feature's split gain, will use ``gain[i] = max(0, feature_contri[i]) * gain[i]`` to replace the split gain of i-th feature\n // desc = you need to specify all features in order\n std::vector feature_contri;\n\n // alias = fs, forced_splits_filename, forced_splits_file, forced_splits\n // desc = path to a ``.json`` file that specifies splits to force at the top of every decision tree before best-first learning commences\n // desc = ``.json`` file can be arbitrarily nested, and each split contains ``feature``, ``threshold`` fields, as well as ``left`` and ``right`` fields representing subsplits\n // desc = categorical splits are forced in a one-hot fashion, with ``left`` representing the split containing the feature value and ``right`` representing other values\n // desc = **Note**: the forced split logic will be ignored, if the split makes gain worse\n // desc = see `this file `__ as an example\n std::string forcedsplits_filename = \"\";\n\n // check = >=0.0\n // check = <=1.0\n // desc = decay rate of ``refit`` task, will use ``leaf_output = refit_decay_rate * old_leaf_output + (1.0 - refit_decay_rate) * new_leaf_output`` to refit trees\n // desc = used only in ``refit`` task in CLI version or as argument in ``refit`` function in language-specific package\n double refit_decay_rate = 0.9;\n\n // check = >=0.0\n // desc = cost-effective gradient boosting multiplier for all penalties\n double cegb_tradeoff = 1.0;\n\n // check = >=0.0\n // desc = cost-effective gradient-boosting penalty for splitting a node\n double cegb_penalty_split = 0.0;\n\n // type = multi-double\n // default = 0,0,...,0\n // desc = cost-effective gradient boosting penalty for using a feature\n // desc = applied per data point\n std::vector cegb_penalty_feature_lazy;\n\n // type = multi-double\n // default = 0,0,...,0\n // desc = cost-effective gradient boosting penalty for using a feature\n // desc = applied once per forest\n std::vector cegb_penalty_feature_coupled;\n\n // check = >= 0.0\n // desc = controls smoothing applied to tree nodes\n // desc = helps prevent overfitting on leaves with few samples\n // desc = if ``0.0`` (the default), no smoothing is applied\n // desc = if ``path_smooth > 0`` then ``min_data_in_leaf`` must be at least ``2``\n // desc = larger values give stronger regularization\n // descl2 = the weight of each node is ``w * (n / path_smooth) / (n / path_smooth + 1) + w_p / (n / path_smooth + 1)``, where ``n`` is the number of samples in the node, ``w`` is the optimal node weight to minimise the loss (approximately ``-sum_gradients / sum_hessians``), and ``w_p`` is the weight of the parent node\n // descl2 = note that the parent output ``w_p`` itself has smoothing applied, unless it is the root node, so that the smoothing effect accumulates with the tree depth\n double path_smooth = 0;\n\n // desc = controls which features can appear in the same branch\n // desc = by default interaction constraints are disabled, to enable them you can specify\n // descl2 = for CLI, lists separated by commas, e.g. ``[0,1,2],[2,3]``\n // descl2 = for Python-package, list of lists, e.g. ``[[0, 1, 2], [2, 3]]``\n // descl2 = for R-package, list of character or numeric vectors, e.g. ``list(c(\"var1\", \"var2\", \"var3\"), c(\"var3\", \"var4\"))`` or ``list(c(1L, 2L, 3L), c(3L, 4L))``. Numeric vectors should use 1-based indexing, where ``1L`` is the first feature, ``2L`` is the second feature, etc.\n // desc = any two features can only appear in the same branch only if there exists a constraint containing both features\n std::string interaction_constraints = \"\";\n\n // alias = verbose\n // desc = controls the level of LightGBM's verbosity\n // desc = ``< 0``: Fatal, ``= 0``: Error (Warning), ``= 1``: Info, ``> 1``: Debug\n int verbosity = 1;\n\n // [no-save]\n // alias = model_input, model_in\n // desc = filename of input model\n // desc = for ``prediction`` task, this model will be applied to prediction data\n // desc = for ``train`` task, training will be continued from this model\n // desc = **Note**: can be used only in CLI version\n std::string input_model = \"\";\n\n // [no-save]\n // alias = model_output, model_out\n // desc = filename of output model in training\n // desc = **Note**: can be used only in CLI version\n std::string output_model = \"LightGBM_model.txt\";\n\n // desc = the feature importance type in the saved model file\n // desc = ``0``: count-based feature importance (numbers of splits are counted); ``1``: gain-based feature importance (values of gain are counted)\n // desc = **Note**: can be used only in CLI version\n int saved_feature_importance_type = 0;\n\n // [no-save]\n // alias = save_period\n // desc = frequency of saving model file snapshot\n // desc = set this to positive value to enable this function. For example, the model file will be snapshotted at each iteration if ``snapshot_freq=1``\n // desc = **Note**: can be used only in CLI version\n int snapshot_freq = -1;\n\n // desc = whether to use gradient quantization when training\n // desc = enabling this will discretize (quantize) the gradients and hessians into bins of ``num_grad_quant_bins``\n // desc = with quantized training, most arithmetics in the training process will be integer operations\n // desc = gradient quantization can accelerate training, with little accuracy drop in most cases\n // desc = **Note**: works only with ``cpu`` and ``cuda`` device type\n // desc = *New in version 4.0.0*\n bool use_quantized_grad = false;\n\n // desc = used only if ``use_quantized_grad=true``\n // desc = number of bins to quantization gradients and hessians\n // desc = with more bins, the quantized training will be closer to full precision training\n // desc = **Note**: works only with ``cpu`` and ``cuda`` device type\n // desc = *New in version 4.0.0*\n int num_grad_quant_bins = 4;\n\n // desc = used only if ``use_quantized_grad=true``\n // desc = whether to renew the leaf values with original gradients when quantized training\n // desc = renewing is very helpful for good quantized training accuracy for ranking objectives\n // desc = **Note**: works only with ``cpu`` and ``cuda`` device type\n // desc = *New in version 4.0.0*\n bool quant_train_renew_leaf = false;\n\n // desc = used only if ``use_quantized_grad=true``\n // desc = whether to use stochastic rounding in gradient quantization\n // desc = **Note**: works only with ``cpu`` and ``cuda`` device type\n // desc = *New in version 4.0.0*\n bool stochastic_rounding = true;\n\n #ifndef __NVCC__\n #pragma endregion\n\n #pragma region IO Parameters\n\n #pragma region Dataset Parameters\n #endif // __NVCC__\n\n // alias = linear_trees\n // desc = fit piecewise linear gradient boosting tree\n // desc = tree splits are chosen in the usual way, but the model at each leaf is linear instead of constant\n // desc = the linear model at each leaf includes all the numerical features in that leaf's branch\n // desc = the first tree has constant leaf values\n // desc = categorical features are used for splits as normal but are not used in the linear models\n // desc = missing values should not be encoded as ``0``. Use ``np.nan`` for Python, ``NA`` for the CLI, and ``NA``, ``NA_real_``, or ``NA_integer_`` for R\n // desc = it is recommended to rescale data before training so that features have similar mean and standard deviation\n // desc = **Note**: works only with ``cpu``, ``gpu`` device type and ``serial`` tree learner\n // desc = **Note**: ``regression_l1`` objective is not supported with linear tree boosting\n // desc = **Note**: setting ``linear_tree=true`` significantly increases the memory use of LightGBM\n // desc = **Note**: if you specify ``monotone_constraints``, constraints will be enforced when choosing the split points, but not when fitting the linear models on leaves\n bool linear_tree = false;\n\n // alias = max_bins\n // check = >1\n // desc = max number of bins that feature values will be bucketed in\n // desc = small number of bins may reduce training accuracy but may increase general power (deal with over-fitting)\n // desc = LightGBM will auto compress memory according to ``max_bin``. For example, LightGBM will use ``uint8_t`` for feature value if ``max_bin=255``\n int max_bin = 255;\n\n // type = multi-int\n // default = None\n // desc = max number of bins for each feature\n // desc = if not specified, will use ``max_bin`` for all features\n std::vector max_bin_by_feature;\n\n // check = >0\n // desc = minimal number of data inside one bin\n // desc = use this to avoid one-data-one-bin (potential over-fitting)\n int min_data_in_bin = 3;\n\n // alias = subsample_for_bin\n // check = >0\n // desc = number of data that sampled to construct feature discrete bins\n // desc = setting this to larger value will give better training result, but may increase data loading time\n // desc = set this to larger value if data is very sparse\n // desc = **Note**: don't set this to small values, otherwise, you may encounter unexpected errors and poor accuracy\n int bin_construct_sample_cnt = 200000;\n\n // alias = data_seed\n // desc = random seed for sampling data to construct histogram bins\n int data_random_seed = 1;\n\n // alias = is_sparse, enable_sparse, sparse\n // desc = used to enable/disable sparse optimization\n bool is_enable_sparse = true;\n\n // alias = is_enable_bundle, bundle\n // desc = set this to ``false`` to disable Exclusive Feature Bundling (EFB), which is described in `LightGBM: A Highly Efficient Gradient Boosting Decision Tree `__\n // desc = **Note**: disabling this may cause the slow training speed for sparse datasets\n bool enable_bundle = true;\n\n // desc = set this to ``false`` to disable the special handle of missing value\n bool use_missing = true;\n\n // desc = set this to ``true`` to treat all zero as missing values (including the unshown values in LibSVM / sparse matrices)\n // desc = set this to ``false`` to use ``na`` for representing missing values\n bool zero_as_missing = false;\n\n // desc = set this to ``true`` (the default) to tell LightGBM to ignore the features that are unsplittable based on ``min_data_in_leaf``\n // desc = as dataset object is initialized only once and cannot be changed after that, you may need to set this to ``false`` when searching parameters with ``min_data_in_leaf``, otherwise features are filtered by ``min_data_in_leaf`` firstly if you don't reconstruct dataset object\n // desc = **Note**: setting this to ``false`` may slow down the training\n bool feature_pre_filter = true;\n\n // alias = is_pre_partition\n // desc = used for distributed learning (excluding the ``feature_parallel`` mode)\n // desc = ``true`` if training data are pre-partitioned, and different machines use different partitions\n bool pre_partition = false;\n\n // alias = two_round_loading, use_two_round_loading\n // desc = set this to ``true`` if data file is too big to fit in memory\n // desc = by default, LightGBM will map data file to memory and load features from memory. This will provide faster data loading speed, but may cause run out of memory error when the data file is very big\n // desc = **Note**: works only in case of loading data directly from text file\n bool two_round = false;\n\n // alias = has_header\n // desc = set this to ``true`` if input data has header\n // desc = **Note**: works only in case of loading data directly from text file\n bool header = false;\n\n // type = int or string\n // alias = label\n // desc = used to specify the label column\n // desc = use number for index, e.g. ``label=0`` means column\\_0 is the label\n // desc = add a prefix ``name:`` for column name, e.g. ``label=name:is_click``\n // desc = if omitted, the first column in the training data is used as the label\n // desc = **Note**: works only in case of loading data directly from text file\n std::string label_column = \"\";\n\n // type = int or string\n // alias = weight\n // desc = used to specify the weight column\n // desc = use number for index, e.g. ``weight=0`` means column\\_0 is the weight\n // desc = add a prefix ``name:`` for column name, e.g. ``weight=name:weight``\n // desc = **Note**: works only in case of loading data directly from text file\n // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``, e.g. when label is column\\_0, and weight is column\\_1, the correct parameter is ``weight=0``\n // desc = **Note**: weights should be non-negative\n std::string weight_column = \"\";\n\n // type = int or string\n // alias = group, group_id, query_column, query, query_id\n // desc = used to specify the query/group id column\n // desc = use number for index, e.g. ``query=0`` means column\\_0 is the query id\n // desc = add a prefix ``name:`` for column name, e.g. ``query=name:query_id``\n // desc = **Note**: works only in case of loading data directly from text file\n // desc = **Note**: data should be grouped by query\\_id, for more information, see `Query Data <#query-data>`__\n // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``, e.g. when label is column\\_0 and query\\_id is column\\_1, the correct parameter is ``query=0``\n std::string group_column = \"\";\n\n // type = multi-int or string\n // alias = ignore_feature, blacklist\n // desc = used to specify some ignoring columns in training\n // desc = use number for index, e.g. ``ignore_column=0,1,2`` means column\\_0, column\\_1 and column\\_2 will be ignored\n // desc = add a prefix ``name:`` for column name, e.g. ``ignore_column=name:c1,c2,c3`` means c1, c2 and c3 will be ignored\n // desc = **Note**: works only in case of loading data directly from text file\n // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``\n // desc = **Note**: despite the fact that specified columns will be completely ignored during the training, they still should have a valid format allowing LightGBM to load file successfully\n std::string ignore_column = \"\";\n\n // type = multi-int or string\n // alias = cat_feature, categorical_column, cat_column, categorical_features\n // desc = used to specify categorical features\n // desc = use number for index, e.g. ``categorical_feature=0,1,2`` means column\\_0, column\\_1 and column\\_2 are categorical features\n // desc = add a prefix ``name:`` for column name, e.g. ``categorical_feature=name:c1,c2,c3`` means c1, c2 and c3 are categorical features\n // desc = **Note**: all values will be cast to ``int32`` (integer codes will be extracted from pandas categoricals in the Python-package)\n // desc = **Note**: index starts from ``0`` and it doesn't count the label column when passing type is ``int``\n // desc = **Note**: all values should be less than ``Int32.MaxValue`` (2147483647)\n // desc = **Note**: using large values could be memory consuming. Tree decision rule works best when categorical features are presented by consecutive integers starting from zero\n // desc = **Note**: all negative values will be treated as **missing values**\n // desc = **Note**: the output cannot be monotonically constrained with respect to a categorical feature\n // desc = **Note**: floating point numbers in categorical features will be rounded towards 0\n std::string categorical_feature = \"\";\n\n // desc = path to a ``.json`` file that specifies bin upper bounds for some or all features\n // desc = ``.json`` file should contain an array of objects, each containing the word ``feature`` (integer feature index) and ``bin_upper_bound`` (array of thresholds for binning)\n // desc = see `this file `__ as an example\n std::string forcedbins_filename = \"\";\n\n // [no-save]\n // alias = is_save_binary, is_save_binary_file\n // desc = if ``true``, LightGBM will save the dataset (including validation data) to a binary file. This speed ups the data loading for the next time\n // desc = **Note**: ``init_score`` is not saved in binary file\n // desc = **Note**: can be used only in CLI version; for language-specific packages you can use the correspondent function\n bool save_binary = false;\n\n // desc = use precise floating point number parsing for text parser (e.g. CSV, TSV, LibSVM input)\n // desc = **Note**: setting this to ``true`` may lead to much slower text parsing\n bool precise_float_parser = false;\n\n // desc = path to a ``.json`` file that specifies customized parser initialized configuration\n // desc = see `lightgbm-transform `__ for usage examples\n // desc = **Note**: ``lightgbm-transform`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should go to `issues page `__\n // desc = *New in version 4.0.0*\n std::string parser_config_file = \"\";\n\n #ifndef __NVCC__\n #pragma endregion\n\n #pragma region Predict Parameters\n #endif // __NVCC__\n\n // [no-save]\n // desc = used only in ``prediction`` task\n // desc = used to specify from which iteration to start the prediction\n // desc = ``<= 0`` means from the first iteration\n int start_iteration_predict = 0;\n\n // [no-save]\n // desc = used only in ``prediction`` task\n // desc = used to specify how many trained iterations will be used in prediction\n // desc = ``<= 0`` means no limit\n int num_iteration_predict = -1;\n\n // [no-save]\n // alias = is_predict_raw_score, predict_rawscore, raw_score\n // desc = used only in ``prediction`` task\n // desc = set this to ``true`` to predict only the raw scores\n // desc = set this to ``false`` to predict transformed scores\n bool predict_raw_score = false;\n\n // [no-save]\n // alias = is_predict_leaf_index, leaf_index\n // desc = used only in ``prediction`` task\n // desc = set this to ``true`` to predict with leaf index of all trees\n bool predict_leaf_index = false;\n\n // [no-save]\n // alias = is_predict_contrib, contrib\n // desc = used only in ``prediction`` task\n // desc = set this to ``true`` to estimate `SHAP values `__, which represent how each feature contributes to each prediction\n // desc = produces ``#features + 1`` values where the last value is the expected value of the model output over the training data\n // desc = **Note**: if you want to get more explanation for your model's predictions using SHAP values like SHAP interaction values, you can install `shap package `__\n // desc = **Note**: unlike the shap package, with ``predict_contrib`` we return a matrix with an extra column, where the last column is the expected value\n // desc = **Note**: this feature is not implemented for linear trees\n bool predict_contrib = false;\n\n // [no-save]\n // desc = used only in ``prediction`` task\n // desc = control whether or not LightGBM raises an error when you try to predict on data with a different number of features than the training data\n // desc = if ``false`` (the default), a fatal error will be raised if the number of features in the dataset you predict on differs from the number seen during training\n // desc = if ``true``, LightGBM will attempt to predict on whatever data you provide. This is dangerous because you might get incorrect predictions, but you could use it in situations where it is difficult or expensive to generate some features and you are very confident that they were never chosen for splits in the model\n // desc = **Note**: be very careful setting this parameter to ``true``\n bool predict_disable_shape_check = false;\n\n // [no-save]\n // desc = used only in ``prediction`` task\n // desc = used only in ``classification`` and ``ranking`` applications\n // desc = used only for predicting normal or raw scores\n // desc = if ``true``, will use early-stopping to speed up the prediction. May affect the accuracy\n // desc = **Note**: cannot be used with ``rf`` boosting type or custom objective function\n bool pred_early_stop = false;\n\n // [no-save]\n // desc = used only in ``prediction`` task and if ``pred_early_stop=true``\n // desc = the frequency of checking early-stopping prediction\n int pred_early_stop_freq = 10;\n\n // [no-save]\n // desc = used only in ``prediction`` task and if ``pred_early_stop=true``\n // desc = the threshold of margin in early-stopping prediction\n double pred_early_stop_margin = 10.0;\n\n // [no-save]\n // alias = predict_result, prediction_result, predict_name, prediction_name, pred_name, name_pred\n // desc = used only in ``prediction`` task\n // desc = filename of prediction result\n // desc = **Note**: can be used only in CLI version\n std::string output_result = \"LightGBM_predict_result.txt\";\n\n #ifndef __NVCC__\n #pragma endregion\n\n #pragma region Convert Parameters\n #endif // __NVCC__\n\n // [no-save]\n // desc = used only in ``convert_model`` task\n // desc = only ``cpp`` is supported yet; for conversion model to other languages consider using `m2cgen `__ utility\n // desc = if ``convert_model_language`` is set and ``task=train``, the model will be also converted\n // desc = **Note**: can be used only in CLI version\n std::string convert_model_language = \"\";\n\n // [no-save]\n // alias = convert_model_file\n // desc = used only in ``convert_model`` task\n // desc = output filename of converted model\n // desc = **Note**: can be used only in CLI version\n std::string convert_model = \"gbdt_prediction.cpp\";\n\n #ifndef __NVCC__\n #pragma endregion\n\n #pragma endregion\n\n #pragma region Objective Parameters\n #endif // __NVCC__\n\n // desc = used only in ``rank_xendcg`` objective\n // desc = random seed for objectives, if random process is needed\n int objective_seed = 5;\n\n // check = >0\n // alias = num_classes\n // desc = used only in ``multi-class`` classification application\n int num_class = 1;\n\n // alias = unbalance, unbalanced_sets\n // desc = used only in ``binary`` and ``multiclassova`` applications\n // desc = set this to ``true`` if training data are unbalanced\n // desc = **Note**: while enabling this should increase the overall performance metric of your model, it will also result in poor estimates of the individual class probabilities\n // desc = **Note**: this parameter cannot be used at the same time with ``scale_pos_weight``, choose only **one** of them\n bool is_unbalance = false;\n\n // check = >0.0\n // desc = used only in ``binary`` and ``multiclassova`` applications\n // desc = weight of labels with positive class\n // desc = **Note**: while enabling this should increase the overall performance metric of your model, it will also result in poor estimates of the individual class probabilities\n // desc = **Note**: this parameter cannot be used at the same time with ``is_unbalance``, choose only **one** of them\n double scale_pos_weight = 1.0;\n\n // check = >0.0\n // desc = used only in ``binary`` and ``multiclassova`` classification and in ``lambdarank`` applications\n // desc = parameter for the sigmoid function\n double sigmoid = 1.0;\n\n // desc = used only in ``regression``, ``binary``, ``multiclassova`` and ``cross-entropy`` applications\n // desc = adjusts initial score to the mean of labels for faster convergence\n bool boost_from_average = true;\n\n // desc = used only in ``regression`` application\n // desc = used to fit ``sqrt(label)`` instead of original values and prediction result will be also automatically converted to ``prediction^2``\n // desc = might be useful in case of large-range labels\n bool reg_sqrt = false;\n\n // check = >0.0\n // desc = used only in ``huber`` and ``quantile`` ``regression`` applications\n // desc = parameter for `Huber loss `__ and `Quantile regression `__\n double alpha = 0.9;\n\n // check = >0.0\n // desc = used only in ``fair`` ``regression`` application\n // desc = parameter for `Fair loss `__\n double fair_c = 1.0;\n\n // check = >0.0\n // desc = used only in ``poisson`` ``regression`` application\n // desc = parameter for `Poisson regression `__ to safeguard optimization\n double poisson_max_delta_step = 0.7;\n\n // check = >=1.0\n // check = <2.0\n // desc = used only in ``tweedie`` ``regression`` application\n // desc = used to control the variance of the tweedie distribution\n // desc = set this closer to ``2`` to shift towards a **Gamma** distribution\n // desc = set this closer to ``1`` to shift towards a **Poisson** distribution\n double tweedie_variance_power = 1.5;\n\n // check = >0\n // desc = used only in ``lambdarank`` application\n // desc = controls the number of top-results to focus on during training, refer to \"truncation level\" in the Sec. 3 of `LambdaMART paper `__\n // desc = this parameter is closely related to the desirable cutoff ``k`` in the metric **NDCG@k** that we aim at optimizing the ranker for. The optimal setting for this parameter is likely to be slightly higher than ``k`` (e.g., ``k + 3``) to include more pairs of documents to train on, but perhaps not too high to avoid deviating too much from the desired target metric **NDCG@k**\n int lambdarank_truncation_level = 30;\n\n // desc = used only in ``lambdarank`` application\n // desc = set this to ``true`` to normalize the lambdas for different queries, and improve the performance for unbalanced data\n // desc = set this to ``false`` to enforce the original lambdarank algorithm\n bool lambdarank_norm = true;\n\n // type = multi-double\n // default = 0,1,3,7,15,31,63,...,2^30-1\n // desc = used only in ``lambdarank`` application\n // desc = relevant gain for labels. For example, the gain of label ``2`` is ``3`` in case of default label gains\n // desc = separate by ``,``\n std::vector label_gain;\n\n // check = >=0.0\n // desc = used only in ``lambdarank`` application when positional information is provided and position bias is modeled\n // desc = larger values reduce the inferred position bias factors\n // desc = *New in version 4.1.0*\n double lambdarank_position_bias_regularization = 0.0;\n\n #ifndef __NVCC__\n #pragma endregion\n\n #pragma region Metric Parameters\n #endif // __NVCC__\n\n // [no-automatically-extract]\n // [no-save]\n // alias = metrics, metric_types\n // default = \"\"\n // type = multi-enum\n // desc = metric(s) to be evaluated on the evaluation set(s)\n // descl2 = ``\"\"`` (empty string or not specified) means that metric corresponding to specified ``objective`` will be used (this is possible only for pre-defined objective functions, otherwise no evaluation metric will be added)\n // descl2 = ``\"None\"`` (string, **not** a ``None`` value) means that no metric will be registered, aliases: ``na``, ``null``, ``custom``\n // descl2 = ``l1``, absolute loss, aliases: ``mean_absolute_error``, ``mae``, ``regression_l1``\n // descl2 = ``l2``, square loss, aliases: ``mean_squared_error``, ``mse``, ``regression_l2``, ``regression``\n // descl2 = ``rmse``, root square loss, aliases: ``root_mean_squared_error``, ``l2_root``\n // descl2 = ``quantile``, `Quantile regression `__\n // descl2 = ``mape``, `MAPE loss `__, aliases: ``mean_absolute_percentage_error``\n // descl2 = ``huber``, `Huber loss `__\n // descl2 = ``fair``, `Fair loss `__\n // descl2 = ``poisson``, negative log-likelihood for `Poisson regression `__\n // descl2 = ``gamma``, negative log-likelihood for **Gamma** regression\n // descl2 = ``gamma_deviance``, residual deviance for **Gamma** regression\n // descl2 = ``tweedie``, negative log-likelihood for **Tweedie** regression\n // descl2 = ``ndcg``, `NDCG `__, aliases: ``lambdarank``, ``rank_xendcg``, ``xendcg``, ``xe_ndcg``, ``xe_ndcg_mart``, ``xendcg_mart``\n // descl2 = ``map``, `MAP `__, aliases: ``mean_average_precision``\n // descl2 = ``auc``, `AUC `__\n // descl2 = ``average_precision``, `average precision score `__\n // descl2 = ``r2``, `R-squared `__\n // descl2 = ``binary_logloss``, `log loss `__, aliases: ``binary``\n // descl2 = ``binary_error``, for one sample: ``0`` for correct classification, ``1`` for error classification\n // descl2 = ``auc_mu``, `AUC-mu `__\n // descl2 = ``multi_logloss``, log loss for multi-class classification, aliases: ``multiclass``, ``softmax``, ``multiclassova``, ``multiclass_ova``, ``ova``, ``ovr``\n // descl2 = ``multi_error``, error rate for multi-class classification\n // descl2 = ``cross_entropy``, cross-entropy (with optional linear weights), aliases: ``xentropy``\n // descl2 = ``cross_entropy_lambda``, \"intensity-weighted\" cross-entropy, aliases: ``xentlambda``\n // descl2 = ``kullback_leibler``, `Kullback-Leibler divergence `__, aliases: ``kldiv``\n // desc = support multiple metrics, separated by ``,``\n std::vector metric;\n\n // [no-save]\n // check = >0\n // alias = output_freq\n // desc = frequency for metric output\n // desc = **Note**: can be used only in CLI version\n int metric_freq = 1;\n\n // [no-save]\n // alias = training_metric, is_training_metric, train_metric\n // desc = set this to ``true`` to output metric result over training dataset\n // desc = **Note**: can be used only in CLI version\n bool is_provide_training_metric = false;\n\n // type = multi-int\n // default = 1,2,3,4,5\n // alias = ndcg_eval_at, ndcg_at, map_eval_at, map_at\n // desc = used only with ``ndcg`` and ``map`` metrics\n // desc = `NDCG `__ and `MAP `__ evaluation positions, separated by ``,``\n std::vector eval_at;\n\n // check = >0\n // desc = used only with ``multi_error`` metric\n // desc = threshold for top-k multi-error metric\n // desc = the error on each sample is ``0`` if the true class is among the top ``multi_error_top_k`` predictions, and ``1`` otherwise\n // descl2 = more precisely, the error on a sample is ``0`` if there are at least ``num_classes - multi_error_top_k`` predictions strictly less than the prediction on the true class\n // desc = when ``multi_error_top_k=1`` this is equivalent to the usual multi-error metric\n int multi_error_top_k = 1;\n\n // type = multi-double\n // default = None\n // desc = used only with ``auc_mu`` metric\n // desc = list representing flattened matrix (in row-major order) giving loss weights for classification errors\n // desc = list should have ``n * n`` elements, where ``n`` is the number of classes\n // desc = the matrix co-ordinate ``[i, j]`` should correspond to the ``i * n + j``-th element of the list\n // desc = if not specified, will use equal weights for all classes\n std::vector auc_mu_weights;\n\n #ifndef __NVCC__\n #pragma endregion\n\n #pragma region Network Parameters\n #endif // __NVCC__\n\n // check = >0\n // alias = num_machine\n // desc = the number of machines for distributed learning application\n // desc = this parameter is needed to be set in both **socket** and **MPI** versions\n int num_machines = 1;\n\n // check = >0\n // default = 12400 (random for Dask-package)\n // alias = local_port, port\n // desc = TCP listen port for local machines\n // desc = **Note**: don't forget to allow this port in firewall settings before training\n int local_listen_port = 12400;\n\n // check = >0\n // desc = socket time-out in minutes\n int time_out = 120;\n\n // alias = machine_list_file, machine_list, mlist\n // desc = path of file that lists machines for this distributed learning application\n // desc = each line contains one IP and one port for one machine. The format is ``ip port`` (space as a separator)\n // desc = **Note**: can be used only in CLI version\n std::string machine_list_filename = \"\";\n\n // alias = workers, nodes\n // desc = list of machines in the following format: ``ip1:port1,ip2:port2``\n std::string machines = \"\";\n\n #ifndef __NVCC__\n #pragma endregion\n\n #pragma region GPU Parameters\n #endif // __NVCC__\n\n // desc = used only with ``gpu`` device type\n // desc = OpenCL platform ID. Usually each GPU vendor exposes one OpenCL platform\n // desc = ``-1`` means the system-wide default platform\n // desc = **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details\n int gpu_platform_id = -1;\n\n // desc = OpenCL device ID in the specified platform or CUDA device ID. Each GPU in the selected platform has a unique device ID\n // desc = ``-1`` means the default device in the selected platform\n // desc = in multi-GPU case (``num_gpu>1``) means ID of the master GPU\n // desc = **Note**: refer to `GPU Targets <./GPU-Targets.rst#query-opencl-devices-in-your-system>`__ for more details\n int gpu_device_id = -1;\n\n // desc = list of CUDA device IDs\n // desc = **Note**: can be used only in CUDA implementation (``device_type=\"cuda\"``) and when ``num_gpu>1``\n // desc = if empty, the devices with the smallest IDs will be used\n std::string gpu_device_id_list = \"\";\n\n // desc = set this to ``true`` to use double precision math on GPU (by default single precision is used)\n // desc = **Note**: can be used only in OpenCL implementation (``device_type=\"gpu\"``), in CUDA implementation only double precision is currently supported\n bool gpu_use_dp = false;\n\n // check = >0\n // desc = number of GPUs used for training in this node\n // desc = **Note**: can be used only in CUDA implementation (``device_type=\"cuda\"``)\n // desc = if ``0``, only 1 GPU will be used\n // desc = used in both single-machine and distributed learning applications\n // desc = in distributed learning application, each machine can use different number of GPUs\n int num_gpu = 1;\n\n #ifndef __NVCC__\n #pragma endregion\n\n #pragma endregion\n #endif // __NVCC__\n\n size_t file_load_progress_interval_bytes = size_t(10) * 1024 * 1024 * 1024;\n\n bool is_parallel = false;\n bool is_data_based_parallel = false;\n LIGHTGBM_EXPORT void Set(const std::unordered_map& params);\n static const std::unordered_map& alias_table();\n static const std::unordered_map>& parameter2aliases();\n static const std::unordered_set& parameter_set();\n std::vector> auc_mu_weights_matrix;\n std::vector> interaction_constraints_vector;\n static const std::unordered_map& ParameterTypes();\n static const std::string DumpAliases();\n\n private:\n void CheckParamConflict(const std::unordered_map& params);\n void GetMembersFromString(const std::unordered_map& params);\n std::string SaveMembersToString() const;\n void GetAucMuWeights();\n void GetInteractionConstraints();\n};\n\ninline bool Config::GetString(\n const std::unordered_map& params,\n const std::string& name, std::string* out) {\n if (params.count(name) > 0 && !params.at(name).empty()) {\n *out = params.at(name);\n return true;\n }\n return false;\n}\n\ninline bool Config::GetInt(\n const std::unordered_map& params,\n const std::string& name, int* out) {\n if (params.count(name) > 0 && !params.at(name).empty()) {\n if (!Common::AtoiAndCheck(params.at(name).c_str(), out)) {\n Log::Fatal(\"Parameter %s should be of type int, got \\\"%s\\\"\",\n name.c_str(), params.at(name).c_str());\n }\n return true;\n }\n return false;\n}\n\ninline bool Config::GetDouble(\n const std::unordered_map& params,\n const std::string& name, double* out) {\n if (params.count(name) > 0 && !params.at(name).empty()) {\n if (!Common::AtofAndCheck(params.at(name).c_str(), out)) {\n Log::Fatal(\"Parameter %s should be of type double, got \\\"%s\\\"\",\n name.c_str(), params.at(name).c_str());\n }\n return true;\n }\n return false;\n}\n\ninline bool Config::GetBool(\n const std::unordered_map& params,\n const std::string& name, bool* out) {\n if (params.count(name) > 0 && !params.at(name).empty()) {\n std::string value = params.at(name);\n std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); });\n if (value == std::string(\"false\") || value == std::string(\"-\")) {\n *out = false;\n } else if (value == std::string(\"true\") || value == std::string(\"+\")) {\n *out = true;\n } else {\n Log::Fatal(\"Parameter %s should be \\\"true\\\"/\\\"+\\\" or \\\"false\\\"/\\\"-\\\", got \\\"%s\\\"\",\n name.c_str(), params.at(name).c_str());\n }\n return true;\n }\n return false;\n}\n\ninline bool Config::SortAlias(const std::string& x, const std::string& y) {\n return x.size() < y.size() || (x.size() == y.size() && x < y);\n}\n\nstruct ParameterAlias {\n static void KeyAliasTransform(std::unordered_map* params) {\n std::unordered_map tmp_map;\n for (const auto& pair : *params) {\n auto alias = Config::alias_table().find(pair.first);\n if (alias != Config::alias_table().end()) { // found alias\n auto alias_set = tmp_map.find(alias->second);\n if (alias_set != tmp_map.end()) { // alias already set\n if (Config::SortAlias(alias_set->second, pair.first)) {\n Log::Warning(\"%s is set with %s=%s, %s=%s will be ignored. Current value: %s=%s\",\n alias->second.c_str(), alias_set->second.c_str(), params->at(alias_set->second).c_str(),\n pair.first.c_str(), pair.second.c_str(), alias->second.c_str(), params->at(alias_set->second).c_str());\n } else {\n Log::Warning(\"%s is set with %s=%s, will be overridden by %s=%s. Current value: %s=%s\",\n alias->second.c_str(), alias_set->second.c_str(), params->at(alias_set->second).c_str(),\n pair.first.c_str(), pair.second.c_str(), alias->second.c_str(), pair.second.c_str());\n tmp_map[alias->second] = pair.first;\n }\n } else { // alias not set\n tmp_map.emplace(alias->second, pair.first);\n }\n } else if (Config::parameter_set().find(pair.first) == Config::parameter_set().end()) {\n Log::Warning(\"Unknown parameter: %s\", pair.first.c_str());\n }\n }\n for (const auto& pair : tmp_map) {\n auto alias = params->find(pair.first);\n if (alias == params->end()) { // not find\n params->emplace(pair.first, params->at(pair.second));\n params->erase(pair.second);\n } else {\n Log::Warning(\"%s is set=%s, %s=%s will be ignored. Current value: %s=%s\",\n pair.first.c_str(), alias->second.c_str(), pair.second.c_str(), params->at(pair.second).c_str(),\n pair.first.c_str(), alias->second.c_str());\n }\n }\n }\n};\n\ninline std::string ParseObjectiveAlias(const std::string& type) {\n if (type == std::string(\"regression\") || type == std::string(\"regression_l2\")\n || type == std::string(\"mean_squared_error\") || type == std::string(\"mse\") || type == std::string(\"l2\")\n || type == std::string(\"l2_root\") || type == std::string(\"root_mean_squared_error\") || type == std::string(\"rmse\")) {\n return \"regression\";\n } else if (type == std::string(\"regression_l1\") || type == std::string(\"mean_absolute_error\")\n || type == std::string(\"l1\") || type == std::string(\"mae\")) {\n return \"regression_l1\";\n } else if (type == std::string(\"multiclass\") || type == std::string(\"softmax\")) {\n return \"multiclass\";\n } else if (type == std::string(\"multiclassova\") || type == std::string(\"multiclass_ova\") || type == std::string(\"ova\") || type == std::string(\"ovr\")) {\n return \"multiclassova\";\n } else if (type == std::string(\"xentropy\") || type == std::string(\"cross_entropy\")) {\n return \"cross_entropy\";\n } else if (type == std::string(\"xentlambda\") || type == std::string(\"cross_entropy_lambda\")) {\n return \"cross_entropy_lambda\";\n } else if (type == std::string(\"mean_absolute_percentage_error\") || type == std::string(\"mape\")) {\n return \"mape\";\n } else if (type == std::string(\"rank_xendcg\") || type == std::string(\"xendcg\") || type == std::string(\"xe_ndcg\")\n || type == std::string(\"xe_ndcg_mart\") || type == std::string(\"xendcg_mart\")) {\n return \"rank_xendcg\";\n } else if (type == std::string(\"none\") || type == std::string(\"null\") || type == std::string(\"custom\") || type == std::string(\"na\")) {\n return \"custom\";\n }\n return type;\n}\n\ninline std::string ParseMetricAlias(const std::string& type) {\n if (type == std::string(\"regression\") || type == std::string(\"regression_l2\") || type == std::string(\"l2\") || type == std::string(\"mean_squared_error\") || type == std::string(\"mse\")) {\n return \"l2\";\n } else if (type == std::string(\"l2_root\") || type == std::string(\"root_mean_squared_error\") || type == std::string(\"rmse\")) {\n return \"rmse\";\n } else if (type == std::string(\"regression_l1\") || type == std::string(\"l1\") || type == std::string(\"mean_absolute_error\") || type == std::string(\"mae\")) {\n return \"l1\";\n } else if (type == std::string(\"binary_logloss\") || type == std::string(\"binary\")) {\n return \"binary_logloss\";\n } else if (type == std::string(\"ndcg\") || type == std::string(\"lambdarank\") || type == std::string(\"rank_xendcg\")\n || type == std::string(\"xendcg\") || type == std::string(\"xe_ndcg\") || type == std::string(\"xe_ndcg_mart\") || type == std::string(\"xendcg_mart\")) {\n return \"ndcg\";\n } else if (type == std::string(\"map\") || type == std::string(\"mean_average_precision\")) {\n return \"map\";\n } else if (type == std::string(\"multi_logloss\") || type == std::string(\"multiclass\") || type == std::string(\"softmax\") || type == std::string(\"multiclassova\") || type == std::string(\"multiclass_ova\") || type == std::string(\"ova\") || type == std::string(\"ovr\")) {\n return \"multi_logloss\";\n } else if (type == std::string(\"xentropy\") || type == std::string(\"cross_entropy\")) {\n return \"cross_entropy\";\n } else if (type == std::string(\"xentlambda\") || type == std::string(\"cross_entropy_lambda\")) {\n return \"cross_entropy_lambda\";\n } else if (type == std::string(\"kldiv\") || type == std::string(\"kullback_leibler\")) {\n return \"kullback_leibler\";\n } else if (type == std::string(\"mean_absolute_percentage_error\") || type == std::string(\"mape\")) {\n return \"mape\";\n } else if (type == std::string(\"none\") || type == std::string(\"null\") || type == std::string(\"custom\") || type == std::string(\"na\")) {\n return \"custom\";\n }\n return type;\n}\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CONFIG_H_\n" }, { "path": "include/LightGBM/cuda/cuda_algorithms.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved.\n */\n\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ALGORITHMS_HPP_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ALGORITHMS_HPP_\n\n#ifdef USE_CUDA\n\n#ifndef USE_ROCM\n#include \n#include \n#endif\n#include \n\n#include \n#include \n#include \n#include \n\n#include \n\n#define GLOBAL_PREFIX_SUM_BLOCK_SIZE (1024)\n#define BITONIC_SORT_NUM_ELEMENTS (1024)\n#define BITONIC_SORT_DEPTH (11)\n#define BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE (10)\n\nnamespace LightGBM {\n\ntemplate \n__device__ __forceinline__ T ShufflePrefixSum(T value, T* shared_mem_buffer) {\n const uint32_t mask = 0xffffffff;\n const uint32_t warpLane = threadIdx.x % warpSize;\n const uint32_t warpID = threadIdx.x / warpSize;\n const uint32_t num_warp = blockDim.x / warpSize;\n for (uint32_t offset = 1; offset < warpSize; offset <<= 1) {\n const T other_value = __shfl_up_sync(mask, value, offset);\n if (warpLane >= offset) {\n value += other_value;\n }\n }\n if (warpLane == warpSize - 1) {\n shared_mem_buffer[warpID] = value;\n }\n __syncthreads();\n if (warpID == 0) {\n T warp_sum = (warpLane < num_warp ? shared_mem_buffer[warpLane] : 0);\n for (uint32_t offset = 1; offset < warpSize; offset <<= 1) {\n const T other_warp_sum = __shfl_up_sync(mask, warp_sum, offset);\n if (warpLane >= offset) {\n warp_sum += other_warp_sum;\n }\n }\n shared_mem_buffer[warpLane] = warp_sum;\n }\n __syncthreads();\n const T warp_base = warpID == 0 ? 0 : shared_mem_buffer[warpID - 1];\n return warp_base + value;\n}\n\ntemplate \n__device__ __forceinline__ T ShufflePrefixSumExclusive(T value, T* shared_mem_buffer) {\n const uint32_t mask = 0xffffffff;\n const uint32_t warpLane = threadIdx.x % warpSize;\n const uint32_t warpID = threadIdx.x / warpSize;\n const uint32_t num_warp = blockDim.x / warpSize;\n for (uint32_t offset = 1; offset < warpSize; offset <<= 1) {\n const T other_value = __shfl_up_sync(mask, value, offset);\n if (warpLane >= offset) {\n value += other_value;\n }\n }\n if (warpLane == warpSize - 1) {\n shared_mem_buffer[warpID] = value;\n }\n __syncthreads();\n if (warpID == 0) {\n T warp_sum = (warpLane < num_warp ? shared_mem_buffer[warpLane] : 0);\n for (uint32_t offset = 1; offset < warpSize; offset <<= 1) {\n const T other_warp_sum = __shfl_up_sync(mask, warp_sum, offset);\n if (warpLane >= offset) {\n warp_sum += other_warp_sum;\n }\n }\n shared_mem_buffer[warpLane] = warp_sum;\n }\n __syncthreads();\n const T warp_base = warpID == 0 ? 0 : shared_mem_buffer[warpID - 1];\n const T inclusive_result = warp_base + value;\n if (threadIdx.x % warpSize == warpSize - 1) {\n shared_mem_buffer[warpLane] = inclusive_result;\n }\n __syncthreads();\n T exclusive_result = __shfl_up_sync(mask, inclusive_result, 1);\n if (threadIdx.x == 0) {\n exclusive_result = 0;\n } else if (threadIdx.x % warpSize == 0) {\n exclusive_result = shared_mem_buffer[warpLane - 1];\n }\n return exclusive_result;\n}\n\ntemplate \nvoid ShufflePrefixSumGlobal(T* values, size_t len, T* block_prefix_sum_buffer);\n\ntemplate \nvoid GlobalInclusiveArgPrefixSum(const INDEX_T* sorted_indices, const VAL_T* in_values, REDUCE_T* out_values, REDUCE_T* block_buffer, size_t n);\n\ntemplate \n__device__ __forceinline__ T ShuffleReduceSumWarp(T value, const data_size_t len) {\n if (len > 0) {\n const uint32_t mask = 0xffffffff;\n for (int offset = warpSize / 2; offset > 0; offset >>= 1) {\n value += __shfl_down_sync(mask, value, offset);\n }\n }\n return value;\n}\n\n// reduce values from an 1-dimensional block (block size must be no greater than 1024)\ntemplate \n__device__ __forceinline__ T ShuffleReduceSum(T value, T* shared_mem_buffer, const size_t len) {\n const uint32_t warpLane = threadIdx.x % warpSize;\n const uint32_t warpID = threadIdx.x / warpSize;\n const data_size_t warp_len = min(static_cast(warpSize), static_cast(len) - static_cast(warpID * warpSize));\n value = ShuffleReduceSumWarp(value, warp_len);\n if (warpLane == 0) {\n shared_mem_buffer[warpID] = value;\n }\n __syncthreads();\n const data_size_t num_warp = static_cast((len + warpSize - 1) / warpSize);\n if (warpID == 0) {\n value = (warpLane < num_warp ? shared_mem_buffer[warpLane] : 0);\n value = ShuffleReduceSumWarp(value, num_warp);\n }\n return value;\n}\n\ntemplate \n__device__ __forceinline__ T ShuffleReduceMaxWarp(T value, const data_size_t len) {\n if (len > 0) {\n const uint32_t mask = 0xffffffff;\n for (int offset = warpSize / 2; offset > 0; offset >>= 1) {\n value = max(value, __shfl_down_sync(mask, value, offset));\n }\n }\n return value;\n}\n\n// reduce values from an 1-dimensional block (block size must be no greater than 1024)\ntemplate \n__device__ __forceinline__ T ShuffleReduceMax(T value, T* shared_mem_buffer, const size_t len) {\n const uint32_t warpLane = threadIdx.x % warpSize;\n const uint32_t warpID = threadIdx.x / warpSize;\n const data_size_t warp_len = min(static_cast(warpSize), static_cast(len) - static_cast(warpID * warpSize));\n value = ShuffleReduceMaxWarp(value, warp_len);\n if (warpLane == 0) {\n shared_mem_buffer[warpID] = value;\n }\n __syncthreads();\n const data_size_t num_warp = static_cast((len + warpSize - 1) / warpSize);\n if (warpID == 0) {\n value = (warpLane < num_warp ? shared_mem_buffer[warpLane] : 0);\n value = ShuffleReduceMaxWarp(value, num_warp);\n }\n return value;\n}\n\n// calculate prefix sum values within an 1-dimensional block in global memory, exclusively\ntemplate \n__device__ __forceinline__ void GlobalMemoryPrefixSum(T* array, const size_t len) {\n const size_t num_values_per_thread = (len + blockDim.x - 1) / blockDim.x;\n const size_t start = threadIdx.x * num_values_per_thread;\n const size_t end = min(start + num_values_per_thread, len);\n T thread_sum = 0;\n for (size_t index = start; index < end; ++index) {\n thread_sum += array[index];\n }\n __shared__ T shared_mem[WARPSIZE];\n const T thread_base = ShufflePrefixSumExclusive(thread_sum, shared_mem);\n if (start < end) {\n array[start] += thread_base;\n }\n for (size_t index = start + 1; index < end; ++index) {\n array[index] += array[index - 1];\n }\n}\n\ntemplate \n__device__ __forceinline__ T ShuffleReduceMinWarp(T value, const data_size_t len) {\n if (len > 0) {\n const uint32_t mask = (0xffffffff >> (warpSize - len));\n for (int offset = warpSize / 2; offset > 0; offset >>= 1) {\n const T other_value = __shfl_down_sync(mask, value, offset);\n value = (other_value < value) ? other_value : value;\n }\n }\n return value;\n}\n\n// reduce values from an 1-dimensional block (block size must be no greater than 1024)\ntemplate \n__device__ __forceinline__ T ShuffleReduceMin(T value, T* shared_mem_buffer, const size_t len) {\n const uint32_t warpLane = threadIdx.x % warpSize;\n const uint32_t warpID = threadIdx.x / warpSize;\n const data_size_t warp_len = min(static_cast(warpSize), static_cast(len) - static_cast(warpID * warpSize));\n value = ShuffleReduceMinWarp(value, warp_len);\n if (warpLane == 0) {\n shared_mem_buffer[warpID] = value;\n }\n __syncthreads();\n const data_size_t num_warp = static_cast((len + warpSize - 1) / warpSize);\n if (warpID == 0) {\n value = (warpLane < num_warp ? shared_mem_buffer[warpLane] : shared_mem_buffer[0]);\n value = ShuffleReduceMinWarp(value, num_warp);\n }\n return value;\n}\n\ntemplate \nvoid ShuffleReduceMinGlobal(const VAL_T* values, size_t n, REDUCE_T* block_buffer);\n\ntemplate \n__device__ __forceinline__ void BitonicArgSort_1024(const VAL_T* scores, INDEX_T* indices, const INDEX_T num_items) {\n INDEX_T depth = 1;\n INDEX_T num_items_aligend = 1;\n INDEX_T num_items_ref = num_items - 1;\n while (num_items_ref > 0) {\n num_items_ref >>= 1;\n num_items_aligend <<= 1;\n ++depth;\n }\n for (INDEX_T outer_depth = depth - 1; outer_depth >= 1; --outer_depth) {\n const INDEX_T outer_segment_length = 1 << (depth - outer_depth);\n const INDEX_T outer_segment_index = threadIdx.x / outer_segment_length;\n const bool ascending = ASCENDING ? (outer_segment_index % 2 == 0) : (outer_segment_index % 2 > 0);\n for (INDEX_T inner_depth = outer_depth; inner_depth < depth; ++inner_depth) {\n const INDEX_T segment_length = 1 << (depth - inner_depth);\n const INDEX_T half_segment_length = segment_length >> 1;\n const INDEX_T half_segment_index = threadIdx.x / half_segment_length;\n if (threadIdx.x < num_items_aligend) {\n if (half_segment_index % 2 == 0) {\n const INDEX_T index_to_compare = threadIdx.x + half_segment_length;\n if ((scores[indices[threadIdx.x]] > scores[indices[index_to_compare]]) == ascending) {\n const INDEX_T index = indices[threadIdx.x];\n indices[threadIdx.x] = indices[index_to_compare];\n indices[index_to_compare] = index;\n }\n }\n }\n __syncthreads();\n }\n }\n}\n\ntemplate \n__device__ __forceinline__ void BitonicArgSort_2048(const VAL_T* scores, INDEX_T* indices) {\n for (INDEX_T base = 0; base < 2048; base += 1024) {\n for (INDEX_T outer_depth = 10; outer_depth >= 1; --outer_depth) {\n const INDEX_T outer_segment_length = 1 << (11 - outer_depth);\n const INDEX_T outer_segment_index = threadIdx.x / outer_segment_length;\n const bool ascending = ((base == 0) ^ ASCENDING) ? (outer_segment_index % 2 > 0) : (outer_segment_index % 2 == 0);\n for (INDEX_T inner_depth = outer_depth; inner_depth < 11; ++inner_depth) {\n const INDEX_T segment_length = 1 << (11 - inner_depth);\n const INDEX_T half_segment_length = segment_length >> 1;\n const INDEX_T half_segment_index = threadIdx.x / half_segment_length;\n if (half_segment_index % 2 == 0) {\n const INDEX_T index_to_compare = threadIdx.x + half_segment_length + base;\n if ((scores[indices[threadIdx.x + base]] > scores[indices[index_to_compare]]) == ascending) {\n const INDEX_T index = indices[threadIdx.x + base];\n indices[threadIdx.x + base] = indices[index_to_compare];\n indices[index_to_compare] = index;\n }\n }\n __syncthreads();\n }\n }\n }\n const unsigned int index_to_compare = threadIdx.x + 1024;\n if (scores[indices[index_to_compare]] > scores[indices[threadIdx.x]]) {\n const INDEX_T temp_index = indices[index_to_compare];\n indices[index_to_compare] = indices[threadIdx.x];\n indices[threadIdx.x] = temp_index;\n }\n __syncthreads();\n for (INDEX_T base = 0; base < 2048; base += 1024) {\n for (INDEX_T inner_depth = 1; inner_depth < 11; ++inner_depth) {\n const INDEX_T segment_length = 1 << (11 - inner_depth);\n const INDEX_T half_segment_length = segment_length >> 1;\n const INDEX_T half_segment_index = threadIdx.x / half_segment_length;\n if (half_segment_index % 2 == 0) {\n const INDEX_T index_to_compare = threadIdx.x + half_segment_length + base;\n if (scores[indices[threadIdx.x + base]] < scores[indices[index_to_compare]]) {\n const INDEX_T index = indices[threadIdx.x + base];\n indices[threadIdx.x + base] = indices[index_to_compare];\n indices[index_to_compare] = index;\n }\n }\n __syncthreads();\n }\n }\n}\n\ntemplate \n__device__ void BitonicArgSortDevice(const VAL_T* values, INDEX_T* indices, const int len) {\n __shared__ VAL_T shared_values[BLOCK_DIM];\n __shared__ INDEX_T shared_indices[BLOCK_DIM];\n int len_to_shift = len - 1;\n int max_depth = 1;\n while (len_to_shift > 0) {\n len_to_shift >>= 1;\n ++max_depth;\n }\n const int num_blocks = (len + static_cast(BLOCK_DIM) - 1) / static_cast(BLOCK_DIM);\n for (int block_index = 0; block_index < num_blocks; ++block_index) {\n const int this_index = block_index * static_cast(BLOCK_DIM) + static_cast(threadIdx.x);\n if (this_index < len) {\n shared_values[threadIdx.x] = values[this_index];\n shared_indices[threadIdx.x] = this_index;\n } else {\n shared_indices[threadIdx.x] = len;\n }\n __syncthreads();\n for (int depth = max_depth - 1; depth > max_depth - static_cast(MAX_DEPTH); --depth) {\n const int segment_length = (1 << (max_depth - depth));\n const int segment_index = this_index / segment_length;\n const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1);\n {\n const int half_segment_length = (segment_length >> 1);\n const int half_segment_index = this_index / half_segment_length;\n const int num_total_segment = (len + segment_length - 1) / segment_length;\n const int offset = (segment_index == num_total_segment - 1 && ascending == ASCENDING) ?\n (num_total_segment * segment_length - len) : 0;\n if (half_segment_index % 2 == 0) {\n const int segment_start = segment_index * segment_length;\n if (this_index >= offset + segment_start) {\n const int other_index = static_cast(threadIdx.x) + half_segment_length - offset;\n const INDEX_T this_data_index = shared_indices[threadIdx.x];\n const INDEX_T other_data_index = shared_indices[other_index];\n const VAL_T this_value = shared_values[threadIdx.x];\n const VAL_T other_value = shared_values[other_index];\n if (other_data_index < len && (this_value > other_value) == ascending) {\n shared_indices[threadIdx.x] = other_data_index;\n shared_indices[other_index] = this_data_index;\n shared_values[threadIdx.x] = other_value;\n shared_values[other_index] = this_value;\n }\n }\n }\n __syncthreads();\n }\n for (int inner_depth = depth + 1; inner_depth < max_depth; ++inner_depth) {\n const int half_segment_length = (1 << (max_depth - inner_depth - 1));\n const int half_segment_index = this_index / half_segment_length;\n if (half_segment_index % 2 == 0) {\n const int other_index = static_cast(threadIdx.x) + half_segment_length;\n const INDEX_T this_data_index = shared_indices[threadIdx.x];\n const INDEX_T other_data_index = shared_indices[other_index];\n const VAL_T this_value = shared_values[threadIdx.x];\n const VAL_T other_value = shared_values[other_index];\n if (other_data_index < len && (this_value > other_value) == ascending) {\n shared_indices[threadIdx.x] = other_data_index;\n shared_indices[other_index] = this_data_index;\n shared_values[threadIdx.x] = other_value;\n shared_values[other_index] = this_value;\n }\n }\n __syncthreads();\n }\n }\n if (this_index < len) {\n indices[this_index] = shared_indices[threadIdx.x];\n }\n __syncthreads();\n }\n for (int depth = max_depth - static_cast(MAX_DEPTH); depth >= 1; --depth) {\n const int segment_length = (1 << (max_depth - depth));\n {\n const int num_total_segment = (len + segment_length - 1) / segment_length;\n const int half_segment_length = (segment_length >> 1);\n for (int block_index = 0; block_index < num_blocks; ++block_index) {\n const int this_index = block_index * static_cast(BLOCK_DIM) + static_cast(threadIdx.x);\n const int segment_index = this_index / segment_length;\n const int half_segment_index = this_index / half_segment_length;\n const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1);\n const int offset = (segment_index == num_total_segment - 1 && ascending == ASCENDING) ?\n (num_total_segment * segment_length - len) : 0;\n if (half_segment_index % 2 == 0) {\n const int segment_start = segment_index * segment_length;\n if (this_index >= offset + segment_start) {\n const int other_index = this_index + half_segment_length - offset;\n if (other_index < len) {\n const INDEX_T this_data_index = indices[this_index];\n const INDEX_T other_data_index = indices[other_index];\n const VAL_T this_value = values[this_data_index];\n const VAL_T other_value = values[other_data_index];\n if ((this_value > other_value) == ascending) {\n indices[this_index] = other_data_index;\n indices[other_index] = this_data_index;\n }\n }\n }\n }\n }\n __syncthreads();\n }\n for (int inner_depth = depth + 1; inner_depth <= max_depth - static_cast(MAX_DEPTH); ++inner_depth) {\n const int half_segment_length = (1 << (max_depth - inner_depth - 1));\n for (int block_index = 0; block_index < num_blocks; ++block_index) {\n const int this_index = block_index * static_cast(BLOCK_DIM) + static_cast(threadIdx.x);\n const int segment_index = this_index / segment_length;\n const int half_segment_index = this_index / half_segment_length;\n const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1);\n if (half_segment_index % 2 == 0) {\n const int other_index = this_index + half_segment_length;\n if (other_index < len) {\n const INDEX_T this_data_index = indices[this_index];\n const INDEX_T other_data_index = indices[other_index];\n const VAL_T this_value = values[this_data_index];\n const VAL_T other_value = values[other_data_index];\n if ((this_value > other_value) == ascending) {\n indices[this_index] = other_data_index;\n indices[other_index] = this_data_index;\n }\n }\n }\n __syncthreads();\n }\n }\n for (int block_index = 0; block_index < num_blocks; ++block_index) {\n const int this_index = block_index * static_cast(BLOCK_DIM) + static_cast(threadIdx.x);\n const int segment_index = this_index / segment_length;\n const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1);\n if (this_index < len) {\n const INDEX_T index = indices[this_index];\n shared_values[threadIdx.x] = values[index];\n shared_indices[threadIdx.x] = index;\n } else {\n shared_indices[threadIdx.x] = len;\n }\n __syncthreads();\n for (int inner_depth = max_depth - static_cast(MAX_DEPTH) + 1; inner_depth < max_depth; ++inner_depth) {\n const int half_segment_length = (1 << (max_depth - inner_depth - 1));\n const int half_segment_index = this_index / half_segment_length;\n if (half_segment_index % 2 == 0) {\n const int other_index = static_cast(threadIdx.x) + half_segment_length;\n const INDEX_T this_data_index = shared_indices[threadIdx.x];\n const INDEX_T other_data_index = shared_indices[other_index];\n const VAL_T this_value = shared_values[threadIdx.x];\n const VAL_T other_value = shared_values[other_index];\n if (other_data_index < len && (this_value > other_value) == ascending) {\n shared_indices[threadIdx.x] = other_data_index;\n shared_indices[other_index] = this_data_index;\n shared_values[threadIdx.x] = other_value;\n shared_values[other_index] = this_value;\n }\n }\n __syncthreads();\n }\n if (this_index < len) {\n indices[this_index] = shared_indices[threadIdx.x];\n }\n __syncthreads();\n }\n }\n}\n\nvoid BitonicArgSortItemsGlobal(\n const double* scores,\n const int num_queries,\n const data_size_t* cuda_query_boundaries,\n data_size_t* out_indices);\n\ntemplate \nvoid BitonicArgSortGlobal(const VAL_T* values, INDEX_T* indices, const size_t len);\n\ntemplate \nvoid ShuffleReduceSumGlobal(const VAL_T* values, size_t n, REDUCE_T* block_buffer);\n\ntemplate \nvoid ShuffleReduceDotProdGlobal(const VAL_T* values1, const VAL_T* values2, size_t n, REDUCE_T* block_buffer);\n\ntemplate \n__device__ void ShuffleSortedPrefixSumDevice(const VAL_T* in_values,\n const INDEX_T* sorted_indices,\n REDUCE_VAL_T* out_values,\n const INDEX_T num_data) {\n __shared__ REDUCE_VAL_T shared_buffer[WARPSIZE];\n const INDEX_T num_data_per_thread = (num_data + static_cast(blockDim.x) - 1) / static_cast(blockDim.x);\n const INDEX_T start = num_data_per_thread * static_cast(threadIdx.x);\n const INDEX_T end = min(start + num_data_per_thread, num_data);\n REDUCE_VAL_T thread_sum = 0;\n for (INDEX_T index = start; index < end; ++index) {\n thread_sum += static_cast(in_values[sorted_indices[index]]);\n }\n __syncthreads();\n thread_sum = ShufflePrefixSumExclusive(thread_sum, shared_buffer);\n const REDUCE_VAL_T thread_base = shared_buffer[threadIdx.x];\n for (INDEX_T index = start; index < end; ++index) {\n out_values[index] = thread_base + static_cast(in_values[sorted_indices[index]]);\n }\n __syncthreads();\n}\n\ntemplate \n__global__ void PercentileGlobalKernel(const VAL_T* values,\n const WEIGHT_T* weights,\n const INDEX_T* sorted_indices,\n const WEIGHT_REDUCE_T* weights_prefix_sum,\n const double alpha,\n const INDEX_T len,\n VAL_T* out_value) {\n if (!USE_WEIGHT) {\n const double float_pos = (1.0f - alpha) * len;\n const INDEX_T pos = static_cast(float_pos);\n if (pos < 1) {\n *out_value = values[sorted_indices[0]];\n } else if (pos >= len) {\n *out_value = values[sorted_indices[len - 1]];\n } else {\n const double bias = float_pos - static_cast(pos);\n const VAL_T v1 = values[sorted_indices[pos - 1]];\n const VAL_T v2 = values[sorted_indices[pos]];\n *out_value = static_cast(v1 - (v1 - v2) * bias);\n }\n } else {\n const WEIGHT_REDUCE_T threshold = weights_prefix_sum[len - 1] * (1.0f - alpha);\n __shared__ INDEX_T pos;\n if (threadIdx.x == 0) {\n pos = len;\n }\n __syncthreads();\n for (INDEX_T index = static_cast(threadIdx.x); index < len; index += static_cast(blockDim.x)) {\n if (weights_prefix_sum[index] > threshold && (index == 0 || weights_prefix_sum[index - 1] <= threshold)) {\n pos = index;\n }\n }\n __syncthreads();\n pos = min(pos, len - 1);\n if (pos == 0 || pos == len - 1) {\n *out_value = values[pos];\n }\n const VAL_T v1 = values[sorted_indices[pos - 1]];\n const VAL_T v2 = values[sorted_indices[pos]];\n *out_value = static_cast(v1 - (v1 - v2) * (threshold - weights_prefix_sum[pos - 1]) / (weights_prefix_sum[pos] - weights_prefix_sum[pos - 1]));\n }\n}\n\ntemplate \nvoid PercentileGlobal(const VAL_T* values,\n const WEIGHT_T* weights,\n INDEX_T* indices,\n WEIGHT_REDUCE_T* weights_prefix_sum,\n WEIGHT_REDUCE_T* weights_prefix_sum_buffer,\n const double alpha,\n const INDEX_T len,\n VAL_T* cuda_out_value) {\n if (len <= 1) {\n CopyFromCUDADeviceToCUDADevice(cuda_out_value, values, 1, __FILE__, __LINE__);\n }\n BitonicArgSortGlobal(values, indices, len);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n if (USE_WEIGHT) {\n GlobalInclusiveArgPrefixSum(indices, weights, weights_prefix_sum, weights_prefix_sum_buffer, static_cast(len));\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n PercentileGlobalKernel<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(values, weights, indices, weights_prefix_sum, alpha, len, cuda_out_value);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\ntemplate \n__device__ VAL_T PercentileDevice(const VAL_T* values,\n const WEIGHT_T* weights,\n INDEX_T* indices,\n REDUCE_WEIGHT_T* weights_prefix_sum,\n const double alpha,\n const INDEX_T len) {\n if (len <= 1) {\n return values[0];\n }\n if (!USE_WEIGHT) {\n BitonicArgSortDevice(values, indices, len);\n const double float_pos = (1.0f - alpha) * len;\n const INDEX_T pos = static_cast(float_pos);\n if (pos < 1) {\n return values[indices[0]];\n } else if (pos >= len) {\n return values[indices[len - 1]];\n } else {\n const double bias = float_pos - pos;\n const VAL_T v1 = values[indices[pos - 1]];\n const VAL_T v2 = values[indices[pos]];\n return static_cast(v1 - (v1 - v2) * bias);\n }\n } else {\n BitonicArgSortDevice(values, indices, len);\n ShuffleSortedPrefixSumDevice(weights, indices, weights_prefix_sum, len);\n const REDUCE_WEIGHT_T threshold = weights_prefix_sum[len - 1] * (1.0f - alpha);\n __shared__ INDEX_T pos;\n if (threadIdx.x == 0) {\n pos = len;\n }\n __syncthreads();\n for (INDEX_T index = static_cast(threadIdx.x); index < len; index += static_cast(blockDim.x)) {\n if (weights_prefix_sum[index] > threshold && (index == 0 || weights_prefix_sum[index - 1] <= threshold)) {\n pos = index;\n }\n }\n __syncthreads();\n pos = min(pos, len - 1);\n if (pos == 0 || pos == len - 1) {\n return values[pos];\n }\n const VAL_T v1 = values[indices[pos - 1]];\n const VAL_T v2 = values[indices[pos]];\n return static_cast(v1 - (v1 - v2) * (threshold - weights_prefix_sum[pos - 1]) / (weights_prefix_sum[pos] - weights_prefix_sum[pos - 1]));\n }\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ALGORITHMS_HPP_\n" }, { "path": "include/LightGBM/cuda/cuda_column_data.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_COLUMN_DATA_HPP_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_COLUMN_DATA_HPP_\n\n#ifdef USE_CUDA\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n\nnamespace LightGBM {\n\nclass CUDAColumnData {\n public:\n CUDAColumnData(const data_size_t num_data, const int gpu_device_id);\n\n ~CUDAColumnData();\n\n void Init(const int num_columns,\n const std::vector& column_data,\n const std::vector& column_bin_iterator,\n const std::vector& column_bit_type,\n const std::vector& feature_max_bin,\n const std::vector& feature_min_bin,\n const std::vector& feature_offset,\n const std::vector& feature_most_freq_bin,\n const std::vector& feature_default_bin,\n const std::vector& feature_missing_is_zero,\n const std::vector& feature_missing_is_na,\n const std::vector& feature_mfb_is_zero,\n const std::vector& feature_mfb_is_na,\n const std::vector& feature_to_column);\n\n const uint8_t* GetColumnData(const int column_index) const { return data_by_column_[column_index]->RawData(); }\n\n void CopySubrow(const CUDAColumnData* full_set, const data_size_t* used_indices, const data_size_t num_used_indices);\n\n uint8_t* const* cuda_data_by_column() const { return cuda_data_by_column_.RawData(); }\n\n uint32_t feature_min_bin(const int feature_index) const { return feature_min_bin_[feature_index]; }\n\n uint32_t feature_max_bin(const int feature_index) const { return feature_max_bin_[feature_index]; }\n\n uint32_t feature_offset(const int feature_index) const { return feature_offset_[feature_index]; }\n\n uint32_t feature_most_freq_bin(const int feature_index) const { return feature_most_freq_bin_[feature_index]; }\n\n uint32_t feature_default_bin(const int feature_index) const { return feature_default_bin_[feature_index]; }\n\n uint8_t feature_missing_is_zero(const int feature_index) const { return feature_missing_is_zero_[feature_index]; }\n\n uint8_t feature_missing_is_na(const int feature_index) const { return feature_missing_is_na_[feature_index]; }\n\n uint8_t feature_mfb_is_zero(const int feature_index) const { return feature_mfb_is_zero_[feature_index]; }\n\n uint8_t feature_mfb_is_na(const int feature_index) const { return feature_mfb_is_na_[feature_index]; }\n\n const uint32_t* cuda_feature_min_bin() const { return cuda_feature_min_bin_.RawData(); }\n\n const uint32_t* cuda_feature_max_bin() const { return cuda_feature_max_bin_.RawData(); }\n\n const uint32_t* cuda_feature_offset() const { return cuda_feature_offset_.RawData(); }\n\n const uint32_t* cuda_feature_most_freq_bin() const { return cuda_feature_most_freq_bin_.RawData(); }\n\n const uint32_t* cuda_feature_default_bin() const { return cuda_feature_default_bin_.RawData(); }\n\n const uint8_t* cuda_feature_missing_is_zero() const { return cuda_feature_missing_is_zero_.RawData(); }\n\n const uint8_t* cuda_feature_missing_is_na() const { return cuda_feature_missing_is_na_.RawData(); }\n\n const uint8_t* cuda_feature_mfb_is_zero() const { return cuda_feature_mfb_is_zero_.RawData(); }\n\n const uint8_t* cuda_feature_mfb_is_na() const { return cuda_feature_mfb_is_na_.RawData(); }\n\n const int* cuda_feature_to_column() const { return cuda_feature_to_column_.RawData(); }\n\n const uint8_t* cuda_column_bit_type() const { return cuda_column_bit_type_.RawData(); }\n\n int feature_to_column(const int feature_index) const { return feature_to_column_[feature_index]; }\n\n uint8_t column_bit_type(const int column_index) const { return column_bit_type_[column_index]; }\n\n private:\n template \n void InitOneColumnData(const void* in_column_data, BinIterator* bin_iterator, CUDAVector* out_column_data_pointer);\n\n void LaunchCopySubrowKernel(uint8_t* const* in_cuda_data_by_column);\n\n void InitColumnMetaInfo();\n\n void ResizeWhenCopySubrow(const data_size_t num_used_indices);\n\n std::vector GetDataByColumnPointers(const std::vector>>& data_by_column) const {\n std::vector data_by_column_pointers(data_by_column.size(), nullptr);\n for (size_t i = 0; i < data_by_column.size(); ++i) {\n data_by_column_pointers[i] = reinterpret_cast(data_by_column[i]->RawData());\n }\n return data_by_column_pointers;\n }\n\n int gpu_device_id_;\n int num_threads_;\n data_size_t num_data_;\n int num_columns_;\n std::vector column_bit_type_;\n std::vector feature_min_bin_;\n std::vector feature_max_bin_;\n std::vector feature_offset_;\n std::vector feature_most_freq_bin_;\n std::vector feature_default_bin_;\n std::vector feature_missing_is_zero_;\n std::vector feature_missing_is_na_;\n std::vector feature_mfb_is_zero_;\n std::vector feature_mfb_is_na_;\n CUDAVector cuda_data_by_column_;\n std::vector feature_to_column_;\n std::vector>> data_by_column_;\n\n CUDAVector cuda_column_bit_type_;\n CUDAVector cuda_feature_min_bin_;\n CUDAVector cuda_feature_max_bin_;\n CUDAVector cuda_feature_offset_;\n CUDAVector cuda_feature_most_freq_bin_;\n CUDAVector cuda_feature_default_bin_;\n CUDAVector cuda_feature_missing_is_zero_;\n CUDAVector cuda_feature_missing_is_na_;\n CUDAVector cuda_feature_mfb_is_zero_;\n CUDAVector cuda_feature_mfb_is_na_;\n CUDAVector cuda_feature_to_column_;\n\n // used when bagging with subset\n CUDAVector cuda_used_indices_;\n data_size_t num_used_indices_;\n data_size_t cur_subset_buffer_size_;\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_COLUMN_DATA_HPP_\n" }, { "path": "include/LightGBM/cuda/cuda_metadata.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METADATA_HPP_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METADATA_HPP_\n\n#ifdef USE_CUDA\n\n#include \n#include \n\n#include \n\nnamespace LightGBM {\n\nclass CUDAMetadata {\n public:\n explicit CUDAMetadata(const int gpu_device_id);\n\n ~CUDAMetadata();\n\n void Init(const std::vector& label,\n const std::vector& weight,\n const std::vector& query_boundaries,\n const std::vector& query_weights,\n const std::vector& init_score);\n\n void SetLabel(const label_t* label, data_size_t len);\n\n void SetWeights(const label_t* weights, data_size_t len);\n\n void SetQuery(const data_size_t* query, const label_t* query_weights, data_size_t num_queries);\n\n void SetInitScore(const double* init_score, data_size_t len);\n\n const label_t* cuda_label() const { return cuda_label_.RawData(); }\n\n const label_t* cuda_weights() const { return cuda_weights_.RawData(); }\n\n const data_size_t* cuda_query_boundaries() const { return cuda_query_boundaries_.RawData(); }\n\n const label_t* cuda_query_weights() const { return cuda_query_weights_.RawData(); }\n\n private:\n CUDAVector cuda_label_;\n CUDAVector cuda_weights_;\n CUDAVector cuda_query_boundaries_;\n CUDAVector cuda_query_weights_;\n CUDAVector cuda_init_score_;\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METADATA_HPP_\n" }, { "path": "include/LightGBM/cuda/cuda_metric.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METRIC_HPP_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METRIC_HPP_\n\n#ifdef USE_CUDA\n\n#include \n#include \n\nnamespace LightGBM {\n\ntemplate \nclass CUDAMetricInterface: public HOST_METRIC {\n public:\n explicit CUDAMetricInterface(const Config& config): HOST_METRIC(config) {\n cuda_labels_ = nullptr;\n cuda_weights_ = nullptr;\n const int gpu_device_id = config.gpu_device_id >= 0 ? config.gpu_device_id : 0;\n SetCUDADevice(gpu_device_id, __FILE__, __LINE__);\n }\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n HOST_METRIC::Init(metadata, num_data);\n cuda_labels_ = metadata.cuda_metadata()->cuda_label();\n cuda_weights_ = metadata.cuda_metadata()->cuda_weights();\n }\n\n bool IsCUDAMetric() const { return true; }\n\n protected:\n const label_t* cuda_labels_;\n const label_t* cuda_weights_;\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_METRIC_HPP_\n" }, { "path": "include/LightGBM/cuda/cuda_nccl_topology.hpp", "content": "/*!\n * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_NCCL_TOPOLOGY_HPP_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_NCCL_TOPOLOGY_HPP_\n\n#ifdef USE_CUDA\n\n#ifdef USE_ROCM\n#include \n#else\n#include \n#endif\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nclass NCCLTopology {\n public:\n NCCLTopology(const int master_gpu_device_id, const int num_gpu, const std::string& gpu_device_id_list, const data_size_t global_num_data) {\n num_gpu_ = num_gpu;\n master_gpu_device_id_ = master_gpu_device_id;\n global_num_data_ = global_num_data;\n int max_num_gpu = 0;\n CUDASUCCESS_OR_FATAL(cudaGetDeviceCount(&max_num_gpu));\n if (gpu_device_id_list != std::string(\"\")) {\n std::set gpu_id_set;\n std::vector gpu_list_str = Common::Split(gpu_device_id_list.c_str(), \",\");\n for (const auto& gpu_str : gpu_list_str) {\n int gpu_id = 0;\n Common::Atoi(gpu_str.c_str(), &gpu_id);\n if (gpu_id < 0 || gpu_id >= max_num_gpu) {\n Log::Warning(\"Invalid GPU device ID %d in gpu_device_list is ignored.\", gpu_id);\n } else {\n gpu_id_set.insert(gpu_id);\n }\n }\n for (const int gpu_id : gpu_id_set) {\n gpu_list_.push_back(gpu_id);\n }\n }\n if (!gpu_list_.empty() && num_gpu_ != static_cast(gpu_list_.size())) {\n Log::Warning(\"num_gpu = %d is different from the number of valid device IDs in gpu_device_list (%d), using %d GPUs instead.\", \\\n num_gpu_, static_cast(gpu_list_.size()), static_cast(gpu_list_.size()));\n num_gpu_ = static_cast(gpu_list_.size());\n }\n\n if (!gpu_list_.empty()) {\n bool check_master_gpu = false;\n for (int i = 0; i < static_cast(gpu_list_.size()); ++i) {\n const int gpu_id = gpu_list_[i];\n if (gpu_id == master_gpu_device_id_) {\n check_master_gpu = true;\n master_gpu_index_ = i;\n break;\n }\n }\n if (!check_master_gpu) {\n Log::Warning(\"Master GPU index not in gpu_device_list. Using %d as the master GPU instead.\", gpu_list_[0]);\n master_gpu_device_id_ = gpu_list_[0];\n master_gpu_index_ = 0;\n }\n } else {\n if (num_gpu_ <= 0) {\n num_gpu_ = 1;\n } else if (num_gpu_ > max_num_gpu) {\n Log::Warning(\"Only %d GPUs available, using num_gpu = %d.\", max_num_gpu, max_num_gpu);\n num_gpu_ = max_num_gpu;\n }\n if (master_gpu_device_id_ < 0 || master_gpu_device_id_ >= num_gpu_) {\n Log::Warning(\"Invalid gpu_device_id = %d for master GPU index, using gpu_device_id = 0 instead.\", master_gpu_device_id_);\n master_gpu_device_id_ = 0;\n master_gpu_index_ = 0;\n }\n for (int i = 0; i < num_gpu_; ++i) {\n gpu_list_.push_back(i);\n }\n }\n\n Log::Info(\"Using GPU devices %s, and local master GPU device %d.\", Common::Join(gpu_list_, \",\").c_str(), master_gpu_device_id_);\n\n const int num_threads = OMP_NUM_THREADS();\n if (num_gpu_ > num_threads) {\n Log::Fatal(\"Number of GPUs %d is greater than the number of threads %d. Please use more threads.\", num_gpu_, num_threads);\n }\n\n host_threads_.resize(num_gpu_);\n }\n\n ~NCCLTopology() {}\n\n void InitNCCL() {\n nccl_gpu_rank_.resize(num_gpu_, -1);\n nccl_communicators_.resize(num_gpu_);\n ncclUniqueId nccl_unique_id;\n if (Network::num_machines() == 1 || Network::rank() == 0) {\n NCCLCHECK(ncclGetUniqueId(&nccl_unique_id));\n }\n if (Network::num_machines() > 1) {\n std::vector output_buffer(Network::num_machines());\n Network::Allgather(\n reinterpret_cast(&nccl_unique_id),\n sizeof(ncclUniqueId) / sizeof(char),\n reinterpret_cast(output_buffer.data()));\n if (Network::rank() > 0) {\n nccl_unique_id = output_buffer[0];\n }\n }\n\n if (Network::num_machines() > 1) {\n node_rank_offset_.resize(Network::num_machines() + 1, 0);\n Network::Allgather(\n reinterpret_cast(&num_gpu_),\n sizeof(int) / sizeof(char),\n reinterpret_cast(node_rank_offset_.data() + 1));\n for (int rank = 1; rank < Network::num_machines() + 1; ++rank) {\n node_rank_offset_[rank] += node_rank_offset_[rank - 1];\n }\n CHECK_EQ(node_rank_offset_[Network::rank() + 1] - node_rank_offset_[Network::rank()], num_gpu_);\n NCCLCHECK(ncclGroupStart());\n for (int gpu_index = 0; gpu_index < num_gpu_; ++gpu_index) {\n SetCUDADevice(gpu_list_[gpu_index], __FILE__, __LINE__);\n nccl_gpu_rank_[gpu_index] = gpu_index + node_rank_offset_[Network::rank()];\n NCCLCHECK(ncclCommInitRank(&nccl_communicators_[gpu_index], node_rank_offset_.back(), nccl_unique_id, nccl_gpu_rank_[gpu_index]));\n }\n NCCLCHECK(ncclGroupEnd());\n } else {\n NCCLCHECK(ncclGroupStart());\n for (int gpu_index = 0; gpu_index < num_gpu_; ++gpu_index) {\n SetCUDADevice(gpu_list_[gpu_index], __FILE__, __LINE__);\n nccl_gpu_rank_[gpu_index] = gpu_index;\n NCCLCHECK(ncclCommInitRank(&nccl_communicators_[gpu_index], num_gpu_, nccl_unique_id, gpu_index));\n }\n NCCLCHECK(ncclGroupEnd());\n }\n\n // return to master gpu device\n CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_));\n }\n\n template \n void RunPerDevice(const std::vector>& objs, const std::function& func) {\n #pragma omp parallel for schedule(static) num_threads(num_gpu_)\n for (int i = 0; i < num_gpu_; ++i) {\n CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_list_[i]));\n func(objs[i].get());\n }\n CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_));\n }\n\n template \n void InitPerDevice(std::vector>* vec) {\n vec->resize(num_gpu_);\n #pragma omp parallel for schedule(static) num_threads(num_gpu_)\n for (int i = 0; i < num_gpu_; ++i) {\n CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_list_[i]));\n RET_T* nccl_info = new RET_T();\n nccl_info->SetNCCLInfo(nccl_communicators_[i], nccl_gpu_rank_[i], i, gpu_list_[i], global_num_data_);\n vec->operator[](i).reset(nccl_info);\n }\n CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_));\n }\n\n template \n void DispatchPerDevice(std::vector>* objs, const std::function& func) {\n for (int i = 0; i < num_gpu_; ++i) {\n host_threads_[i] = std::thread([this, i, &func, objs] () {\n CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_list_[i]))\n func(objs->operator[](i).get());\n });\n }\n for (int i = 0; i < num_gpu_; ++i) {\n host_threads_[i].join();\n }\n CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_));\n }\n\n template \n void RunOnMasterDevice(const std::vector>& objs, const std::function& func) {\n CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_));\n func(objs[master_gpu_index_].get());\n }\n\n template \n void RunOnNonMasterDevice(const std::vector>& objs, const std::function& func) {\n for (int i = 0; i < num_gpu_; ++i) {\n if (i != master_gpu_index_) {\n CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_list_[i]));\n func(objs[i].get());\n }\n }\n CUDASUCCESS_OR_FATAL(cudaSetDevice(master_gpu_device_id_));\n }\n\n int num_gpu() const { return num_gpu_; }\n\n int master_gpu_index() const { return master_gpu_index_; }\n\n int master_gpu_device_id() const { return master_gpu_device_id_; }\n\n const std::vector& gpu_list() const { return gpu_list_; }\n\n private:\n int num_gpu_;\n int master_gpu_index_;\n int master_gpu_device_id_;\n std::vector gpu_list_;\n data_size_t global_num_data_;\n\n ncclUniqueId nccl_unique_id_;\n std::vector node_rank_offset_;\n std::vector nccl_gpu_rank_;\n std::vector nccl_communicators_;\n std::vector host_threads_;\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_NCCL_TOPOLOGY_HPP_\n" }, { "path": "include/LightGBM/cuda/cuda_objective_function.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2026-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_OBJECTIVE_FUNCTION_HPP_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_OBJECTIVE_FUNCTION_HPP_\n\n#ifdef USE_CUDA\n\n#include \n#include \n#include \n\n#include \n#include \n\nnamespace LightGBM {\n\ntemplate \nclass CUDAObjectiveInterface: public HOST_OBJECTIVE, public NCCLInfo {\n public:\n explicit CUDAObjectiveInterface(const Config& config): HOST_OBJECTIVE(config) {\n if (config.num_gpu <= 1) {\n const int gpu_device_id = config.gpu_device_id >= 0 ? config.gpu_device_id : 0;\n SetCUDADevice(gpu_device_id, __FILE__, __LINE__);\n }\n }\n\n explicit CUDAObjectiveInterface(const std::vector& strs): HOST_OBJECTIVE(strs) {}\n\n void Init(const Metadata& metadata, data_size_t num_data) {\n HOST_OBJECTIVE::Init(metadata, num_data);\n cuda_labels_ = metadata.cuda_metadata()->cuda_label();\n cuda_weights_ = metadata.cuda_metadata()->cuda_weights();\n }\n\n void SetNCCLInfo(\n ncclComm_t nccl_communicator,\n int nccl_gpu_rank,\n int local_gpu_rank,\n int gpu_device_id,\n data_size_t global_num_data) override {\n NCCLInfo::SetNCCLInfo(nccl_communicator, nccl_gpu_rank, local_gpu_rank, gpu_device_id, global_num_data);\n }\n\n virtual const double* ConvertOutputCUDA(const data_size_t num_data, const double* input, double* output) const {\n return LaunchConvertOutputCUDAKernel(num_data, input, output);\n }\n\n double BoostFromScore(int class_id) const override {\n return LaunchCalcInitScoreKernel(class_id);\n }\n\n bool IsCUDAObjective() const override { return true; }\n\n void GetGradients(const double* scores, score_t* gradients, score_t* hessians) const override {\n LaunchGetGradientsKernel(scores, gradients, hessians);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n }\n\n void GetGradientsWithSampledQueries(const double* scores, const data_size_t /*num_sampled_queries*/, const data_size_t* /*sampled_query_indices*/, score_t* gradients, score_t* hessians) const override {\n LaunchGetGradientsKernel(scores, gradients, hessians);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n }\n\n void RenewTreeOutputCUDA(const double* score, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf,\n const data_size_t* data_start_in_leaf, const int num_leaves, double* leaf_value) const override {\n global_timer.Start(\"CUDAObjectiveInterface::LaunchRenewTreeOutputCUDAKernel\");\n LaunchRenewTreeOutputCUDAKernel(score, data_indices_in_leaf, num_data_in_leaf, data_start_in_leaf, num_leaves, leaf_value);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n global_timer.Stop(\"CUDAObjectiveInterface::LaunchRenewTreeOutputCUDAKernel\");\n }\n\n protected:\n virtual void LaunchGetGradientsKernel(const double* scores, score_t* gradients, score_t* hessians) const = 0;\n\n virtual double LaunchCalcInitScoreKernel(const int class_id) const {\n return HOST_OBJECTIVE::BoostFromScore(class_id);\n }\n\n virtual const double* LaunchConvertOutputCUDAKernel(const data_size_t /*num_data*/, const double* input, double* /*output*/) const { return input; }\n\n virtual void LaunchRenewTreeOutputCUDAKernel(\n const double* /*score*/, const data_size_t* /*data_indices_in_leaf*/, const data_size_t* /*num_data_in_leaf*/,\n const data_size_t* /*data_start_in_leaf*/, const int /*num_leaves*/, double* /*leaf_value*/) const {}\n\n const label_t* cuda_labels_;\n const label_t* cuda_weights_;\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_OBJECTIVE_FUNCTION_HPP_\n" }, { "path": "include/LightGBM/cuda/cuda_random.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_RANDOM_HPP_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_RANDOM_HPP_\n\n#ifdef USE_CUDA\n\n#ifndef USE_ROCM\n#include \n#include \n#endif\n\nnamespace LightGBM {\n\n/*!\n* \\brief A wrapper for random generator\n*/\nclass CUDARandom {\n public:\n /*!\n * \\brief Set specific seed\n */\n __device__ void SetSeed(int seed) {\n x = seed;\n }\n /*!\n * \\brief Generate random integer, int16 range. [0, 65536]\n * \\param lower_bound lower bound\n * \\param upper_bound upper bound\n * \\return The random integer between [lower_bound, upper_bound)\n */\n __device__ inline int NextShort(int lower_bound, int upper_bound) {\n return (RandInt16()) % (upper_bound - lower_bound) + lower_bound;\n }\n\n /*!\n * \\brief Generate random integer, int32 range\n * \\param lower_bound lower bound\n * \\param upper_bound upper bound\n * \\return The random integer between [lower_bound, upper_bound)\n */\n __device__ inline int NextInt(int lower_bound, int upper_bound) {\n return (RandInt32()) % (upper_bound - lower_bound) + lower_bound;\n }\n\n /*!\n * \\brief Generate random float data\n * \\return The random float between [0.0, 1.0)\n */\n __device__ inline float NextFloat() {\n // get random float in [0,1)\n return static_cast(RandInt16()) / (32768.0f);\n }\n\n private:\n __device__ inline int RandInt16() {\n x = (214013 * x + 2531011);\n return static_cast((x >> 16) & 0x7FFF);\n }\n\n __device__ inline int RandInt32() {\n x = (214013 * x + 2531011);\n return static_cast(x & 0x7FFFFFFF);\n }\n\n unsigned int x = 123456789;\n};\n\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_RANDOM_HPP_\n" }, { "path": "include/LightGBM/cuda/cuda_rocm_interop.h", "content": "/*!\n * Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved.\n */\n\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROCM_INTEROP_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROCM_INTEROP_H_\n\n#ifdef USE_CUDA\n\n#if defined(__HIP_PLATFORM_AMD__)\n\n// ROCm doesn't have atomicAdd_block, but it should be semantically the same as atomicAdd\n#define atomicAdd_block atomicAdd\n\n// hipify\n#include \n#define cudaDeviceProp hipDeviceProp_t\n#define cudaDeviceSynchronize hipDeviceSynchronize\n#define cudaError_t hipError_t\n#define cudaFree hipFree\n#define cudaFreeHost hipFreeHost\n#define cudaGetDevice hipGetDevice\n#define cudaGetDeviceCount hipGetDeviceCount\n#define cudaGetDeviceProperties hipGetDeviceProperties\n#define cudaGetErrorName hipGetErrorName\n#define cudaGetErrorString hipGetErrorString\n#define cudaGetLastError hipGetLastError\n#define cudaHostAlloc hipHostAlloc\n#define cudaHostAllocPortable hipHostAllocPortable\n#define cudaMalloc hipMalloc\n#define cudaMemcpy hipMemcpy\n#define cudaMemcpyAsync hipMemcpyAsync\n#define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice\n#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost\n#define cudaMemcpyHostToDevice hipMemcpyHostToDevice\n#define cudaMemoryTypeHost hipMemoryTypeHost\n#define cudaMemset hipMemset\n#define cudaPointerAttributes hipPointerAttribute_t\n#define cudaPointerGetAttributes hipPointerGetAttributes\n#define cudaSetDevice hipSetDevice\n#define cudaStreamCreate hipStreamCreate\n#define cudaStreamDestroy hipStreamDestroy\n#define cudaStreamSynchronize hipStreamSynchronize\n#define cudaStream_t hipStream_t\n#define cudaSuccess hipSuccess\n\n// ROCm 7.0 did add __shfl_down_sync et al, but the following hack still works.\n// Since mask is full 0xffffffff, we can use __shfl_down instead.\n#define __shfl_down_sync(mask, val, offset) __shfl_down(val, offset)\n#define __shfl_up_sync(mask, val, offset) __shfl_up(val, offset)\n\n// warpSize is only allowed for device code.\n// HIP header used to define warpSize as a constexpr that was either 32 or 64\n// depending on the target device, and then always set it to 64 for host code.\nstatic inline constexpr int WARP_SIZE_INTERNAL() {\n#if defined(__GFX9__)\n return 64;\n#else // __GFX9__\n return 32;\n#endif // __GFX9__\n}\n#define WARPSIZE (WARP_SIZE_INTERNAL())\n\n#else // __HIP_PLATFORM_AMD__\n// CUDA warpSize is not a constexpr, but always 32\n#define WARPSIZE 32\n#endif // defined(__HIP_PLATFORM_AMD__) || defined(__HIP__)\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROCM_INTEROP_H_\n" }, { "path": "include/LightGBM/cuda/cuda_row_data.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROW_DATA_HPP_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROW_DATA_HPP_\n\n#ifdef USE_CUDA\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#define COPY_SUBROW_BLOCK_SIZE_ROW_DATA (1024)\n\n#if CUDART_VERSION == 10000\n#define DP_SHARED_HIST_SIZE (5176)\n#else\n#define DP_SHARED_HIST_SIZE (6144)\n#endif\n#define SP_SHARED_HIST_SIZE (DP_SHARED_HIST_SIZE * 2)\n\nnamespace LightGBM {\n\nclass CUDARowData {\n public:\n CUDARowData(const Dataset* train_data,\n const TrainingShareStates* train_share_state,\n const int gpu_device_id,\n const bool gpu_use_dp);\n\n ~CUDARowData();\n\n void Init(const Dataset* train_data,\n TrainingShareStates* train_share_state);\n\n void CopySubrow(const CUDARowData* full_set, const data_size_t* used_indices, const data_size_t num_used_indices);\n\n void CopySubcol(const CUDARowData* full_set, const std::vector& is_feature_used, const Dataset* train_data);\n\n void CopySubrowAndSubcol(const CUDARowData* full_set, const data_size_t* used_indices,\n const data_size_t num_used_indices, const std::vector& is_feature_used, const Dataset* train_data);\n\n template \n const BIN_TYPE* GetBin() const;\n\n template \n const PTR_TYPE* GetPartitionPtr() const;\n\n template \n const PTR_TYPE* GetRowPtr() const;\n\n int NumLargeBinPartition() const { return static_cast(large_bin_partitions_.size()); }\n\n int num_feature_partitions() const { return num_feature_partitions_; }\n\n int max_num_column_per_partition() const { return max_num_column_per_partition_; }\n\n bool is_sparse() const { return is_sparse_; }\n\n uint8_t bit_type() const { return bit_type_; }\n\n uint8_t row_ptr_bit_type() const { return row_ptr_bit_type_; }\n\n const int* cuda_feature_partition_column_index_offsets() const { return cuda_feature_partition_column_index_offsets_.RawData(); }\n\n const uint32_t* cuda_column_hist_offsets() const { return cuda_column_hist_offsets_.RawData(); }\n\n const uint32_t* cuda_partition_hist_offsets() const { return cuda_partition_hist_offsets_.RawData(); }\n\n int shared_hist_size() const { return shared_hist_size_; }\n\n private:\n void DivideCUDAFeatureGroups(const Dataset* train_data, TrainingShareStates* share_state);\n\n template \n void GetDenseDataPartitioned(const BIN_TYPE* row_wise_data, std::vector* partitioned_data);\n\n template \n void GetSparseDataPartitioned(const BIN_TYPE* row_wise_data,\n const ROW_PTR_TYPE* row_ptr,\n std::vector>* partitioned_data,\n std::vector>* partitioned_row_ptr,\n std::vector* partition_ptr);\n\n template \n void InitSparseData(const BIN_TYPE* host_data,\n const ROW_PTR_TYPE* host_row_ptr,\n CUDAVector* cuda_data,\n CUDAVector* cuda_row_ptr,\n CUDAVector* cuda_partition_ptr);\n\n /*! \\brief number of threads to use */\n int num_threads_;\n /*! \\brief number of training data */\n data_size_t num_data_;\n /*! \\brief number of bins of all features */\n int num_total_bin_;\n /*! \\brief number of feature groups in dataset */\n int num_feature_group_;\n /*! \\brief number of features in dataset */\n int num_feature_;\n /*! \\brief number of bits used to store each bin value */\n uint8_t bit_type_;\n /*! \\brief number of bits used to store each row pointer value */\n uint8_t row_ptr_bit_type_;\n /*! \\brief is sparse row wise data */\n bool is_sparse_;\n /*! \\brief start column index of each feature partition */\n std::vector feature_partition_column_index_offsets_;\n /*! \\brief histogram offset of each column */\n std::vector column_hist_offsets_;\n /*! \\brief hisotgram offset of each partition */\n std::vector partition_hist_offsets_;\n /*! \\brief maximum number of columns among all feature partitions */\n int max_num_column_per_partition_;\n /*! \\brief number of partitions */\n int num_feature_partitions_;\n /*! \\brief used when bagging with subset, number of used indices */\n data_size_t num_used_indices_;\n /*! \\brief used when bagging with subset, number of total elements */\n uint64_t num_total_elements_;\n /*! \\brief used when bagging with column subset, the size of maximum number of feature partitions */\n int cur_num_feature_partition_buffer_size_;\n /*! \\brief CUDA device ID */\n int gpu_device_id_;\n /*! \\brief index of partitions with large bins that its histogram cannot fit into shared memory, each large bin partition contains a single column */\n std::vector large_bin_partitions_;\n /*! \\brief index of partitions with small bins */\n std::vector small_bin_partitions_;\n /*! \\brief shared memory size used by histogram */\n int shared_hist_size_;\n /*! \\brief whether to use double precision in histograms per block */\n bool gpu_use_dp_;\n\n // CUDA memory\n\n /*! \\brief row-wise data stored in CUDA, 8 bits */\n CUDAVector cuda_data_uint8_t_;\n /*! \\brief row-wise data stored in CUDA, 16 bits */\n CUDAVector cuda_data_uint16_t_;\n /*! \\brief row-wise data stored in CUDA, 32 bits */\n CUDAVector cuda_data_uint32_t_;\n /*! \\brief row pointer stored in CUDA, 16 bits */\n CUDAVector cuda_row_ptr_uint16_t_;\n /*! \\brief row pointer stored in CUDA, 32 bits */\n CUDAVector cuda_row_ptr_uint32_t_;\n /*! \\brief row pointer stored in CUDA, 64 bits */\n CUDAVector cuda_row_ptr_uint64_t_;\n /*! \\brief partition bin offsets, 16 bits */\n CUDAVector cuda_partition_ptr_uint16_t_;\n /*! \\brief partition bin offsets, 32 bits */\n CUDAVector cuda_partition_ptr_uint32_t_;\n /*! \\brief partition bin offsets, 64 bits */\n CUDAVector cuda_partition_ptr_uint64_t_;\n /*! \\brief start column index of each feature partition */\n CUDAVector cuda_feature_partition_column_index_offsets_;\n /*! \\brief histogram offset of each column */\n CUDAVector cuda_column_hist_offsets_;\n /*! \\brief hisotgram offset of each partition */\n CUDAVector cuda_partition_hist_offsets_;\n /*! \\brief block buffer when calculating prefix sum */\n CUDAVector cuda_block_buffer_uint16_t_;\n /*! \\brief block buffer when calculating prefix sum */\n CUDAVector cuda_block_buffer_uint32_t_;\n /*! \\brief block buffer when calculating prefix sum */\n CUDAVector cuda_block_buffer_uint64_t_;\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_ROW_DATA_HPP_\n" }, { "path": "include/LightGBM/cuda/cuda_split_info.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved.\n */\n\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_SPLIT_INFO_HPP_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_SPLIT_INFO_HPP_\n\n#ifdef USE_CUDA\n\n#include \n\nnamespace LightGBM {\n\nclass CUDASplitInfo {\n public:\n bool is_valid;\n int leaf_index;\n double gain;\n int inner_feature_index;\n uint32_t threshold;\n bool default_left;\n\n double left_sum_gradients;\n double left_sum_hessians;\n int64_t left_sum_of_gradients_hessians;\n data_size_t left_count;\n double left_gain;\n double left_value;\n\n double right_sum_gradients;\n double right_sum_hessians;\n int64_t right_sum_of_gradients_hessians;\n data_size_t right_count;\n double right_gain;\n double right_value;\n\n int num_cat_threshold = 0;\n uint32_t* cat_threshold = nullptr;\n int* cat_threshold_real = nullptr;\n\n __host__ __device__ CUDASplitInfo() {\n num_cat_threshold = 0;\n cat_threshold = nullptr;\n cat_threshold_real = nullptr;\n }\n\n __host__ __device__ ~CUDASplitInfo() {\n if (num_cat_threshold > 0) {\n if (cat_threshold != nullptr) {\n CUDASUCCESS_OR_FATAL(cudaFree(cat_threshold));\n }\n if (cat_threshold_real != nullptr) {\n CUDASUCCESS_OR_FATAL(cudaFree(cat_threshold_real));\n }\n }\n }\n\n __host__ __device__ CUDASplitInfo& operator=(const CUDASplitInfo& other) {\n is_valid = other.is_valid;\n leaf_index = other.leaf_index;\n gain = other.gain;\n inner_feature_index = other.inner_feature_index;\n threshold = other.threshold;\n default_left = other.default_left;\n\n left_sum_gradients = other.left_sum_gradients;\n left_sum_hessians = other.left_sum_hessians;\n left_count = other.left_count;\n left_gain = other.left_gain;\n left_value = other.left_value;\n\n right_sum_gradients = other.right_sum_gradients;\n right_sum_hessians = other.right_sum_hessians;\n right_count = other.right_count;\n right_gain = other.right_gain;\n right_value = other.right_value;\n\n num_cat_threshold = other.num_cat_threshold;\n if (num_cat_threshold > 0 && cat_threshold == nullptr) {\n cat_threshold = new uint32_t[num_cat_threshold];\n }\n if (num_cat_threshold > 0 && cat_threshold_real == nullptr) {\n cat_threshold_real = new int[num_cat_threshold];\n }\n if (num_cat_threshold > 0) {\n if (other.cat_threshold != nullptr) {\n for (int i = 0; i < num_cat_threshold; ++i) {\n cat_threshold[i] = other.cat_threshold[i];\n }\n }\n if (other.cat_threshold_real != nullptr) {\n for (int i = 0; i < num_cat_threshold; ++i) {\n cat_threshold_real[i] = other.cat_threshold_real[i];\n }\n }\n }\n return *this;\n }\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_SPLIT_INFO_HPP_\n" }, { "path": "include/LightGBM/cuda/cuda_tree.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_TREE_HPP_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_TREE_HPP_\n\n#ifdef USE_CUDA\n\n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\n__device__ void SetDecisionTypeCUDA(int8_t* decision_type, bool input, int8_t mask);\n\n__device__ void SetMissingTypeCUDA(int8_t* decision_type, int8_t input);\n\n__device__ bool GetDecisionTypeCUDA(int8_t decision_type, int8_t mask);\n\n__device__ int8_t GetMissingTypeCUDA(int8_t decision_type);\n\n__device__ bool IsZeroCUDA(double fval);\n\nclass CUDATree : public Tree {\n public:\n /*!\n * \\brief Constructor\n * \\param max_leaves The number of max leaves\n * \\param track_branch_features Whether to keep track of ancestors of leaf nodes\n * \\param is_linear Whether the tree has linear models at each leaf\n */\n explicit CUDATree(int max_leaves, bool track_branch_features, bool is_linear,\n const int gpu_device_id, const bool has_categorical_feature);\n\n explicit CUDATree(const Tree* host_tree);\n\n ~CUDATree() noexcept;\n\n int Split(const int leaf_index,\n const int real_feature_index,\n const double real_threshold,\n const MissingType missing_type,\n const CUDASplitInfo* cuda_split_info);\n\n int SplitCategorical(\n const int leaf_index,\n const int real_feature_index,\n const MissingType missing_type,\n const CUDASplitInfo* cuda_split_info,\n uint32_t* cuda_bitset,\n size_t cuda_bitset_len,\n uint32_t* cuda_bitset_inner,\n size_t cuda_bitset_inner_len);\n\n /*!\n * \\brief Adding prediction value of this tree model to scores\n * \\param data The dataset\n * \\param num_data Number of total data\n * \\param score Will add prediction to score\n */\n void AddPredictionToScore(const Dataset* data,\n data_size_t num_data,\n double* score) const override;\n\n /*!\n * \\brief Adding prediction value of this tree model to scores\n * \\param data The dataset\n * \\param used_data_indices Indices of used data\n * \\param num_data Number of total data\n * \\param score Will add prediction to score\n */\n void AddPredictionToScore(const Dataset* data,\n const data_size_t* used_data_indices,\n data_size_t num_data, double* score) const override;\n\n inline void AsConstantTree(double val, int count) override;\n\n const int* cuda_leaf_parent() const { return cuda_leaf_parent_.RawData(); }\n\n const int* cuda_left_child() const { return cuda_left_child_.RawData(); }\n\n const int* cuda_right_child() const { return cuda_right_child_.RawData(); }\n\n const int* cuda_split_feature_inner() const { return cuda_split_feature_inner_.RawData(); }\n\n const int* cuda_split_feature() const { return cuda_split_feature_.RawData(); }\n\n const uint32_t* cuda_threshold_in_bin() const { return cuda_threshold_in_bin_.RawData(); }\n\n const double* cuda_threshold() const { return cuda_threshold_.RawData(); }\n\n const int8_t* cuda_decision_type() const { return cuda_decision_type_.RawData(); }\n\n const double* cuda_leaf_value() const { return cuda_leaf_value_.RawData(); }\n\n double* cuda_leaf_value_ref() { return cuda_leaf_value_.RawData(); }\n\n inline void Shrinkage(double rate) override;\n\n inline void AddBias(double val) override;\n\n void ToHost();\n\n void SyncLeafOutputFromHostToCUDA();\n\n void SyncLeafOutputFromCUDAToHost();\n\n private:\n void InitCUDAMemory();\n\n void InitCUDA();\n\n void LaunchSplitKernel(const int leaf_index,\n const int real_feature_index,\n const double real_threshold,\n const MissingType missing_type,\n const CUDASplitInfo* cuda_split_info);\n\n void LaunchSplitCategoricalKernel(\n const int leaf_index,\n const int real_feature_index,\n const MissingType missing_type,\n const CUDASplitInfo* cuda_split_info,\n size_t cuda_bitset_len,\n size_t cuda_bitset_inner_len);\n\n void LaunchAddPredictionToScoreKernel(const Dataset* data,\n const data_size_t* used_data_indices,\n data_size_t num_data, double* score) const;\n\n void LaunchShrinkageKernel(const double rate);\n\n void LaunchAddBiasKernel(const double val);\n\n void RecordBranchFeatures(const int left_leaf_index,\n const int right_leaf_index,\n const int real_feature_index);\n\n CUDAVector cuda_left_child_;\n CUDAVector cuda_right_child_;\n CUDAVector cuda_split_feature_inner_;\n CUDAVector cuda_split_feature_;\n CUDAVector cuda_leaf_depth_;\n CUDAVector cuda_leaf_parent_;\n CUDAVector cuda_threshold_in_bin_;\n CUDAVector cuda_threshold_;\n CUDAVector cuda_internal_weight_;\n CUDAVector cuda_internal_value_;\n CUDAVector cuda_decision_type_;\n CUDAVector cuda_leaf_value_;\n CUDAVector cuda_leaf_count_;\n CUDAVector cuda_leaf_weight_;\n CUDAVector cuda_internal_count_;\n CUDAVector cuda_split_gain_;\n CUDAVector cuda_bitset_;\n CUDAVector cuda_bitset_inner_;\n CUDAVector cuda_cat_boundaries_;\n CUDAVector cuda_cat_boundaries_inner_;\n\n cudaStream_t cuda_stream_;\n\n const int num_threads_per_block_add_prediction_to_score_;\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_CUDA_TREE_HPP_\n" }, { "path": "include/LightGBM/cuda/cuda_utils.hu", "content": "/*!\n * Copyright (c) 2020-2021 IBM Corporation, Microsoft Corporation. All rights reserved.\n * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifndef LIGHTGBM_CUDA_CUDA_UTILS_H_\n#define LIGHTGBM_CUDA_CUDA_UTILS_H_\n\n#ifdef USE_CUDA\n\n#if defined(USE_ROCM)\n#include \n#include \n#else\n#include \n#include \n#include \n#endif\n#include \n\n#include \n#include \n\n#include \n#include \n#include \n\nnamespace LightGBM {\n\ntypedef unsigned long long atomic_add_long_t;\n\n#define CUDASUCCESS_OR_FATAL(ans) { gpuAssert((ans), __FILE__, __LINE__); }\ninline void gpuAssert(cudaError_t code, const char *file, int line, bool abort = true) {\n if (code != cudaSuccess) {\n LightGBM::Log::Fatal(\"[CUDA] %s %s %d\\n\", cudaGetErrorString(code), file, line);\n if (abort) exit(code);\n }\n}\n\n#define CUDASUCCESS_OR_FATAL_OUTER(ans) { gpuAssert((ans), file, line); }\n\n#define NCCLCHECK(cmd) do { \\\n ncclResult_t r = cmd; \\\n if (r!= ncclSuccess) { \\\n printf(\"Failed, NCCL error %s:%d '%s'\\n\", \\\n __FILE__,__LINE__,ncclGetErrorString(r)); \\\n exit(EXIT_FAILURE); \\\n } \\\n} while(0)\n\nvoid SetCUDADevice(int gpu_device_id, const char* file, int line);\n\nint GetCUDADevice(const char* file, int line);\n\ntemplate \nvoid AllocateCUDAMemory(T** out_ptr, size_t size, const char* file, const int line) {\n void* tmp_ptr = nullptr;\n CUDASUCCESS_OR_FATAL_OUTER(cudaMalloc(&tmp_ptr, size * sizeof(T)));\n *out_ptr = reinterpret_cast(tmp_ptr);\n}\n\ntemplate \nvoid CopyFromHostToCUDADevice(T* dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) {\n void* void_dst_ptr = reinterpret_cast(dst_ptr);\n const void* void_src_ptr = reinterpret_cast(src_ptr);\n size_t size_in_bytes = size * sizeof(T);\n CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpy(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyHostToDevice));\n}\n\ntemplate \nvoid InitCUDAMemoryFromHostMemory(T** dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) {\n AllocateCUDAMemory(dst_ptr, size, file, line);\n CopyFromHostToCUDADevice(*dst_ptr, src_ptr, size, file, line);\n}\n\ntemplate \nvoid CopyFromCUDADeviceToHost(T* dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) {\n void* void_dst_ptr = reinterpret_cast(dst_ptr);\n const void* void_src_ptr = reinterpret_cast(src_ptr);\n size_t size_in_bytes = size * sizeof(T);\n CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpy(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyDeviceToHost));\n}\n\ntemplate \nvoid CopyFromCUDADeviceToHostAsync(T* dst_ptr, const T* src_ptr, size_t size, cudaStream_t stream, const char* file, const int line) {\n void* void_dst_ptr = reinterpret_cast(dst_ptr);\n const void* void_src_ptr = reinterpret_cast(src_ptr);\n size_t size_in_bytes = size * sizeof(T);\n CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpyAsync(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyDeviceToHost, stream));\n}\n\ntemplate \nvoid CopyFromCUDADeviceToCUDADevice(T* dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) {\n void* void_dst_ptr = reinterpret_cast(dst_ptr);\n const void* void_src_ptr = reinterpret_cast(src_ptr);\n size_t size_in_bytes = size * sizeof(T);\n CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpy(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyDeviceToDevice));\n}\n\ntemplate \nvoid CopyFromCUDADeviceToCUDADeviceAsync(T* dst_ptr, const T* src_ptr, size_t size, const char* file, const int line) {\n void* void_dst_ptr = reinterpret_cast(dst_ptr);\n const void* void_src_ptr = reinterpret_cast(src_ptr);\n size_t size_in_bytes = size * sizeof(T);\n CUDASUCCESS_OR_FATAL_OUTER(cudaMemcpyAsync(void_dst_ptr, void_src_ptr, size_in_bytes, cudaMemcpyDeviceToDevice));\n}\n\nvoid SynchronizeCUDADevice(const char* file, const int line);\n\nvoid SynchronizeCUDAStream(cudaStream_t cuda_stream, const char* file, const int line);\n\ntemplate \nvoid SetCUDAMemory(T* dst_ptr, int value, size_t size, const char* file, const int line) {\n CUDASUCCESS_OR_FATAL_OUTER(cudaMemset(reinterpret_cast(dst_ptr), value, size * sizeof(T)));\n SynchronizeCUDADevice(file, line);\n}\n\ntemplate \nvoid DeallocateCUDAMemory(T** ptr, const char* file, const int line) {\n if (*ptr != nullptr) {\n CUDASUCCESS_OR_FATAL_OUTER(cudaFree(reinterpret_cast(*ptr)));\n *ptr = nullptr;\n }\n}\n\nvoid PrintLastCUDAError();\n\ntemplate \nclass CUDAVector {\n public:\n CUDAVector() {\n size_ = 0;\n data_ = nullptr;\n }\n\n explicit CUDAVector(size_t size) {\n size_ = size;\n AllocateCUDAMemory(&data_, size_, __FILE__, __LINE__);\n }\n\n void Resize(size_t size) {\n if (size == size_) {\n return;\n }\n if (size == 0) {\n Clear();\n return;\n }\n T* new_data = nullptr;\n AllocateCUDAMemory(&new_data, size, __FILE__, __LINE__);\n if (size_ > 0 && data_ != nullptr) {\n const size_t size_for_old_content = std::min(size_, size);\n CopyFromCUDADeviceToCUDADevice(new_data, data_, size_for_old_content, __FILE__, __LINE__);\n }\n DeallocateCUDAMemory(&data_, __FILE__, __LINE__);\n data_ = new_data;\n size_ = size;\n }\n\n void InitFromHostVector(const std::vector& host_vector) {\n Resize(host_vector.size());\n CopyFromHostToCUDADevice(data_, host_vector.data(), host_vector.size(), __FILE__, __LINE__);\n }\n\n void InitFromHostMemory(const T* host_memory, size_t len) {\n Resize(len);\n CopyFromHostToCUDADevice(data_, host_memory, len, __FILE__, __LINE__);\n }\n\n void Clear() {\n if (size_ > 0 && data_ != nullptr) {\n DeallocateCUDAMemory(&data_, __FILE__, __LINE__);\n }\n size_ = 0;\n }\n\n void PushBack(const T* values, size_t len) {\n T* new_data = nullptr;\n AllocateCUDAMemory(&new_data, size_ + len, __FILE__, __LINE__);\n if (size_ > 0 && data_ != nullptr) {\n CopyFromCUDADeviceToCUDADevice(new_data, data_, size_, __FILE__, __LINE__);\n }\n CopyFromCUDADeviceToCUDADevice(new_data + size_, values, len, __FILE__, __LINE__);\n DeallocateCUDAMemory(&data_, __FILE__, __LINE__);\n size_ += len;\n data_ = new_data;\n }\n\n size_t Size() const {\n return size_;\n }\n\n ~CUDAVector() {\n DeallocateCUDAMemory(&data_, __FILE__, __LINE__);\n }\n\n std::vector ToHost() {\n std::vector host_vector(size_);\n if (size_ > 0 && data_ != nullptr) {\n CopyFromCUDADeviceToHost(host_vector.data(), data_, size_, __FILE__, __LINE__);\n }\n return host_vector;\n }\n\n T* RawData() const {\n return data_;\n }\n\n void SetValue(int value) {\n SetCUDAMemory(data_, value, size_, __FILE__, __LINE__);\n }\n\n const T* RawDataReadOnly() const {\n return data_;\n }\n\n T* MoveTo() {\n size_ = 0;\n T* old_data = data_;\n data_ = nullptr;\n return old_data;\n }\n\n template \n void MoveFrom(CUDAVector& other, size_t new_size) {\n data_ = reinterpret_cast(other.MoveTo());\n size_ = new_size;\n }\n\n private:\n T* data_;\n size_t size_;\n};\n\ntemplate \nstatic __device__ T SafeLog(T x) {\n if (x > 0) {\n return std::log(x);\n } else {\n return -INFINITY;\n }\n}\n\nclass NCCLInfo {\n public:\n NCCLInfo() {\n nccl_communicator_ = nullptr;\n nccl_gpu_rank_ = -1;\n local_gpu_rank_ = -1;\n gpu_device_id_ = -1;\n num_gpu_in_node_ = 0;\n global_num_data_ = 0;\n }\n\n virtual void SetNCCLInfo(\n ncclComm_t nccl_communicator,\n int nccl_gpu_rank,\n int local_gpu_rank,\n int gpu_device_id,\n data_size_t global_num_data) {\n nccl_communicator_ = nccl_communicator;\n nccl_gpu_rank_ = nccl_gpu_rank;\n local_gpu_rank_ = local_gpu_rank;\n gpu_device_id_ = gpu_device_id;\n global_num_data_ = global_num_data;\n }\n\n protected:\n ncclComm_t nccl_communicator_ = nullptr;\n int nccl_gpu_rank_ = -1;\n int local_gpu_rank_ = -1;\n int gpu_device_id_ = -1;\n int num_gpu_in_node_ = 0;\n data_size_t global_num_data_ = 0;\n};\n\ncudaStream_t CUDAStreamCreate();\n\nvoid CUDAStreamDestroy(cudaStream_t cuda_stream);\n\nvoid NCCLGroupStart();\n\nvoid NCCLGroupEnd();\n\ntemplate \nvoid NCCLAllReduce(const T* send_buffer, T* recv_buffer, size_t count, ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm, cudaStream_t stream) {\n NCCLCHECK(ncclAllReduce(reinterpret_cast(send_buffer), reinterpret_cast(recv_buffer), count, datatype, op, comm, stream));\n}\n\ntemplate \nvoid NCCLAllReduce(const T* send_buffer, T* recv_buffer, size_t count, ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm) {\n cudaStream_t nccl_stream;\n CUDASUCCESS_OR_FATAL(cudaStreamCreate(&nccl_stream));\n NCCLCHECK(ncclAllReduce(reinterpret_cast(send_buffer), reinterpret_cast(recv_buffer), count, datatype, op, comm, nccl_stream));\n CUDASUCCESS_OR_FATAL(cudaStreamSynchronize(nccl_stream));\n CUDASUCCESS_OR_FATAL(cudaStreamDestroy(nccl_stream));\n}\n\ntemplate \nT NCCLAllReduce(T send_value, ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm, cudaStream_t stream) {\n CUDAVector send_buffer(1);\n CopyFromHostToCUDADevice(send_buffer.RawData(), &send_value, 1, __FILE__, __LINE__);\n NCCLAllReduce(send_buffer.RawDataReadOnly(), send_buffer.RawData(), 1, datatype, op, comm, stream);\n T recv_value = 0;\n CopyFromCUDADeviceToHost(&recv_value, send_buffer.RawDataReadOnly(), 1, __FILE__, __LINE__);\n return recv_value;\n}\n\ntemplate \nT NCCLAllReduce(T send_value, ncclDataType_t datatype, ncclRedOp_t op, ncclComm_t comm) {\n CUDAVector send_buffer(1);\n CopyFromHostToCUDADevice(send_buffer.RawData(), &send_value, 1, __FILE__, __LINE__);\n NCCLAllReduce(send_buffer.RawDataReadOnly(), send_buffer.RawData(), 1, datatype, op, comm);\n T recv_value = 0;\n CopyFromCUDADeviceToHost(&recv_value, send_buffer.RawDataReadOnly(), 1, __FILE__, __LINE__);\n return recv_value;\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_CUDA_CUDA_UTILS_H_\n" }, { "path": "include/LightGBM/cuda/vector_cudahost.h", "content": "/*!\n * Copyright (c) 2020-2021 IBM Corporation, Microsoft Corporation. All rights reserved.\n * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_VECTOR_CUDAHOST_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_VECTOR_CUDAHOST_H_\n\n#include \n\n#ifdef USE_CUDA\n#ifndef USE_ROCM\n#include \n#include \n#endif // USE_ROCM\n#include \n#endif // USE_CUDA\n#include \n\nenum LGBM_Device {\n lgbm_device_cpu,\n lgbm_device_gpu,\n lgbm_device_cuda\n};\n\nenum Use_Learner {\n use_cpu_learner,\n use_gpu_learner,\n use_cuda_learner\n};\n\nnamespace LightGBM {\n\nclass LGBM_config_ {\n public:\n static int current_device; // Default: lgbm_device_cpu\n static int current_learner; // Default: use_cpu_learner\n};\n\n\ntemplate \nstruct CHAllocator {\n typedef T value_type;\n CHAllocator() {}\n template CHAllocator(const CHAllocator& other);\n T* allocate(std::size_t n) {\n T* ptr;\n if (n == 0) return NULL;\n n = SIZE_ALIGNED(n);\n #ifdef USE_CUDA\n if (LGBM_config_::current_device == lgbm_device_cuda) {\n cudaError_t ret = cudaHostAlloc(reinterpret_cast(&ptr), n*sizeof(T), cudaHostAllocPortable);\n if (ret != cudaSuccess) {\n Log::Warning(\"Defaulting to malloc in CHAllocator!!!\");\n ptr = reinterpret_cast(_mm_malloc(n*sizeof(T), 16));\n }\n } else {\n ptr = reinterpret_cast(_mm_malloc(n*sizeof(T), 16));\n }\n #else\n ptr = reinterpret_cast(_mm_malloc(n*sizeof(T), 16));\n #endif\n return ptr;\n }\n\n void deallocate(T* p, std::size_t n) {\n (void)n; // UNUSED\n if (p == NULL) return;\n #ifdef USE_CUDA\n if (LGBM_config_::current_device == lgbm_device_cuda) {\n cudaPointerAttributes attributes;\n CUDASUCCESS_OR_FATAL(cudaPointerGetAttributes(&attributes, p));\n #if CUDA_VERSION >= 10000 || defined(USE_ROCM)\n if ((attributes.type == cudaMemoryTypeHost) && (attributes.devicePointer != NULL)) {\n CUDASUCCESS_OR_FATAL(cudaFreeHost(p));\n }\n #else\n if ((attributes.memoryType == cudaMemoryTypeHost) && (attributes.devicePointer != NULL)) {\n CUDASUCCESS_OR_FATAL(cudaFreeHost(p));\n }\n #endif\n } else {\n _mm_free(p);\n }\n #else\n _mm_free(p);\n #endif\n }\n};\ntemplate \nbool operator==(const CHAllocator&, const CHAllocator&);\ntemplate \nbool operator!=(const CHAllocator&, const CHAllocator&);\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_CUDA_VECTOR_CUDAHOST_H_\n" }, { "path": "include/LightGBM/dataset.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\nnamespace LightGBM {\n\n/*! \\brief forward declaration */\nclass DatasetLoader;\n/*!\n* \\brief This class is used to store some meta(non-feature) data for training data,\n* e.g. labels, weights, initial scores, query level information.\n*\n* Some details:\n* 1. Label, used for training.\n* 2. Weights, weighs of records, optional\n* 3. Query Boundaries, necessary for LambdaRank.\n* The documents of i-th query is in [ query_boundaries[i], query_boundaries[i+1] )\n* 4. Query Weights, auto calculate by weights and query_boundaries(if both of them are existed)\n* the weight for i-th query is sum(query_boundaries[i] , .., query_boundaries[i+1]) / (query_boundaries[i + 1] - query_boundaries[i+1])\n* 5. Initial score. optional. if existing, the model will boost from this score, otherwise will start from 0.\n*/\nclass Metadata {\n public:\n /*!\n * \\brief Null constructor\n */\n Metadata();\n /*!\n * \\brief Initialization will load query level information, since it is need for sampling data\n * \\param data_filename Filename of data\n */\n void Init(const char* data_filename);\n /*!\n * \\brief init as subset\n * \\param metadata Filename of data\n * \\param used_indices\n * \\param num_used_indices\n */\n void Init(const Metadata& metadata, const data_size_t* used_indices, data_size_t num_used_indices);\n /*!\n * \\brief Initial with binary memory\n * \\param memory Pointer to memory\n */\n void LoadFromMemory(const void* memory);\n /*! \\brief Destructor */\n ~Metadata();\n\n /*!\n * \\brief Initial work, will allocate space for label, weight (if exists) and query (if exists)\n * \\param num_data Number of training data\n * \\param weight_idx Index of weight column, < 0 means doesn't exists\n * \\param query_idx Index of query id column, < 0 means doesn't exists\n */\n void Init(data_size_t num_data, int weight_idx, int query_idx);\n\n /*!\n * \\brief Allocate space for label, weight (if exists), initial score (if exists) and query (if exists)\n * \\param num_data Number of data\n * \\param reference Reference metadata\n */\n void InitByReference(data_size_t num_data, const Metadata* reference);\n\n /*!\n * \\brief Allocate space for label, weight (if exists), initial score (if exists) and query (if exists)\n * \\param num_data Number of data rows\n * \\param has_weights Whether the metadata has weights\n * \\param has_init_scores Whether the metadata has initial scores\n * \\param has_queries Whether the metadata has queries\n * \\param nclasses Number of classes for initial scores\n */\n void Init(data_size_t num_data, int32_t has_weights, int32_t has_init_scores, int32_t has_queries, int32_t nclasses);\n\n /*!\n * \\brief Partition label by used indices\n * \\param used_indices Indices of local used\n */\n void PartitionLabel(const std::vector& used_indices);\n\n /*!\n * \\brief Partition meta data according to local used indices if need\n * \\param num_all_data Number of total training data, including other machines' data on distributed learning\n * \\param used_data_indices Indices of local used training data\n */\n void CheckOrPartition(data_size_t num_all_data,\n const std::vector& used_data_indices);\n\n void SetLabel(const label_t* label, data_size_t len);\n void SetLabel(const ArrowChunkedArray& array);\n\n void SetWeights(const label_t* weights, data_size_t len);\n void SetWeights(const ArrowChunkedArray& array);\n\n void SetQuery(const data_size_t* query, data_size_t len);\n void SetQuery(const ArrowChunkedArray& array);\n\n void SetPosition(const data_size_t* position, data_size_t len);\n\n /*!\n * \\brief Set initial scores\n * \\param init_score Initial scores, this class will manage memory for init_score.\n */\n void SetInitScore(const double* init_score, data_size_t len);\n void SetInitScore(const ArrowChunkedArray& array);\n\n\n /*!\n * \\brief Save binary data to file\n * \\param file File want to write\n */\n void SaveBinaryToFile(BinaryWriter* writer) const;\n\n /*!\n * \\brief Get sizes in byte of this object\n */\n size_t SizesInByte() const;\n\n /*!\n * \\brief Get pointer of label\n * \\return Pointer of label\n */\n inline const label_t* label() const { return label_.data(); }\n\n /*!\n * \\brief Set label for one record\n * \\param idx Index of this record\n * \\param value Label value of this record\n */\n inline void SetLabelAt(data_size_t idx, label_t value) {\n label_[idx] = value;\n }\n\n /*!\n * \\brief Set Weight for one record\n * \\param idx Index of this record\n * \\param value Weight value of this record\n */\n inline void SetWeightAt(data_size_t idx, label_t value) {\n weights_[idx] = value;\n }\n\n /*!\n * \\brief Set initial scores for one record. Note that init_score might have multiple columns and is stored in column format.\n * \\param idx Index of this record\n * \\param values Initial score values for this record, one per class\n */\n inline void SetInitScoreAt(data_size_t idx, const double* values) {\n const auto nclasses = num_init_score_classes();\n const double* val_ptr = values;\n for (int i = idx; i < nclasses * num_data_; i += num_data_, ++val_ptr) {\n init_score_[i] = *val_ptr;\n }\n }\n\n /*!\n * \\brief Set Query Id for one record\n * \\param idx Index of this record\n * \\param value Query Id value of this record\n */\n inline void SetQueryAt(data_size_t idx, data_size_t value) {\n queries_[idx] = static_cast(value);\n }\n\n /*! \\brief Load initial scores from file */\n void LoadInitialScore(const std::string& data_filename);\n\n /*!\n * \\brief Insert data from a given data to the current data at a specified index\n * \\param start_index The target index to begin the insertion\n * \\param count Number of records to insert\n * \\param labels Pointer to label data\n * \\param weights Pointer to weight data, or null\n * \\param init_scores Pointer to init-score data, or null\n * \\param queries Pointer to query data, or null\n */\n void InsertAt(data_size_t start_index,\n data_size_t count,\n const float* labels,\n const float* weights,\n const double* init_scores,\n const int32_t* queries);\n\n /*!\n * \\brief Perform any extra operations after all data has been loaded\n */\n void FinishLoad();\n /*!\n * \\brief Get weights, if not exists, will return nullptr\n * \\return Pointer of weights\n */\n inline const label_t* weights() const {\n if (!weights_.empty()) {\n return weights_.data();\n } else {\n return nullptr;\n }\n }\n\n /*!\n * \\brief Get positions, if does not exist then return nullptr\n * \\return Pointer of positions\n */\n inline const data_size_t* positions() const {\n if (!positions_.empty()) {\n return positions_.data();\n } else {\n return nullptr;\n }\n }\n\n /*!\n * \\brief Get position IDs, if does not exist then return nullptr\n * \\return Pointer of position IDs\n */\n inline const std::string* position_ids() const {\n if (!position_ids_.empty()) {\n return position_ids_.data();\n } else {\n return nullptr;\n }\n }\n\n /*!\n * \\brief Get Number of different position IDs\n * \\return number of different position IDs\n */\n inline size_t num_position_ids() const {\n return position_ids_.size();\n }\n\n /*!\n * \\brief Get data boundaries on queries, if not exists, will return nullptr\n * we assume data will order by query,\n * the interval of [query_boundaris[i], query_boundaris[i+1])\n * is the data indices for query i.\n * \\return Pointer of data boundaries on queries\n */\n inline const data_size_t* query_boundaries() const {\n if (!query_boundaries_.empty()) {\n return query_boundaries_.data();\n } else {\n return nullptr;\n }\n }\n\n /*!\n * \\brief Get Number of queries\n * \\return Number of queries\n */\n inline data_size_t num_queries() const { return num_queries_; }\n\n /*!\n * \\brief Get weights for queries, if not exists, will return nullptr\n * \\return Pointer of weights for queries\n */\n inline const label_t* query_weights() const {\n if (!query_weights_.empty()) {\n return query_weights_.data();\n } else {\n return nullptr;\n }\n }\n\n /*!\n * \\brief Get initial scores, if not exists, will return nullptr\n * \\return Pointer of initial scores\n */\n inline const double* init_score() const {\n if (!init_score_.empty()) {\n return init_score_.data();\n } else {\n return nullptr;\n }\n }\n\n /*!\n * \\brief Get size of initial scores\n */\n inline int64_t num_init_score() const { return num_init_score_; }\n\n /*!\n * \\brief Get number of classes\n */\n inline int32_t num_init_score_classes() const {\n if (num_data_ && num_init_score_) {\n return static_cast(num_init_score_ / num_data_);\n }\n return 1;\n }\n\n /*! \\brief Disable copy */\n Metadata& operator=(const Metadata&) = delete;\n /*! \\brief Disable copy */\n Metadata(const Metadata&) = delete;\n\n #ifdef USE_CUDA\n\n CUDAMetadata* cuda_metadata() const { return cuda_metadata_.get(); }\n\n void CreateCUDAMetadata(const int gpu_device_id);\n\n #endif // USE_CUDA\n\n private:\n /*! \\brief Load wights from file */\n void LoadWeights();\n /*! \\brief Load positions from file */\n void LoadPositions();\n /*! \\brief Load query boundaries from file */\n void LoadQueryBoundaries();\n /*! \\brief Calculate query weights from queries */\n void CalculateQueryWeights();\n /*! \\brief Calculate query boundaries from queries */\n void CalculateQueryBoundaries();\n /*! \\brief Insert labels at the given index */\n void InsertLabels(const label_t* labels, data_size_t start_index, data_size_t len);\n /*! \\brief Set labels from pointers to the first element and the end of an iterator. */\n template \n void SetLabelsFromIterator(It first, It last);\n /*! \\brief Insert weights at the given index */\n void InsertWeights(const label_t* weights, data_size_t start_index, data_size_t len);\n /*! \\brief Set weights from pointers to the first element and the end of an iterator. */\n template \n void SetWeightsFromIterator(It first, It last);\n /*! \\brief Insert initial scores at the given index */\n void InsertInitScores(const double* init_scores, data_size_t start_index, data_size_t len, data_size_t source_size);\n /*! \\brief Set init scores from pointers to the first element and the end of an iterator. */\n template \n void SetInitScoresFromIterator(It first, It last);\n /*! \\brief Insert queries at the given index */\n void InsertQueries(const data_size_t* queries, data_size_t start_index, data_size_t len);\n /*! \\brief Set queries from pointers to the first element and the end of an iterator. */\n template \n void SetQueriesFromIterator(It first, It last);\n /*! \\brief Filename of current data */\n std::string data_filename_;\n /*! \\brief Number of data */\n data_size_t num_data_;\n /*! \\brief Number of weights, used to check correct weight file */\n data_size_t num_weights_;\n /*! \\brief Number of positions, used to check correct position file */\n data_size_t num_positions_;\n /*! \\brief Label data */\n std::vector label_;\n /*! \\brief Weights data */\n std::vector weights_;\n /*! \\brief Positions data */\n std::vector positions_;\n /*! \\brief Position identifiers */\n std::vector position_ids_;\n /*! \\brief Query boundaries */\n std::vector query_boundaries_;\n /*! \\brief Query weights */\n std::vector query_weights_;\n /*! \\brief Number of queries */\n data_size_t num_queries_;\n /*! \\brief Number of Initial score, used to check correct weight file */\n int64_t num_init_score_;\n /*! \\brief Initial score */\n std::vector init_score_;\n /*! \\brief Queries data */\n std::vector queries_;\n /*! \\brief mutex for threading safe call */\n std::mutex mutex_;\n bool weight_load_from_file_;\n bool position_load_from_file_;\n bool query_load_from_file_;\n bool init_score_load_from_file_;\n #ifdef USE_CUDA\n std::unique_ptr cuda_metadata_;\n #endif // USE_CUDA\n};\n\n\n/*! \\brief Interface for Parser */\nclass Parser {\n public:\n typedef const char* (*AtofFunc)(const char* p, double* out);\n\n /*! \\brief Default constructor */\n Parser() {}\n\n /*!\n * \\brief Constructor for customized parser. The constructor accepts content not path because need to save/load the config along with model string\n */\n explicit Parser(std::string) {}\n\n /*! \\brief virtual destructor */\n virtual ~Parser() {}\n\n /*!\n * \\brief Parse one line with label\n * \\param str One line record, string format, should end with '\\0'\n * \\param out_features Output columns, store in (column_idx, values)\n * \\param out_label Label will store to this if exists\n */\n virtual void ParseOneLine(const char* str,\n std::vector>* out_features, double* out_label) const = 0;\n\n virtual int NumFeatures() const = 0;\n\n /*!\n * \\brief Create an object of parser, will auto choose the format depend on file\n * \\param filename One Filename of data\n * \\param header whether input file contains header\n * \\param num_features Pass num_features of this data file if you know, <=0 means don't know\n * \\param label_idx index of label column\n * \\param precise_float_parser using precise floating point number parsing if true\n * \\return Object of parser\n */\n static Parser* CreateParser(const char* filename, bool header, int num_features, int label_idx, bool precise_float_parser);\n\n /*!\n * \\brief Create an object of parser, could use customized parser, or auto choose the format depend on file\n * \\param filename One Filename of data\n * \\param header whether input file contains header\n * \\param num_features Pass num_features of this data file if you know, <=0 means don't know\n * \\param label_idx index of label column\n * \\param precise_float_parser using precise floating point number parsing if true\n * \\param parser_config_str Customized parser config content\n * \\return Object of parser\n */\n static Parser* CreateParser(const char* filename, bool header, int num_features, int label_idx, bool precise_float_parser,\n std::string parser_config_str);\n\n /*!\n * \\brief Generate parser config str used for custom parser initialization, may save values of label id and header\n * \\param filename One Filename of data\n * \\param parser_config_filename One Filename of parser config\n * \\param header whether input file contains header\n * \\param label_idx index of label column\n * \\return Parser config str\n */\n static std::string GenerateParserConfigStr(const char* filename, const char* parser_config_filename, bool header, int label_idx);\n};\n\n/*! \\brief Interface for parser factory, used by customized parser */\nclass ParserFactory {\n private:\n ParserFactory() {}\n std::map> object_map_;\n\n public:\n ~ParserFactory() {}\n static ParserFactory& getInstance();\n void Register(std::string class_name, std::function objc);\n Parser* getObject(std::string class_name, std::string config_str);\n};\n\n/*! \\brief Interface for parser reflector, used by customized parser */\nclass ParserReflector {\n public:\n ParserReflector(std::string class_name, std::function objc) {\n ParserFactory::getInstance().Register(class_name, objc);\n }\n virtual ~ParserReflector() {}\n};\n\n/*! \\brief The main class of data set,\n* which are used to training or validation\n*/\nclass Dataset {\n public:\n friend DatasetLoader;\n\n LIGHTGBM_EXPORT Dataset();\n\n LIGHTGBM_EXPORT Dataset(data_size_t num_data);\n\n void Construct(\n std::vector>* bin_mappers,\n int num_total_features,\n const std::vector>& forced_bins,\n int** sample_non_zero_indices,\n double** sample_values,\n const int* num_per_col,\n int num_sample_col,\n size_t total_sample_cnt,\n const Config& io_config);\n\n /*! \\brief Destructor */\n LIGHTGBM_EXPORT ~Dataset();\n\n /*!\n * \\brief Initialize from the given reference\n * \\param num_data Number of data\n * \\param reference Reference dataset\n */\n LIGHTGBM_EXPORT void InitByReference(data_size_t num_data, const Dataset* reference) {\n metadata_.InitByReference(num_data, &reference->metadata());\n }\n\n LIGHTGBM_EXPORT void InitStreaming(data_size_t num_data,\n int32_t has_weights,\n int32_t has_init_scores,\n int32_t has_queries,\n int32_t nclasses,\n int32_t nthreads,\n int32_t omp_max_threads) {\n // Initialize optional max thread count with either parameter or OMP setting\n if (omp_max_threads > 0) {\n omp_max_threads_ = omp_max_threads;\n } else if (omp_max_threads_ <= 0) {\n omp_max_threads_ = OMP_NUM_THREADS();\n }\n\n metadata_.Init(num_data, has_weights, has_init_scores, has_queries, nclasses);\n for (int i = 0; i < num_groups_; ++i) {\n feature_groups_[i]->InitStreaming(nthreads, omp_max_threads_);\n }\n }\n\n LIGHTGBM_EXPORT bool CheckAlign(const Dataset& other) const {\n if (num_features_ != other.num_features_) {\n return false;\n }\n if (num_total_features_ != other.num_total_features_) {\n return false;\n }\n if (label_idx_ != other.label_idx_) {\n return false;\n }\n for (int i = 0; i < num_features_; ++i) {\n if (!FeatureBinMapper(i)->CheckAlign(*(other.FeatureBinMapper(i)))) {\n return false;\n }\n }\n return true;\n }\n\n inline void FinishOneRow(int tid, data_size_t row_idx, const std::vector& is_feature_added) {\n if (is_finish_load_) {\n return;\n }\n for (auto fidx : feature_need_push_zeros_) {\n if (is_feature_added[fidx]) {\n continue;\n }\n const int group = feature2group_[fidx];\n const int sub_feature = feature2subfeature_[fidx];\n feature_groups_[group]->PushData(tid, sub_feature, row_idx, 0.0f);\n }\n }\n\n inline void PushOneValue(int tid, data_size_t row_idx, size_t col_idx, double value) {\n if (this->is_finish_load_)\n return;\n auto feature_idx = this->used_feature_map_[col_idx];\n if (feature_idx >= 0) {\n auto group = this->feature2group_[feature_idx];\n auto sub_feature = this->feature2subfeature_[feature_idx];\n this->feature_groups_[group]->PushData(tid, sub_feature, row_idx, value);\n if (this->has_raw_) {\n auto feat_ind = numeric_feature_map_[feature_idx];\n if (feat_ind >= 0) {\n raw_data_[feat_ind][row_idx] = static_cast(value);\n }\n }\n }\n }\n\n inline void PushOneRow(int tid, data_size_t row_idx, const std::vector& feature_values) {\n for (size_t i = 0; i < feature_values.size() && i < static_cast(num_total_features_); ++i) {\n this->PushOneValue(tid, row_idx, i, feature_values[i]);\n }\n }\n\n inline void PushOneRow(int tid, data_size_t row_idx, const std::vector>& feature_values) {\n if (is_finish_load_) {\n return;\n }\n std::vector is_feature_added(num_features_, false);\n for (auto& inner_data : feature_values) {\n if (inner_data.first >= num_total_features_) {\n continue;\n }\n int feature_idx = used_feature_map_[inner_data.first];\n if (feature_idx >= 0) {\n is_feature_added[feature_idx] = true;\n const int group = feature2group_[feature_idx];\n const int sub_feature = feature2subfeature_[feature_idx];\n feature_groups_[group]->PushData(tid, sub_feature, row_idx, inner_data.second);\n if (has_raw_) {\n int feat_ind = numeric_feature_map_[feature_idx];\n if (feat_ind >= 0) {\n raw_data_[feat_ind][row_idx] = static_cast(inner_data.second);\n }\n }\n }\n }\n FinishOneRow(tid, row_idx, is_feature_added);\n }\n\n inline void PushOneData(int tid, data_size_t row_idx, int group, int feature_idx, int sub_feature, double value) {\n feature_groups_[group]->PushData(tid, sub_feature, row_idx, value);\n if (has_raw_) {\n int feat_ind = numeric_feature_map_[feature_idx];\n if (feat_ind >= 0) {\n raw_data_[feat_ind][row_idx] = static_cast(value);\n }\n }\n }\n\n inline void InsertMetadataAt(data_size_t start_index,\n data_size_t count,\n const label_t* labels,\n const label_t* weights,\n const double* init_scores,\n const data_size_t* queries) {\n metadata_.InsertAt(start_index, count, labels, weights, init_scores, queries);\n }\n\n inline int RealFeatureIndex(int fidx) const {\n return real_feature_idx_[fidx];\n }\n\n inline int InnerFeatureIndex(int col_idx) const {\n return used_feature_map_[col_idx];\n }\n inline int Feature2Group(int feature_idx) const {\n return feature2group_[feature_idx];\n }\n inline int Feature2SubFeature(int feature_idx) const {\n return feature2subfeature_[feature_idx];\n }\n inline uint64_t GroupBinBoundary(int group_idx) const {\n return group_bin_boundaries_[group_idx];\n }\n inline uint64_t NumTotalBin() const {\n return group_bin_boundaries_.back();\n }\n\n inline std::vector ValidFeatureIndices() const {\n std::vector ret;\n for (int i = 0; i < num_total_features_; ++i) {\n if (used_feature_map_[i] >= 0) {\n ret.push_back(i);\n }\n }\n return ret;\n }\n void ReSize(data_size_t num_data);\n\n void CopySubrow(const Dataset* fullset, const data_size_t* used_indices, data_size_t num_used_indices, bool need_meta_data);\n\n void CopySubrowToDevice(const Dataset* fullset, const data_size_t* used_indices, data_size_t num_used_indices, bool need_meta_data, int gpu_device_id);\n\n MultiValBin* GetMultiBinFromSparseFeatures(const std::vector& offsets) const;\n\n MultiValBin* GetMultiBinFromAllFeatures(const std::vector& offsets) const;\n\n template \n TrainingShareStates* GetShareStates(\n score_t* gradients, score_t* hessians,\n const std::vector& is_feature_used, bool is_constant_hessian,\n bool force_col_wise, bool force_row_wise, const int num_grad_quant_bins) const;\n\n LIGHTGBM_EXPORT void FinishLoad();\n\n bool SetFieldFromArrow(const char* field_name, const ArrowChunkedArray& ca);\n\n LIGHTGBM_EXPORT bool SetFloatField(const char* field_name, const float* field_data, data_size_t num_element);\n\n LIGHTGBM_EXPORT bool SetDoubleField(const char* field_name, const double* field_data, data_size_t num_element);\n\n LIGHTGBM_EXPORT bool SetIntField(const char* field_name, const int* field_data, data_size_t num_element);\n\n LIGHTGBM_EXPORT bool GetFloatField(const char* field_name, data_size_t* out_len, const float** out_ptr);\n\n LIGHTGBM_EXPORT bool GetDoubleField(const char* field_name, data_size_t* out_len, const double** out_ptr);\n\n LIGHTGBM_EXPORT bool GetIntField(const char* field_name, data_size_t* out_len, const int** out_ptr);\n\n /*!\n * \\brief Save current dataset into binary file, will save to \"filename.bin\"\n */\n LIGHTGBM_EXPORT void SaveBinaryFile(const char* bin_filename);\n\n /*!\n * \\brief Serialize the overall Dataset definition/schema to a binary buffer (i.e., without data)\n */\n LIGHTGBM_EXPORT void SerializeReference(ByteBuffer* out);\n\n LIGHTGBM_EXPORT void DumpTextFile(const char* text_filename);\n\n LIGHTGBM_EXPORT void CopyFeatureMapperFrom(const Dataset* dataset);\n\n LIGHTGBM_EXPORT void CreateValid(const Dataset* dataset);\n\n void InitTrain(const std::vector& is_feature_used,\n TrainingShareStates* share_state) const;\n\n template \n void ConstructHistogramsInner(const std::vector& is_feature_used,\n const data_size_t* data_indices,\n data_size_t num_data, const score_t* gradients,\n const score_t* hessians,\n score_t* ordered_gradients,\n score_t* ordered_hessians,\n TrainingShareStates* share_state,\n hist_t* hist_data) const;\n\n template \n void ConstructHistogramsMultiVal(const data_size_t* data_indices,\n data_size_t num_data,\n const score_t* gradients,\n const score_t* hessians,\n TrainingShareStates* share_state,\n hist_t* hist_data) const;\n\n template \n inline void ConstructHistograms(\n const std::vector& is_feature_used,\n const data_size_t* data_indices, data_size_t num_data,\n const score_t* gradients, const score_t* hessians,\n score_t* ordered_gradients, score_t* ordered_hessians,\n TrainingShareStates* share_state, hist_t* hist_data) const {\n if (num_data <= 0) {\n return;\n }\n bool use_indices = data_indices != nullptr && (num_data < num_data_);\n if (share_state->is_constant_hessian) {\n if (use_indices) {\n ConstructHistogramsInner(\n is_feature_used, data_indices, num_data, gradients, hessians,\n ordered_gradients, ordered_hessians, share_state, hist_data);\n } else {\n ConstructHistogramsInner(\n is_feature_used, data_indices, num_data, gradients, hessians,\n ordered_gradients, ordered_hessians, share_state, hist_data);\n }\n } else {\n if (use_indices) {\n ConstructHistogramsInner(\n is_feature_used, data_indices, num_data, gradients, hessians,\n ordered_gradients, ordered_hessians, share_state, hist_data);\n } else {\n ConstructHistogramsInner(\n is_feature_used, data_indices, num_data, gradients, hessians,\n ordered_gradients, ordered_hessians, share_state, hist_data);\n }\n }\n }\n\n void FixHistogram(int feature_idx, double sum_gradient, double sum_hessian, hist_t* data) const;\n\n template \n void FixHistogramInt(int feature_idx, int64_t sum_gradient_and_hessian, hist_t* data) const;\n\n inline data_size_t Split(int feature, const uint32_t* threshold,\n int num_threshold, bool default_left,\n const data_size_t* data_indices,\n data_size_t cnt, data_size_t* lte_indices,\n data_size_t* gt_indices) const {\n const int group = feature2group_[feature];\n const int sub_feature = feature2subfeature_[feature];\n return feature_groups_[group]->Split(\n sub_feature, threshold, num_threshold, default_left, data_indices,\n cnt, lte_indices, gt_indices);\n }\n\n inline int SubFeatureBinOffset(int i) const {\n const int sub_feature = feature2subfeature_[i];\n if (sub_feature == 0) {\n return 1;\n } else {\n return 0;\n }\n }\n\n inline int FeatureNumBin(int i) const {\n const int group = feature2group_[i];\n const int sub_feature = feature2subfeature_[i];\n return feature_groups_[group]->bin_mappers_[sub_feature]->num_bin();\n }\n\n inline int FeatureGroupNumBin(int group) const {\n return feature_groups_[group]->num_total_bin_;\n }\n\n inline const BinMapper* FeatureBinMapper(int i) const {\n const int group = feature2group_[i];\n const int sub_feature = feature2subfeature_[i];\n return feature_groups_[group]->bin_mappers_[sub_feature].get();\n }\n\n inline const Bin* FeatureGroupBin(int group) const {\n return feature_groups_[group]->bin_data_.get();\n }\n\n inline BinIterator* FeatureIterator(int i) const {\n const int group = feature2group_[i];\n const int sub_feature = feature2subfeature_[i];\n return feature_groups_[group]->SubFeatureIterator(sub_feature);\n }\n\n inline BinIterator* FeatureGroupIterator(int group) const {\n return feature_groups_[group]->FeatureGroupIterator();\n }\n\n inline bool IsMultiGroup(int i) const {\n return feature_groups_[i]->is_multi_val_;\n }\n\n inline size_t FeatureGroupSizesInByte(int group) const {\n return feature_groups_[group]->FeatureGroupSizesInByte();\n }\n\n inline void* FeatureGroupData(int group) const {\n return feature_groups_[group]->FeatureGroupData();\n }\n\n const void* GetColWiseData(\n const int feature_group_index,\n const int sub_feature_index,\n uint8_t* bit_type,\n bool* is_sparse,\n std::vector* bin_iterator,\n const int num_threads) const;\n\n const void* GetColWiseData(\n const int feature_group_index,\n const int sub_feature_index,\n uint8_t* bit_type,\n bool* is_sparse,\n BinIterator** bin_iterator) const;\n\n inline double RealThreshold(int i, uint32_t threshold) const {\n const int group = feature2group_[i];\n const int sub_feature = feature2subfeature_[i];\n return feature_groups_[group]->bin_mappers_[sub_feature]->BinToValue(threshold);\n }\n\n // given a real threshold, find the closest threshold bin\n inline uint32_t BinThreshold(int i, double threshold_double) const {\n const int group = feature2group_[i];\n const int sub_feature = feature2subfeature_[i];\n return feature_groups_[group]->bin_mappers_[sub_feature]->ValueToBin(threshold_double);\n }\n\n inline int MaxRealCatValue(int i) const {\n const int group = feature2group_[i];\n const int sub_feature = feature2subfeature_[i];\n return feature_groups_[group]->bin_mappers_[sub_feature]->MaxCatValue();\n }\n\n /*!\n * \\brief Get meta data pointer\n * \\return Pointer of meta data\n */\n inline const Metadata& metadata() const { return metadata_; }\n\n /*! \\brief Get Number of used features */\n inline int num_features() const { return num_features_; }\n\n /*! \\brief Get number of numeric features */\n inline int num_numeric_features() const { return num_numeric_features_; }\n\n /*! \\brief Get Number of feature groups */\n inline int num_feature_groups() const { return num_groups_;}\n\n /*! \\brief Get Number of total features */\n inline int num_total_features() const { return num_total_features_; }\n\n /*! \\brief Get the index of label column */\n inline int label_idx() const { return label_idx_; }\n\n /*! \\brief Get names of current data set */\n inline const std::vector& feature_names() const { return feature_names_; }\n\n /*! \\brief Get content of parser config file */\n inline const std::string parser_config_str() const { return parser_config_str_; }\n\n inline void set_feature_names(const std::vector& feature_names) {\n if (feature_names.size() != static_cast(num_total_features_)) {\n Log::Fatal(\"Size of feature_names error, should equal with total number of features\");\n }\n feature_names_ = std::vector(feature_names);\n std::unordered_set feature_name_set;\n // replace ' ' in feature_names with '_'\n bool spaceInFeatureName = false;\n for (auto& feature_name : feature_names_) {\n // check JSON\n if (!Common::CheckAllowedJSON(feature_name)) {\n Log::Fatal(\"Do not support special JSON characters in feature name.\");\n }\n if (feature_name.find(' ') != std::string::npos) {\n spaceInFeatureName = true;\n std::replace(feature_name.begin(), feature_name.end(), ' ', '_');\n }\n if (feature_name_set.count(feature_name) > 0) {\n Log::Fatal(\"Feature (%s) appears more than one time.\", feature_name.c_str());\n }\n feature_name_set.insert(feature_name);\n }\n if (spaceInFeatureName) {\n Log::Warning(\"Found whitespace in feature_names, replace with underlines\");\n }\n }\n\n inline std::vector feature_infos() const {\n std::vector bufs;\n for (int i = 0; i < num_total_features_; ++i) {\n int fidx = used_feature_map_[i];\n if (fidx < 0) {\n bufs.push_back(\"none\");\n } else {\n const auto bin_mapper = FeatureBinMapper(fidx);\n bufs.push_back(bin_mapper->bin_info_string());\n }\n }\n return bufs;\n }\n\n /*! \\brief Get Number of data */\n inline data_size_t num_data() const { return num_data_; }\n\n /*! \\brief Get whether FinishLoad is automatically called when pushing last row. */\n inline bool wait_for_manual_finish() const { return wait_for_manual_finish_; }\n\n /*! \\brief Get the maximum number of OpenMP threads to allocate for. */\n inline int omp_max_threads() const { return omp_max_threads_; }\n\n /*! \\brief Set whether the Dataset is finished automatically when last row is pushed or with a manual\n * MarkFinished API call. Set to true for thread-safe streaming and/or if will be coalesced later.\n * FinishLoad should not be called on any Dataset that will be coalesced.\n */\n inline void set_wait_for_manual_finish(bool value) {\n std::lock_guard lock(mutex_);\n wait_for_manual_finish_ = value;\n }\n\n /*! \\brief Disable copy */\n Dataset& operator=(const Dataset&) = delete;\n /*! \\brief Disable copy */\n Dataset(const Dataset&) = delete;\n\n void AddFeaturesFrom(Dataset* other);\n\n /*! \\brief Get has_raw_ */\n inline bool has_raw() const { return has_raw_; }\n\n /*! \\brief Set has_raw_ */\n inline void SetHasRaw(bool has_raw) { has_raw_ = has_raw; }\n\n /*! \\brief Resize raw_data_ */\n inline void ResizeRaw(int num_rows) {\n if (static_cast(raw_data_.size()) > num_numeric_features_) {\n raw_data_.resize(num_numeric_features_);\n }\n for (size_t i = 0; i < raw_data_.size(); ++i) {\n raw_data_[i].resize(num_rows);\n }\n int curr_size = static_cast(raw_data_.size());\n for (int i = curr_size; i < num_numeric_features_; ++i) {\n raw_data_.push_back(std::vector(num_rows, 0));\n }\n }\n\n /*! \\brief Get pointer to raw_data_ feature */\n inline const float* raw_index(int feat_ind) const {\n return raw_data_[numeric_feature_map_[feat_ind]].data();\n }\n\n inline uint32_t feature_max_bin(const int inner_feature_index) const {\n const int feature_group_index = Feature2Group(inner_feature_index);\n const int sub_feature_index = feature2subfeature_[inner_feature_index];\n return feature_groups_[feature_group_index]->feature_max_bin(sub_feature_index);\n }\n\n inline uint32_t feature_min_bin(const int inner_feature_index) const {\n const int feature_group_index = Feature2Group(inner_feature_index);\n const int sub_feature_index = feature2subfeature_[inner_feature_index];\n return feature_groups_[feature_group_index]->feature_min_bin(sub_feature_index);\n }\n\n #ifdef USE_CUDA\n\n const CUDAColumnData* cuda_column_data() const {\n return cuda_column_data_.get();\n }\n\n #endif // USE_CUDA\n\n private:\n void SerializeHeader(BinaryWriter* serializer);\n\n size_t GetSerializedHeaderSize();\n\n void CreateCUDAColumnData();\n\n void CopySubrowHostPart(const Dataset* fullset, const data_size_t* used_indices, data_size_t num_used_indices, bool need_meta_data);\n\n std::string data_filename_;\n /*! \\brief Store used features */\n std::vector> feature_groups_;\n /*! \\brief Mapper from real feature index to used index*/\n std::vector used_feature_map_;\n /*! \\brief Number of used features*/\n int num_features_;\n /*! \\brief Number of total features*/\n int num_total_features_;\n /*! \\brief Number of total data*/\n data_size_t num_data_;\n /*! \\brief Store some label level data*/\n Metadata metadata_;\n /*! \\brief index of label column */\n int label_idx_ = 0;\n /*! \\brief store feature names */\n std::vector feature_names_;\n /*! \\brief serialized versions */\n static const int kSerializedReferenceVersionLength;\n static const char* serialized_reference_version;\n static const char* binary_file_token;\n static const char* binary_serialized_reference_token;\n int num_groups_;\n std::vector real_feature_idx_;\n std::vector feature2group_;\n std::vector feature2subfeature_;\n std::vector group_bin_boundaries_;\n std::vector group_feature_start_;\n std::vector group_feature_cnt_;\n bool is_finish_load_;\n int max_bin_;\n std::vector max_bin_by_feature_;\n std::vector> forced_bin_bounds_;\n int bin_construct_sample_cnt_;\n int min_data_in_bin_;\n bool use_missing_;\n bool zero_as_missing_;\n std::vector feature_need_push_zeros_;\n std::vector> raw_data_;\n bool wait_for_manual_finish_;\n int omp_max_threads_ = -1;\n bool has_raw_;\n /*! map feature (inner index) to its index in the list of numeric (non-categorical) features */\n std::vector numeric_feature_map_;\n int num_numeric_features_;\n std::string device_type_;\n int gpu_device_id_;\n /*! \\brief mutex for threading safe call */\n std::mutex mutex_;\n\n #ifdef USE_CUDA\n std::unique_ptr cuda_column_data_;\n #endif // USE_CUDA\n\n std::string parser_config_str_;\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_H_\n" }, { "path": "include/LightGBM/dataset_loader.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_LOADER_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_LOADER_H_\n\n#include \n\n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nclass DatasetLoader {\n public:\n LIGHTGBM_EXPORT DatasetLoader(const Config& io_config, const PredictFunction& predict_fun, int num_class, const char* filename);\n\n LIGHTGBM_EXPORT ~DatasetLoader();\n\n LIGHTGBM_EXPORT Dataset* LoadFromFile(const char* filename, int rank, int num_machines);\n\n LIGHTGBM_EXPORT Dataset* LoadFromFile(const char* filename) {\n return LoadFromFile(filename, 0, 1);\n }\n\n LIGHTGBM_EXPORT Dataset* LoadFromFileAlignWithOtherDataset(const char* filename, const Dataset* train_data);\n\n LIGHTGBM_EXPORT Dataset* LoadFromSerializedReference(const char* buffer, size_t buffer_size, data_size_t num_data, int32_t num_classes);\n\n LIGHTGBM_EXPORT Dataset* ConstructFromSampleData(double** sample_values,\n int** sample_indices,\n int num_col,\n const int* num_per_col,\n size_t total_sample_size,\n data_size_t num_local_data,\n int64_t num_dist_data);\n\n /*! \\brief Disable copy */\n DatasetLoader& operator=(const DatasetLoader&) = delete;\n /*! \\brief Disable copy */\n DatasetLoader(const DatasetLoader&) = delete;\n\n static std::vector> GetForcedBins(std::string forced_bins_path, int num_total_features,\n const std::unordered_set& categorical_features);\n\n private:\n void LoadHeaderFromMemory(Dataset* dataset, const char* buffer);\n\n Dataset* LoadFromBinFile(const char* data_filename, const char* bin_filename, int rank, int num_machines, int* num_global_data, std::vector* used_data_indices);\n\n void SetHeader(const char* filename);\n\n void CheckDataset(const Dataset* dataset, bool is_load_from_binary);\n\n std::vector LoadTextDataToMemory(const char* filename, const Metadata& metadata, int rank, int num_machines, int* num_global_data, std::vector* used_data_indices);\n\n std::vector SampleTextDataFromMemory(const std::vector& data);\n\n std::vector SampleTextDataFromFile(const char* filename, const Metadata& metadata, int rank, int num_machines, int* num_global_data, std::vector* used_data_indices);\n\n void ConstructBinMappersFromTextData(int rank, int num_machines, const std::vector& sample_data, const Parser* parser, Dataset* dataset);\n\n /*! \\brief Extract local features from memory */\n void ExtractFeaturesFromMemory(std::vector* text_data, const Parser* parser, Dataset* dataset);\n\n /*! \\brief Extract local features from file */\n void ExtractFeaturesFromFile(const char* filename, const Parser* parser, const std::vector& used_data_indices, Dataset* dataset);\n\n /*! \\brief Check can load from binary file */\n std::string CheckCanLoadFromBin(const char* filename);\n\n /*! \\brief Check the number of bins for categorical features.\n * The number of bins for categorical features may exceed the configured maximum value.\n * Log warnings when such cases happen.\n *\n * \\param bin_mappers the bin_mappers of all features\n * \\param max_bin max_bin from Config\n * \\param max_bin_by_feature max_bin_by_feature from Config\n */\n void CheckCategoricalFeatureNumBin(const std::vector>& bin_mappers, const int max_bin, const std::vector& max_bin_by_feature) const;\n\n const Config& config_;\n /*! \\brief Random generator*/\n Random random_;\n /*! \\brief prediction function for initial model */\n const PredictFunction predict_fun_;\n /*! \\brief number of classes */\n int num_class_;\n /*! \\brief index of label column */\n int label_idx_;\n /*! \\brief index of weight column */\n int weight_idx_;\n /*! \\brief index of group column */\n int group_idx_;\n /*! \\brief Mapper from real feature index to used index*/\n std::unordered_set ignore_features_;\n /*! \\brief store feature names */\n std::vector feature_names_;\n /*! \\brief Mapper from real feature index to used index*/\n std::unordered_set categorical_features_;\n /*! \\brief Whether to store raw feature values */\n bool store_raw_;\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_DATASET_LOADER_H_\n" }, { "path": "include/LightGBM/export.h", "content": "/*!\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_EXPORT_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_EXPORT_H_\n\n/** Macros for exporting symbols in MSVC/GCC/CLANG **/\n\n#ifdef __cplusplus\n#define LIGHTGBM_EXTERN_C extern \"C\"\n#else\n#define LIGHTGBM_EXTERN_C\n#endif\n\n\n#ifdef _MSC_VER\n#define LIGHTGBM_EXPORT __declspec(dllexport)\n#define LIGHTGBM_C_EXPORT LIGHTGBM_EXTERN_C __declspec(dllexport)\n#else\n#define LIGHTGBM_EXPORT\n#define LIGHTGBM_C_EXPORT LIGHTGBM_EXTERN_C\n#endif\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_EXPORT_H_\n" }, { "path": "include/LightGBM/feature_group.h", "content": "/*!\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_FEATURE_GROUP_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_FEATURE_GROUP_H_\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nclass Dataset;\nclass DatasetLoader;\nstruct TrainingShareStates;\nclass MultiValBinWrapper;\n/*! \\brief Using to store data and providing some operations on one feature\n * group*/\nclass FeatureGroup {\n public:\n friend Dataset;\n friend DatasetLoader;\n friend TrainingShareStates;\n friend MultiValBinWrapper;\n /*!\n * \\brief Constructor\n * \\param num_feature number of features of this group\n * \\param bin_mappers Bin mapper for features\n * \\param num_data Total number of data\n * \\param is_enable_sparse True if enable sparse feature\n */\n FeatureGroup(int num_feature, int8_t is_multi_val,\n std::vector>* bin_mappers,\n data_size_t num_data, int group_id) :\n num_feature_(num_feature), is_multi_val_(is_multi_val > 0), is_sparse_(false) {\n CHECK_EQ(static_cast(bin_mappers->size()), num_feature);\n auto& ref_bin_mappers = *bin_mappers;\n double sum_sparse_rate = 0.0f;\n for (int i = 0; i < num_feature_; ++i) {\n bin_mappers_.emplace_back(ref_bin_mappers[i].release());\n sum_sparse_rate += bin_mappers_.back()->sparse_rate();\n }\n sum_sparse_rate /= num_feature_;\n int offset = 1;\n is_dense_multi_val_ = false;\n if (sum_sparse_rate < MultiValBin::multi_val_bin_sparse_threshold && is_multi_val_) {\n // use dense multi val bin\n offset = 0;\n is_dense_multi_val_ = true;\n }\n // use bin at zero to store most_freq_bin only when not using dense multi val bin\n num_total_bin_ = offset;\n // however, we should force to leave one bin, if dense multi val bin is the first bin\n // and its first feature has most freq bin > 0\n if (group_id == 0 && num_feature_ > 0 && is_dense_multi_val_ &&\n bin_mappers_[0]->GetMostFreqBin() > 0) {\n num_total_bin_ = 1;\n }\n bin_offsets_.emplace_back(num_total_bin_);\n for (int i = 0; i < num_feature_; ++i) {\n auto num_bin = bin_mappers_[i]->num_bin();\n if (bin_mappers_[i]->GetMostFreqBin() == 0) {\n num_bin -= offset;\n }\n num_total_bin_ += num_bin;\n bin_offsets_.emplace_back(num_total_bin_);\n }\n CreateBinData(num_data, is_multi_val_, true, false);\n }\n\n FeatureGroup(const FeatureGroup& other, int num_data) {\n num_feature_ = other.num_feature_;\n is_multi_val_ = other.is_multi_val_;\n is_dense_multi_val_ = other.is_dense_multi_val_;\n is_sparse_ = other.is_sparse_;\n num_total_bin_ = other.num_total_bin_;\n bin_offsets_ = other.bin_offsets_;\n\n bin_mappers_.reserve(other.bin_mappers_.size());\n for (auto& bin_mapper : other.bin_mappers_) {\n bin_mappers_.emplace_back(new BinMapper(*bin_mapper));\n }\n CreateBinData(num_data, is_multi_val_, !is_sparse_, is_sparse_);\n }\n\n FeatureGroup(std::vector>* bin_mappers,\n data_size_t num_data) : num_feature_(1), is_multi_val_(false) {\n CHECK_EQ(static_cast(bin_mappers->size()), 1);\n // use bin at zero to store default_bin\n num_total_bin_ = 1;\n is_dense_multi_val_ = false;\n bin_offsets_.emplace_back(num_total_bin_);\n auto& ref_bin_mappers = *bin_mappers;\n for (int i = 0; i < num_feature_; ++i) {\n bin_mappers_.emplace_back(ref_bin_mappers[i].release());\n auto num_bin = bin_mappers_[i]->num_bin();\n if (bin_mappers_[i]->GetMostFreqBin() == 0) {\n num_bin -= 1;\n }\n num_total_bin_ += num_bin;\n bin_offsets_.emplace_back(num_total_bin_);\n }\n CreateBinData(num_data, false, false, false);\n }\n\n /*!\n * \\brief Constructor from memory when data is present\n * \\param memory Pointer of memory\n * \\param num_all_data Number of global data\n * \\param local_used_indices Local used indices, empty means using all data\n * \\param group_id Id of group\n */\n FeatureGroup(const void* memory,\n data_size_t num_all_data,\n const std::vector& local_used_indices,\n int group_id) {\n // Load the definition schema first\n const char* memory_ptr = LoadDefinitionFromMemory(memory, group_id);\n\n // Allocate memory for the data\n data_size_t num_data = num_all_data;\n if (!local_used_indices.empty()) {\n num_data = static_cast(local_used_indices.size());\n }\n AllocateBins(num_data);\n\n // Now load the actual data\n if (is_multi_val_) {\n for (int i = 0; i < num_feature_; ++i) {\n multi_bin_data_[i]->LoadFromMemory(memory_ptr, local_used_indices);\n memory_ptr += multi_bin_data_[i]->SizesInByte();\n }\n } else {\n bin_data_->LoadFromMemory(memory_ptr, local_used_indices);\n }\n }\n\n /*!\n * \\brief Constructor from definition in memory (without data)\n * \\param memory Pointer of memory\n * \\param local_used_indices Local used indices, empty means using all data\n */\n FeatureGroup(const void* memory, data_size_t num_data, int group_id) {\n LoadDefinitionFromMemory(memory, group_id);\n AllocateBins(num_data);\n }\n\n /*! \\brief Destructor */\n ~FeatureGroup() {}\n\n /*!\n * \\brief Load the overall definition of the feature group from binary serialized data\n * \\param memory Pointer of memory\n * \\param group_id Id of group\n */\n const char* LoadDefinitionFromMemory(const void* memory, int group_id) {\n const char* memory_ptr = reinterpret_cast(memory);\n // get is_sparse\n is_multi_val_ = *(reinterpret_cast(memory_ptr));\n memory_ptr += VirtualFileWriter::AlignedSize(sizeof(is_multi_val_));\n is_dense_multi_val_ = *(reinterpret_cast(memory_ptr));\n memory_ptr += VirtualFileWriter::AlignedSize(sizeof(is_dense_multi_val_));\n is_sparse_ = *(reinterpret_cast(memory_ptr));\n memory_ptr += VirtualFileWriter::AlignedSize(sizeof(is_sparse_));\n num_feature_ = *(reinterpret_cast(memory_ptr));\n memory_ptr += VirtualFileWriter::AlignedSize(sizeof(num_feature_));\n\n // get bin mapper(s)\n bin_mappers_.clear();\n for (int i = 0; i < num_feature_; ++i) {\n bin_mappers_.emplace_back(new BinMapper(memory_ptr));\n memory_ptr += bin_mappers_[i]->SizesInByte();\n }\n\n bin_offsets_.clear();\n int offset = 1;\n if (is_dense_multi_val_) {\n offset = 0;\n }\n // use bin at zero to store most_freq_bin only when not using dense multi val bin\n num_total_bin_ = offset;\n // however, we should force to leave one bin, if dense multi val bin is the first bin\n // and its first feature has most freq bin > 0\n if (group_id == 0 && num_feature_ > 0 && is_dense_multi_val_ &&\n bin_mappers_[0]->GetMostFreqBin() > 0) {\n num_total_bin_ = 1;\n }\n bin_offsets_.emplace_back(num_total_bin_);\n for (int i = 0; i < num_feature_; ++i) {\n auto num_bin = bin_mappers_[i]->num_bin();\n if (bin_mappers_[i]->GetMostFreqBin() == 0) {\n num_bin -= offset;\n }\n num_total_bin_ += num_bin;\n bin_offsets_.emplace_back(num_total_bin_);\n }\n\n return memory_ptr;\n }\n\n /*!\n * \\brief Allocate the bins\n * \\param num_all_data Number of global data\n */\n inline void AllocateBins(data_size_t num_data) {\n if (is_multi_val_) {\n for (int i = 0; i < num_feature_; ++i) {\n int addi = bin_mappers_[i]->GetMostFreqBin() == 0 ? 0 : 1;\n if (bin_mappers_[i]->sparse_rate() >= kSparseThreshold) {\n multi_bin_data_.emplace_back(Bin::CreateSparseBin(num_data, bin_mappers_[i]->num_bin() + addi));\n } else {\n multi_bin_data_.emplace_back(Bin::CreateDenseBin(num_data, bin_mappers_[i]->num_bin() + addi));\n }\n }\n } else {\n if (is_sparse_) {\n bin_data_.reset(Bin::CreateSparseBin(num_data, num_total_bin_));\n } else {\n bin_data_.reset(Bin::CreateDenseBin(num_data, num_total_bin_));\n }\n }\n }\n\n /*!\n * \\brief Initialize for pushing in a streaming fashion. By default, no action needed.\n * \\param num_thread The number of external threads that will be calling the push APIs\n * \\param omp_max_threads The maximum number of OpenMP threads to allocate for\n */\n void InitStreaming(int32_t num_thread, int32_t omp_max_threads) {\n if (is_multi_val_) {\n for (int i = 0; i < num_feature_; ++i) {\n multi_bin_data_[i]->InitStreaming(num_thread, omp_max_threads);\n }\n } else {\n bin_data_->InitStreaming(num_thread, omp_max_threads);\n }\n }\n\n /*!\n * \\brief Push one record, will auto convert to bin and push to bin data\n * \\param tid Thread id\n * \\param sub_feature_idx Index of the subfeature\n * \\param line_idx Index of record\n * \\param value feature value of record\n */\n inline void PushData(int tid, int sub_feature_idx, data_size_t line_idx, double value) {\n uint32_t bin = bin_mappers_[sub_feature_idx]->ValueToBin(value);\n if (bin == bin_mappers_[sub_feature_idx]->GetMostFreqBin()) {\n return;\n }\n if (bin_mappers_[sub_feature_idx]->GetMostFreqBin() == 0) {\n bin -= 1;\n }\n if (is_multi_val_) {\n multi_bin_data_[sub_feature_idx]->Push(tid, line_idx, bin + 1);\n } else {\n bin += bin_offsets_[sub_feature_idx];\n bin_data_->Push(tid, line_idx, bin);\n }\n }\n\n void ReSize(int num_data) {\n if (!is_multi_val_) {\n bin_data_->ReSize(num_data);\n } else {\n for (int i = 0; i < num_feature_; ++i) {\n multi_bin_data_[i]->ReSize(num_data);\n }\n }\n }\n\n inline void CopySubrow(const FeatureGroup* full_feature, const data_size_t* used_indices, data_size_t num_used_indices) {\n if (!is_multi_val_) {\n bin_data_->CopySubrow(full_feature->bin_data_.get(), used_indices, num_used_indices);\n } else {\n for (int i = 0; i < num_feature_; ++i) {\n multi_bin_data_[i]->CopySubrow(full_feature->multi_bin_data_[i].get(), used_indices, num_used_indices);\n }\n }\n }\n\n inline void CopySubrowByCol(const FeatureGroup* full_feature, const data_size_t* used_indices, data_size_t num_used_indices, int fidx) {\n if (!is_multi_val_) {\n bin_data_->CopySubrow(full_feature->bin_data_.get(), used_indices, num_used_indices);\n } else {\n multi_bin_data_[fidx]->CopySubrow(full_feature->multi_bin_data_[fidx].get(), used_indices, num_used_indices);\n }\n }\n\n void AddFeaturesFrom(const FeatureGroup* other, int group_id) {\n CHECK(is_multi_val_);\n CHECK(other->is_multi_val_);\n // every time when new features are added, we need to reconsider sparse or dense\n double sum_sparse_rate = 0.0f;\n for (int i = 0; i < num_feature_; ++i) {\n sum_sparse_rate += bin_mappers_[i]->sparse_rate();\n }\n for (int i = 0; i < other->num_feature_; ++i) {\n sum_sparse_rate += other->bin_mappers_[i]->sparse_rate();\n }\n sum_sparse_rate /= (num_feature_ + other->num_feature_);\n int offset = 1;\n is_dense_multi_val_ = false;\n if (sum_sparse_rate < MultiValBin::multi_val_bin_sparse_threshold && is_multi_val_) {\n // use dense multi val bin\n offset = 0;\n is_dense_multi_val_ = true;\n }\n bin_offsets_.clear();\n num_total_bin_ = offset;\n // however, we should force to leave one bin, if dense multi val bin is the first bin\n // and its first feature has most freq bin > 0\n if (group_id == 0 && num_feature_ > 0 && is_dense_multi_val_ &&\n bin_mappers_[0]->GetMostFreqBin() > 0) {\n num_total_bin_ = 1;\n }\n bin_offsets_.emplace_back(num_total_bin_);\n for (int i = 0; i < num_feature_; ++i) {\n auto num_bin = bin_mappers_[i]->num_bin();\n if (bin_mappers_[i]->GetMostFreqBin() == 0) {\n num_bin -= offset;\n }\n num_total_bin_ += num_bin;\n bin_offsets_.emplace_back(num_total_bin_);\n }\n for (int i = 0; i < other->num_feature_; ++i) {\n const auto& other_bin_mapper = other->bin_mappers_[i];\n bin_mappers_.emplace_back(new BinMapper(*other_bin_mapper));\n auto num_bin = other_bin_mapper->num_bin();\n if (other_bin_mapper->GetMostFreqBin() == 0) {\n num_bin -= offset;\n }\n num_total_bin_ += num_bin;\n bin_offsets_.emplace_back(num_total_bin_);\n multi_bin_data_.emplace_back(other->multi_bin_data_[i]->Clone());\n }\n num_feature_ += other->num_feature_;\n }\n\n inline BinIterator* SubFeatureIterator(int sub_feature) {\n uint32_t most_freq_bin = bin_mappers_[sub_feature]->GetMostFreqBin();\n if (!is_multi_val_) {\n uint32_t min_bin = bin_offsets_[sub_feature];\n uint32_t max_bin = bin_offsets_[sub_feature + 1] - 1;\n return bin_data_->GetIterator(min_bin, max_bin, most_freq_bin);\n } else {\n int addi = bin_mappers_[sub_feature]->GetMostFreqBin() == 0 ? 0 : 1;\n uint32_t min_bin = 1;\n uint32_t max_bin = bin_mappers_[sub_feature]->num_bin() - 1 + addi;\n return multi_bin_data_[sub_feature]->GetIterator(min_bin, max_bin,\n most_freq_bin);\n }\n }\n\n inline void FinishLoad() {\n if (is_multi_val_) {\n OMP_INIT_EX();\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided)\n for (int i = 0; i < num_feature_; ++i) {\n OMP_LOOP_EX_BEGIN();\n multi_bin_data_[i]->FinishLoad();\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n } else {\n bin_data_->FinishLoad();\n }\n }\n\n inline BinIterator* FeatureGroupIterator() {\n if (is_multi_val_) {\n return nullptr;\n }\n uint32_t min_bin = bin_offsets_[0];\n uint32_t max_bin = bin_offsets_.back() - 1;\n uint32_t most_freq_bin = 0;\n return bin_data_->GetIterator(min_bin, max_bin, most_freq_bin);\n }\n\n inline size_t FeatureGroupSizesInByte() {\n return bin_data_->SizesInByte();\n }\n\n inline void* FeatureGroupData() {\n if (is_multi_val_) {\n return nullptr;\n }\n return bin_data_->get_data();\n }\n\n inline data_size_t Split(int sub_feature, const uint32_t* threshold,\n int num_threshold, bool default_left,\n const data_size_t* data_indices, data_size_t cnt,\n data_size_t* lte_indices,\n data_size_t* gt_indices) const {\n uint32_t default_bin = bin_mappers_[sub_feature]->GetDefaultBin();\n uint32_t most_freq_bin = bin_mappers_[sub_feature]->GetMostFreqBin();\n if (!is_multi_val_) {\n uint32_t min_bin = bin_offsets_[sub_feature];\n uint32_t max_bin = bin_offsets_[sub_feature + 1] - 1;\n if (bin_mappers_[sub_feature]->bin_type() == BinType::NumericalBin) {\n auto missing_type = bin_mappers_[sub_feature]->missing_type();\n if (num_feature_ == 1) {\n return bin_data_->Split(max_bin, default_bin, most_freq_bin,\n missing_type, default_left, *threshold,\n data_indices, cnt, lte_indices, gt_indices);\n } else {\n return bin_data_->Split(min_bin, max_bin, default_bin, most_freq_bin,\n missing_type, default_left, *threshold,\n data_indices, cnt, lte_indices, gt_indices);\n }\n } else {\n if (num_feature_ == 1) {\n return bin_data_->SplitCategorical(max_bin, most_freq_bin, threshold,\n num_threshold, data_indices, cnt,\n lte_indices, gt_indices);\n } else {\n return bin_data_->SplitCategorical(\n min_bin, max_bin, most_freq_bin, threshold, num_threshold,\n data_indices, cnt, lte_indices, gt_indices);\n }\n }\n } else {\n int addi = bin_mappers_[sub_feature]->GetMostFreqBin() == 0 ? 0 : 1;\n uint32_t max_bin = bin_mappers_[sub_feature]->num_bin() - 1 + addi;\n if (bin_mappers_[sub_feature]->bin_type() == BinType::NumericalBin) {\n auto missing_type = bin_mappers_[sub_feature]->missing_type();\n return multi_bin_data_[sub_feature]->Split(\n max_bin, default_bin, most_freq_bin, missing_type, default_left,\n *threshold, data_indices, cnt, lte_indices, gt_indices);\n } else {\n return multi_bin_data_[sub_feature]->SplitCategorical(\n max_bin, most_freq_bin, threshold, num_threshold, data_indices, cnt,\n lte_indices, gt_indices);\n }\n }\n }\n\n /*!\n * \\brief From bin to feature value\n * \\param bin\n * \\return FeatureGroup value of this bin\n */\n inline double BinToValue(int sub_feature_idx, uint32_t bin) const {\n return bin_mappers_[sub_feature_idx]->BinToValue(bin);\n }\n\n /*!\n * \\brief Write to binary stream\n * \\param writer Writer\n * \\param include_data Whether to write data (true) or just header information (false)\n */\n void SerializeToBinary(BinaryWriter* writer, bool include_data = true) const {\n writer->AlignedWrite(&is_multi_val_, sizeof(is_multi_val_));\n writer->AlignedWrite(&is_dense_multi_val_, sizeof(is_dense_multi_val_));\n writer->AlignedWrite(&is_sparse_, sizeof(is_sparse_));\n writer->AlignedWrite(&num_feature_, sizeof(num_feature_));\n for (int i = 0; i < num_feature_; ++i) {\n bin_mappers_[i]->SaveBinaryToFile(writer);\n }\n\n if (include_data) {\n if (is_multi_val_) {\n for (int i = 0; i < num_feature_; ++i) {\n multi_bin_data_[i]->SaveBinaryToFile(writer);\n }\n } else {\n bin_data_->SaveBinaryToFile(writer);\n }\n }\n }\n\n /*!\n * \\brief Get sizes in byte of this object\n */\n size_t SizesInByte(bool include_data = true) const {\n size_t ret = VirtualFileWriter::AlignedSize(sizeof(is_multi_val_)) +\n VirtualFileWriter::AlignedSize(sizeof(is_dense_multi_val_)) +\n VirtualFileWriter::AlignedSize(sizeof(is_sparse_)) +\n VirtualFileWriter::AlignedSize(sizeof(num_feature_));\n for (int i = 0; i < num_feature_; ++i) {\n ret += bin_mappers_[i]->SizesInByte();\n }\n if (include_data) {\n if (!is_multi_val_) {\n ret += bin_data_->SizesInByte();\n } else {\n for (int i = 0; i < num_feature_; ++i) {\n ret += multi_bin_data_[i]->SizesInByte();\n }\n }\n }\n return ret;\n }\n\n /*! \\brief Disable copy */\n FeatureGroup& operator=(const FeatureGroup&) = delete;\n\n /*! \\brief Deep copy */\n FeatureGroup(const FeatureGroup& other, bool should_handle_dense_mv,\n int group_id) {\n num_feature_ = other.num_feature_;\n is_multi_val_ = other.is_multi_val_;\n is_dense_multi_val_ = other.is_dense_multi_val_;\n is_sparse_ = other.is_sparse_;\n num_total_bin_ = other.num_total_bin_;\n bin_offsets_ = other.bin_offsets_;\n\n bin_mappers_.reserve(other.bin_mappers_.size());\n for (auto& bin_mapper : other.bin_mappers_) {\n bin_mappers_.emplace_back(new BinMapper(*bin_mapper));\n }\n if (!is_multi_val_) {\n bin_data_.reset(other.bin_data_->Clone());\n } else {\n multi_bin_data_.clear();\n for (int i = 0; i < num_feature_; ++i) {\n multi_bin_data_.emplace_back(other.multi_bin_data_[i]->Clone());\n }\n }\n\n if (should_handle_dense_mv && is_dense_multi_val_ && group_id > 0) {\n // this feature group was the first feature group, but now no longer is,\n // so we need to eliminate its special empty bin for multi val dense bin\n if (bin_mappers_[0]->GetMostFreqBin() > 0 && bin_offsets_[0] == 1) {\n for (size_t i = 0; i < bin_offsets_.size(); ++i) {\n bin_offsets_[i] -= 1;\n }\n num_total_bin_ -= 1;\n }\n }\n }\n\n const void* GetColWiseData(const int sub_feature_index,\n uint8_t* bit_type,\n bool* is_sparse,\n std::vector* bin_iterator,\n const int num_threads) const {\n if (sub_feature_index >= 0) {\n CHECK(is_multi_val_);\n return multi_bin_data_[sub_feature_index]->GetColWiseData(bit_type, is_sparse, bin_iterator, num_threads);\n } else {\n CHECK(!is_multi_val_);\n return bin_data_->GetColWiseData(bit_type, is_sparse, bin_iterator, num_threads);\n }\n }\n\n const void* GetColWiseData(const int sub_feature_index,\n uint8_t* bit_type,\n bool* is_sparse,\n BinIterator** bin_iterator) const {\n if (sub_feature_index >= 0) {\n CHECK(is_multi_val_);\n return multi_bin_data_[sub_feature_index]->GetColWiseData(bit_type, is_sparse, bin_iterator);\n } else {\n CHECK(!is_multi_val_);\n return bin_data_->GetColWiseData(bit_type, is_sparse, bin_iterator);\n }\n }\n\n uint32_t feature_max_bin(const int sub_feature_index) {\n if (!is_multi_val_) {\n return bin_offsets_[sub_feature_index + 1] - 1;\n } else {\n int addi = bin_mappers_[sub_feature_index]->GetMostFreqBin() == 0 ? 0 : 1;\n return bin_mappers_[sub_feature_index]->num_bin() - 1 + addi;\n }\n }\n\n uint32_t feature_min_bin(const int sub_feature_index) {\n if (!is_multi_val_) {\n return bin_offsets_[sub_feature_index];\n } else {\n return 1;\n }\n }\n\n private:\n void CreateBinData(int num_data, bool is_multi_val, bool force_dense, bool force_sparse) {\n if (is_multi_val) {\n multi_bin_data_.clear();\n for (int i = 0; i < num_feature_; ++i) {\n int addi = bin_mappers_[i]->GetMostFreqBin() == 0 ? 0 : 1;\n if (bin_mappers_[i]->sparse_rate() >= kSparseThreshold) {\n multi_bin_data_.emplace_back(Bin::CreateSparseBin(\n num_data, bin_mappers_[i]->num_bin() + addi));\n } else {\n multi_bin_data_.emplace_back(\n Bin::CreateDenseBin(num_data, bin_mappers_[i]->num_bin() + addi));\n }\n }\n is_multi_val_ = true;\n } else {\n if (force_sparse ||\n (!force_dense && num_feature_ == 1 &&\n bin_mappers_[0]->sparse_rate() >= kSparseThreshold)) {\n is_sparse_ = true;\n bin_data_.reset(Bin::CreateSparseBin(num_data, num_total_bin_));\n } else {\n is_sparse_ = false;\n bin_data_.reset(Bin::CreateDenseBin(num_data, num_total_bin_));\n }\n is_multi_val_ = false;\n }\n }\n\n /*! \\brief Number of features */\n int num_feature_;\n /*! \\brief Bin mapper for sub features */\n std::vector> bin_mappers_;\n /*! \\brief Bin offsets for sub features */\n std::vector bin_offsets_;\n /*! \\brief Bin data of this feature */\n std::unique_ptr bin_data_;\n std::vector> multi_bin_data_;\n /*! \\brief True if this feature is sparse */\n bool is_multi_val_;\n bool is_dense_multi_val_;\n bool is_sparse_;\n int num_total_bin_;\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_FEATURE_GROUP_H_\n" }, { "path": "include/LightGBM/meta.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_META_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_META_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#if (defined(_MSC_VER) && (defined(_M_IX86) || defined(_M_AMD64))) || defined(__INTEL_COMPILER) || MM_PREFETCH\n #include \n #define PREFETCH_T0(addr) _mm_prefetch(reinterpret_cast(addr), _MM_HINT_T0)\n#elif defined(__GNUC__)\n #define PREFETCH_T0(addr) __builtin_prefetch(reinterpret_cast(addr), 0, 3)\n#else\n #define PREFETCH_T0(addr) do {} while (0)\n#endif\n\nnamespace LightGBM {\n\n/*! \\brief Type of data size, it is better to use signed type*/\ntypedef int32_t data_size_t;\n\n// Enable following macro to use double for score_t\n// #define SCORE_T_USE_DOUBLE\n\n// Enable following macro to use double for label_t\n// #define LABEL_T_USE_DOUBLE\n\n/*! \\brief Type of score, and gradients */\n#ifdef SCORE_T_USE_DOUBLE\ntypedef double score_t;\n#else\ntypedef float score_t;\n#endif\n\n/*! \\brief Type of metadata, include weight and label */\n#ifdef LABEL_T_USE_DOUBLE\ntypedef double label_t;\n#else\ntypedef float label_t;\n#endif\n\nconst score_t kMinScore = -std::numeric_limits::infinity();\n\nconst score_t kMaxScore = std::numeric_limits::infinity();\n\nconst score_t kEpsilon = 1e-15f;\n\nconst double kZeroThreshold = 1e-35f;\n\n\ntypedef int32_t comm_size_t;\n\nusing PredictFunction =\nstd::function>&, double* output)>;\n\nusing PredictSparseFunction =\nstd::function>&, std::vector>* output)>;\n\ntypedef void(*ReduceFunction)(const char* input, char* output, int type_size, comm_size_t array_size);\n\n\ntypedef void(*ReduceScatterFunction)(char* input, comm_size_t input_size, int type_size,\n const comm_size_t* block_start, const comm_size_t* block_len, int num_block, char* output, comm_size_t output_size,\n const ReduceFunction& reducer);\n\ntypedef void(*AllgatherFunction)(char* input, comm_size_t input_size, const comm_size_t* block_start,\n const comm_size_t* block_len, int num_block, char* output, comm_size_t output_size);\n\n\n#define NO_SPECIFIC (-1)\n\nconst int kAlignedSize = 32;\n\n#define SIZE_ALIGNED(t) ((t) + kAlignedSize - 1) / kAlignedSize * kAlignedSize\n\n// Refer to https://docs.microsoft.com/en-us/cpp/error-messages/compiler-warnings/compiler-warning-level-4-c4127?view=vs-2019\n#ifdef _MSC_VER\n#pragma warning(disable : 4127)\n#endif\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_META_H_\n" }, { "path": "include/LightGBM/metric.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_METRIC_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_METRIC_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\nnamespace LightGBM {\n\n/*!\n* \\brief The interface of metric.\n* Metric is used to calculate metric result\n*/\nclass Metric {\n public:\n /*! \\brief virtual destructor */\n virtual ~Metric() {}\n\n /*!\n * \\brief Initialize\n * \\param test_name Specific name for this metric, will output on log\n * \\param metadata Label data\n * \\param num_data Number of data\n */\n virtual void Init(const Metadata& metadata, data_size_t num_data) = 0;\n\n virtual const std::vector& GetName() const = 0;\n\n virtual double factor_to_bigger_better() const = 0;\n /*!\n * \\brief Calculating and printing metric result\n * \\param score Current prediction score\n */\n virtual std::vector Eval(const double* score, const ObjectiveFunction* objective) const = 0;\n\n Metric() = default;\n /*! \\brief Disable copy */\n Metric& operator=(const Metric&) = delete;\n /*! \\brief Disable copy */\n Metric(const Metric&) = delete;\n\n /*!\n * \\brief Create object of metrics\n * \\param type Specific type of metric\n * \\param config Config for metric\n */\n LIGHTGBM_EXPORT static Metric* CreateMetric(const std::string& type, const Config& config);\n\n /*!\n * \\brief Whether boosting is done on CUDA\n */\n virtual bool IsCUDAMetric() const { return false; }\n};\n\n/*!\n* \\brief Static class, used to calculate DCG score\n*/\nclass DCGCalculator {\n public:\n static void DefaultEvalAt(std::vector* eval_at);\n static void DefaultLabelGain(std::vector* label_gain);\n /*!\n * \\brief Initial logic\n * \\param label_gain Gain for labels, default is 2^i - 1\n */\n static void Init(const std::vector& label_gain);\n\n /*!\n * \\brief Calculate the DCG score at multi position\n * \\param ks The positions to evaluate\n * \\param label Pointer of label\n * \\param score Pointer of score\n * \\param num_data Number of data\n * \\param out Output result\n */\n static void CalDCG(const std::vector& ks,\n const label_t* label, const double* score,\n data_size_t num_data, std::vector* out);\n\n /*!\n * \\brief Calculate the Max DCG score at position k\n * \\param k The position want to eval at\n * \\param label Pointer of label\n * \\param num_data Number of data\n * \\return The max DCG score\n */\n static double CalMaxDCGAtK(data_size_t k,\n const label_t* label, data_size_t num_data);\n\n\n /*!\n * \\brief Check the metadata for NDCG and LambdaRank\n * \\param metadata Metadata\n * \\param num_queries Number of queries\n */\n static void CheckMetadata(const Metadata& metadata, data_size_t num_queries);\n\n /*!\n * \\brief Check the label range for NDCG and LambdaRank\n * \\param label Pointer of label\n * \\param num_data Number of data\n */\n static void CheckLabel(const label_t* label, data_size_t num_data);\n\n /*!\n * \\brief Calculate the Max DCG score at multi position\n * \\param ks The positions want to eval at\n * \\param label Pointer of label\n * \\param num_data Number of data\n * \\param out Output result\n */\n static void CalMaxDCG(const std::vector& ks,\n const label_t* label, data_size_t num_data, std::vector* out);\n\n /*!\n * \\brief Get discount score of position k\n * \\param k The position\n * \\return The discount of this position\n */\n inline static double GetDiscount(data_size_t k) { return discount_[k]; }\n\n private:\n /*! \\brief store gains for different label */\n static std::vector label_gain_;\n /*! \\brief store discount score for different position */\n static std::vector discount_;\n /*! \\brief max position for eval */\n static const data_size_t kMaxPosition;\n};\n\n\n} // namespace LightGBM\n\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_METRIC_H_\n" }, { "path": "include/LightGBM/network.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_NETWORK_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_NETWORK_H_\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n\nnamespace LightGBM {\n\n/*! \\brief forward declaration */\nclass Linkers;\n\n/*! \\brief The network structure for all_gather */\nclass BruckMap {\n public:\n /*! \\brief The communication times for one all gather operation */\n int k;\n /*! \\brief in_ranks[i] means the incoming rank on i-th communication */\n std::vector in_ranks;\n /*! \\brief out_ranks[i] means the out rank on i-th communication */\n std::vector out_ranks;\n BruckMap();\n explicit BruckMap(int n);\n /*!\n * \\brief Create the object of bruck map\n * \\param rank Rank of this machine\n * \\param num_machines The total number of machines\n * \\return The object of bruck map\n */\n static BruckMap Construct(int rank, int num_machines);\n};\n\n/*!\n* \\brief node type on recursive halving algorithm\n* When number of machines is not power of 2, need group machines into power of 2 group.\n* And we can let each group has at most 2 machines.\n* if the group only has 1 machine. this machine is the normal node\n* if the group has 2 machines, this group will have two type of nodes, one is the leader.\n* leader will represent this group and communication with others.\n*/\nenum RecursiveHalvingNodeType {\n Normal, // normal node, 1 group only have 1 machine\n GroupLeader, // leader of group when number of machines in this group is 2.\n Other // non-leader machines in group\n};\n\n/*! \\brief Network structure for recursive halving algorithm */\nclass RecursiveHalvingMap {\n public:\n /*! \\brief Communication times for one recursive halving algorithm */\n int k;\n /*! \\brief Node type */\n RecursiveHalvingNodeType type;\n bool is_power_of_2;\n int neighbor;\n /*! \\brief ranks[i] means the machines that will communicate with on i-th communication*/\n std::vector ranks;\n /*! \\brief send_block_start[i] means send block start index at i-th communication*/\n std::vector send_block_start;\n /*! \\brief send_block_start[i] means send block size at i-th communication*/\n std::vector send_block_len;\n /*! \\brief send_block_start[i] means recv block start index at i-th communication*/\n std::vector recv_block_start;\n /*! \\brief send_block_start[i] means recv block size at i-th communication*/\n std::vector recv_block_len;\n\n RecursiveHalvingMap();\n\n RecursiveHalvingMap(int k, RecursiveHalvingNodeType _type, bool _is_power_of_2);\n\n /*!\n * \\brief Create the object of recursive halving map\n * \\param rank Rank of this machine\n * \\param num_machines The total number of machines\n * \\return The object of recursive halving map\n */\n static RecursiveHalvingMap Construct(int rank, int num_machines);\n};\n\n/*! \\brief A static class that contains some collective communication algorithm */\nclass Network {\n public:\n /*!\n * \\brief Initialize\n * \\param config Config of network setting\n */\n static void Init(Config config);\n /*!\n * \\brief Initialize\n */\n static void Init(int num_machines, int rank, ReduceScatterFunction reduce_scatter_ext_fun, AllgatherFunction allgather_ext_fun);\n /*! \\brief Free this static class */\n static void Dispose();\n /*! \\brief Get rank of this machine */\n static int rank();\n /*! \\brief Get total number of machines */\n static int num_machines();\n\n /*!\n * \\brief Perform all_reduce. if data size is small,\n will perform AllreduceByAllGather, else with call ReduceScatter followed allgather\n * \\param input Input data\n * \\param input_size The size of input data\n * \\param type_size The size of one object in the reduce function\n * \\param output Output result\n * \\param reducer Reduce function\n */\n static void Allreduce(char* input, comm_size_t input_size, int type_size,\n char* output, const ReduceFunction& reducer);\n\n /*!\n * \\brief Perform all_reduce by using all_gather. it can be use to reduce communication time when data is small\n * \\param input Input data\n * \\param input_size The size of input data\n * \\param type_size The size of one object in the reduce function\n * \\param output Output result\n * \\param reducer Reduce function\n */\n static void AllreduceByAllGather(char* input, comm_size_t input_size, int type_size, char* output,\n const ReduceFunction& reducer);\n\n /*!\n * \\brief Performing all_gather by using Bruck algorithm.\n Communication times is O(log(n)), and communication cost is O(send_size * number_machine)\n * It can be used when all nodes have same input size.\n * \\param input Input data\n * \\param send_size The size of input data\n * \\param output Output result\n */\n static void Allgather(char* input, comm_size_t send_size, char* output);\n\n /*!\n * \\brief Performing all_gather by using Bruck algorithm.\n Communication times is O(log(n)), and communication cost is O(all_size)\n * It can be used when nodes have different input size.\n * \\param input Input data\n * \\param block_start The block start for different machines\n * \\param block_len The block size for different machines\n * \\param output Output result\n * \\param all_size The size of output data\n */\n static void Allgather(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size);\n\n /*!\n * \\brief Perform reduce scatter by using recursive halving algorithm.\n Communication times is O(log(n)), and communication cost is O(input_size)\n * \\param input Input data\n * \\param input_size The size of input data\n * \\param type_size The size of one object in the reduce function\n * \\param block_start The block start for different machines\n * \\param block_len The block size for different machines\n * \\param output Output result\n * \\param output_size size of output data\n * \\param reducer Reduce function\n */\n static void ReduceScatter(char* input, comm_size_t input_size, int type_size,\n const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t output_size,\n const ReduceFunction& reducer);\n\n template\n static T GlobalSyncUpByMin(T local) {\n T global = local;\n Allreduce(reinterpret_cast(&local),\n sizeof(local), sizeof(local),\n reinterpret_cast(&global),\n [] (const char* src, char* dst, int type_size, comm_size_t len) {\n comm_size_t used_size = 0;\n const T *p1;\n T *p2;\n while (used_size < len) {\n p1 = reinterpret_cast(src);\n p2 = reinterpret_cast(dst);\n if (*p1 < *p2) {\n std::memcpy(dst, src, type_size);\n }\n src += type_size;\n dst += type_size;\n used_size += type_size;\n }\n });\n return global;\n }\n template\n static T GlobalSyncUpByMax(T local) {\n T global = local;\n Allreduce(reinterpret_cast(&local),\n sizeof(local), sizeof(local),\n reinterpret_cast(&global),\n [] (const char* src, char* dst, int type_size, comm_size_t len) {\n comm_size_t used_size = 0;\n const T *p1;\n T *p2;\n while (used_size < len) {\n p1 = reinterpret_cast(src);\n p2 = reinterpret_cast(dst);\n if (*p1 > *p2) {\n std::memcpy(dst, src, type_size);\n }\n src += type_size;\n dst += type_size;\n used_size += type_size;\n }\n });\n return global;\n }\n\n template\n static T GlobalSyncUpBySum(T local) {\n T global = (T)0;\n Allreduce(reinterpret_cast(&local),\n sizeof(local), sizeof(local),\n reinterpret_cast(&global),\n [](const char* src, char* dst, int type_size, comm_size_t len) {\n comm_size_t used_size = 0;\n const T* p1;\n T* p2;\n while (used_size < len) {\n p1 = reinterpret_cast(src);\n p2 = reinterpret_cast(dst);\n *p2 += *p1;\n src += type_size;\n dst += type_size;\n used_size += type_size;\n }\n });\n return static_cast(global);\n }\n\n template\n static T GlobalSyncUpByMean(T local) {\n return static_cast(GlobalSyncUpBySum(local) / num_machines_);\n }\n\n template\n static std::vector GlobalSum(std::vector* local) {\n std::vector global(local->size(), 0);\n Allreduce(reinterpret_cast(local->data()),\n static_cast(sizeof(T) * local->size()), sizeof(T),\n reinterpret_cast(global.data()),\n [](const char* src, char* dst, int type_size, comm_size_t len) {\n comm_size_t used_size = 0;\n const T *p1;\n T *p2;\n while (used_size < len) {\n p1 = reinterpret_cast(src);\n p2 = reinterpret_cast(dst);\n *p2 += *p1;\n src += type_size;\n dst += type_size;\n used_size += type_size;\n }\n });\n return global;\n }\n\n template\n static std::vector GlobalArray(T local) {\n std::vector global(num_machines_, 0);\n int type_size = sizeof(T);\n std::vector block_start(num_machines_);\n std::vector block_len(num_machines_, type_size);\n for (int i = 1; i < num_machines_; ++i) {\n block_start[i] = block_start[i - 1] + block_len[i - 1];\n }\n Allgather(reinterpret_cast(&local), block_start.data(), block_len.data(), reinterpret_cast(global.data()), type_size*num_machines_);\n return global;\n }\n\n private:\n static void AllgatherBruck(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size);\n\n static void AllgatherRecursiveDoubling(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size);\n\n static void AllgatherRing(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size);\n\n static void ReduceScatterRecursiveHalving(char* input, comm_size_t input_size, int type_size,\n const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t output_size,\n const ReduceFunction& reducer);\n\n static void ReduceScatterRing(char* input, comm_size_t input_size, int type_size,\n const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t output_size,\n const ReduceFunction& reducer);\n\n /*! \\brief Number of all machines */\n static THREAD_LOCAL int num_machines_;\n /*! \\brief Rank of local machine */\n static THREAD_LOCAL int rank_;\n /*! \\brief The network interface, provide send/recv functions */\n static THREAD_LOCAL std::unique_ptr linkers_;\n /*! \\brief Bruck map for all gather algorithm*/\n static THREAD_LOCAL BruckMap bruck_map_;\n /*! \\brief Recursive halving map for reduce scatter */\n static THREAD_LOCAL RecursiveHalvingMap recursive_halving_map_;\n /*! \\brief Buffer to store block start index */\n static THREAD_LOCAL std::vector block_start_;\n /*! \\brief Buffer to store block size */\n static THREAD_LOCAL std::vector block_len_;\n /*! \\brief Buffer */\n static THREAD_LOCAL std::vector buffer_;\n /*! \\brief Size of buffer_ */\n static THREAD_LOCAL comm_size_t buffer_size_;\n /*! \\brief Funcs*/\n static THREAD_LOCAL ReduceScatterFunction reduce_scatter_ext_fun_;\n static THREAD_LOCAL AllgatherFunction allgather_ext_fun_;\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_NETWORK_H_\n" }, { "path": "include/LightGBM/objective_function.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_OBJECTIVE_FUNCTION_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_OBJECTIVE_FUNCTION_H_\n\n#include \n#include \n#include \n\n#include \n#include \n\nnamespace LightGBM {\n/*!\n* \\brief The interface of Objective Function.\n*/\nclass ObjectiveFunction {\n public:\n /*! \\brief virtual destructor */\n virtual ~ObjectiveFunction() {}\n\n /*!\n * \\brief Initialize\n * \\param metadata Label data\n * \\param num_data Number of data\n */\n virtual void Init(const Metadata& metadata, data_size_t num_data) = 0;\n\n /*!\n * \\brief calculating first order derivative of loss function\n * \\param score prediction score in this round\n * \\gradients Output gradients\n * \\hessians Output hessians\n */\n virtual void GetGradients(const double* score,\n score_t* gradients, score_t* hessians) const = 0;\n\n /*!\n * \\brief calculating first order derivative of loss function, used only for bagging by query in lambdarank\n * \\param score prediction score in this round\n * \\param num_sampled_queries number of in-bag queries\n * \\param sampled_query_indices indices of in-bag queries\n * \\gradients Output gradients\n * \\hessians Output hessians\n */\n virtual void GetGradientsWithSampledQueries(const double* score, const data_size_t /*num_sampled_queries*/, const data_size_t* /*sampled_query_indices*/,\n score_t* gradients, score_t* hessians) const { GetGradients(score, gradients, hessians); }\n\n virtual const char* GetName() const = 0;\n\n virtual bool IsConstantHessian() const { return false; }\n\n virtual bool IsRenewTreeOutput() const { return false; }\n\n virtual double RenewTreeOutput(double ori_output, std::function,\n const data_size_t*,\n const data_size_t*,\n data_size_t) const { return ori_output; }\n\n virtual void RenewTreeOutputCUDA(const double* /*score*/, const data_size_t* /*data_indices_in_leaf*/, const data_size_t* /*num_data_in_leaf*/,\n const data_size_t* /*data_start_in_leaf*/, const int /*num_leaves*/, double* /*leaf_value*/) const {}\n\n virtual double BoostFromScore(int /*class_id*/) const { return 0.0; }\n\n virtual bool ClassNeedTrain(int /*class_id*/) const { return true; }\n\n virtual bool SkipEmptyClass() const { return false; }\n\n virtual int NumModelPerIteration() const { return 1; }\n\n virtual int NumPredictOneRow() const { return 1; }\n\n /*! \\brief The prediction should be accurate or not. True will disable early stopping for prediction. */\n virtual bool NeedAccuratePrediction() const { return true; }\n\n /*! \\brief Return the number of positive samples. Return 0 if no binary classification tasks.*/\n virtual data_size_t NumPositiveData() const { return 0; }\n\n virtual void ConvertOutput(const double* input, double* output) const {\n output[0] = input[0];\n }\n\n virtual std::string ToString() const = 0;\n\n ObjectiveFunction() = default;\n /*! \\brief Disable copy */\n ObjectiveFunction& operator=(const ObjectiveFunction&) = delete;\n /*! \\brief Disable copy */\n ObjectiveFunction(const ObjectiveFunction&) = delete;\n\n /*!\n * \\brief Create object of objective function\n * \\param type Specific type of objective function\n * \\param config Config for objective function\n */\n LIGHTGBM_EXPORT static ObjectiveFunction* CreateObjectiveFunction(const std::string& type,\n const Config& config);\n\n /*!\n * \\brief Load objective function from string object\n */\n LIGHTGBM_EXPORT static ObjectiveFunction* CreateObjectiveFunction(const std::string& str);\n\n /*!\n * \\brief Whether boosting is done on CUDA\n */\n virtual bool IsCUDAObjective() const { return false; }\n\n #ifdef USE_CUDA\n /*!\n * \\brief Convert output for CUDA version\n */\n virtual const double* ConvertOutputCUDA(data_size_t /*num_data*/, const double* input, double* /*output*/) const {\n return input;\n }\n\n virtual bool NeedConvertOutputCUDA () const { return false; }\n\n virtual void SetNCCLInfo(\n ncclComm_t /*nccl_communicator*/,\n int /*nccl_gpu_rank*/,\n int /*local_gpu_rank*/,\n int /*gpu_device_id*/,\n data_size_t /*global_num_data*/) {}\n\n /*!\n * \\brief Create object of objective function on CUDA\n * \\param type Specific type of objective function\n * \\param config Config for objective function\n */\n LIGHTGBM_EXPORT static ObjectiveFunction* CreateObjectiveFunctionCUDA(const std::string& type,\n const Config& config);\n\n #endif // USE_CUDA\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_OBJECTIVE_FUNCTION_H_\n" }, { "path": "include/LightGBM/prediction_early_stop.h", "content": "/*!\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_PREDICTION_EARLY_STOP_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_PREDICTION_EARLY_STOP_H_\n\n#include \n\n#include \n#include \n\nnamespace LightGBM {\n\nstruct PredictionEarlyStopInstance {\n /// Callback function type for early stopping.\n /// Takes current prediction and number of elements in prediction\n /// @returns true if prediction should stop according to criterion\n using FunctionType = std::function;\n\n FunctionType callback_function; // callback function itself\n int round_period; // call callback_function every `runPeriod` iterations\n};\n\nstruct PredictionEarlyStopConfig {\n int round_period;\n double margin_threshold;\n};\n\n/// Create an early stopping algorithm of type `type`, with given round_period and margin threshold\nLIGHTGBM_EXPORT PredictionEarlyStopInstance CreatePredictionEarlyStopInstance(const std::string& type,\n const PredictionEarlyStopConfig& config);\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_PREDICTION_EARLY_STOP_H_\n" }, { "path": "include/LightGBM/sample_strategy.h", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_SAMPLE_STRATEGY_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_SAMPLE_STRATEGY_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\nnamespace LightGBM {\n\nclass SampleStrategy {\n public:\n SampleStrategy() : balanced_bagging_(false), bagging_runner_(0, bagging_rand_block_), need_resize_gradients_(false) {}\n\n virtual ~SampleStrategy() {}\n\n static SampleStrategy* CreateSampleStrategy(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function, int num_tree_per_iteration);\n\n virtual void Bagging(int iter, TreeLearner* tree_learner, score_t* gradients, score_t* hessians) = 0;\n\n virtual void ResetSampleConfig(const Config* config, bool is_change_dataset) = 0;\n\n bool is_use_subset() const { return is_use_subset_; }\n\n data_size_t bag_data_cnt() const { return bag_data_cnt_; }\n\n std::vector>& bag_data_indices() { return bag_data_indices_; }\n\n #ifdef USE_CUDA\n CUDAVector& cuda_bag_data_indices() { return cuda_bag_data_indices_; }\n #endif // USE_CUDA\n\n void UpdateObjectiveFunction(const ObjectiveFunction* objective_function) {\n objective_function_ = objective_function;\n }\n\n void UpdateTrainingData(const Dataset* train_data) {\n train_data_ = train_data;\n num_data_ = train_data->num_data();\n }\n\n virtual bool IsHessianChange() const = 0;\n\n bool NeedResizeGradients() const { return need_resize_gradients_; }\n\n virtual data_size_t num_sampled_queries() const { return 0; }\n\n virtual const data_size_t* sampled_query_indices() const { return nullptr; }\n\n protected:\n const Config* config_;\n const Dataset* train_data_;\n const ObjectiveFunction* objective_function_;\n std::vector> bag_data_indices_;\n data_size_t bag_data_cnt_;\n data_size_t num_data_;\n int num_tree_per_iteration_;\n std::unique_ptr tmp_subset_;\n bool is_use_subset_;\n bool balanced_bagging_;\n const int bagging_rand_block_ = 1024;\n std::vector bagging_rands_;\n ParallelPartitionRunner bagging_runner_;\n /*! \\brief whether need to resize the gradient vectors */\n bool need_resize_gradients_;\n\n #ifdef USE_CUDA\n /*! \\brief Buffer for bag_data_indices_ on GPU, used only with cuda */\n CUDAVector cuda_bag_data_indices_;\n #endif // USE_CUDA\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_SAMPLE_STRATEGY_H_\n" }, { "path": "include/LightGBM/train_share_states.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_TRAIN_SHARE_STATES_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_TRAIN_SHARE_STATES_H_\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nclass MultiValBinWrapper {\n public:\n MultiValBinWrapper(MultiValBin* bin, data_size_t num_data,\n const std::vector& feature_groups_contained, const int num_grad_quant_bins);\n\n bool IsSparse() {\n if (multi_val_bin_ != nullptr) {\n return multi_val_bin_->IsSparse();\n }\n return false;\n }\n\n void InitTrain(const std::vector& group_feature_start,\n const std::vector>& feature_groups,\n const std::vector& is_feature_used,\n const data_size_t* bagging_use_indices,\n data_size_t bagging_indices_cnt);\n\n template \n void HistMove(const std::vector>& hist_buf);\n\n template \n void HistMerge(std::vector>* hist_buf);\n\n void ResizeHistBuf(std::vector>* hist_buf,\n MultiValBin* sub_multi_val_bin,\n hist_t* origin_hist_data);\n\n template \n void ConstructHistograms(const data_size_t* data_indices,\n data_size_t num_data,\n const score_t* gradients,\n const score_t* hessians,\n std::vector>* hist_buf,\n hist_t* origin_hist_data) {\n const auto cur_multi_val_bin = (is_use_subcol_ || is_use_subrow_)\n ? multi_val_bin_subset_.get()\n : multi_val_bin_.get();\n if (cur_multi_val_bin != nullptr) {\n global_timer.Start(\"Dataset::sparse_bin_histogram\");\n n_data_block_ = 1;\n data_block_size_ = num_data;\n Threading::BlockInfo(num_threads_, num_data, min_block_size_,\n &n_data_block_, &data_block_size_);\n ResizeHistBuf(hist_buf, cur_multi_val_bin, origin_hist_data);\n const int inner_hist_bits = (data_block_size_ * num_grad_quant_bins_ < 256 && HIST_BITS == 16) ? 8 : HIST_BITS;\n OMP_INIT_EX();\n #pragma omp parallel for schedule(static) num_threads(num_threads_)\n for (int block_id = 0; block_id < n_data_block_; ++block_id) {\n OMP_LOOP_EX_BEGIN();\n data_size_t start = block_id * data_block_size_;\n data_size_t end = std::min(start + data_block_size_, num_data);\n if (inner_hist_bits == 8) {\n ConstructHistogramsForBlock(\n cur_multi_val_bin, start, end, data_indices, gradients, hessians,\n block_id, hist_buf);\n } else {\n ConstructHistogramsForBlock(\n cur_multi_val_bin, start, end, data_indices, gradients, hessians,\n block_id, hist_buf);\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n global_timer.Stop(\"Dataset::sparse_bin_histogram\");\n\n global_timer.Start(\"Dataset::sparse_bin_histogram_merge\");\n if (inner_hist_bits == 8) {\n HistMerge(hist_buf);\n } else {\n HistMerge(hist_buf);\n }\n global_timer.Stop(\"Dataset::sparse_bin_histogram_merge\");\n global_timer.Start(\"Dataset::sparse_bin_histogram_move\");\n if (inner_hist_bits == 8) {\n HistMove(*hist_buf);\n } else {\n HistMove(*hist_buf);\n }\n global_timer.Stop(\"Dataset::sparse_bin_histogram_move\");\n }\n }\n\n template \n void ConstructHistogramsForBlock(const MultiValBin* sub_multi_val_bin,\n data_size_t start, data_size_t end, const data_size_t* data_indices,\n const score_t* gradients, const score_t* hessians, int block_id,\n std::vector>* hist_buf) {\n if (USE_QUANT_GRAD) {\n if (HIST_BITS == 8) {\n int8_t* hist_buf_ptr = reinterpret_cast(hist_buf->data());\n int8_t* data_ptr = hist_buf_ptr +\n static_cast(num_bin_aligned_) * block_id * 2;\n std::memset(reinterpret_cast(data_ptr), 0, num_bin_ * kInt8HistBufferEntrySize);\n if (USE_INDICES) {\n if (ORDERED) {\n sub_multi_val_bin->ConstructHistogramOrderedInt8(data_indices, start, end,\n gradients, hessians,\n reinterpret_cast(data_ptr));\n } else {\n sub_multi_val_bin->ConstructHistogramInt8(data_indices, start, end, gradients,\n hessians,\n reinterpret_cast(data_ptr));\n }\n } else {\n sub_multi_val_bin->ConstructHistogramInt8(start, end, gradients, hessians,\n reinterpret_cast(data_ptr));\n }\n } else if (HIST_BITS == 16) {\n int16_t* data_ptr = reinterpret_cast(origin_hist_data_);\n int16_t* hist_buf_ptr = reinterpret_cast(hist_buf->data());\n if (block_id == 0) {\n if (is_use_subcol_) {\n data_ptr = hist_buf_ptr + hist_buf->size() - 2 * static_cast(num_bin_aligned_);\n }\n } else {\n data_ptr = hist_buf_ptr +\n static_cast(num_bin_aligned_) * (block_id - 1) * 2;\n }\n std::memset(reinterpret_cast(data_ptr), 0, num_bin_ * kInt16HistBufferEntrySize);\n if (USE_INDICES) {\n if (ORDERED) {\n sub_multi_val_bin->ConstructHistogramOrderedInt16(data_indices, start, end,\n gradients, hessians,\n reinterpret_cast(data_ptr));\n } else {\n sub_multi_val_bin->ConstructHistogramInt16(data_indices, start, end, gradients,\n hessians,\n reinterpret_cast(data_ptr));\n }\n } else {\n sub_multi_val_bin->ConstructHistogramInt16(start, end, gradients, hessians,\n reinterpret_cast(data_ptr));\n }\n } else {\n int32_t* data_ptr = reinterpret_cast(origin_hist_data_);\n int32_t* hist_buf_ptr = reinterpret_cast(hist_buf->data());\n if (block_id == 0) {\n if (is_use_subcol_) {\n data_ptr = hist_buf_ptr + hist_buf->size() - 2 * static_cast(num_bin_aligned_);\n }\n } else {\n data_ptr = hist_buf_ptr +\n static_cast(num_bin_aligned_) * (block_id - 1) * 2;\n }\n std::memset(reinterpret_cast(data_ptr), 0, num_bin_ * kInt32HistBufferEntrySize);\n if (USE_INDICES) {\n if (ORDERED) {\n sub_multi_val_bin->ConstructHistogramOrderedInt32(data_indices, start, end,\n gradients, hessians,\n reinterpret_cast(data_ptr));\n } else {\n sub_multi_val_bin->ConstructHistogramInt32(data_indices, start, end, gradients,\n hessians,\n reinterpret_cast(data_ptr));\n }\n } else {\n sub_multi_val_bin->ConstructHistogramInt32(start, end, gradients, hessians,\n reinterpret_cast(data_ptr));\n }\n }\n } else {\n hist_t* data_ptr = origin_hist_data_;\n if (block_id == 0) {\n if (is_use_subcol_) {\n data_ptr = hist_buf->data() + hist_buf->size() - 2 * static_cast(num_bin_aligned_);\n }\n } else {\n data_ptr = hist_buf->data() +\n static_cast(num_bin_aligned_) * (block_id - 1) * 2;\n }\n std::memset(reinterpret_cast(data_ptr), 0, num_bin_ * kHistBufferEntrySize);\n if (USE_INDICES) {\n if (ORDERED) {\n sub_multi_val_bin->ConstructHistogramOrdered(data_indices, start, end,\n gradients, hessians, data_ptr);\n } else {\n sub_multi_val_bin->ConstructHistogram(data_indices, start, end, gradients,\n hessians, data_ptr);\n }\n } else {\n sub_multi_val_bin->ConstructHistogram(start, end, gradients, hessians,\n data_ptr);\n }\n }\n }\n\n void CopyMultiValBinSubset(const std::vector& group_feature_start,\n const std::vector>& feature_groups,\n const std::vector& is_feature_used,\n const data_size_t* bagging_use_indices,\n data_size_t bagging_indices_cnt);\n\n void SetUseSubrow(bool is_use_subrow) {\n is_use_subrow_ = is_use_subrow;\n }\n\n void SetSubrowCopied(bool is_subrow_copied) {\n is_subrow_copied_ = is_subrow_copied;\n }\n\n\n #ifdef USE_CUDA\n const void* GetRowWiseData(\n uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const {\n if (multi_val_bin_ == nullptr) {\n *bit_type = 0;\n *total_size = 0;\n *is_sparse = false;\n return nullptr;\n } else {\n return multi_val_bin_->GetRowWiseData(bit_type, total_size, is_sparse, out_data_ptr, data_ptr_bit_type);\n }\n }\n #endif // USE_CUDA\n\n private:\n bool is_use_subcol_ = false;\n bool is_use_subrow_ = false;\n bool is_subrow_copied_ = false;\n std::unique_ptr multi_val_bin_;\n std::unique_ptr multi_val_bin_subset_;\n std::vector hist_move_src_;\n std::vector hist_move_dest_;\n std::vector hist_move_size_;\n const std::vector feature_groups_contained_;\n\n int num_threads_;\n int num_bin_;\n int num_bin_aligned_;\n int n_data_block_;\n int data_block_size_;\n int min_block_size_;\n int num_data_;\n int num_grad_quant_bins_;\n\n hist_t* origin_hist_data_;\n\n const size_t kHistBufferEntrySize = 2 * sizeof(hist_t);\n const size_t kInt32HistBufferEntrySize = 2 * sizeof(int32_t);\n const size_t kInt16HistBufferEntrySize = 2 * sizeof(int16_t);\n const size_t kInt8HistBufferEntrySize = 2 * sizeof(int8_t);\n};\n\nstruct TrainingShareStates {\n int num_threads = 0;\n bool is_col_wise = true;\n bool is_constant_hessian = true;\n const data_size_t* bagging_use_indices;\n data_size_t bagging_indices_cnt;\n\n TrainingShareStates() {\n multi_val_bin_wrapper_.reset(nullptr);\n }\n\n int num_hist_total_bin() { return num_hist_total_bin_; }\n\n const std::vector& feature_hist_offsets() const { return feature_hist_offsets_; }\n\n #ifdef USE_CUDA\n const std::vector& column_hist_offsets() const { return column_hist_offsets_; }\n #endif // USE_CUDA\n\n bool IsSparseRowwise() {\n return (multi_val_bin_wrapper_ != nullptr && multi_val_bin_wrapper_->IsSparse());\n }\n\n void SetMultiValBin(MultiValBin* bin, data_size_t num_data,\n const std::vector>& feature_groups,\n bool dense_only, bool sparse_only, const int num_grad_quant_bins);\n\n void CalcBinOffsets(const std::vector>& feature_groups,\n std::vector* offsets, bool is_col_wise);\n\n void InitTrain(const std::vector& group_feature_start,\n const std::vector>& feature_groups,\n const std::vector& is_feature_used) {\n if (multi_val_bin_wrapper_ != nullptr) {\n multi_val_bin_wrapper_->InitTrain(group_feature_start,\n feature_groups,\n is_feature_used,\n bagging_use_indices,\n bagging_indices_cnt);\n }\n }\n\n template \n void ConstructHistograms(const data_size_t* data_indices,\n data_size_t num_data,\n const score_t* gradients,\n const score_t* hessians,\n hist_t* hist_data) {\n if (multi_val_bin_wrapper_ != nullptr) {\n multi_val_bin_wrapper_->ConstructHistograms(\n data_indices, num_data, gradients, hessians, &hist_buf_, hist_data);\n }\n }\n\n void SetUseSubrow(bool is_use_subrow) {\n if (multi_val_bin_wrapper_ != nullptr) {\n multi_val_bin_wrapper_->SetUseSubrow(is_use_subrow);\n }\n }\n\n void SetSubrowCopied(bool is_subrow_copied) {\n if (multi_val_bin_wrapper_ != nullptr) {\n multi_val_bin_wrapper_->SetSubrowCopied(is_subrow_copied);\n }\n }\n\n\n #ifdef USE_CUDA\n const void* GetRowWiseData(uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) {\n if (multi_val_bin_wrapper_ != nullptr) {\n return multi_val_bin_wrapper_->GetRowWiseData(bit_type, total_size, is_sparse, out_data_ptr, data_ptr_bit_type);\n } else {\n *bit_type = 0;\n *total_size = 0;\n *is_sparse = false;\n return nullptr;\n }\n }\n #endif // USE_CUDA\n\n private:\n std::vector feature_hist_offsets_;\n #ifdef USE_CUDA\n std::vector column_hist_offsets_;\n #endif // USE_CUDA\n int num_hist_total_bin_ = 0;\n std::unique_ptr multi_val_bin_wrapper_;\n std::vector> hist_buf_;\n int num_total_bin_ = 0;\n double num_elements_per_row_ = 0.0f;\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_TRAIN_SHARE_STATES_H_\n" }, { "path": "include/LightGBM/tree.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_TREE_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_TREE_H_\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\n#define kCategoricalMask (1)\n#define kDefaultLeftMask (2)\n\n/*!\n* \\brief Tree model\n*/\nclass Tree {\n public:\n /*!\n * \\brief Constructor\n * \\param max_leaves The number of max leaves\n * \\param track_branch_features Whether to keep track of ancestors of leaf nodes\n * \\param is_linear Whether the tree has linear models at each leaf\n */\n explicit Tree(int max_leaves, bool track_branch_features, bool is_linear);\n\n /*!\n * \\brief Constructor, from a string\n * \\param str Model string\n * \\param used_len used count of str\n */\n Tree(const char* str, size_t* used_len);\n\n virtual ~Tree() noexcept = default;\n\n /*!\n * \\brief Performing a split on tree leaves.\n * \\param leaf Index of leaf to be split\n * \\param feature Index of feature; the converted index after removing useless features\n * \\param real_feature Index of feature, the original index on data\n * \\param threshold_bin Threshold(bin) of split\n * \\param threshold_double Threshold on feature value\n * \\param left_value Model Left child output\n * \\param right_value Model Right child output\n * \\param left_cnt Count of left child\n * \\param right_cnt Count of right child\n * \\param left_weight Weight of left child\n * \\param right_weight Weight of right child\n * \\param gain Split gain\n * \\param missing_type missing type\n * \\param default_left default direction for missing value\n * \\return The index of new leaf.\n */\n int Split(int leaf, int feature, int real_feature, uint32_t threshold_bin,\n double threshold_double, double left_value, double right_value,\n int left_cnt, int right_cnt, double left_weight, double right_weight,\n float gain, MissingType missing_type, bool default_left);\n\n /*!\n * \\brief Performing a split on tree leaves, with categorical feature\n * \\param leaf Index of leaf to be split\n * \\param feature Index of feature; the converted index after removing useless features\n * \\param real_feature Index of feature, the original index on data\n * \\param threshold_bin Threshold(bin) of split, use bitset to represent\n * \\param num_threshold_bin size of threshold_bin\n * \\param threshold Thresholds of real feature value, use bitset to represent\n * \\param num_threshold size of threshold\n * \\param left_value Model Left child output\n * \\param right_value Model Right child output\n * \\param left_cnt Count of left child\n * \\param right_cnt Count of right child\n * \\param left_weight Weight of left child\n * \\param right_weight Weight of right child\n * \\param gain Split gain\n * \\return The index of new leaf.\n */\n int SplitCategorical(int leaf, int feature, int real_feature, const uint32_t* threshold_bin, int num_threshold_bin,\n const uint32_t* threshold, int num_threshold, double left_value, double right_value,\n int left_cnt, int right_cnt, double left_weight, double right_weight, float gain, MissingType missing_type);\n\n /*! \\brief Get the output of one leaf */\n inline double LeafOutput(int leaf) const { return leaf_value_[leaf]; }\n\n /*! \\brief Set the output of one leaf */\n inline void SetLeafOutput(int leaf, double output) {\n leaf_value_[leaf] = MaybeRoundToZero(output);\n }\n\n /*!\n * \\brief Adding prediction value of this tree model to scores\n * \\param data The dataset\n * \\param num_data Number of total data\n * \\param score Will add prediction to score\n */\n virtual void AddPredictionToScore(const Dataset* data,\n data_size_t num_data,\n double* score) const;\n\n /*!\n * \\brief Adding prediction value of this tree model to scores\n * \\param data The dataset\n * \\param used_data_indices Indices of used data\n * \\param num_data Number of total data\n * \\param score Will add prediction to score\n */\n virtual void AddPredictionToScore(const Dataset* data,\n const data_size_t* used_data_indices,\n data_size_t num_data, double* score) const;\n\n /*!\n * \\brief Get upper bound leaf value of this tree model\n */\n double GetUpperBoundValue() const;\n\n /*!\n * \\brief Get lower bound leaf value of this tree model\n */\n double GetLowerBoundValue() const;\n\n /*!\n * \\brief Prediction on one record\n * \\param feature_values Feature value of this record\n * \\return Prediction result\n */\n inline double Predict(const double* feature_values) const;\n inline double PredictByMap(const std::unordered_map& feature_values) const;\n\n inline int PredictLeafIndex(const double* feature_values) const;\n inline int PredictLeafIndexByMap(const std::unordered_map& feature_values) const;\n\n inline void PredictContrib(const double* feature_values, int num_features, double* output);\n inline void PredictContribByMap(const std::unordered_map& feature_values,\n int num_features, std::unordered_map* output);\n\n /*! \\brief Get Number of leaves*/\n inline int num_leaves() const { return num_leaves_; }\n\n /*! \\brief Get depth of specific leaf*/\n inline int leaf_depth(int leaf_idx) const { return leaf_depth_[leaf_idx]; }\n\n /*! \\brief Get parent of specific leaf*/\n inline int leaf_parent(int leaf_idx) const {return leaf_parent_[leaf_idx]; }\n\n /*! \\brief Get feature of specific split (original feature index)*/\n inline int split_feature(int split_idx) const { return split_feature_[split_idx]; }\n\n /*! \\brief Get feature of specific split*/\n inline int split_feature_inner(int split_idx) const { return split_feature_inner_[split_idx]; }\n\n /*! \\brief Get features on leaf's branch*/\n inline std::vector branch_features(int leaf) const { return branch_features_[leaf]; }\n\n inline double split_gain(int split_idx) const { return split_gain_[split_idx]; }\n\n inline double internal_value(int node_idx) const {\n return internal_value_[node_idx];\n }\n\n inline bool IsNumericalSplit(int node_idx) const {\n return !GetDecisionType(decision_type_[node_idx], kCategoricalMask);\n }\n\n inline int left_child(int node_idx) const { return left_child_[node_idx]; }\n\n inline int right_child(int node_idx) const { return right_child_[node_idx]; }\n\n inline uint32_t threshold_in_bin(int node_idx) const {\n return threshold_in_bin_[node_idx];\n }\n\n /*! \\brief Get the number of data points that fall at or below this node*/\n inline int data_count(int node) const { return node >= 0 ? internal_count_[node] : leaf_count_[~node]; }\n\n /*!\n * \\brief Shrinkage for the tree's output\n * shrinkage rate (a.k.a learning rate) is used to tune the training process\n * \\param rate The factor of shrinkage\n */\n virtual inline void Shrinkage(double rate) {\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1024) if (num_leaves_ >= 2048)\n for (int i = 0; i < num_leaves_ - 1; ++i) {\n leaf_value_[i] = MaybeRoundToZero(leaf_value_[i] * rate);\n internal_value_[i] = MaybeRoundToZero(internal_value_[i] * rate);\n if (is_linear_) {\n leaf_const_[i] = MaybeRoundToZero(leaf_const_[i] * rate);\n for (size_t j = 0; j < leaf_coeff_[i].size(); ++j) {\n leaf_coeff_[i][j] = MaybeRoundToZero(leaf_coeff_[i][j] * rate);\n }\n }\n }\n leaf_value_[num_leaves_ - 1] =\n MaybeRoundToZero(leaf_value_[num_leaves_ - 1] * rate);\n if (is_linear_) {\n leaf_const_[num_leaves_ - 1] = MaybeRoundToZero(leaf_const_[num_leaves_ - 1] * rate);\n for (size_t j = 0; j < leaf_coeff_[num_leaves_ - 1].size(); ++j) {\n leaf_coeff_[num_leaves_ - 1][j] = MaybeRoundToZero(leaf_coeff_[num_leaves_ - 1][j] * rate);\n }\n }\n shrinkage_ *= rate;\n }\n\n inline double shrinkage() const { return shrinkage_; }\n\n virtual inline void AddBias(double val) {\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1024) if (num_leaves_ >= 2048)\n for (int i = 0; i < num_leaves_ - 1; ++i) {\n leaf_value_[i] = MaybeRoundToZero(leaf_value_[i] + val);\n internal_value_[i] = MaybeRoundToZero(internal_value_[i] + val);\n }\n leaf_value_[num_leaves_ - 1] =\n MaybeRoundToZero(leaf_value_[num_leaves_ - 1] + val);\n if (is_linear_) {\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1024) if (num_leaves_ >= 2048)\n for (int i = 0; i < num_leaves_ - 1; ++i) {\n leaf_const_[i] = MaybeRoundToZero(leaf_const_[i] + val);\n }\n leaf_const_[num_leaves_ - 1] = MaybeRoundToZero(leaf_const_[num_leaves_ - 1] + val);\n }\n // force to 1.0\n shrinkage_ = 1.0f;\n }\n\n virtual inline void AsConstantTree(double val, int count = 0) {\n num_leaves_ = 1;\n shrinkage_ = 1.0f;\n leaf_value_[0] = val;\n if (is_linear_) {\n leaf_const_[0] = val;\n }\n leaf_count_[0] = count;\n }\n\n /*! \\brief Serialize this object to string*/\n std::string ToString() const;\n\n /*! \\brief Serialize this object to json*/\n std::string ToJSON() const;\n\n /*! \\brief Serialize linear model of tree node to json*/\n std::string LinearModelToJSON(int index) const;\n\n /*! \\brief Serialize this object to if-else statement*/\n std::string ToIfElse(int index, bool predict_leaf_index) const;\n\n inline static bool IsZero(double fval) {\n return (fval >= -kZeroThreshold && fval <= kZeroThreshold);\n }\n\n inline static double MaybeRoundToZero(double fval) {\n return IsZero(fval) ? 0 : fval;\n }\n\n inline static bool GetDecisionType(int8_t decision_type, int8_t mask) {\n return (decision_type & mask) > 0;\n }\n\n inline static void SetDecisionType(int8_t* decision_type, bool input, int8_t mask) {\n if (input) {\n (*decision_type) |= mask;\n } else {\n (*decision_type) &= (127 - mask);\n }\n }\n\n inline static int8_t GetMissingType(int8_t decision_type) {\n return (decision_type >> 2) & 3;\n }\n\n inline static void SetMissingType(int8_t* decision_type, int8_t input) {\n (*decision_type) &= 3;\n (*decision_type) |= (input << 2);\n }\n\n void RecomputeMaxDepth();\n\n int NextLeafId() const { return num_leaves_; }\n\n /*! \\brief Get the linear model constant term (bias) of one leaf */\n inline double LeafConst(int leaf) const { return leaf_const_[leaf]; }\n\n /*! \\brief Get the linear model coefficients of one leaf */\n inline std::vector LeafCoeffs(int leaf) const { return leaf_coeff_[leaf]; }\n\n /*! \\brief Get the linear model features of one leaf */\n inline std::vector LeafFeaturesInner(int leaf) const {return leaf_features_inner_[leaf]; }\n\n /*! \\brief Get the linear model features of one leaf */\n inline std::vector LeafFeatures(int leaf) const {return leaf_features_[leaf]; }\n\n /*! \\brief Set the linear model coefficients on one leaf */\n inline void SetLeafCoeffs(int leaf, const std::vector& output) {\n leaf_coeff_[leaf].resize(output.size());\n for (size_t i = 0; i < output.size(); ++i) {\n leaf_coeff_[leaf][i] = MaybeRoundToZero(output[i]);\n }\n }\n\n /*! \\brief Set the linear model constant term (bias) on one leaf */\n inline void SetLeafConst(int leaf, double output) {\n leaf_const_[leaf] = MaybeRoundToZero(output);\n }\n\n /*! \\brief Set the linear model features on one leaf */\n inline void SetLeafFeaturesInner(int leaf, const std::vector& features) {\n leaf_features_inner_[leaf] = features;\n }\n\n /*! \\brief Set the linear model features on one leaf */\n inline void SetLeafFeatures(int leaf, const std::vector& features) {\n leaf_features_[leaf] = features;\n }\n\n inline bool is_linear() const { return is_linear_; }\n\n #ifdef USE_CUDA\n inline bool is_cuda_tree() const { return is_cuda_tree_; }\n #endif // USE_CUDA\n\n inline void SetIsLinear(bool is_linear) {\n is_linear_ = is_linear;\n }\n\n protected:\n std::string NumericalDecisionIfElse(int node) const;\n\n std::string CategoricalDecisionIfElse(int node) const;\n\n inline int NumericalDecision(double fval, int node) const {\n uint8_t missing_type = GetMissingType(decision_type_[node]);\n if (std::isnan(fval) && missing_type != MissingType::NaN) {\n fval = 0.0f;\n }\n if ((missing_type == MissingType::Zero && IsZero(fval))\n || (missing_type == MissingType::NaN && std::isnan(fval))) {\n if (GetDecisionType(decision_type_[node], kDefaultLeftMask)) {\n return left_child_[node];\n } else {\n return right_child_[node];\n }\n }\n if (fval <= threshold_[node]) {\n return left_child_[node];\n } else {\n return right_child_[node];\n }\n }\n\n inline int NumericalDecisionInner(uint32_t fval, int node, uint32_t default_bin, uint32_t max_bin) const {\n uint8_t missing_type = GetMissingType(decision_type_[node]);\n if ((missing_type == MissingType::Zero && fval == default_bin)\n || (missing_type == MissingType::NaN && fval == max_bin)) {\n if (GetDecisionType(decision_type_[node], kDefaultLeftMask)) {\n return left_child_[node];\n } else {\n return right_child_[node];\n }\n }\n if (fval <= threshold_in_bin_[node]) {\n return left_child_[node];\n } else {\n return right_child_[node];\n }\n }\n\n inline int CategoricalDecision(double fval, int node) const {\n int int_fval;\n if (std::isnan(fval)) {\n return right_child_[node];\n } else {\n int_fval = static_cast(fval);\n if (int_fval < 0) {\n return right_child_[node];\n }\n }\n int cat_idx = static_cast(threshold_[node]);\n if (Common::FindInBitset(cat_threshold_.data() + cat_boundaries_[cat_idx],\n cat_boundaries_[cat_idx + 1] - cat_boundaries_[cat_idx], int_fval)) {\n return left_child_[node];\n }\n return right_child_[node];\n }\n\n inline int CategoricalDecisionInner(uint32_t fval, int node) const {\n int cat_idx = static_cast(threshold_in_bin_[node]);\n if (Common::FindInBitset(cat_threshold_inner_.data() + cat_boundaries_inner_[cat_idx],\n cat_boundaries_inner_[cat_idx + 1] - cat_boundaries_inner_[cat_idx], fval)) {\n return left_child_[node];\n }\n return right_child_[node];\n }\n\n inline int Decision(double fval, int node) const {\n if (GetDecisionType(decision_type_[node], kCategoricalMask)) {\n return CategoricalDecision(fval, node);\n } else {\n return NumericalDecision(fval, node);\n }\n }\n\n inline int DecisionInner(uint32_t fval, int node, uint32_t default_bin, uint32_t max_bin) const {\n if (GetDecisionType(decision_type_[node], kCategoricalMask)) {\n return CategoricalDecisionInner(fval, node);\n } else {\n return NumericalDecisionInner(fval, node, default_bin, max_bin);\n }\n }\n\n inline void Split(int leaf, int feature, int real_feature, double left_value, double right_value, int left_cnt, int right_cnt,\n double left_weight, double right_weight, float gain);\n /*!\n * \\brief Find leaf index of which record belongs by features\n * \\param feature_values Feature value of this record\n * \\return Leaf index\n */\n inline int GetLeaf(const double* feature_values) const;\n inline int GetLeafByMap(const std::unordered_map& feature_values) const;\n\n /*! \\brief Serialize one node to json*/\n std::string NodeToJSON(int index) const;\n\n /*! \\brief Serialize one node to if-else statement*/\n std::string NodeToIfElse(int index, bool predict_leaf_index) const;\n\n std::string NodeToIfElseByMap(int index, bool predict_leaf_index) const;\n\n double ExpectedValue() const;\n\n /*! \\brief This is used fill in leaf_depth_ after reloading a model*/\n inline void RecomputeLeafDepths(int node = 0, int depth = 0);\n\n /*!\n * \\brief Used by TreeSHAP for data we keep about our decision path\n */\n struct PathElement {\n int feature_index;\n double zero_fraction;\n double one_fraction;\n\n // note that pweight is included for convenience and is not tied with the other attributes,\n // the pweight of the i'th path element is the permutation weight of paths with i-1 ones in them\n double pweight;\n\n PathElement() {}\n PathElement(int i, double z, double o, double w) : feature_index(i), zero_fraction(z), one_fraction(o), pweight(w) {}\n };\n\n /*! \\brief Polynomial time algorithm for SHAP values (arXiv:1706.06060)*/\n void TreeSHAP(const double *feature_values, double *phi,\n int node, int unique_depth,\n PathElement *parent_unique_path, double parent_zero_fraction,\n double parent_one_fraction, int parent_feature_index) const;\n\n void TreeSHAPByMap(const std::unordered_map& feature_values,\n std::unordered_map* phi,\n int node, int unique_depth,\n PathElement *parent_unique_path, double parent_zero_fraction,\n double parent_one_fraction, int parent_feature_index) const;\n\n /*! \\brief Extend our decision path with a fraction of one and zero extensions for TreeSHAP*/\n static void ExtendPath(PathElement *unique_path, int unique_depth,\n double zero_fraction, double one_fraction, int feature_index);\n\n /*! \\brief Undo a previous extension of the decision path for TreeSHAP*/\n static void UnwindPath(PathElement *unique_path, int unique_depth, int path_index);\n\n /*! determine what the total permutation weight would be if we unwound a previous extension in the decision path*/\n static double UnwoundPathSum(const PathElement *unique_path, int unique_depth, int path_index);\n\n /*! \\brief Number of max leaves*/\n int max_leaves_;\n /*! \\brief Number of current leaves*/\n int num_leaves_;\n // following values used for non-leaf node\n /*! \\brief A non-leaf node's left child */\n std::vector left_child_;\n /*! \\brief A non-leaf node's right child */\n std::vector right_child_;\n /*! \\brief A non-leaf node's split feature */\n std::vector split_feature_inner_;\n /*! \\brief A non-leaf node's split feature, the original index */\n std::vector split_feature_;\n /*! \\brief A non-leaf node's split threshold in bin */\n std::vector threshold_in_bin_;\n /*! \\brief A non-leaf node's split threshold in feature value */\n std::vector threshold_;\n int num_cat_;\n std::vector cat_boundaries_inner_;\n std::vector cat_threshold_inner_;\n std::vector cat_boundaries_;\n std::vector cat_threshold_;\n /*! \\brief Store the information for categorical feature handle and missing value handle. */\n std::vector decision_type_;\n /*! \\brief A non-leaf node's split gain */\n std::vector split_gain_;\n // used for leaf node\n /*! \\brief The parent of leaf */\n std::vector leaf_parent_;\n /*! \\brief Output of leaves */\n std::vector leaf_value_;\n /*! \\brief weight of leaves */\n std::vector leaf_weight_;\n /*! \\brief DataCount of leaves */\n std::vector leaf_count_;\n /*! \\brief Output of non-leaf nodes */\n std::vector internal_value_;\n /*! \\brief weight of non-leaf nodes */\n std::vector internal_weight_;\n /*! \\brief DataCount of non-leaf nodes */\n std::vector internal_count_;\n /*! \\brief Depth for leaves */\n std::vector leaf_depth_;\n /*! \\brief whether to keep track of ancestor nodes for each leaf (only needed when feature interactions are restricted) */\n bool track_branch_features_;\n /*! \\brief Features on leaf's branch, original index */\n std::vector> branch_features_;\n double shrinkage_;\n int max_depth_;\n /*! \\brief Tree has linear model at each leaf */\n bool is_linear_;\n /*! \\brief coefficients of linear models on leaves */\n std::vector> leaf_coeff_;\n /*! \\brief constant term (bias) of linear models on leaves */\n std::vector leaf_const_;\n /* \\brief features used in leaf linear models; indexing is relative to num_total_features_ */\n std::vector> leaf_features_;\n /* \\brief features used in leaf linear models; indexing is relative to used_features_ */\n std::vector> leaf_features_inner_;\n #ifdef USE_CUDA\n /*! \\brief Marks whether this tree is a CUDATree */\n bool is_cuda_tree_;\n #endif // USE_CUDA\n};\n\ninline void Tree::Split(int leaf, int feature, int real_feature,\n double left_value, double right_value, int left_cnt, int right_cnt,\n double left_weight, double right_weight, float gain) {\n int new_node_idx = num_leaves_ - 1;\n // update parent info\n int parent = leaf_parent_[leaf];\n if (parent >= 0) {\n // if cur node is left child\n if (left_child_[parent] == ~leaf) {\n left_child_[parent] = new_node_idx;\n } else {\n right_child_[parent] = new_node_idx;\n }\n }\n // add new node\n split_feature_inner_[new_node_idx] = feature;\n split_feature_[new_node_idx] = real_feature;\n split_gain_[new_node_idx] = gain;\n // add two new leaves\n left_child_[new_node_idx] = ~leaf;\n right_child_[new_node_idx] = ~num_leaves_;\n // update new leaves\n leaf_parent_[leaf] = new_node_idx;\n leaf_parent_[num_leaves_] = new_node_idx;\n // save current leaf value to internal node before change\n internal_weight_[new_node_idx] = left_weight + right_weight;\n internal_value_[new_node_idx] = leaf_value_[leaf];\n internal_count_[new_node_idx] = left_cnt + right_cnt;\n leaf_value_[leaf] = std::isnan(left_value) ? 0.0f : left_value;\n leaf_weight_[leaf] = left_weight;\n leaf_count_[leaf] = left_cnt;\n leaf_value_[num_leaves_] = std::isnan(right_value) ? 0.0f : right_value;\n leaf_weight_[num_leaves_] = right_weight;\n leaf_count_[num_leaves_] = right_cnt;\n // update leaf depth\n leaf_depth_[num_leaves_] = leaf_depth_[leaf] + 1;\n leaf_depth_[leaf]++;\n if (track_branch_features_) {\n branch_features_[num_leaves_] = branch_features_[leaf];\n branch_features_[num_leaves_].push_back(split_feature_[new_node_idx]);\n branch_features_[leaf].push_back(split_feature_[new_node_idx]);\n }\n}\n\ninline double Tree::Predict(const double* feature_values) const {\n if (is_linear_) {\n int leaf = (num_leaves_ > 1) ? GetLeaf(feature_values) : 0;\n double output = leaf_const_[leaf];\n bool nan_found = false;\n for (size_t i = 0; i < leaf_features_[leaf].size(); ++i) {\n int feat_raw = leaf_features_[leaf][i];\n double feat_val = feature_values[feat_raw];\n if (std::isnan(feat_val)) {\n nan_found = true;\n break;\n } else {\n output += leaf_coeff_[leaf][i] * feat_val;\n }\n }\n if (nan_found) {\n return LeafOutput(leaf);\n } else {\n return output;\n }\n } else {\n if (num_leaves_ > 1) {\n int leaf = GetLeaf(feature_values);\n return LeafOutput(leaf);\n } else {\n return leaf_value_[0];\n }\n }\n}\n\ninline double Tree::PredictByMap(const std::unordered_map& feature_values) const {\n if (is_linear_) {\n int leaf = (num_leaves_ > 1) ? GetLeafByMap(feature_values) : 0;\n double output = leaf_const_[leaf];\n bool nan_found = false;\n for (size_t i = 0; i < leaf_features_[leaf].size(); ++i) {\n int feat = leaf_features_[leaf][i];\n auto val_it = feature_values.find(feat);\n if (val_it != feature_values.end()) {\n double feat_val = val_it->second;\n if (std::isnan(feat_val)) {\n nan_found = true;\n break;\n } else {\n output += leaf_coeff_[leaf][i] * feat_val;\n }\n }\n }\n if (nan_found) {\n return LeafOutput(leaf);\n } else {\n return output;\n }\n } else {\n if (num_leaves_ > 1) {\n int leaf = GetLeafByMap(feature_values);\n return LeafOutput(leaf);\n } else {\n return leaf_value_[0];\n }\n }\n}\n\ninline int Tree::PredictLeafIndex(const double* feature_values) const {\n if (num_leaves_ > 1) {\n int leaf = GetLeaf(feature_values);\n return leaf;\n } else {\n return 0;\n }\n}\n\ninline int Tree::PredictLeafIndexByMap(const std::unordered_map& feature_values) const {\n if (num_leaves_ > 1) {\n int leaf = GetLeafByMap(feature_values);\n return leaf;\n } else {\n return 0;\n }\n}\n\ninline void Tree::PredictContrib(const double* feature_values, int num_features, double* output) {\n output[num_features] += ExpectedValue();\n // Run the recursion with preallocated space for the unique path data\n if (num_leaves_ > 1) {\n CHECK_GE(max_depth_, 0);\n const int max_path_len = max_depth_ + 1;\n std::vector unique_path_data(max_path_len*(max_path_len + 1) / 2);\n TreeSHAP(feature_values, output, 0, 0, unique_path_data.data(), 1, 1, -1);\n }\n}\n\ninline void Tree::PredictContribByMap(const std::unordered_map& feature_values,\n int num_features, std::unordered_map* output) {\n (*output)[num_features] += ExpectedValue();\n // Run the recursion with preallocated space for the unique path data\n if (num_leaves_ > 1) {\n CHECK_GE(max_depth_, 0);\n const int max_path_len = max_depth_ + 1;\n std::vector unique_path_data(max_path_len*(max_path_len + 1) / 2);\n TreeSHAPByMap(feature_values, output, 0, 0, unique_path_data.data(), 1, 1, -1);\n }\n}\n\ninline void Tree::RecomputeLeafDepths(int node, int depth) {\n if (node == 0) leaf_depth_.resize(num_leaves());\n if (node < 0) {\n leaf_depth_[~node] = depth;\n } else {\n RecomputeLeafDepths(left_child_[node], depth + 1);\n RecomputeLeafDepths(right_child_[node], depth + 1);\n }\n}\n\ninline int Tree::GetLeaf(const double* feature_values) const {\n int node = 0;\n if (num_cat_ > 0) {\n while (node >= 0) {\n node = Decision(feature_values[split_feature_[node]], node);\n }\n } else {\n while (node >= 0) {\n node = NumericalDecision(feature_values[split_feature_[node]], node);\n }\n }\n return ~node;\n}\n\ninline int Tree::GetLeafByMap(const std::unordered_map& feature_values) const {\n int node = 0;\n if (num_cat_ > 0) {\n while (node >= 0) {\n node = Decision(feature_values.count(split_feature_[node]) > 0 ? feature_values.at(split_feature_[node]) : 0.0f, node);\n }\n } else {\n while (node >= 0) {\n node = NumericalDecision(feature_values.count(split_feature_[node]) > 0 ? feature_values.at(split_feature_[node]) : 0.0f, node);\n }\n }\n return ~node;\n}\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_TREE_H_\n" }, { "path": "include/LightGBM/tree_learner.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_TREE_LEARNER_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_TREE_LEARNER_H_\n\n#include \n#include \n#include \n\n#include \n#include \n\nnamespace LightGBM {\n\nusing json11_internal_lightgbm::Json;\n\n/*! \\brief forward declaration */\nclass Tree;\nclass Dataset;\nclass ObjectiveFunction;\n\n/*!\n* \\brief Interface for tree learner\n*/\nclass TreeLearner {\n public:\n /*! \\brief virtual destructor */\n virtual ~TreeLearner() {}\n\n /*!\n * \\brief Initialize tree learner with training dataset\n * \\param train_data The used training data\n * \\param is_constant_hessian True if all hessians share the same value\n */\n virtual void Init(const Dataset* train_data, bool is_constant_hessian) = 0;\n\n /*! Initialise some temporary storage, only needed for the linear tree; needs to be a method of TreeLearner since we call it in GBDT::RefitTree */\n virtual void InitLinear(const Dataset* /*train_data*/, const int /*max_leaves*/) {}\n\n virtual void ResetIsConstantHessian(bool is_constant_hessian) = 0;\n\n virtual void ResetTrainingData(const Dataset* train_data,\n bool is_constant_hessian) = 0;\n\n /*!\n * \\brief Reset tree configs\n * \\param config config of tree\n */\n virtual void ResetConfig(const Config* config) = 0;\n\n /*!\n * \\brief Reset boosting_on_gpu_\n * \\param boosting_on_gpu flag for boosting on GPU\n */\n virtual void ResetBoostingOnGPU(const bool /*boosting_on_gpu*/) {}\n\n virtual void SetForcedSplit(const Json* forced_split_json) = 0;\n\n /*!\n * \\brief training tree model on dataset\n * \\param gradients The first order gradients\n * \\param hessians The second order gradients\n * \\param is_first_tree If linear tree learning is enabled, first tree needs to be handled differently\n * \\return A trained tree\n */\n virtual Tree* Train(const score_t* gradients, const score_t* hessians, bool is_first_tree) = 0;\n\n /*!\n * \\brief use an existing tree to fit the new gradients and hessians.\n */\n virtual Tree* FitByExistingTree(const Tree* old_tree, const score_t* gradients, const score_t* hessians) const = 0;\n\n virtual Tree* FitByExistingTree(const Tree* old_tree, const std::vector& leaf_pred,\n const score_t* gradients, const score_t* hessians) const = 0;\n\n /*!\n * \\brief Set bagging data\n * \\param subset subset of bagging\n * \\param used_indices Used data indices\n * \\param num_data Number of used data\n */\n virtual void SetBaggingData(const Dataset* subset,\n const data_size_t* used_indices,\n data_size_t num_data) = 0;\n\n /*!\n * \\brief Using last trained tree to predict score then adding to out_score;\n * \\param out_score output score\n */\n virtual void AddPredictionToScore(const Tree* tree, double* out_score) const = 0;\n\n virtual void RenewTreeOutput(Tree* tree, const ObjectiveFunction* obj, std::function residual_getter,\n data_size_t total_num_data, const data_size_t* bag_indices, data_size_t bag_cnt, const double* train_score) const = 0;\n\n TreeLearner() = default;\n /*! \\brief Disable copy */\n TreeLearner& operator=(const TreeLearner&) = delete;\n /*! \\brief Disable copy */\n TreeLearner(const TreeLearner&) = delete;\n\n /*!\n * \\brief Create object of tree learner\n * \\param learner_type Type of tree learner\n * \\param device_type Type of tree learner\n * \\param booster_type Type of boosting\n * \\param config config of tree\n */\n static TreeLearner* CreateTreeLearner(const std::string& learner_type,\n const std::string& device_type,\n const Config* config,\n const bool boosting_on_cuda);\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_TREE_LEARNER_H_\n" }, { "path": "include/LightGBM/utils/array_args.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_ARRAY_ARGS_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_ARRAY_ARGS_H_\n\n#include \n#include \n\n#include \n#include \n#include \n\nnamespace LightGBM {\n\n/*!\n* \\brief Contains some operation for an array, e.g. ArgMax, TopK.\n*/\ntemplate\nclass ArrayArgs {\n public:\n inline static size_t ArgMaxMT(const std::vector& array) {\n int num_threads = OMP_NUM_THREADS();\n std::vector arg_maxs(num_threads, 0);\n int n_blocks = Threading::For(\n 0, array.size(), 1024,\n [&array, &arg_maxs](int i, size_t start, size_t end) {\n size_t arg_max = start;\n for (size_t j = start + 1; j < end; ++j) {\n if (array[j] > array[arg_max]) {\n arg_max = j;\n }\n }\n arg_maxs[i] = arg_max;\n });\n size_t ret = arg_maxs[0];\n for (int i = 1; i < n_blocks; ++i) {\n if (array[arg_maxs[i]] > array[ret]) {\n ret = arg_maxs[i];\n }\n }\n return ret;\n }\n inline static size_t ArgMax(const std::vector& array) {\n if (array.empty()) {\n return 0;\n }\n if (array.size() > 1024) {\n return ArgMaxMT(array);\n } else {\n size_t arg_max = 0;\n for (size_t i = 1; i < array.size(); ++i) {\n if (array[i] > array[arg_max]) {\n arg_max = i;\n }\n }\n return arg_max;\n }\n }\n\n inline static size_t ArgMin(const std::vector& array) {\n if (array.empty()) {\n return 0;\n }\n size_t arg_min = 0;\n for (size_t i = 1; i < array.size(); ++i) {\n if (array[i] < array[arg_min]) {\n arg_min = i;\n }\n }\n return arg_min;\n }\n\n inline static size_t ArgMax(const VAL_T* array, size_t n) {\n if (n <= 0) {\n return 0;\n }\n size_t arg_max = 0;\n for (size_t i = 1; i < n; ++i) {\n if (array[i] > array[arg_max]) {\n arg_max = i;\n }\n }\n return arg_max;\n }\n\n inline static size_t ArgMin(const VAL_T* array, size_t n) {\n if (n <= 0) {\n return 0;\n }\n size_t arg_min = 0;\n for (size_t i = 1; i < n; ++i) {\n if (array[i] < array[arg_min]) {\n arg_min = i;\n }\n }\n return arg_min;\n }\n\n inline static void Partition(std::vector* arr, int start, int end, int* l, int* r) {\n int i = start - 1;\n int j = end - 1;\n int p = i;\n int q = j;\n if (start >= end - 1) {\n *l = start - 1;\n *r = end;\n return;\n }\n std::vector& ref = *arr;\n VAL_T v = ref[end - 1];\n for (;;) {\n while (ref[++i] > v) {}\n while (v > ref[--j]) {\n if (j == start) {\n break;\n }\n }\n if (i >= j) {\n break;\n }\n std::swap(ref[i], ref[j]);\n if (ref[i] == v) {\n p++;\n std::swap(ref[p], ref[i]);\n }\n if (v == ref[j]) {\n q--;\n std::swap(ref[j], ref[q]);\n }\n }\n std::swap(ref[i], ref[end - 1]);\n j = i - 1;\n i = i + 1;\n for (int k = start; k <= p; k++, j--) {\n std::swap(ref[k], ref[j]);\n }\n for (int k = end - 2; k >= q; k--, i++) {\n std::swap(ref[i], ref[k]);\n }\n *l = j;\n *r = i;\n }\n\n // Note: k refer to index here. e.g. k=0 means get the max number.\n inline static int ArgMaxAtK(std::vector* arr, int start, int end, int k) {\n if (start >= end - 1) {\n return start;\n }\n int l = start;\n int r = end - 1;\n Partition(arr, start, end, &l, &r);\n // if find or all elements are the same.\n if ((k > l && k < r) || (l == start - 1 && r == end - 1)) {\n return k;\n } else if (k <= l) {\n return ArgMaxAtK(arr, start, l + 1, k);\n } else {\n return ArgMaxAtK(arr, r, end, k);\n }\n }\n\n // Note: k is 1-based here. e.g. k=3 means get the top-3 numbers.\n inline static void MaxK(const std::vector& array, int k, std::vector* out) {\n out->clear();\n if (k <= 0) {\n return;\n }\n for (auto val : array) {\n out->push_back(val);\n }\n if (static_cast(k) >= array.size()) {\n return;\n }\n ArgMaxAtK(out, 0, static_cast(out->size()), k - 1);\n out->erase(out->begin() + k, out->end());\n }\n\n inline static void Assign(std::vector* array, VAL_T t, size_t n) {\n array->resize(n);\n for (size_t i = 0; i < array->size(); ++i) {\n (*array)[i] = t;\n }\n }\n\n inline static bool CheckAllZero(const std::vector& array) {\n for (size_t i = 0; i < array.size(); ++i) {\n if (array[i] != VAL_T(0)) {\n return false;\n }\n }\n return true;\n }\n\n inline static bool CheckAll(const std::vector& array, VAL_T t) {\n for (size_t i = 0; i < array.size(); ++i) {\n if (array[i] != t) {\n return false;\n }\n }\n return true;\n }\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_ARRAY_ARGS_H_\n" }, { "path": "include/LightGBM/utils/binary_writer.h", "content": "/*!\n * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BINARY_WRITER_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BINARY_WRITER_H_\n\n#include \n#include \n\nnamespace LightGBM {\n\n/*!\n * \\brief An interface for serializing binary data to a buffer\n */\nstruct BinaryWriter {\n /*!\n * \\brief Append data to this binary target\n * \\param data Buffer to write from\n * \\param bytes Number of bytes to write from buffer\n * \\return Number of bytes written\n */\n virtual size_t Write(const void* data, size_t bytes) = 0;\n\n /*!\n * \\brief Append data to this binary target aligned on a given byte size boundary\n * \\param data Buffer to write from\n * \\param bytes Number of bytes to write from buffer\n * \\param alignment The size of bytes to align to in whole increments\n * \\return Number of bytes written\n */\n size_t AlignedWrite(const void* data, size_t bytes, size_t alignment = 8) {\n auto ret = Write(data, bytes);\n if (bytes % alignment != 0) {\n size_t padding = AlignedSize(bytes, alignment) - bytes;\n std::vector tmp(padding, 0);\n ret += Write(tmp.data(), padding);\n }\n return ret;\n }\n\n /*!\n * \\brief The aligned size of a buffer length.\n * \\param bytes The number of bytes in a buffer\n * \\param alignment The size of bytes to align to in whole increments\n * \\return Number of aligned bytes\n */\n static size_t AlignedSize(size_t bytes, size_t alignment = 8) {\n if (bytes % alignment == 0) {\n return bytes;\n } else {\n return bytes / alignment * alignment + alignment;\n }\n }\n};\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BINARY_WRITER_H_\n" }, { "path": "include/LightGBM/utils/byte_buffer.h", "content": "/*!\n * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BYTE_BUFFER_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BYTE_BUFFER_H_\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\n/*!\n * \\brief An implementation for serializing binary data to an auto-expanding memory buffer\n */\nstruct ByteBuffer final : public BinaryWriter {\n ByteBuffer() {}\n\n explicit ByteBuffer(size_t initial_size) {\n buffer_.reserve(initial_size);\n }\n\n size_t Write(const void* data, size_t bytes) {\n const char* mem_ptr = static_cast(data);\n for (size_t i = 0; i < bytes; ++i) {\n buffer_.push_back(mem_ptr[i]);\n }\n\n return bytes;\n }\n\n LIGHTGBM_EXPORT void Reserve(size_t capacity) {\n buffer_.reserve(capacity);\n }\n\n LIGHTGBM_EXPORT size_t GetSize() {\n return buffer_.size();\n }\n\n LIGHTGBM_EXPORT char GetAt(size_t index) {\n return buffer_.at(index);\n }\n\n LIGHTGBM_EXPORT char* Data() {\n return buffer_.data();\n }\n\n private:\n std::vector buffer_;\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_BYTE_BUFFER_H_\n" }, { "path": "include/LightGBM/utils/chunked_array.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n *\n * Author: Alberto Ferreira\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_CHUNKED_ARRAY_HPP_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_CHUNKED_ARRAY_HPP_\n\n#include \n\n#include \n\n#include \n#include \n#include \n\n\nnamespace LightGBM {\n\n/**\n * Container that manages a dynamic array of fixed-length chunks.\n *\n * The class also takes care of allocation & release of the underlying\n * memory. It can be used with either a high or low-level API.\n *\n * The high-level API allocates chunks as needed, manages addresses automatically and keeps\n * track of number of inserted elements, but is not thread-safe (this is ok as usually input is a streaming iterator).\n * For parallel input sources the low-level API must be used.\n *\n * Note: When using this for `LGBM_DatasetCreateFromMats` use a\n * chunk_size multiple of #num_cols for your dataset, so each chunk\n * contains \"complete\" instances.\n *\n * === High-level insert API intro ===\n *\n * The easiest way to use is:\n * 0. ChunkedArray(chunk_size) # Choose appropriate size\n * 1. add(value) # as many times as you want (will generate chunks as needed)\n * 2. data() or void_data() # retrieves a T** or void** pointer (useful for `LGBM_DatasetCreateFromMats`).\n *\n * Useful query methods (all O(1)):\n * - get_add_count() # total count of added elements.\n * - get_chunks_count() # how many chunks are currently allocated.\n * - get_current_chunk_added_count() # for the last add() chunk, how many items there are.\n * - get_chunk_size() # get constant chunk_size from constructor call.\n *\n * With those you can generate int32_t sizes[]. Last chunk can be smaller than chunk_size, so, for any i:\n * - sizes[i\nclass ChunkedArray {\n public:\n explicit ChunkedArray(size_t chunk_size)\n : _chunk_size(chunk_size), _last_chunk_idx(0), _last_idx_in_last_chunk(0) {\n if (chunk_size == 0) {\n Log::Fatal(\"ChunkedArray chunk size must be larger than 0!\");\n }\n new_chunk();\n }\n\n ~ChunkedArray() {\n release();\n }\n\n /**\n * Adds a value to the chunks sequentially.\n * If the last chunk is full it creates a new one and appends to it.\n *\n * @param value value to insert.\n */\n void add(T value) {\n if (!within_bounds(_last_chunk_idx, _last_idx_in_last_chunk)) {\n new_chunk();\n ++_last_chunk_idx;\n _last_idx_in_last_chunk = 0;\n }\n\n CHECK_EQ(setitem(_last_chunk_idx, _last_idx_in_last_chunk, value), 0);\n ++_last_idx_in_last_chunk;\n }\n\n /**\n * @return Number of add() calls.\n */\n size_t get_add_count() const {\n return _last_chunk_idx * _chunk_size + _last_idx_in_last_chunk;\n }\n\n /**\n * @return Number of allocated chunks.\n */\n size_t get_chunks_count() const {\n return _chunks.size();\n }\n\n /**\n * @return Number of elemends add()'ed in the last chunk.\n */\n size_t get_last_chunk_add_count() const {\n return _last_idx_in_last_chunk;\n }\n\n /**\n * Getter for the chunk size set at the constructor.\n *\n * @return Return the size of chunks.\n */\n size_t get_chunk_size() const {\n return _chunk_size;\n }\n\n /**\n * Returns the pointer to the raw chunks data.\n *\n * @return T** pointer to raw data.\n */\n T **data() noexcept {\n return _chunks.data();\n }\n\n /**\n * Returns the pointer to the raw chunks data, but cast to void**.\n * This is so ``LGBM_DatasetCreateFromMats`` accepts it.\n *\n * @return void** pointer to raw data.\n */\n void **data_as_void() noexcept {\n return reinterpret_cast(_chunks.data());\n }\n\n /**\n * Coalesces (copies chunked data) to a contiguous array of the same type.\n * It assumes that ``other`` has enough space to receive that data.\n *\n * @param other array with elements T of size >= this->get_add_count().\n * @param all_valid_addresses\n * If true exports values from all valid addresses independently of add() count.\n * Otherwise, exports only up to `get_add_count()` addresses.\n */\n void coalesce_to(T *other, bool all_valid_addresses = false) const {\n const size_t full_chunks = this->get_chunks_count() - 1;\n\n // Copy full chunks:\n size_t i = 0;\n for (size_t chunk = 0; chunk < full_chunks; ++chunk) {\n T* chunk_ptr = _chunks[chunk];\n for (size_t in_chunk_idx = 0; in_chunk_idx < _chunk_size; ++in_chunk_idx) {\n other[i++] = chunk_ptr[in_chunk_idx];\n }\n }\n // Copy filled values from last chunk only:\n const size_t last_chunk_elems_to_copy = all_valid_addresses ? _chunk_size : this->get_last_chunk_add_count();\n T* chunk_ptr = _chunks[full_chunks];\n for (size_t in_chunk_idx = 0; in_chunk_idx < last_chunk_elems_to_copy; ++in_chunk_idx) {\n other[i++] = chunk_ptr[in_chunk_idx];\n }\n }\n\n /**\n * Return value from array of chunks.\n *\n * @param chunk_index index of the chunk\n * @param index_within_chunk index within chunk\n * @param on_fail_value sentinel value. If out of bounds returns that value.\n *\n * @return pointer or nullptr if index is out of bounds.\n */\n T getitem(size_t chunk_index, size_t index_within_chunk, T on_fail_value) const noexcept {\n if (within_bounds(chunk_index, index_within_chunk))\n return _chunks[chunk_index][index_within_chunk];\n else\n return on_fail_value;\n }\n\n /**\n * Sets the value at a specific address in one of the chunks.\n *\n * @param chunk_index index of the chunk\n * @param index_within_chunk index within chunk\n * @param value value to store\n *\n * @return 0 = success, -1 = out of bounds access.\n */\n int setitem(size_t chunk_index, size_t index_within_chunk, T value) noexcept {\n if (within_bounds(chunk_index, index_within_chunk)) {\n _chunks[chunk_index][index_within_chunk] = value;\n return 0;\n } else {\n return -1;\n }\n }\n\n /**\n * To reset storage call this.\n * Will release existing resources and prepare for reuse.\n */\n void clear() noexcept {\n release();\n new_chunk();\n }\n\n /**\n * Deletes all the allocated chunks.\n * Do not use container after this! See ``clear()`` instead.\n */\n void release() noexcept {\n std::for_each(_chunks.begin(), _chunks.end(), [](T* c) { delete[] c; });\n _chunks.clear();\n _chunks.shrink_to_fit();\n _last_chunk_idx = 0;\n _last_idx_in_last_chunk = 0;\n }\n\n /**\n * As the array is dynamic, checks whether a given address is currently within bounds.\n *\n * @param chunk_index index of the chunk\n * @param index_within_chunk index within that chunk\n * @return true if that chunk is already allocated and index_within_chunk < chunk size.\n */\n inline bool within_bounds(size_t chunk_index, size_t index_within_chunk) const {\n return (chunk_index < _chunks.size()) && (index_within_chunk < _chunk_size);\n }\n\n /**\n * Adds a new chunk to the array of chunks. Not thread-safe.\n */\n void new_chunk() {\n _chunks.push_back(new (std::nothrow) T[_chunk_size]);\n\n // Check memory allocation success:\n if (!_chunks[_chunks.size() - 1]) {\n release();\n Log::Fatal(\"Memory exhausted! Cannot allocate new ChunkedArray chunk.\");\n }\n }\n\n private:\n const size_t _chunk_size;\n std::vector _chunks;\n\n // For the add() interface & some of the get_*() queries:\n size_t _last_chunk_idx; //\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#define FMT_HEADER_ONLY\n#include \"fast_double_parser.h\"\n#include \"fmt/format.h\"\n\n#ifdef _MSC_VER\n#include \n#pragma intrinsic(_BitScanReverse)\n#endif\n\n#if defined(_MSC_VER)\n#include \n#elif MM_MALLOC\n#include \n// https://gcc.gnu.org/onlinedocs/cpp/Common-Predefined-Macros.html\n// https://www.oreilly.com/library/view/mac-os-x/0596003560/ch05s01s02.html\n#elif defined(__GNUC__) && defined(HAVE_MALLOC_H)\n #include \n #define _mm_malloc(a, b) memalign(b, a)\n #define _mm_free(a) free(a)\n#else\n#include \n#define _mm_malloc(a, b) malloc(a)\n#define _mm_free(a) free(a)\n#endif\n\nnamespace LightGBM {\n\nnamespace Common {\n\nusing json11_internal_lightgbm::Json;\n\n/*!\n* Imbues the stream with the C locale.\n*/\nstatic void C_stringstream(std::stringstream &ss) {\n ss.imbue(std::locale::classic());\n}\n\ninline static std::string Trim(std::string str) {\n if (str.empty()) {\n return str;\n }\n str.erase(str.find_last_not_of(\" \\f\\n\\r\\t\\v\") + 1);\n str.erase(0, str.find_first_not_of(\" \\f\\n\\r\\t\\v\"));\n return str;\n}\n\ninline static std::string RemoveQuotationSymbol(std::string str) {\n if (str.empty()) {\n return str;\n }\n str.erase(str.find_last_not_of(\"'\\\"\") + 1);\n str.erase(0, str.find_first_not_of(\"'\\\"\"));\n return str;\n}\n\ninline static bool StartsWith(const std::string& str, const std::string prefix) {\n if (str.substr(0, prefix.size()) == prefix) {\n return true;\n } else {\n return false;\n }\n}\n\ninline static std::vector Split(const char* c_str, char delimiter) {\n std::vector ret;\n std::string str(c_str);\n size_t i = 0;\n size_t pos = 0;\n while (pos < str.length()) {\n if (str[pos] == delimiter) {\n if (i < pos) {\n ret.push_back(str.substr(i, pos - i));\n }\n ++pos;\n i = pos;\n } else {\n ++pos;\n }\n }\n if (i < pos) {\n ret.push_back(str.substr(i));\n }\n return ret;\n}\n\ninline static std::vector SplitBrackets(const char* c_str, char left_delimiter, char right_delimiter) {\n std::vector ret;\n std::string str(c_str);\n size_t i = 0;\n size_t pos = 0;\n bool open = false;\n while (pos < str.length()) {\n if (str[pos] == left_delimiter) {\n open = true;\n ++pos;\n i = pos;\n } else if (str[pos] == right_delimiter && open) {\n if (i < pos) {\n ret.push_back(str.substr(i, pos - i));\n }\n open = false;\n ++pos;\n } else {\n ++pos;\n }\n }\n return ret;\n}\n\ninline static std::vector SplitLines(const char* c_str) {\n std::vector ret;\n std::string str(c_str);\n size_t i = 0;\n size_t pos = 0;\n while (pos < str.length()) {\n if (str[pos] == '\\n' || str[pos] == '\\r') {\n if (i < pos) {\n ret.push_back(str.substr(i, pos - i));\n }\n // skip the line endings\n while (str[pos] == '\\n' || str[pos] == '\\r') ++pos;\n // new begin\n i = pos;\n } else {\n ++pos;\n }\n }\n if (i < pos) {\n ret.push_back(str.substr(i));\n }\n return ret;\n}\n\ninline static std::vector Split(const char* c_str, const char* delimiters) {\n std::vector ret;\n std::string str(c_str);\n size_t i = 0;\n size_t pos = 0;\n while (pos < str.length()) {\n bool met_delimiters = false;\n for (int j = 0; delimiters[j] != '\\0'; ++j) {\n if (str[pos] == delimiters[j]) {\n met_delimiters = true;\n break;\n }\n }\n if (met_delimiters) {\n if (i < pos) {\n ret.push_back(str.substr(i, pos - i));\n }\n ++pos;\n i = pos;\n } else {\n ++pos;\n }\n }\n if (i < pos) {\n ret.push_back(str.substr(i));\n }\n return ret;\n}\n\ninline static std::string GetFromParserConfig(std::string config_str, std::string key) {\n // parser config should follow json format.\n std::string err;\n Json config_json = Json::parse(config_str, &err);\n if (!err.empty()) {\n Log::Fatal(\"Invalid parser config: %s. Please check if follow json format.\", err.c_str());\n }\n return config_json[key].string_value();\n}\n\ninline static std::string SaveToParserConfig(std::string config_str, std::string key, std::string value) {\n std::string err;\n Json config_json = Json::parse(config_str, &err);\n if (!err.empty()) {\n Log::Fatal(\"Invalid parser config: %s. Please check if follow json format.\", err.c_str());\n }\n CHECK(config_json.is_object());\n std::map config_map = config_json.object_items();\n config_map.insert(std::pair(key, Json(value)));\n return Json(config_map).dump();\n}\n\ntemplate\ninline static const char* Atoi(const char* p, T* out) {\n int sign;\n T value;\n while (*p == ' ') {\n ++p;\n }\n sign = 1;\n if (*p == '-') {\n sign = -1;\n ++p;\n } else if (*p == '+') {\n ++p;\n }\n for (value = 0; *p >= '0' && *p <= '9'; ++p) {\n value = value * 10 + (*p - '0');\n }\n *out = static_cast(sign * value);\n while (*p == ' ') {\n ++p;\n }\n return p;\n}\n\ntemplate\ninline static double Pow(T base, int power) {\n if (power < 0) {\n return 1.0 / Pow(base, -power);\n } else if (power == 0) {\n return 1;\n } else if (power % 2 == 0) {\n return Pow(base*base, power / 2);\n } else if (power % 3 == 0) {\n return Pow(base*base*base, power / 3);\n } else {\n return base * Pow(base, power - 1);\n }\n}\n\ninline static const char* Atof(const char* p, double* out) {\n int frac;\n double sign, value, scale;\n *out = NAN;\n // Skip leading white space, if any.\n while (*p == ' ') {\n ++p;\n }\n // Get sign, if any.\n sign = 1.0;\n if (*p == '-') {\n sign = -1.0;\n ++p;\n } else if (*p == '+') {\n ++p;\n }\n\n // is a number\n if ((*p >= '0' && *p <= '9') || *p == '.' || *p == 'e' || *p == 'E') {\n // Get digits before decimal point or exponent, if any.\n for (value = 0.0; *p >= '0' && *p <= '9'; ++p) {\n value = value * 10.0 + (*p - '0');\n }\n\n // Get digits after decimal point, if any.\n if (*p == '.') {\n double right = 0.0;\n int nn = 0;\n ++p;\n while (*p >= '0' && *p <= '9') {\n right = (*p - '0') + right * 10.0;\n ++nn;\n ++p;\n }\n value += right / Pow(10.0, nn);\n }\n\n // Handle exponent, if any.\n frac = 0;\n scale = 1.0;\n if ((*p == 'e') || (*p == 'E')) {\n uint32_t expon;\n // Get sign of exponent, if any.\n ++p;\n if (*p == '-') {\n frac = 1;\n ++p;\n } else if (*p == '+') {\n ++p;\n }\n // Get digits of exponent, if any.\n for (expon = 0; *p >= '0' && *p <= '9'; ++p) {\n expon = expon * 10 + (*p - '0');\n }\n if (expon > 308) expon = 308;\n // Calculate scaling factor.\n while (expon >= 50) {\n scale *= 1E50;\n expon -= 50;\n }\n while (expon >= 8) {\n scale *= 1E8;\n expon -= 8;\n }\n while (expon > 0) {\n scale *= 10.0;\n expon -= 1;\n }\n }\n // Return signed and scaled floating point result.\n *out = sign * (frac ? (value / scale) : (value * scale));\n } else {\n size_t cnt = 0;\n while (*(p + cnt) != '\\0' && *(p + cnt) != ' '\n && *(p + cnt) != '\\t' && *(p + cnt) != ','\n && *(p + cnt) != '\\n' && *(p + cnt) != '\\r'\n && *(p + cnt) != ':') {\n ++cnt;\n }\n if (cnt > 0) {\n std::string tmp_str(p, cnt);\n std::transform(tmp_str.begin(), tmp_str.end(), tmp_str.begin(), [](unsigned char c){ return std::tolower(c); });\n if (tmp_str == std::string(\"na\") || tmp_str == std::string(\"nan\") ||\n tmp_str == std::string(\"null\")) {\n *out = NAN;\n } else if (tmp_str == std::string(\"inf\") || tmp_str == std::string(\"infinity\")) {\n *out = sign * 1e308;\n } else {\n Log::Fatal(\"Unknown token %s in data file\", tmp_str.c_str());\n }\n p += cnt;\n }\n }\n\n while (*p == ' ') {\n ++p;\n }\n\n return p;\n}\n\n// Use fast_double_parse and strtod (if parse failed) to parse double.\ninline static const char* AtofPrecise(const char* p, double* out) {\n const char* end = fast_double_parser::parse_number(p, out);\n\n if (end != nullptr) {\n return end;\n }\n\n // Rare path: Not in RFC 7159 format. Possible \"inf\", \"nan\", etc. Fallback to standard library:\n char* end2;\n errno = 0; // This is Required before calling strtod.\n *out = std::strtod(p, &end2); // strtod is locale aware.\n if (end2 == p) {\n Log::Fatal(\"no conversion to double for: %s\", p);\n }\n if (errno == ERANGE) {\n Log::Warning(\"convert to double got underflow or overflow: %s\", p);\n }\n return end2;\n}\n\ninline static bool AtoiAndCheck(const char* p, int* out) {\n const char* after = Atoi(p, out);\n if (*after != '\\0') {\n return false;\n }\n return true;\n}\n\ninline static bool AtofAndCheck(const char* p, double* out) {\n const char* after = Atof(p, out);\n if (*after != '\\0') {\n return false;\n }\n return true;\n}\n\ninline static const char* SkipSpaceAndTab(const char* p) {\n while (*p == ' ' || *p == '\\t') {\n ++p;\n }\n return p;\n}\n\ninline static const char* SkipReturn(const char* p) {\n while (*p == '\\n' || *p == '\\r' || *p == ' ') {\n ++p;\n }\n return p;\n}\n\ntemplate\ninline static std::vector ArrayCast(const std::vector& arr) {\n std::vector ret(arr.size());\n for (size_t i = 0; i < arr.size(); ++i) {\n ret[i] = static_cast(arr[i]);\n }\n return ret;\n}\n\ntemplate\nstruct __StringToTHelper {\n T operator()(const std::string& str) const {\n T ret = 0;\n Atoi(str.c_str(), &ret);\n return ret;\n }\n};\n\ntemplate\nstruct __StringToTHelper {\n T operator()(const std::string& str) const {\n return static_cast(std::stod(str));\n }\n};\n\ntemplate\ninline static std::vector StringToArray(const std::string& str, char delimiter) {\n std::vector strs = Split(str.c_str(), delimiter);\n std::vector ret;\n ret.reserve(strs.size());\n __StringToTHelper::value> helper;\n for (const auto& s : strs) {\n ret.push_back(helper(s));\n }\n return ret;\n}\n\ntemplate\ninline static std::vector> StringToArrayofArrays(\n const std::string& str, char left_bracket, char right_bracket, char delimiter) {\n std::vector strs = SplitBrackets(str.c_str(), left_bracket, right_bracket);\n std::vector> ret;\n for (const auto& s : strs) {\n ret.push_back(StringToArray(s, delimiter));\n }\n return ret;\n}\n\ntemplate\ninline static std::vector StringToArray(const std::string& str, int n) {\n if (n == 0) {\n return std::vector();\n }\n std::vector strs = Split(str.c_str(), ' ');\n CHECK_EQ(strs.size(), static_cast(n));\n std::vector ret;\n ret.reserve(strs.size());\n __StringToTHelper::value> helper;\n for (const auto& s : strs) {\n ret.push_back(helper(s));\n }\n return ret;\n}\n\ntemplate\nstruct __StringToTHelperFast {\n const char* operator()(const char*p, T* out) const {\n return Atoi(p, out);\n }\n};\n\ntemplate\nstruct __StringToTHelperFast {\n const char* operator()(const char*p, T* out) const {\n double tmp = 0.0f;\n auto ret = Atof(p, &tmp);\n *out = static_cast(tmp);\n return ret;\n }\n};\n\ntemplate\ninline static std::vector StringToArrayFast(const std::string& str, int n) {\n if (n == 0) {\n return std::vector();\n }\n auto p_str = str.c_str();\n __StringToTHelperFast::value> helper;\n std::vector ret(n);\n for (int i = 0; i < n; ++i) {\n p_str = helper(p_str, &ret[i]);\n }\n return ret;\n}\n\ntemplate\ninline static std::string Join(const std::vector& strs, const char* delimiter, const bool force_C_locale = false) {\n if (strs.empty()) {\n return std::string(\"\");\n }\n std::stringstream str_buf;\n if (force_C_locale) {\n C_stringstream(str_buf);\n }\n str_buf << std::setprecision(std::numeric_limits::digits10 + 2);\n str_buf << strs[0];\n for (size_t i = 1; i < strs.size(); ++i) {\n str_buf << delimiter;\n str_buf << strs[i];\n }\n return str_buf.str();\n}\n\ntemplate<>\ninline std::string Join(const std::vector& strs, const char* delimiter, const bool force_C_locale) {\n if (strs.empty()) {\n return std::string(\"\");\n }\n std::stringstream str_buf;\n if (force_C_locale) {\n C_stringstream(str_buf);\n }\n str_buf << std::setprecision(std::numeric_limits::digits10 + 2);\n str_buf << static_cast(strs[0]);\n for (size_t i = 1; i < strs.size(); ++i) {\n str_buf << delimiter;\n str_buf << static_cast(strs[i]);\n }\n return str_buf.str();\n}\n\ntemplate\ninline static std::string Join(const std::vector& strs, size_t start, size_t end, const char* delimiter, const bool force_C_locale = false) {\n if (end - start <= 0) {\n return std::string(\"\");\n }\n start = std::min(start, static_cast(strs.size()) - 1);\n end = std::min(end, static_cast(strs.size()));\n std::stringstream str_buf;\n if (force_C_locale) {\n C_stringstream(str_buf);\n }\n str_buf << std::setprecision(std::numeric_limits::digits10 + 2);\n str_buf << strs[start];\n for (size_t i = start + 1; i < end; ++i) {\n str_buf << delimiter;\n str_buf << strs[i];\n }\n return str_buf.str();\n}\n\ninline static int64_t Pow2RoundUp(int64_t x) {\n int64_t t = 1;\n for (int i = 0; i < 64; ++i) {\n if (t >= x) {\n return t;\n }\n t <<= 1;\n }\n return 0;\n}\n\n/*!\n * \\brief Do inplace softmax transformation on p_rec\n * \\param p_rec The input/output vector of the values.\n */\ninline static void Softmax(std::vector* p_rec) {\n std::vector &rec = *p_rec;\n double wmax = rec[0];\n for (size_t i = 1; i < rec.size(); ++i) {\n wmax = std::max(rec[i], wmax);\n }\n double wsum = 0.0f;\n for (size_t i = 0; i < rec.size(); ++i) {\n rec[i] = std::exp(rec[i] - wmax);\n wsum += rec[i];\n }\n for (size_t i = 0; i < rec.size(); ++i) {\n rec[i] /= static_cast(wsum);\n }\n}\n\ninline static void Softmax(const double* input, double* output, int len) {\n double wmax = input[0];\n for (int i = 1; i < len; ++i) {\n wmax = std::max(input[i], wmax);\n }\n double wsum = 0.0f;\n for (int i = 0; i < len; ++i) {\n output[i] = std::exp(input[i] - wmax);\n wsum += output[i];\n }\n for (int i = 0; i < len; ++i) {\n output[i] /= static_cast(wsum);\n }\n}\n\ntemplate\nstd::vector ConstPtrInVectorWrapper(const std::vector>& input) {\n std::vector ret;\n for (auto t = input.begin(); t !=input.end(); ++t) {\n ret.push_back(t->get());\n }\n return ret;\n}\n\ntemplate\ninline static void SortForPair(std::vector* keys, std::vector* values, size_t start, bool is_reverse = false) {\n std::vector> arr;\n auto& ref_key = *keys;\n auto& ref_value = *values;\n for (size_t i = start; i < keys->size(); ++i) {\n arr.emplace_back(ref_key[i], ref_value[i]);\n }\n if (!is_reverse) {\n std::stable_sort(arr.begin(), arr.end(), [](const std::pair& a, const std::pair& b) {\n return a.first < b.first;\n });\n } else {\n std::stable_sort(arr.begin(), arr.end(), [](const std::pair& a, const std::pair& b) {\n return a.first > b.first;\n });\n }\n for (size_t i = start; i < arr.size(); ++i) {\n ref_key[i] = arr[i].first;\n ref_value[i] = arr[i].second;\n }\n}\n\ntemplate \ninline static std::vector Vector2Ptr(std::vector>* data) {\n std::vector ptr(data->size());\n auto& ref_data = *data;\n for (size_t i = 0; i < data->size(); ++i) {\n ptr[i] = ref_data[i].data();\n }\n return ptr;\n}\n\ntemplate \ninline static std::vector VectorSize(const std::vector>& data) {\n std::vector ret(data.size());\n for (size_t i = 0; i < data.size(); ++i) {\n ret[i] = static_cast(data[i].size());\n }\n return ret;\n}\n\ninline static double AvoidInf(double x) {\n if (std::isnan(x)) {\n return 0.0;\n } else if (x >= 1e300) {\n return 1e300;\n } else if (x <= -1e300) {\n return -1e300;\n } else {\n return x;\n }\n}\n\ninline static float AvoidInf(float x) {\n if (std::isnan(x)) {\n return 0.0f;\n } else if (x >= 1e38) {\n return 1e38f;\n } else if (x <= -1e38) {\n return -1e38f;\n } else {\n return x;\n }\n}\n\ntemplate inline\nstatic typename std::iterator_traits<_Iter>::value_type* IteratorValType(_Iter) {\n return (0);\n}\n\ntemplate inline\nstatic void ParallelSort(_RanIt _First, _RanIt _Last, _Pr _Pred, _VTRanIt*) {\n size_t len = _Last - _First;\n const size_t kMinInnerLen = 1024;\n int num_threads = OMP_NUM_THREADS();\n if (len <= kMinInnerLen || num_threads <= 1) {\n std::sort(_First, _Last, _Pred);\n return;\n }\n size_t inner_size = (len + num_threads - 1) / num_threads;\n inner_size = std::max(inner_size, kMinInnerLen);\n num_threads = static_cast((len + inner_size - 1) / inner_size);\n#pragma omp parallel for num_threads(num_threads) schedule(static, 1)\n for (int i = 0; i < num_threads; ++i) {\n size_t left = inner_size*i;\n size_t right = left + inner_size;\n right = std::min(right, len);\n if (right > left) {\n std::sort(_First + left, _First + right, _Pred);\n }\n }\n // Buffer for merge.\n std::vector<_VTRanIt> temp_buf(len);\n _RanIt buf = temp_buf.begin();\n size_t s = inner_size;\n // Recursive merge\n while (s < len) {\n int loop_size = static_cast((len + s * 2 - 1) / (s * 2));\n #pragma omp parallel for num_threads(num_threads) schedule(static, 1)\n for (int i = 0; i < loop_size; ++i) {\n size_t left = i * 2 * s;\n size_t mid = left + s;\n size_t right = mid + s;\n right = std::min(len, right);\n if (mid >= right) {\n continue;\n }\n std::copy(_First + left, _First + mid, buf + left);\n std::merge(buf + left, buf + mid, _First + mid, _First + right, _First + left, _Pred);\n }\n s *= 2;\n }\n}\n\ntemplate inline\nstatic void ParallelSort(_RanIt _First, _RanIt _Last, _Pr _Pred) {\n return ParallelSort(_First, _Last, _Pred, IteratorValType(_First));\n}\n\n// Check that all y[] are in interval [ymin, ymax] (end points included); throws error if not\ntemplate \ninline static void CheckElementsIntervalClosed(const T *y, T ymin, T ymax, int ny, const char *callername) {\n auto fatal_msg = [&y, &ymin, &ymax, &callername](int i) {\n std::ostringstream os;\n os << \"[%s]: does not tolerate element [#%i = \" << y[i] << \"] outside [\" << ymin << \", \" << ymax << \"]\";\n Log::Fatal(os.str().c_str(), callername, i);\n };\n for (int i = 1; i < ny; i += 2) {\n if (y[i - 1] < y[i]) {\n if (y[i - 1] < ymin) {\n fatal_msg(i - 1);\n } else if (y[i] > ymax) {\n fatal_msg(i);\n }\n } else {\n if (y[i - 1] > ymax) {\n fatal_msg(i - 1);\n } else if (y[i] < ymin) {\n fatal_msg(i);\n }\n }\n }\n if (ny & 1) { // odd\n if (y[ny - 1] < ymin || y[ny - 1] > ymax) {\n fatal_msg(ny - 1);\n }\n }\n}\n\n// One-pass scan over array w with nw elements: find min, max and sum of elements;\n// this is useful for checking weight requirements.\ntemplate \ninline static void ObtainMinMaxSum(const T1 *w, int nw, T1 *mi, T1 *ma, T2 *su) {\n T1 minw;\n T1 maxw;\n T1 sumw;\n int i;\n if (nw & 1) { // odd\n minw = w[0];\n maxw = w[0];\n sumw = w[0];\n i = 2;\n } else { // even\n if (w[0] < w[1]) {\n minw = w[0];\n maxw = w[1];\n } else {\n minw = w[1];\n maxw = w[0];\n }\n sumw = w[0] + w[1];\n i = 3;\n }\n for (; i < nw; i += 2) {\n if (w[i - 1] < w[i]) {\n minw = std::min(minw, w[i - 1]);\n maxw = std::max(maxw, w[i]);\n } else {\n minw = std::min(minw, w[i]);\n maxw = std::max(maxw, w[i - 1]);\n }\n sumw += w[i - 1] + w[i];\n }\n if (mi != nullptr) {\n *mi = minw;\n }\n if (ma != nullptr) {\n *ma = maxw;\n }\n if (su != nullptr) {\n *su = static_cast(sumw);\n }\n}\n\ninline static std::vector EmptyBitset(int n) {\n int size = n / 32;\n if (n % 32 != 0) ++size;\n return std::vector(size);\n}\n\ntemplate\ninline static void InsertBitset(std::vector* vec, const T val) {\n auto& ref_v = *vec;\n int i1 = val / 32;\n int i2 = val % 32;\n if (static_cast(vec->size()) < i1 + 1) {\n vec->resize(i1 + 1, 0);\n }\n ref_v[i1] |= (1 << i2);\n}\n\ntemplate\ninline static std::vector ConstructBitset(const T* vals, int n) {\n std::vector ret;\n for (int i = 0; i < n; ++i) {\n int i1 = vals[i] / 32;\n int i2 = vals[i] % 32;\n if (static_cast(ret.size()) < i1 + 1) {\n ret.resize(i1 + 1, 0);\n }\n ret[i1] |= (1 << i2);\n }\n return ret;\n}\n\ntemplate\ninline static bool FindInBitset(const uint32_t* bits, int n, T pos) {\n int i1 = pos / 32;\n if (i1 >= n) {\n return false;\n }\n int i2 = pos % 32;\n return (bits[i1] >> i2) & 1;\n}\n\ninline static bool CheckDoubleEqualOrdered(double a, double b) {\n double upper = std::nextafter(a, INFINITY);\n return b <= upper;\n}\n\ninline static double GetDoubleUpperBound(double a) {\n return std::nextafter(a, INFINITY);\n}\n\ninline static size_t GetLine(const char* str) {\n auto start = str;\n while (*str != '\\0' && *str != '\\n' && *str != '\\r') {\n ++str;\n }\n return str - start;\n}\n\ninline static const char* SkipNewLine(const char* str) {\n if (*str == '\\r') {\n ++str;\n }\n if (*str == '\\n') {\n ++str;\n }\n return str;\n}\n\ntemplate \nstatic int Sign(T x) {\n return (x > T(0)) - (x < T(0));\n}\n\ntemplate \nstatic T SafeLog(T x) {\n if (x > 0) {\n return std::log(x);\n } else {\n return -INFINITY;\n }\n}\n\ninline bool CheckAllowedJSON(const std::string& s) {\n unsigned char char_code;\n for (auto c : s) {\n char_code = static_cast(c);\n if (char_code == 34 // \"\n || char_code == 44 // ,\n || char_code == 58 // :\n || char_code == 91 // [\n || char_code == 93 // ]\n || char_code == 123 // {\n || char_code == 125 // }\n ) {\n return false;\n }\n }\n return true;\n}\n\ninline int RoundInt(double x) {\n return static_cast(x + 0.5f);\n}\n\ntemplate \nclass AlignmentAllocator {\n public:\n typedef T value_type;\n typedef std::size_t size_type;\n typedef std::ptrdiff_t difference_type;\n\n typedef T* pointer;\n typedef const T* const_pointer;\n\n typedef T& reference;\n typedef const T& const_reference;\n\n inline AlignmentAllocator() throw() {}\n\n template \n inline AlignmentAllocator(const AlignmentAllocator&) throw() {}\n\n inline ~AlignmentAllocator() throw() {}\n\n inline pointer address(reference r) {\n return &r;\n }\n\n inline const_pointer address(const_reference r) const {\n return &r;\n }\n\n inline pointer allocate(size_type n) {\n return (pointer)_mm_malloc(n * sizeof(value_type), N);\n }\n\n inline void deallocate(pointer p, size_type) {\n _mm_free(p);\n }\n\n inline void construct(pointer p, const value_type& wert) {\n new (p) value_type(wert);\n }\n\n inline void destroy(pointer p) {\n p->~value_type();\n }\n\n inline size_type max_size() const throw() {\n return size_type(-1) / sizeof(value_type);\n }\n\n template \n struct rebind {\n typedef AlignmentAllocator other;\n };\n\n bool operator!=(const AlignmentAllocator& other) const {\n return !(*this == other);\n }\n\n // Returns true if and only if storage allocated from *this\n // can be deallocated from other, and vice versa.\n // Always returns true for stateless allocators.\n bool operator==(const AlignmentAllocator&) const {\n return true;\n }\n};\n\nclass Timer {\n public:\n Timer() {\n#ifdef TIMETAG\n int num_threads = OMP_NUM_THREADS();\n start_time_.resize(num_threads);\n stats_.resize(num_threads);\n#endif // TIMETAG\n }\n\n ~Timer() { Print(); }\n\n#ifdef TIMETAG\n void Start(const std::string& name) {\n auto tid = omp_get_thread_num();\n start_time_[tid][name] = std::chrono::steady_clock::now();\n }\n\n void Stop(const std::string& name) {\n auto cur_time = std::chrono::steady_clock::now();\n auto tid = omp_get_thread_num();\n if (stats_[tid].find(name) == stats_[tid].end()) {\n stats_[tid][name] = std::chrono::duration(0);\n }\n stats_[tid][name] += cur_time - start_time_[tid][name];\n }\n\n#else\n void Start(const std::string&) {}\n\n void Stop(const std::string&) {}\n#endif // TIMETAG\n\n void Print() const {\n#ifdef TIMETAG\n std::unordered_map>\n stats(stats_[0].begin(), stats_[0].end());\n for (size_t i = 1; i < stats_.size(); ++i) {\n for (auto it = stats_[i].begin(); it != stats_[i].end(); ++it) {\n if (stats.find(it->first) == stats.end()) {\n stats[it->first] = it->second;\n } else {\n stats[it->first] += it->second;\n }\n }\n }\n std::map> ordered(\n stats.begin(), stats.end());\n for (auto it = ordered.begin(); it != ordered.end(); ++it) {\n Log::Info(\"%s costs:\\t %f\", it->first.c_str(), it->second * 1e-3);\n }\n#endif // TIMETAG\n }\n#ifdef TIMETAG\n std::vector<\n std::unordered_map>\n start_time_;\n std::vector>>\n stats_;\n#endif // TIMETAG\n};\n\n// Note: this class is not thread-safe, don't use it inside omp blocks\nclass FunctionTimer {\n public:\n#ifdef TIMETAG\n FunctionTimer(const std::string& name, Timer& timer) : timer_(timer) {\n timer.Start(name);\n name_ = name;\n }\n\n ~FunctionTimer() { timer_.Stop(name_); }\n\n private:\n std::string name_;\n Timer& timer_;\n#else\n FunctionTimer(const std::string&, Timer&) {}\n#endif // TIMETAG\n};\n\n} // namespace Common\n\nextern Common::Timer global_timer;\n\n\n/*!\n* Provides locale-independent alternatives to Common's methods.\n* Essential to make models robust to locale settings.\n*/\nnamespace CommonC {\n\ntemplate\ninline static std::string Join(const std::vector& strs, const char* delimiter) {\n return LightGBM::Common::Join(strs, delimiter, true);\n}\n\ntemplate\ninline static std::string Join(const std::vector& strs, size_t start, size_t end, const char* delimiter) {\n return LightGBM::Common::Join(strs, start, end, delimiter, true);\n}\n\ninline static const char* Atof(const char* p, double* out) {\n return LightGBM::Common::Atof(p, out);\n}\n\ntemplate\nstruct __StringToTHelperFast {\n const char* operator()(const char*p, T* out) const {\n return LightGBM::Common::Atoi(p, out);\n }\n};\n\n/*!\n* \\warning Beware that ``Common::Atof`` in ``__StringToTHelperFast``,\n* has **less** floating point precision than ``__StringToTHelper``.\n* Both versions are kept to maintain bit-for-bit the \"legacy\" LightGBM behaviour in terms of precision.\n* Check ``StringToArrayFast`` and ``StringToArray`` for more details on this.\n*/\ntemplate\nstruct __StringToTHelperFast {\n const char* operator()(const char*p, T* out) const {\n double tmp = 0.0f;\n auto ret = Atof(p, &tmp);\n *out = static_cast(tmp);\n return ret;\n }\n};\n\ntemplate\nstruct __StringToTHelper {\n T operator()(const std::string& str) const {\n T ret = 0;\n LightGBM::Common::Atoi(str.c_str(), &ret);\n return ret;\n }\n};\n\n/*!\n* \\warning Beware that ``Common::Atof`` in ``__StringToTHelperFast``,\n* has **less** floating point precision than ``__StringToTHelper``.\n* Both versions are kept to maintain bit-for-bit the \"legacy\" LightGBM behaviour in terms of precision.\n* Check ``StringToArrayFast`` and ``StringToArray`` for more details on this.\n* \\note It is possible that ``fast_double_parser::parse_number`` is faster than ``Common::Atof``.\n*/\ntemplate\nstruct __StringToTHelper {\n T operator()(const std::string& str) const {\n double tmp;\n\n const char* end = Common::AtofPrecise(str.c_str(), &tmp);\n if (end == str.c_str()) {\n Log::Fatal(\"Failed to parse double: %s\", str.c_str());\n }\n\n return static_cast(tmp);\n }\n};\n\n\n/*!\n* \\warning Beware that due to internal use of ``Common::Atof`` in ``__StringToTHelperFast``,\n* this method has less precision for floating point numbers than ``StringToArray``,\n* which calls ``__StringToTHelper``.\n* As such, ``StringToArrayFast`` and ``StringToArray`` are not equivalent!\n* Both versions were kept to maintain bit-for-bit the \"legacy\" LightGBM behaviour in terms of precision.\n*/\ntemplate\ninline static std::vector StringToArrayFast(const std::string& str, int n) {\n if (n == 0) {\n return std::vector();\n }\n auto p_str = str.c_str();\n __StringToTHelperFast::value> helper;\n std::vector ret(n);\n for (int i = 0; i < n; ++i) {\n p_str = helper(p_str, &ret[i]);\n }\n return ret;\n}\n\n/*!\n* \\warning Do not replace calls to this method by ``StringToArrayFast``.\n* This method is more precise for floating point numbers.\n* Check ``StringToArrayFast`` for more details.\n*/\ntemplate\ninline static std::vector StringToArray(const std::string& str, int n) {\n if (n == 0) {\n return std::vector();\n }\n std::vector strs = LightGBM::Common::Split(str.c_str(), ' ');\n CHECK_EQ(strs.size(), static_cast(n));\n std::vector ret;\n ret.reserve(strs.size());\n __StringToTHelper::value> helper;\n for (const auto& s : strs) {\n ret.push_back(helper(s));\n }\n return ret;\n}\n\n/*!\n* \\warning Do not replace calls to this method by ``StringToArrayFast``.\n* This method is more precise for floating point numbers.\n* Check ``StringToArrayFast`` for more details.\n*/\ntemplate\ninline static std::vector StringToArray(const std::string& str, char delimiter) {\n std::vector strs = LightGBM::Common::Split(str.c_str(), delimiter);\n std::vector ret;\n ret.reserve(strs.size());\n __StringToTHelper::value> helper;\n for (const auto& s : strs) {\n ret.push_back(helper(s));\n }\n return ret;\n}\n\n/*!\n* Safely formats a value onto a buffer according to a format string and null-terminates it.\n*\n* \\note It checks that the full value was written or forcefully aborts.\n* This safety check serves to prevent incorrect internal API usage.\n* Correct usage will never incur in this problem:\n* - The received buffer size shall be sufficient at all times for the input format string and value.\n*/\ntemplate \ninline static void format_to_buf(char* buffer, const size_t buf_len, const char* format, const T value) {\n auto result = fmt::format_to_n(buffer, buf_len, format, value);\n if (result.size >= buf_len) {\n Log::Fatal(\"Numerical conversion failed. Buffer is too small.\");\n }\n buffer[result.size] = '\\0';\n}\n\ntemplate\nstruct __TToStringHelper {\n void operator()(T value, char* buffer, size_t buf_len) const {\n format_to_buf(buffer, buf_len, \"{}\", value);\n }\n};\n\ntemplate\nstruct __TToStringHelper {\n void operator()(T value, char* buffer, size_t buf_len) const {\n format_to_buf(buffer, buf_len, \"{:g}\", value);\n }\n};\n\ntemplate\nstruct __TToStringHelper {\n void operator()(T value, char* buffer, size_t buf_len) const {\n format_to_buf(buffer, buf_len, \"{:.17g}\", value);\n }\n};\n\n/*!\n* Converts an array to a string with with values separated by the space character.\n* This method replaces Common's ``ArrayToString`` and ``ArrayToStringFast`` functionality\n* and is locale-independent.\n*\n* \\note If ``high_precision_output`` is set to true,\n* floating point values are output with more digits of precision.\n*/\ntemplate\ninline static std::string ArrayToString(const std::vector& arr, size_t n) {\n if (arr.empty() || n == 0) {\n return std::string(\"\");\n }\n __TToStringHelper::value, high_precision_output> helper;\n const size_t buf_len = high_precision_output ? 32 : 16;\n std::vector buffer(buf_len);\n std::stringstream str_buf;\n Common::C_stringstream(str_buf);\n helper(arr[0], buffer.data(), buf_len);\n str_buf << buffer.data();\n for (size_t i = 1; i < std::min(n, arr.size()); ++i) {\n helper(arr[i], buffer.data(), buf_len);\n str_buf << ' ' << buffer.data();\n }\n return str_buf.str();\n}\n\n\n} // namespace CommonC\n\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_COMMON_H_\n" }, { "path": "include/LightGBM/utils/file_io.h", "content": "/*!\n * Copyright (c) 2018-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2018-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_FILE_IO_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_FILE_IO_H_\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\n/*!\n * \\brief An interface for writing files from buffers\n */\nstruct VirtualFileWriter : BinaryWriter {\n virtual ~VirtualFileWriter() {}\n\n /*!\n * \\brief Initialize the writer\n * \\return True when the file is available for writes\n */\n virtual bool Init() = 0;\n\n /*!\n * \\brief Create appropriate writer for filename\n * \\param filename Filename of the data\n * \\return File writer instance\n */\n static std::unique_ptr Make(const std::string& filename);\n\n /*!\n * \\brief Check filename existence\n * \\param filename Filename of the data\n * \\return True when the file exists\n */\n static bool Exists(const std::string& filename);\n};\n\n/**\n * \\brief An interface for reading files into buffers\n */\nstruct VirtualFileReader {\n /*!\n * \\brief Constructor\n * \\param filename Filename of the data\n */\n virtual ~VirtualFileReader() {}\n /*!\n * \\brief Initialize the reader\n * \\return True when the file is available for read\n */\n virtual bool Init() = 0;\n /*!\n * \\brief Read data into buffer\n * \\param buffer Buffer to read data into\n * \\param bytes Number of bytes to read\n * \\return Number of bytes read\n */\n virtual size_t Read(void* buffer, size_t bytes) const = 0;\n /*!\n * \\brief Create appropriate reader for filename\n * \\param filename Filename of the data\n * \\return File reader instance\n */\n static std::unique_ptr Make(const std::string& filename);\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_FILE_IO_H_\n" }, { "path": "include/LightGBM/utils/json11.h", "content": "/* Copyright (c) 2013 Dropbox, Inc.\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to deal\n * in the Software without restriction, including without limitation the rights\n * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n * copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n * THE SOFTWARE.\n */\n\n/* json11\n *\n * json11 is a tiny JSON library for C++11, providing JSON parsing and\n * serialization.\n *\n * The core object provided by the library is json11::Json. A Json object\n * represents any JSON value: null, bool, number (int or double), string\n * (std::string), array (std::vector), or object (std::map).\n *\n * Json objects act like values: they can be assigned, copied, moved, compared\n * for equality or order, etc. There are also helper methods Json::dump, to\n * serialize a Json to a string, and Json::parse (static) to parse a std::string\n * as a Json object.\n *\n * Internally, the various types of Json object are represented by the JsonValue\n * class hierarchy.\n *\n * A note on numbers - JSON specifies the syntax of number formatting but not\n * its semantics, so some JSON implementations distinguish between integers and\n * floating-point numbers, while some don't. In json11, we choose the latter.\n * Because some JSON implementations (namely Javascript itself) treat all\n * numbers as the same type, distinguishing the two leads to JSON that will be\n * *silently* changed by a round-trip through those implementations. Dangerous!\n * To avoid that risk, json11 stores all numbers as double internally, but also\n * provides integer helpers.\n *\n * Fortunately, double-precision IEEE754 ('double') can precisely store any\n * integer in the range +/-2^53, which includes every 'int' on most systems.\n * (Timestamps often use int64 or long long to avoid the Y2038K problem; a\n * double storing microseconds since some epoch will be exact for +/- 275\n * years.)\n */\n#pragma once\n\n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace json11_internal_lightgbm {\n\nenum JsonParse { STANDARD, COMMENTS };\n\nclass JsonValue;\n\nclass Json final {\n public:\n // Types\n enum Type { NUL, NUMBER, BOOL, STRING, ARRAY, OBJECT };\n\n // Array and object typedefs\n typedef std::vector array;\n typedef std::map object;\n\n // Constructors for the various types of JSON value.\n Json() noexcept; // NUL\n explicit Json(std::nullptr_t) noexcept; // NUL\n explicit Json(double value); // NUMBER\n explicit Json(int value); // NUMBER\n explicit Json(bool value); // BOOL\n explicit Json(const std::string &value); // STRING\n explicit Json(std::string &&value); // STRING\n explicit Json(const char *value); // STRING\n explicit Json(const array &values); // ARRAY\n explicit Json(array &&values); // ARRAY\n explicit Json(const object &values); // OBJECT\n explicit Json(object &&values); // OBJECT\n\n // Implicit constructor: anything with a to_json() function.\n template \n explicit Json(const T &t) : Json(t.to_json()) {}\n\n // Implicit constructor: map-like objects (std::map, std::unordered_map, etc)\n template <\n class M,\n typename std::enable_if<\n std::is_constructible<\n std::string, decltype(std::declval().begin()->first)>::value &&\n std::is_constructible<\n Json, decltype(std::declval().begin()->second)>::value,\n int>::type = 0>\n explicit Json(const M &m) : Json(object(m.begin(), m.end())) {}\n\n // Implicit constructor: vector-like objects (std::list, std::vector,\n // std::set, etc)\n template ().begin())>::value,\n int>::type = 0>\n explicit Json(const V &v) : Json(array(v.begin(), v.end())) {}\n\n // This prevents Json(some_pointer) from accidentally producing a bool. Use\n // Json(bool(some_pointer)) if that behavior is desired.\n explicit Json(void *) = delete;\n\n // Accessors\n Type type() const;\n\n bool is_null() const { return type() == NUL; }\n bool is_number() const { return type() == NUMBER; }\n bool is_bool() const { return type() == BOOL; }\n bool is_string() const { return type() == STRING; }\n bool is_array() const { return type() == ARRAY; }\n bool is_object() const { return type() == OBJECT; }\n\n // Return the enclosed value if this is a number, 0 otherwise. Note that\n // json11 does not distinguish between integer and non-integer numbers -\n // number_value() and int_value() can both be applied to a NUMBER-typed\n // object.\n double number_value() const;\n int int_value() const;\n\n // Return the enclosed value if this is a boolean, false otherwise.\n bool bool_value() const;\n // Return the enclosed string if this is a string, \"\" otherwise.\n const std::string &string_value() const;\n // Return the enclosed std::vector if this is an array, or an empty vector\n // otherwise.\n const array &array_items() const;\n // Return the enclosed std::map if this is an object, or an empty map\n // otherwise.\n const object &object_items() const;\n\n // Return a reference to arr[i] if this is an array, Json() otherwise.\n const Json &operator[](size_t i) const;\n // Return a reference to obj[key] if this is an object, Json() otherwise.\n const Json &operator[](const std::string &key) const;\n\n // Serialize.\n void dump(std::string *out) const;\n std::string dump() const {\n std::string out;\n dump(&out);\n return out;\n }\n\n // Parse. If parse fails, return Json() and assign an error message to err.\n static Json parse(const std::string &in, std::string *err,\n JsonParse strategy = JsonParse::STANDARD);\n static Json parse(const char *in, std::string *err,\n JsonParse strategy = JsonParse::STANDARD) {\n if (in) {\n return parse(std::string(in), err, strategy);\n } else {\n *err = \"null input\";\n return Json(nullptr);\n }\n }\n // Parse multiple objects, concatenated or separated by whitespace\n static std::vector parse_multi(\n const std::string &in, std::string::size_type *parser_stop_pos,\n std::string *err, JsonParse strategy = JsonParse::STANDARD);\n\n static inline std::vector parse_multi(\n const std::string &in, std::string *err,\n JsonParse strategy = JsonParse::STANDARD) {\n std::string::size_type parser_stop_pos;\n return parse_multi(in, &parser_stop_pos, err, strategy);\n }\n\n bool operator==(const Json &rhs) const;\n bool operator<(const Json &rhs) const;\n bool operator!=(const Json &rhs) const { return !(*this == rhs); }\n bool operator<=(const Json &rhs) const { return !(rhs < *this); }\n bool operator>(const Json &rhs) const { return (rhs < *this); }\n bool operator>=(const Json &rhs) const { return !(*this < rhs); }\n\n /* has_shape(types, err)\n *\n * Return true if this is a JSON object and, for each item in types, has a\n * field of the given type. If not, return false and set err to a descriptive\n * message.\n */\n typedef std::initializer_list> shape;\n bool has_shape(const shape &types, std::string *err) const;\n\n private:\n std::shared_ptr m_ptr;\n};\n\n// Internal class hierarchy - JsonValue objects are not exposed to users of this\n// API.\nclass JsonValue {\n protected:\n friend class Json;\n friend class JsonInt;\n friend class JsonDouble;\n virtual Json::Type type() const = 0;\n virtual bool equals(const JsonValue *other) const = 0;\n virtual bool less(const JsonValue *other) const = 0;\n virtual void dump(std::string *out) const = 0;\n virtual double number_value() const;\n virtual int int_value() const;\n virtual bool bool_value() const;\n virtual const std::string &string_value() const;\n virtual const Json::array &array_items() const;\n virtual const Json &operator[](size_t i) const;\n virtual const Json::object &object_items() const;\n virtual const Json &operator[](const std::string &key) const;\n virtual ~JsonValue() {}\n};\n\n} // namespace json11_internal_lightgbm\n" }, { "path": "include/LightGBM/utils/log.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_LOG_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_LOG_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#ifdef LGB_R_BUILD\n\n#ifndef R_NO_REMAP\n#define R_NO_REMAP\n#endif\n\n#ifndef R_USE_C99_IN_CXX\n#define R_USE_C99_IN_CXX\n#endif\n\n#include \nextern \"C\" void R_FlushConsole(void);\n#endif\n\nnamespace LightGBM {\n\n#if defined(_MSC_VER)\n#define THREAD_LOCAL __declspec(thread)\n#else\n#define THREAD_LOCAL thread_local\n#endif\n\n#ifndef CHECK\n#define CHECK(condition) \\\n if (!(condition)) \\\n Log::Fatal(\"Check failed: \" #condition \" at %s, line %d .\\n\", __FILE__, \\\n __LINE__);\n#endif\n\n#ifndef CHECK_EQ\n#define CHECK_EQ(a, b) CHECK((a) == (b))\n#endif\n\n#ifndef CHECK_NE\n#define CHECK_NE(a, b) CHECK((a) != (b))\n#endif\n\n#ifndef CHECK_GE\n#define CHECK_GE(a, b) CHECK((a) >= (b))\n#endif\n\n#ifndef CHECK_LE\n#define CHECK_LE(a, b) CHECK((a) <= (b))\n#endif\n\n#ifndef CHECK_GT\n#define CHECK_GT(a, b) CHECK((a) > (b))\n#endif\n\n#ifndef CHECK_LT\n#define CHECK_LT(a, b) CHECK((a) < (b))\n#endif\n\n#ifndef CHECK_NOTNULL\n#define CHECK_NOTNULL(pointer) \\\n if ((pointer) == nullptr) \\\n LightGBM::Log::Fatal(#pointer \" Can't be NULL at %s, line %d .\\n\", \\\n __FILE__, __LINE__);\n#endif\n\nenum class LogLevel : int {\n Fatal = -1,\n Warning = 0,\n Info = 1,\n Debug = 2,\n};\n\n/*!\n * \\brief A static Log class\n */\nclass Log {\n public:\n using Callback = void (*)(const char *);\n /*!\n * \\brief Resets the minimal log level. It is INFO by default.\n * \\param level The new minimal log level.\n */\n static void ResetLogLevel(LogLevel level) { GetLevel() = level; }\n\n static void ResetCallBack(Callback callback) { GetLogCallBack() = callback; }\n\n static void Debug(const char *format, ...) {\n va_list val;\n va_start(val, format);\n Write(LogLevel::Debug, \"Debug\", format, val);\n va_end(val);\n }\n static void Info(const char *format, ...) {\n va_list val;\n va_start(val, format);\n Write(LogLevel::Info, \"Info\", format, val);\n va_end(val);\n }\n static void Warning(const char *format, ...) {\n va_list val;\n va_start(val, format);\n Write(LogLevel::Warning, \"Warning\", format, val);\n va_end(val);\n }\n static void Fatal(const char *format, ...) {\n va_list val;\n const size_t kBufSize = 1024;\n char str_buf[kBufSize];\n va_start(val, format);\n#ifdef _MSC_VER\n vsnprintf_s(str_buf, kBufSize, format, val);\n#else\n vsnprintf(str_buf, kBufSize, format, val);\n#endif\n va_end(val);\n\n// R code should write back to R's error stream,\n// otherwise to stderr\n#ifndef LGB_R_BUILD\n fprintf(stderr, \"[LightGBM] [Fatal] %s\\n\", str_buf);\n fflush(stderr);\n#else\n REprintf(\"[LightGBM] [Fatal] %s\\n\", str_buf);\n R_FlushConsole();\n#endif\n throw std::runtime_error(std::string(str_buf));\n }\n\n private:\n static void Write(LogLevel level, const char *level_str, const char *format,\n va_list val) {\n if (level <= GetLevel()) { // omit the message with low level\n// R code should write back to R's output stream,\n// otherwise to stdout\n#ifndef LGB_R_BUILD\n if (GetLogCallBack() == nullptr) {\n printf(\"[LightGBM] [%s] \", level_str);\n vprintf(format, val);\n printf(\"\\n\");\n fflush(stdout);\n } else {\n const size_t kBufSize = 512;\n char buf[kBufSize];\n snprintf(buf, kBufSize, \"[LightGBM] [%s] \", level_str);\n GetLogCallBack()(buf);\n vsnprintf(buf, kBufSize, format, val);\n GetLogCallBack()(buf);\n GetLogCallBack()(\"\\n\");\n }\n#else\n Rprintf(\"[LightGBM] [%s] \", level_str);\n Rvprintf(format, val);\n Rprintf(\"\\n\");\n R_FlushConsole();\n#endif\n }\n }\n\n // a trick to use static variable in header file.\n // May be not good, but avoid to use an additional cpp file\n static LogLevel &GetLevel() {\n static THREAD_LOCAL LogLevel level = LogLevel::Info;\n return level;\n }\n\n static Callback &GetLogCallBack() {\n static THREAD_LOCAL Callback callback = nullptr;\n return callback;\n }\n};\n\n} // namespace LightGBM\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_LOG_H_\n" }, { "path": "include/LightGBM/utils/openmp_wrapper.h", "content": "/*!\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_OPENMP_WRAPPER_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_OPENMP_WRAPPER_H_\n\n#include \n\n// this can only be changed by LGBM_SetMaxThreads()\nLIGHTGBM_EXTERN_C int LGBM_MAX_NUM_THREADS;\n\n// this is modified by OMP_SET_NUM_THREADS(), for example\n// by passing num_thread through params\nLIGHTGBM_EXTERN_C int LGBM_DEFAULT_NUM_THREADS;\n\n#ifdef _OPENMP\n\n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n\n/*\n Get number of threads to use in OpenMP parallel regions.\n\n By default, this will return the result of omp_get_max_threads(),\n which is OpenMP-implementation dependent but generally can be controlled\n by environment variable OMP_NUM_THREADS.\n\n ref:\n - https://www.openmp.org/spec-html/5.0/openmpsu112.html\n - https://gcc.gnu.org/onlinedocs/libgomp/omp_005fget_005fmax_005fthreads.html\n*/\nLIGHTGBM_EXTERN_C int OMP_NUM_THREADS();\n\n/*\n Update the default number of threads that'll be used in OpenMP parallel\n regions for LightGBM routines where the number of threads aren't directly\n supplied.\n*/\nLIGHTGBM_EXTERN_C void OMP_SET_NUM_THREADS(int num_threads);\n\nclass ThreadExceptionHelper {\n public:\n ThreadExceptionHelper() {\n ex_ptr_ = nullptr;\n }\n\n ~ThreadExceptionHelper() {\n ReThrow();\n }\n void ReThrow() {\n if (ex_ptr_ != nullptr) {\n std::rethrow_exception(ex_ptr_);\n }\n }\n void CaptureException() {\n // only catch first exception.\n if (ex_ptr_ != nullptr) {\n return;\n }\n std::unique_lock guard(lock_);\n if (ex_ptr_ != nullptr) {\n return;\n }\n ex_ptr_ = std::current_exception();\n }\n\n private:\n std::exception_ptr ex_ptr_;\n std::mutex lock_;\n};\n\n#define OMP_INIT_EX() ThreadExceptionHelper omp_except_helper\n#define OMP_LOOP_EX_BEGIN() try {\n#define OMP_LOOP_EX_END() \\\n } \\\n catch (std::exception & ex) { \\\n Log::Warning(ex.what()); \\\n omp_except_helper.CaptureException(); \\\n } \\\n catch (...) { \\\n omp_except_helper.CaptureException(); \\\n }\n#define OMP_THROW_EX() omp_except_helper.ReThrow()\n\n#else\n\n/*\n * To be compatible with OpenMP, define a nothrow macro which is used by gcc\n * openmp, but not by clang.\n * See also https://github.com/dmlc/dmlc-core/blob/3106c1cbdcc9fc9ef3a2c1d2196a7a6f6616c13d/include/dmlc/omp.h#L14\n */\n#if defined(__clang__)\n#undef __GOMP_NOTHROW\n#define __GOMP_NOTHROW\n#elif defined(__cplusplus)\n#undef __GOMP_NOTHROW\n#define __GOMP_NOTHROW throw()\n#else\n#undef __GOMP_NOTHROW\n#define __GOMP_NOTHROW __attribute__((__nothrow__))\n#endif\n\n#ifdef _MSC_VER\n #pragma warning(disable : 4068) // disable unknown pragma warning\n#endif\n\n#ifdef __cplusplus\n extern \"C\" {\n#endif\n /** Fall here if no OPENMP support, so just\n simulate a single thread running.\n All #pragma omp should be ignored by the compiler **/\n inline void OMP_SET_NUM_THREADS(int) __GOMP_NOTHROW {}\n inline int omp_get_thread_num() __GOMP_NOTHROW {return 0;}\n inline int OMP_NUM_THREADS() __GOMP_NOTHROW { return 1; }\n#ifdef __cplusplus\n} // extern \"C\"\n#endif\n\n#define OMP_INIT_EX()\n#define OMP_LOOP_EX_BEGIN()\n#define OMP_LOOP_EX_END()\n#define OMP_THROW_EX()\n\n#endif\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_OPENMP_WRAPPER_H_\n" }, { "path": "include/LightGBM/utils/pipeline_reader.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_PIPELINE_READER_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_PIPELINE_READER_H_\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\n/*!\n* \\brief A pipeline file reader, use 2 threads, one read block from file, the other process the block\n*/\nclass PipelineReader {\n public:\n /*!\n * \\brief Read data from a file, use pipeline methods\n * \\param filename Filename of data\n * \\process_fun Process function\n */\n static size_t Read(const char* filename, int skip_bytes, const std::function& process_fun) {\n auto reader = VirtualFileReader::Make(filename);\n if (!reader->Init()) {\n return 0;\n }\n size_t cnt = 0;\n const size_t buffer_size = 16 * 1024 * 1024;\n // buffer used for the process_fun\n auto buffer_process = std::vector(buffer_size);\n // buffer used for the file reading\n auto buffer_read = std::vector(buffer_size);\n size_t read_cnt = 0;\n if (skip_bytes > 0) {\n // skip first k bytes\n read_cnt = reader->Read(buffer_process.data(), skip_bytes);\n }\n // read first block\n read_cnt = reader->Read(buffer_process.data(), buffer_size);\n\n size_t last_read_cnt = 0;\n while (read_cnt > 0) {\n // start read thread\n std::thread read_worker = std::thread(\n [=, &last_read_cnt, &reader, &buffer_read] {\n last_read_cnt = reader->Read(buffer_read.data(), buffer_size);\n });\n // start process\n cnt += process_fun(buffer_process.data(), read_cnt);\n // wait for read thread\n read_worker.join();\n // exchange the buffer\n std::swap(buffer_process, buffer_read);\n read_cnt = last_read_cnt;\n }\n return cnt;\n }\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_PIPELINE_READER_H_\n" }, { "path": "include/LightGBM/utils/random.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_RANDOM_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_RANDOM_H_\n\n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\n/*!\n* \\brief A wrapper for random generator\n*/\nclass Random {\n public:\n /*!\n * \\brief Constructor, with random seed\n */\n Random() {\n std::random_device rd;\n auto generator = std::mt19937(rd());\n std::uniform_int_distribution distribution(0, x);\n x = distribution(generator);\n }\n /*!\n * \\brief Constructor, with specific seed\n */\n explicit Random(int seed) {\n x = seed;\n }\n /*!\n * \\brief Generate random integer, int16 range. [0, 65536]\n * \\param lower_bound lower bound\n * \\param upper_bound upper bound\n * \\return The random integer between [lower_bound, upper_bound)\n */\n inline int NextShort(int lower_bound, int upper_bound) {\n return (RandInt16()) % (upper_bound - lower_bound) + lower_bound;\n }\n\n /*!\n * \\brief Generate random integer, int32 range\n * \\param lower_bound lower bound\n * \\param upper_bound upper bound\n * \\return The random integer between [lower_bound, upper_bound)\n */\n inline int NextInt(int lower_bound, int upper_bound) {\n return (RandInt32()) % (upper_bound - lower_bound) + lower_bound;\n }\n\n /*!\n * \\brief Generate random float data\n * \\return The random float between [0.0, 1.0)\n */\n inline float NextFloat() {\n // get random float in [0,1)\n return static_cast(RandInt16()) / (32768.0f);\n }\n /*!\n * \\brief Sample K data from {0,1,...,N-1}\n * \\param N\n * \\param K\n * \\return K Ordered sampled data from {0,1,...,N-1}\n */\n inline std::vector Sample(int N, int K) {\n std::vector ret;\n ret.reserve(K);\n if (K > N || K <= 0) {\n return ret;\n } else if (K == N) {\n for (int i = 0; i < N; ++i) {\n ret.push_back(i);\n }\n } else if (K > 1 && K > (N / std::log2(K))) {\n for (int i = 0; i < N; ++i) {\n double prob = (K - ret.size()) / static_cast(N - i);\n if (NextFloat() < prob) {\n ret.push_back(i);\n }\n }\n } else {\n std::set sample_set;\n for (int r = N - K; r < N; ++r) {\n int v = NextInt(0, r + 1);\n if (!sample_set.insert(v).second) {\n sample_set.insert(r);\n }\n }\n for (auto iter = sample_set.begin(); iter != sample_set.end(); ++iter) {\n ret.push_back(*iter);\n }\n }\n return ret;\n }\n\n private:\n inline int RandInt16() {\n x = (214013 * x + 2531011);\n return static_cast((x >> 16) & 0x7FFF);\n }\n\n inline int RandInt32() {\n x = (214013 * x + 2531011);\n return static_cast(x & 0x7FFFFFFF);\n }\n\n unsigned int x = 123456789;\n};\n\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_RANDOM_H_\n" }, { "path": "include/LightGBM/utils/text_reader.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_TEXT_READER_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_TEXT_READER_H_\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nconst size_t kGbs = size_t(1024) * 1024 * 1024;\n\n/*!\n* \\brief Read text data from file\n*/\ntemplate\nclass TextReader {\n public:\n /*!\n * \\brief Constructor\n * \\param filename Filename of data\n * \\param is_skip_first_line True if need to skip header\n */\n TextReader(const char* filename, bool is_skip_first_line, size_t progress_interval_bytes = SIZE_MAX):\n filename_(filename), is_skip_first_line_(is_skip_first_line), read_progress_interval_bytes_(progress_interval_bytes) {\n if (is_skip_first_line_) {\n auto reader = VirtualFileReader::Make(filename);\n if (!reader->Init()) {\n Log::Fatal(\"Could not open %s\", filename);\n }\n std::stringstream str_buf;\n char read_c;\n size_t nread = reader->Read(&read_c, 1);\n while (nread == 1) {\n if (read_c == '\\n' || read_c == '\\r') {\n break;\n }\n str_buf << read_c;\n ++skip_bytes_;\n nread = reader->Read(&read_c, 1);\n }\n if (read_c == '\\r') {\n reader->Read(&read_c, 1);\n ++skip_bytes_;\n }\n if (read_c == '\\n') {\n reader->Read(&read_c, 1);\n ++skip_bytes_;\n }\n first_line_ = str_buf.str();\n Log::Debug(\"Skipped header \\\"%s\\\" in file %s\", first_line_.c_str(), filename_);\n }\n }\n /*!\n * \\brief Destructor\n */\n ~TextReader() {\n Clear();\n }\n /*!\n * \\brief Clear cached data\n */\n inline void Clear() {\n lines_.clear();\n lines_.shrink_to_fit();\n }\n /*!\n * \\brief return first line of data\n */\n inline std::string first_line() {\n return first_line_;\n }\n /*!\n * \\brief Get text data that read from file\n * \\return Text data, store in std::vector by line\n */\n inline std::vector& Lines() { return lines_; }\n /*!\n * \\brief Get joined text data that read from file\n * \\return Text data, store in std::string, joined all lines by delimiter\n */\n inline std::string JoinedLines(std::string delimiter = \"\\n\") {\n std::stringstream ss;\n for (auto line : lines_) {\n ss << line << delimiter;\n }\n return ss.str();\n }\n\n INDEX_T ReadAllAndProcess(const std::function& process_fun) {\n last_line_ = \"\";\n INDEX_T total_cnt = 0;\n size_t bytes_read = 0;\n PipelineReader::Read(filename_, skip_bytes_,\n [&process_fun, &bytes_read, &total_cnt, this]\n (const char* buffer_process, size_t read_cnt) {\n size_t cnt = 0;\n size_t i = 0;\n size_t last_i = 0;\n // skip the break between \\r and \\n\n if (last_line_.size() == 0 && buffer_process[0] == '\\n') {\n i = 1;\n last_i = i;\n }\n while (i < read_cnt) {\n if (buffer_process[i] == '\\n' || buffer_process[i] == '\\r') {\n if (last_line_.size() > 0) {\n last_line_.append(buffer_process + last_i, i - last_i);\n process_fun(total_cnt, last_line_.c_str(), last_line_.size());\n last_line_ = \"\";\n } else {\n process_fun(total_cnt, buffer_process + last_i, i - last_i);\n }\n ++cnt;\n ++i;\n ++total_cnt;\n // skip end of line\n while ((buffer_process[i] == '\\n' || buffer_process[i] == '\\r') && i < read_cnt) {\n ++i;\n }\n last_i = i;\n } else {\n ++i;\n }\n }\n if (last_i != read_cnt) {\n last_line_.append(buffer_process + last_i, read_cnt - last_i);\n }\n\n size_t prev_bytes_read = bytes_read;\n bytes_read += read_cnt;\n if (prev_bytes_read / read_progress_interval_bytes_ < bytes_read / read_progress_interval_bytes_) {\n Log::Debug(\"Read %.1f GBs from %s.\", 1.0 * bytes_read / kGbs, filename_);\n }\n\n return cnt;\n });\n // if last line of file doesn't contain end of line\n if (last_line_.size() > 0) {\n Log::Info(\"Warning: last line of %s has no end of line, still using this line\", filename_);\n process_fun(total_cnt, last_line_.c_str(), last_line_.size());\n ++total_cnt;\n last_line_ = \"\";\n }\n return total_cnt;\n }\n\n /*!\n * \\brief Read all text data from file in memory\n * \\return number of lines of text data\n */\n INDEX_T ReadAllLines() {\n return ReadAllAndProcess(\n [=](INDEX_T, const char* buffer, size_t size) {\n lines_.emplace_back(buffer, size);\n });\n }\n\n std::vector ReadContent(size_t* out_len) {\n std::vector ret;\n *out_len = 0;\n auto reader = VirtualFileReader::Make(filename_);\n if (!reader->Init()) {\n return ret;\n }\n const size_t buffer_size = 16 * 1024 * 1024;\n auto buffer_read = std::vector(buffer_size);\n size_t read_cnt = 0;\n do {\n read_cnt = reader->Read(buffer_read.data(), buffer_size);\n ret.insert(ret.end(), buffer_read.begin(), buffer_read.begin() + read_cnt);\n *out_len += read_cnt;\n } while (read_cnt > 0);\n return ret;\n }\n\n INDEX_T SampleFromFile(Random* random, INDEX_T sample_cnt, std::vector* out_sampled_data) {\n INDEX_T cur_sample_cnt = 0;\n return ReadAllAndProcess([=, &random, &cur_sample_cnt,\n &out_sampled_data]\n (INDEX_T line_idx, const char* buffer, size_t size) {\n if (cur_sample_cnt < sample_cnt) {\n out_sampled_data->emplace_back(buffer, size);\n ++cur_sample_cnt;\n } else {\n const size_t idx = static_cast(random->NextInt(0, static_cast(line_idx + 1)));\n if (idx < static_cast(sample_cnt)) {\n out_sampled_data->operator[](idx) = std::string(buffer, size);\n }\n }\n });\n }\n /*!\n * \\brief Read part of text data from file in memory, use filter_fun to filter data\n * \\param filter_fun Function that perform data filter\n * \\param out_used_data_indices Store line indices that read text data\n * \\return The number of total data\n */\n INDEX_T ReadAndFilterLines(const std::function& filter_fun, std::vector* out_used_data_indices) {\n out_used_data_indices->clear();\n INDEX_T total_cnt = ReadAllAndProcess(\n [&filter_fun, &out_used_data_indices, this]\n (INDEX_T line_idx , const char* buffer, size_t size) {\n bool is_used = filter_fun(line_idx);\n if (is_used) {\n out_used_data_indices->push_back(line_idx);\n lines_.emplace_back(buffer, size);\n }\n });\n return total_cnt;\n }\n\n INDEX_T SampleAndFilterFromFile(const std::function& filter_fun, std::vector* out_used_data_indices,\n Random* random, INDEX_T sample_cnt, std::vector* out_sampled_data) {\n INDEX_T cur_sample_cnt = 0;\n out_used_data_indices->clear();\n INDEX_T total_cnt = ReadAllAndProcess(\n [=, &filter_fun, &out_used_data_indices, &random, &cur_sample_cnt,\n &out_sampled_data]\n (INDEX_T line_idx, const char* buffer, size_t size) {\n bool is_used = filter_fun(line_idx);\n if (is_used) {\n out_used_data_indices->push_back(line_idx);\n if (cur_sample_cnt < sample_cnt) {\n out_sampled_data->emplace_back(buffer, size);\n ++cur_sample_cnt;\n } else {\n const size_t idx = static_cast(random->NextInt(0, static_cast(out_used_data_indices->size())));\n if (idx < static_cast(sample_cnt)) {\n out_sampled_data->operator[](idx) = std::string(buffer, size);\n }\n }\n }\n });\n return total_cnt;\n }\n\n INDEX_T CountLine() {\n return ReadAllAndProcess(\n [=](INDEX_T, const char*, size_t) {\n });\n }\n\n INDEX_T ReadAllAndProcessParallelWithFilter(const std::function&)>& process_fun, const std::function& filter_fun) {\n last_line_ = \"\";\n INDEX_T total_cnt = 0;\n size_t bytes_read = 0;\n INDEX_T used_cnt = 0;\n PipelineReader::Read(filename_, skip_bytes_,\n [&process_fun, &filter_fun, &total_cnt, &bytes_read, &used_cnt, this]\n (const char* buffer_process, size_t read_cnt) {\n size_t cnt = 0;\n size_t i = 0;\n size_t last_i = 0;\n INDEX_T start_idx = used_cnt;\n // skip the break between \\r and \\n\n if (last_line_.size() == 0 && buffer_process[0] == '\\n') {\n i = 1;\n last_i = i;\n }\n while (i < read_cnt) {\n if (buffer_process[i] == '\\n' || buffer_process[i] == '\\r') {\n if (last_line_.size() > 0) {\n last_line_.append(buffer_process + last_i, i - last_i);\n if (filter_fun(used_cnt, total_cnt)) {\n lines_.push_back(last_line_);\n ++used_cnt;\n }\n last_line_ = \"\";\n } else {\n if (filter_fun(used_cnt, total_cnt)) {\n lines_.emplace_back(buffer_process + last_i, i - last_i);\n ++used_cnt;\n }\n }\n ++cnt;\n ++i;\n ++total_cnt;\n // skip end of line\n while ((buffer_process[i] == '\\n' || buffer_process[i] == '\\r') && i < read_cnt) {\n ++i;\n }\n last_i = i;\n } else {\n ++i;\n }\n }\n process_fun(start_idx, lines_);\n lines_.clear();\n if (last_i != read_cnt) {\n last_line_.append(buffer_process + last_i, read_cnt - last_i);\n }\n\n size_t prev_bytes_read = bytes_read;\n bytes_read += read_cnt;\n if (prev_bytes_read / read_progress_interval_bytes_ < bytes_read / read_progress_interval_bytes_) {\n Log::Debug(\"Read %.1f GBs from %s.\", 1.0 * bytes_read / kGbs, filename_);\n }\n\n return cnt;\n });\n // if last line of file doesn't contain end of line\n if (last_line_.size() > 0) {\n Log::Info(\"Warning: last line of %s has no end of line, still using this line\", filename_);\n if (filter_fun(used_cnt, total_cnt)) {\n lines_.push_back(last_line_);\n process_fun(used_cnt, lines_);\n }\n lines_.clear();\n ++total_cnt;\n ++used_cnt;\n last_line_ = \"\";\n }\n return total_cnt;\n }\n\n INDEX_T ReadAllAndProcessParallel(const std::function&)>& process_fun) {\n return ReadAllAndProcessParallelWithFilter(process_fun, [](INDEX_T, INDEX_T) { return true; });\n }\n\n INDEX_T ReadPartAndProcessParallel(const std::vector& used_data_indices, const std::function&)>& process_fun) {\n return ReadAllAndProcessParallelWithFilter(process_fun,\n [&used_data_indices](INDEX_T used_cnt, INDEX_T total_cnt) {\n if (static_cast(used_cnt) < used_data_indices.size() && total_cnt == used_data_indices[used_cnt]) {\n return true;\n } else {\n return false;\n }\n });\n }\n\n private:\n /*! \\brief Filename of text data */\n const char* filename_;\n /*! \\brief Cache the read text data */\n std::vector lines_;\n /*! \\brief Buffer for last line */\n std::string last_line_;\n /*! \\brief first line */\n std::string first_line_ = \"\";\n /*! \\brief is skip first line */\n bool is_skip_first_line_ = false;\n size_t read_progress_interval_bytes_;\n /*! \\brief is skip first line */\n int skip_bytes_ = 0;\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_TEXT_READER_H_\n" }, { "path": "include/LightGBM/utils/threading.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_THREADING_H_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_THREADING_H_\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n\nnamespace LightGBM {\n\nclass Threading {\n public:\n template \n static inline void BlockInfo(INDEX_T cnt, INDEX_T min_cnt_per_block,\n int* out_nblock, INDEX_T* block_size) {\n int num_threads = OMP_NUM_THREADS();\n BlockInfo(num_threads, cnt, min_cnt_per_block, out_nblock,\n block_size);\n }\n\n template \n static inline void BlockInfo(int num_threads, INDEX_T cnt,\n INDEX_T min_cnt_per_block, int* out_nblock,\n INDEX_T* block_size) {\n *out_nblock = std::min(\n num_threads,\n static_cast((cnt + min_cnt_per_block - 1) / min_cnt_per_block));\n if (*out_nblock > 1) {\n *block_size = SIZE_ALIGNED((cnt + (*out_nblock) - 1) / (*out_nblock));\n } else {\n *block_size = cnt;\n }\n }\n\n template \n static inline void BlockInfoForceSize(int num_threads, INDEX_T cnt,\n INDEX_T min_cnt_per_block,\n int* out_nblock, INDEX_T* block_size) {\n *out_nblock = std::min(\n num_threads,\n static_cast((cnt + min_cnt_per_block - 1) / min_cnt_per_block));\n if (*out_nblock > 1) {\n *block_size = (cnt + (*out_nblock) - 1) / (*out_nblock);\n // force the block size to the times of min_cnt_per_block\n *block_size = (*block_size + min_cnt_per_block - 1) / min_cnt_per_block *\n min_cnt_per_block;\n } else {\n *block_size = cnt;\n }\n }\n\n template \n static inline void BlockInfoForceSize(INDEX_T cnt, INDEX_T min_cnt_per_block,\n int* out_nblock, INDEX_T* block_size) {\n int num_threads = OMP_NUM_THREADS();\n BlockInfoForceSize(num_threads, cnt, min_cnt_per_block, out_nblock,\n block_size);\n }\n\n template \n static inline int For(\n INDEX_T start, INDEX_T end, INDEX_T min_block_size,\n const std::function& inner_fun) {\n int n_block = 1;\n INDEX_T num_inner = end - start;\n BlockInfo(num_inner, min_block_size, &n_block, &num_inner);\n OMP_INIT_EX();\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1)\n for (int i = 0; i < n_block; ++i) {\n OMP_LOOP_EX_BEGIN();\n INDEX_T inner_start = start + num_inner * i;\n INDEX_T inner_end = std::min(end, inner_start + num_inner);\n if (inner_start < inner_end) {\n inner_fun(i, inner_start, inner_end);\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n return n_block;\n }\n};\n\ntemplate \nclass ParallelPartitionRunner {\n public:\n ParallelPartitionRunner(INDEX_T num_data, INDEX_T min_block_size)\n : min_block_size_(min_block_size) {\n num_threads_ = OMP_NUM_THREADS();\n left_.resize(num_data);\n if (TWO_BUFFER) {\n right_.resize(num_data);\n }\n offsets_.resize(num_threads_);\n left_cnts_.resize(num_threads_);\n right_cnts_.resize(num_threads_);\n left_write_pos_.resize(num_threads_);\n right_write_pos_.resize(num_threads_);\n }\n\n ~ParallelPartitionRunner() {}\n\n void ReSize(INDEX_T num_data) {\n left_.resize(num_data);\n if (TWO_BUFFER) {\n right_.resize(num_data);\n }\n }\n\n template\n INDEX_T Run(\n INDEX_T cnt,\n const std::function& func,\n INDEX_T* out) {\n int nblock = 1;\n INDEX_T inner_size = cnt;\n if (FORCE_SIZE) {\n Threading::BlockInfoForceSize(num_threads_, cnt, min_block_size_,\n &nblock, &inner_size);\n } else {\n Threading::BlockInfo(num_threads_, cnt, min_block_size_, &nblock,\n &inner_size);\n }\n\n OMP_INIT_EX();\n#pragma omp parallel for schedule(static, 1) num_threads(num_threads_)\n for (int i = 0; i < nblock; ++i) {\n OMP_LOOP_EX_BEGIN();\n INDEX_T cur_start = i * inner_size;\n INDEX_T cur_cnt = std::min(inner_size, cnt - cur_start);\n offsets_[i] = cur_start;\n if (cur_cnt <= 0) {\n left_cnts_[i] = 0;\n right_cnts_[i] = 0;\n continue;\n }\n auto left_ptr = left_.data() + cur_start;\n INDEX_T* right_ptr = nullptr;\n if (TWO_BUFFER) {\n right_ptr = right_.data() + cur_start;\n }\n // split data inner, reduce the times of function called\n INDEX_T cur_left_count =\n func(i, cur_start, cur_cnt, left_ptr, right_ptr);\n if (!TWO_BUFFER) {\n // reverse for one buffer\n std::reverse(left_ptr + cur_left_count, left_ptr + cur_cnt);\n }\n left_cnts_[i] = cur_left_count;\n right_cnts_[i] = cur_cnt - cur_left_count;\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n\n left_write_pos_[0] = 0;\n right_write_pos_[0] = 0;\n for (int i = 1; i < nblock; ++i) {\n left_write_pos_[i] = left_write_pos_[i - 1] + left_cnts_[i - 1];\n right_write_pos_[i] = right_write_pos_[i - 1] + right_cnts_[i - 1];\n }\n data_size_t left_cnt = left_write_pos_[nblock - 1] + left_cnts_[nblock - 1];\n\n auto right_start = out + left_cnt;\n#pragma omp parallel for schedule(static, 1) num_threads(num_threads_)\n for (int i = 0; i < nblock; ++i) {\n std::copy_n(left_.data() + offsets_[i], left_cnts_[i],\n out + left_write_pos_[i]);\n if (TWO_BUFFER) {\n std::copy_n(right_.data() + offsets_[i], right_cnts_[i],\n right_start + right_write_pos_[i]);\n } else {\n std::copy_n(left_.data() + offsets_[i] + left_cnts_[i], right_cnts_[i],\n right_start + right_write_pos_[i]);\n }\n }\n return left_cnt;\n }\n\n private:\n int num_threads_;\n INDEX_T min_block_size_;\n std::vector left_;\n std::vector right_;\n std::vector offsets_;\n std::vector left_cnts_;\n std::vector right_cnts_;\n std::vector left_write_pos_;\n std::vector right_write_pos_;\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_THREADING_H_\n" }, { "path": "include/LightGBM/utils/yamc/alternate_shared_mutex.hpp", "content": "/*\n * alternate_shared_mutex.hpp\n *\n * MIT License\n *\n * Copyright (c) 2017 yohhoy\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to deal\n * in the Software without restriction, including without limitation the rights\n * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n * copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n * SOFTWARE.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_ALTERNATE_SHARED_MUTEX_HPP_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_ALTERNATE_SHARED_MUTEX_HPP_\n\n#include \n#include \n#include \n#include \n\n#include \"yamc_rwlock_sched.hpp\"\n\nnamespace yamc {\n\n/*\n * alternate implementation of shared mutex variants\n *\n * - yamc::alternate::shared_mutex\n * - yamc::alternate::shared_timed_mutex\n * - yamc::alternate::basic_shared_mutex\n * - yamc::alternate::basic_shared_timed_mutex\n */\nnamespace alternate {\n\nnamespace detail {\n\ntemplate \nclass shared_mutex_base {\n protected:\n typename RwLockPolicy::state state_;\n std::condition_variable cv_;\n std::mutex mtx_;\n\n void lock() {\n std::unique_lock lk(mtx_);\n RwLockPolicy::before_wait_wlock(state_);\n while (RwLockPolicy::wait_wlock(state_)) {\n cv_.wait(lk);\n }\n RwLockPolicy::after_wait_wlock(state_);\n RwLockPolicy::acquire_wlock(&state_);\n }\n\n bool try_lock() {\n std::lock_guard lk(mtx_);\n if (RwLockPolicy::wait_wlock(state_)) return false;\n RwLockPolicy::acquire_wlock(state_);\n return true;\n }\n\n void unlock() {\n std::lock_guard lk(mtx_);\n RwLockPolicy::release_wlock(&state_);\n cv_.notify_all();\n }\n\n void lock_shared() {\n std::unique_lock lk(mtx_);\n while (RwLockPolicy::wait_rlock(state_)) {\n cv_.wait(lk);\n }\n RwLockPolicy::acquire_rlock(&state_);\n }\n\n bool try_lock_shared() {\n std::lock_guard lk(mtx_);\n if (RwLockPolicy::wait_rlock(state_)) return false;\n RwLockPolicy::acquire_rlock(state_);\n return true;\n }\n\n void unlock_shared() {\n std::lock_guard lk(mtx_);\n if (RwLockPolicy::release_rlock(&state_)) {\n cv_.notify_all();\n }\n }\n};\n\n} // namespace detail\n\ntemplate \nclass basic_shared_mutex : private detail::shared_mutex_base {\n using base = detail::shared_mutex_base;\n\n public:\n basic_shared_mutex() = default;\n ~basic_shared_mutex() = default;\n\n basic_shared_mutex(const basic_shared_mutex&) = delete;\n basic_shared_mutex& operator=(const basic_shared_mutex&) = delete;\n\n using base::lock;\n using base::try_lock;\n using base::unlock;\n\n using base::lock_shared;\n using base::try_lock_shared;\n using base::unlock_shared;\n};\n\nusing shared_mutex = basic_shared_mutex;\n\ntemplate \nclass basic_shared_timed_mutex\n : private detail::shared_mutex_base {\n using base = detail::shared_mutex_base;\n\n using base::cv_;\n using base::mtx_;\n using base::state_;\n\n template \n bool do_try_lockwait(const std::chrono::time_point& tp) {\n std::unique_lock lk(mtx_);\n RwLockPolicy::before_wait_wlock(state_);\n while (RwLockPolicy::wait_wlock(state_)) {\n if (cv_.wait_until(lk, tp) == std::cv_status::timeout) {\n if (!RwLockPolicy::wait_wlock(state_)) // re-check predicate\n break;\n RwLockPolicy::after_wait_wlock(state_);\n return false;\n }\n }\n RwLockPolicy::after_wait_wlock(state_);\n RwLockPolicy::acquire_wlock(state_);\n return true;\n }\n\n template \n bool do_try_lock_sharedwait(\n const std::chrono::time_point& tp) {\n std::unique_lock lk(mtx_);\n while (RwLockPolicy::wait_rlock(state_)) {\n if (cv_.wait_until(lk, tp) == std::cv_status::timeout) {\n if (!RwLockPolicy::wait_rlock(state_)) // re-check predicate\n break;\n return false;\n }\n }\n RwLockPolicy::acquire_rlock(state_);\n return true;\n }\n\n public:\n basic_shared_timed_mutex() = default;\n ~basic_shared_timed_mutex() = default;\n\n basic_shared_timed_mutex(const basic_shared_timed_mutex&) = delete;\n basic_shared_timed_mutex& operator=(const basic_shared_timed_mutex&) = delete;\n\n using base::lock;\n using base::try_lock;\n using base::unlock;\n\n template \n bool try_lock_for(const std::chrono::duration& duration) {\n const auto tp = std::chrono::steady_clock::now() + duration;\n return do_try_lockwait(tp);\n }\n\n template \n bool try_lock_until(const std::chrono::time_point& tp) {\n return do_try_lockwait(tp);\n }\n\n using base::lock_shared;\n using base::try_lock_shared;\n using base::unlock_shared;\n\n template \n bool try_lock_shared_for(const std::chrono::duration& duration) {\n const auto tp = std::chrono::steady_clock::now() + duration;\n return do_try_lock_sharedwait(tp);\n }\n\n template \n bool try_lock_shared_until(\n const std::chrono::time_point& tp) {\n return do_try_lock_sharedwait(tp);\n }\n};\n\nusing shared_timed_mutex = basic_shared_timed_mutex;\n\n} // namespace alternate\n} // namespace yamc\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_ALTERNATE_SHARED_MUTEX_HPP_\n" }, { "path": "include/LightGBM/utils/yamc/yamc_rwlock_sched.hpp", "content": "/*\n * yamc_rwlock_sched.hpp\n *\n * MIT License\n *\n * Copyright (c) 2017 yohhoy\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to deal\n * in the Software without restriction, including without limitation the rights\n * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n * copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n * SOFTWARE.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_RWLOCK_SCHED_HPP_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_RWLOCK_SCHED_HPP_\n\n#include \n#include \n\n/// default shared_mutex rwlock policy\n#ifndef YAMC_RWLOCK_SCHED_DEFAULT\n#define YAMC_RWLOCK_SCHED_DEFAULT yamc::rwlock::ReaderPrefer\n#endif\n\nnamespace yamc {\n\n/*\n * readers-writer locking policy for basic_shared_(timed)_mutex\n *\n * - yamc::rwlock::ReaderPrefer\n * - yamc::rwlock::WriterPrefer\n */\nnamespace rwlock {\n\n/// Reader prefer scheduling\n///\n/// NOTE:\n// This policy might introduce \"Writer Starvation\" if readers continuously\n// hold shared lock. PThreads rwlock implementation in Linux use this\n// scheduling policy as default. (see also PTHREAD_RWLOCK_PREFER_READER_NP)\n//\nstruct ReaderPrefer {\n static const std::size_t writer_mask = ~(~std::size_t(0u) >> 1); // MSB 1bit\n static const std::size_t reader_mask = ~std::size_t(0u) >> 1;\n\n struct state {\n std::size_t rwcount = 0;\n };\n\n static void before_wait_wlock(const state&) {}\n static void after_wait_wlock(const state&) {}\n\n static bool wait_wlock(const state& s) { return (s.rwcount != 0); }\n\n static void acquire_wlock(state* s) {\n assert(!(s->rwcount & writer_mask));\n s->rwcount |= writer_mask;\n }\n\n static void release_wlock(state* s) {\n assert(s->rwcount & writer_mask);\n s->rwcount &= ~writer_mask;\n }\n\n static bool wait_rlock(const state& s) { return (s.rwcount & writer_mask) != 0; }\n\n static void acquire_rlock(state* s) {\n assert((s->rwcount & reader_mask) < reader_mask);\n ++(s->rwcount);\n }\n\n static bool release_rlock(state* s) {\n assert(0 < (s->rwcount & reader_mask));\n return (--(s->rwcount) == 0);\n }\n};\n\n/// Writer prefer scheduling\n///\n/// NOTE:\n/// If there are waiting writer, new readers are blocked until all shared lock\n/// are released,\n// and the writer thread can get exclusive lock in preference to blocked\n// reader threads. This policy might introduce \"Reader Starvation\" if writers\n// continuously request exclusive lock.\n/// (see also PTHREAD_RWLOCK_PREFER_WRITER_NONRECURSIVE_NP)\n///\nstruct WriterPrefer {\n static const std::size_t locked = ~(~std::size_t(0u) >> 1); // MSB 1bit\n static const std::size_t wait_mask = ~std::size_t(0u) >> 1;\n\n struct state {\n std::size_t nwriter = 0;\n std::size_t nreader = 0;\n };\n\n static void before_wait_wlock(state* s) {\n assert((s->nwriter & wait_mask) < wait_mask);\n ++(s->nwriter);\n }\n\n static bool wait_wlock(const state& s) {\n return ((s.nwriter & locked) || 0 < s.nreader);\n }\n\n static void after_wait_wlock(state* s) {\n assert(0 < (s->nwriter & wait_mask));\n --(s->nwriter);\n }\n\n static void acquire_wlock(state* s) {\n assert(!(s->nwriter & locked));\n s->nwriter |= locked;\n }\n\n static void release_wlock(state* s) {\n assert(s->nwriter & locked);\n s->nwriter &= ~locked;\n }\n\n static bool wait_rlock(const state& s) { return (s.nwriter != 0); }\n\n static void acquire_rlock(state* s) {\n assert(!(s->nwriter & locked));\n ++(s->nreader);\n }\n\n static bool release_rlock(state* s) {\n assert(0 < s->nreader);\n return (--(s->nreader) == 0);\n }\n};\n\n} // namespace rwlock\n} // namespace yamc\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_RWLOCK_SCHED_HPP_\n" }, { "path": "include/LightGBM/utils/yamc/yamc_shared_lock.hpp", "content": "/*\n * yamc_shared_lock.hpp\n *\n * MIT License\n *\n * Copyright (c) 2017 yohhoy\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to deal\n * in the Software without restriction, including without limitation the rights\n * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n * copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n * SOFTWARE.\n */\n#ifndef LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_SHARED_LOCK_HPP_\n#define LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_SHARED_LOCK_HPP_\n\n#include \n#include \n#include \n#include \n#include // std::swap\n\n/*\n * std::shared_lock in C++14 Standard Library\n *\n * - yamc::shared_lock\n */\nnamespace yamc {\n\ntemplate \nclass shared_lock {\n void locking_precondition(const char* emsg) {\n if (pm_ == nullptr) {\n throw std::system_error(\n std::make_error_code(std::errc::operation_not_permitted), emsg);\n }\n if (owns_) {\n throw std::system_error(\n std::make_error_code(std::errc::resource_deadlock_would_occur), emsg);\n }\n }\n\n public:\n using mutex_type = Mutex;\n\n shared_lock() noexcept = default;\n\n explicit shared_lock(mutex_type* m) {\n m->lock_shared();\n pm_ = m;\n owns_ = true;\n }\n\n shared_lock(const mutex_type& m, std::defer_lock_t) noexcept {\n pm_ = &m;\n owns_ = false;\n }\n\n shared_lock(const mutex_type& m, std::try_to_lock_t) {\n pm_ = &m;\n owns_ = m.try_lock_shared();\n }\n\n shared_lock(const mutex_type& m, std::adopt_lock_t) {\n pm_ = &m;\n owns_ = true;\n }\n\n template \n shared_lock(const mutex_type& m,\n const std::chrono::time_point& abs_time) {\n pm_ = &m;\n owns_ = m.try_lock_shared_until(abs_time);\n }\n\n template \n shared_lock(const mutex_type& m,\n const std::chrono::duration& rel_time) {\n pm_ = &m;\n owns_ = m.try_lock_shared_for(rel_time);\n }\n\n ~shared_lock() {\n if (owns_) {\n assert(pm_ != nullptr);\n pm_->unlock_shared();\n }\n }\n\n shared_lock(const shared_lock&) = delete;\n shared_lock& operator=(const shared_lock&) = delete;\n\n shared_lock(shared_lock&& rhs) noexcept {\n if (pm_ && owns_) {\n pm_->unlock_shared();\n }\n pm_ = rhs.pm_;\n owns_ = rhs.owns_;\n rhs.pm_ = nullptr;\n rhs.owns_ = false;\n }\n\n shared_lock& operator=(shared_lock&& rhs) noexcept {\n if (pm_ && owns_) {\n pm_->unlock_shared();\n }\n pm_ = rhs.pm_;\n owns_ = rhs.owns_;\n rhs.pm_ = nullptr;\n rhs.owns_ = false;\n return *this;\n }\n\n void lock() {\n locking_precondition(\"shared_lock::lock\");\n pm_->lock_shared();\n owns_ = true;\n }\n\n bool try_lock() {\n locking_precondition(\"shared_lock::try_lock\");\n return (owns_ = pm_->try_lock_shared());\n }\n\n template \n bool try_lock_for(const std::chrono::duration& rel_time) {\n locking_precondition(\"shared_lock::try_lock_for\");\n return (owns_ = pm_->try_lock_shared_for(rel_time));\n }\n\n template \n bool try_lock_until(\n const std::chrono::time_point& abs_time) {\n locking_precondition(\"shared_lock::try_lock_until\");\n return (owns_ = pm_->try_lock_shared_until(abs_time));\n }\n\n void unlock() {\n assert(pm_ != nullptr);\n if (!owns_) {\n throw std::system_error(\n std::make_error_code(std::errc::operation_not_permitted),\n \"shared_lock::unlock\");\n }\n pm_->unlock_shared();\n owns_ = false;\n }\n\n void swap(shared_lock& sl) noexcept {\n std::swap(pm_, sl.pm_);\n std::swap(owns_, sl.owns_);\n }\n\n mutex_type* release() noexcept {\n mutex_type* result = pm_;\n pm_ = nullptr;\n owns_ = false;\n return result;\n }\n\n bool owns_lock() const noexcept { return owns_; }\n\n explicit operator bool() const noexcept { return owns_; }\n\n mutex_type* mutex() const noexcept { return pm_; }\n\n private:\n mutex_type* pm_ = nullptr;\n bool owns_ = false;\n};\n\n} // namespace yamc\n\nnamespace std {\n\n/// std::swap() specialization for yamc::shared_lock type\ntemplate \nvoid swap(yamc::shared_lock& lhs,\n yamc::shared_lock& rhs) noexcept {\n lhs.swap(rhs);\n}\n\n} // namespace std\n\n#endif // LIGHTGBM_INCLUDE_LIGHTGBM_UTILS_YAMC_YAMC_SHARED_LOCK_HPP_\n" }, { "path": "python-package/README.rst", "content": "LightGBM Python-package\n=======================\n\n|License| |Python Versions| |PyPI Version| |PyPI Downloads| |conda Version| |conda Downloads| |API Docs|\n\nInstallation\n------------\n\nPreparation\n'''''''''''\n\n32-bit Python is not supported.\nPlease install 64-bit version.\nIf you have a strong need to install with 32-bit Python, refer to `Build 32-bit Version with 32-bit Python section <#build-32-bit-version-with-32-bit-python>`__.\n\n|\n\nInstall from `PyPI `_\n''''''''''''''''''''''''''''''''''''''''''''''''''''''''\n\n.. code:: sh\n\n pip install lightgbm\n\nCompiled library that is included in the wheel file supports both **GPU** (don't confuse with CUDA version) and **CPU** versions out of the box.\nThis feature is available only for **Windows** and **Linux** currently.\nTo use **GPU** version you only need to install OpenCL Runtime libraries.\nFor NVIDIA and AMD GPU they are included in the ordinary drivers for your graphics card, so no action is required.\nIf you would like your AMD or Intel CPU to act like a GPU (for testing and debugging),\nyou can install `AMD APP SDK `_ on **Windows** and `PoCL `_ on **Linux**.\nMany modern Linux distributions provide packages for PoCL, look for ``pocl-opencl-icd`` on Debian-based distributions and ``pocl`` on RedHat-based distributions.\n\nFor **Windows** users, `VC runtime `_ is needed if **Visual Studio** is not installed.\n\nFor **macOS** users, the **OpenMP** library is needed.\nYou can install it by the following command: ``brew install libomp``.\n\n|\n\nUse LightGBM with PyArrow\n*************************\n\nTo install all dependencies needed to use ``PyArrow`` in LightGBM, append ``[arrow]``.\n\n.. code:: sh\n\n pip install 'lightgbm[arrow]'\n\n|\n\nUse LightGBM with Dask\n**********************\n\nWarning: Dask-package is only tested on macOS and Linux.\n\nTo install all dependencies needed to use ``lightgbm.dask``, append ``[dask]``.\n\n.. code:: sh\n\n pip install 'lightgbm[dask]'\n\n|\n\nUse LightGBM with pandas\n************************\n\nTo install all dependencies needed to use ``pandas`` in LightGBM, append ``[pandas]``.\n\n.. code:: sh\n\n pip install 'lightgbm[pandas]'\n\n|\n\nUse LightGBM Plotting Capabilities\n**********************************\n\nTo install all dependencies needed to use ``lightgbm.plotting``, append ``[plotting]``.\n\n.. code:: sh\n\n pip install 'lightgbm[plotting]'\n\n|\n\nUse LightGBM with scikit-learn\n******************************\n\nTo install all dependencies needed to use ``lightgbm.sklearn``, append ``[scikit-learn]``.\n\n.. code:: sh\n\n pip install 'lightgbm[scikit-learn]'\n\n|\n\nBuild from Sources\n******************\n\n.. code:: sh\n\n pip install lightgbm --no-binary lightgbm\n\nFor **macOS** users, you can perform installation either with **Apple Clang** or **gcc**.\n\n- In case you prefer **Apple Clang**, you should install **OpenMP** (details for installation can be found in `Installation Guide `__) first.\n\n- In case you prefer **gcc**, you need to install it (details for installation can be found in `Installation Guide `__) and specify compilers by running ``export CXX=g++-7 CC=gcc-7`` (replace \"7\" with version of **gcc** installed on your machine) first.\n\nFor **Windows** users, **Visual Studio** (or `VS Build Tools `_) is needed.\n\n|\n\nBuild Threadless Version\n~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: sh\n\n pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_OPENMP=OFF\n\nAll requirements, except the **OpenMP** requirement, from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well.\n\nIt is **strongly not recommended** to use this version of LightGBM!\n\n|\n\nBuild MPI Version\n~~~~~~~~~~~~~~~~~\n\n.. code:: sh\n\n pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_MPI=ON\n\nAll requirements from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well.\n\nFor **Windows** users, compilation with **MinGW-w64** is not supported.\n\n**MPI** libraries are needed: details for installation can be found in `Installation Guide `__.\n\n|\n\nBuild GPU Version\n~~~~~~~~~~~~~~~~~\n\n.. code:: sh\n\n pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_GPU=ON\n\nAll requirements from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well.\n\nFor **macOS** users, the GPU version is not supported.\n\n**Boost** and **OpenCL** are needed: details for installation can be found in `Installation Guide `__.\nAlmost always you also need to pass ``OpenCL_INCLUDE_DIR``, ``OpenCL_LIBRARY`` options for **Linux** and ``BOOST_ROOT``, ``BOOST_LIBRARYDIR`` options for **Windows** to **CMake** via ``pip`` options, like\n\n.. code:: sh\n\n pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_GPU=ON --config-settings=cmake.define.OpenCL_INCLUDE_DIR=\"/usr/local/cuda/include/\" --config-settings=cmake.define.OpenCL_LIBRARY=\"/usr/local/cuda/lib64/libOpenCL.so\"\n\nAll available options that can be passed via ``cmake.define.{option}``.\n\n- BOOST_ROOT\n\n- Boost_DIR\n\n- Boost_INCLUDE_DIR\n\n- BOOST_LIBRARYDIR\n\n- OpenCL_INCLUDE_DIR\n\n- OpenCL_LIBRARY\n\nFor more details see `FindBoost `__ and `FindOpenCL `__.\n\nDon't confuse with `CUDA version <#build-cuda-version>`__.\nTo use the GPU version within Python, pass ``{\"device\": \"gpu\"}`` respectively in parameters.\n\n|\n\nBuild CUDA Version\n~~~~~~~~~~~~~~~~~~\n\n.. code:: sh\n\n pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_CUDA=ON\n\nAll requirements from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well.\n\nFor **macOS** and **Windows** users, the CUDA version is not supported.\n\n**CUDA** library is needed: details for installation can be found in `Installation Guide `__.\n\nDon't confuse with `GPU version <#build-gpu-version>`__.\nTo use the CUDA version within Python, pass ``{\"device\": \"cuda\"}`` respectively in parameters.\n\n|\n\nBuild with MinGW-w64 on Windows\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: sh\n\n pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.CMAKE_SH=CMAKE_SH-NOTFOUND --config-settings=cmake.args=\"-GMinGW Makefiles\"\n\n`MinGW-w64 `_ should be installed first.\n\nIt is recommended to use **Visual Studio** for its better multithreading efficiency in **Windows** for many-core systems\n(see `Question 4 `__\nand `Question 8 `__).\n\n|\n\nBuild 32-bit Version with 32-bit Python\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: sh\n\n pip install lightgbm --no-binary lightgbm --config-settings=cmake.args=\"-AWin32\"\n\nFor **Windows** users, compilation with **MinGW-w64** is not supported.\n\nFor **macOS** and **Linux** users, the 32-bit version is not supported.\n\nIt is **strongly not recommended** to use this version of LightGBM!\n\n|\n\nBuild with Time Costs Output\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: sh\n\n pip install lightgbm --no-binary lightgbm --config-settings=cmake.define.USE_TIMETAG=ON\n\nUse this option to make LightGBM output time costs for different internal routines, to investigate and benchmark its performance.\n\n|\n\nInstall from `conda-forge channel `_\n'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''\n\n``lightgbm`` conda packages are available from the ``conda-forge`` channel.\n\n.. code:: sh\n\n conda install -c conda-forge lightgbm\n\nThese packages support **CPU**, **GPU** and **CUDA** versions out of the box.\n\n**GPU**-enabled version is available only for **Windows** and **Linux** currently.\n\n**CUDA**-enabled version (since ``lightgbm>=4.4.0``) is available only for **Linux** currently and will be automatically selected if you are on a system where CUDA is installed.\n\n|\n\nInstall from GitHub\n'''''''''''''''''''\n\nAll requirements from `Build from Sources section <#build-from-sources>`__ apply for this installation option as well.\n\n.. code:: sh\n\n git clone --recursive https://github.com/lightgbm-org/LightGBM.git\n cd LightGBM\n # export CXX=g++-14 CC=gcc-14 # macOS users, if you decided to compile with gcc, don't forget to specify compilers\n sh ./build-python.sh install\n\nNote: ``sudo`` (or administrator rights in **Windows**) may be needed to perform the command.\nRun ``sh ./build-python.sh install --user`` to install into user-specific instead of global site-packages directory.\n\nRun ``sh ./build-python.sh install --no-isolation`` to assume all build and install dependencies are already installed, don't go to the internet to get them.\n\n|\n\nRun ``sh ./build-python.sh install --nomp`` to disable **OpenMP** support.\nAll requirements from `Build Threadless Version section <#build-threadless-version>`__ apply for this installation option as well.\n\nRun ``sh ./build-python.sh install --mpi`` to enable **MPI** support.\nAll requirements from `Build MPI Version section <#build-mpi-version>`__ apply for this installation option as well.\n\nRun ``sh ./build-python.sh install --gpu`` to enable GPU support.\nAll requirements from `Build GPU Version section <#build-gpu-version>`__ apply for this installation option as well.\nTo pass additional options to **CMake** use the following syntax: ``sh ./build-python.sh install --gpu --opencl-include-dir=\"/usr/local/cuda/include/\"``,\nsee `Build GPU Version section <#build-gpu-version>`__ for the complete list of them.\n\nRun ``sh ./build-python.sh install --cuda`` to enable CUDA support.\nAll requirements from `Build CUDA Version section <#build-cuda-version>`__ apply for this installation option as well.\n\nRun ``sh ./build-python.sh install --mingw``, if you want to use **MinGW-w64** on **Windows** instead of **Visual Studio**.\nAll requirements from `Build with MinGW-w64 on Windows section <#build-with-mingw-w64-on-windows>`__ apply for this installation option as well.\n\nRun ``sh ./build-python.sh install --bit32``, if you want to use 32-bit version.\nAll requirements from `Build 32-bit Version with 32-bit Python section <#build-32-bit-version-with-32-bit-python>`__ apply for this installation option as well.\n\nRun ``sh ./build-python.sh install --time-costs``, if you want to output time costs for different internal routines.\nAll requirements from `Build with Time Costs Output section <#build-with-time-costs-output>`__ apply for this installation option as well.\n\n|\n\nIf you get any errors during installation or due to any other reasons,\nyou may want to build dynamic library from sources by any method you prefer\n(see `Installation Guide `__).\nFor example, you can use ``MSBuild`` tool and `solution file `__ from the repo.\n\n.. code:: sh\n\n MSBuild.exe windows/LightGBM.sln /p:Configuration=DLL /p:Platform=x64 /p:PlatformToolset=v143\n\nAfter compiling dynamic library just run ``sh ./build-python.sh install --precompile`` to install the Python-package using that library.\n\n|\n\nBuild Wheel File\n****************\n\nYou can run ``sh ./build-python.sh bdist_wheel`` to build a wheel file but not install it.\n\nThat script requires some dependencies like ``build``, ``scikit-build-core``, and ``wheel``.\nIn environments with restricted or no internet access, install those tools and then pass ``--no-isolation``.\n\n.. code:: sh\n\n sh ./build-python.sh bdist_wheel --no-isolation\n\nTroubleshooting\n---------------\n\nRefer to `FAQ `_.\n\nExamples\n--------\n\nRefer to the walk through examples in `Python guide folder `_.\n\nSupported Python Versions\n-------------------------\n\nThis project supports all Python versions until they reach end-of-life.\nFor details on the support calendar for Python versions, see https://devguide.python.org/versions/.\n\nDevelopment Guide\n-----------------\n\nTo check that a contribution to the package matches its style expectations, run the following from the root of the repo.\n\n.. code:: sh\n\n pre-commit run --all-files\n\nTo run the tests locally and compute test coverage, install the Python package using one of the options mentioned above.\nThen run the following from the root of the repo.\n\n.. code:: sh\n\n pytest \\\n --cov=lightgbm \\\n --cov-report=\"term\" \\\n --cov-report=\"html:htmlcov\" \\\n tests/python_package_test/\n\nThen open `htmlcov/index.html` to view a clickable coverage report.\n\n.. |License| image:: https://img.shields.io/github/license/lightgbm-org/lightgbm.svg\n :target: https://github.com/lightgbm-org/LightGBM/blob/master/LICENSE\n.. |Python Versions| image:: https://img.shields.io/pypi/pyversions/lightgbm.svg?logo=python&logoColor=white\n :target: https://pypi.org/project/lightgbm\n.. |PyPI Version| image:: https://img.shields.io/pypi/v/lightgbm.svg?logo=pypi&logoColor=white\n :target: https://pypi.org/project/lightgbm\n.. |PyPI Downloads| image:: https://img.shields.io/pepy/dt/lightgbm?logo=pypi&logoColor=white&label=pypi%20downloads\n :target: https://pepy.tech/project/lightgbm\n.. |conda Version| image:: https://img.shields.io/conda/vn/conda-forge/lightgbm?logo=conda-forge&logoColor=white&label=conda\n :target: https://anaconda.org/conda-forge/lightgbm\n.. |conda Downloads| image:: https://img.shields.io/conda/d/conda-forge/lightgbm?logo=conda-forge&logoColor=white&label=conda%20downloads\n :target: https://anaconda.org/conda-forge/lightgbm/files\n.. |API Docs| image:: https://readthedocs.org/projects/lightgbm/badge/?version=latest\n :target: https://lightgbm.readthedocs.io/en/latest/Python-API.html\n" }, { "path": "python-package/lightgbm/__init__.py", "content": "# coding: utf-8\n\"\"\"LightGBM, Light Gradient Boosting Machine.\n\nContributors: https://github.com/lightgbm-org/LightGBM/graphs/contributors.\n\"\"\"\n\nfrom pathlib import Path\n\n# .basic is intentionally loaded as early as possible, to dlopen() lib_lightgbm.{dll,dylib,so}\n# and its dependencies as early as possible\nfrom .basic import Booster, Dataset, Sequence, register_logger\nfrom .callback import EarlyStopException, early_stopping, log_evaluation, record_evaluation, reset_parameter\nfrom .engine import CVBooster, cv, train\n\ntry:\n from .sklearn import LGBMClassifier, LGBMModel, LGBMRanker, LGBMRegressor\nexcept ImportError:\n pass\ntry:\n from .plotting import create_tree_digraph, plot_importance, plot_metric, plot_split_value_histogram, plot_tree\nexcept ImportError:\n pass\ntry:\n from .dask import DaskLGBMClassifier, DaskLGBMRanker, DaskLGBMRegressor\nexcept ImportError:\n pass\n\n\n_version_path = Path(__file__).resolve().parent / \"VERSION.txt\"\nif _version_path.is_file():\n __version__ = _version_path.read_text(encoding=\"utf-8\").strip()\n\n__all__ = [\n \"Dataset\",\n \"Booster\",\n \"CVBooster\",\n \"Sequence\",\n \"register_logger\",\n \"train\",\n \"cv\",\n \"LGBMModel\",\n \"LGBMRegressor\",\n \"LGBMClassifier\",\n \"LGBMRanker\",\n \"DaskLGBMRegressor\",\n \"DaskLGBMClassifier\",\n \"DaskLGBMRanker\",\n \"log_evaluation\",\n \"record_evaluation\",\n \"reset_parameter\",\n \"early_stopping\",\n \"EarlyStopException\",\n \"plot_importance\",\n \"plot_split_value_histogram\",\n \"plot_metric\",\n \"plot_tree\",\n \"create_tree_digraph\",\n]\n" }, { "path": "python-package/lightgbm/basic.py", "content": "# coding: utf-8\n\"\"\"Wrapper for C API of LightGBM.\"\"\"\n\n# This import causes lib_lightgbm.{dll,dylib,so} to be loaded.\n# It's intentionally done here, as early as possible, to avoid issues like\n# \"libgomp.so.1: cannot allocate memory in static TLS block\" on aarch64 Linux.\n#\n# For details, see the \"cannot allocate memory in static TLS block\" entry in docs/FAQ.rst.\nfrom .libpath import _LIB # isort: skip\n\nimport abc\nimport ctypes\nimport inspect\nimport json\nimport warnings\nfrom collections import OrderedDict\nfrom copy import deepcopy\nfrom enum import Enum\nfrom functools import wraps\nfrom os import SEEK_END, environ\nfrom os.path import getsize\nfrom pathlib import Path\nfrom tempfile import NamedTemporaryFile\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, Iterator, List, Optional, Set, Tuple, Union\n\nimport numpy as np\nimport scipy.sparse\n\nfrom .compat import (\n CFFI_INSTALLED,\n PANDAS_INSTALLED,\n PYARROW_INSTALLED,\n arrow_cffi,\n arrow_is_boolean,\n arrow_is_floating,\n arrow_is_integer,\n concat,\n pa_Array,\n pa_chunked_array,\n pa_ChunkedArray,\n pa_compute,\n pa_Table,\n pd_CategoricalDtype,\n pd_DataFrame,\n pd_Series,\n)\n\nif TYPE_CHECKING:\n from typing import Literal\n\n # typing.TypeGuard was only introduced in Python 3.10\n try:\n from typing import TypeGuard\n except ImportError:\n from typing_extensions import TypeGuard\n\n\n__all__ = [\n \"Booster\",\n \"Dataset\",\n \"LGBMDeprecationWarning\",\n \"LightGBMError\",\n \"register_logger\",\n \"Sequence\",\n]\n\n_BoosterHandle = ctypes.c_void_p\n_DatasetHandle = ctypes.c_void_p\n_ctypes_int_ptr = Union[\n \"ctypes._Pointer[ctypes.c_int32]\",\n \"ctypes._Pointer[ctypes.c_int64]\",\n]\n_ctypes_int_array = Union[\n \"ctypes.Array[ctypes._Pointer[ctypes.c_int32]]\",\n \"ctypes.Array[ctypes._Pointer[ctypes.c_int64]]\",\n]\n_ctypes_float_ptr = Union[\n \"ctypes._Pointer[ctypes.c_float]\",\n \"ctypes._Pointer[ctypes.c_double]\",\n]\n_ctypes_float_array = Union[\n \"ctypes.Array[ctypes._Pointer[ctypes.c_float]]\",\n \"ctypes.Array[ctypes._Pointer[ctypes.c_double]]\",\n]\n_LGBM_EvalFunctionResultType = Tuple[str, float, bool]\n_LGBM_BoosterBestScoreType = Dict[str, Dict[str, float]]\n_LGBM_BoosterEvalMethodResultType = Tuple[str, str, float, bool]\n_LGBM_BoosterEvalMethodResultWithStandardDeviationType = Tuple[str, str, float, bool, float]\n_LGBM_CategoricalFeatureConfiguration = Union[List[str], List[int], \"Literal['auto']\"]\n_LGBM_FeatureNameConfiguration = Union[List[str], \"Literal['auto']\"]\n_LGBM_GroupType = Union[\n List[float],\n List[int],\n np.ndarray,\n pd_Series,\n pa_Array,\n pa_ChunkedArray,\n]\n_LGBM_PositionType = Union[\n np.ndarray,\n pd_Series,\n]\n_LGBM_InitScoreType = Union[\n List[float],\n List[List[float]],\n np.ndarray,\n pd_Series,\n pd_DataFrame,\n pa_Table,\n pa_Array,\n pa_ChunkedArray,\n]\n_LGBM_TrainDataType = Union[\n str,\n Path,\n np.ndarray,\n pd_DataFrame,\n scipy.sparse.spmatrix,\n \"Sequence\",\n List[\"Sequence\"],\n List[np.ndarray],\n pa_Table,\n]\n_LGBM_LabelType = Union[\n List[float],\n List[int],\n np.ndarray,\n pd_Series,\n pd_DataFrame,\n pa_Array,\n pa_ChunkedArray,\n]\n_LGBM_PredictDataType = Union[\n str,\n Path,\n np.ndarray,\n pd_DataFrame,\n scipy.sparse.spmatrix,\n pa_Table,\n]\n_LGBM_PredictReturnType = Union[\n np.ndarray,\n scipy.sparse.spmatrix,\n List[scipy.sparse.spmatrix],\n]\n_LGBM_PredictSparseReturnType = Union[\n scipy.sparse.spmatrix,\n List[scipy.sparse.spmatrix],\n]\n_LGBM_WeightType = Union[\n List[float],\n List[int],\n np.ndarray,\n pd_Series,\n pa_Array,\n pa_ChunkedArray,\n]\n_LGBM_SetFieldType = Union[\n List[List[float]],\n List[List[int]],\n List[float],\n List[int],\n np.ndarray,\n pd_Series,\n pd_DataFrame,\n pa_Table,\n pa_Array,\n pa_ChunkedArray,\n]\n\nZERO_THRESHOLD = 1e-35\n\n_MULTICLASS_OBJECTIVES = {\"multiclass\", \"multiclassova\", \"multiclass_ova\", \"ova\", \"ovr\", \"softmax\"}\n\n\nclass LightGBMError(Exception):\n \"\"\"Error thrown by LightGBM.\"\"\"\n\n pass\n\n\ndef _is_zero(x: float) -> bool:\n return -ZERO_THRESHOLD <= x <= ZERO_THRESHOLD\n\n\ndef _get_sample_count(total_nrow: int, params: str) -> int:\n sample_cnt = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_GetSampleCount(\n ctypes.c_int32(total_nrow),\n _c_str(params),\n ctypes.byref(sample_cnt),\n )\n )\n return sample_cnt.value\n\n\ndef _np2d_to_np1d(mat: np.ndarray) -> Tuple[np.ndarray, int]:\n dtype: \"np.typing.DTypeLike\"\n if mat.dtype in (np.float32, np.float64):\n dtype = mat.dtype\n else:\n dtype = np.float32\n order: \"Literal['C', 'F']\"\n if mat.flags[\"F_CONTIGUOUS\"]:\n order = \"F\"\n layout = _C_API_IS_COL_MAJOR\n else:\n order = \"C\"\n layout = _C_API_IS_ROW_MAJOR\n # ensure dtype and order, copies if either do not match\n data = np.asarray(mat, dtype=dtype, order=order)\n # flatten array without copying\n return data.ravel(order=order), layout\n\n\nclass _MissingType(Enum):\n NONE = \"None\"\n NAN = \"NaN\"\n ZERO = \"Zero\"\n\n\nclass _DummyLogger:\n def info(self, msg: str) -> None:\n print(msg) # noqa: T201\n\n def warning(self, msg: str) -> None:\n warnings.warn(msg, stacklevel=3)\n\n\n_LOGGER: Any = _DummyLogger()\n_INFO_METHOD_NAME = \"info\"\n_WARNING_METHOD_NAME = \"warning\"\n\n\ndef _has_method(logger: Any, method_name: str) -> bool:\n return callable(getattr(logger, method_name, None))\n\n\ndef register_logger(\n logger: Any,\n info_method_name: str = \"info\",\n warning_method_name: str = \"warning\",\n) -> None:\n \"\"\"Register custom logger.\n\n Parameters\n ----------\n logger : Any\n Custom logger.\n info_method_name : str, optional (default=\"info\")\n Method used to log info messages.\n warning_method_name : str, optional (default=\"warning\")\n Method used to log warning messages.\n \"\"\"\n if not _has_method(logger, info_method_name) or not _has_method(logger, warning_method_name):\n raise TypeError(f\"Logger must provide '{info_method_name}' and '{warning_method_name}' method\")\n\n global _LOGGER, _INFO_METHOD_NAME, _WARNING_METHOD_NAME\n _LOGGER = logger\n _INFO_METHOD_NAME = info_method_name\n _WARNING_METHOD_NAME = warning_method_name\n\n\ndef _normalize_native_string(func: Callable[[str], None]) -> Callable[[str], None]:\n \"\"\"Join log messages from native library which come by chunks.\"\"\"\n msg_normalized: List[str] = []\n\n @wraps(func)\n def wrapper(msg: str) -> None:\n nonlocal msg_normalized\n if msg.strip() == \"\":\n msg = \"\".join(msg_normalized)\n msg_normalized = []\n return func(msg)\n else:\n msg_normalized.append(msg)\n\n return wrapper\n\n\ndef _log_info(msg: str) -> None:\n getattr(_LOGGER, _INFO_METHOD_NAME)(msg)\n\n\ndef _log_warning(msg: str) -> None:\n getattr(_LOGGER, _WARNING_METHOD_NAME)(msg)\n\n\n@_normalize_native_string\ndef _log_native(msg: str) -> None:\n getattr(_LOGGER, _INFO_METHOD_NAME)(msg)\n\n\ndef _log_callback(msg: bytes) -> None:\n \"\"\"Redirect logs from native library into Python.\"\"\"\n _log_native(str(msg.decode(\"utf-8\")))\n\n\n# connect the Python logger to logging in lib_lightgbm\nif environ.get(\"LIGHTGBM_BUILD_DOC\", \"False\") != \"True\":\n _LIB.LGBM_GetLastError.restype = ctypes.c_char_p\n callback = ctypes.CFUNCTYPE(None, ctypes.c_char_p)\n _LIB.callback = callback(_log_callback) # type: ignore[attr-defined]\n if _LIB.LGBM_RegisterLogCallback(_LIB.callback) != 0:\n raise LightGBMError(_LIB.LGBM_GetLastError().decode(\"utf-8\"))\n\n\n_NUMERIC_TYPES = (int, float, bool)\n\n\ndef _safe_call(ret: int) -> None:\n \"\"\"Check the return value from C API call.\n\n Parameters\n ----------\n ret : int\n The return value from C API calls.\n \"\"\"\n if ret != 0:\n raise LightGBMError(_LIB.LGBM_GetLastError().decode(\"utf-8\"))\n\n\ndef _is_numeric(obj: Any) -> bool:\n \"\"\"Check whether object is a number or not, include numpy number, etc.\"\"\"\n try:\n float(obj)\n return True\n except (TypeError, ValueError):\n # TypeError: obj is not a string or a number\n # ValueError: invalid literal\n return False\n\n\ndef _is_numpy_1d_array(data: Any) -> bool:\n \"\"\"Check whether data is a numpy 1-D array.\"\"\"\n return isinstance(data, np.ndarray) and len(data.shape) == 1\n\n\ndef _is_numpy_column_array(data: Any) -> bool:\n \"\"\"Check whether data is a column numpy array.\"\"\"\n if not isinstance(data, np.ndarray):\n return False\n shape = data.shape\n return len(shape) == 2 and shape[1] == 1\n\n\ndef _cast_numpy_array_to_dtype(array: np.ndarray, dtype: \"np.typing.DTypeLike\") -> np.ndarray:\n \"\"\"Cast numpy array to given dtype.\"\"\"\n if array.dtype == dtype:\n return array\n return array.astype(dtype=dtype, copy=False)\n\n\ndef _is_1d_list(data: Any) -> bool:\n \"\"\"Check whether data is a 1-D list.\"\"\"\n return isinstance(data, list) and (not data or _is_numeric(data[0]))\n\n\ndef _is_list_of_numpy_arrays(data: Any) -> \"TypeGuard[List[np.ndarray]]\":\n return isinstance(data, list) and all(isinstance(x, np.ndarray) for x in data)\n\n\ndef _is_list_of_sequences(data: Any) -> \"TypeGuard[List[Sequence]]\":\n return isinstance(data, list) and all(isinstance(x, Sequence) for x in data)\n\n\ndef _is_1d_collection(data: Any) -> bool:\n \"\"\"Check whether data is a 1-D collection.\"\"\"\n return _is_numpy_1d_array(data) or _is_numpy_column_array(data) or _is_1d_list(data) or isinstance(data, pd_Series)\n\n\ndef _list_to_1d_numpy(\n *,\n data: Any,\n dtype: \"np.typing.DTypeLike\",\n name: str,\n) -> np.ndarray:\n \"\"\"Convert data to numpy 1-D array.\"\"\"\n if _is_numpy_1d_array(data):\n return _cast_numpy_array_to_dtype(data, dtype)\n elif _is_numpy_column_array(data):\n _log_warning(\"Converting column-vector to 1d array\")\n array = data.ravel()\n return _cast_numpy_array_to_dtype(array, dtype)\n elif _is_1d_list(data):\n return np.asarray(data, dtype=dtype)\n elif isinstance(data, pd_Series):\n _check_for_bad_pandas_dtypes(data.to_frame().dtypes)\n return np.asarray(data, dtype=dtype) # SparseArray should be supported as well\n else:\n raise TypeError(\n f\"Wrong type({type(data).__name__}) for {name}.\\nIt should be list, numpy 1-D array or pandas Series\"\n )\n\n\ndef _is_numpy_2d_array(data: Any) -> bool:\n \"\"\"Check whether data is a numpy 2-D array.\"\"\"\n return isinstance(data, np.ndarray) and len(data.shape) == 2 and data.shape[1] > 1\n\n\ndef _is_2d_list(data: Any) -> bool:\n \"\"\"Check whether data is a 2-D list.\"\"\"\n return isinstance(data, list) and len(data) > 0 and _is_1d_list(data[0])\n\n\ndef _is_2d_collection(data: Any) -> bool:\n \"\"\"Check whether data is a 2-D collection.\"\"\"\n return _is_numpy_2d_array(data) or _is_2d_list(data) or isinstance(data, pd_DataFrame)\n\n\ndef _is_pyarrow_array(data: Any) -> \"TypeGuard[Union[pa_Array, pa_ChunkedArray]]\":\n \"\"\"Check whether data is a PyArrow array.\"\"\"\n return isinstance(data, (pa_Array, pa_ChunkedArray))\n\n\ndef _is_pyarrow_table(data: Any) -> \"TypeGuard[pa_Table]\":\n \"\"\"Check whether data is a PyArrow table.\"\"\"\n return isinstance(data, pa_Table)\n\n\nclass _ArrowCArray:\n \"\"\"Simple wrapper around the C representation of an Arrow type.\"\"\"\n\n n_chunks: int\n chunks: arrow_cffi.CData\n schema: arrow_cffi.CData\n\n def __init__(self, n_chunks: int, chunks: arrow_cffi.CData, schema: arrow_cffi.CData):\n self.n_chunks = n_chunks\n self.chunks = chunks\n self.schema = schema\n\n @property\n def chunks_ptr(self) -> int:\n \"\"\"Returns the address of the pointer to the list of chunks making up the array.\"\"\"\n return int(arrow_cffi.cast(\"uintptr_t\", arrow_cffi.addressof(self.chunks[0])))\n\n @property\n def schema_ptr(self) -> int:\n \"\"\"Returns the address of the pointer to the schema of the array.\"\"\"\n return int(arrow_cffi.cast(\"uintptr_t\", self.schema))\n\n\ndef _export_arrow_to_c(data: pa_Table) -> _ArrowCArray:\n \"\"\"Export an Arrow type to its C representation.\"\"\"\n # Obtain objects to export\n if isinstance(data, pa_Array):\n export_objects = [data]\n elif isinstance(data, pa_ChunkedArray):\n export_objects = data.chunks\n elif isinstance(data, pa_Table):\n export_objects = data.to_batches()\n else:\n raise ValueError(f\"data of type '{type(data)}' cannot be exported to Arrow\")\n\n # Prepare export\n chunks = arrow_cffi.new(\"struct ArrowArray[]\", len(export_objects))\n schema = arrow_cffi.new(\"struct ArrowSchema*\")\n\n # Export all objects\n for i, obj in enumerate(export_objects):\n chunk_ptr = int(arrow_cffi.cast(\"uintptr_t\", arrow_cffi.addressof(chunks[i])))\n if i == 0:\n schema_ptr = int(arrow_cffi.cast(\"uintptr_t\", schema))\n obj._export_to_c(chunk_ptr, schema_ptr)\n else:\n obj._export_to_c(chunk_ptr)\n\n return _ArrowCArray(len(chunks), chunks, schema)\n\n\ndef _data_to_2d_numpy(\n data: Any,\n dtype: \"np.typing.DTypeLike\",\n name: str,\n) -> np.ndarray:\n \"\"\"Convert data to numpy 2-D array.\"\"\"\n if _is_numpy_2d_array(data):\n return _cast_numpy_array_to_dtype(data, dtype)\n if _is_2d_list(data):\n return np.array(data, dtype=dtype)\n if isinstance(data, pd_DataFrame):\n _check_for_bad_pandas_dtypes(data.dtypes)\n return _cast_numpy_array_to_dtype(data.values, dtype)\n raise TypeError(\n f\"Wrong type({type(data).__name__}) for {name}.\\n\"\n \"It should be list of lists, numpy 2-D array or pandas DataFrame\"\n )\n\n\ndef _cfloat32_array_to_numpy(*, cptr: \"ctypes._Pointer\", length: int) -> np.ndarray:\n \"\"\"Convert a ctypes float pointer array to a numpy array.\"\"\"\n if isinstance(cptr, ctypes.POINTER(ctypes.c_float)):\n return np.ctypeslib.as_array(cptr, shape=(length,)).copy()\n else:\n raise RuntimeError(\"Expected float pointer\")\n\n\ndef _cfloat64_array_to_numpy(*, cptr: \"ctypes._Pointer\", length: int) -> np.ndarray:\n \"\"\"Convert a ctypes double pointer array to a numpy array.\"\"\"\n if isinstance(cptr, ctypes.POINTER(ctypes.c_double)):\n return np.ctypeslib.as_array(cptr, shape=(length,)).copy()\n else:\n raise RuntimeError(\"Expected double pointer\")\n\n\ndef _cint32_array_to_numpy(*, cptr: \"ctypes._Pointer\", length: int) -> np.ndarray:\n \"\"\"Convert a ctypes int pointer array to a numpy array.\"\"\"\n if isinstance(cptr, ctypes.POINTER(ctypes.c_int32)):\n return np.ctypeslib.as_array(cptr, shape=(length,)).copy()\n else:\n raise RuntimeError(\"Expected int32 pointer\")\n\n\ndef _cint64_array_to_numpy(*, cptr: \"ctypes._Pointer\", length: int) -> np.ndarray:\n \"\"\"Convert a ctypes int pointer array to a numpy array.\"\"\"\n if isinstance(cptr, ctypes.POINTER(ctypes.c_int64)):\n return np.ctypeslib.as_array(cptr, shape=(length,)).copy()\n else:\n raise RuntimeError(\"Expected int64 pointer\")\n\n\ndef _c_str(string: str) -> ctypes.c_char_p:\n \"\"\"Convert a Python string to C string.\"\"\"\n return ctypes.c_char_p(string.encode(\"utf-8\"))\n\n\ndef _c_array(ctype: type, values: List[Any]) -> ctypes.Array:\n \"\"\"Convert a Python array to C array.\"\"\"\n return (ctype * len(values))(*values) # type: ignore[operator]\n\n\ndef _json_default_with_numpy(obj: Any) -> Any:\n \"\"\"Convert numpy classes to JSON serializable objects.\"\"\"\n if isinstance(obj, (np.integer, np.floating, np.bool_)):\n return obj.item()\n elif isinstance(obj, np.ndarray):\n return obj.tolist()\n else:\n return obj\n\n\ndef _to_string(x: Union[int, float, str, List]) -> str:\n if isinstance(x, list):\n val_list = \",\".join(str(val) for val in x)\n return f\"[{val_list}]\"\n else:\n return str(x)\n\n\ndef _param_dict_to_str(data: Optional[Dict[str, Any]]) -> str:\n \"\"\"Convert Python dictionary to string, which is passed to C API.\"\"\"\n if data is None or not data:\n return \"\"\n pairs = []\n for key, val in data.items():\n if isinstance(val, (list, tuple, set)) or _is_numpy_1d_array(val):\n pairs.append(f\"{key}={','.join(map(_to_string, val))}\")\n elif isinstance(val, (str, Path, _NUMERIC_TYPES)) or _is_numeric(val):\n pairs.append(f\"{key}={val}\")\n elif val is not None:\n raise TypeError(f\"Unknown type of parameter:{key}, got:{type(val).__name__}\")\n return \" \".join(pairs)\n\n\nclass _TempFile:\n \"\"\"Proxy class to workaround errors on Windows.\"\"\"\n\n def __enter__(self) -> \"_TempFile\":\n with NamedTemporaryFile(prefix=\"lightgbm_tmp_\", delete=True) as f:\n self.name = f.name\n self.path = Path(self.name)\n return self\n\n def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:\n if self.path.is_file():\n self.path.unlink()\n\n\n# DeprecationWarning is not shown by default, so let's create our own with higher level\n# ref: https://peps.python.org/pep-0565/#additional-use-case-for-futurewarning\nclass LGBMDeprecationWarning(FutureWarning):\n \"\"\"Custom deprecation warning.\"\"\"\n\n pass\n\n\nclass _ConfigAliases:\n # lazy evaluation to allow import without dynamic library, e.g., for docs generation\n aliases = None\n\n @staticmethod\n def _get_all_param_aliases() -> Dict[str, List[str]]:\n buffer_len = 1 << 20\n tmp_out_len = ctypes.c_int64(0)\n string_buffer = ctypes.create_string_buffer(buffer_len)\n ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer))\n _safe_call(\n _LIB.LGBM_DumpParamAliases(\n ctypes.c_int64(buffer_len),\n ctypes.byref(tmp_out_len),\n ptr_string_buffer,\n )\n )\n actual_len = tmp_out_len.value\n # if buffer length is not long enough, re-allocate a buffer\n if actual_len > buffer_len:\n string_buffer = ctypes.create_string_buffer(actual_len)\n ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer))\n _safe_call(\n _LIB.LGBM_DumpParamAliases(\n ctypes.c_int64(actual_len),\n ctypes.byref(tmp_out_len),\n ptr_string_buffer,\n )\n )\n return json.loads(\n string_buffer.value.decode(\"utf-8\"), object_hook=lambda obj: {k: [k] + v for k, v in obj.items()}\n )\n\n @classmethod\n def get(cls, *args: str) -> Set[str]:\n if cls.aliases is None:\n cls.aliases = cls._get_all_param_aliases()\n ret = set()\n for i in args:\n ret.update(cls.get_sorted(i))\n return ret\n\n @classmethod\n def get_sorted(cls, name: str) -> List[str]:\n if cls.aliases is None:\n cls.aliases = cls._get_all_param_aliases()\n return cls.aliases.get(name, [name])\n\n @classmethod\n def get_by_alias(cls, *args: str) -> Set[str]:\n if cls.aliases is None:\n cls.aliases = cls._get_all_param_aliases()\n ret = set(args)\n for arg in args:\n for aliases in cls.aliases.values():\n if arg in aliases:\n ret.update(aliases)\n break\n return ret\n\n\ndef _choose_param_value(main_param_name: str, params: Dict[str, Any], default_value: Any) -> Dict[str, Any]:\n \"\"\"Get a single parameter value, accounting for aliases.\n\n Parameters\n ----------\n main_param_name : str\n Name of the main parameter to get a value for. One of the keys of ``_ConfigAliases``.\n params : dict\n Dictionary of LightGBM parameters.\n default_value : Any\n Default value to use for the parameter, if none is found in ``params``.\n\n Returns\n -------\n params : dict\n A ``params`` dict with exactly one value for ``main_param_name``, and all aliases ``main_param_name`` removed.\n If both ``main_param_name`` and one or more aliases for it are found, the value of ``main_param_name`` will be preferred.\n \"\"\"\n # avoid side effects on passed-in parameters\n params = deepcopy(params)\n\n aliases = _ConfigAliases.get_sorted(main_param_name)\n aliases = [a for a in aliases if a != main_param_name]\n\n # if main_param_name was provided, keep that value and remove all aliases\n if main_param_name in params.keys():\n for param in aliases:\n params.pop(param, None)\n return params\n\n # if main param name was not found, search for an alias\n for param in aliases:\n if param in params.keys():\n params[main_param_name] = params[param]\n break\n\n if main_param_name in params.keys():\n for param in aliases:\n params.pop(param, None)\n return params\n\n # neither of main_param_name, aliases were found\n params[main_param_name] = default_value\n\n return params\n\n\n_MAX_INT32 = (1 << 31) - 1\n\n\"\"\"Macro definition of data type in C API of LightGBM\"\"\"\n_C_API_DTYPE_FLOAT32 = 0\n_C_API_DTYPE_FLOAT64 = 1\n_C_API_DTYPE_INT32 = 2\n_C_API_DTYPE_INT64 = 3\n\n\"\"\"Macro definition of data order in matrix\"\"\"\n_C_API_IS_COL_MAJOR = 0\n_C_API_IS_ROW_MAJOR = 1\n\n\"\"\"Macro definition of prediction type in C API of LightGBM\"\"\"\n_C_API_PREDICT_NORMAL = 0\n_C_API_PREDICT_RAW_SCORE = 1\n_C_API_PREDICT_LEAF_INDEX = 2\n_C_API_PREDICT_CONTRIB = 3\n\n\"\"\"Macro definition of sparse matrix type\"\"\"\n_C_API_MATRIX_TYPE_CSR = 0\n_C_API_MATRIX_TYPE_CSC = 1\n\n\"\"\"Macro definition of feature importance type\"\"\"\n_C_API_FEATURE_IMPORTANCE_SPLIT = 0\n_C_API_FEATURE_IMPORTANCE_GAIN = 1\n\n\"\"\"Data type of data field\"\"\"\n_FIELD_TYPE_MAPPER = {\n \"label\": _C_API_DTYPE_FLOAT32,\n \"weight\": _C_API_DTYPE_FLOAT32,\n \"init_score\": _C_API_DTYPE_FLOAT64,\n \"group\": _C_API_DTYPE_INT32,\n \"position\": _C_API_DTYPE_INT32,\n}\n\n\"\"\"String name to int feature importance type mapper\"\"\"\n_FEATURE_IMPORTANCE_TYPE_MAPPER = {\n \"split\": _C_API_FEATURE_IMPORTANCE_SPLIT,\n \"gain\": _C_API_FEATURE_IMPORTANCE_GAIN,\n}\n\n\ndef _convert_from_sliced_object(data: np.ndarray) -> np.ndarray:\n \"\"\"Fix the memory of multi-dimensional sliced object.\"\"\"\n if isinstance(data, np.ndarray) and isinstance(data.base, np.ndarray):\n if not data.flags.c_contiguous:\n _log_warning(\n \"Usage of np.ndarray subset (sliced data) is not recommended \"\n \"due to it will double the peak memory cost in LightGBM.\"\n )\n return np.copy(data)\n return data\n\n\ndef _c_float_array(data: np.ndarray) -> Tuple[_ctypes_float_ptr, int, np.ndarray]:\n \"\"\"Get pointer of float numpy array / list.\"\"\"\n if _is_1d_list(data):\n data = np.asarray(data)\n if _is_numpy_1d_array(data):\n data = _convert_from_sliced_object(data)\n assert data.flags.c_contiguous\n ptr_data: _ctypes_float_ptr\n if data.dtype == np.float32:\n ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_float))\n type_data = _C_API_DTYPE_FLOAT32\n elif data.dtype == np.float64:\n ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_double))\n type_data = _C_API_DTYPE_FLOAT64\n else:\n raise TypeError(f\"Expected np.float32 or np.float64, met type({data.dtype})\")\n else:\n raise TypeError(f\"Unknown type({type(data).__name__})\")\n return (ptr_data, type_data, data) # return `data` to avoid the temporary copy is freed\n\n\ndef _c_int_array(data: np.ndarray) -> Tuple[_ctypes_int_ptr, int, np.ndarray]:\n \"\"\"Get pointer of int numpy array / list.\"\"\"\n if _is_1d_list(data):\n data = np.asarray(data)\n if _is_numpy_1d_array(data):\n data = _convert_from_sliced_object(data)\n assert data.flags.c_contiguous\n ptr_data: _ctypes_int_ptr\n if data.dtype == np.int32:\n ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_int32))\n type_data = _C_API_DTYPE_INT32\n elif data.dtype == np.int64:\n ptr_data = data.ctypes.data_as(ctypes.POINTER(ctypes.c_int64))\n type_data = _C_API_DTYPE_INT64\n else:\n raise TypeError(f\"Expected np.int32 or np.int64, met type({data.dtype})\")\n else:\n raise TypeError(f\"Unknown type({type(data).__name__})\")\n return (ptr_data, type_data, data) # return `data` to avoid the temporary copy is freed\n\n\ndef _is_allowed_numpy_dtype(dtype: type) -> bool:\n float128 = getattr(np, \"float128\", type(None))\n return issubclass(dtype, (np.integer, np.floating, np.bool_)) and not issubclass(dtype, (np.timedelta64, float128))\n\n\ndef _check_for_bad_pandas_dtypes(pandas_dtypes_series: pd_Series) -> None:\n bad_pandas_dtypes = [\n f\"{column_name}: {pandas_dtype}\"\n for column_name, pandas_dtype in pandas_dtypes_series.items()\n if not _is_allowed_numpy_dtype(pandas_dtype.type)\n ]\n if bad_pandas_dtypes:\n raise ValueError(\n f\"pandas dtypes must be int, float or bool.\\nFields with bad pandas dtypes: {', '.join(bad_pandas_dtypes)}\"\n )\n\n\ndef _pandas_to_numpy(\n data: pd_DataFrame,\n target_dtype: \"np.typing.DTypeLike\",\n) -> np.ndarray:\n _check_for_bad_pandas_dtypes(data.dtypes)\n try:\n # most common case (no nullable dtypes)\n return data.to_numpy(dtype=target_dtype, copy=False)\n except TypeError:\n # 1.0 <= pd version < 1.1 and nullable dtypes, least common case\n # raises error because array is casted to type(pd.NA) and there's no na_value argument\n return data.astype(target_dtype, copy=False).values\n except ValueError:\n # data has nullable dtypes, but we can specify na_value argument and copy will be made\n return data.to_numpy(dtype=target_dtype, na_value=np.nan)\n\n\ndef _data_from_pandas(\n data: pd_DataFrame,\n feature_name: _LGBM_FeatureNameConfiguration,\n categorical_feature: _LGBM_CategoricalFeatureConfiguration,\n pandas_categorical: Optional[List[List]],\n) -> Tuple[np.ndarray, List[str], Union[List[str], List[int]], List[List]]:\n if len(data.shape) != 2 or data.shape[0] < 1:\n raise ValueError(\"Input data must be 2 dimensional and non empty.\")\n\n # take shallow copy in case we modify categorical columns\n # whole column modifications don't change the original df\n data = data.copy(deep=False)\n\n # determine feature names\n if feature_name == \"auto\":\n feature_name = [str(col) for col in data.columns]\n\n # determine categorical features\n cat_cols = [col for col, dtype in zip(data.columns, data.dtypes) if isinstance(dtype, pd_CategoricalDtype)]\n cat_cols_not_ordered: List[str] = [col for col in cat_cols if not data[col].cat.ordered]\n if pandas_categorical is None: # train dataset\n pandas_categorical = [list(data[col].cat.categories) for col in cat_cols]\n else:\n if len(cat_cols) != len(pandas_categorical):\n raise ValueError(\"train and valid dataset categorical_feature do not match.\")\n for col, category in zip(cat_cols, pandas_categorical):\n if list(data[col].cat.categories) != list(category):\n data[col] = data[col].cat.set_categories(category)\n if cat_cols: # cat_cols is list\n data[cat_cols] = data[cat_cols].apply(lambda x: x.cat.codes).replace({-1: np.nan})\n\n # use cat cols from DataFrame\n if categorical_feature == \"auto\":\n categorical_feature = cat_cols_not_ordered\n\n df_dtypes = [dtype.type for dtype in data.dtypes]\n # so that the target dtype considers floats\n df_dtypes.append(np.float32)\n target_dtype = np.result_type(*df_dtypes)\n\n return (\n _pandas_to_numpy(data, target_dtype=target_dtype),\n feature_name,\n categorical_feature,\n pandas_categorical,\n )\n\n\ndef _dump_pandas_categorical(\n pandas_categorical: Optional[List[List]],\n file_name: Optional[Union[str, Path]] = None,\n) -> str:\n categorical_json = json.dumps(pandas_categorical, default=_json_default_with_numpy)\n pandas_str = f\"\\npandas_categorical:{categorical_json}\\n\"\n if file_name is not None:\n with open(file_name, \"a\") as f:\n f.write(pandas_str)\n return pandas_str\n\n\ndef _load_pandas_categorical(\n file_name: Optional[Union[str, Path]] = None,\n model_str: Optional[str] = None,\n) -> Optional[List[List]]:\n pandas_key = \"pandas_categorical:\"\n offset = -len(pandas_key)\n if file_name is not None:\n max_offset = -getsize(file_name)\n with open(file_name, \"rb\") as f:\n while True:\n offset = max(offset, max_offset)\n f.seek(offset, SEEK_END)\n lines = f.readlines()\n if len(lines) >= 2:\n break\n offset *= 2\n last_line = lines[-1].decode(\"utf-8\").strip()\n if not last_line.startswith(pandas_key):\n last_line = lines[-2].decode(\"utf-8\").strip()\n elif model_str is not None:\n idx = model_str.rfind(\"\\n\", 0, offset)\n last_line = model_str[idx:].strip()\n if last_line.startswith(pandas_key):\n return json.loads(last_line[len(pandas_key) :])\n else:\n return None\n\n\nclass Sequence(abc.ABC):\n \"\"\"\n Generic data access interface.\n\n Object should support the following operations:\n\n .. code-block::\n\n # Get total row number.\n >>> len(seq)\n # Random access by row index. Used for data sampling.\n >>> seq[10]\n # Range data access. Used to read data in batch when constructing Dataset.\n >>> seq[0:100]\n # Optionally specify batch_size to control range data read size.\n >>> seq.batch_size\n\n - With random access, **data sampling does not need to go through all data**.\n - With range data access, there's **no need to read all data into memory thus reduce memory usage**.\n\n .. versionadded:: 3.3.0\n\n Attributes\n ----------\n batch_size : int\n Default size of a batch.\n \"\"\"\n\n batch_size = 4096 # Defaults to read 4K rows in each batch.\n\n @abc.abstractmethod\n def __getitem__(self, idx: Union[int, slice, List[int]]) -> np.ndarray:\n \"\"\"Return data for given row index.\n\n A basic implementation should look like this:\n\n .. code-block:: python\n\n if isinstance(idx, numbers.Integral):\n return self._get_one_line(idx)\n elif isinstance(idx, slice):\n return np.stack([self._get_one_line(i) for i in range(idx.start, idx.stop)])\n elif isinstance(idx, list):\n # Only required if using ``Dataset.subset()``.\n return np.array([self._get_one_line(i) for i in idx])\n else:\n raise TypeError(f\"Sequence index must be integer, slice or list, got {type(idx).__name__}\")\n\n Parameters\n ----------\n idx : int, slice[int], list[int]\n Item index.\n\n Returns\n -------\n result : numpy 1-D array or numpy 2-D array\n 1-D array if idx is int, 2-D array if idx is slice or list.\n \"\"\"\n raise NotImplementedError(\"Sub-classes of lightgbm.Sequence must implement __getitem__()\")\n\n @abc.abstractmethod\n def __len__(self) -> int:\n \"\"\"Return row count of this sequence.\"\"\"\n raise NotImplementedError(\"Sub-classes of lightgbm.Sequence must implement __len__()\")\n\n\nclass _InnerPredictor:\n \"\"\"_InnerPredictor of LightGBM.\n\n Not exposed to user.\n Used only for prediction, usually used for continued training.\n\n .. note::\n\n Can be converted from Booster, but cannot be converted to Booster.\n \"\"\"\n\n def __init__(\n self,\n booster_handle: _BoosterHandle,\n pandas_categorical: Optional[List[List]],\n pred_parameter: Dict[str, Any],\n manage_handle: bool,\n ):\n \"\"\"Initialize the _InnerPredictor.\n\n Parameters\n ----------\n booster_handle : object\n Handle of Booster.\n pandas_categorical : list of list, or None\n If provided, list of categories for ``pandas`` categorical columns.\n Where the ``i``th element of the list contains the categories for the ``i``th categorical feature.\n pred_parameter : dict\n Other parameters for the prediction.\n manage_handle : bool\n If ``True``, free the corresponding Booster on the C++ side when this Python object is deleted.\n \"\"\"\n self._handle = booster_handle\n self.__is_manage_handle = manage_handle\n self.pandas_categorical = pandas_categorical\n self.pred_parameter = _param_dict_to_str(pred_parameter)\n\n out_num_class = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterGetNumClasses(\n self._handle,\n ctypes.byref(out_num_class),\n )\n )\n self.num_class = out_num_class.value\n\n @classmethod\n def from_booster(\n cls,\n booster: \"Booster\",\n pred_parameter: Dict[str, Any],\n ) -> \"_InnerPredictor\":\n \"\"\"Initialize an ``_InnerPredictor`` from a ``Booster``.\n\n Parameters\n ----------\n booster : Booster\n Booster.\n pred_parameter : dict\n Other parameters for the prediction.\n \"\"\"\n out_cur_iter = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterGetCurrentIteration(\n booster._handle,\n ctypes.byref(out_cur_iter),\n )\n )\n return cls(\n booster_handle=booster._handle,\n pandas_categorical=booster.pandas_categorical,\n pred_parameter=pred_parameter,\n manage_handle=False,\n )\n\n @classmethod\n def from_model_file(\n cls,\n model_file: Union[str, Path],\n pred_parameter: Dict[str, Any],\n ) -> \"_InnerPredictor\":\n \"\"\"Initialize an ``_InnerPredictor`` from a text file containing a LightGBM model.\n\n Parameters\n ----------\n model_file : str or pathlib.Path\n Path to the model file.\n pred_parameter : dict\n Other parameters for the prediction.\n \"\"\"\n booster_handle = ctypes.c_void_p()\n out_num_iterations = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterCreateFromModelfile(\n _c_str(str(model_file)),\n ctypes.byref(out_num_iterations),\n ctypes.byref(booster_handle),\n )\n )\n return cls(\n booster_handle=booster_handle,\n pandas_categorical=_load_pandas_categorical(file_name=model_file),\n pred_parameter=pred_parameter,\n manage_handle=True,\n )\n\n def __del__(self) -> None:\n try:\n if self.__is_manage_handle:\n _safe_call(_LIB.LGBM_BoosterFree(self._handle))\n except AttributeError:\n pass\n\n def __getstate__(self) -> Dict[str, Any]:\n this = self.__dict__.copy()\n this.pop(\"handle\", None)\n this.pop(\"_handle\", None)\n return this\n\n def predict(\n self,\n data: _LGBM_PredictDataType,\n start_iteration: int = 0,\n num_iteration: int = -1,\n raw_score: bool = False,\n pred_leaf: bool = False,\n pred_contrib: bool = False,\n data_has_header: bool = False,\n validate_features: bool = False,\n ) -> _LGBM_PredictReturnType:\n \"\"\"Predict logic.\n\n Parameters\n ----------\n data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse or pyarrow Table\n Data source for prediction.\n If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM).\n start_iteration : int, optional (default=0)\n Start index of the iteration to predict.\n num_iteration : int, optional (default=-1)\n Iteration used for prediction.\n raw_score : bool, optional (default=False)\n Whether to predict raw scores.\n pred_leaf : bool, optional (default=False)\n Whether to predict leaf index.\n pred_contrib : bool, optional (default=False)\n Whether to predict feature contributions.\n data_has_header : bool, optional (default=False)\n Whether data has header.\n Used only for txt data.\n validate_features : bool, optional (default=False)\n If True, ensure that the features used to predict match the ones used to train.\n Used only if data is pandas DataFrame.\n\n .. versionadded:: 4.0.0\n\n Returns\n -------\n result : numpy array, scipy.sparse or list of scipy.sparse\n Prediction result.\n Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``).\n \"\"\"\n if isinstance(data, Dataset):\n raise TypeError(\"Cannot use Dataset instance for prediction, please use raw data instead\")\n if isinstance(data, pd_DataFrame) and validate_features:\n data_names = [str(x) for x in data.columns]\n ptr_names = (ctypes.c_char_p * len(data_names))()\n ptr_names[:] = [x.encode(\"utf-8\") for x in data_names]\n _safe_call(\n _LIB.LGBM_BoosterValidateFeatureNames(\n self._handle,\n ptr_names,\n ctypes.c_int(len(data_names)),\n )\n )\n\n if isinstance(data, pd_DataFrame):\n data = _data_from_pandas(\n data=data,\n feature_name=\"auto\",\n categorical_feature=\"auto\",\n pandas_categorical=self.pandas_categorical,\n )[0]\n\n predict_type = _C_API_PREDICT_NORMAL\n if raw_score:\n predict_type = _C_API_PREDICT_RAW_SCORE\n if pred_leaf:\n predict_type = _C_API_PREDICT_LEAF_INDEX\n if pred_contrib:\n predict_type = _C_API_PREDICT_CONTRIB\n\n if isinstance(data, (str, Path)):\n with _TempFile() as f:\n _safe_call(\n _LIB.LGBM_BoosterPredictForFile(\n self._handle,\n _c_str(str(data)),\n ctypes.c_int(data_has_header),\n ctypes.c_int(predict_type),\n ctypes.c_int(start_iteration),\n ctypes.c_int(num_iteration),\n _c_str(self.pred_parameter),\n _c_str(f.name),\n )\n )\n preds = np.loadtxt(f.name, dtype=np.float64)\n nrow = preds.shape[0]\n elif isinstance(data, scipy.sparse.csr_matrix):\n # TODO: remove 'type: ignore[assignment]' when https://github.com/lightgbm-org/LightGBM/pull/6348 is resolved.\n preds, nrow = self.__pred_for_csr( # type: ignore[assignment]\n csr=data,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n predict_type=predict_type,\n )\n elif isinstance(data, scipy.sparse.csc_matrix):\n # TODO: remove 'type: ignore[assignment]' when https://github.com/lightgbm-org/LightGBM/pull/6348 is resolved.\n preds, nrow = self.__pred_for_csc( # type: ignore[assignment]\n csc=data,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n predict_type=predict_type,\n )\n elif isinstance(data, np.ndarray):\n preds, nrow = self.__pred_for_np2d(\n mat=data,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n predict_type=predict_type,\n )\n elif _is_pyarrow_table(data):\n preds, nrow = self.__pred_for_pyarrow_table(\n table=data,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n predict_type=predict_type,\n )\n elif isinstance(data, list):\n try:\n data = np.array(data)\n except BaseException as err:\n raise ValueError(\"Cannot convert data list to numpy array.\") from err\n preds, nrow = self.__pred_for_np2d(\n mat=data,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n predict_type=predict_type,\n )\n else:\n try:\n _log_warning(\"Converting data to scipy sparse matrix.\")\n csr = scipy.sparse.csr_matrix(data)\n except BaseException as err:\n raise TypeError(f\"Cannot predict data for type {type(data).__name__}\") from err\n # TODO: remove 'type: ignore[assignment]' when https://github.com/lightgbm-org/LightGBM/pull/6348 is resolved.\n preds, nrow = self.__pred_for_csr( # type: ignore[assignment]\n csr=csr,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n predict_type=predict_type,\n )\n if pred_leaf:\n preds = preds.astype(np.int32)\n is_sparse = isinstance(preds, (list, scipy.sparse.spmatrix))\n if not is_sparse and (preds.size != nrow or pred_leaf or pred_contrib):\n if preds.size % nrow == 0:\n preds = preds.reshape(nrow, -1)\n else:\n raise ValueError(f\"Length of predict result ({preds.size}) cannot be divide nrow ({nrow})\")\n return preds\n\n def __get_num_preds(\n self,\n *,\n start_iteration: int,\n num_iteration: int,\n nrow: int,\n predict_type: int,\n ) -> int:\n \"\"\"Get size of prediction result.\"\"\"\n if nrow > _MAX_INT32:\n raise LightGBMError(\n \"LightGBM cannot perform prediction for data \"\n f\"with number of rows greater than MAX_INT32 ({_MAX_INT32}).\\n\"\n \"You can split your data into chunks \"\n \"and then concatenate predictions for them\"\n )\n n_preds = ctypes.c_int64(0)\n _safe_call(\n _LIB.LGBM_BoosterCalcNumPredict(\n self._handle,\n ctypes.c_int(nrow),\n ctypes.c_int(predict_type),\n ctypes.c_int(start_iteration),\n ctypes.c_int(num_iteration),\n ctypes.byref(n_preds),\n )\n )\n return n_preds.value\n\n def __inner_predict_np2d(\n self,\n mat: np.ndarray,\n start_iteration: int,\n num_iteration: int,\n predict_type: int,\n preds: Optional[np.ndarray],\n ) -> Tuple[np.ndarray, int]:\n data, layout = _np2d_to_np1d(mat)\n ptr_data, type_ptr_data, _ = _c_float_array(data)\n n_preds = self.__get_num_preds(\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n nrow=mat.shape[0],\n predict_type=predict_type,\n )\n if preds is None:\n preds = np.empty(n_preds, dtype=np.float64)\n elif len(preds.shape) != 1 or len(preds) != n_preds:\n raise ValueError(\"Wrong length of pre-allocated predict array\")\n out_num_preds = ctypes.c_int64(0)\n _safe_call(\n _LIB.LGBM_BoosterPredictForMat(\n self._handle,\n ptr_data,\n ctypes.c_int(type_ptr_data),\n ctypes.c_int32(mat.shape[0]),\n ctypes.c_int32(mat.shape[1]),\n ctypes.c_int(layout),\n ctypes.c_int(predict_type),\n ctypes.c_int(start_iteration),\n ctypes.c_int(num_iteration),\n _c_str(self.pred_parameter),\n ctypes.byref(out_num_preds),\n preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double)),\n )\n )\n if n_preds != out_num_preds.value:\n raise ValueError(\"Wrong length for predict results\")\n return preds, mat.shape[0]\n\n def __pred_for_np2d(\n self,\n mat: np.ndarray,\n start_iteration: int,\n num_iteration: int,\n predict_type: int,\n ) -> Tuple[np.ndarray, int]:\n \"\"\"Predict for a 2-D numpy matrix.\"\"\"\n if len(mat.shape) != 2:\n raise ValueError(\"Input numpy.ndarray or list must be 2 dimensional\")\n\n nrow = mat.shape[0]\n if nrow > _MAX_INT32:\n sections = np.arange(_MAX_INT32, nrow, _MAX_INT32)\n # __get_num_preds() cannot work with nrow > MAX_INT32, so calculate overall number of predictions piecemeal\n n_preds = [\n self.__get_num_preds(\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n nrow=int(i),\n predict_type=predict_type,\n )\n for i in np.diff([0] + list(sections) + [nrow])\n ]\n n_preds_sections = np.array([0] + n_preds, dtype=np.intp).cumsum()\n preds = np.empty(sum(n_preds), dtype=np.float64)\n for chunk, (start_idx_pred, end_idx_pred) in zip(\n np.array_split(mat, sections), zip(n_preds_sections, n_preds_sections[1:])\n ):\n # avoid memory consumption by arrays concatenation operations\n self.__inner_predict_np2d(\n mat=chunk,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n predict_type=predict_type,\n preds=preds[start_idx_pred:end_idx_pred],\n )\n return preds, nrow\n else:\n return self.__inner_predict_np2d(\n mat=mat,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n predict_type=predict_type,\n preds=None,\n )\n\n def __create_sparse_native(\n self,\n cs: Union[scipy.sparse.csc_matrix, scipy.sparse.csr_matrix],\n out_shape: np.ndarray,\n out_ptr_indptr: \"ctypes._Pointer\",\n out_ptr_indices: \"ctypes._Pointer\",\n out_ptr_data: \"ctypes._Pointer\",\n indptr_type: int,\n data_type: int,\n is_csr: bool,\n ) -> _LGBM_PredictSparseReturnType:\n # create numpy array from output arrays\n data_indices_len = out_shape[0]\n indptr_len = out_shape[1]\n if indptr_type == _C_API_DTYPE_INT32:\n out_indptr = _cint32_array_to_numpy(cptr=out_ptr_indptr, length=indptr_len)\n elif indptr_type == _C_API_DTYPE_INT64:\n out_indptr = _cint64_array_to_numpy(cptr=out_ptr_indptr, length=indptr_len)\n else:\n raise TypeError(\"Expected int32 or int64 type for indptr\")\n if data_type == _C_API_DTYPE_FLOAT32:\n out_data = _cfloat32_array_to_numpy(cptr=out_ptr_data, length=data_indices_len)\n elif data_type == _C_API_DTYPE_FLOAT64:\n out_data = _cfloat64_array_to_numpy(cptr=out_ptr_data, length=data_indices_len)\n else:\n raise TypeError(\"Expected float32 or float64 type for data\")\n out_indices = _cint32_array_to_numpy(cptr=out_ptr_indices, length=data_indices_len)\n # break up indptr based on number of rows (note more than one matrix in multiclass case)\n per_class_indptr_shape = cs.indptr.shape[0]\n # for CSC there is extra column added\n if not is_csr:\n per_class_indptr_shape += 1\n out_indptr_arrays = np.split(out_indptr, out_indptr.shape[0] / per_class_indptr_shape)\n # reformat output into a csr or csc matrix or list of csr or csc matrices\n cs_output_matrices = []\n offset = 0\n for cs_indptr in out_indptr_arrays:\n matrix_indptr_len = cs_indptr[cs_indptr.shape[0] - 1]\n cs_indices = out_indices[offset + cs_indptr[0] : offset + matrix_indptr_len]\n cs_data = out_data[offset + cs_indptr[0] : offset + matrix_indptr_len]\n offset += matrix_indptr_len\n # same shape as input csr or csc matrix except extra column for expected value\n cs_shape = [cs.shape[0], cs.shape[1] + 1]\n # note: make sure we copy data as it will be deallocated next\n if is_csr:\n cs_output_matrices.append(scipy.sparse.csr_matrix((cs_data, cs_indices, cs_indptr), cs_shape))\n else:\n cs_output_matrices.append(scipy.sparse.csc_matrix((cs_data, cs_indices, cs_indptr), cs_shape))\n # free the temporary native indptr, indices, and data\n _safe_call(\n _LIB.LGBM_BoosterFreePredictSparse(\n out_ptr_indptr,\n out_ptr_indices,\n out_ptr_data,\n ctypes.c_int(indptr_type),\n ctypes.c_int(data_type),\n )\n )\n if len(cs_output_matrices) == 1:\n return cs_output_matrices[0]\n return cs_output_matrices\n\n def __inner_predict_csr(\n self,\n csr: scipy.sparse.csr_matrix,\n start_iteration: int,\n num_iteration: int,\n predict_type: int,\n preds: Optional[np.ndarray],\n ) -> Tuple[np.ndarray, int]:\n nrow = len(csr.indptr) - 1\n n_preds = self.__get_num_preds(\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n nrow=nrow,\n predict_type=predict_type,\n )\n if preds is None:\n preds = np.empty(n_preds, dtype=np.float64)\n elif len(preds.shape) != 1 or len(preds) != n_preds:\n raise ValueError(\"Wrong length of pre-allocated predict array\")\n out_num_preds = ctypes.c_int64(0)\n\n ptr_indptr, type_ptr_indptr, _ = _c_int_array(csr.indptr)\n ptr_data, type_ptr_data, _ = _c_float_array(csr.data)\n\n assert csr.shape[1] <= _MAX_INT32\n csr_indices = csr.indices.astype(np.int32, copy=False)\n\n _safe_call(\n _LIB.LGBM_BoosterPredictForCSR(\n self._handle,\n ptr_indptr,\n ctypes.c_int(type_ptr_indptr),\n csr_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),\n ptr_data,\n ctypes.c_int(type_ptr_data),\n ctypes.c_int64(len(csr.indptr)),\n ctypes.c_int64(len(csr.data)),\n ctypes.c_int64(csr.shape[1]),\n ctypes.c_int(predict_type),\n ctypes.c_int(start_iteration),\n ctypes.c_int(num_iteration),\n _c_str(self.pred_parameter),\n ctypes.byref(out_num_preds),\n preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double)),\n )\n )\n if n_preds != out_num_preds.value:\n raise ValueError(\"Wrong length for predict results\")\n return preds, nrow\n\n def __inner_predict_csr_sparse(\n self,\n csr: scipy.sparse.csr_matrix,\n start_iteration: int,\n num_iteration: int,\n predict_type: int,\n ) -> Tuple[_LGBM_PredictSparseReturnType, int]:\n ptr_indptr, type_ptr_indptr, __ = _c_int_array(csr.indptr)\n ptr_data, type_ptr_data, _ = _c_float_array(csr.data)\n csr_indices = csr.indices.astype(np.int32, copy=False)\n matrix_type = _C_API_MATRIX_TYPE_CSR\n out_ptr_indptr: _ctypes_int_ptr\n if type_ptr_indptr == _C_API_DTYPE_INT32:\n out_ptr_indptr = ctypes.POINTER(ctypes.c_int32)()\n else:\n out_ptr_indptr = ctypes.POINTER(ctypes.c_int64)()\n out_ptr_indices = ctypes.POINTER(ctypes.c_int32)()\n out_ptr_data: _ctypes_float_ptr\n if type_ptr_data == _C_API_DTYPE_FLOAT32:\n out_ptr_data = ctypes.POINTER(ctypes.c_float)()\n else:\n out_ptr_data = ctypes.POINTER(ctypes.c_double)()\n out_shape = np.empty(2, dtype=np.int64)\n _safe_call(\n _LIB.LGBM_BoosterPredictSparseOutput(\n self._handle,\n ptr_indptr,\n ctypes.c_int(type_ptr_indptr),\n csr_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),\n ptr_data,\n ctypes.c_int(type_ptr_data),\n ctypes.c_int64(len(csr.indptr)),\n ctypes.c_int64(len(csr.data)),\n ctypes.c_int64(csr.shape[1]),\n ctypes.c_int(predict_type),\n ctypes.c_int(start_iteration),\n ctypes.c_int(num_iteration),\n _c_str(self.pred_parameter),\n ctypes.c_int(matrix_type),\n out_shape.ctypes.data_as(ctypes.POINTER(ctypes.c_int64)),\n ctypes.byref(out_ptr_indptr),\n ctypes.byref(out_ptr_indices),\n ctypes.byref(out_ptr_data),\n )\n )\n matrices = self.__create_sparse_native(\n cs=csr,\n out_shape=out_shape,\n out_ptr_indptr=out_ptr_indptr,\n out_ptr_indices=out_ptr_indices,\n out_ptr_data=out_ptr_data,\n indptr_type=type_ptr_indptr,\n data_type=type_ptr_data,\n is_csr=True,\n )\n nrow = len(csr.indptr) - 1\n return matrices, nrow\n\n def __pred_for_csr(\n self,\n csr: scipy.sparse.csr_matrix,\n start_iteration: int,\n num_iteration: int,\n predict_type: int,\n ) -> Tuple[_LGBM_PredictSparseReturnType, int]:\n \"\"\"Predict for a CSR data.\"\"\"\n if predict_type == _C_API_PREDICT_CONTRIB:\n return self.__inner_predict_csr_sparse(\n csr=csr,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n predict_type=predict_type,\n )\n nrow = len(csr.indptr) - 1\n if nrow > _MAX_INT32:\n sections = [0] + list(np.arange(_MAX_INT32, nrow, _MAX_INT32)) + [nrow]\n # __get_num_preds() cannot work with nrow > MAX_INT32, so calculate overall number of predictions piecemeal\n n_preds = [\n self.__get_num_preds(\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n nrow=int(i),\n predict_type=predict_type,\n )\n for i in np.diff(sections)\n ]\n n_preds_sections = np.array([0] + n_preds, dtype=np.intp).cumsum()\n preds = np.empty(sum(n_preds), dtype=np.float64)\n for (start_idx, end_idx), (start_idx_pred, end_idx_pred) in zip(\n zip(sections, sections[1:]), zip(n_preds_sections, n_preds_sections[1:])\n ):\n # avoid memory consumption by arrays concatenation operations\n self.__inner_predict_csr(\n csr=csr[start_idx:end_idx],\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n predict_type=predict_type,\n preds=preds[start_idx_pred:end_idx_pred],\n )\n return preds, nrow\n else:\n return self.__inner_predict_csr(\n csr=csr,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n predict_type=predict_type,\n preds=None,\n )\n\n def __inner_predict_sparse_csc(\n self,\n csc: scipy.sparse.csc_matrix,\n start_iteration: int,\n num_iteration: int,\n predict_type: int,\n ) -> Tuple[_LGBM_PredictSparseReturnType, int]:\n ptr_indptr, type_ptr_indptr, __ = _c_int_array(csc.indptr)\n ptr_data, type_ptr_data, _ = _c_float_array(csc.data)\n csc_indices = csc.indices.astype(np.int32, copy=False)\n matrix_type = _C_API_MATRIX_TYPE_CSC\n out_ptr_indptr: _ctypes_int_ptr\n if type_ptr_indptr == _C_API_DTYPE_INT32:\n out_ptr_indptr = ctypes.POINTER(ctypes.c_int32)()\n else:\n out_ptr_indptr = ctypes.POINTER(ctypes.c_int64)()\n out_ptr_indices = ctypes.POINTER(ctypes.c_int32)()\n out_ptr_data: _ctypes_float_ptr\n if type_ptr_data == _C_API_DTYPE_FLOAT32:\n out_ptr_data = ctypes.POINTER(ctypes.c_float)()\n else:\n out_ptr_data = ctypes.POINTER(ctypes.c_double)()\n out_shape = np.empty(2, dtype=np.int64)\n _safe_call(\n _LIB.LGBM_BoosterPredictSparseOutput(\n self._handle,\n ptr_indptr,\n ctypes.c_int(type_ptr_indptr),\n csc_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),\n ptr_data,\n ctypes.c_int(type_ptr_data),\n ctypes.c_int64(len(csc.indptr)),\n ctypes.c_int64(len(csc.data)),\n ctypes.c_int64(csc.shape[0]),\n ctypes.c_int(predict_type),\n ctypes.c_int(start_iteration),\n ctypes.c_int(num_iteration),\n _c_str(self.pred_parameter),\n ctypes.c_int(matrix_type),\n out_shape.ctypes.data_as(ctypes.POINTER(ctypes.c_int64)),\n ctypes.byref(out_ptr_indptr),\n ctypes.byref(out_ptr_indices),\n ctypes.byref(out_ptr_data),\n )\n )\n matrices = self.__create_sparse_native(\n cs=csc,\n out_shape=out_shape,\n out_ptr_indptr=out_ptr_indptr,\n out_ptr_indices=out_ptr_indices,\n out_ptr_data=out_ptr_data,\n indptr_type=type_ptr_indptr,\n data_type=type_ptr_data,\n is_csr=False,\n )\n nrow = csc.shape[0]\n return matrices, nrow\n\n def __pred_for_csc(\n self,\n csc: scipy.sparse.csc_matrix,\n start_iteration: int,\n num_iteration: int,\n predict_type: int,\n ) -> Tuple[_LGBM_PredictSparseReturnType, int]:\n \"\"\"Predict for a CSC data.\"\"\"\n nrow = csc.shape[0]\n if nrow > _MAX_INT32:\n return self.__pred_for_csr(\n csr=csc.tocsr(),\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n predict_type=predict_type,\n )\n if predict_type == _C_API_PREDICT_CONTRIB:\n return self.__inner_predict_sparse_csc(\n csc=csc,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n predict_type=predict_type,\n )\n n_preds = self.__get_num_preds(\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n nrow=nrow,\n predict_type=predict_type,\n )\n preds = np.empty(n_preds, dtype=np.float64)\n out_num_preds = ctypes.c_int64(0)\n\n ptr_indptr, type_ptr_indptr, __ = _c_int_array(csc.indptr)\n ptr_data, type_ptr_data, _ = _c_float_array(csc.data)\n\n assert csc.shape[0] <= _MAX_INT32\n csc_indices = csc.indices.astype(np.int32, copy=False)\n\n _safe_call(\n _LIB.LGBM_BoosterPredictForCSC(\n self._handle,\n ptr_indptr,\n ctypes.c_int(type_ptr_indptr),\n csc_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),\n ptr_data,\n ctypes.c_int(type_ptr_data),\n ctypes.c_int64(len(csc.indptr)),\n ctypes.c_int64(len(csc.data)),\n ctypes.c_int64(csc.shape[0]),\n ctypes.c_int(predict_type),\n ctypes.c_int(start_iteration),\n ctypes.c_int(num_iteration),\n _c_str(self.pred_parameter),\n ctypes.byref(out_num_preds),\n preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double)),\n )\n )\n if n_preds != out_num_preds.value:\n raise ValueError(\"Wrong length for predict results\")\n return preds, nrow\n\n def __pred_for_pyarrow_table(\n self,\n table: pa_Table,\n start_iteration: int,\n num_iteration: int,\n predict_type: int,\n ) -> Tuple[np.ndarray, int]:\n \"\"\"Predict for a PyArrow table.\"\"\"\n if not (PYARROW_INSTALLED and CFFI_INSTALLED):\n raise LightGBMError(\"Cannot predict from Arrow without 'pyarrow' and 'cffi' installed.\")\n\n # Check that the input is valid: we only handle numbers (for now)\n if not all(arrow_is_integer(t) or arrow_is_floating(t) or arrow_is_boolean(t) for t in table.schema.types):\n raise ValueError(\"Arrow table may only have integer or floating point datatypes\")\n\n # Prepare prediction output array\n n_preds = self.__get_num_preds(\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n nrow=table.num_rows,\n predict_type=predict_type,\n )\n preds = np.empty(n_preds, dtype=np.float64)\n out_num_preds = ctypes.c_int64(0)\n\n # Export Arrow table to C and run prediction\n c_array = _export_arrow_to_c(table)\n _safe_call(\n _LIB.LGBM_BoosterPredictForArrow(\n self._handle,\n ctypes.c_int64(c_array.n_chunks),\n ctypes.c_void_p(c_array.chunks_ptr),\n ctypes.c_void_p(c_array.schema_ptr),\n ctypes.c_int(predict_type),\n ctypes.c_int(start_iteration),\n ctypes.c_int(num_iteration),\n _c_str(self.pred_parameter),\n ctypes.byref(out_num_preds),\n preds.ctypes.data_as(ctypes.POINTER(ctypes.c_double)),\n )\n )\n if n_preds != out_num_preds.value:\n raise ValueError(\"Wrong length for predict results\")\n return preds, table.num_rows\n\n def current_iteration(self) -> int:\n \"\"\"Get the index of the current iteration.\n\n Returns\n -------\n cur_iter : int\n The index of the current iteration.\n \"\"\"\n out_cur_iter = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterGetCurrentIteration(\n self._handle,\n ctypes.byref(out_cur_iter),\n )\n )\n return out_cur_iter.value\n\n\nclass Dataset:\n \"\"\"\n Dataset in LightGBM.\n\n LightGBM does not train on raw data.\n It discretizes continuous features into histogram bins, tries to combine categorical features,\n and automatically handles missing and infinite values.\n\n This class handles that preprocessing, and holds that alternative representation of the input data.\n \"\"\"\n\n def __init__(\n self,\n data: _LGBM_TrainDataType,\n label: Optional[_LGBM_LabelType] = None,\n reference: Optional[\"Dataset\"] = None,\n weight: Optional[_LGBM_WeightType] = None,\n group: Optional[_LGBM_GroupType] = None,\n init_score: Optional[_LGBM_InitScoreType] = None,\n feature_name: _LGBM_FeatureNameConfiguration = \"auto\",\n categorical_feature: _LGBM_CategoricalFeatureConfiguration = \"auto\",\n params: Optional[Dict[str, Any]] = None,\n free_raw_data: bool = True,\n position: Optional[_LGBM_PositionType] = None,\n ):\n \"\"\"Initialize Dataset.\n\n Parameters\n ----------\n data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array or pyarrow Table\n Data source of Dataset.\n If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM) or a LightGBM Dataset binary file.\n label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None)\n Label of the data.\n reference : Dataset or None, optional (default=None)\n If this is Dataset for validation, training data should be used as reference.\n weight : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None)\n Weight for each instance. Weights should be non-negative.\n group : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None)\n Group/query data.\n Only used in the learning-to-rank task.\n sum(group) = n_samples.\n For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,\n where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.\n init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow Array, pyarrow ChunkedArray, pyarrow Table (for multi-class task) or None, optional (default=None)\n Init score for Dataset.\n feature_name : list of str, or 'auto', optional (default=\"auto\")\n Feature names.\n If 'auto' and data is pandas DataFrame or pyarrow Table, data columns names are used.\n categorical_feature : list of str or int, or 'auto', optional (default=\"auto\")\n Categorical features.\n If list of int, interpreted as indices.\n If list of str, interpreted as feature names (need to specify ``feature_name`` as well).\n If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.\n All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647).\n Large values could be memory consuming. Consider using consecutive integers starting from zero.\n All negative values in categorical features will be treated as missing values.\n The output cannot be monotonically constrained with respect to a categorical feature.\n Floating point numbers in categorical features will be rounded towards 0.\n params : dict or None, optional (default=None)\n Other parameters for Dataset.\n free_raw_data : bool, optional (default=True)\n If True, raw data is freed after constructing inner Dataset.\n position : numpy 1-D array, pandas Series or None, optional (default=None)\n Position of items used in unbiased learning-to-rank task.\n \"\"\"\n self._handle: Optional[_DatasetHandle] = None\n self.data = data\n self.label = label\n self.reference = reference\n self.weight = weight\n self.group = group\n self.position = position\n self.init_score = init_score\n self.feature_name: _LGBM_FeatureNameConfiguration = feature_name\n self.categorical_feature: _LGBM_CategoricalFeatureConfiguration = categorical_feature\n self.params = deepcopy(params)\n self.free_raw_data = free_raw_data\n self.used_indices: Optional[List[int]] = None\n self._need_slice = True\n self._predictor: Optional[_InnerPredictor] = None\n self.pandas_categorical: Optional[List[List]] = None\n self._params_back_up: Optional[Dict[str, Any]] = None\n self.version = 0\n self._start_row = 0 # Used when pushing rows one by one.\n\n def __del__(self) -> None:\n try:\n self._free_handle()\n except AttributeError:\n pass\n\n def _create_sample_indices(self, *, total_nrow: int) -> np.ndarray:\n \"\"\"Get an array of randomly chosen indices from this ``Dataset``.\n\n Indices are sampled without replacement.\n\n Parameters\n ----------\n total_nrow : int\n Total number of rows to sample from.\n If this value is greater than the value of parameter ``bin_construct_sample_cnt``, only ``bin_construct_sample_cnt`` indices will be used.\n If Dataset has multiple input data, this should be the sum of rows of every file.\n\n Returns\n -------\n indices : numpy array\n Indices for sampled data.\n \"\"\"\n param_str = _param_dict_to_str(self.get_params())\n sample_cnt = _get_sample_count(total_nrow, param_str)\n indices = np.empty(sample_cnt, dtype=np.int32)\n ptr_data, _, _ = _c_int_array(indices)\n actual_sample_cnt = ctypes.c_int32(0)\n\n _safe_call(\n _LIB.LGBM_SampleIndices(\n ctypes.c_int32(total_nrow),\n _c_str(param_str),\n ptr_data,\n ctypes.byref(actual_sample_cnt),\n )\n )\n assert sample_cnt == actual_sample_cnt.value\n return indices\n\n def _init_from_ref_dataset(\n self,\n total_nrow: int,\n ref_dataset: _DatasetHandle,\n ) -> \"Dataset\":\n \"\"\"Create dataset from a reference dataset.\n\n Parameters\n ----------\n total_nrow : int\n Number of rows expected to add to dataset.\n ref_dataset : object\n Handle of reference dataset to extract metadata from.\n\n Returns\n -------\n self : Dataset\n Constructed Dataset object.\n \"\"\"\n self._handle = ctypes.c_void_p()\n _safe_call(\n _LIB.LGBM_DatasetCreateByReference(\n ref_dataset,\n ctypes.c_int64(total_nrow),\n ctypes.byref(self._handle),\n )\n )\n return self\n\n def _init_from_sample(\n self,\n sample_data: List[np.ndarray],\n sample_indices: List[np.ndarray],\n sample_cnt: int,\n total_nrow: int,\n ) -> \"Dataset\":\n \"\"\"Create Dataset from sampled data structures.\n\n Parameters\n ----------\n sample_data : list of numpy array\n Sample data for each column.\n sample_indices : list of numpy array\n Sample data row index for each column.\n sample_cnt : int\n Number of samples.\n total_nrow : int\n Total number of rows for all input files.\n\n Returns\n -------\n self : Dataset\n Constructed Dataset object.\n \"\"\"\n ncol = len(sample_indices)\n assert len(sample_data) == ncol, \"#sample data column != #column indices\"\n\n for i in range(ncol):\n if sample_data[i].dtype != np.double:\n raise ValueError(f\"sample_data[{i}] type {sample_data[i].dtype} is not double\")\n if sample_indices[i].dtype != np.int32:\n raise ValueError(f\"sample_indices[{i}] type {sample_indices[i].dtype} is not int32\")\n\n # c type: double**\n # each double* element points to start of each column of sample data.\n sample_col_ptr: _ctypes_float_array = (ctypes.POINTER(ctypes.c_double) * ncol)()\n # c type int**\n # each int* points to start of indices for each column\n indices_col_ptr: _ctypes_int_array = (ctypes.POINTER(ctypes.c_int32) * ncol)()\n for i in range(ncol):\n sample_col_ptr[i] = _c_float_array(sample_data[i])[0]\n indices_col_ptr[i] = _c_int_array(sample_indices[i])[0]\n\n num_per_col = np.array([len(d) for d in sample_indices], dtype=np.int32)\n num_per_col_ptr, _, _ = _c_int_array(num_per_col)\n\n self._handle = ctypes.c_void_p()\n params_str = _param_dict_to_str(self.get_params())\n _safe_call(\n _LIB.LGBM_DatasetCreateFromSampledColumn(\n ctypes.cast(sample_col_ptr, ctypes.POINTER(ctypes.POINTER(ctypes.c_double))),\n ctypes.cast(indices_col_ptr, ctypes.POINTER(ctypes.POINTER(ctypes.c_int32))),\n ctypes.c_int32(ncol),\n num_per_col_ptr,\n ctypes.c_int32(sample_cnt),\n ctypes.c_int32(total_nrow),\n ctypes.c_int64(total_nrow),\n _c_str(params_str),\n ctypes.byref(self._handle),\n )\n )\n return self\n\n def _push_rows(self, data: np.ndarray) -> \"Dataset\":\n \"\"\"Add rows to Dataset.\n\n Parameters\n ----------\n data : numpy 1-D array\n New data to add to the Dataset.\n\n Returns\n -------\n self : Dataset\n Dataset object.\n \"\"\"\n nrow, ncol = data.shape\n data = data.reshape(data.size)\n data_ptr, data_type, _ = _c_float_array(data)\n\n _safe_call(\n _LIB.LGBM_DatasetPushRows(\n self._handle,\n data_ptr,\n data_type,\n ctypes.c_int32(nrow),\n ctypes.c_int32(ncol),\n ctypes.c_int32(self._start_row),\n )\n )\n self._start_row += nrow\n return self\n\n def get_params(self) -> Dict[str, Any]:\n \"\"\"Get the used parameters in the Dataset.\n\n Returns\n -------\n params : dict\n The used parameters in this Dataset object.\n \"\"\"\n if self.params is not None:\n # no min_data, nthreads and verbose in this function\n dataset_params = _ConfigAliases.get(\n \"bin_construct_sample_cnt\",\n \"categorical_feature\",\n \"data_random_seed\",\n \"enable_bundle\",\n \"feature_pre_filter\",\n \"forcedbins_filename\",\n \"group_column\",\n \"header\",\n \"ignore_column\",\n \"is_enable_sparse\",\n \"label_column\",\n \"linear_tree\",\n \"max_bin\",\n \"max_bin_by_feature\",\n \"min_data_in_bin\",\n \"pre_partition\",\n \"precise_float_parser\",\n \"two_round\",\n \"use_missing\",\n \"weight_column\",\n \"zero_as_missing\",\n )\n return {k: v for k, v in self.params.items() if k in dataset_params}\n else:\n return {}\n\n def _free_handle(self) -> \"Dataset\":\n if self._handle is not None:\n _safe_call(_LIB.LGBM_DatasetFree(self._handle))\n self._handle = None\n self._need_slice = True\n if self.used_indices is not None:\n self.data = None\n return self\n\n def _set_init_score_by_predictor(\n self,\n predictor: Optional[_InnerPredictor],\n data: _LGBM_TrainDataType,\n used_indices: Optional[Union[List[int], np.ndarray]],\n ) -> \"Dataset\":\n data_has_header = False\n if isinstance(data, (str, Path)) and self.params is not None:\n # check data has header or not\n data_has_header = any(self.params.get(alias, False) for alias in _ConfigAliases.get(\"header\"))\n num_data = self.num_data()\n if predictor is not None:\n init_score: Union[np.ndarray, scipy.sparse.spmatrix] = predictor.predict(\n data=data,\n raw_score=True,\n data_has_header=data_has_header,\n )\n init_score = init_score.ravel()\n if used_indices is not None:\n assert not self._need_slice\n if isinstance(data, (str, Path)):\n sub_init_score = np.empty(num_data * predictor.num_class, dtype=np.float64)\n assert num_data == len(used_indices)\n for i in range(len(used_indices)):\n for j in range(predictor.num_class):\n sub_init_score[i * predictor.num_class + j] = init_score[\n used_indices[i] * predictor.num_class + j\n ]\n init_score = sub_init_score\n if predictor.num_class > 1:\n # need to regroup init_score\n new_init_score = np.empty(init_score.size, dtype=np.float64)\n for i in range(num_data):\n for j in range(predictor.num_class):\n new_init_score[j * num_data + i] = init_score[i * predictor.num_class + j]\n init_score = new_init_score\n elif self.init_score is not None:\n init_score = np.full_like(self.init_score, fill_value=0.0, dtype=np.float64)\n else:\n return self\n self.set_init_score(init_score)\n return self\n\n def _lazy_init(\n self,\n data: Optional[_LGBM_TrainDataType],\n label: Optional[_LGBM_LabelType],\n reference: Optional[\"Dataset\"],\n weight: Optional[_LGBM_WeightType],\n group: Optional[_LGBM_GroupType],\n init_score: Optional[_LGBM_InitScoreType],\n predictor: Optional[_InnerPredictor],\n feature_name: _LGBM_FeatureNameConfiguration,\n categorical_feature: _LGBM_CategoricalFeatureConfiguration,\n params: Optional[Dict[str, Any]],\n position: Optional[_LGBM_PositionType],\n ) -> \"Dataset\":\n if data is None:\n self._handle = None\n return self\n if reference is not None:\n self.pandas_categorical = reference.pandas_categorical\n categorical_feature = reference.categorical_feature\n if isinstance(data, pd_DataFrame):\n data, feature_name, categorical_feature, self.pandas_categorical = _data_from_pandas(\n data=data,\n feature_name=feature_name,\n categorical_feature=categorical_feature,\n pandas_categorical=self.pandas_categorical,\n )\n elif _is_pyarrow_table(data) and feature_name == \"auto\":\n feature_name = data.column_names\n\n # process for args\n params = {} if params is None else params\n args_names = inspect.signature(self.__class__._lazy_init).parameters.keys()\n for key in params.keys():\n if key in args_names:\n _log_warning(\n f\"{key} keyword has been found in `params` and will be ignored.\\n\"\n f\"Please use {key} argument of the Dataset constructor to pass this parameter.\"\n )\n # get categorical features\n if isinstance(categorical_feature, list):\n categorical_indices = set()\n feature_dict = {}\n if isinstance(feature_name, list):\n feature_dict = {name: i for i, name in enumerate(feature_name)}\n for name in categorical_feature:\n if isinstance(name, str) and name in feature_dict:\n categorical_indices.add(feature_dict[name])\n elif isinstance(name, int):\n categorical_indices.add(name)\n else:\n raise TypeError(f\"Wrong type({type(name).__name__}) or unknown name({name}) in categorical_feature\")\n if categorical_indices:\n for cat_alias in _ConfigAliases.get(\"categorical_feature\"):\n if cat_alias in params:\n # If the params[cat_alias] is equal to categorical_indices, do not report the warning.\n if not (isinstance(params[cat_alias], list) and set(params[cat_alias]) == categorical_indices):\n _log_warning(f\"{cat_alias} in param dict is overridden.\")\n params.pop(cat_alias, None)\n params[\"categorical_column\"] = sorted(categorical_indices)\n\n params_str = _param_dict_to_str(params)\n self.params = params\n # process for reference dataset\n ref_dataset = None\n if isinstance(reference, Dataset):\n ref_dataset = reference.construct()._handle\n elif reference is not None:\n raise TypeError(\"Reference dataset should be None or dataset instance\")\n # start construct data\n if isinstance(data, (str, Path)):\n self._handle = ctypes.c_void_p()\n _safe_call(\n _LIB.LGBM_DatasetCreateFromFile(\n _c_str(str(data)),\n _c_str(params_str),\n ref_dataset,\n ctypes.byref(self._handle),\n )\n )\n elif isinstance(data, scipy.sparse.csr_matrix):\n self.__init_from_csr(csr=data, params_str=params_str, ref_dataset=ref_dataset)\n elif isinstance(data, scipy.sparse.csc_matrix):\n self.__init_from_csc(csc=data, params_str=params_str, ref_dataset=ref_dataset)\n elif isinstance(data, np.ndarray):\n self.__init_from_np2d(mat=data, params_str=params_str, ref_dataset=ref_dataset)\n elif _is_pyarrow_table(data):\n self.__init_from_pyarrow_table(table=data, params_str=params_str, ref_dataset=ref_dataset)\n elif isinstance(data, list) and len(data) > 0:\n if _is_list_of_numpy_arrays(data):\n self.__init_from_list_np2d(mats=data, params_str=params_str, ref_dataset=ref_dataset)\n elif _is_list_of_sequences(data):\n self.__init_from_seqs(seqs=data, ref_dataset=ref_dataset)\n else:\n raise TypeError(\"Data list can only be of ndarray or Sequence\")\n elif isinstance(data, Sequence):\n self.__init_from_seqs(seqs=[data], ref_dataset=ref_dataset)\n else:\n try:\n csr = scipy.sparse.csr_matrix(data)\n self.__init_from_csr(csr=csr, params_str=params_str, ref_dataset=ref_dataset)\n except BaseException as err:\n raise TypeError(f\"Cannot initialize Dataset from {type(data).__name__}\") from err\n if label is not None:\n self.set_label(label)\n if self.get_label() is None:\n raise ValueError(\"Label should not be None\")\n if weight is not None:\n self.set_weight(weight)\n if group is not None:\n self.set_group(group)\n if position is not None:\n self.set_position(position)\n if isinstance(predictor, _InnerPredictor):\n if self._predictor is None and init_score is not None:\n _log_warning(\"The init_score will be overridden by the prediction of init_model.\")\n self._set_init_score_by_predictor(predictor=predictor, data=data, used_indices=None)\n elif init_score is not None:\n self.set_init_score(init_score)\n elif predictor is not None:\n raise TypeError(f\"Wrong predictor type {type(predictor).__name__}\")\n # set feature names\n return self.set_feature_name(feature_name)\n\n @staticmethod\n def _yield_row_from_seqlist(seqs: List[Sequence], indices: Iterable[int]) -> Iterator[np.ndarray]:\n offset = 0\n seq_id = 0\n seq = seqs[seq_id]\n for row_id in indices:\n assert row_id >= offset, \"sample indices are expected to be monotonic\"\n while row_id >= offset + len(seq):\n offset += len(seq)\n seq_id += 1\n seq = seqs[seq_id]\n id_in_seq = row_id - offset\n row = seq[id_in_seq]\n yield row if row.flags[\"OWNDATA\"] else row.copy()\n\n def __sample(self, *, seqs: List[Sequence], total_nrow: int) -> Tuple[List[np.ndarray], List[np.ndarray]]:\n \"\"\"Sample data from seqs.\n\n Mimics behavior in c_api.cpp:LGBM_DatasetCreateFromMats()\n\n Returns\n -------\n sampled_rows, sampled_row_indices\n \"\"\"\n indices = self._create_sample_indices(total_nrow=total_nrow)\n\n # Select sampled rows, transpose to column order.\n sampled = np.array(list(self._yield_row_from_seqlist(seqs, indices)))\n sampled = sampled.T\n\n filtered = []\n filtered_idx = []\n sampled_row_range = np.arange(len(indices), dtype=np.int32)\n for col in sampled:\n col_predicate = (np.abs(col) > ZERO_THRESHOLD) | np.isnan(col)\n filtered_col = col[col_predicate]\n filtered_row_idx = sampled_row_range[col_predicate]\n\n filtered.append(filtered_col)\n filtered_idx.append(filtered_row_idx)\n\n return filtered, filtered_idx\n\n def __init_from_seqs(\n self,\n *,\n seqs: List[Sequence],\n ref_dataset: Optional[_DatasetHandle],\n ) -> \"Dataset\":\n \"\"\"\n Initialize data from list of Sequence objects.\n\n Sequence: Generic Data Access Object\n Supports random access and access by batch if properly defined by user\n\n Data scheme uniformity are trusted, not checked\n \"\"\"\n total_nrow = sum(len(seq) for seq in seqs)\n\n # create validation dataset from ref_dataset\n if ref_dataset is not None:\n self._init_from_ref_dataset(total_nrow, ref_dataset)\n else:\n param_str = _param_dict_to_str(self.get_params())\n sample_cnt = _get_sample_count(total_nrow, param_str)\n\n sample_data, col_indices = self.__sample(seqs=seqs, total_nrow=total_nrow)\n self._init_from_sample(sample_data, col_indices, sample_cnt, total_nrow)\n\n for seq in seqs:\n nrow = len(seq)\n batch_size = getattr(seq, \"batch_size\", None) or Sequence.batch_size\n for start in range(0, nrow, batch_size):\n end = min(start + batch_size, nrow)\n self._push_rows(seq[start:end])\n return self\n\n def __init_from_np2d(\n self,\n *,\n mat: np.ndarray,\n params_str: str,\n ref_dataset: Optional[_DatasetHandle],\n ) -> \"Dataset\":\n \"\"\"Initialize data from a 2-D numpy matrix.\"\"\"\n if len(mat.shape) != 2:\n raise ValueError(\"Input numpy.ndarray must be 2 dimensional\")\n\n self._handle = ctypes.c_void_p()\n data, layout = _np2d_to_np1d(mat)\n ptr_data, type_ptr_data, _ = _c_float_array(data)\n _safe_call(\n _LIB.LGBM_DatasetCreateFromMat(\n ptr_data,\n ctypes.c_int(type_ptr_data),\n ctypes.c_int32(mat.shape[0]),\n ctypes.c_int32(mat.shape[1]),\n ctypes.c_int(layout),\n _c_str(params_str),\n ref_dataset,\n ctypes.byref(self._handle),\n )\n )\n return self\n\n def __init_from_list_np2d(\n self,\n *,\n mats: List[np.ndarray],\n params_str: str,\n ref_dataset: Optional[_DatasetHandle],\n ) -> \"Dataset\":\n \"\"\"Initialize data from a list of 2-D numpy matrices.\"\"\"\n ncol = mats[0].shape[1]\n nrow = np.empty((len(mats),), np.int32)\n ptr_data: _ctypes_float_array\n if mats[0].dtype == np.float64:\n ptr_data = (ctypes.POINTER(ctypes.c_double) * len(mats))()\n else:\n ptr_data = (ctypes.POINTER(ctypes.c_float) * len(mats))()\n layouts = (ctypes.c_int * len(mats))()\n\n holders = []\n type_ptr_data = -1\n\n for i, mat in enumerate(mats):\n if len(mat.shape) != 2:\n raise ValueError(\"Input numpy.ndarray must be 2 dimensional\")\n\n if mat.shape[1] != ncol:\n raise ValueError(\"Input arrays must have same number of columns\")\n\n nrow[i] = mat.shape[0]\n\n mat, layout = _np2d_to_np1d(mat)\n\n chunk_ptr_data, chunk_type_ptr_data, holder = _c_float_array(mat)\n if type_ptr_data != -1 and chunk_type_ptr_data != type_ptr_data:\n raise ValueError(\"Input chunks must have same type\")\n ptr_data[i] = chunk_ptr_data\n layouts[i] = layout\n type_ptr_data = chunk_type_ptr_data\n holders.append(holder)\n\n self._handle = ctypes.c_void_p()\n _safe_call(\n _LIB.LGBM_DatasetCreateFromMats(\n ctypes.c_int32(len(mats)),\n ctypes.cast(ptr_data, ctypes.POINTER(ctypes.POINTER(ctypes.c_double))),\n ctypes.c_int(type_ptr_data),\n nrow.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),\n ctypes.c_int32(ncol),\n layouts,\n _c_str(params_str),\n ref_dataset,\n ctypes.byref(self._handle),\n )\n )\n return self\n\n def __init_from_csr(\n self,\n *,\n csr: scipy.sparse.csr_matrix,\n params_str: str,\n ref_dataset: Optional[_DatasetHandle],\n ) -> \"Dataset\":\n \"\"\"Initialize data from a CSR matrix.\"\"\"\n if len(csr.indices) != len(csr.data):\n raise ValueError(f\"Length mismatch: {len(csr.indices)} vs {len(csr.data)}\")\n self._handle = ctypes.c_void_p()\n\n ptr_indptr, type_ptr_indptr, __ = _c_int_array(csr.indptr)\n ptr_data, type_ptr_data, _ = _c_float_array(csr.data)\n\n assert csr.shape[1] <= _MAX_INT32\n csr_indices = csr.indices.astype(np.int32, copy=False)\n\n _safe_call(\n _LIB.LGBM_DatasetCreateFromCSR(\n ptr_indptr,\n ctypes.c_int(type_ptr_indptr),\n csr_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),\n ptr_data,\n ctypes.c_int(type_ptr_data),\n ctypes.c_int64(len(csr.indptr)),\n ctypes.c_int64(len(csr.data)),\n ctypes.c_int64(csr.shape[1]),\n _c_str(params_str),\n ref_dataset,\n ctypes.byref(self._handle),\n )\n )\n return self\n\n def __init_from_csc(\n self,\n *,\n csc: scipy.sparse.csc_matrix,\n params_str: str,\n ref_dataset: Optional[_DatasetHandle],\n ) -> \"Dataset\":\n \"\"\"Initialize data from a CSC matrix.\"\"\"\n if len(csc.indices) != len(csc.data):\n raise ValueError(f\"Length mismatch: {len(csc.indices)} vs {len(csc.data)}\")\n self._handle = ctypes.c_void_p()\n\n ptr_indptr, type_ptr_indptr, __ = _c_int_array(csc.indptr)\n ptr_data, type_ptr_data, _ = _c_float_array(csc.data)\n\n assert csc.shape[0] <= _MAX_INT32\n csc_indices = csc.indices.astype(np.int32, copy=False)\n\n _safe_call(\n _LIB.LGBM_DatasetCreateFromCSC(\n ptr_indptr,\n ctypes.c_int(type_ptr_indptr),\n csc_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),\n ptr_data,\n ctypes.c_int(type_ptr_data),\n ctypes.c_int64(len(csc.indptr)),\n ctypes.c_int64(len(csc.data)),\n ctypes.c_int64(csc.shape[0]),\n _c_str(params_str),\n ref_dataset,\n ctypes.byref(self._handle),\n )\n )\n return self\n\n def __init_from_pyarrow_table(\n self,\n *,\n table: pa_Table,\n params_str: str,\n ref_dataset: Optional[_DatasetHandle],\n ) -> \"Dataset\":\n \"\"\"Initialize data from a PyArrow table.\"\"\"\n if not (PYARROW_INSTALLED and CFFI_INSTALLED):\n raise LightGBMError(\"Cannot init Dataset from Arrow without 'pyarrow' and 'cffi' installed.\")\n\n # Check that the input is valid: we only handle numbers (for now)\n if not all(arrow_is_integer(t) or arrow_is_floating(t) or arrow_is_boolean(t) for t in table.schema.types):\n raise ValueError(\"Arrow table may only have integer or floating point datatypes\")\n\n # Export Arrow table to C\n c_array = _export_arrow_to_c(table)\n self._handle = ctypes.c_void_p()\n _safe_call(\n _LIB.LGBM_DatasetCreateFromArrow(\n ctypes.c_int64(c_array.n_chunks),\n ctypes.c_void_p(c_array.chunks_ptr),\n ctypes.c_void_p(c_array.schema_ptr),\n _c_str(params_str),\n ref_dataset,\n ctypes.byref(self._handle),\n )\n )\n return self\n\n @staticmethod\n def _compare_params_for_warning(\n *,\n params: Dict[str, Any],\n other_params: Dict[str, Any],\n ignore_keys: Set[str],\n ) -> bool:\n \"\"\"Compare two dictionaries with params ignoring some keys.\n\n It is only for the warning purpose.\n\n Parameters\n ----------\n params : dict\n One dictionary with parameters to compare.\n other_params : dict\n Another dictionary with parameters to compare.\n ignore_keys : set\n Keys that should be ignored during comparing two dictionaries.\n\n Returns\n -------\n compare_result : bool\n Returns whether two dictionaries with params are equal.\n \"\"\"\n for k, v in other_params.items():\n if k not in ignore_keys:\n if k not in params or params[k] != v:\n return False\n for k, v in params.items():\n if k not in ignore_keys:\n if k not in other_params or v != other_params[k]:\n return False\n return True\n\n def construct(self) -> \"Dataset\":\n \"\"\"Lazy init.\n\n Returns\n -------\n self : Dataset\n Constructed Dataset object.\n \"\"\"\n if self._handle is None:\n if self.reference is not None:\n reference_params = self.reference.get_params()\n params = self.get_params()\n if params != reference_params:\n if not self._compare_params_for_warning(\n params=params,\n other_params=reference_params,\n ignore_keys=_ConfigAliases.get(\"categorical_feature\"),\n ):\n _log_warning(\"Overriding the parameters from Reference Dataset.\")\n self._update_params(reference_params)\n if self.used_indices is None:\n # create valid\n self._lazy_init(\n data=self.data,\n label=self.label,\n reference=self.reference,\n weight=self.weight,\n group=self.group,\n position=self.position,\n init_score=self.init_score,\n predictor=self._predictor,\n feature_name=self.feature_name,\n categorical_feature=\"auto\",\n params=self.params,\n )\n else:\n # construct subset\n used_indices = _list_to_1d_numpy(\n data=self.used_indices,\n dtype=np.int32,\n name=\"used_indices\",\n )\n assert used_indices.flags.c_contiguous\n if self.reference.group is not None:\n group_info = np.array(self.reference.group).astype(np.int32, copy=False)\n _, self.group = np.unique(\n np.repeat(range(len(group_info)), repeats=group_info)[self.used_indices], return_counts=True\n )\n self._handle = ctypes.c_void_p()\n params_str = _param_dict_to_str(self.params)\n _safe_call(\n _LIB.LGBM_DatasetGetSubset(\n self.reference.construct()._handle,\n used_indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),\n ctypes.c_int32(used_indices.shape[0]),\n _c_str(params_str),\n ctypes.byref(self._handle),\n )\n )\n if not self.free_raw_data:\n self.get_data()\n if self.group is not None:\n self.set_group(self.group)\n if self.position is not None:\n self.set_position(self.position)\n if self.get_label() is None:\n raise ValueError(\"Label should not be None.\")\n if (\n isinstance(self._predictor, _InnerPredictor)\n and self._predictor is not self.reference._predictor\n ):\n self.get_data()\n self._set_init_score_by_predictor(\n predictor=self._predictor, data=self.data, used_indices=used_indices\n )\n else:\n # create train\n self._lazy_init(\n data=self.data,\n label=self.label,\n reference=None,\n weight=self.weight,\n group=self.group,\n init_score=self.init_score,\n predictor=self._predictor,\n feature_name=self.feature_name,\n categorical_feature=self.categorical_feature,\n params=self.params,\n position=self.position,\n )\n if self.free_raw_data:\n self.data = None\n self.feature_name = self.get_feature_name()\n return self\n\n def create_valid(\n self,\n data: _LGBM_TrainDataType,\n label: Optional[_LGBM_LabelType] = None,\n weight: Optional[_LGBM_WeightType] = None,\n group: Optional[_LGBM_GroupType] = None,\n init_score: Optional[_LGBM_InitScoreType] = None,\n params: Optional[Dict[str, Any]] = None,\n position: Optional[_LGBM_PositionType] = None,\n ) -> \"Dataset\":\n \"\"\"Create validation data align with current Dataset.\n\n Parameters\n ----------\n data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array or pyarrow Table\n Data source of Dataset.\n If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM) or a LightGBM Dataset binary file.\n label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None)\n Label of the data.\n weight : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None)\n Weight for each instance. Weights should be non-negative.\n group : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None)\n Group/query data.\n Only used in the learning-to-rank task.\n sum(group) = n_samples.\n For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,\n where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.\n init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow Array, pyarrow ChunkedArray, pyarrow Table (for multi-class task) or None, optional (default=None)\n Init score for Dataset.\n params : dict or None, optional (default=None)\n Other parameters for validation Dataset.\n position : numpy 1-D array, pandas Series or None, optional (default=None)\n Position of items used in unbiased learning-to-rank task.\n\n Returns\n -------\n valid : Dataset\n Validation Dataset with reference to self.\n \"\"\"\n ret = Dataset(\n data,\n label=label,\n reference=self,\n weight=weight,\n group=group,\n position=position,\n init_score=init_score,\n params=params,\n free_raw_data=self.free_raw_data,\n )\n ret._predictor = self._predictor\n ret.pandas_categorical = self.pandas_categorical\n return ret\n\n def subset(\n self,\n used_indices: List[int],\n params: Optional[Dict[str, Any]] = None,\n ) -> \"Dataset\":\n \"\"\"Get subset of current Dataset.\n\n Parameters\n ----------\n used_indices : list of int\n Indices used to create the subset.\n params : dict or None, optional (default=None)\n These parameters will be passed to Dataset constructor.\n\n Returns\n -------\n subset : Dataset\n Subset of the current Dataset.\n \"\"\"\n if params is None:\n params = self.params\n ret = Dataset(\n None,\n reference=self,\n feature_name=self.feature_name,\n categorical_feature=self.categorical_feature,\n params=params,\n free_raw_data=self.free_raw_data,\n )\n ret._predictor = self._predictor\n ret.pandas_categorical = self.pandas_categorical\n ret.used_indices = sorted(used_indices)\n return ret\n\n def save_binary(self, filename: Union[str, Path]) -> \"Dataset\":\n \"\"\"Save Dataset to a binary file.\n\n .. note::\n\n Please note that `init_score` is not saved in binary file.\n If you need it, please set it again after loading Dataset.\n\n Parameters\n ----------\n filename : str or pathlib.Path\n Name of the output file.\n\n Returns\n -------\n self : Dataset\n Returns self.\n \"\"\"\n _safe_call(\n _LIB.LGBM_DatasetSaveBinary(\n self.construct()._handle,\n _c_str(str(filename)),\n )\n )\n return self\n\n def _update_params(self, params: Optional[Dict[str, Any]]) -> \"Dataset\":\n if not params:\n return self\n params = deepcopy(params)\n\n def update() -> None:\n if not self.params:\n self.params = params\n else:\n self._params_back_up = deepcopy(self.params)\n self.params.update(params)\n\n if self._handle is None:\n update()\n elif params is not None:\n ret = _LIB.LGBM_DatasetUpdateParamChecking(\n _c_str(_param_dict_to_str(self.params)),\n _c_str(_param_dict_to_str(params)),\n )\n if ret != 0:\n # could be updated if data is not freed\n if self.data is not None:\n update()\n self._free_handle()\n else:\n raise LightGBMError(_LIB.LGBM_GetLastError().decode(\"utf-8\"))\n return self\n\n def _reverse_update_params(self) -> \"Dataset\":\n if self._handle is None:\n self.params = deepcopy(self._params_back_up)\n self._params_back_up = None\n return self\n\n def set_field(\n self,\n field_name: str,\n data: Optional[_LGBM_SetFieldType],\n ) -> \"Dataset\":\n \"\"\"Set property into the Dataset.\n\n Parameters\n ----------\n field_name : str\n The field name of the information.\n data : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow Array, pyarrow ChunkedArray, pyarrow Table (for multi-class task) or None\n The data to be set.\n\n Returns\n -------\n self : Dataset\n Dataset with set property.\n \"\"\"\n if self._handle is None:\n raise Exception(f\"Cannot set {field_name} before construct dataset\")\n if data is None:\n # set to None\n _safe_call(\n _LIB.LGBM_DatasetSetField(\n self._handle,\n _c_str(field_name),\n None,\n ctypes.c_int(0),\n ctypes.c_int(_FIELD_TYPE_MAPPER[field_name]),\n )\n )\n return self\n\n # If the data is a arrow data, we can just pass it to C\n if _is_pyarrow_array(data) or _is_pyarrow_table(data):\n # If a table is being passed, we concatenate the columns. This is only valid for\n # 'init_score'.\n if _is_pyarrow_table(data):\n if field_name != \"init_score\":\n raise ValueError(f\"pyarrow tables are not supported for field '{field_name}'\")\n data = pa_chunked_array(\n [\n chunk\n for array in data.columns # type: ignore\n for chunk in array.chunks\n ]\n )\n\n c_array = _export_arrow_to_c(data)\n _safe_call(\n _LIB.LGBM_DatasetSetFieldFromArrow(\n self._handle,\n _c_str(field_name),\n ctypes.c_int64(c_array.n_chunks),\n ctypes.c_void_p(c_array.chunks_ptr),\n ctypes.c_void_p(c_array.schema_ptr),\n )\n )\n self.version += 1\n return self\n\n dtype: \"np.typing.DTypeLike\"\n if field_name == \"init_score\":\n dtype = np.float64\n if _is_1d_collection(data):\n data = _list_to_1d_numpy(data=data, dtype=dtype, name=field_name)\n elif _is_2d_collection(data):\n data = _data_to_2d_numpy(data=data, dtype=dtype, name=field_name)\n data = data.ravel(order=\"F\")\n else:\n raise TypeError(\n \"init_score must be list, numpy 1-D array or pandas Series.\\n\"\n \"In multiclass classification init_score can also be a list of lists, numpy 2-D array or pandas DataFrame.\"\n )\n else:\n if field_name in {\"group\", \"position\"}:\n dtype = np.int32\n else:\n dtype = np.float32\n data = _list_to_1d_numpy(data=data, dtype=dtype, name=field_name)\n\n ptr_data: Union[_ctypes_float_ptr, _ctypes_int_ptr]\n if data.dtype == np.float32 or data.dtype == np.float64:\n ptr_data, type_data, _ = _c_float_array(data)\n elif data.dtype == np.int32:\n ptr_data, type_data, _ = _c_int_array(data)\n else:\n raise TypeError(f\"Expected np.float32/64 or np.int32, met type({data.dtype})\")\n if type_data != _FIELD_TYPE_MAPPER[field_name]:\n raise TypeError(\"Input type error for set_field\")\n _safe_call(\n _LIB.LGBM_DatasetSetField(\n self._handle,\n _c_str(field_name),\n ptr_data,\n ctypes.c_int(len(data)),\n ctypes.c_int(type_data),\n )\n )\n self.version += 1\n return self\n\n def get_field(self, field_name: str) -> Optional[np.ndarray]:\n \"\"\"Get property from the Dataset.\n\n Can only be run on a constructed Dataset.\n\n Unlike ``get_group()``, ``get_init_score()``, ``get_label()``, ``get_position()``, and ``get_weight()``,\n this method ignores any raw data passed into ``lgb.Dataset()`` on the Python side, and will only read\n data from the constructed C++ ``Dataset`` object.\n\n Parameters\n ----------\n field_name : str\n The field name of the information.\n\n Returns\n -------\n info : numpy array or None\n A numpy array with information from the Dataset.\n \"\"\"\n if self._handle is None:\n raise Exception(f\"Cannot get {field_name} before construct Dataset\")\n tmp_out_len = ctypes.c_int(0)\n out_type = ctypes.c_int(0)\n ret = ctypes.POINTER(ctypes.c_void_p)()\n _safe_call(\n _LIB.LGBM_DatasetGetField(\n self._handle,\n _c_str(field_name),\n ctypes.byref(tmp_out_len),\n ctypes.byref(ret),\n ctypes.byref(out_type),\n )\n )\n if out_type.value != _FIELD_TYPE_MAPPER[field_name]:\n raise TypeError(\"Return type error for get_field\")\n if tmp_out_len.value == 0:\n return None\n if out_type.value == _C_API_DTYPE_INT32:\n arr = _cint32_array_to_numpy(\n cptr=ctypes.cast(ret, ctypes.POINTER(ctypes.c_int32)),\n length=tmp_out_len.value,\n )\n elif out_type.value == _C_API_DTYPE_FLOAT32:\n arr = _cfloat32_array_to_numpy(\n cptr=ctypes.cast(ret, ctypes.POINTER(ctypes.c_float)),\n length=tmp_out_len.value,\n )\n elif out_type.value == _C_API_DTYPE_FLOAT64:\n arr = _cfloat64_array_to_numpy(\n cptr=ctypes.cast(ret, ctypes.POINTER(ctypes.c_double)),\n length=tmp_out_len.value,\n )\n else:\n raise TypeError(\"Unknown type\")\n if field_name == \"init_score\":\n num_data = self.num_data()\n num_classes = arr.size // num_data\n if num_classes > 1:\n arr = arr.reshape((num_data, num_classes), order=\"F\")\n return arr\n\n def set_categorical_feature(\n self,\n categorical_feature: _LGBM_CategoricalFeatureConfiguration,\n ) -> \"Dataset\":\n \"\"\"Set categorical features.\n\n Parameters\n ----------\n categorical_feature : list of str or int, or 'auto'\n Names or indices of categorical features.\n\n Returns\n -------\n self : Dataset\n Dataset with set categorical features.\n \"\"\"\n if self.categorical_feature == categorical_feature:\n return self\n if self.data is not None:\n if self.categorical_feature is None:\n self.categorical_feature = categorical_feature\n return self._free_handle()\n elif categorical_feature == \"auto\":\n return self\n else:\n if self.categorical_feature != \"auto\":\n _log_warning(\n \"categorical_feature in Dataset is overridden.\\n\"\n f\"New categorical_feature is {list(categorical_feature)}\"\n )\n self.categorical_feature = categorical_feature\n return self._free_handle()\n else:\n raise LightGBMError(\n \"Cannot set categorical feature after freed raw data, \"\n \"set free_raw_data=False when construct Dataset to avoid this.\"\n )\n\n def _set_predictor(\n self,\n predictor: Optional[_InnerPredictor],\n ) -> \"Dataset\":\n \"\"\"Set predictor for continued training.\n\n It is not recommended for user to call this function.\n Please use init_model argument in engine.train() or engine.cv() instead.\n \"\"\"\n if predictor is None and self._predictor is None:\n return self\n elif isinstance(predictor, _InnerPredictor) and isinstance(self._predictor, _InnerPredictor):\n if (predictor == self._predictor) and (\n predictor.current_iteration() == self._predictor.current_iteration()\n ):\n return self\n if self._handle is None:\n self._predictor = predictor\n elif self.data is not None:\n self._predictor = predictor\n self._set_init_score_by_predictor(\n predictor=self._predictor,\n data=self.data,\n used_indices=None,\n )\n elif self.used_indices is not None and self.reference is not None and self.reference.data is not None:\n self._predictor = predictor\n self._set_init_score_by_predictor(\n predictor=self._predictor,\n data=self.reference.data,\n used_indices=self.used_indices,\n )\n else:\n raise LightGBMError(\n \"Cannot set predictor after freed raw data, \"\n \"set free_raw_data=False when construct Dataset to avoid this.\"\n )\n return self\n\n def set_reference(self, reference: \"Dataset\") -> \"Dataset\":\n \"\"\"Set reference Dataset.\n\n Parameters\n ----------\n reference : Dataset\n Reference that is used as a template to construct the current Dataset.\n\n Returns\n -------\n self : Dataset\n Dataset with set reference.\n \"\"\"\n self.set_categorical_feature(reference.categorical_feature).set_feature_name(\n reference.feature_name\n )._set_predictor(reference._predictor)\n # we're done if self and reference share a common upstream reference\n if self.get_ref_chain().intersection(reference.get_ref_chain()):\n return self\n if self.data is not None:\n self.reference = reference\n return self._free_handle()\n else:\n raise LightGBMError(\n \"Cannot set reference after freed raw data, \"\n \"set free_raw_data=False when construct Dataset to avoid this.\"\n )\n\n def set_feature_name(self, feature_name: _LGBM_FeatureNameConfiguration) -> \"Dataset\":\n \"\"\"Set feature name.\n\n Parameters\n ----------\n feature_name : list of str\n Feature names.\n\n Returns\n -------\n self : Dataset\n Dataset with set feature name.\n \"\"\"\n if feature_name != \"auto\":\n self.feature_name = feature_name\n if self._handle is not None and feature_name is not None and feature_name != \"auto\":\n if len(feature_name) != self.num_feature():\n raise ValueError(\n f\"Length of feature_name({len(feature_name)}) and num_feature({self.num_feature()}) don't match\"\n )\n c_feature_name = [_c_str(name) for name in feature_name]\n _safe_call(\n _LIB.LGBM_DatasetSetFeatureNames(\n self._handle,\n _c_array(ctypes.c_char_p, c_feature_name),\n ctypes.c_int(len(feature_name)),\n )\n )\n return self\n\n def set_label(self, label: Optional[_LGBM_LabelType]) -> \"Dataset\":\n \"\"\"Set label of Dataset.\n\n Parameters\n ----------\n label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array, pyarrow ChunkedArray or None\n The label information to be set into Dataset.\n\n Returns\n -------\n self : Dataset\n Dataset with set label.\n \"\"\"\n self.label = label\n if self._handle is not None:\n if isinstance(label, pd_DataFrame):\n if len(label.columns) > 1:\n raise ValueError(\"DataFrame for label cannot have multiple columns\")\n label_array = np.ravel(_pandas_to_numpy(label, target_dtype=np.float32))\n elif _is_pyarrow_array(label):\n label_array = label\n else:\n label_array = _list_to_1d_numpy(data=label, dtype=np.float32, name=\"label\")\n self.set_field(\"label\", label_array)\n self.label = self.get_field(\"label\") # original values can be modified at cpp side\n return self\n\n def set_weight(\n self,\n weight: Optional[_LGBM_WeightType],\n ) -> \"Dataset\":\n \"\"\"Set weight of each instance.\n\n Parameters\n ----------\n weight : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None\n Weight to be set for each data point. Weights should be non-negative.\n\n Returns\n -------\n self : Dataset\n Dataset with set weight.\n \"\"\"\n # Check if the weight contains values other than one\n if weight is not None:\n if _is_pyarrow_array(weight):\n if pa_compute.all(pa_compute.equal(weight, 1)).as_py():\n weight = None\n elif np.all(weight == 1):\n weight = None\n self.weight = weight\n\n # Set field\n if self._handle is not None and weight is not None:\n if not _is_pyarrow_array(weight):\n weight = _list_to_1d_numpy(data=weight, dtype=np.float32, name=\"weight\")\n self.set_field(\"weight\", weight)\n self.weight = self.get_field(\"weight\") # original values can be modified at cpp side\n return self\n\n def set_init_score(\n self,\n init_score: Optional[_LGBM_InitScoreType],\n ) -> \"Dataset\":\n \"\"\"Set init score of Booster to start from.\n\n Parameters\n ----------\n init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow Array, pyarrow ChunkedArray, pyarrow Table (for multi-class task) or None\n Init score for Booster.\n\n Returns\n -------\n self : Dataset\n Dataset with set init score.\n \"\"\"\n self.init_score = init_score\n if self._handle is not None and init_score is not None:\n self.set_field(\"init_score\", init_score)\n self.init_score = self.get_field(\"init_score\") # original values can be modified at cpp side\n return self\n\n def set_group(\n self,\n group: Optional[_LGBM_GroupType],\n ) -> \"Dataset\":\n \"\"\"Set group size of Dataset (used for ranking).\n\n Parameters\n ----------\n group : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None\n Group/query data.\n Only used in the learning-to-rank task.\n sum(group) = n_samples.\n For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,\n where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.\n\n Returns\n -------\n self : Dataset\n Dataset with set group.\n \"\"\"\n self.group = group\n if self._handle is not None and group is not None:\n if not _is_pyarrow_array(group):\n group = _list_to_1d_numpy(data=group, dtype=np.int32, name=\"group\")\n self.set_field(\"group\", group)\n # original values can be modified at cpp side\n constructed_group = self.get_field(\"group\")\n if constructed_group is not None:\n self.group = np.diff(constructed_group)\n return self\n\n def set_position(\n self,\n position: Optional[_LGBM_PositionType],\n ) -> \"Dataset\":\n \"\"\"Set position of Dataset (used for ranking).\n\n Parameters\n ----------\n position : numpy 1-D array, pandas Series or None, optional (default=None)\n Position of items used in unbiased learning-to-rank task.\n\n Returns\n -------\n self : Dataset\n Dataset with set position.\n \"\"\"\n self.position = position\n if self._handle is not None and position is not None:\n position = _list_to_1d_numpy(data=position, dtype=np.int32, name=\"position\")\n self.set_field(\"position\", position)\n return self\n\n def get_feature_name(self) -> List[str]:\n \"\"\"Get the names of columns (features) in the Dataset.\n\n Returns\n -------\n feature_names : list of str\n The names of columns (features) in the Dataset.\n \"\"\"\n if self._handle is None:\n raise LightGBMError(\"Cannot get feature_name before construct dataset\")\n num_feature = self.num_feature()\n tmp_out_len = ctypes.c_int(0)\n reserved_string_buffer_size = 255\n required_string_buffer_size = ctypes.c_size_t(0)\n string_buffers = [ctypes.create_string_buffer(reserved_string_buffer_size) for _ in range(num_feature)]\n ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc]\n _safe_call(\n _LIB.LGBM_DatasetGetFeatureNames(\n self._handle,\n ctypes.c_int(num_feature),\n ctypes.byref(tmp_out_len),\n ctypes.c_size_t(reserved_string_buffer_size),\n ctypes.byref(required_string_buffer_size),\n ptr_string_buffers,\n )\n )\n if num_feature != tmp_out_len.value:\n raise ValueError(\"Length of feature names doesn't equal with num_feature\")\n actual_string_buffer_size = required_string_buffer_size.value\n # if buffer length is not long enough, reallocate buffers\n if reserved_string_buffer_size < actual_string_buffer_size:\n string_buffers = [ctypes.create_string_buffer(actual_string_buffer_size) for _ in range(num_feature)]\n ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc]\n _safe_call(\n _LIB.LGBM_DatasetGetFeatureNames(\n self._handle,\n ctypes.c_int(num_feature),\n ctypes.byref(tmp_out_len),\n ctypes.c_size_t(actual_string_buffer_size),\n ctypes.byref(required_string_buffer_size),\n ptr_string_buffers,\n )\n )\n return [string_buffers[i].value.decode(\"utf-8\") for i in range(num_feature)]\n\n def get_label(self) -> Optional[_LGBM_LabelType]:\n \"\"\"Get the label of the Dataset.\n\n Returns\n -------\n label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array, pyarrow ChunkedArray or None\n The label information from the Dataset.\n For a constructed ``Dataset``, this will only return a numpy array.\n \"\"\"\n if self.label is None:\n self.label = self.get_field(\"label\")\n return self.label\n\n def get_weight(self) -> Optional[_LGBM_WeightType]:\n \"\"\"Get the weight of the Dataset.\n\n Returns\n -------\n weight : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None\n Weight for each data point from the Dataset. Weights should be non-negative.\n For a constructed ``Dataset``, this will only return ``None`` or a numpy array.\n \"\"\"\n if self.weight is None:\n self.weight = self.get_field(\"weight\")\n return self.weight\n\n def get_init_score(self) -> Optional[_LGBM_InitScoreType]:\n \"\"\"Get the initial score of the Dataset.\n\n Returns\n -------\n init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow Array, pyarrow ChunkedArray, pyarrow Table (for multi-class task) or None\n Init score of Booster.\n For a constructed ``Dataset``, this will only return ``None`` or a numpy array.\n \"\"\"\n if self.init_score is None:\n self.init_score = self.get_field(\"init_score\")\n return self.init_score\n\n def get_data(self) -> Optional[_LGBM_TrainDataType]:\n \"\"\"Get the raw data of the Dataset.\n\n Returns\n -------\n data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array, pyarrow Table or None\n Raw data used in the Dataset construction.\n \"\"\"\n if self._handle is None:\n raise Exception(\"Cannot get data before construct Dataset\")\n if self._need_slice and self.used_indices is not None and self.reference is not None:\n self.data = self.reference.data\n if self.data is not None:\n if isinstance(self.data, (np.ndarray, scipy.sparse.spmatrix)):\n self.data = self.data[self.used_indices, :]\n elif isinstance(self.data, pd_DataFrame):\n self.data = self.data.iloc[self.used_indices].copy()\n elif isinstance(self.data, Sequence):\n self.data = self.data[self.used_indices]\n elif isinstance(self.data, pa_Table):\n self.data = self.data.take(self.used_indices)\n elif _is_list_of_sequences(self.data) and len(self.data) > 0:\n self.data = np.array(list(self._yield_row_from_seqlist(self.data, self.used_indices)))\n else:\n _log_warning(\n f\"Cannot subset {type(self.data).__name__} type of raw data.\\nReturning original raw data\"\n )\n self._need_slice = False\n if self.data is None:\n raise LightGBMError(\n \"Cannot call `get_data` after freed raw data, \"\n \"set free_raw_data=False when construct Dataset to avoid this.\"\n )\n return self.data\n\n def get_group(self) -> Optional[_LGBM_GroupType]:\n \"\"\"Get the group of the Dataset.\n\n Returns\n -------\n group : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None\n Group/query data.\n Only used in the learning-to-rank task.\n sum(group) = n_samples.\n For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,\n where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.\n For a constructed ``Dataset``, this will only return ``None`` or a numpy array.\n \"\"\"\n if self.group is None:\n self.group = self.get_field(\"group\")\n if self.group is not None:\n # group data from LightGBM is boundaries data, need to convert to group size\n self.group = np.diff(self.group)\n return self.group\n\n def get_position(self) -> Optional[_LGBM_PositionType]:\n \"\"\"Get the position of the Dataset.\n\n Returns\n -------\n position : numpy 1-D array, pandas Series or None\n Position of items used in unbiased learning-to-rank task.\n For a constructed ``Dataset``, this will only return ``None`` or a numpy array.\n \"\"\"\n if self.position is None:\n self.position = self.get_field(\"position\")\n return self.position\n\n def num_data(self) -> int:\n \"\"\"Get the number of rows in the Dataset.\n\n Returns\n -------\n number_of_rows : int\n The number of rows in the Dataset.\n \"\"\"\n if self._handle is not None:\n ret = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_DatasetGetNumData(\n self._handle,\n ctypes.byref(ret),\n )\n )\n return ret.value\n else:\n raise LightGBMError(\"Cannot get num_data before construct dataset\")\n\n def num_feature(self) -> int:\n \"\"\"Get the number of columns (features) in the Dataset.\n\n Returns\n -------\n number_of_columns : int\n The number of columns (features) in the Dataset.\n \"\"\"\n if self._handle is not None:\n ret = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_DatasetGetNumFeature(\n self._handle,\n ctypes.byref(ret),\n )\n )\n return ret.value\n else:\n raise LightGBMError(\"Cannot get num_feature before construct dataset\")\n\n def feature_num_bin(self, feature: Union[int, str]) -> int:\n \"\"\"Get the number of bins for a feature.\n\n .. versionadded:: 4.0.0\n\n Parameters\n ----------\n feature : int or str\n Index or name of the feature.\n\n Returns\n -------\n number_of_bins : int\n The number of constructed bins for the feature in the Dataset.\n \"\"\"\n if self._handle is not None:\n if isinstance(feature, str):\n feature_index = self.feature_name.index(feature)\n else:\n feature_index = feature\n ret = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_DatasetGetFeatureNumBin(\n self._handle,\n ctypes.c_int(feature_index),\n ctypes.byref(ret),\n )\n )\n return ret.value\n else:\n raise LightGBMError(\"Cannot get feature_num_bin before construct dataset\")\n\n def get_ref_chain(self, ref_limit: int = 100) -> Set[\"Dataset\"]:\n \"\"\"Get a chain of Dataset objects.\n\n Starts with r, then goes to r.reference (if exists),\n then to r.reference.reference, etc.\n until we hit ``ref_limit`` or a reference loop.\n\n Parameters\n ----------\n ref_limit : int, optional (default=100)\n The limit number of references.\n\n Returns\n -------\n ref_chain : set of Dataset\n Chain of references of the Datasets.\n \"\"\"\n head = self\n ref_chain: Set[Dataset] = set()\n while len(ref_chain) < ref_limit:\n if isinstance(head, Dataset):\n ref_chain.add(head)\n if (head.reference is not None) and (head.reference not in ref_chain):\n head = head.reference\n else:\n break\n else:\n break\n return ref_chain\n\n def add_features_from(self, other: \"Dataset\") -> \"Dataset\":\n \"\"\"Add features from other Dataset to the current Dataset.\n\n Both Datasets must be constructed before calling this method.\n\n Parameters\n ----------\n other : Dataset\n The Dataset to take features from.\n\n Returns\n -------\n self : Dataset\n Dataset with the new features added.\n \"\"\"\n if self._handle is None or other._handle is None:\n raise ValueError(\"Both source and target Datasets must be constructed before adding features\")\n _safe_call(\n _LIB.LGBM_DatasetAddFeaturesFrom(\n self._handle,\n other._handle,\n )\n )\n was_none = self.data is None\n old_self_data_type = type(self.data).__name__\n if other.data is None:\n self.data = None\n elif self.data is not None:\n if isinstance(self.data, np.ndarray):\n if isinstance(other.data, np.ndarray):\n self.data = np.hstack((self.data, other.data))\n elif isinstance(other.data, scipy.sparse.spmatrix):\n self.data = np.hstack((self.data, other.data.toarray()))\n elif isinstance(other.data, pd_DataFrame):\n self.data = np.hstack((self.data, other.data.values))\n else:\n self.data = None\n elif isinstance(self.data, scipy.sparse.spmatrix):\n sparse_format = self.data.getformat()\n if isinstance(other.data, (np.ndarray, scipy.sparse.spmatrix)):\n self.data = scipy.sparse.hstack((self.data, other.data), format=sparse_format)\n elif isinstance(other.data, pd_DataFrame):\n self.data = scipy.sparse.hstack((self.data, other.data.values), format=sparse_format)\n else:\n self.data = None\n elif isinstance(self.data, pd_DataFrame):\n if not PANDAS_INSTALLED:\n raise LightGBMError(\n \"Cannot add features to DataFrame type of raw data \"\n \"without pandas installed. \"\n \"Install pandas and restart your session.\"\n )\n if isinstance(other.data, np.ndarray):\n self.data = concat((self.data, pd_DataFrame(other.data)), axis=1, ignore_index=True)\n elif isinstance(other.data, scipy.sparse.spmatrix):\n self.data = concat((self.data, pd_DataFrame(other.data.toarray())), axis=1, ignore_index=True)\n elif isinstance(other.data, pd_DataFrame):\n self.data = concat((self.data, other.data), axis=1, ignore_index=True)\n else:\n self.data = None\n else:\n self.data = None\n if self.data is None:\n err_msg = (\n f\"Cannot add features from {type(other.data).__name__} type of raw data to \"\n f\"{old_self_data_type} type of raw data.\\n\"\n )\n err_msg += (\n \"Set free_raw_data=False when construct Dataset to avoid this\" if was_none else \"Freeing raw data\"\n )\n _log_warning(err_msg)\n self.feature_name = self.get_feature_name()\n _log_warning(\n \"Resetting categorical features.\\n\"\n \"You can set new categorical features via ``set_categorical_feature`` method\"\n )\n self.categorical_feature = \"auto\"\n self.pandas_categorical = None\n return self\n\n def _dump_text(self, filename: Union[str, Path]) -> \"Dataset\":\n \"\"\"Save Dataset to a text file.\n\n This format cannot be loaded back in by LightGBM, but is useful for debugging purposes.\n\n Parameters\n ----------\n filename : str or pathlib.Path\n Name of the output file.\n\n Returns\n -------\n self : Dataset\n Returns self.\n \"\"\"\n _safe_call(\n _LIB.LGBM_DatasetDumpText(\n self.construct()._handle,\n _c_str(str(filename)),\n )\n )\n return self\n\n\n_LGBM_CustomObjectiveFunction = Callable[\n [np.ndarray, Dataset],\n Tuple[np.ndarray, np.ndarray],\n]\n_LGBM_CustomEvalFunction = Union[\n Callable[\n [np.ndarray, Dataset],\n _LGBM_EvalFunctionResultType,\n ],\n Callable[\n [np.ndarray, Dataset],\n List[_LGBM_EvalFunctionResultType],\n ],\n]\n\n\nclass Booster:\n \"\"\"Booster in LightGBM.\"\"\"\n\n def __init__(\n self,\n params: Optional[Dict[str, Any]] = None,\n train_set: Optional[Dataset] = None,\n model_file: Optional[Union[str, Path]] = None,\n model_str: Optional[str] = None,\n ):\n \"\"\"Initialize the Booster.\n\n Parameters\n ----------\n params : dict or None, optional (default=None)\n Parameters for Booster.\n train_set : Dataset or None, optional (default=None)\n Training dataset.\n model_file : str, pathlib.Path or None, optional (default=None)\n Path to the model file.\n model_str : str or None, optional (default=None)\n Model will be loaded from this string.\n \"\"\"\n self._handle = ctypes.c_void_p()\n self._network = False\n self.__need_reload_eval_info = True\n self._train_data_name = \"training\"\n self.__set_objective_to_none = False\n self.best_iteration = -1\n self.best_score: _LGBM_BoosterBestScoreType = {}\n params = {} if params is None else deepcopy(params)\n if train_set is not None:\n # Training task\n if not isinstance(train_set, Dataset):\n raise TypeError(f\"Training data should be Dataset instance, met {type(train_set).__name__}\")\n params = _choose_param_value(\n main_param_name=\"machines\",\n params=params,\n default_value=None,\n )\n # if \"machines\" is given, assume user wants to do distributed learning, and set up network\n if params[\"machines\"] is None:\n params.pop(\"machines\", None)\n else:\n machines = params[\"machines\"]\n if isinstance(machines, str):\n num_machines_from_machine_list = len(machines.split(\",\"))\n elif isinstance(machines, (list, set)):\n num_machines_from_machine_list = len(machines)\n machines = \",\".join(machines)\n else:\n raise ValueError(\"Invalid machines in params.\")\n\n params = _choose_param_value(\n main_param_name=\"num_machines\",\n params=params,\n default_value=num_machines_from_machine_list,\n )\n params = _choose_param_value(\n main_param_name=\"local_listen_port\",\n params=params,\n default_value=12400,\n )\n self.set_network(\n machines=machines,\n local_listen_port=params[\"local_listen_port\"],\n listen_time_out=params.get(\"time_out\", 120),\n num_machines=params[\"num_machines\"],\n )\n # construct booster object\n train_set.construct()\n # copy the parameters from train_set\n params.update(train_set.get_params())\n params_str = _param_dict_to_str(params)\n _safe_call(\n _LIB.LGBM_BoosterCreate(\n train_set._handle,\n _c_str(params_str),\n ctypes.byref(self._handle),\n )\n )\n # save reference to data\n self.train_set = train_set\n self.valid_sets: List[Dataset] = []\n self.name_valid_sets: List[str] = []\n self.__num_dataset = 1\n self.__init_predictor = train_set._predictor\n if self.__init_predictor is not None:\n _safe_call(\n _LIB.LGBM_BoosterMerge(\n self._handle,\n self.__init_predictor._handle,\n )\n )\n out_num_class = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterGetNumClasses(\n self._handle,\n ctypes.byref(out_num_class),\n )\n )\n self.__num_class = out_num_class.value\n # buffer for inner predict\n self.__inner_predict_buffer: List[Optional[np.ndarray]] = [None]\n self.__is_predicted_cur_iter = [False]\n self.__get_eval_info()\n self.pandas_categorical = train_set.pandas_categorical\n self.train_set_version = train_set.version\n elif model_file is not None:\n # Prediction task\n out_num_iterations = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterCreateFromModelfile(\n _c_str(str(model_file)),\n ctypes.byref(out_num_iterations),\n ctypes.byref(self._handle),\n )\n )\n out_num_class = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterGetNumClasses(\n self._handle,\n ctypes.byref(out_num_class),\n )\n )\n self.__num_class = out_num_class.value\n self.pandas_categorical = _load_pandas_categorical(file_name=model_file)\n if params:\n _log_warning(\"Ignoring params argument, using parameters from model file.\")\n params = self._get_loaded_param()\n elif model_str is not None:\n self.model_from_string(model_str)\n if params:\n _log_warning(\"Ignoring params argument, using parameters from model string.\")\n params = self._get_loaded_param()\n else:\n raise TypeError(\n \"Need at least one training dataset or model file or model string to create Booster instance\"\n )\n self.params = params\n\n def __del__(self) -> None:\n try:\n if self._network:\n self.free_network()\n except AttributeError:\n pass\n try:\n if self._handle is not None:\n _safe_call(_LIB.LGBM_BoosterFree(self._handle))\n except AttributeError:\n pass\n\n def __copy__(self) -> \"Booster\":\n return self.__deepcopy__(None)\n\n def __deepcopy__(self, *args: Any, **kwargs: Any) -> \"Booster\":\n model_str = self.model_to_string(num_iteration=-1)\n return Booster(model_str=model_str)\n\n def __getstate__(self) -> Dict[str, Any]:\n this = self.__dict__.copy()\n handle = this[\"_handle\"]\n this.pop(\"train_set\", None)\n this.pop(\"valid_sets\", None)\n if handle is not None:\n this[\"_handle\"] = self.model_to_string(num_iteration=-1)\n return this\n\n def __setstate__(self, state: Dict[str, Any]) -> None:\n model_str = state.get(\"_handle\", state.get(\"handle\", None))\n if model_str is not None:\n handle = ctypes.c_void_p()\n out_num_iterations = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterLoadModelFromString(\n _c_str(model_str),\n ctypes.byref(out_num_iterations),\n ctypes.byref(handle),\n )\n )\n state[\"_handle\"] = handle\n self.__dict__.update(state)\n\n def _get_loaded_param(self) -> Dict[str, Any]:\n buffer_len = 1 << 20\n tmp_out_len = ctypes.c_int64(0)\n string_buffer = ctypes.create_string_buffer(buffer_len)\n ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer))\n _safe_call(\n _LIB.LGBM_BoosterGetLoadedParam(\n self._handle,\n ctypes.c_int64(buffer_len),\n ctypes.byref(tmp_out_len),\n ptr_string_buffer,\n )\n )\n actual_len = tmp_out_len.value\n # if buffer length is not long enough, re-allocate a buffer\n if actual_len > buffer_len:\n string_buffer = ctypes.create_string_buffer(actual_len)\n ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer))\n _safe_call(\n _LIB.LGBM_BoosterGetLoadedParam(\n self._handle,\n ctypes.c_int64(actual_len),\n ctypes.byref(tmp_out_len),\n ptr_string_buffer,\n )\n )\n return json.loads(string_buffer.value.decode(\"utf-8\"))\n\n def free_dataset(self) -> \"Booster\":\n \"\"\"Free Booster's Datasets.\n\n Returns\n -------\n self : Booster\n Booster without Datasets.\n \"\"\"\n self.__dict__.pop(\"train_set\", None)\n self.__dict__.pop(\"valid_sets\", None)\n self.__num_dataset = 0\n return self\n\n def _free_buffer(self) -> \"Booster\":\n self.__inner_predict_buffer = []\n self.__is_predicted_cur_iter = []\n return self\n\n def set_network(\n self,\n machines: Union[List[str], Set[str], str],\n local_listen_port: int = 12400,\n listen_time_out: int = 120,\n num_machines: int = 1,\n ) -> \"Booster\":\n \"\"\"Set the network configuration.\n\n Parameters\n ----------\n machines : list, set or str\n Names of machines.\n local_listen_port : int, optional (default=12400)\n TCP listen port for local machines.\n listen_time_out : int, optional (default=120)\n Socket time-out in minutes.\n num_machines : int, optional (default=1)\n The number of machines for distributed learning application.\n\n Returns\n -------\n self : Booster\n Booster with set network.\n \"\"\"\n if isinstance(machines, (list, set)):\n machines = \",\".join(machines)\n _safe_call(\n _LIB.LGBM_NetworkInit(\n _c_str(machines),\n ctypes.c_int(local_listen_port),\n ctypes.c_int(listen_time_out),\n ctypes.c_int(num_machines),\n )\n )\n self._network = True\n return self\n\n def free_network(self) -> \"Booster\":\n \"\"\"Free Booster's network.\n\n Returns\n -------\n self : Booster\n Booster with freed network.\n \"\"\"\n _safe_call(_LIB.LGBM_NetworkFree())\n self._network = False\n return self\n\n def trees_to_dataframe(self) -> pd_DataFrame:\n \"\"\"Parse the fitted model and return in an easy-to-read pandas DataFrame.\n\n The returned DataFrame has the following columns.\n\n - ``tree_index`` : int64, which tree a node belongs to. 0-based, so a value of ``6``, for example, means \"this node is in the 7th tree\".\n - ``node_depth`` : int64, how far a node is from the root of the tree. The root node has a value of ``1``, its direct children are ``2``, etc.\n - ``node_index`` : str, unique identifier for a node.\n - ``left_child`` : str, ``node_index`` of the child node to the left of a split. ``None`` for leaf nodes.\n - ``right_child`` : str, ``node_index`` of the child node to the right of a split. ``None`` for leaf nodes.\n - ``parent_index`` : str, ``node_index`` of this node's parent. ``None`` for the root node.\n - ``split_feature`` : str, name of the feature used for splitting. ``None`` for leaf nodes.\n - ``split_gain`` : float64, gain from adding this split to the tree. ``NaN`` for leaf nodes.\n - ``threshold`` : float64, value of the feature used to decide which side of the split a record will go down. ``NaN`` for leaf nodes.\n - ``decision_type`` : str, logical operator describing how to compare a value to ``threshold``.\n For example, ``split_feature = \"Column_10\", threshold = 15, decision_type = \"<=\"`` means that\n records where ``Column_10 <= 15`` follow the left side of the split, otherwise follows the right side of the split. ``None`` for leaf nodes.\n - ``missing_direction`` : str, split direction that missing values should go to. ``None`` for leaf nodes.\n - ``missing_type`` : str, describes what types of values are treated as missing.\n - ``value`` : float64, predicted value for this leaf node, multiplied by the learning rate.\n - ``weight`` : float64 or int64, sum of Hessian (second-order derivative of objective), summed over observations that fall in this node.\n - ``count`` : int64, number of records in the training data that fall into this node.\n\n Returns\n -------\n result : pandas DataFrame\n Returns a pandas DataFrame of the parsed model.\n \"\"\"\n if not PANDAS_INSTALLED:\n raise LightGBMError(\n \"This method cannot be run without pandas installed. \"\n \"You must install pandas and restart your session to use this method.\"\n )\n\n if self.num_trees() == 0:\n raise LightGBMError(\"There are no trees in this Booster and thus nothing to parse\")\n\n def _is_split_node(tree: Dict[str, Any]) -> bool:\n return \"split_index\" in tree.keys()\n\n def create_node_record(\n tree: Dict[str, Any],\n node_depth: int = 1,\n tree_index: Optional[int] = None,\n feature_names: Optional[List[str]] = None,\n parent_node: Optional[str] = None,\n ) -> Dict[str, Any]:\n def _get_node_index(\n tree: Dict[str, Any],\n tree_index: Optional[int],\n ) -> str:\n tree_num = f\"{tree_index}-\" if tree_index is not None else \"\"\n is_split = _is_split_node(tree)\n node_type = \"S\" if is_split else \"L\"\n # if a single node tree it won't have `leaf_index` so return 0\n node_num = tree.get(\"split_index\" if is_split else \"leaf_index\", 0)\n return f\"{tree_num}{node_type}{node_num}\"\n\n def _get_split_feature(\n *,\n tree: Dict[str, Any],\n feature_names: Optional[List[str]],\n ) -> Optional[str]:\n if _is_split_node(tree):\n if feature_names is not None:\n feature_name = feature_names[tree[\"split_feature\"]]\n else:\n feature_name = tree[\"split_feature\"]\n else:\n feature_name = None\n return feature_name\n\n def _is_single_node_tree(tree: Dict[str, Any]) -> bool:\n return set(tree.keys()) == {\"leaf_value\", \"leaf_count\"}\n\n # Create the node record, and populate universal data members\n node: Dict[str, Union[int, str, None]] = OrderedDict()\n node[\"tree_index\"] = tree_index\n node[\"node_depth\"] = node_depth\n node[\"node_index\"] = _get_node_index(tree, tree_index)\n node[\"left_child\"] = None\n node[\"right_child\"] = None\n node[\"parent_index\"] = parent_node\n node[\"split_feature\"] = _get_split_feature(tree=tree, feature_names=feature_names)\n node[\"split_gain\"] = None\n node[\"threshold\"] = None\n node[\"decision_type\"] = None\n node[\"missing_direction\"] = None\n node[\"missing_type\"] = None\n node[\"value\"] = None\n node[\"weight\"] = None\n node[\"count\"] = None\n\n # Update values to reflect node type (leaf or split)\n if _is_split_node(tree):\n node[\"left_child\"] = _get_node_index(tree[\"left_child\"], tree_index)\n node[\"right_child\"] = _get_node_index(tree[\"right_child\"], tree_index)\n node[\"split_gain\"] = tree[\"split_gain\"]\n node[\"threshold\"] = tree[\"threshold\"]\n node[\"decision_type\"] = tree[\"decision_type\"]\n node[\"missing_direction\"] = \"left\" if tree[\"default_left\"] else \"right\"\n node[\"missing_type\"] = tree[\"missing_type\"]\n node[\"value\"] = tree[\"internal_value\"]\n node[\"weight\"] = tree[\"internal_weight\"]\n node[\"count\"] = tree[\"internal_count\"]\n else:\n node[\"value\"] = tree[\"leaf_value\"]\n if not _is_single_node_tree(tree):\n node[\"weight\"] = tree[\"leaf_weight\"]\n node[\"count\"] = tree[\"leaf_count\"]\n\n return node\n\n def tree_dict_to_node_list(\n tree: Dict[str, Any],\n node_depth: int = 1,\n tree_index: Optional[int] = None,\n feature_names: Optional[List[str]] = None,\n parent_node: Optional[str] = None,\n ) -> List[Dict[str, Any]]:\n node = create_node_record(\n tree=tree,\n node_depth=node_depth,\n tree_index=tree_index,\n feature_names=feature_names,\n parent_node=parent_node,\n )\n\n res = [node]\n\n if _is_split_node(tree):\n # traverse the next level of the tree\n children = [\"left_child\", \"right_child\"]\n for child in children:\n subtree_list = tree_dict_to_node_list(\n tree=tree[child],\n node_depth=node_depth + 1,\n tree_index=tree_index,\n feature_names=feature_names,\n parent_node=node[\"node_index\"],\n )\n # In tree format, \"subtree_list\" is a list of node records (dicts),\n # and we add node to the list.\n res.extend(subtree_list)\n return res\n\n model_dict = self.dump_model()\n feature_names = model_dict[\"feature_names\"]\n model_list = []\n for tree in model_dict[\"tree_info\"]:\n model_list.extend(\n tree_dict_to_node_list(\n tree=tree[\"tree_structure\"], tree_index=tree[\"tree_index\"], feature_names=feature_names\n )\n )\n\n return pd_DataFrame(model_list, columns=model_list[0].keys())\n\n def set_train_data_name(self, name: str) -> \"Booster\":\n \"\"\"Set the name to the training Dataset.\n\n Parameters\n ----------\n name : str\n Name for the training Dataset.\n\n Returns\n -------\n self : Booster\n Booster with set training Dataset name.\n \"\"\"\n self._train_data_name = name\n return self\n\n def add_valid(self, data: Dataset, name: str) -> \"Booster\":\n \"\"\"Add validation data.\n\n Parameters\n ----------\n data : Dataset\n Validation data.\n name : str\n Name of validation data.\n\n Returns\n -------\n self : Booster\n Booster with set validation data.\n \"\"\"\n if not isinstance(data, Dataset):\n raise TypeError(f\"Validation data should be Dataset instance, met {type(data).__name__}\")\n if data._predictor is not self.__init_predictor:\n raise LightGBMError(\"Add validation data failed, you should use same predictor for these data\")\n _safe_call(\n _LIB.LGBM_BoosterAddValidData(\n self._handle,\n data.construct()._handle,\n )\n )\n self.valid_sets.append(data)\n self.name_valid_sets.append(name)\n self.__num_dataset += 1\n self.__inner_predict_buffer.append(None)\n self.__is_predicted_cur_iter.append(False)\n return self\n\n def reset_parameter(self, params: Dict[str, Any]) -> \"Booster\":\n \"\"\"Reset parameters of Booster.\n\n Parameters\n ----------\n params : dict\n New parameters for Booster.\n\n Returns\n -------\n self : Booster\n Booster with new parameters.\n \"\"\"\n params_str = _param_dict_to_str(params)\n if params_str:\n _safe_call(\n _LIB.LGBM_BoosterResetParameter(\n self._handle,\n _c_str(params_str),\n )\n )\n self.params.update(params)\n return self\n\n def update(\n self,\n train_set: Optional[Dataset] = None,\n fobj: Optional[_LGBM_CustomObjectiveFunction] = None,\n ) -> bool:\n \"\"\"Update Booster for one iteration.\n\n Parameters\n ----------\n train_set : Dataset or None, optional (default=None)\n Training data.\n If None, last training data is used.\n fobj : callable or None, optional (default=None)\n Customized objective function.\n Should accept two parameters: preds, train_data,\n and return (grad, hess).\n\n preds : numpy 1-D array or numpy 2-D array (for multi-class task)\n The predicted values.\n Predicted values are returned before any transformation,\n e.g. they are raw margin instead of probability of positive class for binary task.\n train_data : Dataset\n The training dataset.\n grad : numpy 1-D array or numpy 2-D array (for multi-class task)\n The value of the first order derivative (gradient) of the loss\n with respect to the elements of preds for each sample point.\n hess : numpy 1-D array or numpy 2-D array (for multi-class task)\n The value of the second order derivative (Hessian) of the loss\n with respect to the elements of preds for each sample point.\n\n For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes],\n and grad and hess should be returned in the same format.\n\n Returns\n -------\n produced_empty_tree : bool\n ``True`` if the tree(s) produced by this iteration did not have any splits.\n This usually means that training is \"finished\" (calling ``update()`` again\n will not change the model's predictions). However, that is not always the\n case. For example, if you have added any randomness (like column sampling by\n setting ``feature_fraction_bynode < 1.0``), it is possible that another call\n to ``update()`` would produce a non-empty tree.\n \"\"\"\n # need reset training data\n if train_set is None and self.train_set_version != self.train_set.version:\n train_set = self.train_set\n is_the_same_train_set = False\n else:\n is_the_same_train_set = train_set is self.train_set and self.train_set_version == train_set.version\n if train_set is not None and not is_the_same_train_set:\n if not isinstance(train_set, Dataset):\n raise TypeError(f\"Training data should be Dataset instance, met {type(train_set).__name__}\")\n if train_set._predictor is not self.__init_predictor:\n raise LightGBMError(\"Replace training data failed, you should use same predictor for these data\")\n self.train_set = train_set\n _safe_call(\n _LIB.LGBM_BoosterResetTrainingData(\n self._handle,\n self.train_set.construct()._handle,\n )\n )\n self.__inner_predict_buffer[0] = None\n self.train_set_version = self.train_set.version\n produced_empty_tree = ctypes.c_int(0)\n if fobj is None:\n if self.__set_objective_to_none:\n raise LightGBMError(\"Cannot update due to null objective function.\")\n _safe_call(\n _LIB.LGBM_BoosterUpdateOneIter(\n self._handle,\n ctypes.byref(produced_empty_tree),\n )\n )\n self.__is_predicted_cur_iter = [False for _ in range(self.__num_dataset)]\n return produced_empty_tree.value == 1\n else:\n if not self.__set_objective_to_none:\n self.reset_parameter({\"objective\": \"none\"}).__set_objective_to_none = True\n grad, hess = fobj(self.__inner_predict(data_idx=0), self.train_set)\n return self.__boost(grad=grad, hess=hess)\n\n def __boost(\n self,\n *,\n grad: np.ndarray,\n hess: np.ndarray,\n ) -> bool:\n \"\"\"Boost Booster for one iteration with customized gradient statistics.\n\n .. note::\n\n Score is returned before any transformation,\n e.g. it is raw margin instead of probability of positive class for binary task.\n For multi-class task, score are numpy 2-D array of shape = [n_samples, n_classes],\n and grad and hess should be returned in the same format.\n\n Parameters\n ----------\n grad : numpy 1-D array or numpy 2-D array (for multi-class task)\n The value of the first order derivative (gradient) of the loss\n with respect to the elements of score for each sample point.\n hess : numpy 1-D array or numpy 2-D array (for multi-class task)\n The value of the second order derivative (Hessian) of the loss\n with respect to the elements of score for each sample point.\n\n Returns\n -------\n produced_empty_tree : bool\n ``True`` if the tree(s) produced by this iteration did not have any splits.\n This usually means that training is \"finished\" (calling ``__boost()`` again\n will not change the model's predictions). However, that is not always the\n case. For example, if you have added any randomness (like column sampling by\n setting ``feature_fraction_bynode < 1.0``), it is possible that another call\n to ``__boost()`` would produce a non-empty tree.\n \"\"\"\n if self.__num_class > 1:\n grad = grad.ravel(order=\"F\")\n hess = hess.ravel(order=\"F\")\n grad = _list_to_1d_numpy(data=grad, dtype=np.float32, name=\"gradient\")\n hess = _list_to_1d_numpy(data=hess, dtype=np.float32, name=\"hessian\")\n assert grad.flags.c_contiguous\n assert hess.flags.c_contiguous\n if len(grad) != len(hess):\n raise ValueError(f\"Lengths of gradient ({len(grad)}) and Hessian ({len(hess)}) don't match\")\n num_train_data = self.train_set.num_data()\n if len(grad) != num_train_data * self.__num_class:\n raise ValueError(\n f\"Lengths of gradient ({len(grad)}) and Hessian ({len(hess)}) \"\n f\"don't match training data length ({num_train_data}) * \"\n f\"number of models per one iteration ({self.__num_class})\"\n )\n produced_empty_tree = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterUpdateOneIterCustom(\n self._handle,\n grad.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),\n hess.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),\n ctypes.byref(produced_empty_tree),\n )\n )\n self.__is_predicted_cur_iter = [False for _ in range(self.__num_dataset)]\n return produced_empty_tree.value == 1\n\n def rollback_one_iter(self) -> \"Booster\":\n \"\"\"Rollback one iteration.\n\n Returns\n -------\n self : Booster\n Booster with rolled back one iteration.\n \"\"\"\n _safe_call(_LIB.LGBM_BoosterRollbackOneIter(self._handle))\n self.__is_predicted_cur_iter = [False for _ in range(self.__num_dataset)]\n return self\n\n def current_iteration(self) -> int:\n \"\"\"Get the index of the current iteration.\n\n Returns\n -------\n cur_iter : int\n The index of the current iteration.\n \"\"\"\n out_cur_iter = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterGetCurrentIteration(\n self._handle,\n ctypes.byref(out_cur_iter),\n )\n )\n return out_cur_iter.value\n\n def num_model_per_iteration(self) -> int:\n \"\"\"Get number of models per iteration.\n\n Returns\n -------\n model_per_iter : int\n The number of models per iteration.\n \"\"\"\n model_per_iter = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterNumModelPerIteration(\n self._handle,\n ctypes.byref(model_per_iter),\n )\n )\n return model_per_iter.value\n\n def num_trees(self) -> int:\n \"\"\"Get number of weak sub-models.\n\n Returns\n -------\n num_trees : int\n The number of weak sub-models.\n \"\"\"\n num_trees = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterNumberOfTotalModel(\n self._handle,\n ctypes.byref(num_trees),\n )\n )\n return num_trees.value\n\n def upper_bound(self) -> float:\n \"\"\"Get upper bound value of a model.\n\n Returns\n -------\n upper_bound : float\n Upper bound value of the model.\n \"\"\"\n ret = ctypes.c_double(0)\n _safe_call(\n _LIB.LGBM_BoosterGetUpperBoundValue(\n self._handle,\n ctypes.byref(ret),\n )\n )\n return ret.value\n\n def lower_bound(self) -> float:\n \"\"\"Get lower bound value of a model.\n\n Returns\n -------\n lower_bound : float\n Lower bound value of the model.\n \"\"\"\n ret = ctypes.c_double(0)\n _safe_call(\n _LIB.LGBM_BoosterGetLowerBoundValue(\n self._handle,\n ctypes.byref(ret),\n )\n )\n return ret.value\n\n def eval(\n self,\n data: Dataset,\n name: str,\n feval: Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]] = None,\n ) -> List[_LGBM_BoosterEvalMethodResultType]:\n \"\"\"Evaluate for data.\n\n Parameters\n ----------\n data : Dataset\n Data for the evaluating.\n name : str\n Name of the data.\n feval : callable, list of callable, or None, optional (default=None)\n Customized evaluation function.\n Each evaluation function should accept two parameters: preds, eval_data,\n and return (eval_name, eval_result, is_higher_better) or list of such tuples.\n\n preds : numpy 1-D array or numpy 2-D array (for multi-class task)\n The predicted values.\n For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes].\n If custom objective function is used, predicted values are returned before any transformation,\n e.g. they are raw margin instead of probability of positive class for binary task in this case.\n eval_data : Dataset\n A ``Dataset`` to evaluate.\n eval_name : str\n The name of evaluation function (without whitespace).\n eval_result : float\n The eval result.\n is_higher_better : bool\n Is eval result higher better, e.g. AUC is ``is_higher_better``.\n\n Returns\n -------\n result : list\n List with (dataset_name, eval_name, eval_result, is_higher_better) tuples.\n \"\"\"\n if not isinstance(data, Dataset):\n raise TypeError(\"Can only eval for Dataset instance\")\n data_idx = -1\n if data is self.train_set:\n data_idx = 0\n else:\n for i in range(len(self.valid_sets)):\n if data is self.valid_sets[i]:\n data_idx = i + 1\n break\n # need to push new valid data\n if data_idx == -1:\n self.add_valid(data, name)\n data_idx = self.__num_dataset - 1\n\n return self.__inner_eval(data_name=name, data_idx=data_idx, feval=feval)\n\n def eval_train(\n self,\n feval: Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]] = None,\n ) -> List[_LGBM_BoosterEvalMethodResultType]:\n \"\"\"Evaluate for training data.\n\n Parameters\n ----------\n feval : callable, list of callable, or None, optional (default=None)\n Customized evaluation function.\n Each evaluation function should accept two parameters: preds, eval_data,\n and return (eval_name, eval_result, is_higher_better) or list of such tuples.\n\n preds : numpy 1-D array or numpy 2-D array (for multi-class task)\n The predicted values.\n For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes].\n If custom objective function is used, predicted values are returned before any transformation,\n e.g. they are raw margin instead of probability of positive class for binary task in this case.\n eval_data : Dataset\n The training dataset.\n eval_name : str\n The name of evaluation function (without whitespace).\n eval_result : float\n The eval result.\n is_higher_better : bool\n Is eval result higher better, e.g. AUC is ``is_higher_better``.\n\n Returns\n -------\n result : list\n List with (train_dataset_name, eval_name, eval_result, is_higher_better) tuples.\n \"\"\"\n return self.__inner_eval(data_name=self._train_data_name, data_idx=0, feval=feval)\n\n def eval_valid(\n self,\n feval: Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]] = None,\n ) -> List[_LGBM_BoosterEvalMethodResultType]:\n \"\"\"Evaluate for validation data.\n\n Parameters\n ----------\n feval : callable, list of callable, or None, optional (default=None)\n Customized evaluation function.\n Each evaluation function should accept two parameters: preds, eval_data,\n and return (eval_name, eval_result, is_higher_better) or list of such tuples.\n\n preds : numpy 1-D array or numpy 2-D array (for multi-class task)\n The predicted values.\n For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes].\n If custom objective function is used, predicted values are returned before any transformation,\n e.g. they are raw margin instead of probability of positive class for binary task in this case.\n eval_data : Dataset\n The validation dataset.\n eval_name : str\n The name of evaluation function (without whitespace).\n eval_result : float\n The eval result.\n is_higher_better : bool\n Is eval result higher better, e.g. AUC is ``is_higher_better``.\n\n Returns\n -------\n result : list\n List with (validation_dataset_name, eval_name, eval_result, is_higher_better) tuples.\n \"\"\"\n return [\n item\n for i in range(1, self.__num_dataset)\n for item in self.__inner_eval(data_name=self.name_valid_sets[i - 1], data_idx=i, feval=feval)\n ]\n\n def save_model(\n self,\n filename: Union[str, Path],\n num_iteration: Optional[int] = None,\n start_iteration: int = 0,\n importance_type: str = \"split\",\n ) -> \"Booster\":\n \"\"\"Save Booster to file.\n\n Parameters\n ----------\n filename : str or pathlib.Path\n Filename to save Booster.\n num_iteration : int or None, optional (default=None)\n Index of the iteration that should be saved.\n If None, if the best iteration exists, it is saved; otherwise, all iterations are saved.\n If <= 0, all iterations are saved.\n start_iteration : int, optional (default=0)\n Start index of the iteration that should be saved.\n importance_type : str, optional (default=\"split\")\n What type of feature importance should be saved.\n If \"split\", result contains numbers of times the feature is used in a model.\n If \"gain\", result contains total gains of splits which use the feature.\n\n Returns\n -------\n self : Booster\n Returns self.\n \"\"\"\n if num_iteration is None:\n num_iteration = self.best_iteration\n importance_type_int = _FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type]\n _safe_call(\n _LIB.LGBM_BoosterSaveModel(\n self._handle,\n ctypes.c_int(start_iteration),\n ctypes.c_int(num_iteration),\n ctypes.c_int(importance_type_int),\n _c_str(str(filename)),\n )\n )\n _dump_pandas_categorical(self.pandas_categorical, filename)\n return self\n\n def shuffle_models(\n self,\n start_iteration: int = 0,\n end_iteration: int = -1,\n ) -> \"Booster\":\n \"\"\"Shuffle models.\n\n Parameters\n ----------\n start_iteration : int, optional (default=0)\n The first iteration that will be shuffled.\n end_iteration : int, optional (default=-1)\n The last iteration that will be shuffled.\n If <= 0, means the last available iteration.\n\n Returns\n -------\n self : Booster\n Booster with shuffled models.\n \"\"\"\n _safe_call(\n _LIB.LGBM_BoosterShuffleModels(\n self._handle,\n ctypes.c_int(start_iteration),\n ctypes.c_int(end_iteration),\n )\n )\n return self\n\n def model_from_string(self, model_str: str) -> \"Booster\":\n \"\"\"Load Booster from a string.\n\n Parameters\n ----------\n model_str : str\n Model will be loaded from this string.\n\n Returns\n -------\n self : Booster\n Loaded Booster object.\n \"\"\"\n # ensure that existing Booster is freed before replacing it\n # with a new one createdfrom file\n _safe_call(_LIB.LGBM_BoosterFree(self._handle))\n self._free_buffer()\n self._handle = ctypes.c_void_p()\n out_num_iterations = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterLoadModelFromString(\n _c_str(model_str),\n ctypes.byref(out_num_iterations),\n ctypes.byref(self._handle),\n )\n )\n out_num_class = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterGetNumClasses(\n self._handle,\n ctypes.byref(out_num_class),\n )\n )\n self.__num_class = out_num_class.value\n self.pandas_categorical = _load_pandas_categorical(model_str=model_str)\n return self\n\n def model_to_string(\n self,\n num_iteration: Optional[int] = None,\n start_iteration: int = 0,\n importance_type: str = \"split\",\n ) -> str:\n \"\"\"Save Booster to string.\n\n Parameters\n ----------\n num_iteration : int or None, optional (default=None)\n Index of the iteration that should be saved.\n If None, if the best iteration exists, it is saved; otherwise, all iterations are saved.\n If <= 0, all iterations are saved.\n start_iteration : int, optional (default=0)\n Start index of the iteration that should be saved.\n importance_type : str, optional (default=\"split\")\n What type of feature importance should be saved.\n If \"split\", result contains numbers of times the feature is used in a model.\n If \"gain\", result contains total gains of splits which use the feature.\n\n Returns\n -------\n str_repr : str\n String representation of Booster.\n \"\"\"\n if num_iteration is None:\n num_iteration = self.best_iteration\n importance_type_int = _FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type]\n buffer_len = 1 << 20\n tmp_out_len = ctypes.c_int64(0)\n string_buffer = ctypes.create_string_buffer(buffer_len)\n ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer))\n _safe_call(\n _LIB.LGBM_BoosterSaveModelToString(\n self._handle,\n ctypes.c_int(start_iteration),\n ctypes.c_int(num_iteration),\n ctypes.c_int(importance_type_int),\n ctypes.c_int64(buffer_len),\n ctypes.byref(tmp_out_len),\n ptr_string_buffer,\n )\n )\n actual_len = tmp_out_len.value\n # if buffer length is not long enough, re-allocate a buffer\n if actual_len > buffer_len:\n string_buffer = ctypes.create_string_buffer(actual_len)\n ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer))\n _safe_call(\n _LIB.LGBM_BoosterSaveModelToString(\n self._handle,\n ctypes.c_int(start_iteration),\n ctypes.c_int(num_iteration),\n ctypes.c_int(importance_type_int),\n ctypes.c_int64(actual_len),\n ctypes.byref(tmp_out_len),\n ptr_string_buffer,\n )\n )\n ret = string_buffer.value.decode(\"utf-8\")\n ret += _dump_pandas_categorical(self.pandas_categorical)\n return ret\n\n def dump_model(\n self,\n num_iteration: Optional[int] = None,\n start_iteration: int = 0,\n importance_type: str = \"split\",\n object_hook: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None,\n ) -> Dict[str, Any]:\n \"\"\"Dump Booster to JSON format.\n\n Parameters\n ----------\n num_iteration : int or None, optional (default=None)\n Index of the iteration that should be dumped.\n If None, if the best iteration exists, it is dumped; otherwise, all iterations are dumped.\n If <= 0, all iterations are dumped.\n start_iteration : int, optional (default=0)\n Start index of the iteration that should be dumped.\n importance_type : str, optional (default=\"split\")\n What type of feature importance should be dumped.\n If \"split\", result contains numbers of times the feature is used in a model.\n If \"gain\", result contains total gains of splits which use the feature.\n object_hook : callable or None, optional (default=None)\n If not None, ``object_hook`` is a function called while parsing the json\n string returned by the C API. It may be used to alter the json, to store\n specific values while building the json structure. It avoids\n walking through the structure again. It saves a significant amount\n of time if the number of trees is huge.\n Signature is ``def object_hook(node: dict) -> dict``.\n None is equivalent to ``lambda node: node``.\n See documentation of ``json.loads()`` for further details.\n\n Returns\n -------\n json_repr : dict\n JSON format of Booster.\n \"\"\"\n if num_iteration is None:\n num_iteration = self.best_iteration\n importance_type_int = _FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type]\n buffer_len = 1 << 20\n tmp_out_len = ctypes.c_int64(0)\n string_buffer = ctypes.create_string_buffer(buffer_len)\n ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer))\n _safe_call(\n _LIB.LGBM_BoosterDumpModel(\n self._handle,\n ctypes.c_int(start_iteration),\n ctypes.c_int(num_iteration),\n ctypes.c_int(importance_type_int),\n ctypes.c_int64(buffer_len),\n ctypes.byref(tmp_out_len),\n ptr_string_buffer,\n )\n )\n actual_len = tmp_out_len.value\n # if buffer length is not long enough, reallocate a buffer\n if actual_len > buffer_len:\n string_buffer = ctypes.create_string_buffer(actual_len)\n ptr_string_buffer = ctypes.c_char_p(ctypes.addressof(string_buffer))\n _safe_call(\n _LIB.LGBM_BoosterDumpModel(\n self._handle,\n ctypes.c_int(start_iteration),\n ctypes.c_int(num_iteration),\n ctypes.c_int(importance_type_int),\n ctypes.c_int64(actual_len),\n ctypes.byref(tmp_out_len),\n ptr_string_buffer,\n )\n )\n ret = json.loads(string_buffer.value.decode(\"utf-8\"), object_hook=object_hook)\n ret[\"pandas_categorical\"] = json.loads(\n json.dumps(\n self.pandas_categorical,\n default=_json_default_with_numpy,\n )\n )\n return ret\n\n def predict(\n self,\n data: _LGBM_PredictDataType,\n start_iteration: int = 0,\n num_iteration: Optional[int] = None,\n raw_score: bool = False,\n pred_leaf: bool = False,\n pred_contrib: bool = False,\n data_has_header: bool = False,\n validate_features: bool = False,\n **kwargs: Any,\n ) -> _LGBM_PredictReturnType:\n \"\"\"Make a prediction.\n\n Parameters\n ----------\n data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse or pyarrow Table\n Data source for prediction.\n If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM).\n start_iteration : int, optional (default=0)\n Start index of the iteration to predict.\n If <= 0, starts from the first iteration.\n num_iteration : int or None, optional (default=None)\n Total number of iterations used in the prediction.\n If None, if the best iteration exists and start_iteration <= 0, the best iteration is used;\n otherwise, all iterations from ``start_iteration`` are used (no limits).\n If <= 0, all iterations from ``start_iteration`` are used (no limits).\n raw_score : bool, optional (default=False)\n Whether to predict raw scores.\n pred_leaf : bool, optional (default=False)\n Whether to predict leaf index.\n pred_contrib : bool, optional (default=False)\n Whether to predict feature contributions.\n\n .. note::\n\n If you want to get more explanations for your model's predictions using SHAP values,\n like SHAP interaction values,\n you can install the shap package (https://github.com/slundberg/shap).\n Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra\n column, where the last column is the expected value.\n\n data_has_header : bool, optional (default=False)\n Whether the data has header.\n Used only if data is str.\n validate_features : bool, optional (default=False)\n If True, ensure that the features used to predict match the ones used to train.\n Used only if data is pandas DataFrame.\n **kwargs\n Other parameters for the prediction.\n\n Returns\n -------\n result : numpy array, scipy.sparse or list of scipy.sparse\n Prediction result.\n Can be sparse or a list of sparse objects (each element represents predictions for one class) for feature contributions (when ``pred_contrib=True``).\n \"\"\"\n predictor = _InnerPredictor.from_booster(\n booster=self,\n pred_parameter=deepcopy(kwargs),\n )\n if num_iteration is None:\n if start_iteration <= 0:\n num_iteration = self.best_iteration\n else:\n num_iteration = -1\n return predictor.predict(\n data=data,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n raw_score=raw_score,\n pred_leaf=pred_leaf,\n pred_contrib=pred_contrib,\n data_has_header=data_has_header,\n validate_features=validate_features,\n )\n\n def refit(\n self,\n data: _LGBM_TrainDataType,\n label: _LGBM_LabelType,\n decay_rate: float = 0.9,\n reference: Optional[Dataset] = None,\n weight: Optional[_LGBM_WeightType] = None,\n group: Optional[_LGBM_GroupType] = None,\n init_score: Optional[_LGBM_InitScoreType] = None,\n feature_name: _LGBM_FeatureNameConfiguration = \"auto\",\n categorical_feature: _LGBM_CategoricalFeatureConfiguration = \"auto\",\n dataset_params: Optional[Dict[str, Any]] = None,\n free_raw_data: bool = True,\n validate_features: bool = False,\n **kwargs: Any,\n ) -> \"Booster\":\n \"\"\"Refit the existing Booster by new data.\n\n Parameters\n ----------\n data : str, pathlib.Path, numpy array, pandas DataFrame, scipy.sparse, Sequence, list of Sequence, list of numpy array or pyarrow Table\n Data source for refit.\n If str or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM).\n label : list, numpy 1-D array, pandas Series / one-column DataFrame, pyarrow Array or pyarrow ChunkedArray\n Label for refit.\n decay_rate : float, optional (default=0.9)\n Decay rate of refit,\n will use ``leaf_output = decay_rate * old_leaf_output + (1.0 - decay_rate) * new_leaf_output`` to refit trees.\n reference : Dataset or None, optional (default=None)\n Reference for ``data``.\n\n .. versionadded:: 4.0.0\n\n weight : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None)\n Weight for each ``data`` instance. Weights should be non-negative.\n\n .. versionadded:: 4.0.0\n\n group : list, numpy 1-D array, pandas Series, pyarrow Array, pyarrow ChunkedArray or None, optional (default=None)\n Group/query size for ``data``.\n Only used in the learning-to-rank task.\n sum(group) = n_samples.\n For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,\n where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.\n\n .. versionadded:: 4.0.0\n\n init_score : list, list of lists (for multi-class task), numpy array, pandas Series, pandas DataFrame (for multi-class task), pyarrow Array, pyarrow ChunkedArray, pyarrow Table (for multi-class task) or None, optional (default=None)\n Init score for ``data``.\n\n .. versionadded:: 4.0.0\n\n feature_name : list of str, or 'auto', optional (default=\"auto\")\n Feature names for ``data``.\n If 'auto' and data is pandas DataFrame, data columns names are used.\n\n .. versionadded:: 4.0.0\n\n categorical_feature : list of str or int, or 'auto', optional (default=\"auto\")\n Categorical features for ``data``.\n If list of int, interpreted as indices.\n If list of str, interpreted as feature names (need to specify ``feature_name`` as well).\n If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.\n All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647).\n Large values could be memory consuming. Consider using consecutive integers starting from zero.\n All negative values in categorical features will be treated as missing values.\n The output cannot be monotonically constrained with respect to a categorical feature.\n Floating point numbers in categorical features will be rounded towards 0.\n\n .. versionadded:: 4.0.0\n\n dataset_params : dict or None, optional (default=None)\n Other parameters for Dataset ``data``.\n\n .. versionadded:: 4.0.0\n\n free_raw_data : bool, optional (default=True)\n If True, raw data is freed after constructing inner Dataset for ``data``.\n\n .. versionadded:: 4.0.0\n\n validate_features : bool, optional (default=False)\n If True, ensure that the features used to refit the model match the original ones.\n Used only if data is pandas DataFrame.\n\n .. versionadded:: 4.0.0\n\n **kwargs\n Other parameters for refit.\n These parameters will be passed to ``predict`` method.\n\n Returns\n -------\n result : Booster\n Refitted Booster.\n \"\"\"\n if self.__set_objective_to_none:\n raise LightGBMError(\"Cannot refit due to null objective function.\")\n if dataset_params is None:\n dataset_params = {}\n predictor = _InnerPredictor.from_booster(booster=self, pred_parameter=deepcopy(kwargs))\n leaf_preds: np.ndarray = predictor.predict( # type: ignore[assignment]\n data=data,\n start_iteration=-1,\n pred_leaf=True,\n validate_features=validate_features,\n )\n nrow, ncol = leaf_preds.shape\n out_is_linear = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterGetLinear(\n self._handle,\n ctypes.byref(out_is_linear),\n )\n )\n new_params = _choose_param_value(\n main_param_name=\"linear_tree\",\n params=self.params,\n default_value=None,\n )\n new_params[\"linear_tree\"] = bool(out_is_linear.value)\n new_params.update(dataset_params)\n train_set = Dataset(\n data=data,\n label=label,\n reference=reference,\n weight=weight,\n group=group,\n init_score=init_score,\n feature_name=feature_name,\n categorical_feature=categorical_feature,\n params=new_params,\n free_raw_data=free_raw_data,\n )\n new_params[\"refit_decay_rate\"] = decay_rate\n new_booster = Booster(new_params, train_set)\n # Copy models\n _safe_call(\n _LIB.LGBM_BoosterMerge(\n new_booster._handle,\n predictor._handle,\n )\n )\n leaf_preds = leaf_preds.reshape(-1)\n ptr_data, _, _ = _c_int_array(leaf_preds)\n _safe_call(\n _LIB.LGBM_BoosterRefit(\n new_booster._handle,\n ptr_data,\n ctypes.c_int32(nrow),\n ctypes.c_int32(ncol),\n )\n )\n new_booster._network = self._network\n return new_booster\n\n def get_leaf_output(self, tree_id: int, leaf_id: int) -> float:\n \"\"\"Get the output of a leaf.\n\n Parameters\n ----------\n tree_id : int\n The index of the tree.\n leaf_id : int\n The index of the leaf in the tree.\n\n Returns\n -------\n result : float\n The output of the leaf.\n \"\"\"\n ret = ctypes.c_double(0)\n _safe_call(\n _LIB.LGBM_BoosterGetLeafValue(\n self._handle,\n ctypes.c_int(tree_id),\n ctypes.c_int(leaf_id),\n ctypes.byref(ret),\n )\n )\n return ret.value\n\n def set_leaf_output(\n self,\n tree_id: int,\n leaf_id: int,\n value: float,\n ) -> \"Booster\":\n \"\"\"Set the output of a leaf.\n\n .. versionadded:: 4.0.0\n\n Parameters\n ----------\n tree_id : int\n The index of the tree.\n leaf_id : int\n The index of the leaf in the tree.\n value : float\n Value to set as the output of the leaf.\n\n Returns\n -------\n self : Booster\n Booster with the leaf output set.\n \"\"\"\n _safe_call(\n _LIB.LGBM_BoosterSetLeafValue(\n self._handle,\n ctypes.c_int(tree_id),\n ctypes.c_int(leaf_id),\n ctypes.c_double(value),\n )\n )\n return self\n\n def num_feature(self) -> int:\n \"\"\"Get number of features.\n\n Returns\n -------\n num_feature : int\n The number of features.\n \"\"\"\n out_num_feature = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterGetNumFeature(\n self._handle,\n ctypes.byref(out_num_feature),\n )\n )\n return out_num_feature.value\n\n def feature_name(self) -> List[str]:\n \"\"\"Get names of features.\n\n Returns\n -------\n result : list of str\n List with names of features.\n \"\"\"\n num_feature = self.num_feature()\n # Get name of features\n tmp_out_len = ctypes.c_int(0)\n reserved_string_buffer_size = 255\n required_string_buffer_size = ctypes.c_size_t(0)\n string_buffers = [ctypes.create_string_buffer(reserved_string_buffer_size) for _ in range(num_feature)]\n ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc]\n _safe_call(\n _LIB.LGBM_BoosterGetFeatureNames(\n self._handle,\n ctypes.c_int(num_feature),\n ctypes.byref(tmp_out_len),\n ctypes.c_size_t(reserved_string_buffer_size),\n ctypes.byref(required_string_buffer_size),\n ptr_string_buffers,\n )\n )\n if num_feature != tmp_out_len.value:\n raise ValueError(\"Length of feature names doesn't equal with num_feature\")\n actual_string_buffer_size = required_string_buffer_size.value\n # if buffer length is not long enough, reallocate buffers\n if reserved_string_buffer_size < actual_string_buffer_size:\n string_buffers = [ctypes.create_string_buffer(actual_string_buffer_size) for _ in range(num_feature)]\n ptr_string_buffers = (ctypes.c_char_p * num_feature)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc]\n _safe_call(\n _LIB.LGBM_BoosterGetFeatureNames(\n self._handle,\n ctypes.c_int(num_feature),\n ctypes.byref(tmp_out_len),\n ctypes.c_size_t(actual_string_buffer_size),\n ctypes.byref(required_string_buffer_size),\n ptr_string_buffers,\n )\n )\n return [string_buffers[i].value.decode(\"utf-8\") for i in range(num_feature)]\n\n def feature_importance(\n self,\n importance_type: str = \"split\",\n iteration: Optional[int] = None,\n ) -> np.ndarray:\n \"\"\"Get feature importances.\n\n Parameters\n ----------\n importance_type : str, optional (default=\"split\")\n How the importance is calculated.\n If \"split\", result contains numbers of times the feature is used in a model.\n If \"gain\", result contains total gains of splits which use the feature.\n iteration : int or None, optional (default=None)\n Limit number of iterations in the feature importance calculation.\n If None, if the best iteration exists, it is used; otherwise, all trees are used.\n If <= 0, all trees are used (no limits).\n\n Returns\n -------\n result : numpy array\n Array with feature importances.\n \"\"\"\n if iteration is None:\n iteration = self.best_iteration\n importance_type_int = _FEATURE_IMPORTANCE_TYPE_MAPPER[importance_type]\n result = np.empty(self.num_feature(), dtype=np.float64)\n _safe_call(\n _LIB.LGBM_BoosterFeatureImportance(\n self._handle,\n ctypes.c_int(iteration),\n ctypes.c_int(importance_type_int),\n result.ctypes.data_as(ctypes.POINTER(ctypes.c_double)),\n )\n )\n if importance_type_int == _C_API_FEATURE_IMPORTANCE_SPLIT:\n return result.astype(np.int32)\n else:\n return result\n\n def get_split_value_histogram(\n self,\n feature: Union[int, str],\n bins: Optional[Union[int, str]] = None,\n xgboost_style: bool = False,\n ) -> Union[Tuple[np.ndarray, np.ndarray], np.ndarray, pd_DataFrame]:\n \"\"\"Get split value histogram for the specified feature.\n\n Parameters\n ----------\n feature : int or str\n The feature name or index the histogram is calculated for.\n If int, interpreted as index.\n If str, interpreted as name.\n\n .. warning::\n\n Categorical features are not supported.\n\n bins : int, str or None, optional (default=None)\n The maximum number of bins.\n If None, or int and > number of unique split values and ``xgboost_style=True``,\n the number of bins equals number of unique split values.\n If str, it should be one from the list of the supported values by ``numpy.histogram()`` function.\n xgboost_style : bool, optional (default=False)\n Whether the returned result should be in the same form as it is in XGBoost.\n If False, the returned value is tuple of 2 numpy arrays as it is in ``numpy.histogram()`` function.\n If True, the returned value is matrix, in which the first column is the right edges of non-empty bins\n and the second one is the histogram values.\n\n Returns\n -------\n result_tuple : tuple of 2 numpy arrays\n If ``xgboost_style=False``, the values of the histogram of used splitting values for the specified feature\n and the bin edges.\n result_array_like : numpy array or pandas DataFrame (if pandas is installed)\n If ``xgboost_style=True``, the histogram of used splitting values for the specified feature.\n \"\"\"\n\n def add(root: Dict[str, Any]) -> None:\n \"\"\"Recursively add thresholds.\"\"\"\n if \"split_index\" in root: # non-leaf\n if feature_names is not None and isinstance(feature, str):\n split_feature = feature_names[root[\"split_feature\"]]\n else:\n split_feature = root[\"split_feature\"]\n if split_feature == feature:\n if isinstance(root[\"threshold\"], str):\n raise LightGBMError(\"Cannot compute split value histogram for the categorical feature\")\n values.append(root[\"threshold\"])\n add(root[\"left_child\"])\n add(root[\"right_child\"])\n\n model = self.dump_model()\n feature_names = model.get(\"feature_names\")\n tree_infos = model[\"tree_info\"]\n values: List[float] = []\n for tree_info in tree_infos:\n add(tree_info[\"tree_structure\"])\n\n if bins is None or isinstance(bins, int) and xgboost_style:\n n_unique = len(np.unique(values))\n bins = max(min(n_unique, bins) if bins is not None else n_unique, 1)\n hist, bin_edges = np.histogram(values, bins=bins)\n if xgboost_style:\n ret = np.column_stack((bin_edges[1:], hist))\n ret = ret[ret[:, 1] > 0]\n if PANDAS_INSTALLED:\n return pd_DataFrame(ret, columns=[\"SplitValue\", \"Count\"])\n else:\n return ret\n else:\n return hist, bin_edges\n\n def __inner_eval(\n self,\n *,\n data_name: str,\n data_idx: int,\n feval: Optional[Union[_LGBM_CustomEvalFunction, List[_LGBM_CustomEvalFunction]]],\n ) -> List[_LGBM_BoosterEvalMethodResultType]:\n \"\"\"Evaluate training or validation data.\"\"\"\n if data_idx >= self.__num_dataset:\n raise ValueError(\"Data_idx should be smaller than number of dataset\")\n self.__get_eval_info()\n ret = []\n if self.__num_inner_eval > 0:\n result = np.empty(self.__num_inner_eval, dtype=np.float64)\n tmp_out_len = ctypes.c_int(0)\n _safe_call(\n _LIB.LGBM_BoosterGetEval(\n self._handle,\n ctypes.c_int(data_idx),\n ctypes.byref(tmp_out_len),\n result.ctypes.data_as(ctypes.POINTER(ctypes.c_double)),\n )\n )\n if tmp_out_len.value != self.__num_inner_eval:\n raise ValueError(\"Wrong length of eval results\")\n for i in range(self.__num_inner_eval):\n ret.append((data_name, self.__name_inner_eval[i], result[i], self.__higher_better_inner_eval[i]))\n if callable(feval):\n feval = [feval]\n if feval is not None:\n if data_idx == 0:\n cur_data = self.train_set\n else:\n cur_data = self.valid_sets[data_idx - 1]\n for eval_function in feval:\n if eval_function is None:\n continue\n feval_ret = eval_function(self.__inner_predict(data_idx=data_idx), cur_data)\n if isinstance(feval_ret, list):\n for eval_name, val, is_higher_better in feval_ret:\n ret.append((data_name, eval_name, val, is_higher_better))\n else:\n eval_name, val, is_higher_better = feval_ret\n ret.append((data_name, eval_name, val, is_higher_better))\n return ret\n\n def __inner_predict(self, *, data_idx: int) -> np.ndarray:\n \"\"\"Predict for training and validation dataset.\"\"\"\n if data_idx >= self.__num_dataset:\n raise ValueError(\"Data_idx should be smaller than number of dataset\")\n if self.__inner_predict_buffer[data_idx] is None:\n if data_idx == 0:\n n_preds = self.train_set.num_data() * self.__num_class\n else:\n n_preds = self.valid_sets[data_idx - 1].num_data() * self.__num_class\n self.__inner_predict_buffer[data_idx] = np.empty(n_preds, dtype=np.float64)\n # avoid to predict many time in one iteration\n if not self.__is_predicted_cur_iter[data_idx]:\n tmp_out_len = ctypes.c_int64(0)\n data_ptr = self.__inner_predict_buffer[data_idx].ctypes.data_as(ctypes.POINTER(ctypes.c_double)) # type: ignore[union-attr]\n _safe_call(\n _LIB.LGBM_BoosterGetPredict(\n self._handle,\n ctypes.c_int(data_idx),\n ctypes.byref(tmp_out_len),\n data_ptr,\n )\n )\n if tmp_out_len.value != len(self.__inner_predict_buffer[data_idx]): # type: ignore[arg-type]\n raise ValueError(f\"Wrong length of predict results for data {data_idx}\")\n self.__is_predicted_cur_iter[data_idx] = True\n result: np.ndarray = self.__inner_predict_buffer[data_idx] # type: ignore[assignment]\n if self.__num_class > 1:\n num_data = result.size // self.__num_class\n result = result.reshape(num_data, self.__num_class, order=\"F\")\n return result\n\n def __get_eval_info(self) -> None:\n \"\"\"Get inner evaluation count and names.\"\"\"\n if self.__need_reload_eval_info:\n self.__need_reload_eval_info = False\n out_num_eval = ctypes.c_int(0)\n # Get num of inner evals\n _safe_call(\n _LIB.LGBM_BoosterGetEvalCounts(\n self._handle,\n ctypes.byref(out_num_eval),\n )\n )\n self.__num_inner_eval = out_num_eval.value\n if self.__num_inner_eval > 0:\n # Get name of eval metrics\n tmp_out_len = ctypes.c_int(0)\n reserved_string_buffer_size = 255\n required_string_buffer_size = ctypes.c_size_t(0)\n string_buffers = [\n ctypes.create_string_buffer(reserved_string_buffer_size) for _ in range(self.__num_inner_eval)\n ]\n ptr_string_buffers = (ctypes.c_char_p * self.__num_inner_eval)(*map(ctypes.addressof, string_buffers)) # type: ignore[misc]\n _safe_call(\n _LIB.LGBM_BoosterGetEvalNames(\n self._handle,\n ctypes.c_int(self.__num_inner_eval),\n ctypes.byref(tmp_out_len),\n ctypes.c_size_t(reserved_string_buffer_size),\n ctypes.byref(required_string_buffer_size),\n ptr_string_buffers,\n )\n )\n if self.__num_inner_eval != tmp_out_len.value:\n raise ValueError(\"Length of eval names doesn't equal with num_evals\")\n actual_string_buffer_size = required_string_buffer_size.value\n # if buffer length is not long enough, reallocate buffers\n if reserved_string_buffer_size < actual_string_buffer_size:\n string_buffers = [\n ctypes.create_string_buffer(actual_string_buffer_size) for _ in range(self.__num_inner_eval)\n ]\n ptr_string_buffers = (ctypes.c_char_p * self.__num_inner_eval)(\n *map(ctypes.addressof, string_buffers)\n ) # type: ignore[misc]\n _safe_call(\n _LIB.LGBM_BoosterGetEvalNames(\n self._handle,\n ctypes.c_int(self.__num_inner_eval),\n ctypes.byref(tmp_out_len),\n ctypes.c_size_t(actual_string_buffer_size),\n ctypes.byref(required_string_buffer_size),\n ptr_string_buffers,\n )\n )\n self.__name_inner_eval = [string_buffers[i].value.decode(\"utf-8\") for i in range(self.__num_inner_eval)]\n self.__higher_better_inner_eval = [\n name.startswith((\"auc\", \"ndcg@\", \"map@\", \"average_precision\")) for name in self.__name_inner_eval\n ]\n" }, { "path": "python-package/lightgbm/callback.py", "content": "# coding: utf-8\n\"\"\"Callbacks library.\"\"\"\n\nfrom collections import OrderedDict\nfrom dataclasses import dataclass\nfrom functools import partial\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union\n\nfrom .basic import (\n Booster,\n _ConfigAliases,\n _LGBM_BoosterEvalMethodResultType,\n _LGBM_BoosterEvalMethodResultWithStandardDeviationType,\n _log_info,\n _log_warning,\n)\n\nif TYPE_CHECKING:\n from .engine import CVBooster\n\n__all__ = [\n \"EarlyStopException\",\n \"early_stopping\",\n \"log_evaluation\",\n \"record_evaluation\",\n \"reset_parameter\",\n]\n\n_EvalResultDict = Dict[str, Dict[str, List[Any]]]\n_EvalResultTuple = Union[\n _LGBM_BoosterEvalMethodResultType,\n _LGBM_BoosterEvalMethodResultWithStandardDeviationType,\n]\n_ListOfEvalResultTuples = Union[\n List[_LGBM_BoosterEvalMethodResultType],\n List[_LGBM_BoosterEvalMethodResultWithStandardDeviationType],\n]\n\n\nclass EarlyStopException(Exception):\n \"\"\"Exception of early stopping.\n\n Raise this from a callback passed in via keyword argument ``callbacks``\n in ``cv()`` or ``train()`` to trigger early stopping.\n \"\"\"\n\n def __init__(self, best_iteration: int, best_score: _ListOfEvalResultTuples) -> None:\n \"\"\"Create early stopping exception.\n\n Parameters\n ----------\n best_iteration : int\n The best iteration stopped.\n 0-based... pass ``best_iteration=2`` to indicate that the third iteration was the best one.\n best_score : list of (eval_name, metric_name, eval_result, is_higher_better) tuple or (eval_name, metric_name, eval_result, is_higher_better, stdv) tuple\n Scores for each metric, on each validation set, as of the best iteration.\n \"\"\"\n super().__init__()\n self.best_iteration = best_iteration\n self.best_score = best_score\n\n\n# Callback environment used by callbacks\n@dataclass\nclass CallbackEnv:\n model: Union[Booster, \"CVBooster\"]\n params: Dict[str, Any]\n iteration: int\n begin_iteration: int\n end_iteration: int\n evaluation_result_list: Optional[_ListOfEvalResultTuples]\n\n\ndef _is_using_cv(env: CallbackEnv) -> bool:\n \"\"\"Check if model in callback env is a CVBooster.\"\"\"\n # this import is here to avoid a circular import\n from .engine import CVBooster # noqa: PLC0415\n\n return isinstance(env.model, CVBooster)\n\n\ndef _format_eval_result(value: _EvalResultTuple, show_stdv: bool) -> str:\n \"\"\"Format metric string.\"\"\"\n dataset_name, metric_name, metric_value, *_ = value\n out = f\"{dataset_name}'s {metric_name}: {metric_value:g}\"\n # tuples from cv() sometimes have a 5th item, with standard deviation of\n # the evaluation metric (taken over all cross-validation folds)\n if show_stdv and len(value) == 5:\n out += f\" + {value[4]:g}\"\n return out\n\n\nclass _LogEvaluationCallback:\n \"\"\"Internal log evaluation callable class.\"\"\"\n\n def __init__(self, period: int = 1, show_stdv: bool = True) -> None:\n self.order = 10\n self.before_iteration = False\n\n self.period = period\n self.show_stdv = show_stdv\n\n def __call__(self, env: CallbackEnv) -> None:\n if self.period > 0 and env.evaluation_result_list and (env.iteration + 1) % self.period == 0:\n result = \"\\t\".join([_format_eval_result(x, self.show_stdv) for x in env.evaluation_result_list])\n _log_info(f\"[{env.iteration + 1}]\\t{result}\")\n\n\ndef log_evaluation(period: int = 1, show_stdv: bool = True) -> _LogEvaluationCallback:\n \"\"\"Create a callback that logs the evaluation results.\n\n By default, standard output resource is used.\n Use ``register_logger()`` function to register a custom logger.\n\n Note\n ----\n Requires at least one validation data.\n\n Parameters\n ----------\n period : int, optional (default=1)\n The period to log the evaluation results.\n The last boosting stage or the boosting stage found by using ``early_stopping`` callback is also logged.\n show_stdv : bool, optional (default=True)\n Whether to log stdv (if provided).\n\n Returns\n -------\n callback : _LogEvaluationCallback\n The callback that logs the evaluation results every ``period`` boosting iteration(s).\n \"\"\"\n return _LogEvaluationCallback(period=period, show_stdv=show_stdv)\n\n\nclass _RecordEvaluationCallback:\n \"\"\"Internal record evaluation callable class.\"\"\"\n\n def __init__(self, eval_result: _EvalResultDict) -> None:\n self.order = 20\n self.before_iteration = False\n\n if not isinstance(eval_result, dict):\n raise TypeError(\"eval_result should be a dictionary\")\n self.eval_result = eval_result\n\n def _init(self, env: CallbackEnv) -> None:\n if env.evaluation_result_list is None:\n raise RuntimeError(\n \"record_evaluation() callback enabled but no evaluation results found. This is a probably bug in LightGBM. \"\n \"Please report it at https://github.com/lightgbm-org/LightGBM/issues\"\n )\n self.eval_result.clear()\n for item in env.evaluation_result_list:\n dataset_name, metric_name, *_ = item\n self.eval_result.setdefault(dataset_name, OrderedDict())\n if len(item) == 4:\n self.eval_result[dataset_name].setdefault(metric_name, [])\n else:\n self.eval_result[dataset_name].setdefault(f\"{metric_name}-mean\", [])\n self.eval_result[dataset_name].setdefault(f\"{metric_name}-stdv\", [])\n\n def __call__(self, env: CallbackEnv) -> None:\n if env.iteration == env.begin_iteration:\n self._init(env)\n if env.evaluation_result_list is None:\n raise RuntimeError(\n \"record_evaluation() callback enabled but no evaluation results found. This is a probably bug in LightGBM. \"\n \"Please report it at https://github.com/lightgbm-org/LightGBM/issues\"\n )\n for item in env.evaluation_result_list:\n # for cv(), 'metric_value' is actually a mean of metric values over all CV folds\n dataset_name, metric_name, metric_value, *_ = item\n if len(item) == 4:\n # train()\n self.eval_result[dataset_name][metric_name].append(metric_value)\n else:\n # cv()\n metric_std_dev = item[4] # type: ignore[misc]\n self.eval_result[dataset_name][f\"{metric_name}-mean\"].append(metric_value)\n self.eval_result[dataset_name][f\"{metric_name}-stdv\"].append(metric_std_dev)\n\n\ndef record_evaluation(eval_result: Dict[str, Dict[str, List[Any]]]) -> Callable:\n \"\"\"Create a callback that records the evaluation history into ``eval_result``.\n\n Parameters\n ----------\n eval_result : dict\n Dictionary used to store all evaluation results of all validation sets.\n This should be initialized outside of your call to ``record_evaluation()`` and should be empty.\n Any initial contents of the dictionary will be deleted.\n\n .. rubric:: Example\n\n With two validation sets named 'eval' and 'train', and one evaluation metric named 'logloss'\n this dictionary after finishing a model training process will have the following structure:\n\n .. code-block::\n\n {\n 'train':\n {\n 'logloss': [0.48253, 0.35953, ...]\n },\n 'eval':\n {\n 'logloss': [0.480385, 0.357756, ...]\n }\n }\n\n Returns\n -------\n callback : _RecordEvaluationCallback\n The callback that records the evaluation history into the passed dictionary.\n \"\"\"\n return _RecordEvaluationCallback(eval_result=eval_result)\n\n\nclass _ResetParameterCallback:\n \"\"\"Internal reset parameter callable class.\"\"\"\n\n def __init__(self, **kwargs: Union[list, Callable]) -> None:\n self.order = 10\n self.before_iteration = True\n\n self.kwargs = kwargs\n\n def __call__(self, env: CallbackEnv) -> None:\n new_parameters = {}\n for key, value in self.kwargs.items():\n if isinstance(value, list):\n if len(value) != env.end_iteration - env.begin_iteration:\n raise ValueError(f\"Length of list {key!r} has to be equal to 'num_boost_round'.\")\n new_param = value[env.iteration - env.begin_iteration]\n elif callable(value):\n new_param = value(env.iteration - env.begin_iteration)\n else:\n raise ValueError(\n \"Only list and callable values are supported \"\n \"as a mapping from boosting round index to new parameter value.\"\n )\n if new_param != env.params.get(key, None):\n new_parameters[key] = new_param\n if new_parameters:\n if isinstance(env.model, Booster):\n env.model.reset_parameter(new_parameters)\n else:\n # CVBooster holds a list of Booster objects, each needs to be updated\n for booster in env.model.boosters:\n booster.reset_parameter(new_parameters)\n env.params.update(new_parameters)\n\n\ndef reset_parameter(**kwargs: Union[list, Callable]) -> Callable:\n \"\"\"Create a callback that resets the parameter after the first iteration.\n\n .. note::\n\n The initial parameter will still take in-effect on first iteration.\n\n Parameters\n ----------\n **kwargs : value should be list or callable\n List of parameters for each boosting round\n or a callable that calculates the parameter in terms of\n current number of round (e.g. yields learning rate decay).\n If list lst, parameter = lst[current_round].\n If callable func, parameter = func(current_round).\n\n Returns\n -------\n callback : _ResetParameterCallback\n The callback that resets the parameter after the first iteration.\n \"\"\"\n return _ResetParameterCallback(**kwargs)\n\n\nclass _EarlyStoppingCallback:\n \"\"\"Internal early stopping callable class.\"\"\"\n\n def __init__(\n self,\n stopping_rounds: int,\n first_metric_only: bool = False,\n verbose: bool = True,\n min_delta: Union[float, List[float]] = 0.0,\n ) -> None:\n self.enabled = _should_enable_early_stopping(stopping_rounds)\n\n self.order = 30\n self.before_iteration = False\n\n self.stopping_rounds = stopping_rounds\n self.first_metric_only = first_metric_only\n self.verbose = verbose\n self.min_delta = min_delta\n\n self._reset_storages()\n\n def _reset_storages(self) -> None:\n self.best_score: List[float] = []\n self.best_iter: List[int] = []\n self.best_score_list: List[_ListOfEvalResultTuples] = []\n self.cmp_op: List[Callable[[float, float, float], bool]] = []\n self.first_metric = \"\"\n\n def _gt_delta(self, *, curr_score: float, best_score: float, delta: float) -> bool:\n return curr_score > best_score + delta\n\n def _lt_delta(self, *, curr_score: float, best_score: float, delta: float) -> bool:\n return curr_score < best_score - delta\n\n def _is_train_set(self, *, dataset_name: str, env: CallbackEnv) -> bool:\n \"\"\"Check, by name, if a given Dataset is the training data.\"\"\"\n # for lgb.cv() with eval_train_metric=True, evaluation is also done on the training set\n # and those metrics are considered for early stopping\n if _is_using_cv(env) and dataset_name == \"train\":\n return True\n\n # for lgb.train(), it's possible to pass the training data via valid_sets with any eval_name\n if isinstance(env.model, Booster) and dataset_name == env.model._train_data_name:\n return True\n\n return False\n\n def _init(self, env: CallbackEnv) -> None:\n if env.evaluation_result_list is None or env.evaluation_result_list == []:\n raise ValueError(\"For early stopping, at least one dataset and eval metric is required for evaluation\")\n\n is_dart = any(env.params.get(alias, \"\") == \"dart\" for alias in _ConfigAliases.get(\"boosting\"))\n if is_dart:\n self.enabled = False\n _log_warning(\"Early stopping is not available in dart mode\")\n return\n\n # get details of the first dataset\n first_dataset_name, first_metric_name, *_ = env.evaluation_result_list[0]\n\n # validation sets are guaranteed to not be identical to the training data in cv()\n if isinstance(env.model, Booster):\n only_train_set = len(env.evaluation_result_list) == 1 and self._is_train_set(\n dataset_name=first_dataset_name,\n env=env,\n )\n if only_train_set:\n self.enabled = False\n _log_warning(\"Only training set found, disabling early stopping.\")\n return\n\n if self.verbose:\n _log_info(f\"Training until validation scores don't improve for {self.stopping_rounds} rounds\")\n\n self._reset_storages()\n\n n_metrics = len({m[1] for m in env.evaluation_result_list})\n n_datasets = len(env.evaluation_result_list) // n_metrics\n if isinstance(self.min_delta, list):\n if not all(t >= 0 for t in self.min_delta):\n raise ValueError(\"Values for early stopping min_delta must be non-negative.\")\n if len(self.min_delta) == 0:\n if self.verbose:\n _log_info(\"Disabling min_delta for early stopping.\")\n deltas = [0.0] * n_datasets * n_metrics\n elif len(self.min_delta) == 1:\n if self.verbose:\n _log_info(f\"Using {self.min_delta[0]} as min_delta for all metrics.\")\n deltas = self.min_delta * n_datasets * n_metrics\n else:\n if len(self.min_delta) != n_metrics:\n raise ValueError(\"Must provide a single value for min_delta or as many as metrics.\")\n if self.first_metric_only and self.verbose:\n _log_info(f\"Using only {self.min_delta[0]} as early stopping min_delta.\")\n deltas = self.min_delta * n_datasets\n else:\n if self.min_delta < 0:\n raise ValueError(\"Early stopping min_delta must be non-negative.\")\n if self.min_delta > 0 and n_metrics > 1 and not self.first_metric_only and self.verbose:\n _log_info(f\"Using {self.min_delta} as min_delta for all metrics.\")\n deltas = [self.min_delta] * n_datasets * n_metrics\n\n self.first_metric = first_metric_name\n for eval_ret, delta in zip(env.evaluation_result_list, deltas):\n self.best_iter.append(0)\n if eval_ret[3]: # greater is better\n self.best_score.append(float(\"-inf\"))\n self.cmp_op.append(partial(self._gt_delta, delta=delta))\n else:\n self.best_score.append(float(\"inf\"))\n self.cmp_op.append(partial(self._lt_delta, delta=delta))\n\n def _final_iteration_check(self, *, env: CallbackEnv, metric_name: str, i: int) -> None:\n if env.iteration == env.end_iteration - 1:\n if self.verbose:\n best_score_str = \"\\t\".join([_format_eval_result(x, show_stdv=True) for x in self.best_score_list[i]])\n _log_info(\n f\"Did not meet early stopping. Best iteration is:\\n[{self.best_iter[i] + 1}]\\t{best_score_str}\"\n )\n if self.first_metric_only:\n _log_info(f\"Evaluated only: {metric_name}\")\n raise EarlyStopException(self.best_iter[i], self.best_score_list[i])\n\n def __call__(self, env: CallbackEnv) -> None:\n if env.iteration == env.begin_iteration:\n self._init(env)\n if not self.enabled:\n return\n if env.evaluation_result_list is None:\n raise RuntimeError(\n \"early_stopping() callback enabled but no evaluation results found. This is a probably bug in LightGBM. \"\n \"Please report it at https://github.com/lightgbm-org/LightGBM/issues\"\n )\n # self.best_score_list is initialized to an empty list\n first_time_updating_best_score_list = self.best_score_list == []\n for i in range(len(env.evaluation_result_list)):\n dataset_name, metric_name, metric_value, *_ = env.evaluation_result_list[i]\n if first_time_updating_best_score_list or self.cmp_op[i]( # type: ignore[call-arg]\n curr_score=metric_value, best_score=self.best_score[i]\n ):\n self.best_score[i] = metric_value\n self.best_iter[i] = env.iteration\n if first_time_updating_best_score_list:\n self.best_score_list.append(env.evaluation_result_list)\n else:\n self.best_score_list[i] = env.evaluation_result_list\n if self.first_metric_only and self.first_metric != metric_name:\n continue # use only the first metric for early stopping\n if self._is_train_set(\n dataset_name=dataset_name,\n env=env,\n ):\n continue # train data for lgb.cv or sklearn wrapper (underlying lgb.train)\n elif env.iteration - self.best_iter[i] >= self.stopping_rounds:\n if self.verbose:\n eval_result_str = \"\\t\".join(\n [_format_eval_result(x, show_stdv=True) for x in self.best_score_list[i]]\n )\n _log_info(f\"Early stopping, best iteration is:\\n[{self.best_iter[i] + 1}]\\t{eval_result_str}\")\n if self.first_metric_only:\n _log_info(f\"Evaluated only: {metric_name}\")\n raise EarlyStopException(self.best_iter[i], self.best_score_list[i])\n self._final_iteration_check(env=env, metric_name=metric_name, i=i)\n\n\ndef _should_enable_early_stopping(stopping_rounds: Any) -> bool:\n \"\"\"Check if early stopping should be activated.\n\n This function will evaluate to True if the early stopping callback should be\n activated (i.e. stopping_rounds > 0). It also provides an informative error if the\n type is not int.\n \"\"\"\n if not isinstance(stopping_rounds, int):\n raise TypeError(f\"early_stopping_round should be an integer. Got '{type(stopping_rounds).__name__}'\")\n return stopping_rounds > 0\n\n\ndef early_stopping(\n stopping_rounds: int,\n first_metric_only: bool = False,\n verbose: bool = True,\n min_delta: Union[float, List[float]] = 0.0,\n) -> _EarlyStoppingCallback:\n \"\"\"Create a callback that activates early stopping.\n\n Activates early stopping.\n The model will train until the validation score doesn't improve by at least ``min_delta``.\n Validation score needs to improve at least every ``stopping_rounds`` round(s)\n to continue training.\n Requires at least one validation data and one metric.\n If there's more than one, will check all of them. But the training data is ignored anyway.\n To check only the first metric set ``first_metric_only`` to True.\n The index of iteration that has the best performance will be saved in the ``best_iteration`` attribute of a model.\n\n .. note::\n\n If using ``boosting_type=\"dart\"``, this callback has no effect and early stopping\n will not be performed.\n\n Parameters\n ----------\n stopping_rounds : int\n The possible number of rounds without the trend occurrence.\n first_metric_only : bool, optional (default=False)\n Whether to use only the first metric for early stopping.\n verbose : bool, optional (default=True)\n Whether to log message with early stopping information.\n By default, standard output resource is used.\n Use ``register_logger()`` function to register a custom logger.\n min_delta : float or list of float, optional (default=0.0)\n Minimum improvement in score to keep training.\n If float, this single value is used for all metrics.\n If list, its length should match the total number of metrics.\n\n .. versionadded:: 4.0.0\n\n Returns\n -------\n callback : _EarlyStoppingCallback\n The callback that activates early stopping.\n \"\"\"\n return _EarlyStoppingCallback(\n stopping_rounds=stopping_rounds,\n first_metric_only=first_metric_only,\n verbose=verbose,\n min_delta=min_delta,\n )\n" }, { "path": "python-package/lightgbm/compat.py", "content": "# coding: utf-8\n\"\"\"Compatibility library.\"\"\"\n\nimport inspect\nfrom typing import TYPE_CHECKING, Any, List\n\n# scikit-learn is intentionally imported first here,\n# see https://github.com/lightgbm-org/LightGBM/issues/6509\n\"\"\"sklearn\"\"\"\ntry:\n from sklearn import __version__ as _sklearn_version\n from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin\n from sklearn.preprocessing import LabelEncoder\n from sklearn.utils.class_weight import compute_sample_weight\n from sklearn.utils.multiclass import check_classification_targets\n from sklearn.utils.validation import assert_all_finite, check_array, check_X_y\n\n try:\n from sklearn.exceptions import NotFittedError\n from sklearn.model_selection import BaseCrossValidator, GroupKFold, StratifiedKFold\n except ImportError:\n from sklearn.cross_validation import BaseCrossValidator, GroupKFold, StratifiedKFold\n from sklearn.utils.validation import NotFittedError\n try:\n from sklearn.utils.validation import _check_sample_weight\n\n # As of https://github.com/scikit-learn/scikit-learn/pull/32212, scikit-learn started raising an error\n # when sample weights are all 0. This argument allow_all_zero_weights can be used switch back\n # to the old behavior of allowing them.\n #\n # This can be removed when the minimum scikit-learn version supported here is v1.9.\n SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG = (\n \"allow_all_zero_weights\" in inspect.signature(_check_sample_weight).parameters\n )\n\n except ImportError:\n from sklearn.utils.validation import check_consistent_length\n\n SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG = False\n\n # dummy function to support older version of scikit-learn\n def _check_sample_weight(sample_weight: Any, X: Any, dtype: Any = None) -> Any:\n check_consistent_length(sample_weight, X)\n return sample_weight\n\n try:\n from sklearn.utils.validation import validate_data\n except ImportError:\n # validate_data() was added in scikit-learn 1.6, this function roughly imitates it for older versions.\n # It can be removed when lightgbm's minimum scikit-learn version is at least 1.6.\n def validate_data(\n _estimator: Any,\n X: Any,\n y: Any = \"no_validation\",\n accept_sparse: bool = True,\n # 'force_all_finite' was renamed to 'ensure_all_finite' in scikit-learn 1.6\n ensure_all_finite: bool = False,\n ensure_min_samples: int = 1,\n # trap other keyword arguments that only work on scikit-learn >=1.6, like 'reset'\n **ignored_kwargs: Any,\n ) -> Any:\n # it's safe to import _num_features unconditionally because:\n #\n # * it was first added in scikit-learn 0.24.2\n # * lightgbm cannot be used with scikit-learn versions older than that\n # * this validate_data() re-implementation will not be called in scikit-learn>=1.6\n #\n from sklearn.utils.validation import _num_features # noqa: PLC0415\n\n # _num_features() raises a TypeError on 1-dimensional input. That's a problem\n # because scikit-learn's 'check_fit1d' estimator check sets that expectation that\n # estimators must raise a ValueError when a 1-dimensional input is passed to fit().\n #\n # So here, lightgbm avoids calling _num_features() on 1-dimensional inputs.\n if hasattr(X, \"shape\") and len(X.shape) == 1:\n n_features_in_ = 1\n else:\n n_features_in_ = _num_features(X)\n\n no_val_y = isinstance(y, str) and y == \"no_validation\"\n\n # NOTE: check_X_y() calls check_array() internally, so only need to call one or the other of them here\n if no_val_y:\n X = check_array(\n X,\n accept_sparse=accept_sparse,\n force_all_finite=ensure_all_finite,\n ensure_min_samples=ensure_min_samples,\n )\n else:\n X, y = check_X_y(\n X,\n y,\n accept_sparse=accept_sparse,\n force_all_finite=ensure_all_finite,\n ensure_min_samples=ensure_min_samples,\n )\n\n # this only needs to be updated at fit() time\n _estimator.n_features_in_ = n_features_in_\n\n # raise the same error that scikit-learn's `validate_data()` does on scikit-learn>=1.6\n if _estimator.__sklearn_is_fitted__() and _estimator._n_features != n_features_in_:\n raise ValueError(\n f\"X has {n_features_in_} features, but {_estimator.__class__.__name__} \"\n f\"is expecting {_estimator._n_features} features as input.\"\n )\n\n if no_val_y:\n return X\n else:\n return X, y\n\n SKLEARN_INSTALLED = True\n _LGBMBaseCrossValidator = BaseCrossValidator\n _LGBMModelBase = BaseEstimator\n _LGBMRegressorBase = RegressorMixin\n _LGBMClassifierBase = ClassifierMixin\n _LGBMLabelEncoder = LabelEncoder\n LGBMNotFittedError = NotFittedError\n _LGBMStratifiedKFold = StratifiedKFold\n _LGBMGroupKFold = GroupKFold\n _LGBMCheckSampleWeight = _check_sample_weight\n _LGBMAssertAllFinite = assert_all_finite\n _LGBMCheckClassificationTargets = check_classification_targets\n _LGBMComputeSampleWeight = compute_sample_weight\n _LGBMValidateData = validate_data\nexcept ImportError:\n SKLEARN_INSTALLED = False\n SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG = False\n\n class _LGBMModelBase: # type: ignore\n \"\"\"Dummy class for sklearn.base.BaseEstimator.\"\"\"\n\n pass\n\n class _LGBMClassifierBase: # type: ignore\n \"\"\"Dummy class for sklearn.base.ClassifierMixin.\"\"\"\n\n pass\n\n class _LGBMRegressorBase: # type: ignore\n \"\"\"Dummy class for sklearn.base.RegressorMixin.\"\"\"\n\n pass\n\n _LGBMBaseCrossValidator = None\n _LGBMLabelEncoder = None\n LGBMNotFittedError = ValueError\n _LGBMStratifiedKFold = None\n _LGBMGroupKFold = None\n _LGBMCheckSampleWeight = None\n _LGBMAssertAllFinite = None\n _LGBMCheckClassificationTargets = None\n _LGBMComputeSampleWeight = None\n _LGBMValidateData = None\n _sklearn_version = None\n\n# additional scikit-learn imports only for type hints\nif TYPE_CHECKING:\n # sklearn.utils.Tags can be imported unconditionally once\n # lightgbm's minimum scikit-learn version is 1.6 or higher\n try:\n from sklearn.utils import Tags as _sklearn_Tags\n except ImportError:\n _sklearn_Tags = None\n\n\n\"\"\"pandas\"\"\"\ntry:\n from pandas import DataFrame as pd_DataFrame\n from pandas import Series as pd_Series\n from pandas import concat\n\n try:\n from pandas import CategoricalDtype as pd_CategoricalDtype\n except ImportError:\n from pandas.api.types import CategoricalDtype as pd_CategoricalDtype\n PANDAS_INSTALLED = True\nexcept ImportError:\n PANDAS_INSTALLED = False\n\n class pd_Series: # type: ignore\n \"\"\"Dummy class for pandas.Series.\"\"\"\n\n def __init__(self, *args: Any, **kwargs: Any):\n pass\n\n class pd_DataFrame: # type: ignore\n \"\"\"Dummy class for pandas.DataFrame.\"\"\"\n\n def __init__(self, *args: Any, **kwargs: Any):\n pass\n\n class pd_CategoricalDtype: # type: ignore\n \"\"\"Dummy class for pandas.CategoricalDtype.\"\"\"\n\n def __init__(self, *args: Any, **kwargs: Any):\n pass\n\n concat = None\n\n\"\"\"matplotlib\"\"\"\ntry:\n import matplotlib # noqa: F401\n\n MATPLOTLIB_INSTALLED = True\nexcept ImportError:\n MATPLOTLIB_INSTALLED = False\n\n\"\"\"graphviz\"\"\"\ntry:\n import graphviz # noqa: F401\n\n GRAPHVIZ_INSTALLED = True\nexcept ImportError:\n GRAPHVIZ_INSTALLED = False\n\n\"\"\"dask\"\"\"\ntry:\n from dask import delayed\n from dask.array import Array as dask_Array\n from dask.array import from_delayed as dask_array_from_delayed\n from dask.bag import from_delayed as dask_bag_from_delayed\n from dask.dataframe import DataFrame as dask_DataFrame\n from dask.dataframe import Series as dask_Series\n from dask.distributed import Client, Future, default_client, wait\n\n DASK_INSTALLED = True\n# catching 'ValueError' here because of this:\n# https://github.com/lightgbm-org/LightGBM/issues/6365#issuecomment-2002330003\n#\n# That's potentially risky as dask does some significant import-time processing,\n# like loading configuration from environment variables and files, and catching\n# ValueError here might hide issues with that config-loading.\n#\n# But in exchange, it's less likely that 'import lightgbm' will fail for\n# dask-related reasons, which is beneficial for any workloads that are using\n# lightgbm but not its Dask functionality.\nexcept (ImportError, ValueError):\n DASK_INSTALLED = False\n\n dask_array_from_delayed = None # type: ignore[assignment]\n dask_bag_from_delayed = None # type: ignore[assignment]\n delayed = None\n default_client = None # type: ignore[assignment]\n wait = None # type: ignore[assignment]\n\n class Client: # type: ignore\n \"\"\"Dummy class for dask.distributed.Client.\"\"\"\n\n def __init__(self, *args: Any, **kwargs: Any):\n pass\n\n class Future: # type: ignore\n \"\"\"Dummy class for dask.distributed.Future.\"\"\"\n\n def __init__(self, *args: Any, **kwargs: Any):\n pass\n\n class dask_Array: # type: ignore\n \"\"\"Dummy class for dask.array.Array.\"\"\"\n\n def __init__(self, *args: Any, **kwargs: Any):\n pass\n\n class dask_DataFrame: # type: ignore\n \"\"\"Dummy class for dask.dataframe.DataFrame.\"\"\"\n\n def __init__(self, *args: Any, **kwargs: Any):\n pass\n\n class dask_Series: # type: ignore\n \"\"\"Dummy class for dask.dataframe.Series.\"\"\"\n\n def __init__(self, *args: Any, **kwargs: Any):\n pass\n\n\n\"\"\"pyarrow\"\"\"\ntry:\n import pyarrow.compute as pa_compute\n from pyarrow import Array as pa_Array\n from pyarrow import ChunkedArray as pa_ChunkedArray\n from pyarrow import Table as pa_Table\n from pyarrow import array as pa_array\n from pyarrow import chunked_array as pa_chunked_array\n from pyarrow.types import is_boolean as arrow_is_boolean\n from pyarrow.types import is_floating as arrow_is_floating\n from pyarrow.types import is_integer as arrow_is_integer\n\n PYARROW_INSTALLED = True\nexcept ImportError:\n PYARROW_INSTALLED = False\n\n class pa_Array: # type: ignore\n \"\"\"Dummy class for pa.Array.\"\"\"\n\n def __init__(self, *args: Any, **kwargs: Any):\n pass\n\n class pa_ChunkedArray: # type: ignore\n \"\"\"Dummy class for pa.ChunkedArray.\"\"\"\n\n def __init__(self, *args: Any, **kwargs: Any):\n pass\n\n class pa_Table: # type: ignore\n \"\"\"Dummy class for pa.Table.\"\"\"\n\n def __init__(self, *args: Any, **kwargs: Any):\n pass\n\n class pa_compute: # type: ignore\n \"\"\"Dummy class for pyarrow.compute module.\"\"\"\n\n all = None\n equal = None\n\n pa_array = None\n pa_chunked_array = None\n arrow_is_boolean = None\n arrow_is_integer = None\n arrow_is_floating = None\n\n\n\"\"\"cffi\"\"\"\ntry:\n from pyarrow.cffi import ffi as arrow_cffi\n\n CFFI_INSTALLED = True\nexcept ImportError:\n CFFI_INSTALLED = False\n\n class arrow_cffi: # type: ignore\n \"\"\"Dummy class for pyarrow.cffi.ffi.\"\"\"\n\n CData = None\n\n def __init__(self, *args: Any, **kwargs: Any):\n pass\n\n\n\"\"\"cpu_count()\"\"\"\ntry:\n from joblib import cpu_count\n\n def _LGBMCpuCount(only_physical_cores: bool = True) -> int:\n return cpu_count(only_physical_cores=only_physical_cores)\nexcept ImportError:\n try:\n from psutil import cpu_count\n\n def _LGBMCpuCount(only_physical_cores: bool = True) -> int:\n return cpu_count(logical=not only_physical_cores) or 1\n except ImportError:\n from multiprocessing import cpu_count\n\n def _LGBMCpuCount(only_physical_cores: bool = True) -> int:\n return cpu_count()\n\n\n__all__: List[str] = []\n" }, { "path": "python-package/lightgbm/dask.py", "content": "# coding: utf-8\n\"\"\"Distributed training with LightGBM and dask.distributed.\n\nThis module enables you to perform distributed training with LightGBM on\ndask.Array and dask.DataFrame collections.\n\nIt is based on dask-lightgbm, which was based on dask-xgboost.\n\"\"\"\n\nimport operator\nimport socket\nfrom collections import defaultdict\nfrom copy import deepcopy\nfrom enum import Enum, auto\nfrom functools import partial\nfrom typing import Any, Dict, Iterable, List, Optional, Tuple, Type, Union\nfrom urllib.parse import urlparse\n\nimport numpy as np\nimport scipy.sparse as ss\n\nfrom .basic import LightGBMError, _choose_param_value, _ConfigAliases, _log_info, _log_warning\nfrom .compat import (\n DASK_INSTALLED,\n PANDAS_INSTALLED,\n SKLEARN_INSTALLED,\n Client,\n Future,\n LGBMNotFittedError,\n concat,\n dask_Array,\n dask_array_from_delayed,\n dask_bag_from_delayed,\n dask_DataFrame,\n dask_Series,\n default_client,\n delayed,\n pd_DataFrame,\n pd_Series,\n wait,\n)\nfrom .sklearn import (\n LGBMClassifier,\n LGBMModel,\n LGBMRanker,\n LGBMRegressor,\n _LGBM_ScikitCustomObjectiveFunction,\n _LGBM_ScikitEvalMetricType,\n _lgbmmodel_doc_custom_eval_note,\n _lgbmmodel_doc_fit,\n _lgbmmodel_doc_predict,\n _validate_eval_set_Xy,\n)\n\n__all__ = [\n \"DaskLGBMClassifier\",\n \"DaskLGBMRanker\",\n \"DaskLGBMRegressor\",\n]\n\n_DaskCollection = Union[dask_Array, dask_DataFrame, dask_Series]\n_DaskMatrixLike = Union[dask_Array, dask_DataFrame]\n_DaskVectorLike = Union[dask_Array, dask_Series]\n_DaskPart = Union[np.ndarray, pd_DataFrame, pd_Series, ss.spmatrix]\n\n\nclass _RemoteSocket:\n def acquire(self) -> int:\n self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n self.socket.bind((\"\", 0))\n return self.socket.getsockname()[1]\n\n def release(self) -> None:\n self.socket.close()\n\n\ndef _acquire_port() -> Tuple[_RemoteSocket, int]:\n s = _RemoteSocket()\n port = s.acquire()\n return s, port\n\n\nclass _DatasetNames(Enum):\n \"\"\"Placeholder names used by lightgbm.dask internals to say 'also evaluate the training data'.\n\n Avoid duplicating the training data when the validation set refers to elements of training data.\n \"\"\"\n\n TRAINSET = auto()\n SAMPLE_WEIGHT = auto()\n INIT_SCORE = auto()\n GROUP = auto()\n\n\ndef _get_dask_client(client: Optional[Client]) -> Client:\n \"\"\"Choose a Dask client to use.\n\n Parameters\n ----------\n client : dask.distributed.Client or None\n Dask client.\n\n Returns\n -------\n client : dask.distributed.Client\n A Dask client.\n \"\"\"\n if client is None:\n return default_client()\n else:\n return client\n\n\ndef _assign_open_ports_to_workers(\n *,\n client: Client,\n workers: List[str],\n) -> Tuple[Dict[str, Future], Dict[str, int]]:\n \"\"\"Assign an open port to each worker.\n\n Returns\n -------\n worker_to_socket_future: dict\n mapping from worker address to a future pointing to the remote socket.\n worker_to_port: dict\n mapping from worker address to an open port in the worker's host.\n \"\"\"\n # Acquire port in worker\n worker_to_future = {}\n for worker in workers:\n worker_to_future[worker] = client.submit(\n _acquire_port,\n workers=[worker],\n allow_other_workers=False,\n pure=False,\n )\n\n # schedule futures to retrieve each element of the tuple\n worker_to_socket_future = {}\n worker_to_port_future = {}\n for worker, socket_future in worker_to_future.items():\n worker_to_socket_future[worker] = client.submit(operator.itemgetter(0), socket_future)\n worker_to_port_future[worker] = client.submit(operator.itemgetter(1), socket_future)\n\n # retrieve ports\n worker_to_port = client.gather(worker_to_port_future)\n\n return worker_to_socket_future, worker_to_port\n\n\ndef _concat(seq: List[_DaskPart]) -> _DaskPart:\n if isinstance(seq[0], np.ndarray):\n return np.concatenate(seq, axis=0)\n elif isinstance(seq[0], (pd_DataFrame, pd_Series)):\n return concat(seq, axis=0)\n elif isinstance(seq[0], ss.spmatrix):\n return ss.vstack(seq, format=\"csr\")\n else:\n raise TypeError(\n f\"Data must be one of: numpy arrays, pandas dataframes, sparse matrices (from scipy). Got {type(seq[0]).__name__}.\"\n )\n\n\ndef _remove_list_padding(*args: Any) -> List[List[Any]]:\n return [[z for z in arg if z is not None] for arg in args]\n\n\ndef _pad_eval_names(\n *,\n lgbm_model: LGBMModel,\n required_names: List[str],\n) -> LGBMModel:\n \"\"\"Append missing (key, value) pairs to a LightGBM model's evals_result_ and best_score_ OrderedDict attrs based on a set of required eval_set names.\n\n Allows users to rely on expected eval_set names being present when fitting DaskLGBM estimators with ``eval_set``.\n \"\"\"\n for eval_name in required_names:\n if eval_name not in lgbm_model.evals_result_:\n lgbm_model.evals_result_[eval_name] = {}\n if eval_name not in lgbm_model.best_score_:\n lgbm_model.best_score_[eval_name] = {}\n\n return lgbm_model\n\n\ndef _train_part(\n *,\n params: Dict[str, Any],\n model_factory: Type[LGBMModel],\n list_of_parts: List[Dict[str, _DaskPart]],\n machines: str,\n local_listen_port: int,\n num_machines: int,\n return_model: bool,\n time_out: int,\n remote_socket: _RemoteSocket,\n **kwargs: Any,\n) -> Optional[LGBMModel]:\n network_params = {\n \"machines\": machines,\n \"local_listen_port\": local_listen_port,\n \"time_out\": time_out,\n \"num_machines\": num_machines,\n }\n params.update(network_params)\n\n is_ranker = issubclass(model_factory, LGBMRanker)\n\n # Concatenate many parts into one\n data = _concat([x[\"data\"] for x in list_of_parts])\n label = _concat([x[\"label\"] for x in list_of_parts])\n\n if \"weight\" in list_of_parts[0]:\n weight = _concat([x[\"weight\"] for x in list_of_parts])\n else:\n weight = None\n\n if \"group\" in list_of_parts[0]:\n group = _concat([x[\"group\"] for x in list_of_parts])\n else:\n group = None\n\n if \"init_score\" in list_of_parts[0]:\n init_score = _concat([x[\"init_score\"] for x in list_of_parts])\n else:\n init_score = None\n\n # construct local eval_set data.\n n_evals = max(len(x.get(\"eval_set\", [])) for x in list_of_parts)\n eval_names = kwargs.pop(\"eval_names\", None)\n eval_class_weight = kwargs.get(\"eval_class_weight\")\n local_eval_set = None\n local_eval_names = None\n local_eval_sample_weight = None\n local_eval_init_score = None\n local_eval_group = None\n\n if n_evals:\n has_eval_sample_weight = any(x.get(\"eval_sample_weight\") is not None for x in list_of_parts)\n has_eval_init_score = any(x.get(\"eval_init_score\") is not None for x in list_of_parts)\n\n local_eval_set = []\n evals_result_names = []\n if has_eval_sample_weight:\n local_eval_sample_weight = []\n if has_eval_init_score:\n local_eval_init_score = []\n if is_ranker:\n local_eval_group = []\n\n # store indices of eval_set components that were not contained within local parts.\n missing_eval_component_idx = []\n\n # consolidate parts of each individual eval component.\n for i in range(n_evals):\n x_e = []\n y_e = []\n w_e = []\n init_score_e = []\n g_e = []\n for part in list_of_parts:\n if not part.get(\"eval_set\"):\n continue\n\n # require that eval_name exists in evaluated result data in case dropped due to padding.\n # in distributed training the 'training' eval_set is not detected, will have name 'valid_'.\n if eval_names:\n evals_result_name = eval_names[i]\n else:\n evals_result_name = f\"valid_{i}\"\n\n eval_set = part[\"eval_set\"][i]\n if eval_set is _DatasetNames.TRAINSET:\n x_e.append(part[\"data\"])\n y_e.append(part[\"label\"])\n else:\n x_e.extend(eval_set[0])\n y_e.extend(eval_set[1])\n\n if evals_result_name not in evals_result_names:\n evals_result_names.append(evals_result_name)\n\n eval_weight = part.get(\"eval_sample_weight\")\n if eval_weight:\n if eval_weight[i] is _DatasetNames.SAMPLE_WEIGHT:\n w_e.append(part[\"weight\"])\n else:\n w_e.extend(eval_weight[i])\n\n eval_init_score = part.get(\"eval_init_score\")\n if eval_init_score:\n if eval_init_score[i] is _DatasetNames.INIT_SCORE:\n init_score_e.append(part[\"init_score\"])\n else:\n init_score_e.extend(eval_init_score[i])\n\n eval_group = part.get(\"eval_group\")\n if eval_group:\n if eval_group[i] is _DatasetNames.GROUP:\n g_e.append(part[\"group\"])\n else:\n g_e.extend(eval_group[i])\n\n # filter padding from eval parts then _concat each eval_set component.\n x_e, y_e, w_e, init_score_e, g_e = _remove_list_padding(x_e, y_e, w_e, init_score_e, g_e)\n if x_e:\n local_eval_set.append((_concat(x_e), _concat(y_e)))\n else:\n missing_eval_component_idx.append(i)\n continue\n\n if w_e:\n local_eval_sample_weight.append(_concat(w_e))\n if init_score_e:\n local_eval_init_score.append(_concat(init_score_e))\n if g_e:\n local_eval_group.append(_concat(g_e))\n\n # reconstruct eval_set fit args/kwargs depending on which components of eval_set are on worker.\n eval_component_idx = [i for i in range(n_evals) if i not in missing_eval_component_idx]\n if eval_names:\n local_eval_names = [eval_names[i] for i in eval_component_idx]\n if eval_class_weight:\n kwargs[\"eval_class_weight\"] = [eval_class_weight[i] for i in eval_component_idx]\n\n if local_eval_set is None:\n local_eval_X = None\n local_eval_y = None\n else:\n local_eval_X = tuple(X for X, _ in local_eval_set)\n local_eval_y = tuple(y for _, y in local_eval_set)\n\n model = model_factory(**params)\n if remote_socket is not None:\n remote_socket.release()\n try:\n if is_ranker:\n model.fit(\n data,\n label,\n sample_weight=weight,\n init_score=init_score,\n group=group,\n eval_X=local_eval_X,\n eval_y=local_eval_y,\n eval_sample_weight=local_eval_sample_weight,\n eval_init_score=local_eval_init_score,\n eval_group=local_eval_group,\n eval_names=local_eval_names,\n **kwargs,\n )\n else:\n model.fit(\n data,\n label,\n sample_weight=weight,\n init_score=init_score,\n eval_X=local_eval_X,\n eval_y=local_eval_y,\n eval_sample_weight=local_eval_sample_weight,\n eval_init_score=local_eval_init_score,\n eval_names=local_eval_names,\n **kwargs,\n )\n\n finally:\n if getattr(model, \"fitted_\", False):\n model.booster_.free_network()\n\n if n_evals:\n # ensure that expected keys for evals_result_ and best_score_ exist regardless of padding.\n model = _pad_eval_names(lgbm_model=model, required_names=evals_result_names)\n\n return model if return_model else None\n\n\ndef _split_to_parts(*, data: _DaskCollection, is_matrix: bool) -> List[_DaskPart]:\n parts = data.to_delayed()\n if isinstance(parts, np.ndarray):\n if is_matrix:\n assert parts.shape[1] == 1\n else:\n assert parts.ndim == 1 or parts.shape[1] == 1\n parts = parts.flatten().tolist()\n return parts\n\n\ndef _machines_to_worker_map(\n *,\n machines: str,\n worker_addresses: Iterable[str],\n) -> Dict[str, int]:\n \"\"\"Create a worker_map from machines list.\n\n Given ``machines`` and a list of Dask worker addresses, return a mapping where the keys are\n ``worker_addresses`` and the values are ports from ``machines``.\n\n Parameters\n ----------\n machines : str\n A comma-delimited list of workers, of the form ``ip1:port,ip2:port``.\n worker_addresses : list of str\n An iterable of Dask worker addresses, of the form ``{protocol}{hostname}:{port}``, where ``port`` is the port Dask's scheduler uses to talk to that worker.\n\n Returns\n -------\n result : Dict[str, int]\n Dictionary where keys are work addresses in the form expected by Dask and values are a port for LightGBM to use.\n \"\"\"\n machine_addresses = machines.split(\",\")\n\n if len(set(machine_addresses)) != len(machine_addresses):\n raise ValueError(\n f\"Found duplicates in 'machines' ({machines}). Each entry in 'machines' must be a unique IP-port combination.\"\n )\n\n machine_to_port = defaultdict(set)\n for address in machine_addresses:\n host, port = address.split(\":\")\n machine_to_port[host].add(int(port))\n\n out = {}\n for address in worker_addresses:\n worker_host = urlparse(address).hostname\n if not worker_host:\n raise ValueError(f\"Could not parse host name from worker address '{address}'\")\n out[address] = machine_to_port[worker_host].pop()\n\n return out\n\n\ndef _train(\n *,\n client: Client,\n data: _DaskMatrixLike,\n label: _DaskCollection,\n params: Dict[str, Any],\n model_factory: Type[LGBMModel],\n sample_weight: Optional[_DaskVectorLike] = None,\n init_score: Optional[_DaskCollection] = None,\n group: Optional[_DaskVectorLike] = None,\n eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None,\n eval_names: Optional[List[str]] = None,\n eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None,\n eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None,\n eval_sample_weight: Optional[List[_DaskVectorLike]] = None,\n eval_class_weight: Optional[List[Union[dict, str]]] = None,\n eval_init_score: Optional[List[_DaskCollection]] = None,\n eval_group: Optional[List[_DaskVectorLike]] = None,\n eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None,\n eval_at: Optional[Union[List[int], Tuple[int, ...]]] = None,\n **kwargs: Any,\n) -> LGBMModel:\n \"\"\"Inner train routine.\n\n Parameters\n ----------\n client : dask.distributed.Client\n Dask client.\n data : Dask Array or Dask DataFrame of shape = [n_samples, n_features]\n Input feature matrix.\n label : Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]\n The target values (class labels in classification, real numbers in regression).\n params : dict\n Parameters passed to constructor of the local underlying model.\n model_factory : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class\n Class of the local underlying model.\n sample_weight : Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)\n Weights of training data. Weights should be non-negative.\n init_score : Dask Array or Dask Series of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task), or Dask Array or Dask DataFrame of shape = [n_samples, n_classes] (for multi-class task), or None, optional (default=None)\n Init score of training data.\n group : Dask Array or Dask Series or None, optional (default=None)\n Group/query data.\n Only used in the learning-to-rank task.\n sum(group) = n_samples.\n For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,\n where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.\n eval_set : list of (X, y) tuples of Dask data collections, or None, optional (default=None)\n List of (X, y) tuple pairs to use as validation sets.\n Note, that not all workers may receive chunks of every eval set within ``eval_set``. When the returned\n lightgbm estimator is not trained using any chunks of a particular eval set, its corresponding component\n of ``evals_result_`` and ``best_score_`` will be empty dictionaries.\n eval_names : list of str, or None, optional (default=None)\n Names of eval_set.\n eval_X : Dask Array or Dask DataFrame, tuple thereof or None, optional (default=None)\n Feature matrix or tuple thereof, e.g. ``(X_val0, X_val1)``, to use as validation sets.\n eval_y : Dask Array or Dask DataFrame or Dask Series, tuple thereof or None, optional (default=None)\n Target values or tuple thereof, e.g. ``(y_val0, y_val1)``, to use as validation sets.\n eval_sample_weight : list of Dask Array or Dask Series, or None, optional (default=None)\n Weights for each validation set in eval_set. Weights should be non-negative.\n eval_class_weight : list of dict or str, or None, optional (default=None)\n Class weights, one dict or str for each validation set in eval_set.\n eval_init_score : list of Dask Array, Dask Series or Dask DataFrame (for multi-class task), or None, optional (default=None)\n Initial model score for each validation set in eval_set.\n eval_group : list of Dask Array or Dask Series, or None, optional (default=None)\n Group/query for each validation set in eval_set.\n eval_metric : str, callable, list or None, optional (default=None)\n If str, it should be a built-in evaluation metric to use.\n If callable, it should be a custom evaluation metric, see note below for more details.\n If list, it can be a list of built-in metrics, a list of custom evaluation metrics, or a mix of both.\n In either case, the ``metric`` from the Dask model parameters (or inferred from the objective) will be evaluated and used as well.\n Default: 'l2' for DaskLGBMRegressor, 'binary(multi)_logloss' for DaskLGBMClassifier, 'ndcg' for DaskLGBMRanker.\n eval_at : list or tuple of int, optional (default=None)\n The evaluation positions of the specified ranking metric.\n **kwargs\n Other parameters passed to ``fit`` method of the local underlying model.\n\n Returns\n -------\n model : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class\n Returns fitted underlying model.\n\n Note\n ----\n\n This method handles setting up the following network parameters based on information\n about the Dask cluster referenced by ``client``.\n\n * ``local_listen_port``: port that each LightGBM worker opens a listening socket on,\n to accept connections from other workers. This can differ from LightGBM worker\n to LightGBM worker, but does not have to.\n * ``machines``: a comma-delimited list of all workers in the cluster, in the\n form ``ip:port,ip:port``. If running multiple Dask workers on the same host, use different\n ports for each worker. For example, for ``LocalCluster(n_workers=3)``, you might\n pass ``\"127.0.0.1:12400,127.0.0.1:12401,127.0.0.1:12402\"``.\n * ``num_machines``: number of LightGBM workers.\n * ``timeout``: time in minutes to wait before closing unused sockets.\n\n The default behavior of this function is to generate ``machines`` from the list of\n Dask workers which hold some piece of the training data, and to search for an open\n port on each worker to be used as ``local_listen_port``.\n\n If ``machines`` is provided explicitly in ``params``, this function uses the hosts\n and ports in that list directly, and does not do any searching. This means that if\n any of the Dask workers are missing from the list or any of those ports are not free\n when training starts, training will fail.\n\n If ``local_listen_port`` is provided in ``params`` and ``machines`` is not, this function\n constructs ``machines`` from the list of Dask workers which hold some piece of the\n training data, assuming that each one will use the same ``local_listen_port``.\n \"\"\"\n params = deepcopy(params)\n\n # capture whether local_listen_port or its aliases were provided\n listen_port_in_params = any(alias in params for alias in _ConfigAliases.get(\"local_listen_port\"))\n\n # capture whether machines or its aliases were provided\n machines_in_params = any(alias in params for alias in _ConfigAliases.get(\"machines\"))\n\n params = _choose_param_value(\n main_param_name=\"tree_learner\",\n params=params,\n default_value=\"data\",\n )\n allowed_tree_learners = {\n \"data\",\n \"data_parallel\",\n \"feature\",\n \"feature_parallel\",\n \"voting\",\n \"voting_parallel\",\n }\n if params[\"tree_learner\"] not in allowed_tree_learners:\n _log_warning(\n f'Parameter tree_learner set to {params[\"tree_learner\"]}, which is not allowed. Using \"data\" as default'\n )\n params[\"tree_learner\"] = \"data\"\n\n # Some passed-in parameters can be removed:\n # * 'num_machines': set automatically from Dask worker list\n # * 'num_threads': overridden to match nthreads on each Dask process\n for param_alias in _ConfigAliases.get(\"num_machines\", \"num_threads\"):\n if param_alias in params:\n _log_warning(f\"Parameter {param_alias} will be ignored.\")\n params.pop(param_alias)\n\n # Split arrays/dataframes into parts. Arrange parts into dicts to enforce co-locality\n data_parts = _split_to_parts(data=data, is_matrix=True)\n label_parts = _split_to_parts(data=label, is_matrix=False)\n parts = [{\"data\": x, \"label\": y} for (x, y) in zip(data_parts, label_parts)]\n n_parts = len(parts)\n\n if sample_weight is not None:\n weight_parts = _split_to_parts(data=sample_weight, is_matrix=False)\n for i in range(n_parts):\n parts[i][\"weight\"] = weight_parts[i]\n\n if group is not None:\n group_parts = _split_to_parts(data=group, is_matrix=False)\n for i in range(n_parts):\n parts[i][\"group\"] = group_parts[i]\n\n if init_score is not None:\n init_score_parts = _split_to_parts(data=init_score, is_matrix=False)\n for i in range(n_parts):\n parts[i][\"init_score\"] = init_score_parts[i]\n\n eval_set = _validate_eval_set_Xy(eval_set=eval_set, eval_X=eval_X, eval_y=eval_y)\n # evals_set will to be re-constructed into smaller lists of (X, y) tuples, where\n # X and y are each delayed sub-lists of original eval dask Collections.\n if eval_set:\n # find maximum number of parts in an individual eval set so that we can\n # pad eval sets when they come in different sizes.\n n_largest_eval_parts = max(x[0].npartitions for x in eval_set)\n\n eval_sets: Dict[\n int, List[Union[_DatasetNames, Tuple[List[Optional[_DaskMatrixLike]], List[Optional[_DaskVectorLike]]]]]\n ] = defaultdict(list)\n if eval_sample_weight:\n eval_sample_weights: Dict[int, List[Union[_DatasetNames, List[Optional[_DaskVectorLike]]]]] = defaultdict(\n list\n )\n if eval_group:\n eval_groups: Dict[int, List[Union[_DatasetNames, List[Optional[_DaskVectorLike]]]]] = defaultdict(list)\n if eval_init_score:\n eval_init_scores: Dict[int, List[Union[_DatasetNames, List[Optional[_DaskMatrixLike]]]]] = defaultdict(list)\n\n for i, (X_eval, y_eval) in enumerate(eval_set):\n n_this_eval_parts = X_eval.npartitions\n\n # when individual eval set is equivalent to training data, skip recomputing parts.\n if X_eval is data and y_eval is label:\n for parts_idx in range(n_parts):\n eval_sets[parts_idx].append(_DatasetNames.TRAINSET)\n else:\n eval_x_parts = _split_to_parts(data=X_eval, is_matrix=True)\n eval_y_parts = _split_to_parts(data=y_eval, is_matrix=False)\n for j in range(n_largest_eval_parts):\n parts_idx = j % n_parts\n\n # add None-padding for individual eval_set member if it is smaller than the largest member.\n if j < n_this_eval_parts:\n x_e = eval_x_parts[j]\n y_e = eval_y_parts[j]\n else:\n x_e = None\n y_e = None\n\n if j < n_parts:\n # first time a chunk of this eval set is added to this part.\n eval_sets[parts_idx].append(([x_e], [y_e]))\n else:\n # append additional chunks of this eval set to this part.\n eval_sets[parts_idx][-1][0].append(x_e) # type: ignore[index, union-attr]\n eval_sets[parts_idx][-1][1].append(y_e) # type: ignore[index, union-attr]\n\n if eval_sample_weight:\n if eval_sample_weight[i] is sample_weight:\n for parts_idx in range(n_parts):\n eval_sample_weights[parts_idx].append(_DatasetNames.SAMPLE_WEIGHT)\n else:\n eval_w_parts = _split_to_parts(data=eval_sample_weight[i], is_matrix=False)\n\n # ensure that all evaluation parts map uniquely to one part.\n for j in range(n_largest_eval_parts):\n if j < n_this_eval_parts:\n w_e = eval_w_parts[j]\n else:\n w_e = None\n\n parts_idx = j % n_parts\n if j < n_parts:\n eval_sample_weights[parts_idx].append([w_e])\n else:\n eval_sample_weights[parts_idx][-1].append(w_e) # type: ignore[union-attr]\n\n if eval_init_score:\n if eval_init_score[i] is init_score:\n for parts_idx in range(n_parts):\n eval_init_scores[parts_idx].append(_DatasetNames.INIT_SCORE)\n else:\n eval_init_score_parts = _split_to_parts(data=eval_init_score[i], is_matrix=False)\n for j in range(n_largest_eval_parts):\n if j < n_this_eval_parts:\n init_score_e = eval_init_score_parts[j]\n else:\n init_score_e = None\n\n parts_idx = j % n_parts\n if j < n_parts:\n eval_init_scores[parts_idx].append([init_score_e])\n else:\n eval_init_scores[parts_idx][-1].append(init_score_e) # type: ignore[union-attr]\n\n if eval_group:\n if eval_group[i] is group:\n for parts_idx in range(n_parts):\n eval_groups[parts_idx].append(_DatasetNames.GROUP)\n else:\n eval_g_parts = _split_to_parts(data=eval_group[i], is_matrix=False)\n for j in range(n_largest_eval_parts):\n if j < n_this_eval_parts:\n g_e = eval_g_parts[j]\n else:\n g_e = None\n\n parts_idx = j % n_parts\n if j < n_parts:\n eval_groups[parts_idx].append([g_e])\n else:\n eval_groups[parts_idx][-1].append(g_e) # type: ignore[union-attr]\n\n # assign sub-eval_set components to worker parts.\n for parts_idx, e_set in eval_sets.items():\n parts[parts_idx][\"eval_set\"] = e_set\n if eval_sample_weight:\n parts[parts_idx][\"eval_sample_weight\"] = eval_sample_weights[parts_idx]\n if eval_init_score:\n parts[parts_idx][\"eval_init_score\"] = eval_init_scores[parts_idx]\n if eval_group:\n parts[parts_idx][\"eval_group\"] = eval_groups[parts_idx]\n\n # Start computation in the background\n parts = list(map(delayed, parts))\n parts = client.compute(parts)\n wait(parts)\n\n for part in parts:\n if part.status == \"error\": # type: ignore\n # trigger error locally\n return part # type: ignore[return-value]\n\n # Find locations of all parts and map them to particular Dask workers\n key_to_part_dict = {part.key: part for part in parts} # type: ignore\n who_has = client.who_has(parts)\n worker_map = defaultdict(list)\n for key, workers in who_has.items():\n worker_map[next(iter(workers))].append(key_to_part_dict[key])\n\n # Check that all workers were provided some of eval_set. Otherwise warn user that validation\n # data artifacts may not be populated depending on worker returning final estimator.\n if eval_set:\n for worker in worker_map:\n has_eval_set = False\n for part in worker_map[worker]:\n if \"eval_set\" in part.result(): # type: ignore[attr-defined]\n has_eval_set = True\n break\n\n if not has_eval_set:\n _log_warning(\n f\"Worker {worker} was not allocated eval_set data. Therefore evals_result_ and best_score_ data may be unreliable. \"\n \"Try rebalancing data across workers.\"\n )\n\n # assign general validation set settings to fit kwargs.\n if eval_names:\n kwargs[\"eval_names\"] = eval_names\n if eval_class_weight:\n kwargs[\"eval_class_weight\"] = eval_class_weight\n if eval_metric:\n kwargs[\"eval_metric\"] = eval_metric\n if eval_at:\n kwargs[\"eval_at\"] = eval_at\n\n master_worker = next(iter(worker_map))\n worker_ncores = client.ncores()\n\n # resolve aliases for network parameters and pop the result off params.\n # these values are added back in calls to `_train_part()`\n params = _choose_param_value(\n main_param_name=\"local_listen_port\",\n params=params,\n default_value=12400,\n )\n local_listen_port = params.pop(\"local_listen_port\")\n\n params = _choose_param_value(\n main_param_name=\"machines\",\n params=params,\n default_value=None,\n )\n machines = params.pop(\"machines\")\n\n # figure out network params\n worker_to_socket_future: Dict[str, Future] = {}\n worker_addresses = worker_map.keys()\n if machines is not None:\n _log_info(\"Using passed-in 'machines' parameter\")\n worker_address_to_port = _machines_to_worker_map(\n machines=machines,\n worker_addresses=worker_addresses,\n )\n else:\n if listen_port_in_params:\n _log_info(\"Using passed-in 'local_listen_port' for all workers\")\n unique_hosts = {urlparse(a).hostname for a in worker_addresses}\n if len(unique_hosts) < len(worker_addresses):\n msg = (\n \"'local_listen_port' was provided in Dask training parameters, but at least one \"\n \"machine in the cluster has multiple Dask worker processes running on it. Please omit \"\n \"'local_listen_port' or pass 'machines'.\"\n )\n raise LightGBMError(msg)\n\n worker_address_to_port = dict.fromkeys(worker_addresses, local_listen_port)\n else:\n _log_info(\"Finding random open ports for workers\")\n worker_to_socket_future, worker_address_to_port = _assign_open_ports_to_workers(\n client=client,\n workers=list(worker_map.keys()),\n )\n\n machines = \",\".join(\n [f\"{urlparse(worker_address).hostname}:{port}\" for worker_address, port in worker_address_to_port.items()]\n )\n\n num_machines = len(worker_address_to_port)\n\n # Tell each worker to train on the parts that it has locally\n #\n # This code treats ``_train_part()`` calls as not \"pure\" because:\n # 1. there is randomness in the training process unless parameters ``seed``\n # and ``deterministic`` are set\n # 2. even with those parameters set, the output of one ``_train_part()`` call\n # relies on global state (it and all the other LightGBM training processes\n # coordinate with each other)\n futures_classifiers = [\n client.submit(\n _train_part,\n model_factory=model_factory,\n params={**params, \"num_threads\": worker_ncores[worker]},\n list_of_parts=list_of_parts,\n machines=machines,\n local_listen_port=worker_address_to_port[worker],\n num_machines=num_machines,\n time_out=params.get(\"time_out\", 120),\n remote_socket=worker_to_socket_future.get(worker, None),\n return_model=(worker == master_worker),\n workers=[worker],\n allow_other_workers=False,\n pure=False,\n **kwargs,\n )\n for worker, list_of_parts in worker_map.items()\n ]\n\n results = client.gather(futures_classifiers)\n results = [v for v in results if v]\n model = results[0]\n\n # if network parameters were changed during training, remove them from the\n # returned model so that they're generated dynamically on every run based\n # on the Dask cluster you're connected to and which workers have pieces of\n # the training data\n if not listen_port_in_params:\n for param in _ConfigAliases.get(\"local_listen_port\"):\n model._other_params.pop(param, None)\n\n if not machines_in_params:\n for param in _ConfigAliases.get(\"machines\"):\n model._other_params.pop(param, None)\n\n for param in _ConfigAliases.get(\"num_machines\", \"timeout\"):\n model._other_params.pop(param, None)\n\n return model\n\n\ndef _predict_part(\n part: _DaskPart,\n *,\n model: LGBMModel,\n raw_score: bool,\n pred_proba: bool,\n pred_leaf: bool,\n pred_contrib: bool,\n **kwargs: Any,\n) -> _DaskPart:\n result: _DaskPart\n if part.shape[0] == 0:\n result = np.array([])\n elif pred_proba:\n result = model.predict_proba(\n part,\n raw_score=raw_score,\n pred_leaf=pred_leaf,\n pred_contrib=pred_contrib,\n **kwargs,\n )\n else:\n result = model.predict(\n part,\n raw_score=raw_score,\n pred_leaf=pred_leaf,\n pred_contrib=pred_contrib,\n **kwargs,\n )\n\n # dask.DataFrame.map_partitions() expects each call to return a pandas DataFrame or Series\n if isinstance(part, pd_DataFrame):\n # assert that 'result' is an array, only necessary because predict(..., pred_contrib=True) on\n # sparse matrices returns a list.\n #\n # This can be removed when https://github.com/lightgbm-org/LightGBM/pull/6348 is resolved.\n error_msg = (\n f\"predict(X) for lightgbm.dask estimators should always return an array, not '{type(result)}', when X is a pandas Dataframe. \"\n \"If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues.\"\n )\n assert hasattr(result, \"shape\"), error_msg\n if len(result.shape) == 2:\n result = pd_DataFrame(result, index=part.index)\n else:\n result = pd_Series(result, index=part.index, name=\"predictions\")\n\n return result\n\n\ndef _predict(\n *,\n model: LGBMModel,\n data: _DaskMatrixLike,\n client: Client,\n raw_score: bool = False,\n pred_proba: bool = False,\n pred_leaf: bool = False,\n pred_contrib: bool = False,\n **kwargs: Any,\n) -> Union[dask_Array, List[dask_Array]]:\n \"\"\"Inner predict routine.\n\n Parameters\n ----------\n model : lightgbm.LGBMClassifier, lightgbm.LGBMRegressor, or lightgbm.LGBMRanker class\n Fitted underlying model.\n data : Dask Array or Dask DataFrame of shape = [n_samples, n_features]\n Input feature matrix.\n raw_score : bool, optional (default=False)\n Whether to predict raw scores.\n pred_proba : bool, optional (default=False)\n Should method return results of ``predict_proba`` (``pred_proba=True``) or ``predict`` (``pred_proba=False``).\n pred_leaf : bool, optional (default=False)\n Whether to predict leaf index.\n pred_contrib : bool, optional (default=False)\n Whether to predict feature contributions.\n **kwargs\n Other parameters passed to ``predict`` or ``predict_proba`` method.\n\n Returns\n -------\n predicted_result : Dask Array of shape = [n_samples] or shape = [n_samples, n_classes]\n The predicted values.\n X_leaves : Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]\n If ``pred_leaf=True``, the predicted leaf of every tree for each sample.\n X_SHAP_values : Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of ``n_classes`` Dask Arrays of shape = [n_samples, n_features + 1]\n If ``pred_contrib=True``, the feature contributions for each sample.\n \"\"\"\n if not all((DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED)):\n raise LightGBMError(\"dask, pandas and scikit-learn are required for lightgbm.dask\")\n if isinstance(data, dask_DataFrame):\n return data.map_partitions(\n _predict_part,\n model=model,\n raw_score=raw_score,\n pred_proba=pred_proba,\n pred_leaf=pred_leaf,\n pred_contrib=pred_contrib,\n **kwargs,\n ).values\n elif isinstance(data, dask_Array):\n # for multi-class classification with sparse matrices, pred_contrib predictions\n # are returned as a list of sparse matrices (one per class)\n num_classes = model._n_classes\n\n if num_classes > 2 and pred_contrib and isinstance(data._meta, ss.spmatrix):\n predict_function = partial(\n _predict_part,\n model=model,\n raw_score=False,\n pred_proba=pred_proba,\n pred_leaf=False,\n pred_contrib=True,\n **kwargs,\n )\n\n delayed_chunks = data.to_delayed()\n bag = dask_bag_from_delayed(delayed_chunks[:, 0])\n\n @delayed\n def _extract(items: List[Any], i: int) -> Any:\n return items[i]\n\n preds = bag.map_partitions(predict_function)\n\n # pred_contrib output will have one column per feature,\n # plus one more for the base value\n num_cols = model.n_features_ + 1\n\n nrows_per_chunk = data.chunks[0]\n out: List[List[dask_Array]] = [[] for _ in range(num_classes)]\n\n # need to tell Dask the expected type and shape of individual preds\n pred_meta = data._meta\n\n for j, partition in enumerate(preds.to_delayed()):\n for i in range(num_classes):\n part = dask_array_from_delayed(\n value=_extract(partition, i),\n shape=(nrows_per_chunk[j], num_cols),\n meta=pred_meta,\n )\n out[i].append(part)\n\n # by default, dask.array.concatenate() concatenates sparse arrays into a COO matrix\n # the code below is used instead to ensure that the sparse type is preserved during concatenation\n if isinstance(pred_meta, ss.csr_matrix):\n concat_fn = partial(ss.vstack, format=\"csr\")\n elif isinstance(pred_meta, ss.csc_matrix):\n concat_fn = partial(ss.vstack, format=\"csc\")\n else:\n concat_fn = ss.vstack\n\n # At this point, `out` is a list of lists of delayeds (each of which points to a matrix).\n # Concatenate them to return a list of Dask Arrays.\n out_arrays: List[dask_Array] = []\n for i in range(num_classes):\n out_arrays.append(\n dask_array_from_delayed(\n value=delayed(concat_fn)(out[i]),\n shape=(data.shape[0], num_cols),\n meta=pred_meta,\n )\n )\n\n return out_arrays\n\n data_row = client.compute(data[[0]]).result()\n predict_fn = partial(\n _predict_part,\n model=model,\n raw_score=raw_score,\n pred_proba=pred_proba,\n pred_leaf=pred_leaf,\n pred_contrib=pred_contrib,\n **kwargs,\n )\n pred_row = predict_fn(data_row) # type: ignore[misc]\n chunks: Tuple[int, ...] = (data.chunks[0],)\n map_blocks_kwargs = {}\n if len(pred_row.shape) > 1:\n chunks += (pred_row.shape[1],)\n else:\n map_blocks_kwargs[\"drop_axis\"] = 1\n return data.map_blocks(\n predict_fn,\n chunks=chunks,\n meta=pred_row,\n **map_blocks_kwargs,\n )\n else:\n raise TypeError(f\"Data must be either Dask Array or Dask DataFrame. Got {type(data).__name__}.\")\n\n\nclass _DaskLGBMModel:\n @property\n def client_(self) -> Client:\n \"\"\":obj:`dask.distributed.Client`: Dask client.\n\n This property can be passed in the constructor or updated\n with ``model.set_params(client=client)``.\n \"\"\"\n if not getattr(self, \"fitted_\", False):\n raise LGBMNotFittedError(\"Cannot access property client_ before calling fit().\")\n\n return _get_dask_client(client=self.client)\n\n def _lgb_dask_getstate(self) -> Dict[Any, Any]:\n \"\"\"Remove un-picklable attributes before serialization.\"\"\"\n client = self.__dict__.pop(\"client\", None)\n self._other_params.pop(\"client\", None) # type: ignore[attr-defined]\n out = deepcopy(self.__dict__)\n out.update({\"client\": None})\n self.client = client\n return out\n\n def _lgb_dask_fit(\n self,\n *,\n model_factory: Type[LGBMModel],\n X: _DaskMatrixLike,\n y: _DaskCollection,\n sample_weight: Optional[_DaskVectorLike] = None,\n init_score: Optional[_DaskCollection] = None,\n group: Optional[_DaskVectorLike] = None,\n eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None,\n eval_names: Optional[List[str]] = None,\n eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None,\n eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None,\n eval_sample_weight: Optional[List[_DaskVectorLike]] = None,\n eval_class_weight: Optional[List[Union[dict, str]]] = None,\n eval_init_score: Optional[List[_DaskCollection]] = None,\n eval_group: Optional[List[_DaskVectorLike]] = None,\n eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None,\n eval_at: Optional[Union[List[int], Tuple[int, ...]]] = None,\n **kwargs: Any,\n ) -> \"_DaskLGBMModel\":\n if not DASK_INSTALLED:\n raise LightGBMError(\"dask is required for lightgbm.dask\")\n if not all((DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED)):\n raise LightGBMError(\"dask, pandas and scikit-learn are required for lightgbm.dask\")\n\n params = self.get_params(True) # type: ignore[attr-defined]\n params.pop(\"client\", None)\n\n model = _train(\n client=_get_dask_client(self.client),\n data=X,\n label=y,\n params=params,\n model_factory=model_factory,\n sample_weight=sample_weight,\n init_score=init_score,\n group=group,\n eval_set=eval_set,\n eval_names=eval_names,\n eval_X=eval_X,\n eval_y=eval_y,\n eval_sample_weight=eval_sample_weight,\n eval_class_weight=eval_class_weight,\n eval_init_score=eval_init_score,\n eval_group=eval_group,\n eval_metric=eval_metric,\n eval_at=eval_at,\n **kwargs,\n )\n\n self.set_params(**model.get_params()) # type: ignore[attr-defined]\n self._lgb_dask_copy_extra_params(source=model, dest=self) # type: ignore[attr-defined]\n\n return self\n\n def _lgb_dask_to_local(self, model_factory: Type[LGBMModel]) -> LGBMModel:\n params = self.get_params() # type: ignore[attr-defined]\n params.pop(\"client\", None)\n model = model_factory(**params)\n self._lgb_dask_copy_extra_params(source=self, dest=model)\n model._other_params.pop(\"client\", None)\n return model\n\n @staticmethod\n def _lgb_dask_copy_extra_params(\n *,\n source: Union[\"_DaskLGBMModel\", LGBMModel],\n dest: Union[\"_DaskLGBMModel\", LGBMModel],\n ) -> None:\n params = source.get_params() # type: ignore[union-attr]\n attributes = source.__dict__\n extra_param_names = set(attributes.keys()).difference(params.keys())\n for name in extra_param_names:\n setattr(dest, name, attributes[name])\n\n\nclass DaskLGBMClassifier(LGBMClassifier, _DaskLGBMModel):\n \"\"\"Distributed version of lightgbm.LGBMClassifier.\"\"\"\n\n def __init__(\n self,\n *,\n boosting_type: str = \"gbdt\",\n num_leaves: int = 31,\n max_depth: int = -1,\n learning_rate: float = 0.1,\n n_estimators: int = 100,\n subsample_for_bin: int = 200000,\n objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None,\n class_weight: Optional[Union[dict, str]] = None,\n min_split_gain: float = 0.0,\n min_child_weight: float = 1e-3,\n min_child_samples: int = 20,\n subsample: float = 1.0,\n subsample_freq: int = 0,\n colsample_bytree: float = 1.0,\n reg_alpha: float = 0.0,\n reg_lambda: float = 0.0,\n random_state: Optional[Union[int, np.random.RandomState, \"np.random.Generator\"]] = None,\n n_jobs: Optional[int] = None,\n importance_type: str = \"split\",\n client: Optional[Client] = None,\n **kwargs: Any,\n ):\n \"\"\"Docstring is inherited from the lightgbm.LGBMClassifier.__init__.\"\"\"\n self.client = client\n super().__init__(\n boosting_type=boosting_type,\n num_leaves=num_leaves,\n max_depth=max_depth,\n learning_rate=learning_rate,\n n_estimators=n_estimators,\n subsample_for_bin=subsample_for_bin,\n objective=objective,\n class_weight=class_weight,\n min_split_gain=min_split_gain,\n min_child_weight=min_child_weight,\n min_child_samples=min_child_samples,\n subsample=subsample,\n subsample_freq=subsample_freq,\n colsample_bytree=colsample_bytree,\n reg_alpha=reg_alpha,\n reg_lambda=reg_lambda,\n random_state=random_state,\n n_jobs=n_jobs,\n importance_type=importance_type,\n **kwargs,\n )\n\n _base_doc = LGBMClassifier.__init__.__doc__\n _before_kwargs, _kwargs, _after_kwargs = _base_doc.partition(\"**kwargs\") # type: ignore\n __init__.__doc__ = f\"\"\"\n {_before_kwargs}client : dask.distributed.Client or None, optional (default=None)\n {\" \":4}Dask client. If ``None``, ``distributed.default_client()`` will be used at runtime. The Dask client used by this class will not be saved if the model object is pickled.\n {_kwargs}{_after_kwargs}\n \"\"\"\n\n def __getstate__(self) -> Dict[Any, Any]:\n return self._lgb_dask_getstate()\n\n def fit( # type: ignore[override]\n self,\n X: _DaskMatrixLike,\n y: _DaskCollection,\n sample_weight: Optional[_DaskVectorLike] = None,\n init_score: Optional[_DaskCollection] = None,\n eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None,\n eval_names: Optional[List[str]] = None,\n eval_sample_weight: Optional[List[_DaskVectorLike]] = None,\n eval_class_weight: Optional[List[Union[dict, str]]] = None,\n eval_init_score: Optional[List[_DaskCollection]] = None,\n eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None,\n *,\n eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None,\n eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None,\n **kwargs: Any,\n ) -> \"DaskLGBMClassifier\":\n \"\"\"Docstring is inherited from the lightgbm.LGBMClassifier.fit.\"\"\"\n self._lgb_dask_fit(\n model_factory=LGBMClassifier,\n X=X,\n y=y,\n sample_weight=sample_weight,\n init_score=init_score,\n eval_set=eval_set,\n eval_names=eval_names,\n eval_X=eval_X,\n eval_y=eval_y,\n eval_sample_weight=eval_sample_weight,\n eval_class_weight=eval_class_weight,\n eval_init_score=eval_init_score,\n eval_metric=eval_metric,\n **kwargs,\n )\n return self\n\n _base_doc = _lgbmmodel_doc_fit.format(\n X_shape=\"Dask Array or Dask DataFrame of shape = [n_samples, n_features]\",\n y_shape=\"Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]\",\n sample_weight_shape=\"Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)\",\n init_score_shape=\"Dask Array or Dask Series of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task), or Dask Array or Dask DataFrame of shape = [n_samples, n_classes] (for multi-class task), or None, optional (default=None)\",\n group_shape=\"Dask Array or Dask Series or None, optional (default=None)\",\n eval_sample_weight_shape=\"list of Dask Array or Dask Series, or None, optional (default=None)\",\n eval_init_score_shape=\"list of Dask Array, Dask Series or Dask DataFrame (for multi-class task), or None, optional (default=None)\",\n eval_group_shape=\"list of Dask Array or Dask Series, or None, optional (default=None)\",\n )\n\n # DaskLGBMClassifier does not support group, eval_group.\n _base_doc = _base_doc[: _base_doc.find(\"group :\")] + _base_doc[_base_doc.find(\"eval_set :\") :]\n\n _base_doc = _base_doc[: _base_doc.find(\"eval_group :\")] + _base_doc[_base_doc.find(\"eval_metric :\") :]\n\n # DaskLGBMClassifier support for callbacks and init_model is not tested\n fit.__doc__ = f\"\"\"{_base_doc[: _base_doc.find(\"callbacks :\")]}**kwargs\n Other parameters passed through to ``LGBMClassifier.fit()``.\n\n Returns\n -------\n self : lightgbm.DaskLGBMClassifier\n Returns self.\n\n {_lgbmmodel_doc_custom_eval_note}\n \"\"\"\n\n def predict(\n self,\n X: _DaskMatrixLike, # type: ignore[override]\n raw_score: bool = False,\n start_iteration: int = 0,\n num_iteration: Optional[int] = None,\n pred_leaf: bool = False,\n pred_contrib: bool = False,\n validate_features: bool = False,\n **kwargs: Any,\n ) -> dask_Array:\n \"\"\"Docstring is inherited from the lightgbm.LGBMClassifier.predict.\"\"\"\n return _predict(\n model=self.to_local(),\n data=X,\n client=_get_dask_client(self.client),\n raw_score=raw_score,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n pred_leaf=pred_leaf,\n pred_contrib=pred_contrib,\n validate_features=validate_features,\n **kwargs,\n )\n\n predict.__doc__ = _lgbmmodel_doc_predict.format(\n description=\"Return the predicted value for each sample.\",\n X_shape=\"Dask Array or Dask DataFrame of shape = [n_samples, n_features]\",\n output_name=\"predicted_result\",\n predicted_result_shape=\"Dask Array of shape = [n_samples] or shape = [n_samples, n_classes]\",\n X_leaves_shape=\"Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]\",\n X_SHAP_values_shape=\"Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of ``n_classes`` Dask Arrays of shape = [n_samples, n_features + 1]\",\n )\n\n def predict_proba(\n self,\n X: _DaskMatrixLike, # type: ignore[override]\n raw_score: bool = False,\n start_iteration: int = 0,\n num_iteration: Optional[int] = None,\n pred_leaf: bool = False,\n pred_contrib: bool = False,\n validate_features: bool = False,\n **kwargs: Any,\n ) -> dask_Array:\n \"\"\"Docstring is inherited from the lightgbm.LGBMClassifier.predict_proba.\"\"\"\n return _predict(\n model=self.to_local(),\n data=X,\n pred_proba=True,\n client=_get_dask_client(self.client),\n raw_score=raw_score,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n pred_leaf=pred_leaf,\n pred_contrib=pred_contrib,\n validate_features=validate_features,\n **kwargs,\n )\n\n predict_proba.__doc__ = _lgbmmodel_doc_predict.format(\n description=\"Return the predicted probability for each class for each sample.\",\n X_shape=\"Dask Array or Dask DataFrame of shape = [n_samples, n_features]\",\n output_name=\"predicted_probability\",\n predicted_result_shape=\"Dask Array of shape = [n_samples] or shape = [n_samples, n_classes]\",\n X_leaves_shape=\"Dask Array of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]\",\n X_SHAP_values_shape=\"Dask Array of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or (if multi-class and using sparse inputs) a list of ``n_classes`` Dask Arrays of shape = [n_samples, n_features + 1]\",\n )\n\n def to_local(self) -> LGBMClassifier:\n \"\"\"Create regular version of lightgbm.LGBMClassifier from the distributed version.\n\n Returns\n -------\n model : lightgbm.LGBMClassifier\n Local underlying model.\n \"\"\"\n return self._lgb_dask_to_local(LGBMClassifier)\n\n\nclass DaskLGBMRegressor(LGBMRegressor, _DaskLGBMModel):\n \"\"\"Distributed version of lightgbm.LGBMRegressor.\"\"\"\n\n def __init__(\n self,\n *,\n boosting_type: str = \"gbdt\",\n num_leaves: int = 31,\n max_depth: int = -1,\n learning_rate: float = 0.1,\n n_estimators: int = 100,\n subsample_for_bin: int = 200000,\n objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None,\n class_weight: Optional[Union[dict, str]] = None,\n min_split_gain: float = 0.0,\n min_child_weight: float = 1e-3,\n min_child_samples: int = 20,\n subsample: float = 1.0,\n subsample_freq: int = 0,\n colsample_bytree: float = 1.0,\n reg_alpha: float = 0.0,\n reg_lambda: float = 0.0,\n random_state: Optional[Union[int, np.random.RandomState, \"np.random.Generator\"]] = None,\n n_jobs: Optional[int] = None,\n importance_type: str = \"split\",\n client: Optional[Client] = None,\n **kwargs: Any,\n ):\n \"\"\"Docstring is inherited from the lightgbm.LGBMRegressor.__init__.\"\"\"\n self.client = client\n super().__init__(\n boosting_type=boosting_type,\n num_leaves=num_leaves,\n max_depth=max_depth,\n learning_rate=learning_rate,\n n_estimators=n_estimators,\n subsample_for_bin=subsample_for_bin,\n objective=objective,\n class_weight=class_weight,\n min_split_gain=min_split_gain,\n min_child_weight=min_child_weight,\n min_child_samples=min_child_samples,\n subsample=subsample,\n subsample_freq=subsample_freq,\n colsample_bytree=colsample_bytree,\n reg_alpha=reg_alpha,\n reg_lambda=reg_lambda,\n random_state=random_state,\n n_jobs=n_jobs,\n importance_type=importance_type,\n **kwargs,\n )\n\n _base_doc = LGBMRegressor.__init__.__doc__\n _before_kwargs, _kwargs, _after_kwargs = _base_doc.partition(\"**kwargs\") # type: ignore\n __init__.__doc__ = f\"\"\"\n {_before_kwargs}client : dask.distributed.Client or None, optional (default=None)\n {\" \":4}Dask client. If ``None``, ``distributed.default_client()`` will be used at runtime. The Dask client used by this class will not be saved if the model object is pickled.\n {_kwargs}{_after_kwargs}\n \"\"\"\n\n def __getstate__(self) -> Dict[Any, Any]:\n return self._lgb_dask_getstate()\n\n def fit( # type: ignore[override]\n self,\n X: _DaskMatrixLike,\n y: _DaskCollection,\n sample_weight: Optional[_DaskVectorLike] = None,\n init_score: Optional[_DaskVectorLike] = None,\n eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None,\n eval_names: Optional[List[str]] = None,\n eval_sample_weight: Optional[List[_DaskVectorLike]] = None,\n eval_init_score: Optional[List[_DaskVectorLike]] = None,\n eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None,\n *,\n eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None,\n eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None,\n **kwargs: Any,\n ) -> \"DaskLGBMRegressor\":\n \"\"\"Docstring is inherited from the lightgbm.LGBMRegressor.fit.\"\"\"\n self._lgb_dask_fit(\n model_factory=LGBMRegressor,\n X=X,\n y=y,\n sample_weight=sample_weight,\n init_score=init_score,\n eval_set=eval_set,\n eval_names=eval_names,\n eval_X=eval_X,\n eval_y=eval_y,\n eval_sample_weight=eval_sample_weight,\n eval_init_score=eval_init_score,\n eval_metric=eval_metric,\n **kwargs,\n )\n return self\n\n _base_doc = _lgbmmodel_doc_fit.format(\n X_shape=\"Dask Array or Dask DataFrame of shape = [n_samples, n_features]\",\n y_shape=\"Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]\",\n sample_weight_shape=\"Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)\",\n init_score_shape=\"Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)\",\n group_shape=\"Dask Array or Dask Series or None, optional (default=None)\",\n eval_sample_weight_shape=\"list of Dask Array or Dask Series, or None, optional (default=None)\",\n eval_init_score_shape=\"list of Dask Array or Dask Series, or None, optional (default=None)\",\n eval_group_shape=\"list of Dask Array or Dask Series, or None, optional (default=None)\",\n )\n\n # DaskLGBMRegressor does not support group, eval_class_weight, eval_group.\n _base_doc = _base_doc[: _base_doc.find(\"group :\")] + _base_doc[_base_doc.find(\"eval_set :\") :]\n\n _base_doc = _base_doc[: _base_doc.find(\"eval_class_weight :\")] + _base_doc[_base_doc.find(\"eval_init_score :\") :]\n\n _base_doc = _base_doc[: _base_doc.find(\"eval_group :\")] + _base_doc[_base_doc.find(\"eval_metric :\") :]\n\n # DaskLGBMRegressor support for callbacks and init_model is not tested\n fit.__doc__ = f\"\"\"{_base_doc[: _base_doc.find(\"callbacks :\")]}**kwargs\n Other parameters passed through to ``LGBMRegressor.fit()``.\n\n Returns\n -------\n self : lightgbm.DaskLGBMRegressor\n Returns self.\n\n {_lgbmmodel_doc_custom_eval_note}\n \"\"\"\n\n def predict(\n self,\n X: _DaskMatrixLike, # type: ignore[override]\n raw_score: bool = False,\n start_iteration: int = 0,\n num_iteration: Optional[int] = None,\n pred_leaf: bool = False,\n pred_contrib: bool = False,\n validate_features: bool = False,\n **kwargs: Any,\n ) -> dask_Array:\n \"\"\"Docstring is inherited from the lightgbm.LGBMRegressor.predict.\"\"\"\n return _predict(\n model=self.to_local(),\n data=X,\n client=_get_dask_client(self.client),\n raw_score=raw_score,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n pred_leaf=pred_leaf,\n pred_contrib=pred_contrib,\n validate_features=validate_features,\n **kwargs,\n )\n\n predict.__doc__ = _lgbmmodel_doc_predict.format(\n description=\"Return the predicted value for each sample.\",\n X_shape=\"Dask Array or Dask DataFrame of shape = [n_samples, n_features]\",\n output_name=\"predicted_result\",\n predicted_result_shape=\"Dask Array of shape = [n_samples]\",\n X_leaves_shape=\"Dask Array of shape = [n_samples, n_trees]\",\n X_SHAP_values_shape=\"Dask Array of shape = [n_samples, n_features + 1]\",\n )\n\n def to_local(self) -> LGBMRegressor:\n \"\"\"Create regular version of lightgbm.LGBMRegressor from the distributed version.\n\n Returns\n -------\n model : lightgbm.LGBMRegressor\n Local underlying model.\n \"\"\"\n return self._lgb_dask_to_local(LGBMRegressor)\n\n\nclass DaskLGBMRanker(LGBMRanker, _DaskLGBMModel):\n \"\"\"Distributed version of lightgbm.LGBMRanker.\"\"\"\n\n def __init__(\n self,\n *,\n boosting_type: str = \"gbdt\",\n num_leaves: int = 31,\n max_depth: int = -1,\n learning_rate: float = 0.1,\n n_estimators: int = 100,\n subsample_for_bin: int = 200000,\n objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None,\n class_weight: Optional[Union[dict, str]] = None,\n min_split_gain: float = 0.0,\n min_child_weight: float = 1e-3,\n min_child_samples: int = 20,\n subsample: float = 1.0,\n subsample_freq: int = 0,\n colsample_bytree: float = 1.0,\n reg_alpha: float = 0.0,\n reg_lambda: float = 0.0,\n random_state: Optional[Union[int, np.random.RandomState, \"np.random.Generator\"]] = None,\n n_jobs: Optional[int] = None,\n importance_type: str = \"split\",\n client: Optional[Client] = None,\n **kwargs: Any,\n ):\n \"\"\"Docstring is inherited from the lightgbm.LGBMRanker.__init__.\"\"\"\n self.client = client\n super().__init__(\n boosting_type=boosting_type,\n num_leaves=num_leaves,\n max_depth=max_depth,\n learning_rate=learning_rate,\n n_estimators=n_estimators,\n subsample_for_bin=subsample_for_bin,\n objective=objective,\n class_weight=class_weight,\n min_split_gain=min_split_gain,\n min_child_weight=min_child_weight,\n min_child_samples=min_child_samples,\n subsample=subsample,\n subsample_freq=subsample_freq,\n colsample_bytree=colsample_bytree,\n reg_alpha=reg_alpha,\n reg_lambda=reg_lambda,\n random_state=random_state,\n n_jobs=n_jobs,\n importance_type=importance_type,\n **kwargs,\n )\n\n _base_doc = LGBMRanker.__init__.__doc__\n _before_kwargs, _kwargs, _after_kwargs = _base_doc.partition(\"**kwargs\") # type: ignore\n __init__.__doc__ = f\"\"\"\n {_before_kwargs}client : dask.distributed.Client or None, optional (default=None)\n {\" \":4}Dask client. If ``None``, ``distributed.default_client()`` will be used at runtime. The Dask client used by this class will not be saved if the model object is pickled.\n {_kwargs}{_after_kwargs}\n \"\"\"\n\n def __getstate__(self) -> Dict[Any, Any]:\n return self._lgb_dask_getstate()\n\n def fit( # type: ignore[override]\n self,\n X: _DaskMatrixLike,\n y: _DaskCollection,\n sample_weight: Optional[_DaskVectorLike] = None,\n init_score: Optional[_DaskVectorLike] = None,\n group: Optional[_DaskVectorLike] = None,\n eval_set: Optional[List[Tuple[_DaskMatrixLike, _DaskCollection]]] = None,\n eval_names: Optional[List[str]] = None,\n eval_sample_weight: Optional[List[_DaskVectorLike]] = None,\n eval_init_score: Optional[List[_DaskVectorLike]] = None,\n eval_group: Optional[List[_DaskVectorLike]] = None,\n eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None,\n eval_at: Union[List[int], Tuple[int, ...]] = (1, 2, 3, 4, 5),\n *,\n eval_X: Optional[Union[_DaskMatrixLike, Tuple[_DaskMatrixLike]]] = None,\n eval_y: Optional[Union[_DaskCollection, Tuple[_DaskCollection]]] = None,\n **kwargs: Any,\n ) -> \"DaskLGBMRanker\":\n \"\"\"Docstring is inherited from the lightgbm.LGBMRanker.fit.\"\"\"\n self._lgb_dask_fit(\n model_factory=LGBMRanker,\n X=X,\n y=y,\n sample_weight=sample_weight,\n init_score=init_score,\n group=group,\n eval_set=eval_set,\n eval_names=eval_names,\n eval_X=eval_X,\n eval_y=eval_y,\n eval_sample_weight=eval_sample_weight,\n eval_init_score=eval_init_score,\n eval_group=eval_group,\n eval_metric=eval_metric,\n eval_at=eval_at,\n **kwargs,\n )\n return self\n\n _base_doc = _lgbmmodel_doc_fit.format(\n X_shape=\"Dask Array or Dask DataFrame of shape = [n_samples, n_features]\",\n y_shape=\"Dask Array, Dask DataFrame or Dask Series of shape = [n_samples]\",\n sample_weight_shape=\"Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)\",\n init_score_shape=\"Dask Array or Dask Series of shape = [n_samples] or None, optional (default=None)\",\n group_shape=\"Dask Array or Dask Series or None, optional (default=None)\",\n eval_sample_weight_shape=\"list of Dask Array or Dask Series, or None, optional (default=None)\",\n eval_init_score_shape=\"list of Dask Array or Dask Series, or None, optional (default=None)\",\n eval_group_shape=\"list of Dask Array or Dask Series, or None, optional (default=None)\",\n )\n\n # DaskLGBMRanker does not support eval_class_weight or early stopping\n _base_doc = _base_doc[: _base_doc.find(\"eval_class_weight :\")] + _base_doc[_base_doc.find(\"eval_init_score :\") :]\n\n _base_doc = (\n _base_doc[: _base_doc.find(\"feature_name :\")]\n + \"eval_at : list or tuple of int, optional (default=(1, 2, 3, 4, 5))\\n\"\n + f\"{' ':8}The evaluation positions of the specified metric.\\n\"\n + f\"{' ':4}{_base_doc[_base_doc.find('feature_name :') :]}\"\n )\n\n # DaskLGBMRanker support for callbacks and init_model is not tested\n fit.__doc__ = f\"\"\"{_base_doc[: _base_doc.find(\"callbacks :\")]}**kwargs\n Other parameters passed through to ``LGBMRanker.fit()``.\n\n Returns\n -------\n self : lightgbm.DaskLGBMRanker\n Returns self.\n\n {_lgbmmodel_doc_custom_eval_note}\n \"\"\"\n\n def predict(\n self,\n X: _DaskMatrixLike, # type: ignore[override]\n raw_score: bool = False,\n start_iteration: int = 0,\n num_iteration: Optional[int] = None,\n pred_leaf: bool = False,\n pred_contrib: bool = False,\n validate_features: bool = False,\n **kwargs: Any,\n ) -> dask_Array:\n \"\"\"Docstring is inherited from the lightgbm.LGBMRanker.predict.\"\"\"\n return _predict(\n model=self.to_local(),\n data=X,\n client=_get_dask_client(self.client),\n raw_score=raw_score,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n pred_leaf=pred_leaf,\n pred_contrib=pred_contrib,\n validate_features=validate_features,\n **kwargs,\n )\n\n predict.__doc__ = _lgbmmodel_doc_predict.format(\n description=\"Return the predicted value for each sample.\",\n X_shape=\"Dask Array or Dask DataFrame of shape = [n_samples, n_features]\",\n output_name=\"predicted_result\",\n predicted_result_shape=\"Dask Array of shape = [n_samples]\",\n X_leaves_shape=\"Dask Array of shape = [n_samples, n_trees]\",\n X_SHAP_values_shape=\"Dask Array of shape = [n_samples, n_features + 1]\",\n )\n\n def to_local(self) -> LGBMRanker:\n \"\"\"Create regular version of lightgbm.LGBMRanker from the distributed version.\n\n Returns\n -------\n model : lightgbm.LGBMRanker\n Local underlying model.\n \"\"\"\n return self._lgb_dask_to_local(LGBMRanker)\n" }, { "path": "python-package/lightgbm/engine.py", "content": "# coding: utf-8\n\"\"\"Library with training routines of LightGBM.\"\"\"\n\nimport copy\nimport json\nfrom collections import OrderedDict, defaultdict\nfrom operator import attrgetter\nfrom pathlib import Path\nfrom typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union\n\nimport numpy as np\n\nfrom . import callback\nfrom .basic import (\n Booster,\n Dataset,\n LightGBMError,\n _choose_param_value,\n _ConfigAliases,\n _InnerPredictor,\n _LGBM_BoosterEvalMethodResultType,\n _LGBM_BoosterEvalMethodResultWithStandardDeviationType,\n _LGBM_CustomObjectiveFunction,\n _LGBM_EvalFunctionResultType,\n _log_warning,\n)\nfrom .compat import SKLEARN_INSTALLED, _LGBMBaseCrossValidator, _LGBMGroupKFold, _LGBMStratifiedKFold\n\n__all__ = [\n \"cv\",\n \"CVBooster\",\n \"train\",\n]\n\n\n_LGBM_CustomMetricFunction = Union[\n Callable[\n [np.ndarray, Dataset],\n _LGBM_EvalFunctionResultType,\n ],\n Callable[\n [np.ndarray, Dataset],\n List[_LGBM_EvalFunctionResultType],\n ],\n]\n\n_LGBM_PreprocFunction = Callable[\n [Dataset, Dataset, Dict[str, Any]],\n Tuple[Dataset, Dataset, Dict[str, Any]],\n]\n\n\ndef _choose_num_iterations(*, num_boost_round_kwarg: int, params: Dict[str, Any]) -> Dict[str, Any]:\n \"\"\"Choose number of boosting rounds.\n\n In ``train()`` and ``cv()``, there are multiple ways to provide configuration for\n the number of boosting rounds to perform:\n\n * the ``num_boost_round`` keyword argument\n * any of the ``num_iterations`` or its aliases via the ``params`` dictionary\n\n These should be preferred in the following order (first one found wins):\n\n 1. ``num_iterations`` provided via ``params`` (because it's the main parameter name)\n 2. any other aliases of ``num_iterations`` provided via ``params``\n 3. the ``num_boost_round`` keyword argument\n\n This function handles that choice, and issuing helpful warnings in the cases where the\n result might be surprising.\n\n Returns\n -------\n params : dict\n Parameters, with ``\"num_iterations\"`` set to the preferred value and all other\n aliases of ``num_iterations`` removed.\n \"\"\"\n num_iteration_configs_provided = {\n alias: params[alias] for alias in _ConfigAliases.get(\"num_iterations\") if alias in params\n }\n\n # now that the relevant information has been pulled out of params, it's safe to overwrite it\n # with the content that should be used for training (i.e. with aliases resolved)\n params = _choose_param_value(\n main_param_name=\"num_iterations\",\n params=params,\n default_value=num_boost_round_kwarg,\n )\n\n # if there were not multiple boosting rounds configurations provided in params,\n # then by definition they cannot have conflicting values... no need to warn\n if len(num_iteration_configs_provided) <= 1:\n return params\n\n # if all the aliases have the same value, no need to warn\n if len(set(num_iteration_configs_provided.values())) <= 1:\n return params\n\n # if this line is reached, lightgbm should warn\n value_string = \", \".join(f\"{alias}={val}\" for alias, val in num_iteration_configs_provided.items())\n _log_warning(\n f\"Found conflicting values for num_iterations provided via 'params': {value_string}. \"\n f\"LightGBM will perform up to {params['num_iterations']} boosting rounds. \"\n \"To be confident in the maximum number of boosting rounds LightGBM will perform and to \"\n \"suppress this warning, modify 'params' so that only one of those is present.\"\n )\n return params\n\n\ndef train(\n params: Dict[str, Any],\n train_set: Dataset,\n num_boost_round: int = 100,\n valid_sets: Optional[List[Dataset]] = None,\n valid_names: Optional[List[str]] = None,\n feval: Optional[Union[_LGBM_CustomMetricFunction, List[_LGBM_CustomMetricFunction]]] = None,\n init_model: Optional[Union[str, Path, Booster]] = None,\n keep_training_booster: bool = False,\n callbacks: Optional[List[Callable]] = None,\n) -> Booster:\n \"\"\"Perform the training with given parameters.\n\n Parameters\n ----------\n params : dict\n Parameters for training. Values passed through ``params`` take precedence over those\n supplied via arguments.\n train_set : Dataset\n Data to be trained on.\n num_boost_round : int, optional (default=100)\n Number of boosting iterations.\n valid_sets : list of Dataset, or None, optional (default=None)\n List of data to be evaluated on during training.\n valid_names : list of str, or None, optional (default=None)\n Names of ``valid_sets``.\n feval : callable, list of callable, or None, optional (default=None)\n Customized evaluation function.\n Each evaluation function should accept two parameters: preds, eval_data,\n and return (eval_name, eval_result, is_higher_better) or list of such tuples.\n\n preds : numpy 1-D array or numpy 2-D array (for multi-class task)\n The predicted values.\n For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes].\n If custom objective function is used, predicted values are returned before any transformation,\n e.g. they are raw margin instead of probability of positive class for binary task in this case.\n eval_data : Dataset\n A ``Dataset`` to evaluate.\n eval_name : str\n The name of evaluation function (without whitespaces).\n eval_result : float\n The eval result.\n is_higher_better : bool\n Is eval result higher better, e.g. AUC is ``is_higher_better``.\n\n To ignore the default metric corresponding to the used objective,\n set the ``metric`` parameter to the string ``\"None\"`` in ``params``.\n init_model : str, pathlib.Path, Booster or None, optional (default=None)\n Filename of LightGBM model or Booster instance used for continue training.\n keep_training_booster : bool, optional (default=False)\n Whether the returned Booster will be used to keep training.\n If False, the returned value will be converted into _InnerPredictor before returning.\n This means you won't be able to use ``eval``, ``eval_train`` or ``eval_valid`` methods of the returned Booster.\n When your model is very large and cause the memory error,\n you can try to set this param to ``True`` to avoid the model conversion performed during the internal call of ``model_to_string``.\n You can still use _InnerPredictor as ``init_model`` for future continue training.\n callbacks : list of callable, or None, optional (default=None)\n List of callback functions that are applied at each iteration.\n See Callbacks in Python API for more information.\n\n Note\n ----\n A custom objective function can be provided for the ``objective`` parameter.\n It should accept two parameters: preds, train_data and return (grad, hess).\n\n preds : numpy 1-D array or numpy 2-D array (for multi-class task)\n The predicted values.\n Predicted values are returned before any transformation,\n e.g. they are raw margin instead of probability of positive class for binary task.\n train_data : Dataset\n The training dataset.\n grad : numpy 1-D array or numpy 2-D array (for multi-class task)\n The value of the first order derivative (gradient) of the loss\n with respect to the elements of preds for each sample point.\n hess : numpy 1-D array or numpy 2-D array (for multi-class task)\n The value of the second order derivative (Hessian) of the loss\n with respect to the elements of preds for each sample point.\n\n For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes],\n and grad and hess should be returned in the same format.\n\n Returns\n -------\n booster : Booster\n The trained Booster model.\n \"\"\"\n if not isinstance(train_set, Dataset):\n raise TypeError(f\"train() only accepts Dataset object, train_set has type '{type(train_set).__name__}'.\")\n\n if isinstance(valid_sets, list):\n for i, valid_item in enumerate(valid_sets):\n if not isinstance(valid_item, Dataset):\n raise TypeError(\n \"Every item in valid_sets must be a Dataset object. \"\n f\"Item {i} has type '{type(valid_item).__name__}'.\"\n )\n\n # create predictor first\n params = copy.deepcopy(params)\n params = _choose_param_value(\n main_param_name=\"objective\",\n params=params,\n default_value=None,\n )\n fobj: Optional[_LGBM_CustomObjectiveFunction] = None\n if callable(params[\"objective\"]):\n fobj = params[\"objective\"]\n params[\"objective\"] = \"none\"\n\n params = _choose_num_iterations(num_boost_round_kwarg=num_boost_round, params=params)\n num_boost_round = params[\"num_iterations\"]\n if num_boost_round <= 0:\n raise ValueError(f\"Number of boosting rounds must be greater than 0. Got {num_boost_round}.\")\n\n # setting early stopping via global params should be possible\n params = _choose_param_value(\n main_param_name=\"early_stopping_round\",\n params=params,\n default_value=None,\n )\n if params[\"early_stopping_round\"] is None:\n params.pop(\"early_stopping_round\")\n first_metric_only = params.get(\"first_metric_only\", False)\n\n predictor: Optional[_InnerPredictor] = None\n if isinstance(init_model, (str, Path)):\n predictor = _InnerPredictor.from_model_file(model_file=init_model, pred_parameter=params)\n elif isinstance(init_model, Booster):\n predictor = _InnerPredictor.from_booster(booster=init_model, pred_parameter=dict(init_model.params, **params))\n\n if predictor is not None:\n init_iteration = predictor.current_iteration()\n else:\n init_iteration = 0\n\n train_set._update_params(params)._set_predictor(predictor)\n\n is_valid_contain_train = False\n train_data_name = \"training\"\n reduced_valid_sets = []\n name_valid_sets = []\n if valid_sets is not None:\n if isinstance(valid_sets, Dataset):\n valid_sets = [valid_sets]\n if isinstance(valid_names, str):\n valid_names = [valid_names]\n for i, valid_data in enumerate(valid_sets):\n # reduce cost for prediction training data\n if valid_data is train_set:\n is_valid_contain_train = True\n if valid_names is not None:\n train_data_name = valid_names[i]\n continue\n reduced_valid_sets.append(valid_data._update_params(params).set_reference(train_set))\n if valid_names is not None and len(valid_names) > i:\n name_valid_sets.append(valid_names[i])\n else:\n name_valid_sets.append(f\"valid_{i}\")\n # process callbacks\n if callbacks is None:\n callbacks_set = set()\n else:\n for i, cb in enumerate(callbacks):\n cb.__dict__.setdefault(\"order\", i - len(callbacks))\n callbacks_set = set(callbacks)\n\n if callback._should_enable_early_stopping(params.get(\"early_stopping_round\", 0)):\n callbacks_set.add(\n callback.early_stopping(\n stopping_rounds=params[\"early_stopping_round\"], # type: ignore[arg-type]\n first_metric_only=first_metric_only,\n min_delta=params.get(\"early_stopping_min_delta\", 0.0),\n verbose=_choose_param_value(\n main_param_name=\"verbosity\",\n params=params,\n default_value=1,\n ).pop(\"verbosity\")\n > 0,\n )\n )\n\n callbacks_before_iter_set = {cb for cb in callbacks_set if getattr(cb, \"before_iteration\", False)}\n callbacks_after_iter_set = callbacks_set - callbacks_before_iter_set\n callbacks_before_iter = sorted(callbacks_before_iter_set, key=attrgetter(\"order\"))\n callbacks_after_iter = sorted(callbacks_after_iter_set, key=attrgetter(\"order\"))\n\n # construct booster\n try:\n booster = Booster(params=params, train_set=train_set)\n if is_valid_contain_train:\n booster.set_train_data_name(train_data_name)\n for valid_set, name_valid_set in zip(reduced_valid_sets, name_valid_sets):\n booster.add_valid(valid_set, name_valid_set)\n finally:\n train_set._reverse_update_params()\n for valid_set in reduced_valid_sets:\n valid_set._reverse_update_params()\n booster.best_iteration = 0\n\n # start training\n for i in range(init_iteration, init_iteration + num_boost_round):\n for cb in callbacks_before_iter:\n cb(\n callback.CallbackEnv(\n model=booster,\n params=params,\n iteration=i,\n begin_iteration=init_iteration,\n end_iteration=init_iteration + num_boost_round,\n evaluation_result_list=None,\n )\n )\n\n booster.update(fobj=fobj)\n\n evaluation_result_list: List[_LGBM_BoosterEvalMethodResultType] = []\n # check evaluation result.\n if valid_sets is not None:\n if is_valid_contain_train:\n evaluation_result_list.extend(booster.eval_train(feval))\n evaluation_result_list.extend(booster.eval_valid(feval))\n try:\n for cb in callbacks_after_iter:\n cb(\n callback.CallbackEnv(\n model=booster,\n params=params,\n iteration=i,\n begin_iteration=init_iteration,\n end_iteration=init_iteration + num_boost_round,\n evaluation_result_list=evaluation_result_list,\n )\n )\n except callback.EarlyStopException as earlyStopException:\n booster.best_iteration = earlyStopException.best_iteration + 1\n # eval results from cv() have a 5th element with the standard deviation of metrics,\n # which is not needed for early stopping\n evaluation_result_list = [item[:4] for item in earlyStopException.best_score]\n break\n booster.best_score = defaultdict(OrderedDict)\n for dataset_name, eval_name, score, _ in evaluation_result_list:\n booster.best_score[dataset_name][eval_name] = score\n if not keep_training_booster:\n booster.model_from_string(booster.model_to_string()).free_dataset()\n return booster\n\n\nclass CVBooster:\n \"\"\"CVBooster in LightGBM.\n\n Auxiliary data structure to hold and redirect all boosters of ``cv()`` function.\n This class has the same methods as Booster class.\n All method calls, except for the following methods, are actually performed for underlying Boosters and\n then all returned results are returned in a list.\n\n - ``model_from_string()``\n - ``model_to_string()``\n - ``save_model()``\n\n Attributes\n ----------\n boosters : list of Booster\n The list of underlying fitted models.\n best_iteration : int\n The best iteration of fitted model.\n \"\"\"\n\n def __init__(\n self,\n model_file: Optional[Union[str, Path]] = None,\n ):\n \"\"\"Initialize the CVBooster.\n\n Parameters\n ----------\n model_file : str, pathlib.Path or None, optional (default=None)\n Path to the CVBooster model file.\n \"\"\"\n self.boosters: List[Booster] = []\n self.best_iteration = -1\n\n if model_file is not None:\n with open(model_file, \"r\") as file:\n self._from_dict(json.load(file))\n\n def _from_dict(self, models: Dict[str, Any]) -> None:\n \"\"\"Load CVBooster from dict.\"\"\"\n self.best_iteration = models[\"best_iteration\"]\n self.boosters = []\n for model_str in models[\"boosters\"]:\n self.boosters.append(Booster(model_str=model_str))\n\n def _to_dict(\n self,\n *,\n num_iteration: Optional[int],\n start_iteration: int,\n importance_type: str,\n ) -> Dict[str, Any]:\n \"\"\"Serialize CVBooster to dict.\"\"\"\n models_str = []\n for booster in self.boosters:\n models_str.append(\n booster.model_to_string(\n num_iteration=num_iteration, start_iteration=start_iteration, importance_type=importance_type\n )\n )\n return {\"boosters\": models_str, \"best_iteration\": self.best_iteration}\n\n def __getattr__(self, name: str) -> Callable[[Any, Any], List[Any]]:\n \"\"\"Redirect methods call of CVBooster.\"\"\"\n\n def handler_function(*args: Any, **kwargs: Any) -> List[Any]:\n \"\"\"Call methods with each booster, and concatenate their results.\"\"\"\n ret = []\n for booster in self.boosters:\n ret.append(getattr(booster, name)(*args, **kwargs))\n return ret\n\n return handler_function\n\n def __getstate__(self) -> Dict[str, Any]:\n return vars(self)\n\n def __setstate__(self, state: Dict[str, Any]) -> None:\n vars(self).update(state)\n\n def model_from_string(self, model_str: str) -> \"CVBooster\":\n \"\"\"Load CVBooster from a string.\n\n Parameters\n ----------\n model_str : str\n Model will be loaded from this string.\n\n Returns\n -------\n self : CVBooster\n Loaded CVBooster object.\n \"\"\"\n self._from_dict(json.loads(model_str))\n return self\n\n def model_to_string(\n self,\n num_iteration: Optional[int] = None,\n start_iteration: int = 0,\n importance_type: str = \"split\",\n ) -> str:\n \"\"\"Save CVBooster to JSON string.\n\n Parameters\n ----------\n num_iteration : int or None, optional (default=None)\n Index of the iteration that should be saved.\n If None, if the best iteration exists, it is saved; otherwise, all iterations are saved.\n If <= 0, all iterations are saved.\n start_iteration : int, optional (default=0)\n Start index of the iteration that should be saved.\n importance_type : str, optional (default=\"split\")\n What type of feature importance should be saved.\n If \"split\", result contains numbers of times the feature is used in a model.\n If \"gain\", result contains total gains of splits which use the feature.\n\n Returns\n -------\n str_repr : str\n JSON string representation of CVBooster.\n \"\"\"\n return json.dumps(\n self._to_dict(num_iteration=num_iteration, start_iteration=start_iteration, importance_type=importance_type)\n )\n\n def save_model(\n self,\n filename: Union[str, Path],\n num_iteration: Optional[int] = None,\n start_iteration: int = 0,\n importance_type: str = \"split\",\n ) -> \"CVBooster\":\n \"\"\"Save CVBooster to a file as JSON text.\n\n Parameters\n ----------\n filename : str or pathlib.Path\n Filename to save CVBooster.\n num_iteration : int or None, optional (default=None)\n Index of the iteration that should be saved.\n If None, if the best iteration exists, it is saved; otherwise, all iterations are saved.\n If <= 0, all iterations are saved.\n start_iteration : int, optional (default=0)\n Start index of the iteration that should be saved.\n importance_type : str, optional (default=\"split\")\n What type of feature importance should be saved.\n If \"split\", result contains numbers of times the feature is used in a model.\n If \"gain\", result contains total gains of splits which use the feature.\n\n Returns\n -------\n self : CVBooster\n Returns self.\n \"\"\"\n with open(filename, \"w\") as file:\n json.dump(\n self._to_dict(\n num_iteration=num_iteration, start_iteration=start_iteration, importance_type=importance_type\n ),\n file,\n )\n\n return self\n\n\ndef _make_n_folds(\n *,\n full_data: Dataset,\n folds: Optional[Union[Iterable[Tuple[np.ndarray, np.ndarray]], _LGBMBaseCrossValidator]],\n nfold: int,\n params: Dict[str, Any],\n seed: int,\n fpreproc: Optional[_LGBM_PreprocFunction],\n stratified: bool,\n shuffle: bool,\n eval_train_metric: bool,\n) -> CVBooster:\n \"\"\"Make a n-fold list of Booster from random indices.\"\"\"\n full_data = full_data.construct()\n num_data = full_data.num_data()\n if folds is not None:\n if not hasattr(folds, \"__iter__\") and not hasattr(folds, \"split\"):\n raise AttributeError(\n \"folds should be a generator or iterator of (train_idx, test_idx) tuples \"\n \"or scikit-learn splitter object with split method\"\n )\n if hasattr(folds, \"split\"):\n group_info = full_data.get_group()\n if group_info is not None:\n group_info = np.asarray(group_info, dtype=np.int32)\n flatted_group = np.repeat(range(len(group_info)), repeats=group_info)\n else:\n flatted_group = np.zeros(num_data, dtype=np.int32)\n folds = folds.split(X=np.empty(num_data), y=full_data.get_label(), groups=flatted_group)\n else:\n if any(\n params.get(obj_alias, \"\")\n in {\"lambdarank\", \"rank_xendcg\", \"xendcg\", \"xe_ndcg\", \"xe_ndcg_mart\", \"xendcg_mart\"}\n for obj_alias in _ConfigAliases.get(\"objective\")\n ):\n if not SKLEARN_INSTALLED:\n raise LightGBMError(\"scikit-learn is required for ranking cv\")\n # ranking task, split according to groups\n group_info = np.asarray(full_data.get_group(), dtype=np.int32)\n flatted_group = np.repeat(range(len(group_info)), repeats=group_info)\n group_kfold = _LGBMGroupKFold(n_splits=nfold)\n folds = group_kfold.split(X=np.empty(num_data), groups=flatted_group)\n elif stratified:\n if not SKLEARN_INSTALLED:\n raise LightGBMError(\"scikit-learn is required for stratified cv\")\n skf = _LGBMStratifiedKFold(n_splits=nfold, shuffle=shuffle, random_state=seed)\n folds = skf.split(X=np.empty(num_data), y=full_data.get_label())\n else:\n if shuffle:\n randidx = np.random.RandomState(seed).permutation(num_data)\n else:\n randidx = np.arange(num_data)\n kstep = int(num_data / nfold)\n test_id = [randidx[i : i + kstep] for i in range(0, num_data, kstep)]\n train_id = [np.concatenate([test_id[i] for i in range(nfold) if k != i]) for k in range(nfold)]\n folds = zip(train_id, test_id)\n\n ret = CVBooster()\n for train_idx, test_idx in folds:\n train_set = full_data.subset(sorted(train_idx))\n valid_set = full_data.subset(sorted(test_idx))\n # run preprocessing on the data set if needed\n if fpreproc is not None:\n train_set, valid_set, tparam = fpreproc(train_set, valid_set, params.copy())\n else:\n tparam = params\n booster_for_fold = Booster(tparam, train_set)\n if eval_train_metric:\n booster_for_fold.add_valid(train_set, \"train\")\n booster_for_fold.add_valid(valid_set, \"valid\")\n ret.boosters.append(booster_for_fold)\n return ret\n\n\ndef _agg_cv_result(\n raw_results: List[List[_LGBM_BoosterEvalMethodResultType]],\n) -> List[_LGBM_BoosterEvalMethodResultWithStandardDeviationType]:\n \"\"\"Aggregate cross-validation results.\"\"\"\n # build up 2 maps, of the form:\n #\n # OrderedDict{\n # (, ): \n # }\n #\n # OrderedDict{\n # (, ): list[]\n # }\n #\n metric_types: Dict[Tuple[str, str], bool] = OrderedDict()\n metric_values: Dict[Tuple[str, str], List[float]] = OrderedDict()\n for one_result in raw_results:\n for dataset_name, metric_name, metric_value, is_higher_better in one_result:\n key = (dataset_name, metric_name)\n metric_types[key] = is_higher_better\n metric_values.setdefault(key, [])\n metric_values[key].append(metric_value)\n\n # turn that into a list of tuples of the form:\n #\n # [\n # (, , mean(), , std_dev())\n # ]\n return [(k[0], k[1], float(np.mean(v)), metric_types[k], float(np.std(v))) for k, v in metric_values.items()]\n\n\ndef cv(\n params: Dict[str, Any],\n train_set: Dataset,\n num_boost_round: int = 100,\n folds: Optional[Union[Iterable[Tuple[np.ndarray, np.ndarray]], _LGBMBaseCrossValidator]] = None,\n nfold: int = 5,\n stratified: bool = True,\n shuffle: bool = True,\n metrics: Optional[Union[str, List[str]]] = None,\n feval: Optional[Union[_LGBM_CustomMetricFunction, List[_LGBM_CustomMetricFunction]]] = None,\n init_model: Optional[Union[str, Path, Booster]] = None,\n fpreproc: Optional[_LGBM_PreprocFunction] = None,\n seed: int = 0,\n callbacks: Optional[List[Callable]] = None,\n eval_train_metric: bool = False,\n return_cvbooster: bool = False,\n) -> Dict[str, Union[List[float], CVBooster]]:\n \"\"\"Perform the cross-validation with given parameters.\n\n Parameters\n ----------\n params : dict\n Parameters for training. Values passed through ``params`` take precedence over those\n supplied via arguments.\n train_set : Dataset\n Data to be trained on.\n num_boost_round : int, optional (default=100)\n Number of boosting iterations.\n folds : generator or iterator of (train_idx, test_idx) tuples, scikit-learn splitter object or None, optional (default=None)\n If generator or iterator, it should yield the train and test indices for each fold.\n If object, it should be one of the scikit-learn splitter classes\n (https://scikit-learn.org/stable/modules/classes.html#splitter-classes)\n and have ``split`` method.\n This argument has highest priority over other data split arguments.\n nfold : int, optional (default=5)\n Number of folds in CV.\n stratified : bool, optional (default=True)\n Whether to perform stratified sampling.\n shuffle : bool, optional (default=True)\n Whether to shuffle before splitting data.\n metrics : str, list of str, or None, optional (default=None)\n Evaluation metrics to be monitored while CV.\n If not None, the metric in ``params`` will be overridden.\n feval : callable, list of callable, or None, optional (default=None)\n Customized evaluation function.\n Each evaluation function should accept two parameters: preds, eval_data,\n and return (eval_name, eval_result, is_higher_better) or list of such tuples.\n\n preds : numpy 1-D array or numpy 2-D array (for multi-class task)\n The predicted values.\n For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes].\n If custom objective function is used, predicted values are returned before any transformation,\n e.g. they are raw margin instead of probability of positive class for binary task in this case.\n eval_data : Dataset\n A ``Dataset`` to evaluate.\n eval_name : str\n The name of evaluation function (without whitespace).\n eval_result : float\n The eval result.\n is_higher_better : bool\n Is eval result higher better, e.g. AUC is ``is_higher_better``.\n\n To ignore the default metric corresponding to the used objective,\n set ``metrics`` to the string ``\"None\"``.\n init_model : str, pathlib.Path, Booster or None, optional (default=None)\n Filename of LightGBM model or Booster instance used for continue training.\n fpreproc : callable or None, optional (default=None)\n Preprocessing function that takes (dtrain, dtest, params)\n and returns transformed versions of those.\n seed : int, optional (default=0)\n Seed used to generate the folds (passed to numpy.random.seed).\n callbacks : list of callable, or None, optional (default=None)\n List of callback functions that are applied at each iteration.\n See Callbacks in Python API for more information.\n eval_train_metric : bool, optional (default=False)\n Whether to display the train metric in progress.\n The score of the metric is calculated again after each training step, so there is some impact on performance.\n return_cvbooster : bool, optional (default=False)\n Whether to return Booster models trained on each fold through ``CVBooster``.\n\n Note\n ----\n A custom objective function can be provided for the ``objective`` parameter.\n It should accept two parameters: preds, train_data and return (grad, hess).\n\n preds : numpy 1-D array or numpy 2-D array (for multi-class task)\n The predicted values.\n Predicted values are returned before any transformation,\n e.g. they are raw margin instead of probability of positive class for binary task.\n train_data : Dataset\n The training dataset.\n grad : numpy 1-D array or numpy 2-D array (for multi-class task)\n The value of the first order derivative (gradient) of the loss\n with respect to the elements of preds for each sample point.\n hess : numpy 1-D array or numpy 2-D array (for multi-class task)\n The value of the second order derivative (Hessian) of the loss\n with respect to the elements of preds for each sample point.\n\n For multi-class task, preds are numpy 2-D array of shape = [n_samples, n_classes],\n and grad and hess should be returned in the same format.\n\n Returns\n -------\n eval_results : dict\n History of evaluation results of each metric.\n The dictionary has the following format:\n {'valid metric1-mean': [values], 'valid metric1-stdv': [values],\n 'valid metric2-mean': [values], 'valid metric2-stdv': [values],\n ...}.\n If ``return_cvbooster=True``, also returns trained boosters wrapped in a ``CVBooster`` object via ``cvbooster`` key.\n If ``eval_train_metric=True``, also returns the train metric history.\n In this case, the dictionary has the following format:\n {'train metric1-mean': [values], 'valid metric1-mean': [values],\n 'train metric2-mean': [values], 'valid metric2-mean': [values],\n ...}.\n \"\"\"\n if not isinstance(train_set, Dataset):\n raise TypeError(f\"cv() only accepts Dataset object, train_set has type '{type(train_set).__name__}'.\")\n\n params = copy.deepcopy(params)\n params = _choose_param_value(\n main_param_name=\"objective\",\n params=params,\n default_value=None,\n )\n fobj: Optional[_LGBM_CustomObjectiveFunction] = None\n if callable(params[\"objective\"]):\n fobj = params[\"objective\"]\n params[\"objective\"] = \"none\"\n\n params = _choose_num_iterations(num_boost_round_kwarg=num_boost_round, params=params)\n num_boost_round = params[\"num_iterations\"]\n if num_boost_round <= 0:\n raise ValueError(f\"Number of boosting rounds must be greater than 0. Got {num_boost_round}.\")\n\n # setting early stopping via global params should be possible\n params = _choose_param_value(\n main_param_name=\"early_stopping_round\",\n params=params,\n default_value=None,\n )\n if params[\"early_stopping_round\"] is None:\n params.pop(\"early_stopping_round\")\n first_metric_only = params.get(\"first_metric_only\", False)\n\n if isinstance(init_model, (str, Path)):\n predictor = _InnerPredictor.from_model_file(\n model_file=init_model,\n pred_parameter=params,\n )\n elif isinstance(init_model, Booster):\n predictor = _InnerPredictor.from_booster(\n booster=init_model,\n pred_parameter=dict(init_model.params, **params),\n )\n else:\n predictor = None\n\n if metrics is not None:\n for metric_alias in _ConfigAliases.get(\"metric\"):\n params.pop(metric_alias, None)\n params[\"metric\"] = metrics\n\n train_set._update_params(params)._set_predictor(predictor)\n\n results = defaultdict(list)\n cvbooster = _make_n_folds(\n full_data=train_set,\n folds=folds,\n nfold=nfold,\n params=params,\n seed=seed,\n fpreproc=fpreproc,\n stratified=stratified,\n shuffle=shuffle,\n eval_train_metric=eval_train_metric,\n )\n\n # setup callbacks\n if callbacks is None:\n callbacks_set = set()\n else:\n for i, cb in enumerate(callbacks):\n cb.__dict__.setdefault(\"order\", i - len(callbacks))\n callbacks_set = set(callbacks)\n\n if callback._should_enable_early_stopping(params.get(\"early_stopping_round\", 0)):\n callbacks_set.add(\n callback.early_stopping(\n stopping_rounds=params[\"early_stopping_round\"], # type: ignore[arg-type]\n first_metric_only=first_metric_only,\n min_delta=params.get(\"early_stopping_min_delta\", 0.0),\n verbose=_choose_param_value(\n main_param_name=\"verbosity\",\n params=params,\n default_value=1,\n ).pop(\"verbosity\")\n > 0,\n )\n )\n\n callbacks_before_iter_set = {cb for cb in callbacks_set if getattr(cb, \"before_iteration\", False)}\n callbacks_after_iter_set = callbacks_set - callbacks_before_iter_set\n callbacks_before_iter = sorted(callbacks_before_iter_set, key=attrgetter(\"order\"))\n callbacks_after_iter = sorted(callbacks_after_iter_set, key=attrgetter(\"order\"))\n\n for i in range(num_boost_round):\n for cb in callbacks_before_iter:\n cb(\n callback.CallbackEnv(\n model=cvbooster,\n params=params,\n iteration=i,\n begin_iteration=0,\n end_iteration=num_boost_round,\n evaluation_result_list=None,\n )\n )\n cvbooster.update(fobj=fobj) # type: ignore[call-arg]\n res = _agg_cv_result(cvbooster.eval_valid(feval)) # type: ignore[call-arg]\n for dataset_name, metric_name, metric_mean, _, metric_std_dev in res:\n results[f\"{dataset_name} {metric_name}-mean\"].append(metric_mean)\n results[f\"{dataset_name} {metric_name}-stdv\"].append(metric_std_dev)\n try:\n for cb in callbacks_after_iter:\n cb(\n callback.CallbackEnv(\n model=cvbooster,\n params=params,\n iteration=i,\n begin_iteration=0,\n end_iteration=num_boost_round,\n evaluation_result_list=res,\n )\n )\n except callback.EarlyStopException as earlyStopException:\n cvbooster.best_iteration = earlyStopException.best_iteration + 1\n for bst in cvbooster.boosters:\n bst.best_iteration = cvbooster.best_iteration\n for k in results:\n results[k] = results[k][: cvbooster.best_iteration]\n break\n\n if return_cvbooster:\n results[\"cvbooster\"] = cvbooster # type: ignore[assignment]\n\n return dict(results)\n" }, { "path": "python-package/lightgbm/libpath.py", "content": "# coding: utf-8\n\"\"\"Find the path to LightGBM dynamic library files.\"\"\"\n\nimport ctypes\nfrom os import environ\nfrom pathlib import Path\nfrom platform import system\nfrom typing import List\n\n__all__: List[str] = []\n\n\ndef _find_lib_path() -> List[str]:\n \"\"\"Find the path to LightGBM library files.\n\n Returns\n -------\n lib_path: list of str\n List of all found library paths to LightGBM.\n \"\"\"\n curr_path = Path(__file__).resolve()\n dll_path = [\n curr_path.parents[1],\n curr_path.parents[0] / \"bin\",\n curr_path.parents[0] / \"lib\",\n ]\n if system() in (\"Windows\", \"Microsoft\"):\n dll_path.append(curr_path.parents[1] / \"Release\")\n dll_path.append(curr_path.parents[1] / \"windows\" / \"x64\" / \"DLL\")\n dll_path = [p / \"lib_lightgbm.dll\" for p in dll_path]\n elif system() == \"Darwin\":\n dll_path = [p / \"lib_lightgbm.dylib\" for p in dll_path]\n else:\n dll_path = [p / \"lib_lightgbm.so\" for p in dll_path]\n lib_path = [str(p) for p in dll_path if p.is_file()]\n if not lib_path:\n dll_path_joined = \"\\n\".join(map(str, dll_path))\n raise Exception(f\"Cannot find lightgbm library file in following paths:\\n{dll_path_joined}\")\n return lib_path\n\n\n# we don't need lib_lightgbm while building docs\n_LIB: ctypes.CDLL\nif environ.get(\"LIGHTGBM_BUILD_DOC\", \"False\") == \"True\":\n from unittest.mock import Mock # isort: skip\n\n _LIB = Mock(ctypes.CDLL) # type: ignore\nelse:\n _LIB = ctypes.cdll.LoadLibrary(_find_lib_path()[0])\n" }, { "path": "python-package/lightgbm/plotting.py", "content": "# coding: utf-8\n\"\"\"Plotting library.\"\"\"\n\nimport math\nfrom copy import deepcopy\nfrom io import BytesIO\nfrom typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\n\nfrom .basic import Booster, _data_from_pandas, _is_zero, _log_warning, _MissingType\nfrom .compat import GRAPHVIZ_INSTALLED, MATPLOTLIB_INSTALLED, pd_DataFrame\nfrom .sklearn import LGBMModel\n\n__all__ = [\n \"create_tree_digraph\",\n \"plot_importance\",\n \"plot_metric\",\n \"plot_split_value_histogram\",\n \"plot_tree\",\n]\n\nif TYPE_CHECKING:\n import matplotlib\n\n\ndef _check_not_tuple_of_2_elements(obj: Any, obj_name: str) -> None:\n \"\"\"Check object is not tuple or does not have 2 elements.\"\"\"\n if not isinstance(obj, tuple) or len(obj) != 2:\n raise TypeError(f\"{obj_name} must be a tuple of 2 elements.\")\n\n\ndef _float2str(value: float, precision: Optional[int]) -> str:\n return f\"{value:.{precision}f}\" if precision is not None and not isinstance(value, str) else str(value)\n\n\ndef plot_importance(\n booster: Union[Booster, LGBMModel],\n ax: \"Optional[matplotlib.axes.Axes]\" = None,\n height: float = 0.2,\n xlim: Optional[Tuple[float, float]] = None,\n ylim: Optional[Tuple[float, float]] = None,\n title: Optional[str] = \"Feature importance\",\n xlabel: Optional[str] = \"Feature importance\",\n ylabel: Optional[str] = \"Features\",\n importance_type: str = \"auto\",\n max_num_features: Optional[int] = None,\n ignore_zero: bool = True,\n figsize: Optional[Tuple[float, float]] = None,\n dpi: Optional[int] = None,\n grid: bool = True,\n precision: Optional[int] = 3,\n **kwargs: Any,\n) -> Any:\n \"\"\"Plot model's feature importances.\n\n Parameters\n ----------\n booster : Booster or LGBMModel\n Booster or LGBMModel instance which feature importance should be plotted.\n ax : matplotlib.axes.Axes or None, optional (default=None)\n Target axes instance.\n If None, new figure and axes will be created.\n height : float, optional (default=0.2)\n Bar height, passed to ``ax.barh()``.\n xlim : tuple of 2 elements or None, optional (default=None)\n Tuple passed to ``ax.xlim()``.\n ylim : tuple of 2 elements or None, optional (default=None)\n Tuple passed to ``ax.ylim()``.\n title : str or None, optional (default=\"Feature importance\")\n Axes title.\n If None, title is disabled.\n xlabel : str or None, optional (default=\"Feature importance\")\n X-axis title label.\n If None, title is disabled.\n @importance_type@ placeholder can be used, and it will be replaced with the value of ``importance_type`` parameter.\n ylabel : str or None, optional (default=\"Features\")\n Y-axis title label.\n If None, title is disabled.\n importance_type : str, optional (default=\"auto\")\n How the importance is calculated.\n If \"auto\", if ``booster`` parameter is LGBMModel, ``booster.importance_type`` attribute is used; \"split\" otherwise.\n If \"split\", result contains numbers of times the feature is used in a model.\n If \"gain\", result contains total gains of splits which use the feature.\n max_num_features : int or None, optional (default=None)\n Max number of top features displayed on plot.\n If None or <1, all features will be displayed.\n ignore_zero : bool, optional (default=True)\n Whether to ignore features with zero importance.\n figsize : tuple of 2 elements or None, optional (default=None)\n Figure size.\n dpi : int or None, optional (default=None)\n Resolution of the figure.\n grid : bool, optional (default=True)\n Whether to add a grid for axes.\n precision : int or None, optional (default=3)\n Used to restrict the display of floating point values to a certain precision.\n **kwargs\n Other parameters passed to ``ax.barh()``.\n\n Returns\n -------\n ax : matplotlib.axes.Axes\n The plot with model's feature importances.\n \"\"\"\n if MATPLOTLIB_INSTALLED:\n import matplotlib.pyplot as plt # noqa: PLC0415\n else:\n raise ImportError(\"You must install matplotlib and restart your session to plot importance.\")\n\n if isinstance(booster, LGBMModel):\n if importance_type == \"auto\":\n importance_type = booster.importance_type\n booster = booster.booster_\n elif isinstance(booster, Booster):\n if importance_type == \"auto\":\n importance_type = \"split\"\n else:\n raise TypeError(\"booster must be Booster or LGBMModel.\")\n\n importance = booster.feature_importance(importance_type=importance_type)\n feature_name = booster.feature_name()\n\n if not len(importance):\n raise ValueError(\"Booster's feature_importance is empty.\")\n\n tuples = sorted(zip(feature_name, importance), key=lambda x: x[1])\n if ignore_zero:\n tuples = [x for x in tuples if x[1] > 0]\n if max_num_features is not None and max_num_features > 0:\n tuples = tuples[-max_num_features:]\n if not tuples:\n raise ValueError(\n \"No non-zero feature importances found. The model may have no splits. \"\n \"Use ignore_zero=False to show all features.\"\n )\n labels, values = zip(*tuples)\n\n if ax is None:\n if figsize is not None:\n _check_not_tuple_of_2_elements(figsize, \"figsize\")\n _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi)\n\n ylocs = np.arange(len(values))\n ax.barh(ylocs, values, align=\"center\", height=height, **kwargs)\n\n for x, y in zip(values, ylocs):\n ax.text(x + 1, float(y), _float2str(x, precision) if importance_type == \"gain\" else x, va=\"center\")\n\n ax.set_yticks(ylocs)\n ax.set_yticklabels(labels)\n\n if xlim is not None:\n _check_not_tuple_of_2_elements(xlim, \"xlim\")\n else:\n xlim = (0, max(values) * 1.1)\n ax.set_xlim(xlim)\n\n if ylim is not None:\n _check_not_tuple_of_2_elements(ylim, \"ylim\")\n else:\n ylim = (-1, len(values))\n ax.set_ylim(ylim)\n\n if title is not None:\n ax.set_title(title)\n if xlabel is not None:\n xlabel = xlabel.replace(\"@importance_type@\", importance_type)\n ax.set_xlabel(xlabel)\n if ylabel is not None:\n ax.set_ylabel(ylabel)\n ax.grid(grid)\n return ax\n\n\ndef plot_split_value_histogram(\n booster: Union[Booster, LGBMModel],\n feature: Union[int, str],\n bins: Union[int, str, None] = None,\n ax: \"Optional[matplotlib.axes.Axes]\" = None,\n width_coef: float = 0.8,\n xlim: Optional[Tuple[float, float]] = None,\n ylim: Optional[Tuple[float, float]] = None,\n title: Optional[str] = \"Split value histogram for feature with @index/name@ @feature@\",\n xlabel: Optional[str] = \"Feature split value\",\n ylabel: Optional[str] = \"Count\",\n figsize: Optional[Tuple[float, float]] = None,\n dpi: Optional[int] = None,\n grid: bool = True,\n **kwargs: Any,\n) -> Any:\n \"\"\"Plot split value histogram for the specified feature of the model.\n\n Parameters\n ----------\n booster : Booster or LGBMModel\n Booster or LGBMModel instance of which feature split value histogram should be plotted.\n feature : int or str\n The feature name or index the histogram is plotted for.\n If int, interpreted as index.\n If str, interpreted as name.\n bins : int, str or None, optional (default=None)\n The maximum number of bins.\n If None, the number of bins equals number of unique split values.\n If str, it should be one from the list of the supported values by ``numpy.histogram()`` function.\n ax : matplotlib.axes.Axes or None, optional (default=None)\n Target axes instance.\n If None, new figure and axes will be created.\n width_coef : float, optional (default=0.8)\n Coefficient for histogram bar width.\n xlim : tuple of 2 elements or None, optional (default=None)\n Tuple passed to ``ax.xlim()``.\n ylim : tuple of 2 elements or None, optional (default=None)\n Tuple passed to ``ax.ylim()``.\n title : str or None, optional (default=\"Split value histogram for feature with @index/name@ @feature@\")\n Axes title.\n If None, title is disabled.\n @feature@ placeholder can be used, and it will be replaced with the value of ``feature`` parameter.\n @index/name@ placeholder can be used,\n and it will be replaced with ``index`` word in case of ``int`` type ``feature`` parameter\n or ``name`` word in case of ``str`` type ``feature`` parameter.\n xlabel : str or None, optional (default=\"Feature split value\")\n X-axis title label.\n If None, title is disabled.\n ylabel : str or None, optional (default=\"Count\")\n Y-axis title label.\n If None, title is disabled.\n figsize : tuple of 2 elements or None, optional (default=None)\n Figure size.\n dpi : int or None, optional (default=None)\n Resolution of the figure.\n grid : bool, optional (default=True)\n Whether to add a grid for axes.\n **kwargs\n Other parameters passed to ``ax.bar()``.\n\n Returns\n -------\n ax : matplotlib.axes.Axes\n The plot with specified model's feature split value histogram.\n \"\"\"\n if MATPLOTLIB_INSTALLED:\n import matplotlib.pyplot as plt # noqa: PLC0415\n from matplotlib.ticker import MaxNLocator # noqa: PLC0415\n else:\n raise ImportError(\"You must install matplotlib and restart your session to plot split value histogram.\")\n\n if isinstance(booster, LGBMModel):\n booster = booster.booster_\n elif not isinstance(booster, Booster):\n raise TypeError(\"booster must be Booster or LGBMModel.\")\n\n hist, split_bins = booster.get_split_value_histogram(feature=feature, bins=bins, xgboost_style=False)\n if np.count_nonzero(hist) == 0:\n raise ValueError(f\"Cannot plot split value histogram, because feature {feature} was not used in splitting\")\n width = width_coef * (split_bins[1] - split_bins[0])\n centred = (split_bins[:-1] + split_bins[1:]) / 2\n\n if ax is None:\n if figsize is not None:\n _check_not_tuple_of_2_elements(figsize, \"figsize\")\n _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi)\n\n ax.bar(centred, hist, align=\"center\", width=width, **kwargs)\n\n if xlim is not None:\n _check_not_tuple_of_2_elements(xlim, \"xlim\")\n else:\n range_result = split_bins[-1] - split_bins[0]\n xlim = (split_bins[0] - range_result * 0.2, split_bins[-1] + range_result * 0.2)\n ax.set_xlim(xlim)\n\n ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n if ylim is not None:\n _check_not_tuple_of_2_elements(ylim, \"ylim\")\n else:\n ylim = (0, max(hist) * 1.1)\n ax.set_ylim(ylim)\n\n if title is not None:\n title = title.replace(\"@feature@\", str(feature))\n title = title.replace(\"@index/name@\", (\"name\" if isinstance(feature, str) else \"index\"))\n ax.set_title(title)\n if xlabel is not None:\n ax.set_xlabel(xlabel)\n if ylabel is not None:\n ax.set_ylabel(ylabel)\n ax.grid(grid)\n return ax\n\n\ndef plot_metric(\n booster: Union[Dict, LGBMModel],\n metric: Optional[str] = None,\n dataset_names: Optional[List[str]] = None,\n ax: \"Optional[matplotlib.axes.Axes]\" = None,\n xlim: Optional[Tuple[float, float]] = None,\n ylim: Optional[Tuple[float, float]] = None,\n title: Optional[str] = \"Metric during training\",\n xlabel: Optional[str] = \"Iterations\",\n ylabel: Optional[str] = \"@metric@\",\n figsize: Optional[Tuple[float, float]] = None,\n dpi: Optional[int] = None,\n grid: bool = True,\n) -> Any:\n \"\"\"Plot one metric during training.\n\n Parameters\n ----------\n booster : dict or LGBMModel\n Dictionary returned from ``lightgbm.train()`` or LGBMModel instance.\n metric : str or None, optional (default=None)\n The metric name to plot.\n Only one metric supported because different metrics have various scales.\n If None, first metric picked from dictionary (according to hashcode).\n dataset_names : list of str, or None, optional (default=None)\n List of the dataset names which are used to calculate metric to plot.\n If None, all datasets are used.\n ax : matplotlib.axes.Axes or None, optional (default=None)\n Target axes instance.\n If None, new figure and axes will be created.\n xlim : tuple of 2 elements or None, optional (default=None)\n Tuple passed to ``ax.xlim()``.\n ylim : tuple of 2 elements or None, optional (default=None)\n Tuple passed to ``ax.ylim()``.\n title : str or None, optional (default=\"Metric during training\")\n Axes title.\n If None, title is disabled.\n xlabel : str or None, optional (default=\"Iterations\")\n X-axis title label.\n If None, title is disabled.\n ylabel : str or None, optional (default=\"@metric@\")\n Y-axis title label.\n If 'auto', metric name is used.\n If None, title is disabled.\n @metric@ placeholder can be used, and it will be replaced with metric name.\n figsize : tuple of 2 elements or None, optional (default=None)\n Figure size.\n dpi : int or None, optional (default=None)\n Resolution of the figure.\n grid : bool, optional (default=True)\n Whether to add a grid for axes.\n\n Returns\n -------\n ax : matplotlib.axes.Axes\n The plot with metric's history over the training.\n \"\"\"\n if MATPLOTLIB_INSTALLED:\n import matplotlib.pyplot as plt # noqa: PLC0415\n else:\n raise ImportError(\"You must install matplotlib and restart your session to plot metric.\")\n\n if isinstance(booster, LGBMModel):\n eval_results = deepcopy(booster.evals_result_)\n elif isinstance(booster, dict):\n eval_results = deepcopy(booster)\n elif isinstance(booster, Booster):\n raise TypeError(\n \"booster must be dict or LGBMModel. To use plot_metric with Booster type, first record the metrics using record_evaluation callback then pass that to plot_metric as argument `booster`\"\n )\n else:\n raise TypeError(\"booster must be dict or LGBMModel.\")\n\n num_data = len(eval_results)\n\n if not num_data:\n raise ValueError(\"eval results cannot be empty.\")\n\n if ax is None:\n if figsize is not None:\n _check_not_tuple_of_2_elements(figsize, \"figsize\")\n _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi)\n\n if dataset_names is None:\n dataset_names_iter = iter(eval_results.keys())\n elif not isinstance(dataset_names, (list, tuple, set)) or not dataset_names:\n raise ValueError(\"dataset_names should be iterable and cannot be empty\")\n else:\n dataset_names_iter = iter(dataset_names)\n\n name = next(dataset_names_iter) # take one as sample\n metrics_for_one = eval_results[name]\n num_metric = len(metrics_for_one)\n if metric is None:\n if num_metric > 1:\n _log_warning(\"More than one metric available, picking one to plot.\")\n metric, results = metrics_for_one.popitem()\n else:\n if metric not in metrics_for_one:\n raise KeyError(\"No given metric in eval results.\")\n results = metrics_for_one[metric]\n num_iteration = len(results)\n max_result = max(results)\n min_result = min(results)\n x_ = range(num_iteration)\n ax.plot(x_, results, label=name)\n\n for name in dataset_names_iter:\n metrics_for_one = eval_results[name]\n results = metrics_for_one[metric]\n max_result = max(*results, max_result)\n min_result = min(*results, min_result)\n ax.plot(x_, results, label=name)\n\n ax.legend(loc=\"best\")\n\n if xlim is not None:\n _check_not_tuple_of_2_elements(xlim, \"xlim\")\n else:\n xlim = (0, num_iteration)\n ax.set_xlim(xlim)\n\n if ylim is not None:\n _check_not_tuple_of_2_elements(ylim, \"ylim\")\n else:\n range_result = max_result - min_result\n ylim = (min_result - range_result * 0.2, max_result + range_result * 0.2)\n ax.set_ylim(ylim)\n\n if title is not None:\n ax.set_title(title)\n if xlabel is not None:\n ax.set_xlabel(xlabel)\n if ylabel is not None:\n ylabel = ylabel.replace(\"@metric@\", metric)\n ax.set_ylabel(ylabel)\n ax.grid(grid)\n return ax\n\n\ndef _determine_direction_for_numeric_split(\n *,\n fval: float,\n threshold: float,\n missing_type_str: str,\n default_left: bool,\n) -> str:\n missing_type = _MissingType(missing_type_str)\n if math.isnan(fval) and missing_type != _MissingType.NAN:\n fval = 0.0\n if (missing_type == _MissingType.ZERO and _is_zero(fval)) or (\n missing_type == _MissingType.NAN and math.isnan(fval)\n ):\n direction = \"left\" if default_left else \"right\"\n else:\n direction = \"left\" if fval <= threshold else \"right\"\n return direction\n\n\ndef _determine_direction_for_categorical_split(fval: float, thresholds: str) -> str:\n if math.isnan(fval) or int(fval) < 0:\n return \"right\"\n int_thresholds = {int(t) for t in thresholds.split(\"||\")}\n return \"left\" if int(fval) in int_thresholds else \"right\"\n\n\ndef _to_graphviz(\n *,\n tree_info: Dict[str, Any],\n show_info: List[str],\n feature_names: Union[List[str], None],\n precision: Optional[int],\n orientation: str,\n constraints: Optional[List[int]],\n example_case: Optional[Union[np.ndarray, pd_DataFrame]],\n max_category_values: int,\n **kwargs: Any,\n) -> Any:\n \"\"\"Convert specified tree to graphviz instance.\n\n See:\n - https://graphviz.readthedocs.io/en/stable/api.html#digraph\n \"\"\"\n if GRAPHVIZ_INSTALLED:\n from graphviz import Digraph # noqa: PLC0415\n else:\n raise ImportError(\"You must install graphviz and restart your session to plot tree.\")\n\n def add(\n root: Dict[str, Any], total_count: int, parent: Optional[str], decision: Optional[str], highlight: bool\n ) -> None:\n \"\"\"Recursively add node or edge.\"\"\"\n fillcolor = \"white\"\n style = \"\"\n tooltip = None\n if highlight:\n color = \"blue\"\n penwidth = \"3\"\n else:\n color = \"black\"\n penwidth = \"1\"\n if \"split_index\" in root: # non-leaf\n shape = \"rectangle\"\n l_dec = \"yes\"\n r_dec = \"no\"\n threshold = root[\"threshold\"]\n if root[\"decision_type\"] == \"<=\":\n operator = \"≤\"\n elif root[\"decision_type\"] == \"==\":\n operator = \"=\"\n else:\n raise ValueError(\"Invalid decision type in tree model.\")\n name = f\"split{root['split_index']}\"\n split_feature = root[\"split_feature\"]\n if feature_names is not None:\n label = f\"{feature_names[split_feature]} {operator}\"\n else:\n label = f\"feature {split_feature} {operator} \"\n direction = None\n if example_case is not None:\n if root[\"decision_type\"] == \"==\":\n direction = _determine_direction_for_categorical_split(\n fval=example_case[split_feature], thresholds=root[\"threshold\"]\n )\n else:\n direction = _determine_direction_for_numeric_split(\n fval=example_case[split_feature],\n threshold=root[\"threshold\"],\n missing_type_str=root[\"missing_type\"],\n default_left=root[\"default_left\"],\n )\n if root[\"decision_type\"] == \"==\":\n category_values = root[\"threshold\"].split(\"||\")\n if len(category_values) > max_category_values:\n tooltip = root[\"threshold\"]\n threshold = \"||\".join(category_values[:2]) + \"||...||\" + category_values[-1]\n\n label += f\"{_float2str(threshold, precision)}\"\n for info in [\"split_gain\", \"internal_value\", \"internal_weight\", \"internal_count\", \"data_percentage\"]:\n if info in show_info:\n output = info.split(\"_\")[-1]\n if info in {\"split_gain\", \"internal_value\", \"internal_weight\"}:\n label += f\"
{_float2str(root[info], precision)} {output}\"\n elif info == \"internal_count\":\n label += f\"
{output}: {root[info]}\"\n elif info == \"data_percentage\":\n label += f\"
{_float2str(root['internal_count'] / total_count * 100, 2)}% of data\"\n\n if constraints:\n if constraints[root[\"split_feature\"]] == 1:\n fillcolor = \"#ddffdd\" # light green\n if constraints[root[\"split_feature\"]] == -1:\n fillcolor = \"#ffdddd\" # light red\n style = \"filled\"\n label = f\"<{label}>\"\n add(\n root=root[\"left_child\"],\n total_count=total_count,\n parent=name,\n decision=l_dec,\n highlight=highlight and direction == \"left\",\n )\n add(\n root=root[\"right_child\"],\n total_count=total_count,\n parent=name,\n decision=r_dec,\n highlight=highlight and direction == \"right\",\n )\n else: # leaf\n shape = \"ellipse\"\n name = f\"leaf{root['leaf_index']}\"\n label = f\"leaf {root['leaf_index']}: \"\n label += f\"{_float2str(root['leaf_value'], precision)}\"\n if \"leaf_weight\" in show_info:\n label += f\"
{_float2str(root['leaf_weight'], precision)} weight\"\n if \"leaf_count\" in show_info:\n label += f\"
count: {root['leaf_count']}\"\n if \"data_percentage\" in show_info:\n label += f\"
{_float2str(root['leaf_count'] / total_count * 100, 2)}% of data\"\n label = f\"<{label}>\"\n graph.node(\n name,\n label=label,\n shape=shape,\n style=style,\n fillcolor=fillcolor,\n color=color,\n penwidth=penwidth,\n tooltip=tooltip,\n )\n if parent is not None:\n graph.edge(parent, name, decision, color=color, penwidth=penwidth)\n\n graph = Digraph(**kwargs)\n rankdir = \"LR\" if orientation == \"horizontal\" else \"TB\"\n graph.attr(\"graph\", nodesep=\"0.05\", ranksep=\"0.3\", rankdir=rankdir)\n if \"internal_count\" in tree_info[\"tree_structure\"]:\n add(\n root=tree_info[\"tree_structure\"],\n total_count=tree_info[\"tree_structure\"][\"internal_count\"],\n parent=None,\n decision=None,\n highlight=example_case is not None,\n )\n else:\n raise Exception(\"Cannot plot trees with no split\")\n\n if constraints:\n # \"#ddffdd\" is light green, \"#ffdddd\" is light red\n legend = \"\"\"<\n \n \n \n \n \n \n \n \n \n \n \n \n
Monotone constraints
Increasing
Decreasing
\n >\"\"\"\n graph.node(\"legend\", label=legend, shape=\"rectangle\", color=\"white\")\n return graph\n\n\ndef create_tree_digraph(\n booster: Union[Booster, LGBMModel],\n tree_index: int = 0,\n show_info: Optional[List[str]] = None,\n precision: Optional[int] = 3,\n orientation: str = \"horizontal\",\n example_case: Optional[Union[np.ndarray, pd_DataFrame]] = None,\n max_category_values: int = 10,\n **kwargs: Any,\n) -> Any:\n \"\"\"Create a digraph representation of specified tree.\n\n Each node in the graph represents a node in the tree.\n\n Non-leaf nodes have labels like ``Column_10 <= 875.9``, which means\n \"this node splits on the feature named \"Column_10\", with threshold 875.9\".\n\n Leaf nodes have labels like ``leaf 2: 0.422``, which means \"this node is a\n leaf node, and the predicted value for records that fall into this node\n is 0.422\". The number (``2``) is an internal unique identifier and doesn't\n have any special meaning.\n\n .. note::\n\n For more information please visit\n https://graphviz.readthedocs.io/en/stable/api.html#digraph.\n\n Parameters\n ----------\n booster : Booster or LGBMModel\n Booster or LGBMModel instance to be converted.\n tree_index : int, optional (default=0)\n The index of a target tree to convert.\n show_info : list of str, or None, optional (default=None)\n What information should be shown in nodes.\n\n - ``'split_gain'`` : gain from adding this split to the model\n - ``'internal_value'`` : raw predicted value that would be produced by this node if it was a leaf node\n - ``'internal_count'`` : number of records from the training data that fall into this non-leaf node\n - ``'internal_weight'`` : total weight of all nodes that fall into this non-leaf node\n - ``'leaf_count'`` : number of records from the training data that fall into this leaf node\n - ``'leaf_weight'`` : total weight (sum of Hessian) of all observations that fall into this leaf node\n - ``'data_percentage'`` : percentage of training data that fall into this node\n precision : int or None, optional (default=3)\n Used to restrict the display of floating point values to a certain precision.\n orientation : str, optional (default='horizontal')\n Orientation of the tree.\n Can be 'horizontal' or 'vertical'.\n example_case : numpy 2-D array, pandas DataFrame or None, optional (default=None)\n Single row with the same structure as the training data.\n If not None, the plot will highlight the path that sample takes through the tree.\n\n .. versionadded:: 4.0.0\n\n max_category_values : int, optional (default=10)\n The maximum number of category values to display in tree nodes, if the number of thresholds is greater than this value, thresholds will be collapsed and displayed on the label tooltip instead.\n\n .. warning::\n\n Consider wrapping the SVG string of the tree graph with ``IPython.display.HTML`` when running on JupyterLab to get the `tooltip `_ working right.\n\n Example:\n\n .. code-block:: python\n\n from IPython.display import HTML\n\n graph = lgb.create_tree_digraph(clf, max_category_values=5)\n HTML(graph._repr_image_svg_xml())\n\n .. versionadded:: 4.0.0\n\n **kwargs\n Other parameters passed to ``Digraph`` constructor.\n Check https://graphviz.readthedocs.io/en/stable/api.html#digraph for the full list of supported parameters.\n\n Returns\n -------\n graph : graphviz.Digraph\n The digraph representation of specified tree.\n \"\"\"\n if isinstance(booster, LGBMModel):\n booster = booster.booster_\n elif not isinstance(booster, Booster):\n raise TypeError(\"booster must be Booster or LGBMModel.\")\n\n model = booster.dump_model()\n tree_infos = model[\"tree_info\"]\n feature_names = model.get(\"feature_names\", None)\n monotone_constraints = model.get(\"monotone_constraints\", None)\n\n if tree_index < len(tree_infos):\n tree_info = tree_infos[tree_index]\n else:\n raise IndexError(\"tree_index is out of range.\")\n\n if show_info is None:\n show_info = []\n\n if example_case is not None:\n if not isinstance(example_case, (np.ndarray, pd_DataFrame)) or example_case.ndim != 2:\n raise ValueError(\"example_case must be a numpy 2-D array or a pandas DataFrame\")\n if example_case.shape[0] != 1:\n raise ValueError(\"example_case must have a single row.\")\n if isinstance(example_case, pd_DataFrame):\n example_case = _data_from_pandas(\n data=example_case,\n feature_name=\"auto\",\n categorical_feature=\"auto\",\n pandas_categorical=booster.pandas_categorical,\n )[0]\n example_case = example_case[0]\n\n return _to_graphviz(\n tree_info=tree_info,\n show_info=show_info,\n feature_names=feature_names,\n precision=precision,\n orientation=orientation,\n constraints=monotone_constraints,\n example_case=example_case,\n max_category_values=max_category_values,\n **kwargs,\n )\n\n\ndef plot_tree(\n booster: Union[Booster, LGBMModel],\n ax: \"Optional[matplotlib.axes.Axes]\" = None,\n tree_index: int = 0,\n figsize: Optional[Tuple[float, float]] = None,\n dpi: Optional[int] = None,\n show_info: Optional[List[str]] = None,\n precision: Optional[int] = 3,\n orientation: str = \"horizontal\",\n example_case: Optional[Union[np.ndarray, pd_DataFrame]] = None,\n **kwargs: Any,\n) -> Any:\n \"\"\"Plot specified tree.\n\n Each node in the graph represents a node in the tree.\n\n Non-leaf nodes have labels like ``Column_10 <= 875.9``, which means\n \"this node splits on the feature named \"Column_10\", with threshold 875.9\".\n\n Leaf nodes have labels like ``leaf 2: 0.422``, which means \"this node is a\n leaf node, and the predicted value for records that fall into this node\n is 0.422\". The number (``2``) is an internal unique identifier and doesn't\n have any special meaning.\n\n .. note::\n\n It is preferable to use ``create_tree_digraph()`` because of its lossless quality\n and returned objects can be also rendered and displayed directly inside a Jupyter notebook.\n\n Parameters\n ----------\n booster : Booster or LGBMModel\n Booster or LGBMModel instance to be plotted.\n ax : matplotlib.axes.Axes or None, optional (default=None)\n Target axes instance.\n If None, new figure and axes will be created.\n tree_index : int, optional (default=0)\n The index of a target tree to plot.\n figsize : tuple of 2 elements or None, optional (default=None)\n Figure size.\n dpi : int or None, optional (default=None)\n Resolution of the figure.\n show_info : list of str, or None, optional (default=None)\n What information should be shown in nodes.\n\n - ``'split_gain'`` : gain from adding this split to the model\n - ``'internal_value'`` : raw predicted value that would be produced by this node if it was a leaf node\n - ``'internal_count'`` : number of records from the training data that fall into this non-leaf node\n - ``'internal_weight'`` : total weight of all nodes that fall into this non-leaf node\n - ``'leaf_count'`` : number of records from the training data that fall into this leaf node\n - ``'leaf_weight'`` : total weight (sum of Hessian) of all observations that fall into this leaf node\n - ``'data_percentage'`` : percentage of training data that fall into this node\n precision : int or None, optional (default=3)\n Used to restrict the display of floating point values to a certain precision.\n orientation : str, optional (default='horizontal')\n Orientation of the tree.\n Can be 'horizontal' or 'vertical'.\n example_case : numpy 2-D array, pandas DataFrame or None, optional (default=None)\n Single row with the same structure as the training data.\n If not None, the plot will highlight the path that sample takes through the tree.\n\n .. versionadded:: 4.0.0\n\n **kwargs\n Other parameters passed to ``Digraph`` constructor.\n Check https://graphviz.readthedocs.io/en/stable/api.html#digraph for the full list of supported parameters.\n\n Returns\n -------\n ax : matplotlib.axes.Axes\n The plot with single tree.\n \"\"\"\n if MATPLOTLIB_INSTALLED:\n import matplotlib.image # noqa: PLC0415\n import matplotlib.pyplot as plt # noqa: PLC0415\n else:\n raise ImportError(\"You must install matplotlib and restart your session to plot tree.\")\n\n if ax is None:\n if figsize is not None:\n _check_not_tuple_of_2_elements(figsize, \"figsize\")\n _, ax = plt.subplots(1, 1, figsize=figsize, dpi=dpi)\n\n graph = create_tree_digraph(\n booster=booster,\n tree_index=tree_index,\n show_info=show_info,\n precision=precision,\n orientation=orientation,\n example_case=example_case,\n **kwargs,\n )\n\n s = BytesIO()\n s.write(graph.pipe(format=\"png\"))\n s.seek(0)\n img = matplotlib.image.imread(s)\n\n ax.imshow(img)\n ax.axis(\"off\")\n return ax\n" }, { "path": "python-package/lightgbm/py.typed", "content": "" }, { "path": "python-package/lightgbm/sklearn.py", "content": "# coding: utf-8\n\"\"\"Scikit-learn wrapper interface for LightGBM.\"\"\"\n\nimport copy\nimport warnings\nfrom inspect import signature\nfrom pathlib import Path\nfrom typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union\n\nimport numpy as np\nimport scipy.sparse\n\nfrom .basic import (\n _MULTICLASS_OBJECTIVES,\n Booster,\n Dataset,\n LGBMDeprecationWarning,\n LightGBMError,\n _choose_param_value,\n _ConfigAliases,\n _LGBM_BoosterBestScoreType,\n _LGBM_CategoricalFeatureConfiguration,\n _LGBM_EvalFunctionResultType,\n _LGBM_FeatureNameConfiguration,\n _LGBM_GroupType,\n _LGBM_InitScoreType,\n _LGBM_LabelType,\n _LGBM_PredictReturnType,\n _LGBM_WeightType,\n _log_warning,\n)\nfrom .callback import _EvalResultDict, record_evaluation\nfrom .compat import (\n SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG,\n SKLEARN_INSTALLED,\n LGBMNotFittedError,\n _LGBMAssertAllFinite,\n _LGBMCheckClassificationTargets,\n _LGBMCheckSampleWeight,\n _LGBMClassifierBase,\n _LGBMComputeSampleWeight,\n _LGBMCpuCount,\n _LGBMLabelEncoder,\n _LGBMModelBase,\n _LGBMRegressorBase,\n _LGBMValidateData,\n _sklearn_version,\n pa_Table,\n pd_DataFrame,\n)\nfrom .engine import train\n\nif TYPE_CHECKING:\n from .compat import _sklearn_Tags\n\n\n__all__ = [\n \"LGBMClassifier\",\n \"LGBMModel\",\n \"LGBMRanker\",\n \"LGBMRegressor\",\n]\n\n_LGBM_ScikitMatrixLike = Union[\n List[Union[List[float], List[int]]],\n np.ndarray,\n pd_DataFrame,\n pa_Table,\n scipy.sparse.spmatrix,\n]\n_LGBM_ScikitCustomObjectiveFunction = Union[\n # f(labels, preds)\n Callable[\n [Optional[np.ndarray], np.ndarray],\n Tuple[np.ndarray, np.ndarray],\n ],\n # f(labels, preds, weights)\n Callable[\n [Optional[np.ndarray], np.ndarray, Optional[np.ndarray]],\n Tuple[np.ndarray, np.ndarray],\n ],\n # f(labels, preds, weights, group)\n Callable[\n [Optional[np.ndarray], np.ndarray, Optional[np.ndarray], Optional[np.ndarray]],\n Tuple[np.ndarray, np.ndarray],\n ],\n]\n_LGBM_ScikitCustomEvalFunction = Union[\n # f(labels, preds)\n Callable[\n [Optional[np.ndarray], np.ndarray],\n _LGBM_EvalFunctionResultType,\n ],\n Callable[\n [Optional[np.ndarray], np.ndarray],\n List[_LGBM_EvalFunctionResultType],\n ],\n # f(labels, preds, weights)\n Callable[\n [Optional[np.ndarray], np.ndarray, Optional[np.ndarray]],\n _LGBM_EvalFunctionResultType,\n ],\n Callable[\n [Optional[np.ndarray], np.ndarray, Optional[np.ndarray]],\n List[_LGBM_EvalFunctionResultType],\n ],\n # f(labels, preds, weights, group)\n Callable[\n [Optional[np.ndarray], np.ndarray, Optional[np.ndarray], Optional[np.ndarray]],\n _LGBM_EvalFunctionResultType,\n ],\n Callable[\n [Optional[np.ndarray], np.ndarray, Optional[np.ndarray], Optional[np.ndarray]],\n List[_LGBM_EvalFunctionResultType],\n ],\n]\n_LGBM_ScikitEvalMetricType = Union[\n str,\n _LGBM_ScikitCustomEvalFunction,\n List[Union[str, _LGBM_ScikitCustomEvalFunction]],\n]\n_LGBM_ScikitValidSet = Tuple[_LGBM_ScikitMatrixLike, _LGBM_LabelType]\n\n\ndef _get_group_from_constructed_dataset(dataset: Dataset) -> Optional[np.ndarray]:\n group = dataset.get_group()\n error_msg = (\n \"Estimators in lightgbm.sklearn should only retrieve query groups from a constructed Dataset. \"\n \"If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues.\"\n )\n assert group is None or isinstance(group, np.ndarray), error_msg\n return group\n\n\ndef _get_label_from_constructed_dataset(dataset: Dataset) -> np.ndarray:\n label = dataset.get_label()\n error_msg = (\n \"Estimators in lightgbm.sklearn should only retrieve labels from a constructed Dataset. \"\n \"If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues.\"\n )\n assert isinstance(label, np.ndarray), error_msg\n return label\n\n\ndef _get_weight_from_constructed_dataset(dataset: Dataset) -> Optional[np.ndarray]:\n weight = dataset.get_weight()\n error_msg = (\n \"Estimators in lightgbm.sklearn should only retrieve weights from a constructed Dataset. \"\n \"If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues.\"\n )\n assert weight is None or isinstance(weight, np.ndarray), error_msg\n return weight\n\n\nclass _ObjectiveFunctionWrapper:\n \"\"\"Proxy class for objective function.\"\"\"\n\n def __init__(self, func: _LGBM_ScikitCustomObjectiveFunction):\n \"\"\"Construct a proxy class.\n\n This class transforms objective function to match objective function with signature ``new_func(preds, dataset)``\n as expected by ``lightgbm.engine.train``.\n\n Parameters\n ----------\n func : callable\n Expects a callable with following signatures:\n ``func(y_true, y_pred)``,\n ``func(y_true, y_pred, weight)``\n or ``func(y_true, y_pred, weight, group)``\n and returns (grad, hess):\n\n y_true : numpy 1-D array of shape = [n_samples]\n The target values.\n y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)\n The predicted values.\n Predicted values are returned before any transformation,\n e.g. they are raw margin instead of probability of positive class for binary task.\n weight : numpy 1-D array of shape = [n_samples]\n The weight of samples. Weights should be non-negative.\n group : numpy 1-D array\n Group/query data.\n Only used in the learning-to-rank task.\n sum(group) = n_samples.\n For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,\n where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.\n grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape [n_samples, n_classes] (for multi-class task)\n The value of the first order derivative (gradient) of the loss\n with respect to the elements of y_pred for each sample point.\n hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)\n The value of the second order derivative (Hessian) of the loss\n with respect to the elements of y_pred for each sample point.\n\n .. note::\n\n For multi-class task, y_pred is a numpy 2-D array of shape = [n_samples, n_classes],\n and grad and hess should be returned in the same format.\n \"\"\"\n self.func = func\n\n def __call__(\n self,\n preds: np.ndarray,\n dataset: Dataset,\n ) -> Tuple[np.ndarray, np.ndarray]:\n \"\"\"Call passed function with appropriate arguments.\n\n Parameters\n ----------\n preds : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)\n The predicted values.\n dataset : Dataset\n The training dataset.\n\n Returns\n -------\n grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)\n The value of the first order derivative (gradient) of the loss\n with respect to the elements of preds for each sample point.\n hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)\n The value of the second order derivative (Hessian) of the loss\n with respect to the elements of preds for each sample point.\n \"\"\"\n labels = _get_label_from_constructed_dataset(dataset)\n argc = len(signature(self.func).parameters)\n if argc == 2:\n grad, hess = self.func(labels, preds) # type: ignore[call-arg]\n return grad, hess\n\n weight = _get_weight_from_constructed_dataset(dataset)\n if argc == 3:\n grad, hess = self.func(labels, preds, weight) # type: ignore[call-arg]\n return grad, hess\n\n if argc == 4:\n group = _get_group_from_constructed_dataset(dataset)\n return self.func(labels, preds, weight, group) # type: ignore[call-arg]\n\n raise TypeError(f\"Self-defined objective function should have 2, 3 or 4 arguments, got {argc}\")\n\n\nclass _EvalFunctionWrapper:\n \"\"\"Proxy class for evaluation function.\"\"\"\n\n def __init__(self, func: _LGBM_ScikitCustomEvalFunction):\n \"\"\"Construct a proxy class.\n\n This class transforms evaluation function to match evaluation function with signature ``new_func(preds, dataset)``\n as expected by ``lightgbm.engine.train``.\n\n Parameters\n ----------\n func : callable\n Expects a callable with following signatures:\n ``func(y_true, y_pred)``,\n ``func(y_true, y_pred, weight)``\n or ``func(y_true, y_pred, weight, group)``\n and returns (eval_name, eval_result, is_higher_better) or\n list of (eval_name, eval_result, is_higher_better):\n\n y_true : numpy 1-D array of shape = [n_samples]\n The target values.\n y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array shape = [n_samples, n_classes] (for multi-class task)\n The predicted values.\n In case of custom ``objective``, predicted values are returned before any transformation,\n e.g. they are raw margin instead of probability of positive class for binary task in this case.\n weight : numpy 1-D array of shape = [n_samples]\n The weight of samples. Weights should be non-negative.\n group : numpy 1-D array\n Group/query data.\n Only used in the learning-to-rank task.\n sum(group) = n_samples.\n For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,\n where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.\n eval_name : str\n The name of evaluation function (without whitespace).\n eval_result : float\n The eval result.\n is_higher_better : bool\n Is eval result higher better, e.g. AUC is ``is_higher_better``.\n \"\"\"\n self.func = func\n\n def __call__(\n self,\n preds: np.ndarray,\n dataset: Dataset,\n ) -> Union[_LGBM_EvalFunctionResultType, List[_LGBM_EvalFunctionResultType]]:\n \"\"\"Call passed function with appropriate arguments.\n\n Parameters\n ----------\n preds : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)\n The predicted values.\n dataset : Dataset\n The training dataset.\n\n Returns\n -------\n eval_name : str\n The name of evaluation function (without whitespace).\n eval_result : float\n The eval result.\n is_higher_better : bool\n Is eval result higher better, e.g. AUC is ``is_higher_better``.\n \"\"\"\n labels = _get_label_from_constructed_dataset(dataset)\n argc = len(signature(self.func).parameters)\n if argc == 2:\n return self.func(labels, preds) # type: ignore[call-arg]\n\n weight = _get_weight_from_constructed_dataset(dataset)\n if argc == 3:\n return self.func(labels, preds, weight) # type: ignore[call-arg]\n\n if argc == 4:\n group = _get_group_from_constructed_dataset(dataset)\n return self.func(labels, preds, weight, group) # type: ignore[call-arg]\n\n raise TypeError(f\"Self-defined eval function should have 2, 3 or 4 arguments, got {argc}\")\n\n\n# documentation templates for LGBMModel methods are shared between the classes in\n# this module and those in the ``dask`` module\n\n_lgbmmodel_doc_fit = \"\"\"\n Build a gradient boosting model from the training set (X, y).\n\n Parameters\n ----------\n X : {X_shape}\n Input feature matrix.\n y : {y_shape}\n The target values (class labels in classification, real numbers in regression).\n sample_weight : {sample_weight_shape}\n Weights of training data. Weights should be non-negative.\n init_score : {init_score_shape}\n Init score of training data.\n group : {group_shape}\n Group/query data.\n Only used in the learning-to-rank task.\n sum(group) = n_samples.\n For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,\n where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.\n eval_set : list or None, optional (default=None)\n .. deprecated:: 4.7.0\n A list of (X, y) tuple pairs to use as validation sets.\n Use ``eval_X`` and ``eval_y`` instead.\n eval_names : list of str, or None, optional (default=None)\n Names of eval_set.\n eval_sample_weight : {eval_sample_weight_shape}\n Weights of eval data. Weights should be non-negative.\n eval_class_weight : list or None, optional (default=None)\n Class weights of eval data.\n eval_init_score : {eval_init_score_shape}\n Init score of eval data.\n eval_group : {eval_group_shape}\n Group data of eval data.\n eval_metric : str, callable, list or None, optional (default=None)\n If str, it should be a built-in evaluation metric to use.\n If callable, it should be a custom evaluation metric, see note below for more details.\n If list, it can be a list of built-in metrics, a list of custom evaluation metrics, or a mix of both.\n In either case, the ``metric`` from the model parameters will be evaluated and used as well.\n Default: 'l2' for LGBMRegressor, 'logloss' for LGBMClassifier, 'ndcg' for LGBMRanker.\n feature_name : list of str, or 'auto', optional (default='auto')\n Feature names.\n If 'auto' and data is pandas DataFrame, data columns names are used.\n categorical_feature : list of str or int, or 'auto', optional (default='auto')\n Categorical features.\n If list of int, interpreted as indices.\n If list of str, interpreted as feature names (need to specify ``feature_name`` as well).\n If 'auto' and data is pandas DataFrame, pandas unordered categorical columns are used.\n All values in categorical features will be cast to int32 and thus should be less than int32 max value (2147483647).\n Large values could be memory consuming. Consider using consecutive integers starting from zero.\n All negative values in categorical features will be treated as missing values.\n The output cannot be monotonically constrained with respect to a categorical feature.\n Floating point numbers in categorical features will be rounded towards 0.\n callbacks : list of callable, or None, optional (default=None)\n List of callback functions that are applied at each iteration.\n See Callbacks in Python API for more information.\n init_model : str, pathlib.Path, Booster, LGBMModel or None, optional (default=None)\n Filename of LightGBM model, Booster instance or LGBMModel instance used for continue training.\n eval_X : {X_shape}, or tuple of such inputs, or None, optional (default=None)\n Feature matrix or tuple thereof, e.g. ``(X_val0, X_val1)``, to use as validation sets.\n eval_y : {y_shape}, or tuple of such inputs, or None, optional (default=None)\n Target values or tuple thereof, e.g. ``(y_val0, y_val1)``, to use as validation sets.\n\n Returns\n -------\n self : LGBMModel\n Returns self.\n \"\"\"\n\n_lgbmmodel_doc_custom_eval_note = \"\"\"\n Note\n ----\n Custom eval function expects a callable with following signatures:\n ``func(y_true, y_pred)``, ``func(y_true, y_pred, weight)`` or\n ``func(y_true, y_pred, weight, group)``\n and returns (eval_name, eval_result, is_higher_better) or\n list of (eval_name, eval_result, is_higher_better):\n\n y_true : numpy 1-D array of shape = [n_samples]\n The target values.\n y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)\n The predicted values.\n In case of custom ``objective``, predicted values are returned before any transformation,\n e.g. they are raw margin instead of probability of positive class for binary task in this case.\n weight : numpy 1-D array of shape = [n_samples]\n The weight of samples. Weights should be non-negative.\n group : numpy 1-D array\n Group/query data.\n Only used in the learning-to-rank task.\n sum(group) = n_samples.\n For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,\n where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.\n eval_name : str\n The name of evaluation function (without whitespace).\n eval_result : float\n The eval result.\n is_higher_better : bool\n Is eval result higher better, e.g. AUC is ``is_higher_better``.\n\"\"\"\n\n_lgbmmodel_doc_predict = \"\"\"\n {description}\n\n Parameters\n ----------\n X : {X_shape}\n Input features matrix.\n raw_score : bool, optional (default=False)\n Whether to predict raw scores.\n start_iteration : int, optional (default=0)\n Start index of the iteration to predict.\n If <= 0, starts from the first iteration.\n num_iteration : int or None, optional (default=None)\n Total number of iterations used in the prediction.\n If None, if the best iteration exists and start_iteration <= 0, the best iteration is used;\n otherwise, all iterations from ``start_iteration`` are used (no limits).\n If <= 0, all iterations from ``start_iteration`` are used (no limits).\n pred_leaf : bool, optional (default=False)\n Whether to predict leaf index.\n pred_contrib : bool, optional (default=False)\n Whether to predict feature contributions.\n\n .. note::\n\n If you want to get more explanations for your model's predictions using SHAP values,\n like SHAP interaction values,\n you can install the shap package (https://github.com/slundberg/shap).\n Note that unlike the shap package, with ``pred_contrib`` we return a matrix with an extra\n column, where the last column is the expected value.\n\n validate_features : bool, optional (default=False)\n If True, ensure that the features used to predict match the ones used to train.\n Used only if data is pandas DataFrame.\n **kwargs\n Other parameters for the prediction.\n\n Returns\n -------\n {output_name} : {predicted_result_shape}\n The predicted values.\n X_leaves : {X_leaves_shape}\n If ``pred_leaf=True``, the predicted leaf of every tree for each sample.\n X_SHAP_values : {X_SHAP_values_shape}\n If ``pred_contrib=True``, the feature contributions for each sample.\n \"\"\"\n\n\ndef _extract_evaluation_meta_data(\n *,\n collection: Optional[Union[Dict[Any, Any], List[Any]]],\n name: str,\n i: int,\n) -> Optional[Any]:\n \"\"\"Try to extract the ith element of one of the ``eval_*`` inputs.\"\"\"\n if collection is None:\n return None\n elif isinstance(collection, list):\n # It's possible, for example, to pass 3 eval sets through `eval_set`,\n # but only 1 init_score through `eval_init_score`.\n #\n # This if-else accounts for that possibility.\n if len(collection) > i:\n return collection[i]\n else:\n return None\n elif isinstance(collection, dict):\n return collection.get(i, None)\n else:\n raise TypeError(f\"{name} should be dict or list\")\n\n\ndef _validate_eval_set_Xy(\n *,\n eval_set: Optional[List[_LGBM_ScikitValidSet]],\n eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]],\n eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]],\n) -> Optional[List[_LGBM_ScikitValidSet]]:\n \"\"\"Validate eval args.\n\n Returns\n -------\n eval_set\n \"\"\"\n if eval_set is not None:\n msg = \"The argument 'eval_set' is deprecated, use 'eval_X' and 'eval_y' instead.\"\n warnings.warn(msg, category=LGBMDeprecationWarning, stacklevel=2)\n if eval_X is not None or eval_y is not None:\n raise ValueError(\"Specify either 'eval_set' or 'eval_X' and 'eval_y', but not both.\")\n if isinstance(eval_set, tuple):\n return [eval_set]\n else:\n return eval_set\n if (eval_X is None) != (eval_y is None):\n raise ValueError(\"You must specify eval_X and eval_y, not just one of them.\")\n if eval_set is None and eval_X is not None:\n if isinstance(eval_X, tuple) != isinstance(eval_y, tuple):\n raise ValueError(\"If eval_X is a tuple, y_val must be a tuple of same length, and vice versa.\")\n if isinstance(eval_X, tuple) and isinstance(eval_y, tuple):\n if len(eval_X) != len(eval_y):\n raise ValueError(\"If eval_X is a tuple, y_val must be a tuple of same length, and vice versa.\")\n if isinstance(eval_X, tuple) and isinstance(eval_y, tuple):\n eval_set = list(zip(eval_X, eval_y))\n else:\n eval_set = [(eval_X, eval_y)]\n return eval_set\n\n\nclass LGBMModel(_LGBMModelBase):\n \"\"\"Implementation of the scikit-learn API for LightGBM.\"\"\"\n\n def __init__(\n self,\n *,\n boosting_type: str = \"gbdt\",\n num_leaves: int = 31,\n max_depth: int = -1,\n learning_rate: float = 0.1,\n n_estimators: int = 100,\n subsample_for_bin: int = 200000,\n objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None,\n class_weight: Optional[Union[Dict, str]] = None,\n min_split_gain: float = 0.0,\n min_child_weight: float = 1e-3,\n min_child_samples: int = 20,\n subsample: float = 1.0,\n subsample_freq: int = 0,\n colsample_bytree: float = 1.0,\n reg_alpha: float = 0.0,\n reg_lambda: float = 0.0,\n random_state: Optional[Union[int, np.random.RandomState, np.random.Generator]] = None,\n n_jobs: Optional[int] = None,\n importance_type: str = \"split\",\n **kwargs: Any,\n ):\n r\"\"\"Construct a gradient boosting model.\n\n Parameters\n ----------\n boosting_type : str, optional (default='gbdt')\n 'gbdt', traditional Gradient Boosting Decision Tree.\n 'dart', Dropouts meet Multiple Additive Regression Trees.\n 'rf', Random Forest.\n num_leaves : int, optional (default=31)\n Maximum tree leaves for base learners.\n max_depth : int, optional (default=-1)\n Maximum tree depth for base learners, <=0 means no limit.\n If setting this to a positive value, consider also changing ``num_leaves`` to ``<= 2^max_depth``.\n learning_rate : float, optional (default=0.1)\n Boosting learning rate.\n You can use ``callbacks`` parameter of ``fit`` method to shrink/adapt learning rate\n in training using ``reset_parameter`` callback.\n Note, that this will ignore the ``learning_rate`` argument in training.\n n_estimators : int, optional (default=100)\n Number of boosted trees to fit.\n subsample_for_bin : int, optional (default=200000)\n Number of samples for constructing bins.\n objective : str, callable or None, optional (default=None)\n Specify the learning task and the corresponding learning objective or\n a custom objective function to be used (see note below).\n Default: 'regression' for LGBMRegressor, 'binary' or 'multiclass' for LGBMClassifier, 'lambdarank' for LGBMRanker.\n class_weight : dict, 'balanced' or None, optional (default=None)\n Weights associated with classes in the form ``{class_label: weight}``.\n Use this parameter only for multi-class classification task;\n for binary classification task you may use ``is_unbalance`` or ``scale_pos_weight`` parameters.\n Note, that the usage of all these parameters will result in poor estimates of the individual class probabilities.\n You may want to consider performing probability calibration\n (https://scikit-learn.org/stable/modules/calibration.html) of your model.\n The 'balanced' mode uses the values of y to automatically adjust weights\n inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``.\n If None, all classes are supposed to have weight one.\n Note, that these weights will be multiplied with ``sample_weight`` (passed through the ``fit`` method)\n if ``sample_weight`` is specified.\n min_split_gain : float, optional (default=0.)\n Minimum loss reduction required to make a further partition on a leaf node of the tree.\n min_child_weight : float, optional (default=1e-3)\n Minimum sum of instance weight (Hessian) needed in a child (leaf).\n min_child_samples : int, optional (default=20)\n Minimum number of data needed in a child (leaf).\n subsample : float, optional (default=1.)\n Subsample ratio of the training instance.\n subsample_freq : int, optional (default=0)\n Frequency of subsample, <=0 means no enable.\n colsample_bytree : float, optional (default=1.)\n Subsample ratio of columns when constructing each tree.\n reg_alpha : float, optional (default=0.)\n L1 regularization term on weights.\n reg_lambda : float, optional (default=0.)\n L2 regularization term on weights.\n random_state : int, RandomState object or None, optional (default=None)\n Random number seed.\n If int, this number is used to seed the C++ code.\n If RandomState or Generator object (numpy), a random integer is picked based on its state to seed the C++ code.\n If None, default seeds in C++ code are used.\n n_jobs : int or None, optional (default=None)\n Number of parallel threads to use for training (can be changed at prediction time by\n passing it as an extra keyword argument).\n\n For better performance, it is recommended to set this to the number of physical cores\n in the CPU.\n\n Negative integers are interpreted as following joblib's formula (n_cpus + 1 + n_jobs), just like\n scikit-learn (so e.g. -1 means using all threads). A value of zero corresponds the default number of\n threads configured for OpenMP in the system. A value of ``None`` (the default) corresponds\n to using the number of physical cores in the system (its correct detection requires\n either the ``joblib`` or the ``psutil`` util libraries to be installed).\n\n .. versionchanged:: 4.0.0\n\n importance_type : str, optional (default='split')\n The type of feature importance to be filled into ``feature_importances_``.\n If 'split', result contains numbers of times the feature is used in a model.\n If 'gain', result contains total gains of splits which use the feature.\n **kwargs\n Other parameters for the model.\n Check http://lightgbm.readthedocs.io/en/latest/Parameters.html for more parameters.\n\n .. warning::\n\n \\*\\*kwargs is not supported in sklearn, it may cause unexpected issues.\n\n Note\n ----\n A custom objective function can be provided for the ``objective`` parameter.\n In this case, it should have the signature\n ``objective(y_true, y_pred) -> grad, hess``,\n ``objective(y_true, y_pred, weight) -> grad, hess``\n or ``objective(y_true, y_pred, weight, group) -> grad, hess``:\n\n y_true : numpy 1-D array of shape = [n_samples]\n The target values.\n y_pred : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)\n The predicted values.\n Predicted values are returned before any transformation,\n e.g. they are raw margin instead of probability of positive class for binary task.\n weight : numpy 1-D array of shape = [n_samples]\n The weight of samples. Weights should be non-negative.\n group : numpy 1-D array\n Group/query data.\n Only used in the learning-to-rank task.\n sum(group) = n_samples.\n For example, if you have a 100-document dataset with ``group = [10, 20, 40, 10, 10, 10]``, that means that you have 6 groups,\n where the first 10 records are in the first group, records 11-30 are in the second group, records 31-70 are in the third group, etc.\n grad : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)\n The value of the first order derivative (gradient) of the loss\n with respect to the elements of y_pred for each sample point.\n hess : numpy 1-D array of shape = [n_samples] or numpy 2-D array of shape = [n_samples, n_classes] (for multi-class task)\n The value of the second order derivative (Hessian) of the loss\n with respect to the elements of y_pred for each sample point.\n\n For multi-class task, y_pred is a numpy 2-D array of shape = [n_samples, n_classes],\n and grad and hess should be returned in the same format.\n \"\"\"\n if not SKLEARN_INSTALLED:\n raise LightGBMError(\n \"scikit-learn is required for lightgbm.sklearn. \"\n \"You must install scikit-learn and restart your session to use this module.\"\n )\n\n self.boosting_type = boosting_type\n self.objective = objective\n self.num_leaves = num_leaves\n self.max_depth = max_depth\n self.learning_rate = learning_rate\n self.n_estimators = n_estimators\n self.subsample_for_bin = subsample_for_bin\n self.min_split_gain = min_split_gain\n self.min_child_weight = min_child_weight\n self.min_child_samples = min_child_samples\n self.subsample = subsample\n self.subsample_freq = subsample_freq\n self.colsample_bytree = colsample_bytree\n self.reg_alpha = reg_alpha\n self.reg_lambda = reg_lambda\n self.random_state = random_state\n self.n_jobs = n_jobs\n self.importance_type = importance_type\n self._Booster: Optional[Booster] = None\n self._evals_result: _EvalResultDict = {}\n self._best_score: _LGBM_BoosterBestScoreType = {}\n self._best_iteration: int = -1\n self._other_params: Dict[str, Any] = {}\n self._objective = objective\n self.class_weight = class_weight\n self._class_weight: Optional[Union[Dict, str]] = None\n self._class_map: Optional[Dict[int, int]] = None\n self._n_features: int = -1\n self._n_features_in: int = -1\n self._classes: Optional[np.ndarray] = None\n self._n_classes: int = -1\n self.set_params(**kwargs)\n\n # scikit-learn 1.6 introduced an __sklearn__tags() method intended to replace _more_tags().\n # _more_tags() can be removed whenever lightgbm's minimum supported scikit-learn version\n # is >=1.6.\n # ref: https://github.com/lightgbm-org/LightGBM/pull/6651\n def _more_tags(self) -> Dict[str, Any]:\n check_sample_weight_str = (\n \"In LightGBM, setting a sample's weight to 0 can produce a different result than omitting the sample. \"\n \"Such samples intentionally still affect count-based measures like 'min_data_in_leaf' \"\n \"(https://github.com/lightgbm-org/LightGBM/issues/5626#issuecomment-1712706678) and the estimated distribution \"\n \"of features for Dataset construction (see https://github.com/lightgbm-org/LightGBM/issues/5553).\"\n )\n # \"check_sample_weight_equivalence\" can be removed when lightgbm's\n # minimum supported scikit-learn version is at least 1.6\n # ref: https://github.com/scikit-learn/scikit-learn/pull/30137\n return {\n \"allow_nan\": True,\n \"X_types\": [\"2darray\", \"sparse\", \"1dlabels\"],\n \"_xfail_checks\": {\n \"check_no_attributes_set_in_init\": (\n \"scikit-learn incorrectly asserts that private attributes \"\n \"cannot be set in __init__: \"\n \"(see https://github.com/lightgbm-org/LightGBM/issues/2628)\"\n ),\n \"check_all_zero_sample_weights_error\": (\n \"Beginning in scikit-learn 1.9, by default estimators are expected to reject \"\n \"sample weight arrays that are all-0. LightGBM intentionally accepts such arrays. \"\n \"LightGBM supports some operations where training on an all-0-weight input could make sense, \"\n \"like batch updates with training continuation or manual model creation with forced splits.\"\n ),\n \"check_sample_weight_equivalence\": check_sample_weight_str,\n \"check_sample_weight_equivalence_on_dense_data\": check_sample_weight_str,\n \"check_sample_weight_equivalence_on_sparse_data\": check_sample_weight_str,\n },\n }\n\n @staticmethod\n def _update_sklearn_tags_from_dict(\n *,\n tags: \"_sklearn_Tags\",\n tags_dict: Dict[str, Any],\n ) -> \"_sklearn_Tags\":\n \"\"\"Update ``sklearn.utils.Tags`` inherited from ``scikit-learn`` base classes.\n\n ``scikit-learn`` 1.6 introduced a dataclass-based interface for estimator tags.\n ref: https://github.com/scikit-learn/scikit-learn/pull/29677\n\n This method handles updating that instance based on the value in ``self._more_tags()``.\n \"\"\"\n tags.input_tags.allow_nan = tags_dict[\"allow_nan\"]\n tags.input_tags.sparse = \"sparse\" in tags_dict[\"X_types\"]\n tags.target_tags.one_d_labels = \"1dlabels\" in tags_dict[\"X_types\"]\n return tags\n\n def __sklearn_tags__(self) -> Optional[\"_sklearn_Tags\"]:\n # _LGBMModelBase.__sklearn_tags__() cannot be called unconditionally,\n # because that method isn't defined for scikit-learn<1.6\n if not hasattr(_LGBMModelBase, \"__sklearn_tags__\"):\n err_msg = (\n \"__sklearn_tags__() should not be called when using scikit-learn<1.6. \"\n f\"Detected version: {_sklearn_version}\"\n )\n raise AttributeError(err_msg)\n\n # take whatever tags are provided by BaseEstimator, then modify\n # them with LightGBM-specific values\n return self._update_sklearn_tags_from_dict(\n tags=super().__sklearn_tags__(),\n tags_dict=self._more_tags(),\n )\n\n def __sklearn_is_fitted__(self) -> bool:\n return getattr(self, \"fitted_\", False)\n\n def get_params(self, deep: bool = True) -> Dict[str, Any]:\n \"\"\"Get parameters for this estimator.\n\n Parameters\n ----------\n deep : bool, optional (default=True)\n If True, will return the parameters for this estimator and\n contained subobjects that are estimators.\n\n Returns\n -------\n params : dict\n Parameter names mapped to their values.\n \"\"\"\n # Based on: https://github.com/dmlc/xgboost/blob/bd92b1c9c0db3e75ec3dfa513e1435d518bb535d/python-package/xgboost/sklearn.py#L941\n # which was based on: https://stackoverflow.com/questions/59248211\n #\n # `get_params()` flows like this:\n #\n # 0. Get parameters in subclass (self.__class__) first, by using inspect.\n # 1. Get parameters in all parent classes (especially `LGBMModel`).\n # 2. Get whatever was passed via `**kwargs`.\n # 3. Merge them.\n #\n # This needs to accommodate being called recursively in the following\n # inheritance graphs (and similar for classification and ranking):\n #\n # DaskLGBMRegressor -> LGBMRegressor -> LGBMModel -> BaseEstimator\n # (custom subclass) -> LGBMRegressor -> LGBMModel -> BaseEstimator\n # LGBMRegressor -> LGBMModel -> BaseEstimator\n # (custom subclass) -> LGBMModel -> BaseEstimator\n # LGBMModel -> BaseEstimator\n #\n params = super().get_params(deep=deep)\n cp = copy.copy(self)\n # If the immediate parent defines get_params(), use that.\n if callable(getattr(cp.__class__.__bases__[0], \"get_params\", None)):\n cp.__class__ = cp.__class__.__bases__[0]\n # Otherwise, skip it and assume the next class will have it.\n # This is here primarily for cases where the first class in MRO is a scikit-learn mixin.\n else:\n cp.__class__ = cp.__class__.__bases__[1]\n params.update(cp.__class__.get_params(cp, deep))\n params.update(self._other_params)\n return params\n\n def set_params(self, **params: Any) -> \"LGBMModel\":\n \"\"\"Set the parameters of this estimator.\n\n Parameters\n ----------\n **params\n Parameter names with their new values.\n\n Returns\n -------\n self : object\n Returns self.\n \"\"\"\n for key, value in params.items():\n setattr(self, key, value)\n if hasattr(self, f\"_{key}\"):\n setattr(self, f\"_{key}\", value)\n self._other_params[key] = value\n return self\n\n def _process_params(self, stage: str) -> Dict[str, Any]:\n \"\"\"Process the parameters of this estimator based on its type, parameter aliases, etc.\n\n Parameters\n ----------\n stage : str\n Name of the stage (can be ``fit`` or ``predict``) this method is called from.\n\n Returns\n -------\n processed_params : dict\n Processed parameter names mapped to their values.\n \"\"\"\n assert stage in {\"fit\", \"predict\"}\n params = self.get_params()\n\n params.pop(\"objective\", None)\n for alias in _ConfigAliases.get(\"objective\"):\n if alias in params:\n obj = params.pop(alias)\n _log_warning(f\"Found '{alias}' in params. Will use it instead of 'objective' argument\")\n if stage == \"fit\":\n self._objective = obj\n if stage == \"fit\":\n if self._objective is None:\n if isinstance(self, LGBMRegressor):\n self._objective = \"regression\"\n elif isinstance(self, LGBMClassifier):\n if self._n_classes > 2:\n self._objective = \"multiclass\"\n else:\n self._objective = \"binary\"\n elif isinstance(self, LGBMRanker):\n self._objective = \"lambdarank\"\n else:\n raise ValueError(\"Unknown LGBMModel type.\")\n if callable(self._objective):\n if stage == \"fit\":\n params[\"objective\"] = _ObjectiveFunctionWrapper(self._objective)\n else:\n params[\"objective\"] = \"None\"\n else:\n params[\"objective\"] = self._objective\n\n params.pop(\"importance_type\", None)\n params.pop(\"n_estimators\", None)\n params.pop(\"class_weight\", None)\n\n if isinstance(params[\"random_state\"], np.random.RandomState):\n params[\"random_state\"] = params[\"random_state\"].randint(np.iinfo(np.int32).max)\n elif isinstance(params[\"random_state\"], np.random.Generator):\n params[\"random_state\"] = int(params[\"random_state\"].integers(np.iinfo(np.int32).max))\n if self._n_classes > 2:\n for alias in _ConfigAliases.get(\"num_class\"):\n params.pop(alias, None)\n params[\"num_class\"] = self._n_classes\n if hasattr(self, \"_eval_at\"):\n eval_at = self._eval_at\n for alias in _ConfigAliases.get(\"eval_at\"):\n if alias in params:\n _log_warning(f\"Found '{alias}' in params. Will use it instead of 'eval_at' argument\")\n eval_at = params.pop(alias)\n params[\"eval_at\"] = eval_at\n\n # register default metric for consistency with callable eval_metric case\n original_metric = self._objective if isinstance(self._objective, str) else None\n if original_metric is None:\n # try to deduce from class instance\n if isinstance(self, LGBMRegressor):\n original_metric = \"l2\"\n elif isinstance(self, LGBMClassifier):\n original_metric = \"multi_logloss\" if self._n_classes > 2 else \"binary_logloss\"\n elif isinstance(self, LGBMRanker):\n original_metric = \"ndcg\"\n\n # overwrite default metric by explicitly set metric\n params = _choose_param_value(\"metric\", params, original_metric)\n\n # use joblib conventions for negative n_jobs, just like scikit-learn\n # at predict time, this is handled later due to the order of parameter updates\n if stage == \"fit\":\n params = _choose_param_value(\"num_threads\", params, self.n_jobs)\n params[\"num_threads\"] = self._process_n_jobs(params[\"num_threads\"])\n\n return params\n\n def _process_n_jobs(self, n_jobs: Optional[int]) -> int:\n \"\"\"Convert special values of n_jobs to their actual values according to the formulas that apply.\n\n Parameters\n ----------\n n_jobs : int or None\n The original value of n_jobs, potentially having special values such as 'None' or\n negative integers.\n\n Returns\n -------\n n_jobs : int\n The value of n_jobs with special values converted to actual number of threads.\n \"\"\"\n if n_jobs is None:\n n_jobs = _LGBMCpuCount(only_physical_cores=True)\n elif n_jobs < 0:\n n_jobs = max(_LGBMCpuCount(only_physical_cores=False) + 1 + n_jobs, 1)\n return n_jobs\n\n def fit(\n self,\n X: _LGBM_ScikitMatrixLike,\n y: _LGBM_LabelType,\n sample_weight: Optional[_LGBM_WeightType] = None,\n init_score: Optional[_LGBM_InitScoreType] = None,\n group: Optional[_LGBM_GroupType] = None,\n eval_set: Optional[List[_LGBM_ScikitValidSet]] = None,\n eval_names: Optional[List[str]] = None,\n eval_sample_weight: Optional[List[_LGBM_WeightType]] = None,\n eval_class_weight: Optional[List[float]] = None,\n eval_init_score: Optional[List[_LGBM_InitScoreType]] = None,\n eval_group: Optional[List[_LGBM_GroupType]] = None,\n eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None,\n feature_name: _LGBM_FeatureNameConfiguration = \"auto\",\n categorical_feature: _LGBM_CategoricalFeatureConfiguration = \"auto\",\n callbacks: Optional[List[Callable]] = None,\n init_model: Optional[Union[str, Path, Booster, \"LGBMModel\"]] = None,\n *,\n eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None,\n eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None,\n ) -> \"LGBMModel\":\n \"\"\"Docstring is set after definition, using a template.\"\"\"\n params = self._process_params(stage=\"fit\")\n\n # Do not modify original args in fit function\n # Refer to https://github.com/lightgbm-org/LightGBM/pull/2619\n eval_metric_list: List[Union[str, _LGBM_ScikitCustomEvalFunction]]\n if eval_metric is None:\n eval_metric_list = []\n elif isinstance(eval_metric, list):\n eval_metric_list = copy.deepcopy(eval_metric)\n else:\n eval_metric_list = [copy.deepcopy(eval_metric)]\n\n # Separate built-in from callable evaluation metrics\n eval_metrics_callable = [_EvalFunctionWrapper(f) for f in eval_metric_list if callable(f)]\n eval_metrics_builtin = [m for m in eval_metric_list if isinstance(m, str)]\n\n # concatenate metric from params (or default if not provided in params) and eval_metric\n params[\"metric\"] = [params[\"metric\"]] if isinstance(params[\"metric\"], (str, type(None))) else params[\"metric\"]\n params[\"metric\"] = [e for e in eval_metrics_builtin if e not in params[\"metric\"]] + params[\"metric\"]\n params[\"metric\"] = [metric for metric in params[\"metric\"] if metric is not None]\n\n if not isinstance(X, (pd_DataFrame, pa_Table)):\n _X, _y = _LGBMValidateData(\n self,\n X,\n y,\n reset=True,\n # allow any input type (this validation is done further down, in lgb.Dataset())\n accept_sparse=True,\n # do not raise an error if Inf of NaN values are found (LightGBM handles these internally)\n ensure_all_finite=False,\n # raise an error on 0-row and 1-row inputs\n ensure_min_samples=2,\n )\n if sample_weight is not None:\n if SKLEARN_CHECK_SAMPLE_WEIGHT_HAS_ALLOW_ZERO_WEIGHTS_ARG:\n sample_weight = _LGBMCheckSampleWeight(sample_weight, _X, allow_all_zero_weights=True)\n else:\n sample_weight = _LGBMCheckSampleWeight(sample_weight, _X)\n else:\n _X, _y = X, y\n\n # for other data types, setting n_features_in_ is handled by _LGBMValidateData() in the branch above\n self.n_features_in_ = _X.shape[1]\n\n if self._class_weight is None:\n self._class_weight = self.class_weight\n if self._class_weight is not None:\n class_sample_weight = _LGBMComputeSampleWeight(self._class_weight, y)\n if sample_weight is None or len(sample_weight) == 0:\n sample_weight = class_sample_weight\n else:\n sample_weight = np.multiply(sample_weight, class_sample_weight)\n\n train_set = Dataset(\n data=_X,\n label=_y,\n weight=sample_weight,\n group=group,\n init_score=init_score,\n categorical_feature=categorical_feature,\n feature_name=feature_name,\n params=params,\n )\n\n valid_sets: List[Dataset] = []\n eval_set = _validate_eval_set_Xy(eval_set=eval_set, eval_X=eval_X, eval_y=eval_y)\n if eval_set is not None:\n # check eval_group (only relevant for ranking tasks)\n if eval_group is not None:\n if len(eval_group) != len(eval_set):\n raise ValueError(\n f\"Length of eval_group ({len(eval_group)}) not equal to length of eval_set ({len(eval_set)})\"\n )\n\n for i, valid_data in enumerate(eval_set):\n # reduce cost for prediction training data\n if valid_data[0] is X and valid_data[1] is y:\n valid_set = train_set\n else:\n valid_weight = _extract_evaluation_meta_data(\n collection=eval_sample_weight,\n name=\"eval_sample_weight\",\n i=i,\n )\n valid_class_weight = _extract_evaluation_meta_data(\n collection=eval_class_weight,\n name=\"eval_class_weight\",\n i=i,\n )\n if valid_class_weight is not None:\n if isinstance(valid_class_weight, dict) and self._class_map is not None:\n valid_class_weight = {self._class_map[k]: v for k, v in valid_class_weight.items()}\n valid_class_sample_weight = _LGBMComputeSampleWeight(valid_class_weight, valid_data[1])\n if valid_weight is None or len(valid_weight) == 0:\n valid_weight = valid_class_sample_weight\n else:\n valid_weight = np.multiply(valid_weight, valid_class_sample_weight)\n valid_init_score = _extract_evaluation_meta_data(\n collection=eval_init_score,\n name=\"eval_init_score\",\n i=i,\n )\n valid_group = _extract_evaluation_meta_data(\n collection=eval_group,\n name=\"eval_group\",\n i=i,\n )\n valid_set = Dataset(\n data=valid_data[0],\n label=valid_data[1],\n weight=valid_weight,\n group=valid_group,\n init_score=valid_init_score,\n categorical_feature=\"auto\",\n params=params,\n )\n\n valid_sets.append(valid_set)\n\n if isinstance(init_model, LGBMModel):\n init_model = init_model.booster_\n\n if callbacks is None:\n callbacks = []\n else:\n callbacks = copy.copy(callbacks) # don't use deepcopy here to allow non-serializable objects\n\n evals_result: _EvalResultDict = {}\n callbacks.append(record_evaluation(evals_result))\n\n self._Booster = train(\n params=params,\n train_set=train_set,\n num_boost_round=self.n_estimators,\n valid_sets=valid_sets,\n valid_names=eval_names,\n feval=eval_metrics_callable, # type: ignore[arg-type]\n init_model=init_model,\n callbacks=callbacks,\n )\n\n # This populates the property self.n_features_, the number of features in the fitted model,\n # and so should only be set after fitting.\n #\n # The related property self._n_features_in, which populates self.n_features_in_,\n # is set BEFORE fitting.\n self._n_features = self._Booster.num_feature()\n\n self._evals_result = evals_result\n self._best_iteration = self._Booster.best_iteration\n self._best_score = self._Booster.best_score\n\n self.fitted_ = True\n\n # free dataset\n self._Booster.free_dataset()\n del train_set, valid_sets\n return self\n\n fit.__doc__ = (\n _lgbmmodel_doc_fit.format(\n X_shape=\"numpy array, pandas DataFrame, pyarrow Table, scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]\",\n y_shape=\"numpy array, pandas DataFrame, pandas Series, list of int or float, pyarrow Array, pyarrow ChunkedArray of shape = [n_samples]\",\n sample_weight_shape=\"numpy array, pandas Series, list of int or float, pyarrow Array, pyarrow ChunkedArray of shape = [n_samples] or None, optional (default=None)\",\n init_score_shape=\"numpy array, pandas DataFrame, pandas Series, list of int or float, list of lists, pyarrow Array, pyarrow ChunkedArray, pyarrow Table of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task) or shape = [n_samples, n_classes] (for multi-class task) or None, optional (default=None)\",\n group_shape=\"numpy array, pandas Series, pyarrow Array, pyarrow ChunkedArray, list of int or float, or None, optional (default=None)\",\n eval_sample_weight_shape=\"list of array (same types as ``sample_weight`` supports), or None, optional (default=None)\",\n eval_init_score_shape=\"list of array (same types as ``init_score`` supports), or None, optional (default=None)\",\n eval_group_shape=\"list of array (same types as ``group`` supports), or None, optional (default=None)\",\n )\n + \"\\n\\n\"\n + _lgbmmodel_doc_custom_eval_note\n )\n\n def predict(\n self,\n X: _LGBM_ScikitMatrixLike,\n raw_score: bool = False,\n start_iteration: int = 0,\n num_iteration: Optional[int] = None,\n pred_leaf: bool = False,\n pred_contrib: bool = False,\n validate_features: bool = False,\n **kwargs: Any,\n ) -> _LGBM_PredictReturnType:\n \"\"\"Docstring is set after definition, using a template.\"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\"Estimator not fitted, call fit before exploiting the model.\")\n if not isinstance(X, (pd_DataFrame, pa_Table)):\n X = _LGBMValidateData(\n self,\n X,\n # 'y' being omitted = run scikit-learn's check_array() instead of check_X_y()\n #\n # Prevent scikit-learn from deleting or modifying attributes like 'feature_names_in_' and 'n_features_in_'.\n # These shouldn't be changed at predict() time.\n reset=False,\n # allow any input type (this validation is done further down, in lgb.Dataset())\n accept_sparse=True,\n # do not raise an error if Inf of NaN values are found (LightGBM handles these internally)\n ensure_all_finite=False,\n # raise an error on 0-row inputs\n ensure_min_samples=1,\n )\n # retrieve original params that possibly can be used in both training and prediction\n # and then overwrite them (considering aliases) with params that were passed directly in prediction\n predict_params = self._process_params(stage=\"predict\")\n for alias in _ConfigAliases.get_by_alias(\n \"data\",\n \"X\",\n \"raw_score\",\n \"start_iteration\",\n \"num_iteration\",\n \"pred_leaf\",\n \"pred_contrib\",\n *kwargs.keys(),\n ):\n predict_params.pop(alias, None)\n predict_params.update(kwargs)\n\n # number of threads can have values with special meaning which is only applied\n # in the scikit-learn interface, these should not reach the c++ side as-is\n predict_params = _choose_param_value(\"num_threads\", predict_params, self.n_jobs)\n predict_params[\"num_threads\"] = self._process_n_jobs(predict_params[\"num_threads\"])\n\n return self._Booster.predict( # type: ignore[union-attr]\n X,\n raw_score=raw_score,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n pred_leaf=pred_leaf,\n pred_contrib=pred_contrib,\n validate_features=validate_features,\n **predict_params,\n )\n\n predict.__doc__ = _lgbmmodel_doc_predict.format(\n description=\"Return the predicted value for each sample.\",\n X_shape=\"numpy array, pandas DataFrame, scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]\",\n output_name=\"predicted_result\",\n predicted_result_shape=\"array-like of shape = [n_samples] or shape = [n_samples, n_classes]\",\n X_leaves_shape=\"array-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]\",\n X_SHAP_values_shape=\"array-like of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or list with n_classes length of such objects\",\n )\n\n @property\n def n_features_(self) -> int:\n \"\"\":obj:`int`: The number of features of fitted model.\"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\"No n_features found. Need to call fit beforehand.\")\n return self._n_features\n\n @property\n def n_features_in_(self) -> int:\n \"\"\":obj:`int`: The number of features of fitted model.\"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\"No n_features_in found. Need to call fit beforehand.\")\n return self._n_features_in\n\n @n_features_in_.setter\n def n_features_in_(self, value: int) -> None:\n \"\"\"Set number of features found in passed-in dataset.\n\n Starting with ``scikit-learn`` 1.6, ``scikit-learn`` expects to be able to directly\n set this property in functions like ``validate_data()``.\n\n .. note::\n\n Do not call ``estimator.n_features_in_ = some_int`` or anything else that invokes\n this method. It is only here for compatibility with ``scikit-learn`` validation\n functions used internally in ``lightgbm``.\n \"\"\"\n self._n_features_in = value\n\n @property\n def best_score_(self) -> _LGBM_BoosterBestScoreType:\n \"\"\":obj:`dict`: The best score of fitted model.\"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\"No best_score found. Need to call fit beforehand.\")\n return self._best_score\n\n @property\n def best_iteration_(self) -> int:\n \"\"\":obj:`int`: The best iteration of fitted model if ``early_stopping()`` callback has been specified.\"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\n \"No best_iteration found. Need to call fit with early_stopping callback beforehand.\"\n )\n return self._best_iteration\n\n @property\n def objective_(self) -> Union[str, _LGBM_ScikitCustomObjectiveFunction]:\n \"\"\":obj:`str` or :obj:`callable`: The concrete objective used while fitting this model.\"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\"No objective found. Need to call fit beforehand.\")\n return self._objective # type: ignore[return-value]\n\n @property\n def n_estimators_(self) -> int:\n \"\"\":obj:`int`: True number of boosting iterations performed.\n\n This might be less than parameter ``n_estimators`` if early stopping was enabled or\n if boosting stopped early due to limits on complexity like ``min_gain_to_split``.\n\n .. versionadded:: 4.0.0\n \"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\"No n_estimators found. Need to call fit beforehand.\")\n return self._Booster.current_iteration() # type: ignore\n\n @property\n def n_iter_(self) -> int:\n \"\"\":obj:`int`: True number of boosting iterations performed.\n\n This might be less than parameter ``n_estimators`` if early stopping was enabled or\n if boosting stopped early due to limits on complexity like ``min_gain_to_split``.\n\n .. versionadded:: 4.0.0\n \"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\"No n_iter found. Need to call fit beforehand.\")\n return self._Booster.current_iteration() # type: ignore\n\n @property\n def booster_(self) -> Booster:\n \"\"\"Booster: The underlying Booster of this model.\"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\"No booster found. Need to call fit beforehand.\")\n return self._Booster # type: ignore[return-value]\n\n @property\n def evals_result_(self) -> _EvalResultDict:\n \"\"\":obj:`dict`: The evaluation results if validation sets have been specified.\"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\"No results found. Need to call fit with eval_set beforehand.\")\n return self._evals_result\n\n @property\n def feature_importances_(self) -> np.ndarray:\n \"\"\":obj:`array` of shape = [n_features]: The feature importances (the higher, the more important).\n\n .. note::\n\n ``importance_type`` attribute is passed to the function\n to configure the type of importance values to be extracted.\n \"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\"No feature_importances found. Need to call fit beforehand.\")\n return self._Booster.feature_importance(importance_type=self.importance_type) # type: ignore[union-attr]\n\n @property\n def feature_name_(self) -> List[str]:\n \"\"\":obj:`list` of shape = [n_features]: The names of features.\n\n .. note::\n\n If input does not contain feature names, they will be added during fitting in the format ``Column_0``, ``Column_1``, ..., ``Column_N``.\n \"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\"No feature_name found. Need to call fit beforehand.\")\n return self._Booster.feature_name() # type: ignore[union-attr]\n\n @property\n def feature_names_in_(self) -> np.ndarray:\n \"\"\":obj:`array` of shape = [n_features]: scikit-learn compatible version of ``.feature_name_``.\n\n .. versionadded:: 4.5.0\n \"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\"No feature_names_in_ found. Need to call fit beforehand.\")\n return np.array(self.feature_name_)\n\n @feature_names_in_.deleter\n def feature_names_in_(self) -> None:\n \"\"\"Intercept calls to delete ``feature_names_in_``.\n\n Some code paths in ``scikit-learn`` try to delete the ``feature_names_in_`` attribute\n on estimators when a new training dataset that doesn't have features is passed.\n LightGBM automatically assigns feature names to such datasets\n (like ``Column_0``, ``Column_1``, etc.) and so does not want that behavior.\n\n However, that behavior is coupled to ``scikit-learn`` automatically updating\n ``n_features_in_`` in those same code paths, which is necessary for compliance\n with its API (via argument ``reset`` to functions like ``validate_data()`` and\n ``check_array()``).\n\n .. note::\n\n Do not call ``del estimator.feature_names_in_`` or anything else that invokes\n this method. It is only here for compatibility with ``scikit-learn`` validation\n functions used internally in ``lightgbm``.\n \"\"\"\n pass\n\n\nclass LGBMRegressor(_LGBMRegressorBase, LGBMModel):\n \"\"\"LightGBM regressor.\"\"\"\n\n # NOTE: all args from LGBMModel.__init__() are intentionally repeated here for\n # docs, help(), and tab completion.\n def __init__(\n self,\n *,\n boosting_type: str = \"gbdt\",\n num_leaves: int = 31,\n max_depth: int = -1,\n learning_rate: float = 0.1,\n n_estimators: int = 100,\n subsample_for_bin: int = 200000,\n objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None,\n class_weight: Optional[Union[Dict, str]] = None,\n min_split_gain: float = 0.0,\n min_child_weight: float = 1e-3,\n min_child_samples: int = 20,\n subsample: float = 1.0,\n subsample_freq: int = 0,\n colsample_bytree: float = 1.0,\n reg_alpha: float = 0.0,\n reg_lambda: float = 0.0,\n random_state: Optional[Union[int, np.random.RandomState, np.random.Generator]] = None,\n n_jobs: Optional[int] = None,\n importance_type: str = \"split\",\n **kwargs: Any,\n ) -> None:\n super().__init__(\n boosting_type=boosting_type,\n num_leaves=num_leaves,\n max_depth=max_depth,\n learning_rate=learning_rate,\n n_estimators=n_estimators,\n subsample_for_bin=subsample_for_bin,\n objective=objective,\n class_weight=class_weight,\n min_split_gain=min_split_gain,\n min_child_weight=min_child_weight,\n min_child_samples=min_child_samples,\n subsample=subsample,\n subsample_freq=subsample_freq,\n colsample_bytree=colsample_bytree,\n reg_alpha=reg_alpha,\n reg_lambda=reg_lambda,\n random_state=random_state,\n n_jobs=n_jobs,\n importance_type=importance_type,\n **kwargs,\n )\n\n __init__.__doc__ = LGBMModel.__init__.__doc__\n\n def _more_tags(self) -> Dict[str, Any]:\n # handle the case where RegressorMixin possibly provides _more_tags()\n if callable(getattr(_LGBMRegressorBase, \"_more_tags\", None)):\n tags = _LGBMRegressorBase._more_tags(self)\n else:\n tags = {}\n # override those with LightGBM-specific preferences\n tags.update(LGBMModel._more_tags(self))\n return tags\n\n def __sklearn_tags__(self) -> \"_sklearn_Tags\":\n return super().__sklearn_tags__()\n\n def fit( # type: ignore[override]\n self,\n X: _LGBM_ScikitMatrixLike,\n y: _LGBM_LabelType,\n sample_weight: Optional[_LGBM_WeightType] = None,\n init_score: Optional[_LGBM_InitScoreType] = None,\n eval_set: Optional[List[_LGBM_ScikitValidSet]] = None,\n eval_names: Optional[List[str]] = None,\n eval_sample_weight: Optional[List[_LGBM_WeightType]] = None,\n eval_init_score: Optional[List[_LGBM_InitScoreType]] = None,\n eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None,\n feature_name: _LGBM_FeatureNameConfiguration = \"auto\",\n categorical_feature: _LGBM_CategoricalFeatureConfiguration = \"auto\",\n callbacks: Optional[List[Callable]] = None,\n init_model: Optional[Union[str, Path, Booster, LGBMModel]] = None,\n *,\n eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None,\n eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None,\n ) -> \"LGBMRegressor\":\n \"\"\"Docstring is inherited from the LGBMModel.\"\"\"\n super().fit(\n X,\n y,\n sample_weight=sample_weight,\n init_score=init_score,\n eval_set=eval_set,\n eval_X=eval_X,\n eval_y=eval_y,\n eval_names=eval_names,\n eval_sample_weight=eval_sample_weight,\n eval_init_score=eval_init_score,\n eval_metric=eval_metric,\n feature_name=feature_name,\n categorical_feature=categorical_feature,\n callbacks=callbacks,\n init_model=init_model,\n )\n return self\n\n _base_doc = LGBMModel.fit.__doc__.replace(\"self : LGBMModel\", \"self : LGBMRegressor\") # type: ignore\n _base_doc = (\n _base_doc[: _base_doc.find(\"group :\")] # type: ignore\n + _base_doc[_base_doc.find(\"eval_set :\") :]\n ) # type: ignore\n _base_doc = _base_doc[: _base_doc.find(\"eval_class_weight :\")] + _base_doc[_base_doc.find(\"eval_init_score :\") :]\n fit.__doc__ = _base_doc[: _base_doc.find(\"eval_group :\")] + _base_doc[_base_doc.find(\"eval_metric :\") :]\n\n\nclass LGBMClassifier(_LGBMClassifierBase, LGBMModel):\n \"\"\"LightGBM classifier.\"\"\"\n\n # NOTE: all args from LGBMModel.__init__() are intentionally repeated here for\n # docs, help(), and tab completion.\n def __init__(\n self,\n *,\n boosting_type: str = \"gbdt\",\n num_leaves: int = 31,\n max_depth: int = -1,\n learning_rate: float = 0.1,\n n_estimators: int = 100,\n subsample_for_bin: int = 200000,\n objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None,\n class_weight: Optional[Union[Dict, str]] = None,\n min_split_gain: float = 0.0,\n min_child_weight: float = 1e-3,\n min_child_samples: int = 20,\n subsample: float = 1.0,\n subsample_freq: int = 0,\n colsample_bytree: float = 1.0,\n reg_alpha: float = 0.0,\n reg_lambda: float = 0.0,\n random_state: Optional[Union[int, np.random.RandomState, np.random.Generator]] = None,\n n_jobs: Optional[int] = None,\n importance_type: str = \"split\",\n **kwargs: Any,\n ) -> None:\n super().__init__(\n boosting_type=boosting_type,\n num_leaves=num_leaves,\n max_depth=max_depth,\n learning_rate=learning_rate,\n n_estimators=n_estimators,\n subsample_for_bin=subsample_for_bin,\n objective=objective,\n class_weight=class_weight,\n min_split_gain=min_split_gain,\n min_child_weight=min_child_weight,\n min_child_samples=min_child_samples,\n subsample=subsample,\n subsample_freq=subsample_freq,\n colsample_bytree=colsample_bytree,\n reg_alpha=reg_alpha,\n reg_lambda=reg_lambda,\n random_state=random_state,\n n_jobs=n_jobs,\n importance_type=importance_type,\n **kwargs,\n )\n\n __init__.__doc__ = LGBMModel.__init__.__doc__\n\n def _more_tags(self) -> Dict[str, Any]:\n # handle the case where ClassifierMixin possibly provides _more_tags()\n if callable(getattr(_LGBMClassifierBase, \"_more_tags\", None)):\n tags = _LGBMClassifierBase._more_tags(self)\n else:\n tags = {}\n # override those with LightGBM-specific preferences\n tags.update(LGBMModel._more_tags(self))\n return tags\n\n def __sklearn_tags__(self) -> \"_sklearn_Tags\":\n tags = super().__sklearn_tags__()\n if tags is not None:\n tags.classifier_tags.multi_class = True\n tags.classifier_tags.multi_label = False\n return tags\n\n def fit( # type: ignore[override]\n self,\n X: _LGBM_ScikitMatrixLike,\n y: _LGBM_LabelType,\n sample_weight: Optional[_LGBM_WeightType] = None,\n init_score: Optional[_LGBM_InitScoreType] = None,\n eval_set: Optional[List[_LGBM_ScikitValidSet]] = None,\n eval_names: Optional[List[str]] = None,\n eval_sample_weight: Optional[List[_LGBM_WeightType]] = None,\n eval_class_weight: Optional[List[float]] = None,\n eval_init_score: Optional[List[_LGBM_InitScoreType]] = None,\n eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None,\n feature_name: _LGBM_FeatureNameConfiguration = \"auto\",\n categorical_feature: _LGBM_CategoricalFeatureConfiguration = \"auto\",\n callbacks: Optional[List[Callable]] = None,\n init_model: Optional[Union[str, Path, Booster, LGBMModel]] = None,\n *,\n eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None,\n eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None,\n ) -> \"LGBMClassifier\":\n \"\"\"Docstring is inherited from the LGBMModel.\"\"\"\n _LGBMAssertAllFinite(y)\n _LGBMCheckClassificationTargets(y)\n self._le = _LGBMLabelEncoder().fit(y)\n _y = self._le.transform(y)\n self._class_map = dict(zip(self._le.classes_, self._le.transform(self._le.classes_)))\n if isinstance(self.class_weight, dict):\n self._class_weight = {self._class_map[k]: v for k, v in self.class_weight.items()}\n\n self._classes = self._le.classes_\n self._n_classes = len(self._classes) # type: ignore[arg-type]\n if self.objective is None:\n self._objective = None\n\n # adjust eval metrics to match whether binary or multiclass\n # classification is being performed\n if not callable(eval_metric):\n if isinstance(eval_metric, list):\n eval_metric_list = eval_metric\n elif isinstance(eval_metric, str):\n eval_metric_list = [eval_metric]\n else:\n eval_metric_list = []\n if self.__is_multiclass:\n for index, metric in enumerate(eval_metric_list):\n if metric in {\"logloss\", \"binary_logloss\"}:\n eval_metric_list[index] = \"multi_logloss\"\n elif metric in {\"error\", \"binary_error\"}:\n eval_metric_list[index] = \"multi_error\"\n else:\n for index, metric in enumerate(eval_metric_list):\n if metric in {\"logloss\", \"multi_logloss\"}:\n eval_metric_list[index] = \"binary_logloss\"\n elif metric in {\"error\", \"multi_error\"}:\n eval_metric_list[index] = \"binary_error\"\n eval_metric = eval_metric_list\n\n # do not modify args, as it causes errors in model selection tools\n valid_sets: Optional[List[_LGBM_ScikitValidSet]] = None\n if eval_set is not None:\n if isinstance(eval_set, tuple):\n eval_set = [eval_set]\n valid_sets = []\n for valid_x, valid_y in eval_set:\n if valid_x is X and valid_y is y:\n valid_sets.append((valid_x, _y))\n else:\n valid_sets.append((valid_x, self._le.transform(valid_y)))\n\n super().fit(\n X,\n _y,\n sample_weight=sample_weight,\n init_score=init_score,\n eval_set=valid_sets,\n eval_names=eval_names,\n eval_X=eval_X,\n eval_y=eval_y,\n eval_sample_weight=eval_sample_weight,\n eval_class_weight=eval_class_weight,\n eval_init_score=eval_init_score,\n eval_metric=eval_metric,\n feature_name=feature_name,\n categorical_feature=categorical_feature,\n callbacks=callbacks,\n init_model=init_model,\n )\n return self\n\n _base_doc = LGBMModel.fit.__doc__.replace(\"self : LGBMModel\", \"self : LGBMClassifier\") # type: ignore\n _base_doc = (\n _base_doc[: _base_doc.find(\"group :\")] # type: ignore\n + _base_doc[_base_doc.find(\"eval_set :\") :]\n ) # type: ignore\n fit.__doc__ = _base_doc[: _base_doc.find(\"eval_group :\")] + _base_doc[_base_doc.find(\"eval_metric :\") :]\n\n def predict(\n self,\n X: _LGBM_ScikitMatrixLike,\n raw_score: bool = False,\n start_iteration: int = 0,\n num_iteration: Optional[int] = None,\n pred_leaf: bool = False,\n pred_contrib: bool = False,\n validate_features: bool = False,\n **kwargs: Any,\n ) -> _LGBM_PredictReturnType:\n \"\"\"Docstring is inherited from the LGBMModel.\"\"\"\n result = self.predict_proba(\n X=X,\n raw_score=raw_score,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n pred_leaf=pred_leaf,\n pred_contrib=pred_contrib,\n validate_features=validate_features,\n **kwargs,\n )\n if callable(self._objective) or raw_score or pred_leaf or pred_contrib:\n return result\n else:\n class_index = np.argmax(result, axis=1)\n return self._le.inverse_transform(class_index)\n\n predict.__doc__ = LGBMModel.predict.__doc__\n\n def predict_proba(\n self,\n X: _LGBM_ScikitMatrixLike,\n raw_score: bool = False,\n start_iteration: int = 0,\n num_iteration: Optional[int] = None,\n pred_leaf: bool = False,\n pred_contrib: bool = False,\n validate_features: bool = False,\n **kwargs: Any,\n ) -> _LGBM_PredictReturnType:\n \"\"\"Docstring is set after definition, using a template.\"\"\"\n result = super().predict(\n X=X,\n raw_score=raw_score,\n start_iteration=start_iteration,\n num_iteration=num_iteration,\n pred_leaf=pred_leaf,\n pred_contrib=pred_contrib,\n validate_features=validate_features,\n **kwargs,\n )\n if callable(self._objective) and not (raw_score or pred_leaf or pred_contrib):\n _log_warning(\n \"Cannot compute class probabilities or labels \"\n \"due to the usage of customized objective function.\\n\"\n \"Returning raw scores instead.\"\n )\n return result\n elif self.__is_multiclass or raw_score or pred_leaf or pred_contrib: # type: ignore [operator]\n return result\n else:\n error_msg = (\n \"predict() should return np.ndarray when pred_contrib=False. \"\n \"If you're seeing this message, it's a bug in lightgbm. Please report it at https://github.com/lightgbm-org/LightGBM/issues.\"\n )\n assert isinstance(result, np.ndarray), error_msg\n return np.vstack((1.0 - result, result)).transpose()\n\n predict_proba.__doc__ = _lgbmmodel_doc_predict.format(\n description=\"Return the predicted probability for each class for each sample.\",\n X_shape=\"numpy array, pandas DataFrame, scipy.sparse, list of lists of int or float of shape = [n_samples, n_features]\",\n output_name=\"predicted_probability\",\n predicted_result_shape=\"array-like of shape = [n_samples] or shape = [n_samples, n_classes]\",\n X_leaves_shape=\"array-like of shape = [n_samples, n_trees] or shape = [n_samples, n_trees * n_classes]\",\n X_SHAP_values_shape=\"array-like of shape = [n_samples, n_features + 1] or shape = [n_samples, (n_features + 1) * n_classes] or list with n_classes length of such objects\",\n )\n\n @property\n def classes_(self) -> np.ndarray:\n \"\"\":obj:`array` of shape = [n_classes]: The class label array.\"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\"No classes found. Need to call fit beforehand.\")\n return self._classes # type: ignore[return-value]\n\n @property\n def n_classes_(self) -> int:\n \"\"\":obj:`int`: The number of classes.\"\"\"\n if not self.__sklearn_is_fitted__():\n raise LGBMNotFittedError(\"No classes found. Need to call fit beforehand.\")\n return self._n_classes\n\n @property\n def __is_multiclass(self) -> bool:\n \"\"\":obj:`bool`: Indicator of whether the classifier is used for multiclass.\"\"\"\n return self._n_classes > 2 or (isinstance(self._objective, str) and self._objective in _MULTICLASS_OBJECTIVES)\n\n\nclass LGBMRanker(LGBMModel):\n \"\"\"LightGBM ranker.\n\n .. warning::\n\n scikit-learn doesn't support ranking applications yet,\n therefore this class is not really compatible with the sklearn ecosystem.\n Please use this class mainly for training and applying ranking models in common sklearnish way.\n \"\"\"\n\n # NOTE: all args from LGBMModel.__init__() are intentionally repeated here for\n # docs, help(), and tab completion.\n def __init__(\n self,\n *,\n boosting_type: str = \"gbdt\",\n num_leaves: int = 31,\n max_depth: int = -1,\n learning_rate: float = 0.1,\n n_estimators: int = 100,\n subsample_for_bin: int = 200000,\n objective: Optional[Union[str, _LGBM_ScikitCustomObjectiveFunction]] = None,\n class_weight: Optional[Union[Dict, str]] = None,\n min_split_gain: float = 0.0,\n min_child_weight: float = 1e-3,\n min_child_samples: int = 20,\n subsample: float = 1.0,\n subsample_freq: int = 0,\n colsample_bytree: float = 1.0,\n reg_alpha: float = 0.0,\n reg_lambda: float = 0.0,\n random_state: Optional[Union[int, np.random.RandomState, np.random.Generator]] = None,\n n_jobs: Optional[int] = None,\n importance_type: str = \"split\",\n **kwargs: Any,\n ) -> None:\n super().__init__(\n boosting_type=boosting_type,\n num_leaves=num_leaves,\n max_depth=max_depth,\n learning_rate=learning_rate,\n n_estimators=n_estimators,\n subsample_for_bin=subsample_for_bin,\n objective=objective,\n class_weight=class_weight,\n min_split_gain=min_split_gain,\n min_child_weight=min_child_weight,\n min_child_samples=min_child_samples,\n subsample=subsample,\n subsample_freq=subsample_freq,\n colsample_bytree=colsample_bytree,\n reg_alpha=reg_alpha,\n reg_lambda=reg_lambda,\n random_state=random_state,\n n_jobs=n_jobs,\n importance_type=importance_type,\n **kwargs,\n )\n\n __init__.__doc__ = LGBMModel.__init__.__doc__\n\n def fit( # type: ignore[override]\n self,\n X: _LGBM_ScikitMatrixLike,\n y: _LGBM_LabelType,\n sample_weight: Optional[_LGBM_WeightType] = None,\n init_score: Optional[_LGBM_InitScoreType] = None,\n group: Optional[_LGBM_GroupType] = None,\n eval_set: Optional[List[_LGBM_ScikitValidSet]] = None,\n eval_names: Optional[List[str]] = None,\n eval_sample_weight: Optional[List[_LGBM_WeightType]] = None,\n eval_init_score: Optional[List[_LGBM_InitScoreType]] = None,\n eval_group: Optional[List[_LGBM_GroupType]] = None,\n eval_metric: Optional[_LGBM_ScikitEvalMetricType] = None,\n eval_at: Union[List[int], Tuple[int, ...]] = (1, 2, 3, 4, 5),\n feature_name: _LGBM_FeatureNameConfiguration = \"auto\",\n categorical_feature: _LGBM_CategoricalFeatureConfiguration = \"auto\",\n callbacks: Optional[List[Callable]] = None,\n init_model: Optional[Union[str, Path, Booster, LGBMModel]] = None,\n *,\n eval_X: Optional[Union[_LGBM_ScikitMatrixLike, Tuple[_LGBM_ScikitMatrixLike]]] = None,\n eval_y: Optional[Union[_LGBM_LabelType, Tuple[_LGBM_LabelType]]] = None,\n ) -> \"LGBMRanker\":\n \"\"\"Docstring is inherited from the LGBMModel.\"\"\"\n # check group data\n if group is None:\n raise ValueError(\"Should set group for ranking task\")\n\n if eval_group is None and (eval_set is not None or eval_X is not None or eval_y is not None):\n raise ValueError(\"eval_group cannot be None if any of eval_set, eval_X, or eval_y are provided\")\n\n self._eval_at = eval_at\n super().fit(\n X,\n y,\n sample_weight=sample_weight,\n init_score=init_score,\n group=group,\n eval_set=eval_set,\n eval_names=eval_names,\n eval_X=eval_X,\n eval_y=eval_y,\n eval_sample_weight=eval_sample_weight,\n eval_init_score=eval_init_score,\n eval_group=eval_group,\n eval_metric=eval_metric,\n feature_name=feature_name,\n categorical_feature=categorical_feature,\n callbacks=callbacks,\n init_model=init_model,\n )\n return self\n\n _base_doc = LGBMModel.fit.__doc__.replace(\"self : LGBMModel\", \"self : LGBMRanker\") # type: ignore\n fit.__doc__ = (\n _base_doc[: _base_doc.find(\"eval_class_weight :\")] # type: ignore\n + _base_doc[_base_doc.find(\"eval_init_score :\") :]\n ) # type: ignore\n _base_doc = fit.__doc__\n _before_feature_name, _feature_name, _after_feature_name = _base_doc.partition(\"feature_name :\")\n fit.__doc__ = f\"\"\"{_before_feature_name}eval_at : list or tuple of int, optional (default=(1, 2, 3, 4, 5))\n The evaluation positions of the specified metric.\n {_feature_name}{_after_feature_name}\"\"\"\n" }, { "path": "python-package/pyproject.toml", "content": "[project]\n\nclassifiers = [\n \"Development Status :: 5 - Production/Stable\",\n \"Intended Audience :: Science/Research\",\n \"Natural Language :: English\",\n \"Operating System :: MacOS\",\n \"Operating System :: Microsoft :: Windows\",\n \"Operating System :: POSIX\",\n \"Operating System :: Unix\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.9\",\n \"Programming Language :: Python :: 3.10\",\n \"Programming Language :: Python :: 3.11\",\n \"Programming Language :: Python :: 3.12\",\n \"Programming Language :: Python :: 3.13\",\n \"Topic :: Scientific/Engineering :: Artificial Intelligence\"\n]\ndependencies = [\n \"numpy>=1.17.0\",\n \"scipy\"\n]\ndescription = \"LightGBM Python-package\"\nlicense = \"MIT\"\nlicense-files = [\n \"LICENSE\"\n]\nmaintainers = [\n {name = \"Yu Shi\", email = \"yushi@microsoft.com\"}\n]\nname = \"lightgbm\"\nreadme = \"README.rst\"\nrequires-python = \">=3.9\"\nversion = \"4.6.0.99\"\n\n[project.optional-dependencies]\narrow = [\n \"cffi>=1.15.1\",\n \"pyarrow>=6.0.1\"\n]\ndask = [\n \"dask[array,dataframe,distributed]>=2.0.0\",\n \"pandas>=0.24.0\"\n]\npandas = [\n \"pandas>=0.24.0\"\n]\nplotting = [\n \"graphviz\",\n \"matplotlib\"\n]\nscikit-learn = [\n \"scikit-learn>=0.24.2\"\n]\n\n[project.urls]\nhomepage = \"https://github.com/lightgbm-org/LightGBM\"\ndocumentation = \"https://lightgbm.readthedocs.io/en/latest/\"\nrepository = \"https://github.com/lightgbm-org/LightGBM.git\"\nchangelog = \"https://github.com/lightgbm-org/LightGBM/releases\"\n\n# start:build-system\n[build-system]\n\nrequires = [\"scikit-build-core>=0.11.0\"]\nbuild-backend = \"scikit_build_core.build\"\n\n# based on https://github.com/scikit-build/scikit-build-core#configuration\n[tool.scikit-build]\nninja.version = \">=1.11\"\nninja.make-fallback = true\nbuild.verbose = false\nbuild.targets = [\"_lightgbm\"]\ninstall.strip = true\nlogging.level = \"INFO\"\nsdist.reproducible = true\nwheel.py-api = \"py3\"\nexperimental = false\nstrict-config = false\nminimum-version = \"build-system.requires\"\n\n[tool.scikit-build.cmake]\nversion = \"CMakeLists.txt\"\nbuild-type = \"Release\"\n\n[tool.scikit-build.cmake.define]\n__BUILD_FOR_PYTHON = \"ON\"\n\n# end:build-system\n\n[tool.mypy]\ndisallow_untyped_defs = true\nexclude = 'build/*|compile/*|docs/*|examples/*|external_libs/*|lightgbm-python/*|tests/*'\nignore_missing_imports = true\n\n[tool.rstcheck]\nreport_level = \"WARNING\"\nignore_directives = [\n \"autoclass\",\n \"autofunction\",\n \"autosummary\",\n \"doxygenfile\"\n]\n\n[tool.ruff]\nexclude = [\n \"build\",\n \"compile\",\n \"external_libs\",\n \"lightgbm-python\",\n]\nline-length = 120\n\n# this should be set to the oldest version of python LightGBM supports\ntarget-version = \"py39\"\n\n[tool.ruff.format]\ndocstring-code-format = false\nexclude = [\n \"build/*.py\",\n \"compile/*.py\",\n \"external_libs/*.py\",\n \"lightgbm-python/*.py\",\n]\nindent-style = \"space\"\nquote-style = \"double\"\nskip-magic-trailing-comma = false\n\n[tool.ruff.lint]\nignore = [\n # (pydocstyle) Missing docstring in magic method\n \"D105\",\n # (pycodestyle) Line too long\n \"E501\",\n # (pylint) Too many branches\n \"PLR0912\",\n # (pylint) Too many arguments in function definition\n \"PLR0913\",\n # (pylint) Too many statements\n \"PLR0915\",\n # (pylint) Consider merging multiple comparisons\n \"PLR1714\",\n # (pylint) Magic value used in comparison\n \"PLR2004\",\n # (pylint) for loop variable overwritten by assignment target\n \"PLW2901\",\n # (pylint) use 'elif' instead of 'else' then 'if', to reduce indentation\n \"PLR5501\",\n # (flake8-pytest-style) `scope='function'` is implied in `@pytest.fixture()`\n \"PT003\"\n]\nselect = [\n # flake8-bugbear\n \"B\",\n # flake8-comprehensions\n \"C4\",\n # pydocstyle\n \"D\",\n # pycodestyle (errors)\n \"E\",\n # pyflakes\n \"F\",\n # isort\n \"I\",\n # NumPy-specific rules\n \"NPY\",\n # pylint\n \"PL\",\n # flake8-pytest-style\n \"PT\",\n # flake8-return: unnecessary assignment before return\n \"RET504\",\n # flake8-return: superfluous-else-raise\n \"RET506\",\n # flake8-simplify: use dict.get() instead of an if-else block\n \"SIM401\",\n # flake8-print\n \"T\",\n # pycodestyle (warnings)\n \"W\",\n]\n\n[tool.ruff.lint.flake8-pytest-style]\nraises-extend-require-match-for = [\"*Exception\", \"*Error\"]\n\n[tool.ruff.lint.per-file-ignores]\n\".ci/create-nuget.py\" = [\n # (flake8-print) flake8-print\n \"T201\"\n]\n\"docs/conf.py\" = [\n # (flake8-bugbear) raise exceptions with \"raise ... from err\"\n \"B904\",\n # (flake8-print) flake8-print\n \"T\"\n]\n\"examples/*\" = [\n # pydocstyle\n \"D\",\n # flake8-print\n \"T\"\n]\n\"python-package/lightgbm/basic.py\" = [\n # (pylint) Using the global statement is discouraged\n \"PLW0603\"\n]\n\"tests/*\" = [\n # (flake8-bugbear) Found useless expression\n \"B018\",\n # pydocstyle\n \"D\",\n # flake8-print\n \"T\"\n]\n\n[tool.ruff.lint.pydocstyle]\nconvention = \"numpy\"\n\n[tool.ruff.lint.isort]\nknown-first-party = [\"lightgbm\"]\n" }, { "path": "src/application/application.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"predictor.hpp\"\n\nnamespace LightGBM {\n\nApplication::Application(int argc, char** argv) {\n LoadParameters(argc, argv);\n // set number of threads for openmp\n OMP_SET_NUM_THREADS(config_.num_threads);\n if (config_.data.size() == 0 && config_.task != TaskType::kConvertModel) {\n Log::Fatal(\"No training/prediction data, application quit\");\n }\n\n if (config_.device_type == std::string(\"cuda\")) {\n LGBM_config_::current_device = lgbm_device_cuda;\n }\n}\n\nApplication::~Application() {\n if (config_.is_parallel) {\n Network::Dispose();\n }\n}\n\nvoid Application::LoadParameters(int argc, char** argv) {\n std::unordered_map> all_params;\n std::unordered_map params;\n for (int i = 1; i < argc; ++i) {\n Config::KV2Map(&all_params, argv[i]);\n }\n // read parameters from config file\n bool config_file_ok = true;\n if (all_params.count(\"config\") > 0) {\n TextReader config_reader(all_params[\"config\"][0].c_str(), false);\n config_reader.ReadAllLines();\n if (!config_reader.Lines().empty()) {\n for (auto& line : config_reader.Lines()) {\n // remove str after \"#\"\n if (line.size() > 0 && std::string::npos != line.find_first_of(\"#\")) {\n line.erase(line.find_first_of(\"#\"));\n }\n line = Common::Trim(line);\n if (line.size() == 0) {\n continue;\n }\n Config::KV2Map(&all_params, line.c_str());\n }\n } else {\n config_file_ok = false;\n }\n }\n Config::SetVerbosity(all_params);\n // de-duplicate params\n Config::KeepFirstValues(all_params, ¶ms);\n if (!config_file_ok) {\n Log::Warning(\"Config file %s doesn't exist, will ignore\", params[\"config\"].c_str());\n }\n ParameterAlias::KeyAliasTransform(¶ms);\n config_.Set(params);\n Log::Info(\"Finished loading parameters\");\n}\n\nvoid Application::LoadData() {\n auto start_time = std::chrono::high_resolution_clock::now();\n std::unique_ptr predictor;\n // prediction is needed if using input initial model(continued train)\n PredictFunction predict_fun = nullptr;\n // need to continue training\n if (boosting_->NumberOfTotalModel() > 0 && config_.task != TaskType::KRefitTree) {\n predictor.reset(new Predictor(boosting_.get(), 0, -1, true, false, false, false, -1, -1));\n predict_fun = predictor->GetPredictFunction();\n }\n\n // sync up random seed for data partition\n if (config_.is_data_based_parallel) {\n config_.data_random_seed = Network::GlobalSyncUpByMin(config_.data_random_seed);\n }\n\n Log::Debug(\"Loading train file...\");\n DatasetLoader dataset_loader(config_, predict_fun,\n config_.num_class, config_.data.c_str());\n // load Training data\n if (config_.is_data_based_parallel) {\n // load data for distributed training\n train_data_.reset(dataset_loader.LoadFromFile(config_.data.c_str(),\n Network::rank(), Network::num_machines()));\n } else {\n // load data for single machine\n train_data_.reset(dataset_loader.LoadFromFile(config_.data.c_str(), 0, 1));\n }\n // need save binary file\n if (config_.save_binary) {\n train_data_->SaveBinaryFile(nullptr);\n }\n // create training metric\n if (config_.is_provide_training_metric) {\n for (auto metric_type : config_.metric) {\n auto metric = std::unique_ptr(Metric::CreateMetric(metric_type, config_));\n if (metric == nullptr) {\n continue;\n }\n metric->Init(train_data_->metadata(), train_data_->num_data());\n train_metric_.push_back(std::move(metric));\n }\n }\n train_metric_.shrink_to_fit();\n\n if (!config_.metric.empty()) {\n // only when have metrics then need to construct validation data\n\n // Add validation data, if it exists\n for (size_t i = 0; i < config_.valid.size(); ++i) {\n Log::Debug(\"Loading validation file #%zu...\", (i + 1));\n // add\n auto new_dataset = std::unique_ptr(\n dataset_loader.LoadFromFileAlignWithOtherDataset(\n config_.valid[i].c_str(),\n train_data_.get()));\n valid_datas_.push_back(std::move(new_dataset));\n // need save binary file\n if (config_.save_binary) {\n valid_datas_.back()->SaveBinaryFile(nullptr);\n }\n\n // add metric for validation data\n valid_metrics_.emplace_back();\n for (auto metric_type : config_.metric) {\n auto metric = std::unique_ptr(Metric::CreateMetric(metric_type, config_));\n if (metric == nullptr) {\n continue;\n }\n metric->Init(valid_datas_.back()->metadata(),\n valid_datas_.back()->num_data());\n valid_metrics_.back().push_back(std::move(metric));\n }\n valid_metrics_.back().shrink_to_fit();\n }\n valid_datas_.shrink_to_fit();\n valid_metrics_.shrink_to_fit();\n }\n auto end_time = std::chrono::high_resolution_clock::now();\n // output used time on each iteration\n Log::Info(\"Finished loading data in %f seconds\",\n std::chrono::duration(end_time - start_time) * 1e-3);\n}\n\nvoid Application::InitTrain() {\n if (config_.is_parallel) {\n // need init network\n Network::Init(config_);\n Log::Info(\"Finished initializing network\");\n config_.feature_fraction_seed =\n Network::GlobalSyncUpByMin(config_.feature_fraction_seed);\n config_.feature_fraction =\n Network::GlobalSyncUpByMin(config_.feature_fraction);\n config_.drop_seed =\n Network::GlobalSyncUpByMin(config_.drop_seed);\n }\n\n // create boosting\n boosting_.reset(\n Boosting::CreateBoosting(config_.boosting,\n config_.input_model.c_str(), config_.device_type, config_.num_gpu));\n // create objective function\n objective_fun_.reset(\n ObjectiveFunction::CreateObjectiveFunction(config_.objective,\n config_));\n // load training data\n LoadData();\n if (config_.task == TaskType::kSaveBinary) {\n Log::Info(\"Save data as binary finished, exit\");\n exit(0);\n }\n // initialize the objective function\n objective_fun_->Init(train_data_->metadata(), train_data_->num_data());\n // initialize the boosting\n boosting_->Init(&config_, train_data_.get(), objective_fun_.get(),\n Common::ConstPtrInVectorWrapper(train_metric_));\n // add validation data into boosting\n for (size_t i = 0; i < valid_datas_.size(); ++i) {\n boosting_->AddValidDataset(valid_datas_[i].get(),\n Common::ConstPtrInVectorWrapper(valid_metrics_[i]));\n Log::Debug(\"Number of data points in validation set #%zu: %d\", i + 1, valid_datas_[i]->num_data());\n }\n Log::Info(\"Finished initializing training\");\n}\n\nvoid Application::Train() {\n Log::Info(\"Started training...\");\n boosting_->Train(config_.snapshot_freq, config_.output_model);\n boosting_->SaveModelToFile(0, -1, config_.saved_feature_importance_type,\n config_.output_model.c_str());\n // convert model to if-else statement code\n if (config_.convert_model_language == std::string(\"cpp\")) {\n boosting_->SaveModelToIfElse(-1, config_.convert_model.c_str());\n }\n Log::Info(\"Finished training\");\n}\n\nvoid Application::Predict() {\n if (config_.task == TaskType::KRefitTree) {\n // create predictor\n Predictor predictor(boosting_.get(), 0, -1, false, true, false, false, 1, 1);\n predictor.Predict(config_.data.c_str(), config_.output_result.c_str(), config_.header, config_.predict_disable_shape_check,\n config_.precise_float_parser);\n TextReader result_reader(config_.output_result.c_str(), false);\n result_reader.ReadAllLines();\n\n size_t nrow = result_reader.Lines().size();\n size_t ncol = 0;\n if (nrow > 0) {\n ncol = Common::StringToArray(result_reader.Lines()[0], '\\t').size();\n }\n std::vector pred_leaf;\n pred_leaf.resize(nrow * ncol);\n\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int irow = 0; irow < static_cast(nrow); ++irow) {\n auto line_vec = Common::StringToArray(result_reader.Lines()[irow], '\\t');\n CHECK_EQ(line_vec.size(), ncol);\n for (int i_row_item = 0; i_row_item < static_cast(ncol); ++i_row_item) {\n pred_leaf[irow * ncol + i_row_item] = line_vec[i_row_item];\n }\n // Free memory\n result_reader.Lines()[irow].clear();\n }\n DatasetLoader dataset_loader(config_, nullptr,\n config_.num_class, config_.data.c_str());\n train_data_.reset(dataset_loader.LoadFromFile(config_.data.c_str(), 0, 1));\n train_metric_.clear();\n objective_fun_.reset(ObjectiveFunction::CreateObjectiveFunction(config_.objective,\n config_));\n objective_fun_->Init(train_data_->metadata(), train_data_->num_data());\n boosting_->Init(&config_, train_data_.get(), objective_fun_.get(),\n Common::ConstPtrInVectorWrapper(train_metric_));\n\n boosting_->RefitTree(pred_leaf.data(), nrow, ncol);\n boosting_->SaveModelToFile(0, -1, config_.saved_feature_importance_type,\n config_.output_model.c_str());\n Log::Info(\"Finished RefitTree\");\n } else {\n // create predictor\n Predictor predictor(boosting_.get(), config_.start_iteration_predict, config_.num_iteration_predict, config_.predict_raw_score,\n config_.predict_leaf_index, config_.predict_contrib,\n config_.pred_early_stop, config_.pred_early_stop_freq,\n config_.pred_early_stop_margin);\n predictor.Predict(config_.data.c_str(),\n config_.output_result.c_str(), config_.header, config_.predict_disable_shape_check,\n config_.precise_float_parser);\n Log::Info(\"Finished prediction\");\n }\n}\n\nvoid Application::InitPredict() {\n boosting_.reset(\n Boosting::CreateBoosting(\"gbdt\", config_.input_model.c_str(), config_.device_type, config_.num_gpu));\n Log::Info(\"Finished initializing prediction, total used %d iterations\", boosting_->GetCurrentIteration());\n}\n\nvoid Application::ConvertModel() {\n boosting_.reset(\n Boosting::CreateBoosting(config_.boosting, config_.input_model.c_str(), config_.device_type, config_.num_gpu));\n boosting_->SaveModelToIfElse(-1, config_.convert_model.c_str());\n}\n\n\n} // namespace LightGBM\n" }, { "path": "src/application/predictor.hpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_APPLICATION_PREDICTOR_HPP_\n#define LIGHTGBM_SRC_APPLICATION_PREDICTOR_HPP_\n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\n/*!\n* \\brief Used to predict data with input model\n*/\nclass Predictor {\n public:\n /*!\n * \\brief Constructor\n * \\param boosting Input boosting model\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration Number of boosting round\n * \\param is_raw_score True if need to predict result with raw score\n * \\param predict_leaf_index True to output leaf index instead of prediction score\n * \\param predict_contrib True to output feature contributions instead of prediction score\n */\n Predictor(Boosting* boosting, int start_iteration, int num_iteration, bool is_raw_score,\n bool predict_leaf_index, bool predict_contrib, bool early_stop,\n int early_stop_freq, double early_stop_margin) {\n early_stop_ = CreatePredictionEarlyStopInstance(\n \"none\", LightGBM::PredictionEarlyStopConfig());\n if (early_stop && !boosting->NeedAccuratePrediction()) {\n PredictionEarlyStopConfig pred_early_stop_config;\n CHECK_GT(early_stop_freq, 0);\n CHECK_GE(early_stop_margin, 0);\n pred_early_stop_config.margin_threshold = early_stop_margin;\n pred_early_stop_config.round_period = early_stop_freq;\n if (boosting->NumberOfClasses() == 1) {\n early_stop_ =\n CreatePredictionEarlyStopInstance(\"binary\", pred_early_stop_config);\n } else {\n early_stop_ = CreatePredictionEarlyStopInstance(\"multiclass\",\n pred_early_stop_config);\n }\n }\n\n boosting->InitPredict(start_iteration, num_iteration, predict_contrib);\n boosting_ = boosting;\n num_pred_one_row_ = boosting_->NumPredictOneRow(start_iteration,\n num_iteration, predict_leaf_index, predict_contrib);\n num_feature_ = boosting_->MaxFeatureIdx() + 1;\n predict_buf_.resize(\n OMP_NUM_THREADS(),\n std::vector>(\n num_feature_, 0.0f));\n const int kFeatureThreshold = 100000;\n const size_t KSparseThreshold = static_cast(0.01 * num_feature_);\n if (predict_leaf_index) {\n predict_fun_ = [=](const std::vector>& features,\n double* output) {\n int tid = omp_get_thread_num();\n if (num_feature_ > kFeatureThreshold &&\n features.size() < KSparseThreshold) {\n auto buf = CopyToPredictMap(features);\n boosting_->PredictLeafIndexByMap(buf, output);\n } else {\n CopyToPredictBuffer(predict_buf_[tid].data(), features);\n // get result for leaf index\n boosting_->PredictLeafIndex(predict_buf_[tid].data(), output);\n ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(),\n features);\n }\n };\n } else if (predict_contrib) {\n if (boosting_->IsLinear()) {\n Log::Fatal(\"Predicting SHAP feature contributions is not implemented for linear trees.\");\n }\n predict_fun_ = [=](const std::vector>& features,\n double* output) {\n int tid = omp_get_thread_num();\n CopyToPredictBuffer(predict_buf_[tid].data(), features);\n // get feature importances\n boosting_->PredictContrib(predict_buf_[tid].data(), output);\n ClearPredictBuffer(predict_buf_[tid].data(), predict_buf_[tid].size(),\n features);\n };\n predict_sparse_fun_ = [=](const std::vector>& features,\n std::vector>* output) {\n auto buf = CopyToPredictMap(features);\n // get sparse feature importances\n boosting_->PredictContribByMap(buf, output);\n };\n\n } else {\n if (is_raw_score) {\n predict_fun_ = [=](const std::vector>& features,\n double* output) {\n int tid = omp_get_thread_num();\n if (num_feature_ > kFeatureThreshold &&\n features.size() < KSparseThreshold) {\n auto buf = CopyToPredictMap(features);\n boosting_->PredictRawByMap(buf, output, &early_stop_);\n } else {\n CopyToPredictBuffer(predict_buf_[tid].data(), features);\n boosting_->PredictRaw(predict_buf_[tid].data(), output,\n &early_stop_);\n ClearPredictBuffer(predict_buf_[tid].data(),\n predict_buf_[tid].size(), features);\n }\n };\n } else {\n predict_fun_ = [=](const std::vector>& features,\n double* output) {\n int tid = omp_get_thread_num();\n if (num_feature_ > kFeatureThreshold &&\n features.size() < KSparseThreshold) {\n auto buf = CopyToPredictMap(features);\n boosting_->PredictByMap(buf, output, &early_stop_);\n } else {\n CopyToPredictBuffer(predict_buf_[tid].data(), features);\n boosting_->Predict(predict_buf_[tid].data(), output, &early_stop_);\n ClearPredictBuffer(predict_buf_[tid].data(),\n predict_buf_[tid].size(), features);\n }\n };\n }\n }\n }\n\n /*!\n * \\brief Destructor\n */\n ~Predictor() {\n }\n\n inline const PredictFunction& GetPredictFunction() const {\n return predict_fun_;\n }\n\n\n inline const PredictSparseFunction& GetPredictSparseFunction() const {\n return predict_sparse_fun_;\n }\n\n /*!\n * \\brief predicting on data, then saving result to disk\n * \\param data_filename Filename of data\n * \\param result_filename Filename of output result\n */\n void Predict(const char* data_filename, const char* result_filename, bool header, bool disable_shape_check, bool precise_float_parser) {\n auto writer = VirtualFileWriter::Make(result_filename);\n if (!writer->Init()) {\n Log::Fatal(\"Prediction results file %s cannot be created\", result_filename);\n }\n auto label_idx = header ? -1 : boosting_->LabelIdx();\n auto parser = std::unique_ptr(Parser::CreateParser(data_filename, header, boosting_->MaxFeatureIdx() + 1, label_idx,\n precise_float_parser, boosting_->ParserConfigStr()));\n\n if (parser == nullptr) {\n Log::Fatal(\"Could not recognize the data format of data file %s\", data_filename);\n }\n if (!header && !disable_shape_check && parser->NumFeatures() != boosting_->MaxFeatureIdx() + 1) {\n Log::Fatal(\"The number of features in data (%d) is not the same as it was in training data (%d).\\n\" \\\n \"You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.\", parser->NumFeatures(), boosting_->MaxFeatureIdx() + 1);\n }\n TextReader predict_data_reader(data_filename, header);\n std::vector feature_remapper(parser->NumFeatures(), -1);\n bool need_adjust = false;\n // skip raw feature remapping if trained model has parser config str which may contain actual feature names.\n if (header && boosting_->ParserConfigStr().empty()) {\n std::string first_line = predict_data_reader.first_line();\n std::vector header_words = Common::Split(first_line.c_str(), \"\\t,\");\n std::unordered_map header_mapper;\n for (int i = 0; i < static_cast(header_words.size()); ++i) {\n if (header_mapper.count(header_words[i]) > 0) {\n Log::Fatal(\"Feature (%s) appears more than one time.\", header_words[i].c_str());\n }\n header_mapper[header_words[i]] = i;\n }\n const auto& fnames = boosting_->FeatureNames();\n for (int i = 0; i < static_cast(fnames.size()); ++i) {\n if (header_mapper.count(fnames[i]) <= 0) {\n Log::Warning(\"Feature (%s) is missed in data file. If it is weight/query/group/ignore_column, you can ignore this warning.\", fnames[i].c_str());\n } else {\n feature_remapper[header_mapper.at(fnames[i])] = i;\n }\n }\n for (int i = 0; i < static_cast(feature_remapper.size()); ++i) {\n if (feature_remapper[i] >= 0 && i != feature_remapper[i]) {\n need_adjust = true;\n break;\n }\n }\n }\n // function for parse data\n std::function>*)> parser_fun;\n double tmp_label;\n parser_fun = [&parser, &feature_remapper, &tmp_label, need_adjust]\n (const char* buffer, std::vector>* feature) {\n parser->ParseOneLine(buffer, feature, &tmp_label);\n if (need_adjust) {\n int i = 0, j = static_cast(feature->size());\n while (i < j) {\n if (feature_remapper[(*feature)[i].first] >= 0) {\n (*feature)[i].first = feature_remapper[(*feature)[i].first];\n ++i;\n } else {\n // move the non-used features to the end of the feature vector\n std::swap((*feature)[i], (*feature)[--j]);\n }\n }\n feature->resize(i);\n }\n };\n\n std::function&)>\n process_fun = [&parser_fun, &writer, this](\n data_size_t, const std::vector& lines) {\n std::vector> oneline_features;\n std::vector result_to_write(lines.size());\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) firstprivate(oneline_features)\n for (data_size_t i = 0; i < static_cast(lines.size()); ++i) {\n OMP_LOOP_EX_BEGIN();\n oneline_features.clear();\n // parser\n parser_fun(lines[i].c_str(), &oneline_features);\n // predict\n std::vector result(num_pred_one_row_);\n predict_fun_(oneline_features, result.data());\n auto str_result = Common::Join(result, \"\\t\");\n result_to_write[i] = str_result;\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n for (data_size_t i = 0; i < static_cast(result_to_write.size()); ++i) {\n writer->Write(result_to_write[i].c_str(), result_to_write[i].size());\n writer->Write(\"\\n\", 1);\n }\n };\n predict_data_reader.ReadAllAndProcessParallel(process_fun);\n }\n\n private:\n void CopyToPredictBuffer(double* pred_buf, const std::vector>& features) {\n for (const auto &feature : features) {\n if (feature.first < num_feature_) {\n pred_buf[feature.first] = feature.second;\n }\n }\n }\n\n void ClearPredictBuffer(double* pred_buf, size_t buf_size, const std::vector>& features) {\n if (features.size() > static_cast(buf_size / 2)) {\n std::memset(pred_buf, 0, sizeof(double)*(buf_size));\n } else {\n for (const auto &feature : features) {\n if (feature.first < num_feature_) {\n pred_buf[feature.first] = 0.0f;\n }\n }\n }\n }\n\n std::unordered_map CopyToPredictMap(const std::vector>& features) {\n std::unordered_map buf;\n for (const auto &feature : features) {\n if (feature.first < num_feature_) {\n buf[feature.first] = feature.second;\n }\n }\n return buf;\n }\n\n /*! \\brief Boosting model */\n const Boosting* boosting_;\n /*! \\brief function for prediction */\n PredictFunction predict_fun_;\n PredictSparseFunction predict_sparse_fun_;\n PredictionEarlyStopInstance early_stop_;\n int num_feature_;\n int num_pred_one_row_;\n std::vector>> predict_buf_;\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_SRC_APPLICATION_PREDICTOR_HPP_\n" }, { "path": "src/boosting/bagging.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifndef LIGHTGBM_SRC_BOOSTING_BAGGING_HPP_\n#define LIGHTGBM_SRC_BOOSTING_BAGGING_HPP_\n\n#include \n#include \n\nnamespace LightGBM {\n\nclass BaggingSampleStrategy : public SampleStrategy {\n public:\n BaggingSampleStrategy(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function, int num_tree_per_iteration)\n : need_re_bagging_(false) {\n config_ = config;\n train_data_ = train_data;\n num_data_ = train_data->num_data();\n num_queries_ = train_data->metadata().num_queries();\n query_boundaries_ = train_data->metadata().query_boundaries();\n objective_function_ = objective_function;\n num_tree_per_iteration_ = num_tree_per_iteration;\n num_threads_ = OMP_NUM_THREADS();\n }\n\n ~BaggingSampleStrategy() {}\n\n void Bagging(int iter, TreeLearner* tree_learner, score_t* /*gradients*/, score_t* /*hessians*/) override {\n Common::FunctionTimer fun_timer(\"GBDT::Bagging\", global_timer);\n // if need bagging\n if ((bag_data_cnt_ < num_data_ && iter % config_->bagging_freq == 0) ||\n need_re_bagging_) {\n need_re_bagging_ = false;\n if (!config_->bagging_by_query) {\n auto left_cnt = bagging_runner_.Run(\n num_data_,\n [=](int, data_size_t cur_start, data_size_t cur_cnt, data_size_t* left,\n data_size_t*) {\n data_size_t cur_left_count = 0;\n if (balanced_bagging_) {\n cur_left_count =\n BalancedBaggingHelper(cur_start, cur_cnt, left);\n } else {\n cur_left_count = BaggingHelper(cur_start, cur_cnt, left);\n }\n return cur_left_count;\n },\n bag_data_indices_.data());\n bag_data_cnt_ = left_cnt;\n } else {\n num_sampled_queries_ = bagging_runner_.Run(\n num_queries_,\n [=](int, data_size_t cur_start, data_size_t cur_cnt, data_size_t* left,\n data_size_t*) {\n data_size_t cur_left_count = 0;\n cur_left_count = BaggingHelper(cur_start, cur_cnt, left);\n return cur_left_count;\n }, bag_query_indices_.data());\n\n sampled_query_boundaries_[0] = 0;\n OMP_INIT_EX();\n #pragma omp parallel for schedule(static) num_threads(num_threads_)\n for (data_size_t i = 0; i < num_sampled_queries_; ++i) {\n OMP_LOOP_EX_BEGIN();\n sampled_query_boundaries_[i + 1] = query_boundaries_[bag_query_indices_[i] + 1] - query_boundaries_[bag_query_indices_[i]];\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n\n const int num_blocks = Threading::For(0, num_sampled_queries_ + 1, 128, [this](int thread_index, data_size_t start_index, data_size_t end_index) {\n for (data_size_t i = start_index + 1; i < end_index; ++i) {\n sampled_query_boundaries_[i] += sampled_query_boundaries_[i - 1];\n }\n sampled_query_boundaries_thread_buffer_[thread_index] = sampled_query_boundaries_[end_index - 1];\n });\n\n for (int thread_index = 1; thread_index < num_blocks; ++thread_index) {\n sampled_query_boundaries_thread_buffer_[thread_index] += sampled_query_boundaries_thread_buffer_[thread_index - 1];\n }\n\n Threading::For(0, num_sampled_queries_ + 1, 128, [this](int thread_index, data_size_t start_index, data_size_t end_index) {\n if (thread_index > 0) {\n for (data_size_t i = start_index; i < end_index; ++i) {\n sampled_query_boundaries_[i] += sampled_query_boundaries_thread_buffer_[thread_index - 1];\n }\n }\n });\n\n bag_data_cnt_ = sampled_query_boundaries_[num_sampled_queries_];\n\n Threading::For(0, num_sampled_queries_, 1, [this](int /*thread_index*/, data_size_t start_index, data_size_t end_index) {\n for (data_size_t sampled_query_id = start_index; sampled_query_id < end_index; ++sampled_query_id) {\n const data_size_t query_index = bag_query_indices_[sampled_query_id];\n const data_size_t data_index_start = query_boundaries_[query_index];\n const data_size_t data_index_end = query_boundaries_[query_index + 1];\n const data_size_t sampled_query_start = sampled_query_boundaries_[sampled_query_id];\n for (data_size_t i = data_index_start; i < data_index_end; ++i) {\n bag_data_indices_[sampled_query_start + i - data_index_start] = i;\n }\n }\n });\n }\n Log::Debug(\"Re-bagging, using %d data to train\", bag_data_cnt_);\n // set bagging data to tree learner\n if (!is_use_subset_) {\n #ifdef USE_CUDA\n if (config_->device_type == std::string(\"cuda\")) {\n CopyFromHostToCUDADevice(cuda_bag_data_indices_.RawData(), bag_data_indices_.data(), static_cast(num_data_), __FILE__, __LINE__);\n tree_learner->SetBaggingData(nullptr, cuda_bag_data_indices_.RawData(), bag_data_cnt_);\n } else {\n #endif // USE_CUDA\n tree_learner->SetBaggingData(nullptr, bag_data_indices_.data(), bag_data_cnt_);\n #ifdef USE_CUDA\n }\n #endif // USE_CUDA\n } else {\n // get subset\n tmp_subset_->ReSize(bag_data_cnt_);\n tmp_subset_->CopySubrow(train_data_, bag_data_indices_.data(),\n bag_data_cnt_, false);\n #ifdef USE_CUDA\n if (config_->device_type == std::string(\"cuda\")) {\n CopyFromHostToCUDADevice(cuda_bag_data_indices_.RawData(), bag_data_indices_.data(), static_cast(num_data_), __FILE__, __LINE__);\n tree_learner->SetBaggingData(tmp_subset_.get(), cuda_bag_data_indices_.RawData(),\n bag_data_cnt_);\n } else {\n #endif // USE_CUDA\n tree_learner->SetBaggingData(tmp_subset_.get(), bag_data_indices_.data(),\n bag_data_cnt_);\n #ifdef USE_CUDA\n }\n #endif // USE_CUDA\n }\n }\n }\n\n void ResetSampleConfig(const Config* config, bool is_change_dataset) override {\n need_resize_gradients_ = false;\n // if need bagging, create buffer\n data_size_t num_pos_data = 0;\n if (objective_function_ != nullptr) {\n num_pos_data = objective_function_->NumPositiveData();\n }\n bool balance_bagging_cond = (config->pos_bagging_fraction < 1.0 || config->neg_bagging_fraction < 1.0) && (num_pos_data > 0);\n if ((config->bagging_fraction < 1.0 || balance_bagging_cond) && config->bagging_freq > 0) {\n need_re_bagging_ = false;\n if (!is_change_dataset &&\n config_ != nullptr && config_->bagging_fraction == config->bagging_fraction && config_->bagging_freq == config->bagging_freq\n && config_->pos_bagging_fraction == config->pos_bagging_fraction && config_->neg_bagging_fraction == config->neg_bagging_fraction) {\n config_ = config;\n return;\n }\n config_ = config;\n if (balance_bagging_cond) {\n balanced_bagging_ = true;\n bag_data_cnt_ = static_cast(num_pos_data * config_->pos_bagging_fraction)\n + static_cast((num_data_ - num_pos_data) * config_->neg_bagging_fraction);\n } else {\n bag_data_cnt_ = static_cast(config_->bagging_fraction * num_data_);\n }\n bag_data_indices_.resize(num_data_);\n #ifdef USE_CUDA\n if (config_->device_type == std::string(\"cuda\")) {\n cuda_bag_data_indices_.Resize(num_data_);\n }\n #endif // USE_CUDA\n if (!config_->bagging_by_query) {\n bagging_runner_.ReSize(num_data_);\n } else {\n bagging_runner_.ReSize(num_queries_);\n sampled_query_boundaries_.resize(num_queries_ + 1, 0);\n sampled_query_boundaries_thread_buffer_.resize(num_threads_, 0);\n bag_query_indices_.resize(num_data_);\n }\n bagging_rands_.clear();\n for (int i = 0;\n i < (num_data_ + bagging_rand_block_ - 1) / bagging_rand_block_; ++i) {\n bagging_rands_.emplace_back(config_->bagging_seed + i);\n }\n\n double average_bag_rate =\n (static_cast(bag_data_cnt_) / num_data_) / config_->bagging_freq;\n is_use_subset_ = false;\n if (config_->device_type != std::string(\"cuda\")) {\n const int group_threshold_usesubset = 100;\n const double average_bag_rate_threshold = 0.5;\n if (average_bag_rate <= average_bag_rate_threshold\n && (train_data_->num_feature_groups() < group_threshold_usesubset)) {\n if (tmp_subset_ == nullptr || is_change_dataset) {\n tmp_subset_.reset(new Dataset(bag_data_cnt_));\n tmp_subset_->CopyFeatureMapperFrom(train_data_);\n }\n is_use_subset_ = true;\n Log::Debug(\"Use subset for bagging\");\n }\n }\n\n need_re_bagging_ = true;\n\n if (is_use_subset_ && bag_data_cnt_ < num_data_) {\n // resize gradient vectors to copy the customized gradients for using subset data\n need_resize_gradients_ = true;\n }\n } else {\n bag_data_cnt_ = num_data_;\n bag_data_indices_.clear();\n #ifdef USE_CUDA\n cuda_bag_data_indices_.Clear();\n #endif // USE_CUDA\n bagging_runner_.ReSize(0);\n is_use_subset_ = false;\n }\n }\n\n bool IsHessianChange() const override {\n return false;\n }\n\n data_size_t num_sampled_queries() const override {\n return num_sampled_queries_;\n }\n\n const data_size_t* sampled_query_indices() const override {\n return bag_query_indices_.data();\n }\n\n private:\n data_size_t BaggingHelper(data_size_t start, data_size_t cnt, data_size_t* buffer) {\n if (cnt <= 0) {\n return 0;\n }\n data_size_t cur_left_cnt = 0;\n data_size_t cur_right_pos = cnt;\n // random bagging, minimal unit is one record\n for (data_size_t i = 0; i < cnt; ++i) {\n auto cur_idx = start + i;\n if (bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() < config_->bagging_fraction) {\n buffer[cur_left_cnt++] = cur_idx;\n } else {\n buffer[--cur_right_pos] = cur_idx;\n }\n }\n return cur_left_cnt;\n }\n\n data_size_t BalancedBaggingHelper(data_size_t start, data_size_t cnt, data_size_t* buffer) {\n if (cnt <= 0) {\n return 0;\n }\n auto label_ptr = train_data_->metadata().label();\n data_size_t cur_left_cnt = 0;\n data_size_t cur_right_pos = cnt;\n // random bagging, minimal unit is one record\n for (data_size_t i = 0; i < cnt; ++i) {\n auto cur_idx = start + i;\n bool is_pos = label_ptr[start + i] > 0;\n bool is_in_bag = false;\n if (is_pos) {\n is_in_bag = bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() <\n config_->pos_bagging_fraction;\n } else {\n is_in_bag = bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() <\n config_->neg_bagging_fraction;\n }\n if (is_in_bag) {\n buffer[cur_left_cnt++] = cur_idx;\n } else {\n buffer[--cur_right_pos] = cur_idx;\n }\n }\n return cur_left_cnt;\n }\n\n /*! \\brief whether need restart bagging in continued training */\n bool need_re_bagging_;\n /*! \\brief number of threads */\n int num_threads_;\n /*! \\brief query boundaries of the in-bag queries */\n std::vector sampled_query_boundaries_;\n /*! \\brief buffer for calculating sampled_query_boundaries_ */\n std::vector sampled_query_boundaries_thread_buffer_;\n /*! \\brief in-bag query indices */\n std::vector> bag_query_indices_;\n /*! \\brief number of queries in the training dataset */\n data_size_t num_queries_;\n /*! \\brief number of in-bag queries */\n data_size_t num_sampled_queries_;\n /*! \\brief query boundaries of the whole training dataset */\n const data_size_t* query_boundaries_;\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_SRC_BOOSTING_BAGGING_HPP_\n" }, { "path": "src/boosting/boosting.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n\n#include \n#include \n\n#include \"dart.hpp\"\n#include \"gbdt.h\"\n#include \"rf.hpp\"\n\n#ifdef USE_CUDA\n#include \"cuda/nccl_gbdt.hpp\"\n#endif // USE_CUDA\n\nnamespace LightGBM {\n\nstd::string GetBoostingTypeFromModelFile(const char* filename) {\n TextReader model_reader(filename, true);\n std::string type = model_reader.first_line();\n return type;\n}\n\nbool Boosting::LoadFileToBoosting(Boosting* boosting, const char* filename) {\n auto start_time = std::chrono::steady_clock::now();\n if (boosting != nullptr) {\n TextReader model_reader(filename, true);\n size_t buffer_len = 0;\n auto buffer = model_reader.ReadContent(&buffer_len);\n if (!boosting->LoadModelFromString(buffer.data(), buffer_len)) {\n return false;\n }\n }\n std::chrono::duration delta = (std::chrono::steady_clock::now() - start_time);\n Log::Debug(\"Time for loading model: %f seconds\", 1e-3*delta);\n return true;\n}\n\nBoosting* Boosting::CreateBoosting(const std::string& type, const char* filename,\n const std::string&\n #ifdef USE_CUDA\n device_type\n #endif // USE_CUDA\n , const int\n #ifdef USE_CUDA\n num_gpu\n #endif // USE_CUDA\n ) {\n if (filename == nullptr || filename[0] == '\\0') {\n if (type == std::string(\"gbdt\")) {\n #ifdef USE_CUDA\n if (device_type == std::string(\"cuda\") && num_gpu > 1) {\n return new NCCLGBDT();\n } else {\n #endif // USE_CUDA\n return new GBDT();\n #ifdef USE_CUDA\n }\n #endif // USE_CUDA\n } else if (type == std::string(\"dart\")) {\n return new DART();\n } else if (type == std::string(\"goss\")) {\n return new GBDT();\n } else if (type == std::string(\"rf\")) {\n return new RF();\n } else {\n return nullptr;\n }\n } else {\n std::unique_ptr ret;\n if (GetBoostingTypeFromModelFile(filename) == std::string(\"tree\")) {\n if (type == std::string(\"gbdt\")) {\n #ifdef USE_CUDA\n if (device_type == std::string(\"cuda\") && num_gpu > 1) {\n ret.reset(new NCCLGBDT());\n } else {\n #endif // USE_CUDA\n ret.reset(new GBDT());\n #ifdef USE_CUDA\n }\n #endif // USE_CUDA\n } else if (type == std::string(\"dart\")) {\n ret.reset(new DART());\n } else if (type == std::string(\"goss\")) {\n ret.reset(new GBDT());\n } else if (type == std::string(\"rf\")) {\n ret.reset(new RF());\n } else {\n Log::Fatal(\"Unknown boosting type %s\", type.c_str());\n }\n LoadFileToBoosting(ret.get(), filename);\n } else {\n Log::Fatal(\"Unknown model format or submodel type in model file %s\", filename);\n }\n return ret.release();\n }\n}\n\n} // namespace LightGBM\n" }, { "path": "src/boosting/cuda/cuda_score_updater.cpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#include \"cuda_score_updater.hpp\"\n\n#ifdef USE_CUDA\n\nnamespace LightGBM {\n\nCUDAScoreUpdater::CUDAScoreUpdater(const Dataset* data, int num_tree_per_iteration, const bool boosting_on_cuda):\n ScoreUpdater(data, num_tree_per_iteration), num_threads_per_block_(1024), boosting_on_cuda_(boosting_on_cuda) {\n num_data_ = data->num_data();\n int64_t total_size = static_cast(num_data_) * num_tree_per_iteration;\n InitCUDA(total_size);\n has_init_score_ = false;\n const double* init_score = data->metadata().init_score();\n // if exists initial score, will start from it\n if (init_score != nullptr) {\n if ((data->metadata().num_init_score() % num_data_) != 0\n || (data->metadata().num_init_score() / num_data_) != num_tree_per_iteration) {\n Log::Fatal(\"Number of class for initial score error\");\n }\n has_init_score_ = true;\n CopyFromHostToCUDADevice(cuda_score_.RawData(), init_score, total_size, __FILE__, __LINE__);\n } else {\n SetCUDAMemory(cuda_score_.RawData(), 0, static_cast(total_size), __FILE__, __LINE__);\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n if (boosting_on_cuda_) {\n // clear host score buffer\n score_.clear();\n score_.shrink_to_fit();\n }\n}\n\nvoid CUDAScoreUpdater::InitCUDA(const size_t total_size) {\n cuda_score_.Resize(total_size);\n}\n\nCUDAScoreUpdater::~CUDAScoreUpdater() {}\n\ninline void CUDAScoreUpdater::AddScore(double val, int cur_tree_id) {\n Common::FunctionTimer fun_timer(\"CUDAScoreUpdater::AddScore\", global_timer);\n const size_t offset = static_cast(num_data_) * cur_tree_id;\n LaunchAddScoreConstantKernel(val, offset);\n if (!boosting_on_cuda_) {\n CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__);\n }\n}\n\ninline void CUDAScoreUpdater::AddScore(const Tree* tree, int cur_tree_id) {\n Common::FunctionTimer fun_timer(\"ScoreUpdater::AddScore\", global_timer);\n const size_t offset = static_cast(num_data_) * cur_tree_id;\n tree->AddPredictionToScore(data_, num_data_, cuda_score_.RawData() + offset);\n if (!boosting_on_cuda_) {\n CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__);\n }\n}\n\ninline void CUDAScoreUpdater::AddScore(const TreeLearner* tree_learner, const Tree* tree, int cur_tree_id) {\n Common::FunctionTimer fun_timer(\"ScoreUpdater::AddScore\", global_timer);\n const size_t offset = static_cast(num_data_) * cur_tree_id;\n tree_learner->AddPredictionToScore(tree, cuda_score_.RawData() + offset);\n if (!boosting_on_cuda_) {\n CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__);\n }\n}\n\ninline void CUDAScoreUpdater::AddScore(const Tree* tree, const data_size_t* data_indices,\n data_size_t data_cnt, int cur_tree_id) {\n Common::FunctionTimer fun_timer(\"ScoreUpdater::AddScore\", global_timer);\n const size_t offset = static_cast(num_data_) * cur_tree_id;\n tree->AddPredictionToScore(data_, data_indices, data_cnt, cuda_score_.RawData() + offset);\n if (!boosting_on_cuda_) {\n CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__);\n }\n}\n\ninline void CUDAScoreUpdater::MultiplyScore(double val, int cur_tree_id) {\n Common::FunctionTimer fun_timer(\"CUDAScoreUpdater::MultiplyScore\", global_timer);\n const size_t offset = static_cast(num_data_) * cur_tree_id;\n LaunchMultiplyScoreConstantKernel(val, offset);\n if (!boosting_on_cuda_) {\n CopyFromCUDADeviceToHost(score_.data() + offset, cuda_score_.RawData() + offset, static_cast(num_data_), __FILE__, __LINE__);\n }\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/boosting/cuda/cuda_score_updater.cu", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#include \"cuda_score_updater.hpp\"\n\n#ifdef USE_CUDA\n\nnamespace LightGBM {\n\n__global__ void AddScoreConstantKernel(\n const double val,\n const data_size_t num_data,\n double* score) {\n const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n if (data_index < num_data) {\n score[data_index] += val;\n }\n}\n\nvoid CUDAScoreUpdater::LaunchAddScoreConstantKernel(const double val, const size_t offset) {\n const int num_blocks = (num_data_ + num_threads_per_block_) / num_threads_per_block_;\n Log::Debug(\"Adding init score = %lf\", val);\n AddScoreConstantKernel<<>>(val, num_data_, cuda_score_.RawData() + offset);\n}\n\n__global__ void MultiplyScoreConstantKernel(\n const double val,\n const data_size_t num_data,\n double* score) {\n const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n if (data_index < num_data) {\n score[data_index] *= val;\n }\n}\n\nvoid CUDAScoreUpdater::LaunchMultiplyScoreConstantKernel(const double val, const size_t offset) {\n const int num_blocks = (num_data_ + num_threads_per_block_) / num_threads_per_block_;\n MultiplyScoreConstantKernel<<>>(val, num_data_, cuda_score_.RawData() + offset);\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/boosting/cuda/cuda_score_updater.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifndef LIGHTGBM_SRC_BOOSTING_CUDA_CUDA_SCORE_UPDATER_HPP_\n#define LIGHTGBM_SRC_BOOSTING_CUDA_CUDA_SCORE_UPDATER_HPP_\n\n#ifdef USE_CUDA\n\n#include \n\n#include \"../score_updater.hpp\"\n\nnamespace LightGBM {\n\nclass CUDAScoreUpdater: public ScoreUpdater {\n public:\n CUDAScoreUpdater(const Dataset* data, int num_tree_per_iteration, const bool boosting_on_cuda);\n\n ~CUDAScoreUpdater();\n\n void AddScore(double val, int cur_tree_id) override;\n\n inline void AddScore(const Tree* tree, int cur_tree_id) override;\n\n void AddScore(const TreeLearner* tree_learner, const Tree* tree, int cur_tree_id) override;\n\n inline void AddScore(const Tree* tree, const data_size_t* data_indices,\n data_size_t data_cnt, int cur_tree_id) override;\n\n inline void MultiplyScore(double val, int cur_tree_id) override;\n\n inline const double* score() const override {\n if (boosting_on_cuda_) {\n return cuda_score_.RawData();\n } else {\n return score_.data();\n }\n }\n\n /*! \\brief Disable copy */\n CUDAScoreUpdater& operator=(const CUDAScoreUpdater&) = delete;\n\n CUDAScoreUpdater(const CUDAScoreUpdater&) = delete;\n\n private:\n void InitCUDA(const size_t total_size);\n\n void LaunchAddScoreConstantKernel(const double val, const size_t offset);\n\n void LaunchMultiplyScoreConstantKernel(const double val, const size_t offset);\n\n CUDAVector cuda_score_;\n\n const int num_threads_per_block_;\n\n const bool boosting_on_cuda_;\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_SRC_BOOSTING_CUDA_CUDA_SCORE_UPDATER_HPP_\n" }, { "path": "src/boosting/cuda/nccl_gbdt.cpp", "content": "/*!\n * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#include \n\n#include \n#include \n\n#include \"nccl_gbdt.hpp\"\n#include \"nccl_gbdt_component.hpp\"\n\n#ifdef USE_CUDA\n\nnamespace LightGBM {\n\ntemplate \nNCCLGBDT::NCCLGBDT(): GBDT_T() {}\n\ntemplate \nNCCLGBDT::~NCCLGBDT() {}\n\ntemplate \nvoid NCCLGBDT::Init(\n const Config* gbdt_config, const Dataset* train_data,\n const ObjectiveFunction* objective_function,\n const std::vector& training_metrics) {\n GBDT_T::Init(gbdt_config, train_data, objective_function, training_metrics);\n\n this->tree_learner_.reset();\n\n nccl_topology_.reset(new NCCLTopology(this->config_->gpu_device_id, this->config_->num_gpu, this->config_->gpu_device_id_list, train_data->num_data()));\n\n nccl_topology_->InitNCCL();\n\n nccl_topology_->InitPerDevice(&nccl_gbdt_components_);\n nccl_topology_->RunPerDevice(nccl_gbdt_components_, [this, gbdt_config, train_data]\n (NCCLGBDTComponent* nccl_gbdt_component) { nccl_gbdt_component->Init(\n gbdt_config, train_data, this->num_tree_per_iteration_, this->boosting_on_gpu_, this->is_constant_hessian_);\n });\n}\n\ntemplate \nvoid NCCLGBDT::BoostingThread(NCCLGBDTComponent* thread_data) {\n const ObjectiveFunction* objective_function = thread_data->objective_function();\n score_t* gradients = thread_data->gradients();\n score_t* hessians = thread_data->hessians();\n const double* score = thread_data->train_score_updater()->score();\n objective_function->GetGradients(score, gradients, hessians);\n}\n\ntemplate \nvoid NCCLGBDT::Boosting() {\n Common::FunctionTimer fun_timer(\"NCCLGBDT::Boosting\", global_timer);\n if (this->objective_function_ == nullptr) {\n Log::Fatal(\"No object function provided\");\n }\n nccl_topology_->DispatchPerDevice(&nccl_gbdt_components_, BoostingThread);\n}\n\ntemplate \ndouble NCCLGBDT::BoostFromAverage(int class_id, bool update_scorer) {\n double init_score = GBDT_T::BoostFromAverage(class_id, update_scorer);\n\n if (init_score != 0.0) {\n nccl_topology_->RunPerDevice(nccl_gbdt_components_, [init_score, class_id] (NCCLGBDTComponent* thread_data) {\n thread_data->train_score_updater()->AddScore(init_score, class_id);\n });\n }\n\n return init_score;\n}\n\ntemplate \nvoid NCCLGBDT::TrainTreeLearnerThread(NCCLGBDTComponent* thread_data, const int class_id, const bool is_first_tree) {\n const data_size_t num_data_in_gpu = thread_data->num_data_in_gpu();\n const score_t* gradients = thread_data->gradients() + class_id * num_data_in_gpu;\n const score_t* hessians = thread_data->hessians() + class_id * num_data_in_gpu;\n thread_data->SetTree(thread_data->tree_learner()->Train(gradients, hessians, is_first_tree));\n}\n\ntemplate \nbool NCCLGBDT::TrainOneIter(const score_t* gradients, const score_t* hessians) {\n Common::FunctionTimer fun_timer(\"NCCLGBDT::TrainOneIter\", global_timer);\n std::vector init_scores(this->num_tree_per_iteration_, 0.0);\n // boosting first\n if (gradients == nullptr || hessians == nullptr) {\n for (int cur_tree_id = 0; cur_tree_id < this->num_tree_per_iteration_; ++cur_tree_id) {\n init_scores[cur_tree_id] = BoostFromAverage(cur_tree_id, true);\n }\n Boosting();\n } else {\n nccl_topology_->RunPerDevice(nccl_gbdt_components_, [this, gradients, hessians] (NCCLGBDTComponent* thread_data) {\n const data_size_t data_start_index = thread_data->data_start_index();\n const data_size_t num_data_in_gpu = thread_data->num_data_in_gpu();\n\n for (int class_id = 0; class_id < this->num_class_; ++class_id) {\n CopyFromHostToCUDADevice(\n thread_data->gradients() + class_id * num_data_in_gpu,\n gradients + class_id * this->num_data_ + data_start_index, num_data_in_gpu, __FILE__, __LINE__);\n CopyFromHostToCUDADevice(\n thread_data->hessians() + class_id * num_data_in_gpu,\n hessians + class_id * this->num_data_ + data_start_index, num_data_in_gpu, __FILE__, __LINE__);\n }\n });\n }\n\n bool should_continue = false;\n for (int cur_tree_id = 0; cur_tree_id < this->num_tree_per_iteration_; ++cur_tree_id) {\n if (this->class_need_train_[cur_tree_id] && this->train_data_->num_features() > 0) {\n if (this->data_sample_strategy_->is_use_subset() && this->data_sample_strategy_->bag_data_cnt() < this->num_data_) {\n Log::Fatal(\"Bagging is not supported for NCCLGBDT\");\n }\n bool is_first_tree = this->models_.size() < static_cast(this->num_tree_per_iteration_);\n nccl_topology_->DispatchPerDevice(&nccl_gbdt_components_,\n [is_first_tree, cur_tree_id] (NCCLGBDTComponent* thread_data) -> void {\n TrainTreeLearnerThread(thread_data, cur_tree_id, is_first_tree);\n });\n }\n\n nccl_topology_->DispatchPerDevice(&nccl_gbdt_components_, [cur_tree_id, this, init_scores] (NCCLGBDTComponent* thread_data) -> void {\n this->UpdateScoreThread(thread_data, cur_tree_id, this->config_->learning_rate, init_scores[cur_tree_id]);\n });\n\n nccl_topology_->RunOnMasterDevice(nccl_gbdt_components_, [&should_continue, this, cur_tree_id] (NCCLGBDTComponent* thread_data) -> void {\n if (thread_data->new_tree()->num_leaves() > 1) {\n should_continue = true;\n }\n for (auto& score_updater : this->valid_score_updater_) {\n score_updater->AddScore(thread_data->new_tree(), cur_tree_id);\n }\n });\n\n if (!should_continue) {\n if (this->models_.size() < static_cast(this->num_tree_per_iteration_)) {\n Log::Warning(\"Training stopped with no splits.\");\n }\n }\n\n // add model\n nccl_topology_->RunOnMasterDevice(nccl_gbdt_components_, [this] (NCCLGBDTComponent* thread_data) -> void {\n this->models_.emplace_back(thread_data->release_new_tree());\n });\n\n nccl_topology_->RunOnNonMasterDevice(nccl_gbdt_components_, [this] (NCCLGBDTComponent* thread_data) -> void {\n thread_data->clear_new_tree();\n });\n }\n\n if (!should_continue) {\n Log::Warning(\"Stopped training because there are no more leaves that meet the split requirements\");\n if (this->models_.size() > static_cast(this->num_tree_per_iteration_)) {\n for (int cur_tree_id = 0; cur_tree_id < this->num_tree_per_iteration_; ++cur_tree_id) {\n this->models_.pop_back();\n }\n }\n return true;\n }\n\n ++this->iter_;\n return false;\n}\n\ntemplate \nvoid NCCLGBDT::UpdateScoreThread(NCCLGBDTComponent* thread_data, const int cur_tree_id, const double shrinkage_rate, const double init_score) {\n if (thread_data->new_tree()->num_leaves() > 1) {\n // TODO(shiyu1994): implement bagging\n if (thread_data->objective_function() != nullptr && thread_data->objective_function()->IsRenewTreeOutput()) {\n // TODO(shiyu1994): implement renewing\n }\n thread_data->new_tree()->Shrinkage(shrinkage_rate);\n thread_data->train_score_updater()->AddScore(\n thread_data->tree_learner(),\n thread_data->new_tree(),\n cur_tree_id);\n if (std::fabs(init_score) > kEpsilon) {\n thread_data->new_tree()->AddBias(init_score);\n }\n }\n}\n\ntemplate \nstd::vector NCCLGBDT::EvalOneMetric(const Metric* metric, const double* score, const data_size_t num_data) const {\n if (score == this->train_score_updater_->score()) {\n // delegate to per gpu train score updater\n std::vector tmp_score(num_data * this->num_class_, 0.0f);\n\n nccl_topology_->RunPerDevice(nccl_gbdt_components_, [this, &tmp_score] (NCCLGBDTComponent* thread_data) {\n const data_size_t data_start = thread_data->data_start_index();\n const data_size_t num_data_in_gpu = thread_data->num_data_in_gpu();\n for (int class_id = 0; class_id < this->num_class_; ++class_id) {\n CopyFromCUDADeviceToHost(tmp_score.data() + class_id * this->num_data_ + data_start,\n thread_data->train_score_updater()->score() + class_id * num_data_in_gpu,\n static_cast(num_data_in_gpu), __FILE__, __LINE__);\n }\n });\n\n return metric->Eval(tmp_score.data(), this->objective_function_);\n } else {\n return GBDT_T::EvalOneMetric(metric, score, num_data);\n }\n}\n\ntemplate class NCCLGBDT;\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/boosting/cuda/nccl_gbdt.hpp", "content": "/*!\n * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifndef LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_HPP_\n#define LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_HPP_\n\n#ifdef USE_CUDA\n\n#include \n\n#include \n#include \n\n#include \n#include \n\n#include \n\n#include \"cuda_score_updater.hpp\"\n#include \"nccl_gbdt_component.hpp\"\n\n#include \"../gbdt.h\"\n\nnamespace LightGBM {\n\ntemplate \nclass NCCLGBDT: public GBDT_T {\n public:\n NCCLGBDT();\n\n ~NCCLGBDT();\n\n void Init(const Config* gbdt_config, const Dataset* train_data,\n const ObjectiveFunction* objective_function,\n const std::vector& training_metrics) override;\n\n void Boosting() override;\n\n void RefitTree(const int* /*tree_leaf_prediction*/, const size_t /*nrow*/, const size_t /*ncol*/) override {\n Log::Fatal(\"RefitTree is not supported for NCCLGBDT.\");\n }\n\n bool TrainOneIter(const score_t* gradients, const score_t* hessians) override;\n\n const double* GetTrainingScore(int64_t* /*out_len*/) override {\n Log::Fatal(\"GetTrainingScore is not supported for NCCLGBDT.\");\n }\n\n void ResetTrainingData(const Dataset* /*train_data*/, const ObjectiveFunction* /*objective_function*/,\n const std::vector& /*training_metrics*/) override {\n Log::Fatal(\"ResetTrainingData is not supported for NCCLGBDT.\");\n }\n\n void ResetConfig(const Config* /*gbdt_config*/) override {\n Log::Fatal(\"ResetConfig is not supported for NCCLGBDT.\");\n }\n\n private:\n struct BoostingThreadData {\n int gpu_index;\n ObjectiveFunction* gpu_objective_function;\n score_t* gradients;\n score_t* hessians;\n const double* score;\n\n BoostingThreadData() {\n gpu_index = 0;\n gpu_objective_function = nullptr;\n }\n };\n\n struct TrainTreeLearnerThreadData {\n int gpu_index;\n TreeLearner* gpu_tree_learner;\n const score_t* gradients;\n const score_t* hessians;\n bool is_first_time;\n int class_id;\n data_size_t num_data_in_gpu;\n std::unique_ptr tree;\n\n TrainTreeLearnerThreadData() {\n gpu_index = 0;\n gpu_tree_learner = nullptr;\n gradients = nullptr;\n hessians = nullptr;\n is_first_time = false;\n class_id = 0;\n num_data_in_gpu = 0;\n tree.reset(nullptr);\n }\n };\n\n struct UpdateScoreThreadData {\n int gpu_index;\n ScoreUpdater* gpu_score_updater;\n TreeLearner* gpu_tree_learner;\n Tree* tree;\n int cur_tree_id;\n\n UpdateScoreThreadData() {\n gpu_index = 0;\n gpu_score_updater = nullptr;\n gpu_tree_learner = nullptr;\n tree = nullptr;\n cur_tree_id = 0;\n }\n };\n\n static void BoostingThread(NCCLGBDTComponent* thread_data);\n\n static void TrainTreeLearnerThread(NCCLGBDTComponent* thread_data, const int class_id, const bool is_first_tree);\n\n static void UpdateScoreThread(NCCLGBDTComponent* thread_data, const int cur_tree_id, const double shrinkage_rate, const double init_score);\n\n double BoostFromAverage(int class_id, bool update_scorer) override;\n\n void UpdateScore(const std::vector>& tree, const int cur_tree_id);\n\n void UpdateScore(const Tree* /*tree*/, const int /*cur_tree_id*/) {\n Log::Fatal(\"UpdateScore is not supported for NCCLGBDT.\");\n }\n\n void RollbackOneIter() override {\n Log::Fatal(\"RollbackOneIter is not supported for NCCLGBDT.\");\n }\n\n std::vector EvalOneMetric(const Metric* metric, const double* score, const data_size_t num_data) const override;\n\n\n int num_threads_;\n std::unique_ptr nccl_topology_;\n\n std::vector nccl_gpu_rank_;\n std::vector nccl_communicators_;\n\n std::vector> nccl_gbdt_components_;\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n#endif // LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_HPP_\n" }, { "path": "src/boosting/cuda/nccl_gbdt_component.hpp", "content": "/*!\n * Copyright (c) 2023-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2023-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifndef LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_COMPONENT_HPP_\n#define LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_COMPONENT_HPP_\n\n#ifdef USE_CUDA\n\n#include \n#include \n\n#include \n#include \n#include \n\n#include \n#include \"cuda_score_updater.hpp\"\n#include \"../../treelearner/cuda/cuda_single_gpu_tree_learner.hpp\"\n\nnamespace LightGBM {\n\nclass NCCLGBDTComponent: public NCCLInfo {\n public:\n NCCLGBDTComponent() {}\n\n ~NCCLGBDTComponent() {}\n\n void Init(const Config* config, const Dataset* train_data, const int num_tree_per_iteration, const bool boosting_on_gpu, const bool is_constant_hessian) {\n CUDASUCCESS_OR_FATAL(cudaGetDeviceCount(&num_gpu_in_node_));\n const data_size_t num_data_per_gpu = (train_data->num_data() + num_gpu_in_node_ - 1) / num_gpu_in_node_;\n data_start_index_ = num_data_per_gpu * local_gpu_rank_;\n data_end_index_ = std::min(data_start_index_ + num_data_per_gpu, train_data->num_data());\n num_data_in_gpu_ = data_end_index_ - data_start_index_;\n\n dataset_.reset(new Dataset(num_data_in_gpu_));\n dataset_->ReSize(num_data_in_gpu_);\n dataset_->CopyFeatureMapperFrom(train_data);\n std::vector used_indices(num_data_in_gpu_);\n for (data_size_t data_index = data_start_index_; data_index < data_end_index_; ++data_index) {\n used_indices[data_index - data_start_index_] = data_index;\n }\n dataset_->CopySubrowToDevice(train_data, used_indices.data(), num_data_in_gpu_, true, gpu_device_id_);\n\n objective_function_.reset(ObjectiveFunction::CreateObjectiveFunctionCUDA(config->objective, *config));\n objective_function_->SetNCCLInfo(nccl_communicator_, nccl_gpu_rank_, local_gpu_rank_, gpu_device_id_, train_data->num_data());\n train_score_updater_.reset(new CUDAScoreUpdater(dataset_.get(), num_tree_per_iteration, boosting_on_gpu));\n gradients_.reset(new CUDAVector(num_data_in_gpu_));\n hessians_.reset(new CUDAVector(num_data_in_gpu_));\n tree_learner_.reset(new CUDASingleGPUTreeLearner(config, boosting_on_gpu));\n\n tree_learner_->SetNCCLInfo(nccl_communicator_, nccl_gpu_rank_, local_gpu_rank_, gpu_device_id_, train_data->num_data());\n\n objective_function_->Init(dataset_->metadata(), dataset_->num_data());\n tree_learner_->Init(dataset_.get(), is_constant_hessian);\n }\n\n ObjectiveFunction* objective_function() { return objective_function_.get(); }\n\n ScoreUpdater* train_score_updater() { return train_score_updater_.get(); }\n\n score_t* gradients() { return gradients_->RawData(); }\n\n score_t* hessians() { return hessians_->RawData(); }\n\n data_size_t num_data_in_gpu() const { return num_data_in_gpu_; }\n\n CUDASingleGPUTreeLearner* tree_learner() { return tree_learner_.get(); }\n\n void SetTree(Tree* tree) {\n new_tree_.reset(tree);\n }\n\n data_size_t data_start_index() const { return data_start_index_; }\n\n data_size_t data_end_index() const { return data_end_index_; }\n\n Tree* new_tree() { return new_tree_.get(); }\n\n Tree* release_new_tree() { return new_tree_.release(); }\n\n void clear_new_tree() { new_tree_.reset(nullptr); }\n\n private:\n std::unique_ptr objective_function_;\n std::unique_ptr train_score_updater_;\n std::unique_ptr> gradients_;\n std::unique_ptr> hessians_;\n std::unique_ptr dataset_;\n std::unique_ptr tree_learner_;\n std::unique_ptr new_tree_;\n\n data_size_t data_start_index_;\n data_size_t data_end_index_;\n data_size_t num_data_in_gpu_;\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_SRC_BOOSTING_CUDA_NCCL_GBDT_COMPONENT_HPP_\n" }, { "path": "src/boosting/dart.hpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_BOOSTING_DART_HPP_\n#define LIGHTGBM_SRC_BOOSTING_DART_HPP_\n\n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \"gbdt.h\"\n#include \"score_updater.hpp\"\n\nnamespace LightGBM {\n/*!\n* \\brief DART algorithm implementation. including Training, prediction, bagging.\n*/\nclass DART: public GBDT {\n public:\n /*!\n * \\brief Constructor\n */\n DART() : GBDT() { }\n /*!\n * \\brief Destructor\n */\n ~DART() { }\n /*!\n * \\brief Initialization logic\n * \\param config Config for boosting\n * \\param train_data Training data\n * \\param objective_function Training objective function\n * \\param training_metrics Training metrics\n * \\param output_model_filename Filename of output model\n */\n void Init(const Config* config, const Dataset* train_data,\n const ObjectiveFunction* objective_function,\n const std::vector& training_metrics) override {\n GBDT::Init(config, train_data, objective_function, training_metrics);\n random_for_drop_ = Random(config_->drop_seed);\n sum_weight_ = 0.0f;\n }\n\n void ResetConfig(const Config* config) override {\n GBDT::ResetConfig(config);\n random_for_drop_ = Random(config_->drop_seed);\n sum_weight_ = 0.0f;\n }\n\n /*!\n * \\brief one training iteration\n */\n bool TrainOneIter(const score_t* gradient, const score_t* hessian) override {\n is_update_score_cur_iter_ = false;\n bool ret = GBDT::TrainOneIter(gradient, hessian);\n if (ret) {\n return ret;\n }\n // normalize\n Normalize();\n if (!config_->uniform_drop) {\n tree_weight_.push_back(shrinkage_rate_);\n sum_weight_ += shrinkage_rate_;\n }\n return false;\n }\n\n /*!\n * \\brief Get current training score\n * \\param out_len length of returned score\n * \\return training score\n */\n const double* GetTrainingScore(int64_t* out_len) override {\n if (!is_update_score_cur_iter_) {\n // only drop one time in one iteration\n DroppingTrees();\n is_update_score_cur_iter_ = true;\n }\n *out_len = static_cast(train_score_updater_->num_data()) * num_class_;\n return train_score_updater_->score();\n }\n\n bool EvalAndCheckEarlyStopping() override {\n GBDT::OutputMetric(iter_);\n return false;\n }\n\n private:\n /*!\n * \\brief drop trees based on drop_rate\n */\n void DroppingTrees() {\n drop_index_.clear();\n bool is_skip = random_for_drop_.NextFloat() < config_->skip_drop;\n // select dropping tree indices based on drop_rate and tree weights\n if (!is_skip) {\n double drop_rate = config_->drop_rate;\n if (!config_->uniform_drop) {\n double inv_average_weight = static_cast(tree_weight_.size()) / sum_weight_;\n if (config_->max_drop > 0) {\n drop_rate = std::min(drop_rate, config_->max_drop * inv_average_weight / sum_weight_);\n }\n for (int i = 0; i < iter_; ++i) {\n if (random_for_drop_.NextFloat() < drop_rate * tree_weight_[i] * inv_average_weight) {\n drop_index_.push_back(num_init_iteration_ + i);\n if (drop_index_.size() >= static_cast(config_->max_drop)) {\n break;\n }\n }\n }\n } else {\n if (config_->max_drop > 0) {\n drop_rate = std::min(drop_rate, config_->max_drop / static_cast(iter_));\n }\n for (int i = 0; i < iter_; ++i) {\n if (random_for_drop_.NextFloat() < drop_rate) {\n drop_index_.push_back(num_init_iteration_ + i);\n if (drop_index_.size() >= static_cast(config_->max_drop)) {\n break;\n }\n }\n }\n }\n }\n // drop trees\n for (auto i : drop_index_) {\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n auto curr_tree = i * num_tree_per_iteration_ + cur_tree_id;\n models_[curr_tree]->Shrinkage(-1.0);\n train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id);\n }\n }\n if (!config_->xgboost_dart_mode) {\n shrinkage_rate_ = config_->learning_rate / (1.0f + static_cast(drop_index_.size()));\n } else {\n if (drop_index_.empty()) {\n shrinkage_rate_ = config_->learning_rate;\n } else {\n shrinkage_rate_ = config_->learning_rate / (config_->learning_rate + static_cast(drop_index_.size()));\n }\n }\n }\n /*!\n * \\brief normalize dropped trees\n * NOTE: num_drop_tree(k), learning_rate(lr), shrinkage_rate_ = lr / (k + 1)\n * step 1: shrink tree to -1 -> drop tree\n * step 2: shrink tree to k / (k + 1) - 1 from -1, by 1/(k+1)\n * -> normalize for valid data\n * step 3: shrink tree to k / (k + 1) from k / (k + 1) - 1, by -k\n * -> normalize for train data\n * end with tree weight = (k / (k + 1)) * old_weight\n */\n void Normalize() {\n double k = static_cast(drop_index_.size());\n if (!config_->xgboost_dart_mode) {\n for (auto i : drop_index_) {\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n auto curr_tree = i * num_tree_per_iteration_ + cur_tree_id;\n // update validation score\n models_[curr_tree]->Shrinkage(1.0f / (k + 1.0f));\n for (auto& score_updater : valid_score_updater_) {\n score_updater->AddScore(models_[curr_tree].get(), cur_tree_id);\n }\n // update training score\n models_[curr_tree]->Shrinkage(-k);\n train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id);\n }\n if (!config_->uniform_drop) {\n sum_weight_ -= tree_weight_[i - num_init_iteration_] * (1.0f / (k + 1.0f));\n tree_weight_[i - num_init_iteration_] *= (k / (k + 1.0f));\n }\n }\n } else {\n for (auto i : drop_index_) {\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n auto curr_tree = i * num_tree_per_iteration_ + cur_tree_id;\n // update validation score\n models_[curr_tree]->Shrinkage(shrinkage_rate_);\n for (auto& score_updater : valid_score_updater_) {\n score_updater->AddScore(models_[curr_tree].get(), cur_tree_id);\n }\n // update training score\n models_[curr_tree]->Shrinkage(-k / config_->learning_rate);\n train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id);\n }\n if (!config_->uniform_drop) {\n sum_weight_ -= tree_weight_[i - num_init_iteration_] * (1.0f / (k + config_->learning_rate));;\n tree_weight_[i - num_init_iteration_] *= (k / (k + config_->learning_rate));\n }\n }\n }\n }\n /*! \\brief The weights of all trees, used to choose drop trees */\n std::vector tree_weight_;\n /*! \\brief sum weights of all trees */\n double sum_weight_;\n /*! \\brief The indices of dropping trees */\n std::vector drop_index_;\n /*! \\brief Random generator, used to select dropping trees */\n Random random_for_drop_;\n /*! \\brief Flag that the score is update on current iter or not*/\n bool is_update_score_cur_iter_;\n};\n\n} // namespace LightGBM\n#endif // LIGHTGBM_SRC_BOOSTING_DART_HPP_\n" }, { "path": "src/boosting/gbdt.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \"gbdt.h\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nCommon::Timer global_timer;\n\nint LGBM_config_::current_device = lgbm_device_cpu;\nint LGBM_config_::current_learner = use_cpu_learner;\n\nGBDT::GBDT()\n : iter_(0),\n train_data_(nullptr),\n config_(nullptr),\n objective_function_(nullptr),\n early_stopping_round_(0),\n early_stopping_min_delta_(0.0),\n es_first_metric_only_(false),\n max_feature_idx_(0),\n num_tree_per_iteration_(1),\n num_class_(1),\n num_iteration_for_pred_(0),\n shrinkage_rate_(0.1f),\n num_init_iteration_(0) {\n average_output_ = false;\n tree_learner_ = nullptr;\n linear_tree_ = false;\n data_sample_strategy_.reset(nullptr);\n gradients_pointer_ = nullptr;\n hessians_pointer_ = nullptr;\n boosting_on_gpu_ = false;\n}\n\nGBDT::~GBDT() {\n}\n\nvoid GBDT::Init(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function,\n const std::vector& training_metrics) {\n CHECK_NOTNULL(train_data);\n train_data_ = train_data;\n if (!config->monotone_constraints.empty()) {\n CHECK_EQ(static_cast(train_data_->num_total_features()), config->monotone_constraints.size());\n }\n if (!config->feature_contri.empty()) {\n CHECK_EQ(static_cast(train_data_->num_total_features()), config->feature_contri.size());\n }\n iter_ = 0;\n num_iteration_for_pred_ = 0;\n max_feature_idx_ = 0;\n num_class_ = config->num_class;\n config_ = std::unique_ptr(new Config(*config));\n early_stopping_round_ = config_->early_stopping_round;\n early_stopping_min_delta_ = config->early_stopping_min_delta;\n es_first_metric_only_ = config_->first_metric_only;\n shrinkage_rate_ = config_->learning_rate;\n\n if (config_->device_type == std::string(\"cuda\")) {\n LGBM_config_::current_learner = use_cuda_learner;\n #ifdef USE_CUDA\n if (config_->device_type == std::string(\"cuda\")) {\n const int gpu_device_id = config_->gpu_device_id >= 0 ? config_->gpu_device_id : 0;\n CUDASUCCESS_OR_FATAL(cudaSetDevice(gpu_device_id));\n }\n #endif // USE_CUDA\n }\n\n // load forced_splits file\n if (!config->forcedsplits_filename.empty()) {\n std::ifstream forced_splits_file(config->forcedsplits_filename.c_str());\n std::stringstream buffer;\n buffer << forced_splits_file.rdbuf();\n std::string err;\n forced_splits_json_ = Json::parse(buffer.str(), &err);\n }\n\n objective_function_ = objective_function;\n num_tree_per_iteration_ = num_class_;\n if (objective_function_ != nullptr) {\n num_tree_per_iteration_ = objective_function_->NumModelPerIteration();\n if (objective_function_->IsRenewTreeOutput() && !config->monotone_constraints.empty()) {\n Log::Fatal(\"Cannot use ``monotone_constraints`` in %s objective, please disable it.\", objective_function_->GetName());\n }\n }\n\n data_sample_strategy_.reset(SampleStrategy::CreateSampleStrategy(config_.get(), train_data_, objective_function_, num_tree_per_iteration_));\n is_constant_hessian_ = GetIsConstHessian(objective_function);\n\n boosting_on_gpu_ = objective_function_ != nullptr && objective_function_->IsCUDAObjective() &&\n !data_sample_strategy_->IsHessianChange(); // for sample strategy with Hessian change, fall back to boosting on CPU\n\n tree_learner_ = std::unique_ptr(TreeLearner::CreateTreeLearner(config_->tree_learner, config_->device_type,\n config_.get(), boosting_on_gpu_));\n\n // init tree learner\n tree_learner_->Init(train_data_, is_constant_hessian_);\n tree_learner_->SetForcedSplit(&forced_splits_json_);\n\n // push training metrics\n training_metrics_.clear();\n for (const auto& metric : training_metrics) {\n training_metrics_.push_back(metric);\n }\n training_metrics_.shrink_to_fit();\n\n #ifdef USE_CUDA\n if (config_->device_type == std::string(\"cuda\")) {\n train_score_updater_.reset(new CUDAScoreUpdater(train_data_, num_tree_per_iteration_, boosting_on_gpu_));\n } else {\n #endif // USE_CUDA\n train_score_updater_.reset(new ScoreUpdater(train_data_, num_tree_per_iteration_));\n #ifdef USE_CUDA\n }\n #endif // USE_CUDA\n\n num_data_ = train_data_->num_data();\n\n // get max feature index\n max_feature_idx_ = train_data_->num_total_features() - 1;\n // get label index\n label_idx_ = train_data_->label_idx();\n // get feature names\n feature_names_ = train_data_->feature_names();\n feature_infos_ = train_data_->feature_infos();\n monotone_constraints_ = config->monotone_constraints;\n // get parser config file content\n parser_config_str_ = train_data_->parser_config_str();\n\n // check that forced splits does not use feature indices larger than dataset size\n CheckForcedSplitFeatures();\n\n // if need bagging, create buffer\n data_sample_strategy_->ResetSampleConfig(config_.get(), true);\n ResetGradientBuffers();\n\n class_need_train_ = std::vector(num_tree_per_iteration_, true);\n if (objective_function_ != nullptr && objective_function_->SkipEmptyClass()) {\n CHECK_EQ(num_tree_per_iteration_, num_class_);\n for (int i = 0; i < num_class_; ++i) {\n class_need_train_[i] = objective_function_->ClassNeedTrain(i);\n }\n }\n\n if (config_->linear_tree) {\n linear_tree_ = true;\n }\n}\n\nvoid GBDT::CheckForcedSplitFeatures() {\n std::queue forced_split_nodes;\n forced_split_nodes.push(forced_splits_json_);\n while (!forced_split_nodes.empty()) {\n Json node = forced_split_nodes.front();\n forced_split_nodes.pop();\n const int feature_index = node[\"feature\"].int_value();\n if (feature_index > max_feature_idx_) {\n Log::Fatal(\"Forced splits file includes feature index %d, but maximum feature index in dataset is %d\",\n feature_index, max_feature_idx_);\n }\n if (node.object_items().count(\"left\") > 0) {\n forced_split_nodes.push(node[\"left\"]);\n }\n if (node.object_items().count(\"right\") > 0) {\n forced_split_nodes.push(node[\"right\"]);\n }\n }\n}\n\nvoid GBDT::AddValidDataset(const Dataset* valid_data,\n const std::vector& valid_metrics) {\n if (!train_data_->CheckAlign(*valid_data)) {\n Log::Fatal(\"Cannot add validation data, since it has different bin mappers with training data\");\n }\n // for a validation dataset, we need its score and metric\n auto new_score_updater =\n #ifdef USE_CUDA\n config_->device_type == std::string(\"cuda\") ?\n std::unique_ptr(new CUDAScoreUpdater(valid_data, num_tree_per_iteration_,\n objective_function_ != nullptr && objective_function_->IsCUDAObjective())) :\n #endif // USE_CUDA\n std::unique_ptr(new ScoreUpdater(valid_data, num_tree_per_iteration_));\n // update score\n for (int i = 0; i < iter_; ++i) {\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n auto curr_tree = (i + num_init_iteration_) * num_tree_per_iteration_ + cur_tree_id;\n new_score_updater->AddScore(models_[curr_tree].get(), cur_tree_id);\n }\n }\n valid_score_updater_.push_back(std::move(new_score_updater));\n valid_metrics_.emplace_back();\n for (const auto& metric : valid_metrics) {\n valid_metrics_.back().push_back(metric);\n }\n valid_metrics_.back().shrink_to_fit();\n\n if (early_stopping_round_ > 0) {\n auto num_metrics = valid_metrics.size();\n if (es_first_metric_only_) {\n num_metrics = 1;\n }\n best_iter_.emplace_back(num_metrics, 0);\n best_score_.emplace_back(num_metrics, kMinScore);\n best_msg_.emplace_back(num_metrics);\n }\n}\n\nvoid GBDT::Boosting() {\n Common::FunctionTimer fun_timer(\"GBDT::Boosting\", global_timer);\n if (objective_function_ == nullptr) {\n Log::Fatal(\"No objective function provided\");\n }\n // objective function will calculate gradients and hessians\n int64_t num_score = 0;\n if (config_->bagging_by_query) {\n data_sample_strategy_->Bagging(iter_, tree_learner_.get(), gradients_.data(), hessians_.data());\n objective_function_->\n GetGradientsWithSampledQueries(GetTrainingScore(&num_score), data_sample_strategy_->num_sampled_queries(), data_sample_strategy_->sampled_query_indices(), gradients_pointer_, hessians_pointer_);\n } else {\n objective_function_->\n GetGradients(GetTrainingScore(&num_score), gradients_pointer_, hessians_pointer_);\n }\n}\n\nvoid GBDT::Train(int snapshot_freq, const std::string& model_output_path) {\n Common::FunctionTimer fun_timer(\"GBDT::Train\", global_timer);\n bool is_finished = false;\n auto start_time = std::chrono::steady_clock::now();\n for (int iter = 0; iter < config_->num_iterations && !is_finished; ++iter) {\n is_finished = TrainOneIter(nullptr, nullptr);\n if (!is_finished) {\n is_finished = EvalAndCheckEarlyStopping();\n }\n auto end_time = std::chrono::steady_clock::now();\n // output used time per iteration\n Log::Info(\"%f seconds elapsed, finished iteration %d\", std::chrono::duration(end_time - start_time) * 1e-3, iter + 1);\n if (snapshot_freq > 0\n && (iter + 1) % snapshot_freq == 0) {\n std::string snapshot_out = model_output_path + \".snapshot_iter_\" + std::to_string(iter + 1);\n SaveModelToFile(0, -1, config_->saved_feature_importance_type, snapshot_out.c_str());\n }\n }\n}\n\nvoid GBDT::RefitTree(const int* tree_leaf_prediction, const size_t nrow, const size_t ncol) {\n CHECK_GT(nrow * ncol, 0);\n CHECK_EQ(static_cast(num_data_), nrow);\n CHECK_EQ(models_.size(), ncol);\n\n int num_iterations = static_cast(models_.size() / num_tree_per_iteration_);\n std::vector leaf_pred(num_data_);\n if (linear_tree_) {\n std::vector max_leaves_by_thread = std::vector(OMP_NUM_THREADS(), 0);\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = 0; i < static_cast(nrow); ++i) {\n int tid = omp_get_thread_num();\n for (size_t j = 0; j < ncol; ++j) {\n max_leaves_by_thread[tid] = std::max(max_leaves_by_thread[tid], tree_leaf_prediction[i * ncol + j]);\n }\n }\n int max_leaves = *std::max_element(max_leaves_by_thread.begin(), max_leaves_by_thread.end());\n max_leaves += 1;\n tree_learner_->InitLinear(train_data_, max_leaves);\n }\n\n for (int iter = 0; iter < num_iterations; ++iter) {\n Boosting();\n for (int tree_id = 0; tree_id < num_tree_per_iteration_; ++tree_id) {\n int model_index = iter * num_tree_per_iteration_ + tree_id;\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = 0; i < num_data_; ++i) {\n leaf_pred[i] = tree_leaf_prediction[i * ncol + model_index];\n CHECK_LT(leaf_pred[i], models_[model_index]->num_leaves());\n }\n size_t offset = static_cast(tree_id) * num_data_;\n auto grad = gradients_pointer_ + offset;\n auto hess = hessians_pointer_ + offset;\n auto new_tree = tree_learner_->FitByExistingTree(models_[model_index].get(), leaf_pred, grad, hess);\n train_score_updater_->AddScore(tree_learner_.get(), new_tree, tree_id);\n models_[model_index].reset(new_tree);\n }\n }\n}\n\n/* If the custom \"average\" is implemented it will be used in place of the label average (if enabled)\n*\n* An improvement to this is to have options to explicitly choose\n* (i) standard average\n* (ii) custom average if available\n* (iii) any user defined scalar bias (e.g. using a new option \"init_score\" that overrides (i) and (ii) )\n*\n* (i) and (ii) could be selected as say \"auto_init_score\" = 0 or 1 etc..\n*\n*/\ndouble ObtainAutomaticInitialScore(const ObjectiveFunction* fobj, int class_id) {\n double init_score = 0.0;\n if (fobj != nullptr) {\n init_score = fobj->BoostFromScore(class_id);\n }\n if (Network::num_machines() > 1) {\n init_score = Network::GlobalSyncUpByMean(init_score);\n }\n return init_score;\n}\n\ndouble GBDT::BoostFromAverage(int class_id, bool update_scorer) {\n Common::FunctionTimer fun_timer(\"GBDT::BoostFromAverage\", global_timer);\n // boosting from average label; or customized \"average\" if implemented for the current objective\n if (models_.empty() && !train_score_updater_->has_init_score() && objective_function_ != nullptr) {\n if (config_->boost_from_average || (train_data_ != nullptr && train_data_->num_features() == 0)) {\n double init_score = ObtainAutomaticInitialScore(objective_function_, class_id);\n if (std::fabs(init_score) > kEpsilon) {\n if (update_scorer) {\n train_score_updater_->AddScore(init_score, class_id);\n for (auto& score_updater : valid_score_updater_) {\n score_updater->AddScore(init_score, class_id);\n }\n }\n Log::Info(\"Start training from score %lf\", init_score);\n return init_score;\n }\n } else if (std::string(objective_function_->GetName()) == std::string(\"regression_l1\")\n || std::string(objective_function_->GetName()) == std::string(\"quantile\")\n || std::string(objective_function_->GetName()) == std::string(\"mape\")) {\n Log::Warning(\"Disabling boost_from_average in %s may cause the slow convergence\", objective_function_->GetName());\n }\n }\n return 0.0f;\n}\n\nbool GBDT::TrainOneIter(const score_t* gradients, const score_t* hessians) {\n Common::FunctionTimer fun_timer(\"GBDT::TrainOneIter\", global_timer);\n std::vector init_scores(num_tree_per_iteration_, 0.0);\n // boosting first\n if (gradients == nullptr || hessians == nullptr) {\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n init_scores[cur_tree_id] = BoostFromAverage(cur_tree_id, true);\n }\n Boosting();\n gradients = gradients_pointer_;\n hessians = hessians_pointer_;\n } else {\n // use customized objective function\n // the check below fails unless objective=custom is provided in the parameters on Booster creation\n CHECK(objective_function_ == nullptr);\n if (data_sample_strategy_->IsHessianChange()) {\n // need to copy customized gradients when using GOSS\n int64_t total_size = static_cast(num_data_) * num_tree_per_iteration_;\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int64_t i = 0; i < total_size; ++i) {\n gradients_[i] = gradients[i];\n hessians_[i] = hessians[i];\n }\n CHECK_EQ(gradients_pointer_, gradients_.data());\n CHECK_EQ(hessians_pointer_, hessians_.data());\n gradients = gradients_pointer_;\n hessians = hessians_pointer_;\n }\n }\n\n // bagging logic\n if (!config_->bagging_by_query) {\n data_sample_strategy_->Bagging(iter_, tree_learner_.get(), gradients_.data(), hessians_.data());\n }\n const bool is_use_subset = data_sample_strategy_->is_use_subset();\n const data_size_t bag_data_cnt = data_sample_strategy_->bag_data_cnt();\n const std::vector>& bag_data_indices = data_sample_strategy_->bag_data_indices();\n\n if (objective_function_ == nullptr && is_use_subset && bag_data_cnt < num_data_ && !boosting_on_gpu_ && !data_sample_strategy_->IsHessianChange()) {\n ResetGradientBuffers();\n }\n\n bool should_continue = false;\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n const size_t offset = static_cast(cur_tree_id) * num_data_;\n std::unique_ptr new_tree(new Tree(2, false, false));\n if (class_need_train_[cur_tree_id] && train_data_->num_features() > 0) {\n auto grad = gradients + offset;\n auto hess = hessians + offset;\n // need to copy gradients for bagging subset.\n if (is_use_subset && bag_data_cnt < num_data_ && !boosting_on_gpu_) {\n for (int i = 0; i < bag_data_cnt; ++i) {\n gradients_pointer_[offset + i] = grad[bag_data_indices[i]];\n hessians_pointer_[offset + i] = hess[bag_data_indices[i]];\n }\n grad = gradients_pointer_ + offset;\n hess = hessians_pointer_ + offset;\n }\n bool is_first_tree = models_.size() < static_cast(num_tree_per_iteration_);\n new_tree.reset(tree_learner_->Train(grad, hess, is_first_tree));\n }\n\n if (new_tree->num_leaves() > 1) {\n should_continue = true;\n auto score_ptr = train_score_updater_->score() + offset;\n auto residual_getter = [score_ptr](const label_t* label, int i) {return static_cast(label[i]) - score_ptr[i]; };\n tree_learner_->RenewTreeOutput(new_tree.get(), objective_function_, residual_getter,\n num_data_, bag_data_indices.data(), bag_data_cnt, train_score_updater_->score());\n // shrinkage by learning rate\n new_tree->Shrinkage(shrinkage_rate_);\n // update score\n UpdateScore(new_tree.get(), cur_tree_id);\n if (std::fabs(init_scores[cur_tree_id]) > kEpsilon) {\n new_tree->AddBias(init_scores[cur_tree_id]);\n }\n } else {\n // only add default score one-time\n if (models_.size() < static_cast(num_tree_per_iteration_)) {\n if (objective_function_ != nullptr && !config_->boost_from_average && !train_score_updater_->has_init_score()) {\n init_scores[cur_tree_id] = ObtainAutomaticInitialScore(objective_function_, cur_tree_id);\n // updates scores\n train_score_updater_->AddScore(init_scores[cur_tree_id], cur_tree_id);\n for (auto& score_updater : valid_score_updater_) {\n score_updater->AddScore(init_scores[cur_tree_id], cur_tree_id);\n }\n }\n new_tree->AsConstantTree(init_scores[cur_tree_id], num_data_);\n } else {\n // extend init_scores with zeros\n new_tree->AsConstantTree(0, num_data_);\n }\n }\n // add model\n models_.push_back(std::move(new_tree));\n }\n\n if (!should_continue) {\n Log::Warning(\"Stopped training because there are no more leaves that meet the split requirements\");\n if (models_.size() > static_cast(num_tree_per_iteration_)) {\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n models_.pop_back();\n }\n }\n return true;\n }\n\n ++iter_;\n return false;\n}\n\nvoid GBDT::RollbackOneIter() {\n if (iter_ <= 0) {\n return;\n }\n // reset score\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n auto curr_tree = models_.size() - num_tree_per_iteration_ + cur_tree_id;\n models_[curr_tree]->Shrinkage(-1.0);\n train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id);\n for (auto& score_updater : valid_score_updater_) {\n score_updater->AddScore(models_[curr_tree].get(), cur_tree_id);\n }\n }\n // remove model\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n models_.pop_back();\n }\n --iter_;\n}\n\nbool GBDT::EvalAndCheckEarlyStopping() {\n bool is_met_early_stopping = false;\n // print message for metric\n auto best_msg = OutputMetric(iter_);\n\n\n is_met_early_stopping = !best_msg.empty();\n if (is_met_early_stopping) {\n Log::Info(\"Early stopping at iteration %d, the best iteration round is %d\",\n iter_, iter_ - early_stopping_round_);\n Log::Info(\"Output of best iteration round:\\n%s\", best_msg.c_str());\n // pop last early_stopping_round_ models\n for (int i = 0; i < early_stopping_round_ * num_tree_per_iteration_; ++i) {\n models_.pop_back();\n }\n }\n return is_met_early_stopping;\n}\n\nvoid GBDT::UpdateScore(const Tree* tree, const int cur_tree_id) {\n Common::FunctionTimer fun_timer(\"GBDT::UpdateScore\", global_timer);\n // update training score\n if (!data_sample_strategy_->is_use_subset()) {\n train_score_updater_->AddScore(tree_learner_.get(), tree, cur_tree_id);\n\n const data_size_t bag_data_cnt = data_sample_strategy_->bag_data_cnt();\n // we need to predict out-of-bag scores of data for boosting\n if (num_data_ - bag_data_cnt > 0) {\n #ifdef USE_CUDA\n if (config_->device_type == std::string(\"cuda\")) {\n train_score_updater_->AddScore(tree, data_sample_strategy_->cuda_bag_data_indices().RawData() + bag_data_cnt, num_data_ - bag_data_cnt, cur_tree_id);\n } else {\n #endif // USE_CUDA\n train_score_updater_->AddScore(tree, data_sample_strategy_->bag_data_indices().data() + bag_data_cnt, num_data_ - bag_data_cnt, cur_tree_id);\n #ifdef USE_CUDA\n }\n #endif // USE_CUDA\n }\n\n } else {\n train_score_updater_->AddScore(tree, cur_tree_id);\n }\n\n\n // update validation score\n for (auto& score_updater : valid_score_updater_) {\n score_updater->AddScore(tree, cur_tree_id);\n }\n}\n\n#ifdef USE_CUDA\nstd::vector GBDT::EvalOneMetric(const Metric* metric, const double* score, const data_size_t num_data) const {\n#else\nstd::vector GBDT::EvalOneMetric(const Metric* metric, const double* score, const data_size_t /*num_data*/) const {\n#endif // USE_CUDA\n #ifdef USE_CUDA\n const bool evaluation_on_cuda = metric->IsCUDAMetric();\n if ((boosting_on_gpu_ && evaluation_on_cuda) || (!boosting_on_gpu_ && !evaluation_on_cuda)) {\n #endif // USE_CUDA\n return metric->Eval(score, objective_function_);\n #ifdef USE_CUDA\n } else if (boosting_on_gpu_ && !evaluation_on_cuda) {\n const size_t total_size = static_cast(num_data) * static_cast(num_tree_per_iteration_);\n if (total_size > host_score_.size()) {\n host_score_.resize(total_size, 0.0f);\n }\n CopyFromCUDADeviceToHost(host_score_.data(), score, total_size, __FILE__, __LINE__);\n return metric->Eval(host_score_.data(), objective_function_);\n } else {\n const size_t total_size = static_cast(num_data) * static_cast(num_tree_per_iteration_);\n if (total_size > cuda_score_.Size()) {\n cuda_score_.Resize(total_size);\n }\n CopyFromHostToCUDADevice(cuda_score_.RawData(), score, total_size, __FILE__, __LINE__);\n return metric->Eval(cuda_score_.RawData(), objective_function_);\n }\n #endif // USE_CUDA\n}\n\nstd::string GBDT::OutputMetric(int iter) {\n bool need_output = (iter % config_->metric_freq) == 0;\n std::string ret = \"\";\n std::stringstream msg_buf;\n std::vector> meet_early_stopping_pairs;\n // print training metric\n if (need_output) {\n for (auto& sub_metric : training_metrics_) {\n auto name = sub_metric->GetName();\n auto scores = EvalOneMetric(sub_metric, train_score_updater_->score(), train_score_updater_->num_data());\n for (size_t k = 0; k < name.size(); ++k) {\n std::stringstream tmp_buf;\n tmp_buf << \"Iteration:\" << iter\n << \", training \" << name[k]\n << \" : \" << scores[k];\n Log::Info(tmp_buf.str().c_str());\n if (early_stopping_round_ > 0) {\n msg_buf << tmp_buf.str() << '\\n';\n }\n }\n }\n }\n // print validation metric\n if (need_output || early_stopping_round_ > 0) {\n for (size_t i = 0; i < valid_metrics_.size(); ++i) {\n for (size_t j = 0; j < valid_metrics_[i].size(); ++j) {\n auto test_scores = EvalOneMetric(valid_metrics_[i][j], valid_score_updater_[i]->score(), valid_score_updater_[i]->num_data());\n auto name = valid_metrics_[i][j]->GetName();\n for (size_t k = 0; k < name.size(); ++k) {\n std::stringstream tmp_buf;\n tmp_buf << \"Iteration:\" << iter\n << \", valid_\" << i + 1 << \" \" << name[k]\n << \" : \" << test_scores[k];\n if (need_output) {\n Log::Info(tmp_buf.str().c_str());\n }\n if (early_stopping_round_ > 0) {\n msg_buf << tmp_buf.str() << '\\n';\n }\n }\n if (es_first_metric_only_ && j > 0) {\n continue;\n }\n if (ret.empty() && early_stopping_round_ > 0) {\n auto cur_score = valid_metrics_[i][j]->factor_to_bigger_better() * test_scores.back();\n if (cur_score - best_score_[i][j] > early_stopping_min_delta_) {\n best_score_[i][j] = cur_score;\n best_iter_[i][j] = iter;\n meet_early_stopping_pairs.emplace_back(i, j);\n } else {\n if (iter - best_iter_[i][j] >= early_stopping_round_) {\n ret = best_msg_[i][j];\n }\n }\n }\n }\n }\n }\n for (auto& pair : meet_early_stopping_pairs) {\n best_msg_[pair.first][pair.second] = msg_buf.str();\n }\n return ret;\n}\n\n/*! \\brief Get eval result */\nstd::vector GBDT::GetEvalAt(int data_idx) const {\n CHECK(data_idx >= 0 && data_idx <= static_cast(valid_score_updater_.size()));\n std::vector ret;\n if (data_idx == 0) {\n for (auto& sub_metric : training_metrics_) {\n auto scores = EvalOneMetric(sub_metric, train_score_updater_->score(), train_score_updater_->num_data());\n for (auto score : scores) {\n ret.push_back(score);\n }\n }\n } else {\n auto used_idx = data_idx - 1;\n for (size_t j = 0; j < valid_metrics_[used_idx].size(); ++j) {\n auto test_scores = EvalOneMetric(valid_metrics_[used_idx][j], valid_score_updater_[used_idx]->score(), valid_score_updater_[used_idx]->num_data());\n for (auto score : test_scores) {\n ret.push_back(score);\n }\n }\n }\n return ret;\n}\n\n/*! \\brief Get training scores result */\nconst double* GBDT::GetTrainingScore(int64_t* out_len) {\n *out_len = static_cast(train_score_updater_->num_data()) * num_class_;\n return train_score_updater_->score();\n}\n\nvoid GBDT::PredictContrib(const double* features, double* output) const {\n // set zero\n const int num_features = max_feature_idx_ + 1;\n std::memset(output, 0, sizeof(double) * num_tree_per_iteration_ * (num_features + 1));\n const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_;\n for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) {\n // predict all the trees for one iteration\n for (int k = 0; k < num_tree_per_iteration_; ++k) {\n models_[i * num_tree_per_iteration_ + k]->PredictContrib(features, num_features, output + k*(num_features + 1));\n }\n }\n}\n\nvoid GBDT::PredictContribByMap(const std::unordered_map& features,\n std::vector>* output) const {\n const int num_features = max_feature_idx_ + 1;\n const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_;\n for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) {\n // predict all the trees for one iteration\n for (int k = 0; k < num_tree_per_iteration_; ++k) {\n models_[i * num_tree_per_iteration_ + k]->PredictContribByMap(features, num_features, &((*output)[k]));\n }\n }\n}\n\nvoid GBDT::GetPredictAt(int data_idx, double* out_result, int64_t* out_len) {\n CHECK(data_idx >= 0 && data_idx <= static_cast(valid_score_updater_.size()));\n\n const double* raw_scores = nullptr;\n data_size_t num_data = 0;\n if (data_idx == 0) {\n raw_scores = GetTrainingScore(out_len);\n num_data = train_score_updater_->num_data();\n } else {\n auto used_idx = data_idx - 1;\n raw_scores = valid_score_updater_[used_idx]->score();\n num_data = valid_score_updater_[used_idx]->num_data();\n *out_len = static_cast(num_data) * num_class_;\n }\n #ifdef USE_CUDA\n std::vector host_raw_scores;\n if (boosting_on_gpu_) {\n host_raw_scores.resize(static_cast(*out_len), 0.0);\n CopyFromCUDADeviceToHost(host_raw_scores.data(), raw_scores, static_cast(*out_len), __FILE__, __LINE__);\n raw_scores = host_raw_scores.data();\n }\n #endif // USE_CUDA\n if (objective_function_ != nullptr) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (data_size_t i = 0; i < num_data; ++i) {\n std::vector tree_pred(num_tree_per_iteration_);\n for (int j = 0; j < num_tree_per_iteration_; ++j) {\n tree_pred[j] = raw_scores[j * num_data + i];\n }\n std::vector tmp_result(num_class_);\n objective_function_->ConvertOutput(tree_pred.data(), tmp_result.data());\n for (int j = 0; j < num_class_; ++j) {\n out_result[j * num_data + i] = static_cast(tmp_result[j]);\n }\n }\n } else {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (data_size_t i = 0; i < num_data; ++i) {\n for (int j = 0; j < num_tree_per_iteration_; ++j) {\n out_result[j * num_data + i] = static_cast(raw_scores[j * num_data + i]);\n }\n }\n }\n}\n\ndouble GBDT::GetUpperBoundValue() const {\n double max_value = 0.0;\n for (const auto &tree : models_) {\n max_value += tree->GetUpperBoundValue();\n }\n return max_value;\n}\n\ndouble GBDT::GetLowerBoundValue() const {\n double min_value = 0.0;\n for (const auto &tree : models_) {\n min_value += tree->GetLowerBoundValue();\n }\n return min_value;\n}\n\nvoid GBDT::ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function,\n const std::vector& training_metrics) {\n if (train_data != train_data_ && !train_data_->CheckAlign(*train_data)) {\n Log::Fatal(\"Cannot reset training data, since new training data has different bin mappers\");\n }\n\n objective_function_ = objective_function;\n data_sample_strategy_->UpdateObjectiveFunction(objective_function);\n if (objective_function_ != nullptr) {\n CHECK_EQ(num_tree_per_iteration_, objective_function_->NumModelPerIteration());\n if (objective_function_->IsRenewTreeOutput() && !config_->monotone_constraints.empty()) {\n Log::Fatal(\"Cannot use ``monotone_constraints`` in %s objective, please disable it.\", objective_function_->GetName());\n }\n }\n is_constant_hessian_ = GetIsConstHessian(objective_function);\n\n // push training metrics\n training_metrics_.clear();\n for (const auto& metric : training_metrics) {\n training_metrics_.push_back(metric);\n }\n training_metrics_.shrink_to_fit();\n\n #ifdef USE_CUDA\n boosting_on_gpu_ = objective_function_ != nullptr && objective_function_->IsCUDAObjective() &&\n !data_sample_strategy_->IsHessianChange(); // for sample strategy with Hessian change, fall back to boosting on CPU\n tree_learner_->ResetBoostingOnGPU(boosting_on_gpu_);\n #endif // USE_CUDA\n\n if (train_data != train_data_) {\n train_data_ = train_data;\n data_sample_strategy_->UpdateTrainingData(train_data);\n // not same training data, need reset score and others\n // create score tracker\n #ifdef USE_CUDA\n if (config_->device_type == std::string(\"cuda\")) {\n train_score_updater_.reset(new CUDAScoreUpdater(train_data_, num_tree_per_iteration_, boosting_on_gpu_));\n } else {\n #endif // USE_CUDA\n train_score_updater_.reset(new ScoreUpdater(train_data_, num_tree_per_iteration_));\n #ifdef USE_CUDA\n }\n #endif // USE_CUDA\n\n // update score\n for (int i = 0; i < iter_; ++i) {\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n auto curr_tree = (i + num_init_iteration_) * num_tree_per_iteration_ + cur_tree_id;\n train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id);\n }\n }\n\n num_data_ = train_data_->num_data();\n\n ResetGradientBuffers();\n\n max_feature_idx_ = train_data_->num_total_features() - 1;\n label_idx_ = train_data_->label_idx();\n feature_names_ = train_data_->feature_names();\n feature_infos_ = train_data_->feature_infos();\n parser_config_str_ = train_data_->parser_config_str();\n\n tree_learner_->ResetTrainingData(train_data, is_constant_hessian_);\n data_sample_strategy_->ResetSampleConfig(config_.get(), true);\n } else {\n tree_learner_->ResetIsConstantHessian(is_constant_hessian_);\n }\n}\n\nvoid GBDT::ResetConfig(const Config* config) {\n auto new_config = std::unique_ptr(new Config(*config));\n if (!config->monotone_constraints.empty()) {\n CHECK_EQ(static_cast(train_data_->num_total_features()), config->monotone_constraints.size());\n }\n if (!config->feature_contri.empty()) {\n CHECK_EQ(static_cast(train_data_->num_total_features()), config->feature_contri.size());\n }\n if (objective_function_ != nullptr && objective_function_->IsRenewTreeOutput() && !config->monotone_constraints.empty()) {\n Log::Fatal(\"Cannot use ``monotone_constraints`` in %s objective, please disable it.\", objective_function_->GetName());\n }\n early_stopping_round_ = new_config->early_stopping_round;\n shrinkage_rate_ = new_config->learning_rate;\n if (tree_learner_ != nullptr) {\n tree_learner_->ResetConfig(new_config.get());\n }\n\n boosting_on_gpu_ = objective_function_ != nullptr && objective_function_->IsCUDAObjective() &&\n !data_sample_strategy_->IsHessianChange(); // for sample strategy with Hessian change, fall back to boosting on CPU\n tree_learner_->ResetBoostingOnGPU(boosting_on_gpu_);\n\n if (train_data_ != nullptr) {\n data_sample_strategy_->ResetSampleConfig(new_config.get(), false);\n if (data_sample_strategy_->NeedResizeGradients()) {\n // resize gradient vectors to copy the customized gradients for goss or bagging with subset\n ResetGradientBuffers();\n }\n }\n if (config_.get() != nullptr && config_->forcedsplits_filename != new_config->forcedsplits_filename) {\n // load forced_splits file\n if (!new_config->forcedsplits_filename.empty()) {\n std::ifstream forced_splits_file(\n new_config->forcedsplits_filename.c_str());\n std::stringstream buffer;\n buffer << forced_splits_file.rdbuf();\n std::string err;\n forced_splits_json_ = Json::parse(buffer.str(), &err);\n tree_learner_->SetForcedSplit(&forced_splits_json_);\n } else {\n forced_splits_json_ = Json();\n tree_learner_->SetForcedSplit(nullptr);\n }\n }\n config_.reset(new_config.release());\n}\n\nvoid GBDT::ResetGradientBuffers() {\n const size_t total_size = static_cast(num_data_) * num_tree_per_iteration_;\n const bool is_use_subset = data_sample_strategy_->is_use_subset();\n const data_size_t bag_data_cnt = data_sample_strategy_->bag_data_cnt();\n if (objective_function_ != nullptr) {\n #ifdef USE_CUDA\n if (config_->device_type == std::string(\"cuda\") && boosting_on_gpu_) {\n if (cuda_gradients_.Size() < total_size) {\n cuda_gradients_.Resize(total_size);\n cuda_hessians_.Resize(total_size);\n }\n gradients_pointer_ = cuda_gradients_.RawData();\n hessians_pointer_ = cuda_hessians_.RawData();\n } else {\n #endif // USE_CUDA\n if (gradients_.size() < total_size) {\n gradients_.resize(total_size);\n hessians_.resize(total_size);\n }\n gradients_pointer_ = gradients_.data();\n hessians_pointer_ = hessians_.data();\n #ifdef USE_CUDA\n }\n #endif // USE_CUDA\n } else if (data_sample_strategy_->IsHessianChange() || (is_use_subset && bag_data_cnt < num_data_ && !boosting_on_gpu_)) {\n if (gradients_.size() < total_size) {\n gradients_.resize(total_size);\n hessians_.resize(total_size);\n }\n gradients_pointer_ = gradients_.data();\n hessians_pointer_ = hessians_.data();\n }\n}\n\n} // namespace LightGBM\n" }, { "path": "src/boosting/gbdt.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_BOOSTING_GBDT_H_\n#define LIGHTGBM_SRC_BOOSTING_GBDT_H_\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"cuda/cuda_score_updater.hpp\"\n#include \"score_updater.hpp\"\n\nnamespace LightGBM {\n\nusing json11_internal_lightgbm::Json;\n\n/*!\n* \\brief GBDT algorithm implementation. including Training, prediction, bagging.\n*/\nclass GBDT : public GBDTBase {\n public:\n /*!\n * \\brief Constructor\n */\n GBDT();\n\n /*!\n * \\brief Destructor\n */\n ~GBDT();\n\n\n /*!\n * \\brief Initialization logic\n * \\param gbdt_config Config for boosting\n * \\param train_data Training data\n * \\param objective_function Training objective function\n * \\param training_metrics Training metrics\n */\n void Init(const Config* gbdt_config, const Dataset* train_data,\n const ObjectiveFunction* objective_function,\n const std::vector& training_metrics) override;\n\n /*!\n * \\brief Traverse the tree of forced splits and check that all indices are less than the number of features.\n */\n void CheckForcedSplitFeatures();\n\n /*!\n * \\brief Merge model from other boosting object. Will insert to the front of current boosting object\n * \\param other\n */\n void MergeFrom(const Boosting* other) override {\n auto other_gbdt = reinterpret_cast(other);\n // tmp move to other vector\n auto original_models = std::move(models_);\n models_ = std::vector>();\n // push model from other first\n for (const auto& tree : other_gbdt->models_) {\n auto new_tree = std::unique_ptr(new Tree(*(tree.get())));\n models_.push_back(std::move(new_tree));\n }\n num_init_iteration_ = static_cast(models_.size()) / num_tree_per_iteration_;\n // push model in current object\n for (const auto& tree : original_models) {\n auto new_tree = std::unique_ptr(new Tree(*(tree.get())));\n models_.push_back(std::move(new_tree));\n }\n num_iteration_for_pred_ = static_cast(models_.size()) / num_tree_per_iteration_;\n }\n\n void ShuffleModels(int start_iter, int end_iter) override {\n int total_iter = static_cast(models_.size()) / num_tree_per_iteration_;\n start_iter = std::max(0, start_iter);\n if (end_iter <= 0) {\n end_iter = total_iter;\n }\n end_iter = std::min(total_iter, end_iter);\n auto original_models = std::move(models_);\n std::vector indices(total_iter);\n for (int i = 0; i < total_iter; ++i) {\n indices[i] = i;\n }\n Random tmp_rand(17);\n for (int i = start_iter; i < end_iter - 1; ++i) {\n int j = tmp_rand.NextShort(i + 1, end_iter);\n std::swap(indices[i], indices[j]);\n }\n models_ = std::vector>();\n for (int i = 0; i < total_iter; ++i) {\n for (int j = 0; j < num_tree_per_iteration_; ++j) {\n int tree_idx = indices[i] * num_tree_per_iteration_ + j;\n auto new_tree = std::unique_ptr(new Tree(*(original_models[tree_idx].get())));\n models_.push_back(std::move(new_tree));\n }\n }\n }\n\n /*!\n * \\brief Reset the training data\n * \\param train_data New Training data\n * \\param objective_function Training objective function\n * \\param training_metrics Training metrics\n */\n void ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function,\n const std::vector& training_metrics) override;\n\n /*!\n * \\brief Reset Boosting Config\n * \\param gbdt_config Config for boosting\n */\n void ResetConfig(const Config* gbdt_config) override;\n\n /*!\n * \\brief Adding a validation dataset\n * \\param valid_data Validation dataset\n * \\param valid_metrics Metrics for validation dataset\n */\n void AddValidDataset(const Dataset* valid_data,\n const std::vector& valid_metrics) override;\n\n /*!\n * \\brief Perform a full training procedure\n * \\param snapshot_freq frequency of snapshot\n * \\param model_output_path path of model file\n */\n void Train(int snapshot_freq, const std::string& model_output_path) override;\n\n void RefitTree(const int* tree_leaf_prediction, const size_t nrow, const size_t ncol) override;\n\n /*!\n * \\brief Training logic\n * \\param gradients nullptr for using default objective, otherwise use self-defined boosting\n * \\param hessians nullptr for using default objective, otherwise use self-defined boosting\n * \\return True if cannot train any more\n */\n bool TrainOneIter(const score_t* gradients, const score_t* hessians) override;\n\n /*!\n * \\brief Rollback one iteration\n */\n void RollbackOneIter() override;\n\n /*!\n * \\brief Get current iteration\n */\n int GetCurrentIteration() const override { return static_cast(models_.size()) / num_tree_per_iteration_; }\n\n /*!\n * \\brief Get parameters as a JSON string\n */\n std::string GetLoadedParam() const override {\n if (loaded_parameter_.empty()) {\n return std::string(\"{}\");\n }\n const auto param_types = Config::ParameterTypes();\n const auto lines = Common::Split(loaded_parameter_.c_str(), \"\\n\");\n bool first = true;\n std::stringstream str_buf;\n str_buf << \"{\";\n for (const auto& line : lines) {\n const auto pair = Common::Split(line.c_str(), \":\");\n if (pair[1] == \" ]\")\n continue;\n const auto param = pair[0].substr(1);\n const auto value_str = pair[1].substr(1, pair[1].size() - 2);\n auto iter = param_types.find(param);\n if (iter == param_types.end()) {\n Log::Warning(\"Ignoring unrecognized parameter '%s' found in model string.\", param.c_str());\n continue;\n }\n std::string param_type = iter->second;\n if (first) {\n first = false;\n str_buf << \"\\\"\";\n } else {\n str_buf << \",\\\"\";\n }\n str_buf << param << \"\\\": \";\n if (param_type == \"string\") {\n str_buf << \"\\\"\" << value_str << \"\\\"\";\n } else if (param_type == \"int\") {\n int value;\n Common::Atoi(value_str.c_str(), &value);\n str_buf << value;\n } else if (param_type == \"double\") {\n double value;\n Common::Atof(value_str.c_str(), &value);\n str_buf << value;\n } else if (param_type == \"bool\") {\n bool value = value_str == \"1\";\n str_buf << std::boolalpha << value;\n } else if (param_type.substr(0, 6) == \"vector\") {\n str_buf << \"[\";\n if (param_type.substr(7, 6) == \"string\") {\n const auto parts = Common::Split(value_str.c_str(), \",\");\n str_buf << \"\\\"\" << Common::Join(parts, \"\\\",\\\"\") << \"\\\"\";\n } else {\n str_buf << value_str;\n }\n str_buf << \"]\";\n }\n }\n str_buf << \"}\";\n return str_buf.str();\n }\n\n /*!\n * \\brief Can use early stopping for prediction or not\n * \\return True if cannot use early stopping for prediction\n */\n bool NeedAccuratePrediction() const override {\n if (objective_function_ == nullptr) {\n return true;\n } else {\n return objective_function_->NeedAccuratePrediction();\n }\n }\n\n /*!\n * \\brief Get evaluation result at data_idx data\n * \\param data_idx 0: training data, 1: 1st validation data\n * \\return evaluation result\n */\n std::vector GetEvalAt(int data_idx) const override;\n\n /*!\n * \\brief Get current training score\n * \\param out_len length of returned score\n * \\return training score\n */\n const double* GetTrainingScore(int64_t* out_len) override;\n\n /*!\n * \\brief Get size of prediction at data_idx data\n * \\param data_idx 0: training data, 1: 1st validation data\n * \\return The size of prediction\n */\n int64_t GetNumPredictAt(int data_idx) const override {\n CHECK(data_idx >= 0 && data_idx <= static_cast(valid_score_updater_.size()));\n data_size_t num_data = train_data_->num_data();\n if (data_idx > 0) {\n num_data = valid_score_updater_[data_idx - 1]->num_data();\n }\n return static_cast(num_data) * num_class_;\n }\n\n /*!\n * \\brief Get prediction result at data_idx data\n * \\param data_idx 0: training data, 1: 1st validation data\n * \\param result used to store prediction result, should allocate memory before call this function\n * \\param out_len length of returned score\n */\n void GetPredictAt(int data_idx, double* out_result, int64_t* out_len) override;\n\n /*!\n * \\brief Get number of prediction for one data\n * \\param start_iteration Start index of the iteration to predict\n * \\param num_iteration number of used iterations\n * \\param is_pred_leaf True if predicting leaf index\n * \\param is_pred_contrib True if predicting feature contribution\n * \\return number of prediction\n */\n inline int NumPredictOneRow(int start_iteration, int num_iteration, bool is_pred_leaf, bool is_pred_contrib) const override {\n int num_pred_in_one_row = num_class_;\n if (is_pred_leaf) {\n int max_iteration = GetCurrentIteration();\n start_iteration = std::max(start_iteration, 0);\n start_iteration = std::min(start_iteration, max_iteration);\n if (num_iteration > 0) {\n num_pred_in_one_row *= static_cast(std::min(max_iteration - start_iteration, num_iteration));\n } else {\n num_pred_in_one_row *= (max_iteration - start_iteration);\n }\n } else if (is_pred_contrib) {\n num_pred_in_one_row = num_tree_per_iteration_ * (max_feature_idx_ + 2); // +1 for 0-based indexing, +1 for baseline\n }\n return num_pred_in_one_row;\n }\n\n void PredictRaw(const double* features, double* output,\n const PredictionEarlyStopInstance* earlyStop) const override;\n\n void PredictRawByMap(const std::unordered_map& features, double* output,\n const PredictionEarlyStopInstance* early_stop) const override;\n\n void Predict(const double* features, double* output,\n const PredictionEarlyStopInstance* earlyStop) const override;\n\n void PredictByMap(const std::unordered_map& features, double* output,\n const PredictionEarlyStopInstance* early_stop) const override;\n\n void PredictLeafIndex(const double* features, double* output) const override;\n\n void PredictLeafIndexByMap(const std::unordered_map& features, double* output) const override;\n\n void PredictContrib(const double* features, double* output) const override;\n\n void PredictContribByMap(const std::unordered_map& features,\n std::vector>* output) const override;\n\n /*!\n * \\brief Dump model to json format string\n * \\param start_iteration The model will be saved start from\n * \\param num_iteration Number of iterations that want to dump, -1 means dump all\n * \\param feature_importance_type Type of feature importance, 0: split, 1: gain\n * \\return Json format string of model\n */\n std::string DumpModel(int start_iteration, int num_iteration,\n int feature_importance_type) const override;\n\n /*!\n * \\brief Translate model to if-else statement\n * \\param num_iteration Number of iterations that want to translate, -1 means translate all\n * \\return if-else format codes of model\n */\n std::string ModelToIfElse(int num_iteration) const override;\n\n /*!\n * \\brief Translate model to if-else statement\n * \\param num_iteration Number of iterations that want to translate, -1 means translate all\n * \\param filename Filename that want to save to\n * \\return is_finish Is training finished or not\n */\n bool SaveModelToIfElse(int num_iteration, const char* filename) const override;\n\n /*!\n * \\brief Save model to file\n * \\param start_iteration The model will be saved start from\n * \\param num_iterations Number of model that want to save, -1 means save all\n * \\param feature_importance_type Type of feature importance, 0: split, 1: gain\n * \\param filename Filename that want to save to\n * \\return is_finish Is training finished or not\n */\n bool SaveModelToFile(int start_iteration, int num_iterations,\n int feature_importance_type,\n const char* filename) const override;\n\n /*!\n * \\brief Save model to string\n * \\param start_iteration The model will be saved start from\n * \\param num_iterations Number of model that want to save, -1 means save all\n * \\param feature_importance_type Type of feature importance, 0: split, 1: gain\n * \\return Non-empty string if succeeded\n */\n std::string SaveModelToString(int start_iteration, int num_iterations, int feature_importance_type) const override;\n\n /*!\n * \\brief Restore from a serialized buffer\n */\n bool LoadModelFromString(const char* buffer, size_t len) override;\n\n /*!\n * \\brief Calculate feature importances\n * \\param num_iteration Number of model that want to use for feature importance, -1 means use all\n * \\param importance_type: 0 for split, 1 for gain\n * \\return vector of feature_importance\n */\n std::vector FeatureImportance(int num_iteration, int importance_type) const override;\n\n /*!\n * \\brief Calculate upper bound value\n * \\return upper bound value\n */\n double GetUpperBoundValue() const override;\n\n /*!\n * \\brief Calculate lower bound value\n * \\return lower bound value\n */\n double GetLowerBoundValue() const override;\n\n /*!\n * \\brief Get max feature index of this model\n * \\return Max feature index of this model\n */\n inline int MaxFeatureIdx() const override { return max_feature_idx_; }\n\n /*!\n * \\brief Get feature names of this model\n * \\return Feature names of this model\n */\n inline std::vector FeatureNames() const override { return feature_names_; }\n\n /*!\n * \\brief Get index of label column\n * \\return index of label column\n */\n inline int LabelIdx() const override { return label_idx_; }\n\n /*!\n * \\brief Get number of weak sub-models\n * \\return Number of weak sub-models\n */\n inline int NumberOfTotalModel() const override { return static_cast(models_.size()); }\n\n /*!\n * \\brief Get number of tree per iteration\n * \\return number of tree per iteration\n */\n inline int NumModelPerIteration() const override { return num_tree_per_iteration_; }\n\n /*!\n * \\brief Get number of classes\n * \\return Number of classes\n */\n inline int NumberOfClasses() const override { return num_class_; }\n\n inline void InitPredict(int start_iteration, int num_iteration, bool is_pred_contrib) override {\n num_iteration_for_pred_ = static_cast(models_.size()) / num_tree_per_iteration_;\n start_iteration = std::max(start_iteration, 0);\n start_iteration = std::min(start_iteration, num_iteration_for_pred_);\n if (num_iteration > 0) {\n num_iteration_for_pred_ = std::min(num_iteration, num_iteration_for_pred_ - start_iteration);\n } else {\n num_iteration_for_pred_ = num_iteration_for_pred_ - start_iteration;\n }\n start_iteration_for_pred_ = start_iteration;\n\n if (is_pred_contrib && !models_initialized_) {\n std::lock_guard lock(instance_mutex_);\n if (models_initialized_)\n return;\n\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = 0; i < static_cast(models_.size()); ++i) {\n models_[i]->RecomputeMaxDepth();\n }\n\n models_initialized_ = true;\n }\n }\n\n inline double GetLeafValue(int tree_idx, int leaf_idx) const override {\n CHECK(tree_idx >= 0 && static_cast(tree_idx) < models_.size());\n CHECK(leaf_idx >= 0 && leaf_idx < models_[tree_idx]->num_leaves());\n return models_[tree_idx]->LeafOutput(leaf_idx);\n }\n\n inline void SetLeafValue(int tree_idx, int leaf_idx, double val) override {\n CHECK(tree_idx >= 0 && static_cast(tree_idx) < models_.size());\n CHECK(leaf_idx >= 0 && leaf_idx < models_[tree_idx]->num_leaves());\n models_[tree_idx]->SetLeafOutput(leaf_idx, val);\n }\n\n /*!\n * \\brief Get Type name of this boosting object\n */\n const char* SubModelName() const override { return \"tree\"; }\n\n bool IsLinear() const override { return linear_tree_; }\n\n inline std::string ParserConfigStr() const override {return parser_config_str_;}\n\n protected:\n virtual bool GetIsConstHessian(const ObjectiveFunction* objective_function) {\n if (objective_function != nullptr && !data_sample_strategy_->IsHessianChange()) {\n return objective_function->IsConstantHessian();\n } else {\n return false;\n }\n }\n /*!\n * \\brief Print eval result and check early stopping\n */\n virtual bool EvalAndCheckEarlyStopping();\n\n /*!\n * \\brief reset config for bagging\n */\n void ResetBaggingConfig(const Config* config, bool is_change_dataset);\n\n /*!\n * \\brief calculate the objective function\n */\n virtual void Boosting();\n\n /*!\n * \\brief updating score after tree was trained\n * \\param tree Trained tree of this iteration\n * \\param cur_tree_id Current tree for multiclass training\n */\n virtual void UpdateScore(const Tree* tree, const int cur_tree_id);\n\n /*!\n * \\brief eval results for one metric\n\n */\n virtual std::vector EvalOneMetric(const Metric* metric, const double* score, const data_size_t num_data) const;\n\n /*!\n * \\brief Print metric result of current iteration\n * \\param iter Current iteration\n * \\return best_msg if met early_stopping\n */\n std::string OutputMetric(int iter);\n\n virtual double BoostFromAverage(int class_id, bool update_scorer);\n\n /*!\n * \\brief Reset gradient buffers, must be called after sample strategy is reset\n */\n void ResetGradientBuffers();\n\n /*! \\brief current iteration */\n int iter_;\n /*! \\brief Pointer to training data */\n const Dataset* train_data_;\n /*! \\brief Config of gbdt */\n std::unique_ptr config_;\n /*! \\brief Tree learner, will use this class to learn trees */\n std::unique_ptr tree_learner_;\n /*! \\brief Objective function */\n const ObjectiveFunction* objective_function_;\n /*! \\brief Store and update training data's score */\n std::unique_ptr train_score_updater_;\n /*! \\brief Metrics for training data */\n std::vector training_metrics_;\n /*! \\brief Store and update validation data's scores */\n std::vector> valid_score_updater_;\n /*! \\brief Metric for validation data */\n std::vector> valid_metrics_;\n /*! \\brief Number of rounds for early stopping */\n int early_stopping_round_;\n /*! \\brief Minimum improvement for early stopping */\n double early_stopping_min_delta_;\n /*! \\brief Only use first metric for early stopping */\n bool es_first_metric_only_;\n /*! \\brief Best iteration(s) for early stopping */\n std::vector> best_iter_;\n /*! \\brief Best score(s) for early stopping */\n std::vector> best_score_;\n /*! \\brief output message of best iteration */\n std::vector> best_msg_;\n /*! \\brief Trained models(trees) */\n std::vector> models_;\n /*! \\brief Max feature index of training data*/\n int max_feature_idx_;\n /*! \\brief Parser config file content */\n std::string parser_config_str_ = \"\";\n /*! \\brief Are the models initialized (passed RecomputeMaxDepth phase) */\n bool models_initialized_ = false;\n /*! \\brief Mutex for exclusive models initialization */\n std::mutex instance_mutex_;\n\n#ifdef USE_CUDA\n /*! \\brief First order derivative of training data */\n std::vector> gradients_;\n /*! \\brief Second order derivative of training data */\n std::vector> hessians_;\n#else\n /*! \\brief First order derivative of training data */\n std::vector> gradients_;\n /*! \\brief Second order derivative of training data */\n std::vector> hessians_;\n#endif\n /*! \\brief Pointer to gradient vector, can be on CPU or GPU */\n score_t* gradients_pointer_;\n /*! \\brief Pointer to hessian vector, can be on CPU or GPU */\n score_t* hessians_pointer_;\n /*! \\brief Whether boosting is done on GPU, used for device_type=cuda */\n bool boosting_on_gpu_;\n #ifdef USE_CUDA\n /*! \\brief Gradient vector on GPU */\n CUDAVector cuda_gradients_;\n /*! \\brief Hessian vector on GPU */\n CUDAVector cuda_hessians_;\n /*! \\brief Buffer for scores when boosting is on GPU but evaluation is not, used only with device_type=cuda */\n mutable std::vector host_score_;\n /*! \\brief Buffer for scores when boosting is not on GPU but evaluation is, used only with device_type=cuda */\n mutable CUDAVector cuda_score_;\n #endif // USE_CUDA\n\n /*! \\brief Number of training data */\n data_size_t num_data_;\n /*! \\brief Number of trees per iterations */\n int num_tree_per_iteration_;\n /*! \\brief Number of class */\n int num_class_;\n /*! \\brief Index of label column */\n data_size_t label_idx_;\n /*! \\brief number of used model */\n int num_iteration_for_pred_;\n /*! \\brief Start iteration of used model */\n int start_iteration_for_pred_;\n /*! \\brief Shrinkage rate for one iteration */\n double shrinkage_rate_;\n /*! \\brief Number of loaded initial models */\n int num_init_iteration_;\n /*! \\brief Feature names */\n std::vector feature_names_;\n std::vector feature_infos_;\n std::vector class_need_train_;\n bool is_constant_hessian_;\n std::unique_ptr loaded_objective_;\n bool average_output_;\n bool need_re_bagging_;\n bool balanced_bagging_;\n std::string loaded_parameter_;\n std::vector monotone_constraints_;\n Json forced_splits_json_;\n bool linear_tree_;\n std::unique_ptr data_sample_strategy_;\n};\n\n} // namespace LightGBM\n#endif // LIGHTGBM_SRC_BOOSTING_GBDT_H_\n" }, { "path": "src/boosting/gbdt_model_text.cpp", "content": "/*!\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"gbdt.h\"\n\nnamespace LightGBM {\n\nconst char* kModelVersion = \"v4\";\n\nstd::string GBDT::DumpModel(int start_iteration, int num_iteration, int feature_importance_type) const {\n std::stringstream str_buf;\n Common::C_stringstream(str_buf);\n\n str_buf << \"{\";\n str_buf << \"\\\"name\\\":\\\"\" << SubModelName() << \"\\\",\" << '\\n';\n str_buf << \"\\\"version\\\":\\\"\" << kModelVersion << \"\\\",\" << '\\n';\n str_buf << \"\\\"num_class\\\":\" << num_class_ << \",\" << '\\n';\n str_buf << \"\\\"num_tree_per_iteration\\\":\" << num_tree_per_iteration_ << \",\" << '\\n';\n str_buf << \"\\\"label_index\\\":\" << label_idx_ << \",\" << '\\n';\n str_buf << \"\\\"max_feature_idx\\\":\" << max_feature_idx_ << \",\" << '\\n';\n if (objective_function_ != nullptr) {\n str_buf << \"\\\"objective\\\":\\\"\" << objective_function_->ToString() << \"\\\",\\n\";\n }\n\n str_buf << \"\\\"average_output\\\":\" << (average_output_ ? \"true\" : \"false\") << \",\\n\";\n\n str_buf << \"\\\"feature_names\\\":[\\\"\" << CommonC::Join(feature_names_, \"\\\",\\\"\")\n << \"\\\"],\" << '\\n';\n\n str_buf << \"\\\"monotone_constraints\\\":[\"\n << CommonC::Join(monotone_constraints_, \",\") << \"],\" << '\\n';\n\n str_buf << \"\\\"feature_infos\\\":\" << \"{\";\n bool first_obj = true;\n for (size_t i = 0; i < feature_infos_.size(); ++i) {\n std::stringstream json_str_buf;\n Common::C_stringstream(json_str_buf);\n auto strs = Common::Split(feature_infos_[i].c_str(), \":\");\n if (strs[0][0] == '[') {\n strs[0].erase(0, 1); // remove '['\n strs[1].erase(strs[1].size() - 1); // remove ']'\n double max_, min_;\n Common::Atof(strs[0].c_str(), &min_);\n Common::Atof(strs[1].c_str(), &max_);\n json_str_buf << std::setprecision(std::numeric_limits::digits10 + 2);\n json_str_buf << \"{\\\"min_value\\\":\" << Common::AvoidInf(min_) << \",\";\n json_str_buf << \"\\\"max_value\\\":\" << Common::AvoidInf(max_) << \",\";\n json_str_buf << \"\\\"values\\\":[]}\";\n } else if (strs[0] != \"none\") { // categorical feature\n auto vals = CommonC::StringToArray(feature_infos_[i], ':');\n auto max_idx = ArrayArgs::ArgMax(vals);\n auto min_idx = ArrayArgs::ArgMin(vals);\n json_str_buf << \"{\\\"min_value\\\":\" << vals[min_idx] << \",\";\n json_str_buf << \"\\\"max_value\\\":\" << vals[max_idx] << \",\";\n json_str_buf << \"\\\"values\\\":[\" << CommonC::Join(vals, \",\") << \"]}\";\n } else { // unused feature\n continue;\n }\n if (!first_obj) {\n str_buf << \",\";\n }\n str_buf << \"\\\"\" << feature_names_[i] << \"\\\":\";\n str_buf << json_str_buf.str();\n first_obj = false;\n }\n str_buf << \"},\" << '\\n';\n\n str_buf << \"\\\"tree_info\\\":[\";\n int num_used_model = static_cast(models_.size());\n int total_iteration = num_used_model / num_tree_per_iteration_;\n start_iteration = std::max(start_iteration, 0);\n start_iteration = std::min(start_iteration, total_iteration);\n if (num_iteration > 0) {\n int end_iteration = start_iteration + num_iteration;\n num_used_model = std::min(end_iteration * num_tree_per_iteration_ , num_used_model);\n }\n int start_model = start_iteration * num_tree_per_iteration_;\n for (int i = start_model; i < num_used_model; ++i) {\n if (i > start_model) {\n str_buf << \",\";\n }\n str_buf << \"{\";\n str_buf << \"\\\"tree_index\\\":\" << i << \",\";\n str_buf << models_[i]->ToJSON();\n str_buf << \"}\";\n }\n str_buf << \"],\" << '\\n';\n\n std::vector feature_importances = FeatureImportance(\n num_iteration, feature_importance_type);\n // store the importance first\n std::vector> pairs;\n for (size_t i = 0; i < feature_importances.size(); ++i) {\n size_t feature_importances_int = static_cast(feature_importances[i]);\n if (feature_importances_int > 0) {\n pairs.emplace_back(feature_importances_int, feature_names_[i]);\n }\n }\n str_buf << '\\n' << \"\\\"feature_importances\\\":\" << \"{\";\n for (size_t i = 0; i < pairs.size(); ++i) {\n if (i > 0) {\n str_buf << \",\";\n }\n str_buf << \"\\\"\" << pairs[i].second << \"\\\":\" << std::to_string(pairs[i].first);\n }\n str_buf << \"}\" << '\\n';\n\n str_buf << \"}\" << '\\n';\n\n return str_buf.str();\n}\n\nstd::string GBDT::ModelToIfElse(int num_iteration) const {\n std::stringstream str_buf;\n Common::C_stringstream(str_buf);\n\n str_buf << \"#include \\\"gbdt.h\\\"\" << '\\n';\n str_buf << \"#include \" << '\\n';\n str_buf << \"#include \" << '\\n';\n str_buf << \"#include \" << '\\n';\n str_buf << \"#include \" << '\\n';\n str_buf << \"#include \" << '\\n';\n str_buf << \"#include \" << '\\n';\n str_buf << \"#include \" << '\\n';\n str_buf << \"#include \" << '\\n';\n str_buf << \"#include \" << '\\n';\n str_buf << \"#include \" << '\\n';\n str_buf << \"namespace LightGBM {\" << '\\n';\n\n int num_used_model = static_cast(models_.size());\n if (num_iteration > 0) {\n num_used_model = std::min(num_iteration * num_tree_per_iteration_, num_used_model);\n }\n\n // PredictRaw\n for (int i = 0; i < num_used_model; ++i) {\n str_buf << models_[i]->ToIfElse(i, false) << '\\n';\n }\n\n str_buf << \"double (*PredictTreePtr[])(const double*) = { \";\n for (int i = 0; i < num_used_model; ++i) {\n if (i > 0) {\n str_buf << \" , \";\n }\n str_buf << \"PredictTree\" << i;\n }\n str_buf << \" };\" << '\\n' << '\\n';\n\n std::stringstream pred_str_buf;\n Common::C_stringstream(pred_str_buf);\n\n pred_str_buf << \"\\t\" << \"int early_stop_round_counter = 0;\" << '\\n';\n pred_str_buf << \"\\t\" << \"std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);\" << '\\n';\n pred_str_buf << \"\\t\" << \"for (int i = 0; i < num_iteration_for_pred_; ++i) {\" << '\\n';\n pred_str_buf << \"\\t\\t\" << \"for (int k = 0; k < num_tree_per_iteration_; ++k) {\" << '\\n';\n pred_str_buf << \"\\t\\t\\t\" << \"output[k] += (*PredictTreePtr[i * num_tree_per_iteration_ + k])(features);\" << '\\n';\n pred_str_buf << \"\\t\\t\" << \"}\" << '\\n';\n pred_str_buf << \"\\t\\t\" << \"++early_stop_round_counter;\" << '\\n';\n pred_str_buf << \"\\t\\t\" << \"if (early_stop->round_period == early_stop_round_counter) {\" << '\\n';\n pred_str_buf << \"\\t\\t\\t\" << \"if (early_stop->callback_function(output, num_tree_per_iteration_))\" << '\\n';\n pred_str_buf << \"\\t\\t\\t\\t\" << \"return;\" << '\\n';\n pred_str_buf << \"\\t\\t\\t\" << \"early_stop_round_counter = 0;\" << '\\n';\n pred_str_buf << \"\\t\\t\" << \"}\" << '\\n';\n pred_str_buf << \"\\t\" << \"}\" << '\\n';\n\n str_buf << \"void GBDT::PredictRaw(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {\" << '\\n';\n str_buf << pred_str_buf.str();\n str_buf << \"}\" << '\\n';\n str_buf << '\\n';\n\n // PredictRawByMap\n str_buf << \"double (*PredictTreeByMapPtr[])(const std::unordered_map&) = { \";\n for (int i = 0; i < num_used_model; ++i) {\n if (i > 0) {\n str_buf << \" , \";\n }\n str_buf << \"PredictTree\" << i << \"ByMap\";\n }\n str_buf << \" };\" << '\\n' << '\\n';\n\n std::stringstream pred_str_buf_map;\n Common::C_stringstream(pred_str_buf_map);\n\n pred_str_buf_map << \"\\t\" << \"int early_stop_round_counter = 0;\" << '\\n';\n pred_str_buf_map << \"\\t\" << \"std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);\" << '\\n';\n pred_str_buf_map << \"\\t\" << \"for (int i = 0; i < num_iteration_for_pred_; ++i) {\" << '\\n';\n pred_str_buf_map << \"\\t\\t\" << \"for (int k = 0; k < num_tree_per_iteration_; ++k) {\" << '\\n';\n pred_str_buf_map << \"\\t\\t\\t\" << \"output[k] += (*PredictTreeByMapPtr[i * num_tree_per_iteration_ + k])(features);\" << '\\n';\n pred_str_buf_map << \"\\t\\t\" << \"}\" << '\\n';\n pred_str_buf_map << \"\\t\\t\" << \"++early_stop_round_counter;\" << '\\n';\n pred_str_buf_map << \"\\t\\t\" << \"if (early_stop->round_period == early_stop_round_counter) {\" << '\\n';\n pred_str_buf_map << \"\\t\\t\\t\" << \"if (early_stop->callback_function(output, num_tree_per_iteration_))\" << '\\n';\n pred_str_buf_map << \"\\t\\t\\t\\t\" << \"return;\" << '\\n';\n pred_str_buf_map << \"\\t\\t\\t\" << \"early_stop_round_counter = 0;\" << '\\n';\n pred_str_buf_map << \"\\t\\t\" << \"}\" << '\\n';\n pred_str_buf_map << \"\\t\" << \"}\" << '\\n';\n\n str_buf << \"void GBDT::PredictRawByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const {\" << '\\n';\n str_buf << pred_str_buf_map.str();\n str_buf << \"}\" << '\\n';\n str_buf << '\\n';\n\n // Predict\n str_buf << \"void GBDT::Predict(const double* features, double *output, const PredictionEarlyStopInstance* early_stop) const {\" << '\\n';\n str_buf << \"\\t\" << \"PredictRaw(features, output, early_stop);\" << '\\n';\n str_buf << \"\\t\" << \"if (average_output_) {\" << '\\n';\n str_buf << \"\\t\\t\" << \"for (int k = 0; k < num_tree_per_iteration_; ++k) {\" << '\\n';\n str_buf << \"\\t\\t\\t\" << \"output[k] /= num_iteration_for_pred_;\" << '\\n';\n str_buf << \"\\t\\t\" << \"}\" << '\\n';\n str_buf << \"\\t\" << \"}\" << '\\n';\n str_buf << \"\\t\" << \"if (objective_function_ != nullptr) {\" << '\\n';\n str_buf << \"\\t\\t\" << \"objective_function_->ConvertOutput(output, output);\" << '\\n';\n str_buf << \"\\t\" << \"}\" << '\\n';\n str_buf << \"}\" << '\\n';\n str_buf << '\\n';\n\n // PredictByMap\n str_buf << \"void GBDT::PredictByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const {\" << '\\n';\n str_buf << \"\\t\" << \"PredictRawByMap(features, output, early_stop);\" << '\\n';\n str_buf << \"\\t\" << \"if (average_output_) {\" << '\\n';\n str_buf << \"\\t\\t\" << \"for (int k = 0; k < num_tree_per_iteration_; ++k) {\" << '\\n';\n str_buf << \"\\t\\t\\t\" << \"output[k] /= num_iteration_for_pred_;\" << '\\n';\n str_buf << \"\\t\\t\" << \"}\" << '\\n';\n str_buf << \"\\t\" << \"}\" << '\\n';\n str_buf << \"\\t\" << \"if (objective_function_ != nullptr) {\" << '\\n';\n str_buf << \"\\t\\t\" << \"objective_function_->ConvertOutput(output, output);\" << '\\n';\n str_buf << \"\\t\" << \"}\" << '\\n';\n str_buf << \"}\" << '\\n';\n str_buf << '\\n';\n\n\n // PredictLeafIndex\n for (int i = 0; i < num_used_model; ++i) {\n str_buf << models_[i]->ToIfElse(i, true) << '\\n';\n }\n\n str_buf << \"double (*PredictTreeLeafPtr[])(const double*) = { \";\n for (int i = 0; i < num_used_model; ++i) {\n if (i > 0) {\n str_buf << \" , \";\n }\n str_buf << \"PredictTree\" << i << \"Leaf\";\n }\n str_buf << \" };\" << '\\n' << '\\n';\n\n str_buf << \"void GBDT::PredictLeafIndex(const double* features, double *output) const {\" << '\\n';\n str_buf << \"\\t\" << \"int total_tree = num_iteration_for_pred_ * num_tree_per_iteration_;\" << '\\n';\n str_buf << \"\\t\" << \"for (int i = 0; i < total_tree; ++i) {\" << '\\n';\n str_buf << \"\\t\\t\" << \"output[i] = (*PredictTreeLeafPtr[i])(features);\" << '\\n';\n str_buf << \"\\t\" << \"}\" << '\\n';\n str_buf << \"}\" << '\\n';\n\n // PredictLeafIndexByMap\n str_buf << \"double (*PredictTreeLeafByMapPtr[])(const std::unordered_map&) = { \";\n for (int i = 0; i < num_used_model; ++i) {\n if (i > 0) {\n str_buf << \" , \";\n }\n str_buf << \"PredictTree\" << i << \"LeafByMap\";\n }\n str_buf << \" };\" << '\\n' << '\\n';\n\n str_buf << \"void GBDT::PredictLeafIndexByMap(const std::unordered_map& features, double* output) const {\" << '\\n';\n str_buf << \"\\t\" << \"int total_tree = num_iteration_for_pred_ * num_tree_per_iteration_;\" << '\\n';\n str_buf << \"\\t\" << \"for (int i = 0; i < total_tree; ++i) {\" << '\\n';\n str_buf << \"\\t\\t\" << \"output[i] = (*PredictTreeLeafByMapPtr[i])(features);\" << '\\n';\n str_buf << \"\\t\" << \"}\" << '\\n';\n str_buf << \"}\" << '\\n';\n\n str_buf << \"} // namespace LightGBM\" << '\\n';\n\n return str_buf.str();\n}\n\nbool GBDT::SaveModelToIfElse(int num_iteration, const char* filename) const {\n /*! \\brief File to write models */\n std::ofstream output_file;\n std::ifstream ifs(filename);\n if (ifs.good()) {\n std::string origin((std::istreambuf_iterator(ifs)),\n (std::istreambuf_iterator()));\n output_file.open(filename);\n output_file << \"#define USE_HARD_CODE 0\" << '\\n';\n output_file << \"#ifndef USE_HARD_CODE\" << '\\n';\n output_file << origin << '\\n';\n output_file << \"#else\" << '\\n';\n output_file << ModelToIfElse(num_iteration);\n output_file << \"#endif\" << '\\n';\n } else {\n output_file.open(filename);\n output_file << ModelToIfElse(num_iteration);\n }\n\n ifs.close();\n output_file.close();\n\n return static_cast(output_file);\n}\n\nstd::string GBDT::SaveModelToString(int start_iteration, int num_iteration, int feature_importance_type) const {\n std::stringstream ss;\n Common::C_stringstream(ss);\n\n // output model type\n ss << SubModelName() << '\\n';\n ss << \"version=\" << kModelVersion << '\\n';\n // output number of class\n ss << \"num_class=\" << num_class_ << '\\n';\n ss << \"num_tree_per_iteration=\" << num_tree_per_iteration_ << '\\n';\n // output label index\n ss << \"label_index=\" << label_idx_ << '\\n';\n // output max_feature_idx\n ss << \"max_feature_idx=\" << max_feature_idx_ << '\\n';\n // output objective\n if (objective_function_ != nullptr) {\n ss << \"objective=\" << objective_function_->ToString() << '\\n';\n }\n\n if (average_output_) {\n ss << \"average_output\" << '\\n';\n }\n\n ss << \"feature_names=\" << CommonC::Join(feature_names_, \" \") << '\\n';\n\n if (monotone_constraints_.size() != 0) {\n ss << \"monotone_constraints=\" << CommonC::Join(monotone_constraints_, \" \")\n << '\\n';\n }\n\n ss << \"feature_infos=\" << CommonC::Join(feature_infos_, \" \") << '\\n';\n\n int num_used_model = static_cast(models_.size());\n int total_iteration = num_used_model / num_tree_per_iteration_;\n start_iteration = std::max(start_iteration, 0);\n start_iteration = std::min(start_iteration, total_iteration);\n if (num_iteration > 0) {\n int end_iteration = start_iteration + num_iteration;\n num_used_model = std::min(end_iteration * num_tree_per_iteration_, num_used_model);\n }\n\n int start_model = start_iteration * num_tree_per_iteration_;\n\n std::vector tree_strs(num_used_model - start_model);\n std::vector tree_sizes(num_used_model - start_model);\n // output tree models\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = start_model; i < num_used_model; ++i) {\n const int idx = i - start_model;\n tree_strs[idx] = \"Tree=\" + std::to_string(idx) + '\\n';\n tree_strs[idx] += models_[i]->ToString() + '\\n';\n tree_sizes[idx] = tree_strs[idx].size();\n }\n\n ss << \"tree_sizes=\" << CommonC::Join(tree_sizes, \" \") << '\\n';\n ss << '\\n';\n\n for (int i = 0; i < num_used_model - start_model; ++i) {\n ss << tree_strs[i];\n tree_strs[i].clear();\n }\n ss << \"end of trees\" << \"\\n\";\n std::vector feature_importances = FeatureImportance(\n num_iteration, feature_importance_type);\n // store the importance first\n std::vector> pairs;\n for (size_t i = 0; i < feature_importances.size(); ++i) {\n size_t feature_importances_int = static_cast(feature_importances[i]);\n if (feature_importances_int > 0) {\n pairs.emplace_back(feature_importances_int, feature_names_[i]);\n }\n }\n // sort the importance\n std::stable_sort(pairs.begin(), pairs.end(),\n [](const std::pair& lhs,\n const std::pair& rhs) {\n return lhs.first > rhs.first;\n });\n ss << '\\n' << \"feature_importances:\" << '\\n';\n for (size_t i = 0; i < pairs.size(); ++i) {\n ss << pairs[i].second << \"=\" << std::to_string(pairs[i].first) << '\\n';\n }\n if (config_ != nullptr) {\n ss << \"\\nparameters:\" << '\\n';\n ss << config_->ToString() << \"\\n\";\n ss << \"end of parameters\" << '\\n';\n } else if (!loaded_parameter_.empty()) {\n ss << \"\\nparameters:\" << '\\n';\n ss << loaded_parameter_ << \"\\n\";\n ss << \"end of parameters\" << '\\n';\n }\n if (!parser_config_str_.empty()) {\n ss << \"\\nparser:\" << '\\n';\n ss << parser_config_str_ << \"\\n\";\n ss << \"end of parser\" << '\\n';\n }\n return ss.str();\n}\n\nbool GBDT::SaveModelToFile(int start_iteration, int num_iteration, int feature_importance_type, const char* filename) const {\n /*! \\brief File to write models */\n auto writer = VirtualFileWriter::Make(filename);\n if (!writer->Init()) {\n Log::Fatal(\"Model file %s is not available for writes\", filename);\n }\n std::string str_to_write = SaveModelToString(start_iteration, num_iteration, feature_importance_type);\n auto size = writer->Write(str_to_write.c_str(), str_to_write.size());\n return size > 0;\n}\n\nbool GBDT::LoadModelFromString(const char* buffer, size_t len) {\n // use serialized string to restore this object\n models_.clear();\n auto c_str = buffer;\n auto p = c_str;\n auto end = p + len;\n std::unordered_map key_vals;\n while (p < end) {\n auto line_len = Common::GetLine(p);\n if (line_len > 0) {\n std::string cur_line(p, line_len);\n if (!Common::StartsWith(cur_line, \"Tree=\")) {\n auto strs = Common::Split(cur_line.c_str(), '=');\n if (strs.size() == 1) {\n key_vals[strs[0]] = \"\";\n } else if (strs.size() == 2) {\n key_vals[strs[0]] = strs[1];\n } else if (strs.size() > 2) {\n if (strs[0] == \"feature_names\") {\n key_vals[strs[0]] = cur_line.substr(std::strlen(\"feature_names=\"));\n } else if (strs[0] == \"monotone_constraints\") {\n key_vals[strs[0]] = cur_line.substr(std::strlen(\"monotone_constraints=\"));\n } else {\n // Use first 128 chars to avoid exceed the message buffer.\n Log::Fatal(\"Wrong line at model file: %s\", cur_line.substr(0, std::min(128, cur_line.size())).c_str());\n }\n }\n } else {\n break;\n }\n }\n p += line_len;\n p = Common::SkipNewLine(p);\n }\n\n // get number of classes\n if (key_vals.count(\"num_class\")) {\n Common::Atoi(key_vals[\"num_class\"].c_str(), &num_class_);\n } else {\n Log::Fatal(\"Model file doesn't specify the number of classes\");\n return false;\n }\n\n if (key_vals.count(\"num_tree_per_iteration\")) {\n Common::Atoi(key_vals[\"num_tree_per_iteration\"].c_str(), &num_tree_per_iteration_);\n } else {\n num_tree_per_iteration_ = num_class_;\n }\n\n // get index of label\n if (key_vals.count(\"label_index\")) {\n Common::Atoi(key_vals[\"label_index\"].c_str(), &label_idx_);\n } else {\n Log::Fatal(\"Model file doesn't specify the label index\");\n return false;\n }\n\n // get max_feature_idx first\n if (key_vals.count(\"max_feature_idx\")) {\n Common::Atoi(key_vals[\"max_feature_idx\"].c_str(), &max_feature_idx_);\n } else {\n Log::Fatal(\"Model file doesn't specify max_feature_idx\");\n return false;\n }\n\n // get average_output\n if (key_vals.count(\"average_output\")) {\n average_output_ = true;\n }\n\n // get feature names\n if (key_vals.count(\"feature_names\")) {\n feature_names_ = Common::Split(key_vals[\"feature_names\"].c_str(), ' ');\n if (feature_names_.size() != static_cast(max_feature_idx_ + 1)) {\n Log::Fatal(\"Wrong size of feature_names\");\n return false;\n }\n } else {\n Log::Fatal(\"Model file doesn't contain feature_names\");\n return false;\n }\n\n // get monotone_constraints\n if (key_vals.count(\"monotone_constraints\")) {\n monotone_constraints_ = CommonC::StringToArray(key_vals[\"monotone_constraints\"].c_str(), ' ');\n if (monotone_constraints_.size() != static_cast(max_feature_idx_ + 1)) {\n Log::Fatal(\"Wrong size of monotone_constraints\");\n return false;\n }\n }\n\n if (key_vals.count(\"feature_infos\")) {\n feature_infos_ = Common::Split(key_vals[\"feature_infos\"].c_str(), ' ');\n if (feature_infos_.size() != static_cast(max_feature_idx_ + 1)) {\n Log::Fatal(\"Wrong size of feature_infos\");\n return false;\n }\n } else {\n Log::Fatal(\"Model file doesn't contain feature_infos\");\n return false;\n }\n\n if (key_vals.count(\"objective\")) {\n auto str = key_vals[\"objective\"];\n loaded_objective_.reset(ObjectiveFunction::CreateObjectiveFunction(ParseObjectiveAlias(str)));\n objective_function_ = loaded_objective_.get();\n }\n\n if (!key_vals.count(\"tree_sizes\")) {\n while (p < end) {\n auto line_len = Common::GetLine(p);\n if (line_len > 0) {\n std::string cur_line(p, line_len);\n if (Common::StartsWith(cur_line, \"Tree=\")) {\n p += line_len;\n p = Common::SkipNewLine(p);\n size_t used_len = 0;\n models_.emplace_back(new Tree(p, &used_len));\n p += used_len;\n } else {\n break;\n }\n }\n p = Common::SkipNewLine(p);\n }\n } else {\n std::vector tree_sizes = CommonC::StringToArray(key_vals[\"tree_sizes\"].c_str(), ' ');\n std::vector tree_boundaries(tree_sizes.size() + 1, 0);\n int num_trees = static_cast(tree_sizes.size());\n for (int i = 0; i < num_trees; ++i) {\n tree_boundaries[i + 1] = tree_boundaries[i] + tree_sizes[i];\n models_.emplace_back(nullptr);\n }\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = 0; i < num_trees; ++i) {\n OMP_LOOP_EX_BEGIN();\n auto cur_p = p + tree_boundaries[i];\n auto line_len = Common::GetLine(cur_p);\n std::string cur_line(cur_p, line_len);\n if (Common::StartsWith(cur_line, \"Tree=\")) {\n cur_p += line_len;\n cur_p = Common::SkipNewLine(cur_p);\n size_t used_len = 0;\n models_[i].reset(new Tree(cur_p, &used_len));\n } else {\n Log::Fatal(\"Model format error, expect a tree here. met %s\", cur_line.c_str());\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n }\n num_iteration_for_pred_ = static_cast(models_.size()) / num_tree_per_iteration_;\n num_init_iteration_ = num_iteration_for_pred_;\n iter_ = 0;\n bool is_inparameter = false, is_inparser = false;\n std::stringstream ss;\n Common::C_stringstream(ss);\n while (p < end) {\n auto line_len = Common::GetLine(p);\n if (line_len > 0) {\n std::string cur_line(p, line_len);\n if (cur_line == std::string(\"parameters:\")) {\n is_inparameter = true;\n } else if (cur_line == std::string(\"end of parameters\")) {\n break;\n } else if (is_inparameter) {\n ss << cur_line << \"\\n\";\n if (Common::StartsWith(cur_line, \"[linear_tree: \")) {\n int is_linear = 0;\n Common::Atoi(cur_line.substr(14, 1).c_str(), &is_linear);\n linear_tree_ = static_cast(is_linear);\n }\n }\n }\n p += line_len;\n p = Common::SkipNewLine(p);\n }\n if (!ss.str().empty()) {\n loaded_parameter_ = ss.str();\n }\n ss.clear();\n ss.str(\"\");\n while (p < end) {\n auto line_len = Common::GetLine(p);\n if (line_len > 0) {\n std::string cur_line(p, line_len);\n if (cur_line == std::string(\"parser:\")) {\n is_inparser = true;\n } else if (cur_line == std::string(\"end of parser\")) {\n p += line_len;\n p = Common::SkipNewLine(p);\n break;\n } else if (is_inparser) {\n ss << cur_line << \"\\n\";\n }\n }\n p += line_len;\n p = Common::SkipNewLine(p);\n }\n parser_config_str_ = ss.str();\n ss.clear();\n ss.str(\"\");\n return true;\n}\n\nstd::vector GBDT::FeatureImportance(int num_iteration, int importance_type) const {\n int num_used_model = static_cast(models_.size());\n if (num_iteration > 0) {\n num_iteration += 0;\n num_used_model = std::min(num_iteration * num_tree_per_iteration_, num_used_model);\n }\n\n std::vector feature_importances(max_feature_idx_ + 1, 0.0);\n if (importance_type == 0) {\n for (int iter = 0; iter < num_used_model; ++iter) {\n for (int split_idx = 0; split_idx < models_[iter]->num_leaves() - 1; ++split_idx) {\n if (models_[iter]->split_gain(split_idx) > 0) {\n#ifdef DEBUG\n CHECK_GE(models_[iter]->split_feature(split_idx), 0);\n#endif\n feature_importances[models_[iter]->split_feature(split_idx)] += 1.0;\n }\n }\n }\n } else if (importance_type == 1) {\n for (int iter = 0; iter < num_used_model; ++iter) {\n for (int split_idx = 0; split_idx < models_[iter]->num_leaves() - 1; ++split_idx) {\n if (models_[iter]->split_gain(split_idx) > 0) {\n#ifdef DEBUG\n CHECK_GE(models_[iter]->split_feature(split_idx), 0);\n#endif\n feature_importances[models_[iter]->split_feature(split_idx)] += models_[iter]->split_gain(split_idx);\n }\n }\n }\n } else {\n Log::Fatal(\"Unknown importance type: only support split=0 and gain=1\");\n }\n return feature_importances;\n}\n\n} // namespace LightGBM\n" }, { "path": "src/boosting/gbdt_prediction.cpp", "content": "/*!\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n#include \n#include \n\n#include \n\n#include \"gbdt.h\"\n\nnamespace LightGBM {\n\nvoid GBDT::PredictRaw(const double* features, double* output, const PredictionEarlyStopInstance* early_stop) const {\n int early_stop_round_counter = 0;\n // set zero\n std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);\n const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_;\n for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) {\n // predict all the trees for one iteration\n for (int k = 0; k < num_tree_per_iteration_; ++k) {\n output[k] += models_[i * num_tree_per_iteration_ + k]->Predict(features);\n }\n // check early stopping\n ++early_stop_round_counter;\n if (early_stop->round_period == early_stop_round_counter) {\n if (early_stop->callback_function(output, num_tree_per_iteration_)) {\n return;\n }\n early_stop_round_counter = 0;\n }\n }\n}\n\nvoid GBDT::PredictRawByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const {\n int early_stop_round_counter = 0;\n // set zero\n std::memset(output, 0, sizeof(double) * num_tree_per_iteration_);\n const int end_iteration_for_pred = start_iteration_for_pred_ + num_iteration_for_pred_;\n for (int i = start_iteration_for_pred_; i < end_iteration_for_pred; ++i) {\n // predict all the trees for one iteration\n for (int k = 0; k < num_tree_per_iteration_; ++k) {\n output[k] += models_[i * num_tree_per_iteration_ + k]->PredictByMap(features);\n }\n // check early stopping\n ++early_stop_round_counter;\n if (early_stop->round_period == early_stop_round_counter) {\n if (early_stop->callback_function(output, num_tree_per_iteration_)) {\n return;\n }\n early_stop_round_counter = 0;\n }\n }\n}\n\nvoid GBDT::Predict(const double* features, double* output, const PredictionEarlyStopInstance* early_stop) const {\n PredictRaw(features, output, early_stop);\n if (average_output_) {\n for (int k = 0; k < num_tree_per_iteration_; ++k) {\n output[k] /= num_iteration_for_pred_;\n }\n }\n if (objective_function_ != nullptr) {\n objective_function_->ConvertOutput(output, output);\n }\n}\n\nvoid GBDT::PredictByMap(const std::unordered_map& features, double* output, const PredictionEarlyStopInstance* early_stop) const {\n PredictRawByMap(features, output, early_stop);\n if (average_output_) {\n for (int k = 0; k < num_tree_per_iteration_; ++k) {\n output[k] /= num_iteration_for_pred_;\n }\n }\n if (objective_function_ != nullptr) {\n objective_function_->ConvertOutput(output, output);\n }\n}\n\nvoid GBDT::PredictLeafIndex(const double* features, double* output) const {\n int start_tree = start_iteration_for_pred_ * num_tree_per_iteration_;\n int num_trees = num_iteration_for_pred_ * num_tree_per_iteration_;\n const auto* models_ptr = models_.data() + start_tree;\n for (int i = 0; i < num_trees; ++i) {\n output[i] = models_ptr[i]->PredictLeafIndex(features);\n }\n}\n\nvoid GBDT::PredictLeafIndexByMap(const std::unordered_map& features, double* output) const {\n int start_tree = start_iteration_for_pred_ * num_tree_per_iteration_;\n int num_trees = num_iteration_for_pred_ * num_tree_per_iteration_;\n const auto* models_ptr = models_.data() + start_tree;\n for (int i = 0; i < num_trees; ++i) {\n output[i] = models_ptr[i]->PredictLeafIndexByMap(features);\n }\n}\n\n} // namespace LightGBM\n" }, { "path": "src/boosting/goss.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifndef LIGHTGBM_SRC_BOOSTING_GOSS_HPP_\n#define LIGHTGBM_SRC_BOOSTING_GOSS_HPP_\n\n#include \n#include \n\n#include \n#include \n#include \n\nnamespace LightGBM {\n\nclass GOSSStrategy : public SampleStrategy {\n public:\n GOSSStrategy(const Config* config, const Dataset* train_data, int num_tree_per_iteration) {\n config_ = config;\n train_data_ = train_data;\n num_tree_per_iteration_ = num_tree_per_iteration;\n num_data_ = train_data->num_data();\n }\n\n ~GOSSStrategy() {\n }\n\n void Bagging(int iter, TreeLearner* tree_learner, score_t* gradients, score_t* hessians) override {\n bag_data_cnt_ = num_data_;\n // not subsample for first iterations\n if (iter < static_cast(1.0f / config_->learning_rate)) {\n return;\n }\n auto left_cnt = bagging_runner_.Run(\n num_data_,\n [=](int, data_size_t cur_start, data_size_t cur_cnt, data_size_t* left,\n data_size_t*) {\n data_size_t cur_left_count = 0;\n cur_left_count = Helper(cur_start, cur_cnt, left, gradients, hessians);\n return cur_left_count;\n },\n bag_data_indices_.data());\n bag_data_cnt_ = left_cnt;\n // set bagging data to tree learner\n if (!is_use_subset_) {\n #ifdef USE_CUDA\n if (config_->device_type == std::string(\"cuda\")) {\n CopyFromHostToCUDADevice(cuda_bag_data_indices_.RawData(), bag_data_indices_.data(), static_cast(num_data_), __FILE__, __LINE__);\n tree_learner->SetBaggingData(nullptr, cuda_bag_data_indices_.RawData(), bag_data_cnt_);\n } else {\n #endif // USE_CUDA\n tree_learner->SetBaggingData(nullptr, bag_data_indices_.data(), bag_data_cnt_);\n #ifdef USE_CUDA\n }\n #endif // USE_CUDA\n } else {\n // get subset\n tmp_subset_->ReSize(bag_data_cnt_);\n tmp_subset_->CopySubrow(train_data_, bag_data_indices_.data(),\n bag_data_cnt_, false);\n #ifdef USE_CUDA\n if (config_->device_type == std::string(\"cuda\")) {\n CopyFromHostToCUDADevice(cuda_bag_data_indices_.RawData(), bag_data_indices_.data(), static_cast(num_data_), __FILE__, __LINE__);\n tree_learner->SetBaggingData(tmp_subset_.get(), cuda_bag_data_indices_.RawData(),\n bag_data_cnt_);\n } else {\n #endif // USE_CUDA\n tree_learner->SetBaggingData(tmp_subset_.get(), bag_data_indices_.data(),\n bag_data_cnt_);\n #ifdef USE_CUDA\n }\n #endif // USE_CUDA\n }\n }\n\n void ResetSampleConfig(const Config* config, bool /*is_change_dataset*/) override {\n // Cannot use bagging in GOSS\n config_ = config;\n need_resize_gradients_ = false;\n if (objective_function_ == nullptr) {\n // resize gradient vectors to copy the customized gradients for goss\n need_resize_gradients_ = true;\n }\n\n CHECK_LE(config_->top_rate + config_->other_rate, 1.0f);\n CHECK(config_->top_rate > 0.0f && config_->other_rate > 0.0f);\n if (config_->bagging_freq > 0 && config_->bagging_fraction != 1.0f) {\n Log::Fatal(\"Cannot use bagging in GOSS\");\n }\n Log::Info(\"Using GOSS\");\n balanced_bagging_ = false;\n bag_data_indices_.resize(num_data_);\n bagging_runner_.ReSize(num_data_);\n bagging_rands_.clear();\n for (int i = 0;\n i < (num_data_ + bagging_rand_block_ - 1) / bagging_rand_block_; ++i) {\n bagging_rands_.emplace_back(config_->bagging_seed + i);\n }\n is_use_subset_ = false;\n if (config_->top_rate + config_->other_rate <= 0.5) {\n auto bag_data_cnt = static_cast((config_->top_rate + config_->other_rate) * num_data_);\n bag_data_cnt = std::max(1, bag_data_cnt);\n tmp_subset_.reset(new Dataset(bag_data_cnt));\n tmp_subset_->CopyFeatureMapperFrom(train_data_);\n is_use_subset_ = true;\n }\n // flag to not bagging first\n bag_data_cnt_ = num_data_;\n }\n\n bool IsHessianChange() const override {\n return true;\n }\n\n private:\n data_size_t Helper(data_size_t start, data_size_t cnt, data_size_t* buffer, score_t* gradients, score_t* hessians) {\n if (cnt <= 0) {\n return 0;\n }\n std::vector tmp_gradients(cnt, 0.0f);\n for (data_size_t i = 0; i < cnt; ++i) {\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n size_t idx = static_cast(cur_tree_id) * num_data_ + start + i;\n tmp_gradients[i] += std::fabs(gradients[idx] * hessians[idx]);\n }\n }\n data_size_t top_k = static_cast(cnt * config_->top_rate);\n data_size_t other_k = static_cast(cnt * config_->other_rate);\n top_k = std::max(1, top_k);\n ArrayArgs::ArgMaxAtK(&tmp_gradients, 0, static_cast(tmp_gradients.size()), top_k - 1);\n score_t threshold = tmp_gradients[top_k - 1];\n\n score_t multiply = static_cast(cnt - top_k) / other_k;\n data_size_t cur_left_cnt = 0;\n data_size_t cur_right_pos = cnt;\n data_size_t big_weight_cnt = 0;\n for (data_size_t i = 0; i < cnt; ++i) {\n auto cur_idx = start + i;\n score_t grad = 0.0f;\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n size_t idx = static_cast(cur_tree_id) * num_data_ + cur_idx;\n grad += std::fabs(gradients[idx] * hessians[idx]);\n }\n if (grad >= threshold) {\n buffer[cur_left_cnt++] = cur_idx;\n ++big_weight_cnt;\n } else {\n data_size_t sampled = cur_left_cnt - big_weight_cnt;\n data_size_t rest_need = other_k - sampled;\n data_size_t rest_all = (cnt - i) - (top_k - big_weight_cnt);\n double prob = (rest_need) / static_cast(rest_all);\n if (bagging_rands_[cur_idx / bagging_rand_block_].NextFloat() < prob) {\n buffer[cur_left_cnt++] = cur_idx;\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n size_t idx = static_cast(cur_tree_id) * num_data_ + cur_idx;\n gradients[idx] *= multiply;\n hessians[idx] *= multiply;\n }\n } else {\n buffer[--cur_right_pos] = cur_idx;\n }\n }\n }\n return cur_left_cnt;\n }\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_SRC_BOOSTING_GOSS_HPP_\n" }, { "path": "src/boosting/prediction_early_stop.cpp", "content": "/*!\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nPredictionEarlyStopInstance CreateNone(const PredictionEarlyStopConfig&) {\n return PredictionEarlyStopInstance{\n [](const double*, int) {\n return false;\n },\n std::numeric_limits::max() // make sure the lambda is almost never called\n };\n}\n\nPredictionEarlyStopInstance CreateMulticlass(const PredictionEarlyStopConfig& config) {\n // margin_threshold will be captured by value\n const double margin_threshold = config.margin_threshold;\n\n return PredictionEarlyStopInstance{\n [margin_threshold](const double* pred, int sz) {\n if (sz < 2) {\n Log::Fatal(\"Multiclass early stopping needs predictions to be of length two or larger\");\n }\n\n // copy and sort\n std::vector votes(static_cast(sz));\n for (int i = 0; i < sz; ++i) {\n votes[i] = pred[i];\n }\n std::partial_sort(votes.begin(), votes.begin() + 2, votes.end(), std::greater());\n\n const auto margin = votes[0] - votes[1];\n\n if (margin > margin_threshold) {\n return true;\n }\n\n return false;\n },\n config.round_period\n };\n}\n\nPredictionEarlyStopInstance CreateBinary(const PredictionEarlyStopConfig& config) {\n // margin_threshold will be captured by value\n const double margin_threshold = config.margin_threshold;\n\n return PredictionEarlyStopInstance{\n [margin_threshold](const double* pred, int sz) {\n if (sz != 1) {\n Log::Fatal(\"Binary early stopping needs predictions to be of length one\");\n }\n const auto margin = 2.0 * fabs(pred[0]);\n\n if (margin > margin_threshold) {\n return true;\n }\n\n return false;\n },\n config.round_period\n };\n}\n\nPredictionEarlyStopInstance CreatePredictionEarlyStopInstance(const std::string& type,\n const PredictionEarlyStopConfig& config) {\n if (type == \"none\") {\n return CreateNone(config);\n } else if (type == \"multiclass\") {\n return CreateMulticlass(config);\n } else if (type == \"binary\") {\n return CreateBinary(config);\n } else {\n Log::Fatal(\"Unknown early stopping type: %s\", type.c_str());\n }\n\n // Fix for compiler warnings about reaching end of control\n return CreateNone(config);\n}\n\n} // namespace LightGBM\n" }, { "path": "src/boosting/rf.hpp", "content": "/*!\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_BOOSTING_RF_HPP_\n#define LIGHTGBM_SRC_BOOSTING_RF_HPP_\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"gbdt.h\"\n#include \"score_updater.hpp\"\n\nnamespace LightGBM {\n/*!\n* \\brief Random Forest implementation\n*/\nclass RF : public GBDT {\n public:\n RF() : GBDT() {\n average_output_ = true;\n }\n\n ~RF() {}\n\n void Init(const Config* config, const Dataset* train_data, const ObjectiveFunction* objective_function,\n const std::vector& training_metrics) override {\n if (config->data_sample_strategy == std::string(\"bagging\")) {\n CHECK((config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f) ||\n (config->feature_fraction < 1.0f && config->feature_fraction > 0.0f));\n } else {\n CHECK_EQ(config->data_sample_strategy, std::string(\"goss\"));\n }\n GBDT::Init(config, train_data, objective_function, training_metrics);\n\n if (num_init_iteration_ > 0) {\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n MultiplyScore(cur_tree_id, 1.0f / num_init_iteration_);\n }\n } else {\n CHECK_EQ(train_data->metadata().init_score(), nullptr);\n }\n CHECK_EQ(num_tree_per_iteration_, num_class_);\n // not shrinkage rate for the RF\n shrinkage_rate_ = 1.0f;\n // only boosting one time\n Boosting();\n if (data_sample_strategy_->is_use_subset() && data_sample_strategy_->bag_data_cnt() < num_data_) {\n tmp_grad_.resize(num_data_);\n tmp_hess_.resize(num_data_);\n }\n }\n\n void ResetConfig(const Config* config) override {\n if (config->data_sample_strategy == std::string(\"bagging\")) {\n CHECK((config->bagging_freq > 0 && config->bagging_fraction < 1.0f && config->bagging_fraction > 0.0f) ||\n (config->feature_fraction < 1.0f && config->feature_fraction > 0.0f));\n } else {\n CHECK_EQ(config->data_sample_strategy, std::string(\"goss\"));\n }\n GBDT::ResetConfig(config);\n // not shrinkage rate for the RF\n shrinkage_rate_ = 1.0f;\n }\n\n void ResetTrainingData(const Dataset* train_data, const ObjectiveFunction* objective_function,\n const std::vector& training_metrics) override {\n GBDT::ResetTrainingData(train_data, objective_function, training_metrics);\n if (iter_ + num_init_iteration_ > 0) {\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n train_score_updater_->MultiplyScore(1.0f / (iter_ + num_init_iteration_), cur_tree_id);\n }\n }\n CHECK_EQ(num_tree_per_iteration_, num_class_);\n // only boosting one time\n Boosting();\n if (data_sample_strategy_->is_use_subset() && data_sample_strategy_->bag_data_cnt() < num_data_) {\n tmp_grad_.resize(num_data_);\n tmp_hess_.resize(num_data_);\n }\n }\n\n void Boosting() override {\n if (objective_function_ == nullptr) {\n Log::Fatal(\"RF mode do not support custom objective function, please use built-in objectives.\");\n }\n init_scores_.resize(num_tree_per_iteration_, 0.0);\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n init_scores_[cur_tree_id] = BoostFromAverage(cur_tree_id, false);\n }\n size_t total_size = static_cast(num_data_) * num_tree_per_iteration_;\n std::vector tmp_scores(total_size, 0.0f);\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int j = 0; j < num_tree_per_iteration_; ++j) {\n size_t offset = static_cast(j)* num_data_;\n for (data_size_t i = 0; i < num_data_; ++i) {\n tmp_scores[offset + i] = init_scores_[j];\n }\n }\n objective_function_->\n GetGradients(tmp_scores.data(), gradients_.data(), hessians_.data());\n }\n\n bool TrainOneIter(const score_t* gradients, const score_t* hessians) override {\n // bagging logic\n data_sample_strategy_ ->Bagging(iter_, tree_learner_.get(), gradients_.data(), hessians_.data());\n const bool is_use_subset = data_sample_strategy_->is_use_subset();\n const data_size_t bag_data_cnt = data_sample_strategy_->bag_data_cnt();\n const std::vector>& bag_data_indices = data_sample_strategy_->bag_data_indices();\n\n // GOSSStrategy->Bagging may modify value of bag_data_cnt_\n if (is_use_subset && bag_data_cnt < num_data_) {\n tmp_grad_.resize(num_data_);\n tmp_hess_.resize(num_data_);\n }\n\n CHECK_EQ(gradients, nullptr);\n CHECK_EQ(hessians, nullptr);\n\n gradients = gradients_.data();\n hessians = hessians_.data();\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n std::unique_ptr new_tree(new Tree(2, false, false));\n size_t offset = static_cast(cur_tree_id)* num_data_;\n if (class_need_train_[cur_tree_id]) {\n auto grad = gradients + offset;\n auto hess = hessians + offset;\n\n if (is_use_subset && bag_data_cnt < num_data_ && !boosting_on_gpu_) {\n for (int i = 0; i < bag_data_cnt; ++i) {\n tmp_grad_[i] = grad[bag_data_indices[i]];\n tmp_hess_[i] = hess[bag_data_indices[i]];\n }\n grad = tmp_grad_.data();\n hess = tmp_hess_.data();\n }\n\n new_tree.reset(tree_learner_->Train(grad, hess, false));\n }\n\n if (new_tree->num_leaves() > 1) {\n double pred = init_scores_[cur_tree_id];\n auto residual_getter = [pred](const label_t* label, int i) {return static_cast(label[i]) - pred; };\n tree_learner_->RenewTreeOutput(new_tree.get(), objective_function_, residual_getter,\n num_data_, bag_data_indices.data(), bag_data_cnt, train_score_updater_->score());\n if (std::fabs(init_scores_[cur_tree_id]) > kEpsilon) {\n new_tree->AddBias(init_scores_[cur_tree_id]);\n }\n // update score\n MultiplyScore(cur_tree_id, (iter_ + num_init_iteration_));\n UpdateScore(new_tree.get(), cur_tree_id);\n MultiplyScore(cur_tree_id, 1.0 / (iter_ + num_init_iteration_ + 1));\n } else {\n // only add default score one-time\n if (models_.size() < static_cast(num_tree_per_iteration_)) {\n double output = 0.0;\n if (!class_need_train_[cur_tree_id]) {\n if (objective_function_ != nullptr) {\n output = objective_function_->BoostFromScore(cur_tree_id);\n } else {\n output = init_scores_[cur_tree_id];\n }\n }\n new_tree->AsConstantTree(output, num_data_);\n MultiplyScore(cur_tree_id, (iter_ + num_init_iteration_));\n UpdateScore(new_tree.get(), cur_tree_id);\n MultiplyScore(cur_tree_id, 1.0 / (iter_ + num_init_iteration_ + 1));\n }\n }\n // add model\n models_.push_back(std::move(new_tree));\n }\n ++iter_;\n return false;\n }\n\n void RollbackOneIter() override {\n if (iter_ <= 0) {\n return;\n }\n int cur_iter = iter_ + num_init_iteration_ - 1;\n // reset score\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n auto curr_tree = cur_iter * num_tree_per_iteration_ + cur_tree_id;\n models_[curr_tree]->Shrinkage(-1.0);\n MultiplyScore(cur_tree_id, (iter_ + num_init_iteration_));\n train_score_updater_->AddScore(models_[curr_tree].get(), cur_tree_id);\n for (auto& score_updater : valid_score_updater_) {\n score_updater->AddScore(models_[curr_tree].get(), cur_tree_id);\n }\n MultiplyScore(cur_tree_id, 1.0f / (iter_ + num_init_iteration_ - 1));\n }\n // remove model\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n models_.pop_back();\n }\n --iter_;\n }\n\n void MultiplyScore(const int cur_tree_id, double val) {\n train_score_updater_->MultiplyScore(val, cur_tree_id);\n for (auto& score_updater : valid_score_updater_) {\n score_updater->MultiplyScore(val, cur_tree_id);\n }\n }\n\n void AddValidDataset(const Dataset* valid_data,\n const std::vector& valid_metrics) override {\n GBDT::AddValidDataset(valid_data, valid_metrics);\n if (iter_ + num_init_iteration_ > 0) {\n for (int cur_tree_id = 0; cur_tree_id < num_tree_per_iteration_; ++cur_tree_id) {\n valid_score_updater_.back()->MultiplyScore(1.0f / (iter_ + num_init_iteration_), cur_tree_id);\n }\n }\n }\n\n bool NeedAccuratePrediction() const override {\n // No early stopping for prediction\n return true;\n };\n\n private:\n std::vector tmp_grad_;\n std::vector tmp_hess_;\n std::vector init_scores_;\n};\n\n} // namespace LightGBM\n#endif // LIGHTGBM_SRC_BOOSTING_RF_HPP_\n" }, { "path": "src/boosting/sample_strategy.cpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#include \n\n#include \n\n#include \"goss.hpp\"\n#include \"bagging.hpp\"\n\nnamespace LightGBM {\n\nSampleStrategy* SampleStrategy::CreateSampleStrategy(\n const Config* config,\n const Dataset* train_data,\n const ObjectiveFunction* objective_function,\n int num_tree_per_iteration) {\n if (config->data_sample_strategy == std::string(\"goss\")) {\n return new GOSSStrategy(config, train_data, num_tree_per_iteration);\n } else {\n return new BaggingSampleStrategy(config, train_data, objective_function, num_tree_per_iteration);\n }\n}\n\n} // namespace LightGBM\n" }, { "path": "src/boosting/score_updater.hpp", "content": "/*!\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_BOOSTING_SCORE_UPDATER_HPP_\n#define LIGHTGBM_SRC_BOOSTING_SCORE_UPDATER_HPP_\n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\nnamespace LightGBM {\n/*!\n* \\brief Used to store and update score for data\n*/\nclass ScoreUpdater {\n public:\n /*!\n * \\brief Constructor, will pass a const pointer of dataset\n * \\param data This class will bind with this data set\n */\n ScoreUpdater(const Dataset* data, int num_tree_per_iteration) : data_(data) {\n num_data_ = data->num_data();\n int64_t total_size = static_cast(num_data_) * num_tree_per_iteration;\n score_.resize(total_size);\n // default start score is zero\n std::memset(score_.data(), 0, total_size * sizeof(double));\n has_init_score_ = false;\n const double* init_score = data->metadata().init_score();\n // if exists initial score, will start from it\n if (init_score != nullptr) {\n if ((data->metadata().num_init_score() % num_data_) != 0\n || (data->metadata().num_init_score() / num_data_) != num_tree_per_iteration) {\n Log::Fatal(\"Number of class for initial score error\");\n }\n has_init_score_ = true;\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (total_size >= 1024)\n for (int64_t i = 0; i < total_size; ++i) {\n score_[i] = init_score[i];\n }\n }\n }\n /*! \\brief Destructor */\n virtual ~ScoreUpdater() {\n }\n\n inline bool has_init_score() const { return has_init_score_; }\n\n virtual inline void AddScore(double val, int cur_tree_id) {\n Common::FunctionTimer fun_timer(\"ScoreUpdater::AddScore\", global_timer);\n const size_t offset = static_cast(num_data_) * cur_tree_id;\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data_ >= 1024)\n for (int i = 0; i < num_data_; ++i) {\n score_[offset + i] += val;\n }\n }\n\n virtual inline void MultiplyScore(double val, int cur_tree_id) {\n const size_t offset = static_cast(num_data_) * cur_tree_id;\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data_ >= 1024)\n for (int i = 0; i < num_data_; ++i) {\n score_[offset + i] *= val;\n }\n }\n /*!\n * \\brief Using tree model to get prediction number, then adding to scores for all data\n * Note: this function generally will be used on validation data too.\n * \\param tree Trained tree model\n * \\param cur_tree_id Current tree for multiclass training\n */\n virtual inline void AddScore(const Tree* tree, int cur_tree_id) {\n Common::FunctionTimer fun_timer(\"ScoreUpdater::AddScore\", global_timer);\n const size_t offset = static_cast(num_data_) * cur_tree_id;\n tree->AddPredictionToScore(data_, num_data_, score_.data() + offset);\n }\n /*!\n * \\brief Adding prediction score, only used for training data.\n * The training data is partitioned into tree leaves after training\n * Based on which We can get prediction quickly.\n * \\param tree_learner\n * \\param cur_tree_id Current tree for multiclass training\n */\n virtual inline void AddScore(const TreeLearner* tree_learner, const Tree* tree, int cur_tree_id) {\n Common::FunctionTimer fun_timer(\"ScoreUpdater::AddScore\", global_timer);\n const size_t offset = static_cast(num_data_) * cur_tree_id;\n tree_learner->AddPredictionToScore(tree, score_.data() + offset);\n }\n /*!\n * \\brief Using tree model to get prediction number, then adding to scores for parts of data\n * Used for prediction of training out-of-bag data\n * \\param tree Trained tree model\n * \\param data_indices Indices of data that will be processed\n * \\param data_cnt Number of data that will be processed\n * \\param cur_tree_id Current tree for multiclass training\n */\n virtual inline void AddScore(const Tree* tree, const data_size_t* data_indices,\n data_size_t data_cnt, int cur_tree_id) {\n Common::FunctionTimer fun_timer(\"ScoreUpdater::AddScore\", global_timer);\n const size_t offset = static_cast(num_data_) * cur_tree_id;\n tree->AddPredictionToScore(data_, data_indices, data_cnt, score_.data() + offset);\n }\n /*! \\brief Pointer of score */\n virtual inline const double* score() const { return score_.data(); }\n\n inline data_size_t num_data() const { return num_data_; }\n\n /*! \\brief Disable copy */\n ScoreUpdater& operator=(const ScoreUpdater&) = delete;\n /*! \\brief Disable copy */\n ScoreUpdater(const ScoreUpdater&) = delete;\n\n protected:\n /*! \\brief Number of total data */\n data_size_t num_data_;\n /*! \\brief Pointer of data set */\n const Dataset* data_;\n /*! \\brief Scores for data set */\n std::vector> score_;\n bool has_init_score_;\n};\n\n} // namespace LightGBM\n#endif // LIGHTGBM_SRC_BOOSTING_SCORE_UPDATER_HPP_\n" }, { "path": "src/c_api.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"application/predictor.hpp\"\n#include \n#include \n\nnamespace LightGBM {\n\ninline int LGBM_APIHandleException(const std::exception& ex) {\n LGBM_SetLastError(ex.what());\n return -1;\n}\ninline int LGBM_APIHandleException(const std::string& ex) {\n LGBM_SetLastError(ex.c_str());\n return -1;\n}\n\n#define API_BEGIN() try {\n#define API_END() } \\\ncatch(std::exception& ex) { return LGBM_APIHandleException(ex); } \\\ncatch(std::string& ex) { return LGBM_APIHandleException(ex); } \\\ncatch(...) { return LGBM_APIHandleException(\"unknown exception\"); } \\\nreturn 0;\n\n#define UNIQUE_LOCK(mtx) \\\nstd::unique_lock lock(mtx);\n\n#define SHARED_LOCK(mtx) \\\nyamc::shared_lock lock(&mtx);\n\nconst int PREDICTOR_TYPES = 4;\n\n// Single row predictor to abstract away caching logic\nclass SingleRowPredictorInner {\n public:\n PredictFunction predict_function;\n int64_t num_pred_in_one_row;\n\n SingleRowPredictorInner(int predict_type, Boosting* boosting, const Config& config, int start_iter, int num_iter) {\n bool is_predict_leaf = false;\n bool is_raw_score = false;\n bool predict_contrib = false;\n if (predict_type == C_API_PREDICT_LEAF_INDEX) {\n is_predict_leaf = true;\n } else if (predict_type == C_API_PREDICT_RAW_SCORE) {\n is_raw_score = true;\n } else if (predict_type == C_API_PREDICT_CONTRIB) {\n predict_contrib = true;\n }\n early_stop_ = config.pred_early_stop;\n early_stop_freq_ = config.pred_early_stop_freq;\n early_stop_margin_ = config.pred_early_stop_margin;\n iter_ = num_iter;\n predictor_.reset(new Predictor(boosting, start_iter, iter_, is_raw_score, is_predict_leaf, predict_contrib,\n early_stop_, early_stop_freq_, early_stop_margin_));\n num_pred_in_one_row = boosting->NumPredictOneRow(start_iter, iter_, is_predict_leaf, predict_contrib);\n predict_function = predictor_->GetPredictFunction();\n num_total_model_ = boosting->NumberOfTotalModel();\n }\n\n ~SingleRowPredictorInner() {}\n\n bool IsPredictorEqual(const Config& config, int iter, Boosting* boosting) {\n return early_stop_ == config.pred_early_stop &&\n early_stop_freq_ == config.pred_early_stop_freq &&\n early_stop_margin_ == config.pred_early_stop_margin &&\n iter_ == iter &&\n num_total_model_ == boosting->NumberOfTotalModel();\n }\n\n private:\n std::unique_ptr predictor_;\n bool early_stop_;\n int early_stop_freq_;\n double early_stop_margin_;\n int iter_;\n int num_total_model_;\n};\n\n/*!\n * \\brief Object to store resources meant for single-row Fast Predict methods.\n *\n * For legacy reasons this is called `FastConfig` in the public C API.\n *\n * Meant to be used by the *Fast* predict methods only.\n * It stores the configuration and prediction resources for reuse across predictions.\n */\nstruct SingleRowPredictor {\n public:\n SingleRowPredictor(yamc::alternate::shared_mutex *booster_mutex,\n const char *parameters,\n const int data_type,\n const int32_t num_cols,\n int predict_type,\n Boosting *boosting,\n int start_iter,\n int num_iter) : config(Config::Str2Map(parameters)), data_type(data_type), num_cols(num_cols), single_row_predictor_inner(predict_type, boosting, config, start_iter, num_iter), booster_mutex(booster_mutex) {\n if (!config.predict_disable_shape_check && num_cols != boosting->MaxFeatureIdx() + 1) {\n Log::Fatal(\"The number of features in data (%d) is not the same as it was in training data (%d).\\n\"\\\n \"You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.\", num_cols, boosting->MaxFeatureIdx() + 1);\n }\n }\n\n void Predict(std::function>(int row_idx)> get_row_fun,\n double* out_result, int64_t* out_len) const {\n UNIQUE_LOCK(single_row_predictor_mutex)\n yamc::shared_lock booster_shared_lock(booster_mutex);\n\n auto one_row = get_row_fun(0);\n single_row_predictor_inner.predict_function(one_row, out_result);\n\n *out_len = single_row_predictor_inner.num_pred_in_one_row;\n }\n\n public:\n Config config;\n const int data_type;\n const int32_t num_cols;\n\n private:\n SingleRowPredictorInner single_row_predictor_inner;\n\n // Prevent the booster from being modified while we have a predictor relying on it during prediction\n yamc::alternate::shared_mutex *booster_mutex;\n\n // If several threads try to predict at the same time using the same SingleRowPredictor\n // we want them to still provide correct values, so the mutex is necessary due to the shared\n // resources in the predictor.\n // However the recommended approach is to instantiate one SingleRowPredictor per thread,\n // to avoid contention here.\n mutable yamc::alternate::shared_mutex single_row_predictor_mutex;\n};\n\nclass Booster {\n public:\n explicit Booster(const char* filename) {\n boosting_.reset(Boosting::CreateBoosting(\"gbdt\", filename, std::string(\"cpu\"), 0));\n }\n\n Booster(const Dataset* train_data,\n const char* parameters) {\n auto param = Config::Str2Map(parameters);\n config_.Set(param);\n OMP_SET_NUM_THREADS(config_.num_threads);\n // create boosting\n if (config_.input_model.size() > 0) {\n Log::Warning(\"Continued train from model is not supported for c_api,\\n\"\n \"please use continued train with input score\");\n }\n\n boosting_.reset(Boosting::CreateBoosting(config_.boosting, nullptr, config_.device_type, config_.num_gpu));\n\n train_data_ = train_data;\n CreateObjectiveAndMetrics();\n // initialize the boosting\n if (config_.tree_learner == std::string(\"feature\")) {\n Log::Fatal(\"Do not support feature parallel in c api\");\n }\n if (Network::num_machines() == 1 && config_.tree_learner != std::string(\"serial\")) {\n Log::Warning(\"Only find one worker, will switch to serial tree learner\");\n config_.tree_learner = \"serial\";\n }\n boosting_->Init(&config_, train_data_, objective_fun_.get(),\n Common::ConstPtrInVectorWrapper(train_metric_));\n }\n\n void MergeFrom(const Booster* other) {\n UNIQUE_LOCK(mutex_)\n boosting_->MergeFrom(other->boosting_.get());\n }\n\n ~Booster() {\n }\n\n void CreateObjectiveAndMetrics() {\n // create objective function\n objective_fun_.reset(ObjectiveFunction::CreateObjectiveFunction(config_.objective,\n config_));\n if (objective_fun_ == nullptr) {\n Log::Info(\"Using self-defined objective function\");\n }\n // initialize the objective function\n if (objective_fun_ != nullptr) {\n objective_fun_->Init(train_data_->metadata(), train_data_->num_data());\n }\n\n // create training metric\n train_metric_.clear();\n for (auto metric_type : config_.metric) {\n auto metric = std::unique_ptr(\n Metric::CreateMetric(metric_type, config_));\n if (metric == nullptr) {\n continue;\n }\n metric->Init(train_data_->metadata(), train_data_->num_data());\n train_metric_.push_back(std::move(metric));\n }\n train_metric_.shrink_to_fit();\n }\n\n void ResetTrainingData(const Dataset* train_data) {\n if (train_data != train_data_) {\n UNIQUE_LOCK(mutex_)\n train_data_ = train_data;\n CreateObjectiveAndMetrics();\n // reset the boosting\n boosting_->ResetTrainingData(train_data_,\n objective_fun_.get(), Common::ConstPtrInVectorWrapper(train_metric_));\n }\n }\n\n static void CheckDatasetResetConfig(\n const Config& old_config,\n const std::unordered_map& new_param) {\n Config new_config;\n new_config.Set(new_param);\n if (new_param.count(\"data_random_seed\") &&\n new_config.data_random_seed != old_config.data_random_seed) {\n Log::Fatal(\"Cannot change data_random_seed after constructed Dataset handle.\");\n }\n if (new_param.count(\"max_bin\") &&\n new_config.max_bin != old_config.max_bin) {\n Log::Fatal(\"Cannot change max_bin after constructed Dataset handle.\");\n }\n if (new_param.count(\"max_bin_by_feature\") &&\n new_config.max_bin_by_feature != old_config.max_bin_by_feature) {\n Log::Fatal(\n \"Cannot change max_bin_by_feature after constructed Dataset handle.\");\n }\n if (new_param.count(\"bin_construct_sample_cnt\") &&\n new_config.bin_construct_sample_cnt !=\n old_config.bin_construct_sample_cnt) {\n Log::Fatal(\n \"Cannot change bin_construct_sample_cnt after constructed Dataset \"\n \"handle.\");\n }\n if (new_param.count(\"min_data_in_bin\") &&\n new_config.min_data_in_bin != old_config.min_data_in_bin) {\n Log::Fatal(\n \"Cannot change min_data_in_bin after constructed Dataset handle.\");\n }\n if (new_param.count(\"use_missing\") &&\n new_config.use_missing != old_config.use_missing) {\n Log::Fatal(\"Cannot change use_missing after constructed Dataset handle.\");\n }\n if (new_param.count(\"zero_as_missing\") &&\n new_config.zero_as_missing != old_config.zero_as_missing) {\n Log::Fatal(\n \"Cannot change zero_as_missing after constructed Dataset handle.\");\n }\n if (new_param.count(\"categorical_feature\") &&\n new_config.categorical_feature != old_config.categorical_feature) {\n Log::Fatal(\n \"Cannot change categorical_feature after constructed Dataset \"\n \"handle.\");\n }\n if (new_param.count(\"feature_pre_filter\") &&\n new_config.feature_pre_filter != old_config.feature_pre_filter) {\n Log::Fatal(\n \"Cannot change feature_pre_filter after constructed Dataset handle.\");\n }\n if (new_param.count(\"is_enable_sparse\") &&\n new_config.is_enable_sparse != old_config.is_enable_sparse) {\n Log::Fatal(\n \"Cannot change is_enable_sparse after constructed Dataset handle.\");\n }\n if (new_param.count(\"pre_partition\") &&\n new_config.pre_partition != old_config.pre_partition) {\n Log::Fatal(\n \"Cannot change pre_partition after constructed Dataset handle.\");\n }\n if (new_param.count(\"enable_bundle\") &&\n new_config.enable_bundle != old_config.enable_bundle) {\n Log::Fatal(\n \"Cannot change enable_bundle after constructed Dataset handle.\");\n }\n if (new_param.count(\"header\") && new_config.header != old_config.header) {\n Log::Fatal(\"Cannot change header after constructed Dataset handle.\");\n }\n if (new_param.count(\"two_round\") &&\n new_config.two_round != old_config.two_round) {\n Log::Fatal(\"Cannot change two_round after constructed Dataset handle.\");\n }\n if (new_param.count(\"label_column\") &&\n new_config.label_column != old_config.label_column) {\n Log::Fatal(\n \"Cannot change label_column after constructed Dataset handle.\");\n }\n if (new_param.count(\"weight_column\") &&\n new_config.weight_column != old_config.weight_column) {\n Log::Fatal(\n \"Cannot change weight_column after constructed Dataset handle.\");\n }\n if (new_param.count(\"group_column\") &&\n new_config.group_column != old_config.group_column) {\n Log::Fatal(\n \"Cannot change group_column after constructed Dataset handle.\");\n }\n if (new_param.count(\"ignore_column\") &&\n new_config.ignore_column != old_config.ignore_column) {\n Log::Fatal(\n \"Cannot change ignore_column after constructed Dataset handle.\");\n }\n if (new_param.count(\"forcedbins_filename\")) {\n Log::Fatal(\"Cannot change forced bins after constructed Dataset handle.\");\n }\n if (new_param.count(\"min_data_in_leaf\") &&\n new_config.min_data_in_leaf < old_config.min_data_in_leaf &&\n old_config.feature_pre_filter) {\n Log::Fatal(\n \"Reducing `min_data_in_leaf` with `feature_pre_filter=true` may \"\n \"cause unexpected behaviour \"\n \"for features that were pre-filtered by the larger \"\n \"`min_data_in_leaf`.\\n\"\n \"You need to set `feature_pre_filter=false` to dynamically change \"\n \"the `min_data_in_leaf`.\");\n }\n if (new_param.count(\"linear_tree\") && new_config.linear_tree != old_config.linear_tree) {\n Log::Fatal(\"Cannot change linear_tree after constructed Dataset handle.\");\n }\n if (new_param.count(\"precise_float_parser\") &&\n new_config.precise_float_parser != old_config.precise_float_parser) {\n Log::Fatal(\"Cannot change precise_float_parser after constructed Dataset handle.\");\n }\n }\n\n void ResetConfig(const char* parameters) {\n UNIQUE_LOCK(mutex_)\n auto param = Config::Str2Map(parameters);\n Config new_config;\n new_config.Set(param);\n if (param.count(\"num_class\") && new_config.num_class != config_.num_class) {\n Log::Fatal(\"Cannot change num_class during training\");\n }\n if (param.count(\"boosting\") && new_config.boosting != config_.boosting) {\n Log::Fatal(\"Cannot change boosting during training\");\n }\n if (param.count(\"metric\") && new_config.metric != config_.metric) {\n Log::Fatal(\"Cannot change metric during training\");\n }\n CheckDatasetResetConfig(config_, param);\n\n config_.Set(param);\n\n OMP_SET_NUM_THREADS(config_.num_threads);\n\n if (param.count(\"objective\")) {\n // create objective function\n objective_fun_.reset(ObjectiveFunction::CreateObjectiveFunction(config_.objective,\n config_));\n if (objective_fun_ == nullptr) {\n Log::Info(\"Using self-defined objective function\");\n }\n // initialize the objective function\n if (objective_fun_ != nullptr) {\n objective_fun_->Init(train_data_->metadata(), train_data_->num_data());\n }\n boosting_->ResetTrainingData(train_data_,\n objective_fun_.get(), Common::ConstPtrInVectorWrapper(train_metric_));\n }\n\n boosting_->ResetConfig(&config_);\n }\n\n void AddValidData(const Dataset* valid_data) {\n UNIQUE_LOCK(mutex_)\n valid_metrics_.emplace_back();\n for (auto metric_type : config_.metric) {\n auto metric = std::unique_ptr(Metric::CreateMetric(metric_type, config_));\n if (metric == nullptr) {\n continue;\n }\n metric->Init(valid_data->metadata(), valid_data->num_data());\n valid_metrics_.back().push_back(std::move(metric));\n }\n valid_metrics_.back().shrink_to_fit();\n boosting_->AddValidDataset(valid_data,\n Common::ConstPtrInVectorWrapper(valid_metrics_.back()));\n }\n\n bool TrainOneIter() {\n UNIQUE_LOCK(mutex_)\n return boosting_->TrainOneIter(nullptr, nullptr);\n }\n\n void Refit(const int32_t* leaf_preds, int32_t nrow, int32_t ncol) {\n UNIQUE_LOCK(mutex_)\n boosting_->RefitTree(leaf_preds, nrow, ncol);\n }\n\n bool TrainOneIter(const score_t* gradients, const score_t* hessians) {\n UNIQUE_LOCK(mutex_)\n return boosting_->TrainOneIter(gradients, hessians);\n }\n\n void RollbackOneIter() {\n UNIQUE_LOCK(mutex_)\n boosting_->RollbackOneIter();\n }\n\n void SetSingleRowPredictorInner(int start_iteration, int num_iteration, int predict_type, const Config& config) {\n UNIQUE_LOCK(mutex_)\n if (single_row_predictor_[predict_type].get() == nullptr ||\n !single_row_predictor_[predict_type]->IsPredictorEqual(config, num_iteration, boosting_.get())) {\n single_row_predictor_[predict_type].reset(new SingleRowPredictorInner(predict_type, boosting_.get(),\n config, start_iteration, num_iteration));\n }\n }\n\n std::unique_ptr InitSingleRowPredictor(int predict_type, int start_iteration, int num_iteration, int data_type, int32_t num_cols, const char *parameters) {\n // Workaround https://github.com/lightgbm-org/LightGBM/issues/6142 by locking here\n // This is only a workaround because if predictors are initialized differently it may still behave incorrectly,\n // and because multiple racing Predictor initializations through LGBM_BoosterPredictForMat suffers from that same issue of Predictor init writing things in the booster.\n // Once #6142 is fixed (predictor doesn't write in the Booster as should have been the case since 1c35c3b9ede9adab8ccc5fd7b4b2b6af188a79f0), this line can be removed.\n UNIQUE_LOCK(mutex_)\n\n return std::unique_ptr(new SingleRowPredictor(\n &mutex_, parameters, data_type, num_cols, predict_type, boosting_.get(), start_iteration, num_iteration));\n }\n\n void PredictSingleRow(int predict_type, int ncol,\n std::function>(int row_idx)> get_row_fun,\n const Config& config,\n double* out_result, int64_t* out_len) const {\n if (!config.predict_disable_shape_check && ncol != boosting_->MaxFeatureIdx() + 1) {\n Log::Fatal(\"The number of features in data (%d) is not the same as it was in training data (%d).\\n\"\\\n \"You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.\", ncol, boosting_->MaxFeatureIdx() + 1);\n }\n UNIQUE_LOCK(mutex_)\n const auto& single_row_predictor = single_row_predictor_[predict_type];\n auto one_row = get_row_fun(0);\n auto pred_wrt_ptr = out_result;\n single_row_predictor->predict_function(one_row, pred_wrt_ptr);\n\n *out_len = single_row_predictor->num_pred_in_one_row;\n }\n\n std::shared_ptr CreatePredictor(int start_iteration, int num_iteration, int predict_type, int ncol, const Config& config) const {\n if (!config.predict_disable_shape_check && ncol != boosting_->MaxFeatureIdx() + 1) {\n Log::Fatal(\"The number of features in data (%d) is not the same as it was in training data (%d).\\n\" \\\n \"You can set ``predict_disable_shape_check=true`` to discard this error, but please be aware what you are doing.\", ncol, boosting_->MaxFeatureIdx() + 1);\n }\n bool is_predict_leaf = false;\n bool is_raw_score = false;\n bool predict_contrib = false;\n if (predict_type == C_API_PREDICT_LEAF_INDEX) {\n is_predict_leaf = true;\n } else if (predict_type == C_API_PREDICT_RAW_SCORE) {\n is_raw_score = true;\n } else if (predict_type == C_API_PREDICT_CONTRIB) {\n predict_contrib = true;\n } else {\n is_raw_score = false;\n }\n\n return std::make_shared(boosting_.get(), start_iteration, num_iteration, is_raw_score, is_predict_leaf, predict_contrib,\n config.pred_early_stop, config.pred_early_stop_freq, config.pred_early_stop_margin);\n }\n\n void Predict(int start_iteration, int num_iteration, int predict_type, int nrow, int ncol,\n std::function>(int row_idx)> get_row_fun,\n const Config& config,\n double* out_result, int64_t* out_len) const {\n SHARED_LOCK(mutex_);\n auto predictor = CreatePredictor(start_iteration, num_iteration, predict_type, ncol, config);\n bool is_predict_leaf = false;\n bool predict_contrib = false;\n if (predict_type == C_API_PREDICT_LEAF_INDEX) {\n is_predict_leaf = true;\n } else if (predict_type == C_API_PREDICT_CONTRIB) {\n predict_contrib = true;\n }\n int64_t num_pred_in_one_row = boosting_->NumPredictOneRow(start_iteration, num_iteration, is_predict_leaf, predict_contrib);\n auto pred_fun = predictor->GetPredictFunction();\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = 0; i < nrow; ++i) {\n OMP_LOOP_EX_BEGIN();\n auto one_row = get_row_fun(i);\n auto pred_wrt_ptr = out_result + static_cast(num_pred_in_one_row) * i;\n pred_fun(one_row, pred_wrt_ptr);\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n *out_len = num_pred_in_one_row * nrow;\n }\n\n void PredictSparse(int start_iteration, int num_iteration, int predict_type, int64_t nrow, int ncol,\n std::function>(int64_t row_idx)> get_row_fun,\n const Config& config, int64_t* out_elements_size,\n std::vector>>* agg_ptr,\n int32_t** out_indices, void** out_data, int data_type,\n bool* is_data_float32_ptr, int num_matrices) const {\n auto predictor = CreatePredictor(start_iteration, num_iteration, predict_type, ncol, config);\n auto pred_sparse_fun = predictor->GetPredictSparseFunction();\n std::vector>>& agg = *agg_ptr;\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int64_t i = 0; i < nrow; ++i) {\n OMP_LOOP_EX_BEGIN();\n auto one_row = get_row_fun(i);\n agg[i] = std::vector>(num_matrices);\n pred_sparse_fun(one_row, &agg[i]);\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n // calculate the nonzero data and indices size\n int64_t elements_size = 0;\n for (int64_t i = 0; i < static_cast(agg.size()); ++i) {\n auto row_vector = agg[i];\n for (int j = 0; j < static_cast(row_vector.size()); ++j) {\n elements_size += static_cast(row_vector[j].size());\n }\n }\n *out_elements_size = elements_size;\n *is_data_float32_ptr = false;\n // allocate data and indices arrays\n if (data_type == C_API_DTYPE_FLOAT32) {\n *out_data = new float[elements_size];\n *is_data_float32_ptr = true;\n } else if (data_type == C_API_DTYPE_FLOAT64) {\n *out_data = new double[elements_size];\n } else {\n Log::Fatal(\"Unknown data type in PredictSparse\");\n return;\n }\n *out_indices = new int32_t[elements_size];\n }\n\n void PredictSparseCSR(int start_iteration, int num_iteration, int predict_type, int64_t nrow, int ncol,\n std::function>(int64_t row_idx)> get_row_fun,\n const Config& config,\n int64_t* out_len, void** out_indptr, int indptr_type,\n int32_t** out_indices, void** out_data, int data_type) const {\n SHARED_LOCK(mutex_);\n // Get the number of trees per iteration (for multiclass scenario we output multiple sparse matrices)\n int num_matrices = boosting_->NumModelPerIteration();\n bool is_indptr_int32 = false;\n bool is_data_float32 = false;\n int64_t indptr_size = (nrow + 1) * num_matrices;\n if (indptr_type == C_API_DTYPE_INT32) {\n *out_indptr = new int32_t[indptr_size];\n is_indptr_int32 = true;\n } else if (indptr_type == C_API_DTYPE_INT64) {\n *out_indptr = new int64_t[indptr_size];\n } else {\n Log::Fatal(\"Unknown indptr type in PredictSparseCSR\");\n return;\n }\n // aggregated per row feature contribution results\n std::vector>> agg(nrow);\n int64_t elements_size = 0;\n PredictSparse(start_iteration, num_iteration, predict_type, nrow, ncol, get_row_fun, config, &elements_size, &agg,\n out_indices, out_data, data_type, &is_data_float32, num_matrices);\n std::vector row_sizes(num_matrices * nrow);\n std::vector row_matrix_offsets(num_matrices * nrow);\n std::vector matrix_offsets(num_matrices);\n int64_t row_vector_cnt = 0;\n for (int m = 0; m < num_matrices; ++m) {\n for (int64_t i = 0; i < static_cast(agg.size()); ++i) {\n auto row_vector = agg[i];\n auto row_vector_size = row_vector[m].size();\n // keep track of the row_vector sizes for parallelization\n row_sizes[row_vector_cnt] = static_cast(row_vector_size);\n if (i == 0) {\n row_matrix_offsets[row_vector_cnt] = 0;\n } else {\n row_matrix_offsets[row_vector_cnt] = static_cast(row_sizes[row_vector_cnt - 1] + row_matrix_offsets[row_vector_cnt - 1]);\n }\n row_vector_cnt++;\n }\n if (m == 0) {\n matrix_offsets[m] = 0;\n }\n if (m + 1 < num_matrices) {\n matrix_offsets[m + 1] = static_cast(matrix_offsets[m] + row_matrix_offsets[row_vector_cnt - 1] + row_sizes[row_vector_cnt - 1]);\n }\n }\n // copy vector results to output for each row\n int64_t indptr_index = 0;\n for (int m = 0; m < num_matrices; ++m) {\n if (is_indptr_int32) {\n (reinterpret_cast(*out_indptr))[indptr_index] = 0;\n } else {\n (reinterpret_cast(*out_indptr))[indptr_index] = 0;\n }\n indptr_index++;\n int64_t matrix_start_index = m * static_cast(agg.size());\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int64_t i = 0; i < static_cast(agg.size()); ++i) {\n OMP_LOOP_EX_BEGIN();\n auto row_vector = agg[i];\n int64_t row_start_index = matrix_start_index + i;\n int64_t element_index = row_matrix_offsets[row_start_index] + matrix_offsets[m];\n int64_t indptr_loop_index = indptr_index + i;\n for (auto it = row_vector[m].begin(); it != row_vector[m].end(); ++it) {\n (*out_indices)[element_index] = it->first;\n if (is_data_float32) {\n (reinterpret_cast(*out_data))[element_index] = static_cast(it->second);\n } else {\n (reinterpret_cast(*out_data))[element_index] = it->second;\n }\n element_index++;\n }\n int64_t indptr_value = row_matrix_offsets[row_start_index] + row_sizes[row_start_index];\n if (is_indptr_int32) {\n (reinterpret_cast(*out_indptr))[indptr_loop_index] = static_cast(indptr_value);\n } else {\n (reinterpret_cast(*out_indptr))[indptr_loop_index] = indptr_value;\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n indptr_index += static_cast(agg.size());\n }\n out_len[0] = elements_size;\n out_len[1] = indptr_size;\n }\n\n void PredictSparseCSC(int start_iteration, int num_iteration, int predict_type, int64_t nrow, int ncol,\n std::function>(int64_t row_idx)> get_row_fun,\n const Config& config,\n int64_t* out_len, void** out_col_ptr, int col_ptr_type,\n int32_t** out_indices, void** out_data, int data_type) const {\n SHARED_LOCK(mutex_);\n // Get the number of trees per iteration (for multiclass scenario we output multiple sparse matrices)\n int num_matrices = boosting_->NumModelPerIteration();\n auto predictor = CreatePredictor(start_iteration, num_iteration, predict_type, ncol, config);\n auto pred_sparse_fun = predictor->GetPredictSparseFunction();\n bool is_col_ptr_int32 = false;\n bool is_data_float32 = false;\n int num_output_cols = ncol + 1;\n int col_ptr_size = (num_output_cols + 1) * num_matrices;\n if (col_ptr_type == C_API_DTYPE_INT32) {\n *out_col_ptr = new int32_t[col_ptr_size];\n is_col_ptr_int32 = true;\n } else if (col_ptr_type == C_API_DTYPE_INT64) {\n *out_col_ptr = new int64_t[col_ptr_size];\n } else {\n Log::Fatal(\"Unknown col_ptr type in PredictSparseCSC\");\n return;\n }\n // aggregated per row feature contribution results\n std::vector>> agg(nrow);\n int64_t elements_size = 0;\n PredictSparse(start_iteration, num_iteration, predict_type, nrow, ncol, get_row_fun, config, &elements_size, &agg,\n out_indices, out_data, data_type, &is_data_float32, num_matrices);\n // calculate number of elements per column to construct\n // the CSC matrix with random access\n std::vector> column_sizes(num_matrices);\n for (int m = 0; m < num_matrices; ++m) {\n column_sizes[m] = std::vector(num_output_cols, 0);\n for (int64_t i = 0; i < static_cast(agg.size()); ++i) {\n auto row_vector = agg[i];\n for (auto it = row_vector[m].begin(); it != row_vector[m].end(); ++it) {\n column_sizes[m][it->first] += 1;\n }\n }\n }\n // keep track of column counts\n std::vector> column_counts(num_matrices);\n // keep track of beginning index for each column\n std::vector> column_start_indices(num_matrices);\n // keep track of beginning index for each matrix\n std::vector matrix_start_indices(num_matrices, 0);\n int col_ptr_index = 0;\n for (int m = 0; m < num_matrices; ++m) {\n int64_t col_ptr_value = 0;\n column_start_indices[m] = std::vector(num_output_cols, 0);\n column_counts[m] = std::vector(num_output_cols, 0);\n if (is_col_ptr_int32) {\n (reinterpret_cast(*out_col_ptr))[col_ptr_index] = static_cast(col_ptr_value);\n } else {\n (reinterpret_cast(*out_col_ptr))[col_ptr_index] = col_ptr_value;\n }\n col_ptr_index++;\n for (int64_t i = 1; i < static_cast(column_sizes[m].size()); ++i) {\n column_start_indices[m][i] = column_sizes[m][i - 1] + column_start_indices[m][i - 1];\n if (is_col_ptr_int32) {\n (reinterpret_cast(*out_col_ptr))[col_ptr_index] = static_cast(column_start_indices[m][i]);\n } else {\n (reinterpret_cast(*out_col_ptr))[col_ptr_index] = column_start_indices[m][i];\n }\n col_ptr_index++;\n }\n int64_t last_elem_index = static_cast(column_sizes[m].size()) - 1;\n int64_t last_column_start_index = column_start_indices[m][last_elem_index];\n int64_t last_column_size = column_sizes[m][last_elem_index];\n if (is_col_ptr_int32) {\n (reinterpret_cast(*out_col_ptr))[col_ptr_index] = static_cast(last_column_start_index + last_column_size);\n } else {\n (reinterpret_cast(*out_col_ptr))[col_ptr_index] = last_column_start_index + last_column_size;\n }\n if (m + 1 < num_matrices) {\n matrix_start_indices[m + 1] = matrix_start_indices[m] + last_column_start_index + last_column_size;\n }\n col_ptr_index++;\n }\n // Note: we parallelize across matrices instead of rows because of the column_counts[m][col_idx] increment inside the loop\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int m = 0; m < num_matrices; ++m) {\n OMP_LOOP_EX_BEGIN();\n for (int64_t i = 0; i < static_cast(agg.size()); ++i) {\n auto row_vector = agg[i];\n for (auto it = row_vector[m].begin(); it != row_vector[m].end(); ++it) {\n int64_t col_idx = it->first;\n int64_t element_index = column_start_indices[m][col_idx] +\n matrix_start_indices[m] +\n column_counts[m][col_idx];\n // store the row index\n (*out_indices)[element_index] = static_cast(i);\n // update column count\n column_counts[m][col_idx]++;\n if (is_data_float32) {\n (reinterpret_cast(*out_data))[element_index] = static_cast(it->second);\n } else {\n (reinterpret_cast(*out_data))[element_index] = it->second;\n }\n }\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n out_len[0] = elements_size;\n out_len[1] = col_ptr_size;\n }\n\n void Predict(int start_iteration, int num_iteration, int predict_type, const char* data_filename,\n int data_has_header, const Config& config,\n const char* result_filename) const {\n SHARED_LOCK(mutex_)\n bool is_predict_leaf = false;\n bool is_raw_score = false;\n bool predict_contrib = false;\n if (predict_type == C_API_PREDICT_LEAF_INDEX) {\n is_predict_leaf = true;\n } else if (predict_type == C_API_PREDICT_RAW_SCORE) {\n is_raw_score = true;\n } else if (predict_type == C_API_PREDICT_CONTRIB) {\n predict_contrib = true;\n } else {\n is_raw_score = false;\n }\n Predictor predictor(boosting_.get(), start_iteration, num_iteration, is_raw_score, is_predict_leaf, predict_contrib,\n config.pred_early_stop, config.pred_early_stop_freq, config.pred_early_stop_margin);\n bool bool_data_has_header = data_has_header > 0 ? true : false;\n predictor.Predict(data_filename, result_filename, bool_data_has_header, config.predict_disable_shape_check,\n config.precise_float_parser);\n }\n\n void GetPredictAt(int data_idx, double* out_result, int64_t* out_len) const {\n boosting_->GetPredictAt(data_idx, out_result, out_len);\n }\n\n void SaveModelToFile(int start_iteration, int num_iteration, int feature_importance_type, const char* filename) const {\n boosting_->SaveModelToFile(start_iteration, num_iteration, feature_importance_type, filename);\n }\n\n void LoadModelFromString(const char* model_str) {\n size_t len = std::strlen(model_str);\n boosting_->LoadModelFromString(model_str, len);\n }\n\n std::string SaveModelToString(int start_iteration, int num_iteration,\n int feature_importance_type) const {\n return boosting_->SaveModelToString(start_iteration,\n num_iteration, feature_importance_type);\n }\n\n std::string DumpModel(int start_iteration, int num_iteration,\n int feature_importance_type) const {\n return boosting_->DumpModel(start_iteration, num_iteration,\n feature_importance_type);\n }\n\n std::vector FeatureImportance(int num_iteration, int importance_type) const {\n return boosting_->FeatureImportance(num_iteration, importance_type);\n }\n\n double UpperBoundValue() const {\n SHARED_LOCK(mutex_)\n return boosting_->GetUpperBoundValue();\n }\n\n double LowerBoundValue() const {\n SHARED_LOCK(mutex_)\n return boosting_->GetLowerBoundValue();\n }\n\n double GetLeafValue(int tree_idx, int leaf_idx) const {\n SHARED_LOCK(mutex_)\n return dynamic_cast(boosting_.get())->GetLeafValue(tree_idx, leaf_idx);\n }\n\n void SetLeafValue(int tree_idx, int leaf_idx, double val) {\n UNIQUE_LOCK(mutex_)\n dynamic_cast(boosting_.get())->SetLeafValue(tree_idx, leaf_idx, val);\n }\n\n void ShuffleModels(int start_iter, int end_iter) {\n UNIQUE_LOCK(mutex_)\n boosting_->ShuffleModels(start_iter, end_iter);\n }\n\n int GetEvalCounts() const {\n SHARED_LOCK(mutex_)\n int ret = 0;\n for (const auto& metric : train_metric_) {\n ret += static_cast(metric->GetName().size());\n }\n return ret;\n }\n\n int GetEvalNames(char** out_strs, const int len, const size_t buffer_len, size_t *out_buffer_len) const {\n SHARED_LOCK(mutex_)\n *out_buffer_len = 0;\n int idx = 0;\n for (const auto& metric : train_metric_) {\n for (const auto& name : metric->GetName()) {\n if (idx < len) {\n std::memcpy(out_strs[idx], name.c_str(), std::min(name.size() + 1, buffer_len));\n out_strs[idx][buffer_len - 1] = '\\0';\n }\n *out_buffer_len = std::max(name.size() + 1, *out_buffer_len);\n ++idx;\n }\n }\n return idx;\n }\n\n int GetFeatureNames(char** out_strs, const int len, const size_t buffer_len, size_t *out_buffer_len) const {\n SHARED_LOCK(mutex_)\n *out_buffer_len = 0;\n int idx = 0;\n for (const auto& name : boosting_->FeatureNames()) {\n if (idx < len) {\n std::memcpy(out_strs[idx], name.c_str(), std::min(name.size() + 1, buffer_len));\n out_strs[idx][buffer_len - 1] = '\\0';\n }\n *out_buffer_len = std::max(name.size() + 1, *out_buffer_len);\n ++idx;\n }\n return idx;\n }\n\n const Boosting* GetBoosting() const { return boosting_.get(); }\n\n private:\n const Dataset* train_data_;\n std::unique_ptr boosting_;\n std::unique_ptr single_row_predictor_[PREDICTOR_TYPES];\n\n /*! \\brief All configs */\n Config config_;\n /*! \\brief Metric for training data */\n std::vector> train_metric_;\n /*! \\brief Metrics for validation data */\n std::vector>> valid_metrics_;\n /*! \\brief Training objective function */\n std::unique_ptr objective_fun_;\n /*! \\brief mutex for threading safe call */\n mutable yamc::alternate::shared_mutex mutex_;\n};\n\n} // namespace LightGBM\n\n// explicitly declare symbols from LightGBM namespace\nusing LightGBM::AllgatherFunction;\nusing LightGBM::ArrowChunkedArray;\nusing LightGBM::ArrowTable;\nusing LightGBM::Booster;\nusing LightGBM::Common::CheckElementsIntervalClosed;\nusing LightGBM::Common::RemoveQuotationSymbol;\nusing LightGBM::Common::Vector2Ptr;\nusing LightGBM::Common::VectorSize;\nusing LightGBM::Config;\nusing LightGBM::data_size_t;\nusing LightGBM::Dataset;\nusing LightGBM::DatasetLoader;\nusing LightGBM::kZeroThreshold;\nusing LightGBM::LGBM_APIHandleException;\nusing LightGBM::Log;\nusing LightGBM::Network;\nusing LightGBM::Random;\nusing LightGBM::ReduceScatterFunction;\nusing LightGBM::SingleRowPredictor;\n\n// some help functions used to convert data\n\nstd::function(int row_idx)>\nRowFunctionFromDenseMatrix(const void* data, int num_row, int num_col, int data_type, int is_row_major);\n\nstd::function>(int row_idx)>\nRowPairFunctionFromDenseMatrix(const void* data, int num_row, int num_col, int data_type, int is_row_major);\n\nstd::function>(int row_idx)>\nRowPairFunctionFromDenseRows(const void** data, int num_col, int data_type);\n\ntemplate\nstd::function>(T idx)>\nRowFunctionFromCSR(const void* indptr, int indptr_type, const int32_t* indices,\n const void* data, int data_type, int64_t nindptr, int64_t nelem);\n\n// Row iterator of on column for CSC matrix\nclass CSC_RowIterator {\n public:\n CSC_RowIterator(const void* col_ptr, int col_ptr_type, const int32_t* indices,\n const void* data, int data_type, int64_t ncol_ptr, int64_t nelem, int col_idx);\n ~CSC_RowIterator() {}\n // return value at idx, only can access by ascent order\n double Get(int idx);\n // return next non-zero pair, if index < 0, means no more data\n std::pair NextNonZero();\n\n private:\n int nonzero_idx_ = 0;\n int cur_idx_ = -1;\n double cur_val_ = 0.0f;\n bool is_end_ = false;\n std::function(int idx)> iter_fun_;\n};\n\n// start of c_api functions\n\nconst char* LGBM_GetLastError() {\n return LastErrorMsg();\n}\n\nint LGBM_DumpParamAliases(int64_t buffer_len,\n int64_t* out_len,\n char* out_str) {\n API_BEGIN();\n std::string aliases = Config::DumpAliases();\n *out_len = static_cast(aliases.size()) + 1;\n if (*out_len <= buffer_len) {\n std::memcpy(out_str, aliases.c_str(), *out_len);\n }\n API_END();\n}\n\nint LGBM_RegisterLogCallback(void (*callback)(const char*)) {\n API_BEGIN();\n Log::ResetCallBack(callback);\n API_END();\n}\n\nstatic inline int SampleCount(int32_t total_nrow, const Config& config) {\n return static_cast(total_nrow < config.bin_construct_sample_cnt ? total_nrow : config.bin_construct_sample_cnt);\n}\n\nstatic inline std::vector CreateSampleIndices(int32_t total_nrow, const Config& config) {\n Random rand(config.data_random_seed);\n int sample_cnt = SampleCount(total_nrow, config);\n return rand.Sample(total_nrow, sample_cnt);\n}\n\nint LGBM_GetSampleCount(int32_t num_total_row,\n const char* parameters,\n int* out) {\n API_BEGIN();\n if (out == nullptr) {\n Log::Fatal(\"LGBM_GetSampleCount output is nullptr\");\n }\n auto param = Config::Str2Map(parameters);\n Config config;\n config.Set(param);\n\n *out = SampleCount(num_total_row, config);\n API_END();\n}\n\nint LGBM_SampleIndices(int32_t num_total_row,\n const char* parameters,\n void* out,\n int32_t* out_len) {\n // This API is to keep python binding's behavior the same with C++ implementation.\n // Sample count, random seed etc. should be provided in parameters.\n API_BEGIN();\n if (out == nullptr) {\n Log::Fatal(\"LGBM_SampleIndices output is nullptr\");\n }\n auto param = Config::Str2Map(parameters);\n Config config;\n config.Set(param);\n\n auto sample_indices = CreateSampleIndices(num_total_row, config);\n memcpy(out, sample_indices.data(), sizeof(int32_t) * sample_indices.size());\n *out_len = static_cast(sample_indices.size());\n API_END();\n}\n\nint LGBM_ByteBufferGetAt(ByteBufferHandle handle, int32_t index, uint8_t* out_val) {\n API_BEGIN();\n LightGBM::ByteBuffer* byteBuffer = reinterpret_cast(handle);\n *out_val = byteBuffer->GetAt(index);\n API_END();\n}\n\nint LGBM_ByteBufferFree(ByteBufferHandle handle) {\n API_BEGIN();\n delete reinterpret_cast(handle);\n API_END();\n}\n\nint LGBM_DatasetCreateFromFile(const char* filename,\n const char* parameters,\n const DatasetHandle reference,\n DatasetHandle* out) {\n API_BEGIN();\n auto param = Config::Str2Map(parameters);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n DatasetLoader loader(config, nullptr, 1, filename);\n if (reference == nullptr) {\n if (Network::num_machines() == 1) {\n *out = loader.LoadFromFile(filename);\n } else {\n *out = loader.LoadFromFile(filename, Network::rank(), Network::num_machines());\n }\n } else {\n *out = loader.LoadFromFileAlignWithOtherDataset(filename,\n reinterpret_cast(reference));\n }\n API_END();\n}\n\nint LGBM_DatasetCreateFromSampledColumn(double** sample_data,\n int** sample_indices,\n int32_t ncol,\n const int* num_per_col,\n int32_t num_sample_row,\n int32_t num_local_row,\n int64_t num_dist_row,\n const char* parameters,\n DatasetHandle* out) {\n API_BEGIN();\n auto param = Config::Str2Map(parameters);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n DatasetLoader loader(config, nullptr, 1, nullptr);\n *out = loader.ConstructFromSampleData(sample_data,\n sample_indices,\n ncol,\n num_per_col,\n num_sample_row,\n static_cast(num_local_row),\n num_dist_row);\n API_END();\n}\n\nint LGBM_DatasetCreateByReference(const DatasetHandle reference,\n int64_t num_total_row,\n DatasetHandle* out) {\n API_BEGIN();\n std::unique_ptr ret;\n data_size_t nrows = static_cast(num_total_row);\n ret.reset(new Dataset(nrows));\n const Dataset* reference_dataset = reinterpret_cast(reference);\n ret->CreateValid(reference_dataset);\n ret->InitByReference(nrows, reference_dataset);\n *out = ret.release();\n API_END();\n}\n\nint LGBM_DatasetCreateFromSerializedReference(const void* ref_buffer,\n int32_t ref_buffer_size,\n int64_t num_row,\n int32_t num_classes,\n const char* parameters,\n DatasetHandle* out) {\n API_BEGIN();\n auto param = Config::Str2Map(parameters);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n DatasetLoader loader(config, nullptr, 1, nullptr);\n *out = loader.LoadFromSerializedReference(static_cast(ref_buffer),\n static_cast(ref_buffer_size),\n static_cast(num_row),\n num_classes);\n API_END();\n}\n\nint LGBM_DatasetInitStreaming(DatasetHandle dataset,\n int32_t has_weights,\n int32_t has_init_scores,\n int32_t has_queries,\n int32_t nclasses,\n int32_t nthreads,\n int32_t omp_max_threads) {\n API_BEGIN();\n auto p_dataset = reinterpret_cast(dataset);\n auto num_data = p_dataset->num_data();\n p_dataset->InitStreaming(num_data, has_weights, has_init_scores, has_queries, nclasses, nthreads, omp_max_threads);\n p_dataset->set_wait_for_manual_finish(true);\n API_END();\n}\n\nint LGBM_DatasetPushRows(DatasetHandle dataset,\n const void* data,\n int data_type,\n int32_t nrow,\n int32_t ncol,\n int32_t start_row) {\n API_BEGIN();\n auto p_dataset = reinterpret_cast(dataset);\n auto get_row_fun = RowFunctionFromDenseMatrix(data, nrow, ncol, data_type, 1);\n if (p_dataset->has_raw()) {\n p_dataset->ResizeRaw(p_dataset->num_numeric_features() + nrow);\n }\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = 0; i < nrow; ++i) {\n OMP_LOOP_EX_BEGIN();\n const int tid = omp_get_thread_num();\n auto one_row = get_row_fun(i);\n p_dataset->PushOneRow(tid, start_row + i, one_row);\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n if (!p_dataset->wait_for_manual_finish() && (start_row + nrow == p_dataset->num_data())) {\n p_dataset->FinishLoad();\n }\n API_END();\n}\n\nint LGBM_DatasetPushRowsWithMetadata(DatasetHandle dataset,\n const void* data,\n int data_type,\n int32_t nrow,\n int32_t ncol,\n int32_t start_row,\n const float* labels,\n const float* weights,\n const double* init_scores,\n const int32_t* queries,\n int32_t tid) {\n API_BEGIN();\n#ifdef LABEL_T_USE_DOUBLE\n Log::Fatal(\"Don't support LABEL_T_USE_DOUBLE\");\n#endif\n if (!data) {\n Log::Fatal(\"data cannot be null.\");\n }\n auto p_dataset = reinterpret_cast(dataset);\n auto get_row_fun = RowFunctionFromDenseMatrix(data, nrow, ncol, data_type, 1);\n if (p_dataset->has_raw()) {\n p_dataset->ResizeRaw(p_dataset->num_numeric_features() + nrow);\n }\n\n const int max_omp_threads = p_dataset->omp_max_threads() > 0 ? p_dataset->omp_max_threads() : OMP_NUM_THREADS();\n\n OMP_INIT_EX();\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = 0; i < nrow; ++i) {\n OMP_LOOP_EX_BEGIN();\n // convert internal thread id to be unique based on external thread id\n const int internal_tid = omp_get_thread_num() + (max_omp_threads * tid);\n auto one_row = get_row_fun(i);\n p_dataset->PushOneRow(internal_tid, start_row + i, one_row);\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n\n p_dataset->InsertMetadataAt(start_row, nrow, labels, weights, init_scores, queries);\n\n if (!p_dataset->wait_for_manual_finish() && (start_row + nrow == p_dataset->num_data())) {\n p_dataset->FinishLoad();\n }\n API_END();\n}\n\nint LGBM_DatasetPushRowsByCSR(DatasetHandle dataset,\n const void* indptr,\n int indptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t nindptr,\n int64_t nelem,\n int64_t,\n int64_t start_row) {\n API_BEGIN();\n auto p_dataset = reinterpret_cast(dataset);\n auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem);\n int32_t nrow = static_cast(nindptr - 1);\n if (p_dataset->has_raw()) {\n p_dataset->ResizeRaw(p_dataset->num_numeric_features() + nrow);\n }\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = 0; i < nrow; ++i) {\n OMP_LOOP_EX_BEGIN();\n const int tid = omp_get_thread_num();\n auto one_row = get_row_fun(i);\n p_dataset->PushOneRow(tid, static_cast(start_row + i), one_row);\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n if (!p_dataset->wait_for_manual_finish() && (start_row + nrow == static_cast(p_dataset->num_data()))) {\n p_dataset->FinishLoad();\n }\n API_END();\n}\n\nint LGBM_DatasetPushRowsByCSRWithMetadata(DatasetHandle dataset,\n const void* indptr,\n int indptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t nindptr,\n int64_t nelem,\n int64_t start_row,\n const float* labels,\n const float* weights,\n const double* init_scores,\n const int32_t* queries,\n int32_t tid) {\n API_BEGIN();\n#ifdef LABEL_T_USE_DOUBLE\n Log::Fatal(\"Don't support LABEL_T_USE_DOUBLE\");\n#endif\n if (!data) {\n Log::Fatal(\"data cannot be null.\");\n }\n auto p_dataset = reinterpret_cast(dataset);\n auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem);\n int32_t nrow = static_cast(nindptr - 1);\n if (p_dataset->has_raw()) {\n p_dataset->ResizeRaw(p_dataset->num_numeric_features() + nrow);\n }\n\n const int max_omp_threads = p_dataset->omp_max_threads() > 0 ? p_dataset->omp_max_threads() : OMP_NUM_THREADS();\n\n OMP_INIT_EX();\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = 0; i < nrow; ++i) {\n OMP_LOOP_EX_BEGIN();\n // convert internal thread id to be unique based on external thread id\n const int internal_tid = omp_get_thread_num() + (max_omp_threads * tid);\n auto one_row = get_row_fun(i);\n p_dataset->PushOneRow(internal_tid, static_cast(start_row + i), one_row);\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n\n p_dataset->InsertMetadataAt(static_cast(start_row), nrow, labels, weights, init_scores, queries);\n\n if (!p_dataset->wait_for_manual_finish() && (start_row + nrow == static_cast(p_dataset->num_data()))) {\n p_dataset->FinishLoad();\n }\n API_END();\n}\n\nint LGBM_DatasetSetWaitForManualFinish(DatasetHandle dataset, int wait) {\n API_BEGIN();\n auto p_dataset = reinterpret_cast(dataset);\n p_dataset->set_wait_for_manual_finish(wait);\n API_END();\n}\n\nint LGBM_DatasetMarkFinished(DatasetHandle dataset) {\n API_BEGIN();\n auto p_dataset = reinterpret_cast(dataset);\n p_dataset->FinishLoad();\n API_END();\n}\n\nint LGBM_DatasetCreateFromMat(const void* data,\n int data_type,\n int32_t nrow,\n int32_t ncol,\n int is_row_major,\n const char* parameters,\n const DatasetHandle reference,\n DatasetHandle* out) {\n return LGBM_DatasetCreateFromMats(1,\n &data,\n data_type,\n &nrow,\n ncol,\n &is_row_major,\n parameters,\n reference,\n out);\n}\n\nint LGBM_DatasetCreateFromMats(int32_t nmat,\n const void** data,\n int data_type,\n int32_t* nrow,\n int32_t ncol,\n int* is_row_major,\n const char* parameters,\n const DatasetHandle reference,\n DatasetHandle* out) {\n API_BEGIN();\n auto param = Config::Str2Map(parameters);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n std::unique_ptr ret;\n int32_t total_nrow = 0;\n for (int j = 0; j < nmat; ++j) {\n total_nrow += nrow[j];\n }\n\n std::vector(int row_idx)>> get_row_fun;\n for (int j = 0; j < nmat; ++j) {\n get_row_fun.push_back(RowFunctionFromDenseMatrix(data[j], nrow[j], ncol, data_type, is_row_major[j]));\n }\n\n if (reference == nullptr) {\n // sample data first\n auto sample_indices = CreateSampleIndices(total_nrow, config);\n int sample_cnt = static_cast(sample_indices.size());\n std::vector> sample_values(ncol);\n std::vector> sample_idx(ncol);\n\n int offset = 0;\n int j = 0;\n for (size_t i = 0; i < sample_indices.size(); ++i) {\n auto idx = sample_indices[i];\n while ((idx - offset) >= nrow[j]) {\n offset += nrow[j];\n ++j;\n }\n\n auto row = get_row_fun[j](static_cast(idx - offset));\n for (size_t k = 0; k < row.size(); ++k) {\n if (std::fabs(row[k]) > kZeroThreshold || std::isnan(row[k])) {\n sample_values[k].emplace_back(row[k]);\n sample_idx[k].emplace_back(static_cast(i));\n }\n }\n }\n DatasetLoader loader(config, nullptr, 1, nullptr);\n ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(),\n Vector2Ptr(&sample_idx).data(),\n ncol,\n VectorSize(sample_values).data(),\n sample_cnt,\n total_nrow,\n total_nrow));\n } else {\n ret.reset(new Dataset(total_nrow));\n ret->CreateValid(\n reinterpret_cast(reference));\n if (ret->has_raw()) {\n ret->ResizeRaw(total_nrow);\n }\n }\n int32_t start_row = 0;\n for (int j = 0; j < nmat; ++j) {\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = 0; i < nrow[j]; ++i) {\n OMP_LOOP_EX_BEGIN();\n const int tid = omp_get_thread_num();\n auto one_row = get_row_fun[j](i);\n ret->PushOneRow(tid, start_row + i, one_row);\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n\n start_row += nrow[j];\n }\n ret->FinishLoad();\n *out = ret.release();\n API_END();\n}\n\nint LGBM_DatasetCreateFromCSR(const void* indptr,\n int indptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t nindptr,\n int64_t nelem,\n int64_t num_col,\n const char* parameters,\n const DatasetHandle reference,\n DatasetHandle* out) {\n API_BEGIN();\n if (num_col <= 0) {\n Log::Fatal(\"The number of columns should be greater than zero.\");\n } else if (num_col >= INT32_MAX) {\n Log::Fatal(\"The number of columns should be smaller than INT32_MAX.\");\n }\n auto param = Config::Str2Map(parameters);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n std::unique_ptr ret;\n auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem);\n int32_t nrow = static_cast(nindptr - 1);\n if (reference == nullptr) {\n // sample data first\n auto sample_indices = CreateSampleIndices(nrow, config);\n int sample_cnt = static_cast(sample_indices.size());\n std::vector> sample_values(num_col);\n std::vector> sample_idx(num_col);\n for (size_t i = 0; i < sample_indices.size(); ++i) {\n auto idx = sample_indices[i];\n auto row = get_row_fun(static_cast(idx));\n for (std::pair& inner_data : row) {\n CHECK_LT(inner_data.first, num_col);\n if (std::fabs(inner_data.second) > kZeroThreshold || std::isnan(inner_data.second)) {\n sample_values[inner_data.first].emplace_back(inner_data.second);\n sample_idx[inner_data.first].emplace_back(static_cast(i));\n }\n }\n }\n DatasetLoader loader(config, nullptr, 1, nullptr);\n ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(),\n Vector2Ptr(&sample_idx).data(),\n static_cast(num_col),\n VectorSize(sample_values).data(),\n sample_cnt,\n nrow,\n nrow));\n } else {\n ret.reset(new Dataset(nrow));\n ret->CreateValid(\n reinterpret_cast(reference));\n if (ret->has_raw()) {\n ret->ResizeRaw(nrow);\n }\n }\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = 0; i < static_cast(nindptr - 1); ++i) {\n OMP_LOOP_EX_BEGIN();\n const int tid = omp_get_thread_num();\n auto one_row = get_row_fun(i);\n ret->PushOneRow(tid, i, one_row);\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n ret->FinishLoad();\n *out = ret.release();\n API_END();\n}\n\nint LGBM_DatasetCreateFromCSRFunc(void* get_row_funptr,\n int num_rows,\n int64_t num_col,\n const char* parameters,\n const DatasetHandle reference,\n DatasetHandle* out) {\n API_BEGIN();\n if (num_col <= 0) {\n Log::Fatal(\"The number of columns should be greater than zero.\");\n } else if (num_col >= INT32_MAX) {\n Log::Fatal(\"The number of columns should be smaller than INT32_MAX.\");\n }\n auto get_row_fun = *static_cast>&)>*>(get_row_funptr);\n auto param = Config::Str2Map(parameters);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n std::unique_ptr ret;\n int32_t nrow = num_rows;\n if (reference == nullptr) {\n // sample data first\n auto sample_indices = CreateSampleIndices(nrow, config);\n int sample_cnt = static_cast(sample_indices.size());\n std::vector> sample_values(num_col);\n std::vector> sample_idx(num_col);\n // local buffer to re-use memory\n std::vector> buffer;\n for (size_t i = 0; i < sample_indices.size(); ++i) {\n auto idx = sample_indices[i];\n get_row_fun(static_cast(idx), buffer);\n for (std::pair& inner_data : buffer) {\n CHECK_LT(inner_data.first, num_col);\n if (std::fabs(inner_data.second) > kZeroThreshold || std::isnan(inner_data.second)) {\n sample_values[inner_data.first].emplace_back(inner_data.second);\n sample_idx[inner_data.first].emplace_back(static_cast(i));\n }\n }\n }\n DatasetLoader loader(config, nullptr, 1, nullptr);\n ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(),\n Vector2Ptr(&sample_idx).data(),\n static_cast(num_col),\n VectorSize(sample_values).data(),\n sample_cnt,\n nrow,\n nrow));\n } else {\n ret.reset(new Dataset(nrow));\n ret->CreateValid(\n reinterpret_cast(reference));\n if (ret->has_raw()) {\n ret->ResizeRaw(nrow);\n }\n }\n\n OMP_INIT_EX();\n std::vector> thread_buffer;\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(thread_buffer)\n for (int i = 0; i < num_rows; ++i) {\n OMP_LOOP_EX_BEGIN();\n {\n const int tid = omp_get_thread_num();\n get_row_fun(i, thread_buffer);\n ret->PushOneRow(tid, i, thread_buffer);\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n ret->FinishLoad();\n *out = ret.release();\n API_END();\n}\n\nint LGBM_DatasetCreateFromCSC(const void* col_ptr,\n int col_ptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t ncol_ptr,\n int64_t nelem,\n int64_t num_row,\n const char* parameters,\n const DatasetHandle reference,\n DatasetHandle* out) {\n API_BEGIN();\n auto param = Config::Str2Map(parameters);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n std::unique_ptr ret;\n int32_t nrow = static_cast(num_row);\n if (reference == nullptr) {\n // sample data first\n auto sample_indices = CreateSampleIndices(nrow, config);\n int sample_cnt = static_cast(sample_indices.size());\n std::vector> sample_values(ncol_ptr - 1);\n std::vector> sample_idx(ncol_ptr - 1);\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = 0; i < static_cast(sample_values.size()); ++i) {\n OMP_LOOP_EX_BEGIN();\n CSC_RowIterator col_it(col_ptr, col_ptr_type, indices, data, data_type, ncol_ptr, nelem, i);\n for (int j = 0; j < sample_cnt; j++) {\n auto val = col_it.Get(sample_indices[j]);\n if (std::fabs(val) > kZeroThreshold || std::isnan(val)) {\n sample_values[i].emplace_back(val);\n sample_idx[i].emplace_back(j);\n }\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n DatasetLoader loader(config, nullptr, 1, nullptr);\n ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(),\n Vector2Ptr(&sample_idx).data(),\n static_cast(sample_values.size()),\n VectorSize(sample_values).data(),\n sample_cnt,\n nrow,\n nrow));\n } else {\n ret.reset(new Dataset(nrow));\n ret->CreateValid(\n reinterpret_cast(reference));\n }\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = 0; i < static_cast(ncol_ptr - 1); ++i) {\n OMP_LOOP_EX_BEGIN();\n const int tid = omp_get_thread_num();\n int feature_idx = ret->InnerFeatureIndex(i);\n if (feature_idx < 0) {\n continue;\n }\n int group = ret->Feature2Group(feature_idx);\n int sub_feature = ret->Feature2SubFeature(feature_idx);\n CSC_RowIterator col_it(col_ptr, col_ptr_type, indices, data, data_type, ncol_ptr, nelem, i);\n auto bin_mapper = ret->FeatureBinMapper(feature_idx);\n if (bin_mapper->GetDefaultBin() == bin_mapper->GetMostFreqBin()) {\n int row_idx = 0;\n while (row_idx < nrow) {\n auto pair = col_it.NextNonZero();\n row_idx = pair.first;\n // no more data\n if (row_idx < 0) {\n break;\n }\n ret->PushOneData(tid, row_idx, group, feature_idx, sub_feature, pair.second);\n }\n } else {\n for (int row_idx = 0; row_idx < nrow; ++row_idx) {\n auto val = col_it.Get(row_idx);\n ret->PushOneData(tid, row_idx, group, feature_idx, sub_feature, val);\n }\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n ret->FinishLoad();\n *out = ret.release();\n API_END();\n}\n\nint LGBM_DatasetCreateFromArrow(int64_t n_chunks,\n const struct ArrowArray* chunks,\n const struct ArrowSchema* schema,\n const char* parameters,\n const DatasetHandle reference,\n DatasetHandle *out) {\n API_BEGIN();\n\n auto param = Config::Str2Map(parameters);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n\n std::unique_ptr ret;\n\n // Prepare the Arrow data\n ArrowTable table(n_chunks, chunks, schema);\n\n // Initialize the dataset\n if (reference == nullptr) {\n // If there is no reference dataset, we first sample indices\n auto sample_indices = CreateSampleIndices(static_cast(table.get_num_rows()), config);\n auto sample_count = static_cast(sample_indices.size());\n std::vector> sample_values(table.get_num_columns());\n std::vector> sample_idx(table.get_num_columns());\n\n // Then, we obtain sample values by parallelizing across columns\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int64_t j = 0; j < table.get_num_columns(); ++j) {\n OMP_LOOP_EX_BEGIN();\n\n // Values need to be copied from the record batches.\n sample_values[j].reserve(sample_indices.size());\n sample_idx[j].reserve(sample_indices.size());\n\n // The chunks are iterated over in the inner loop as columns can be treated independently.\n int last_idx = 0;\n int i = 0;\n auto it = table.get_column(j).begin();\n for (auto idx : sample_indices) {\n std::advance(it, idx - last_idx);\n auto v = *it;\n if (std::fabs(v) > kZeroThreshold || std::isnan(v)) {\n sample_values[j].emplace_back(v);\n sample_idx[j].emplace_back(i);\n }\n last_idx = idx;\n i++;\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n\n // Finally, we initialize a loader from the sampled values\n DatasetLoader loader(config, nullptr, 1, nullptr);\n ret.reset(loader.ConstructFromSampleData(Vector2Ptr(&sample_values).data(),\n Vector2Ptr(&sample_idx).data(),\n table.get_num_columns(),\n VectorSize(sample_values).data(),\n sample_count,\n table.get_num_rows(),\n table.get_num_rows()));\n } else {\n ret.reset(new Dataset(static_cast(table.get_num_rows())));\n ret->CreateValid(reinterpret_cast(reference));\n if (ret->has_raw()) {\n ret->ResizeRaw(static_cast(table.get_num_rows()));\n }\n }\n\n // After sampling and properly initializing all bins, we can add our data to the dataset. Here,\n // we parallelize across rows.\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int64_t j = 0; j < table.get_num_columns(); ++j) {\n OMP_LOOP_EX_BEGIN();\n const int tid = omp_get_thread_num();\n data_size_t idx = 0;\n auto column = table.get_column(j);\n for (auto it = column.begin(), end = column.end(); it != end; ++it) {\n ret->PushOneValue(tid, idx++, j, *it);\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n\n ret->FinishLoad();\n *out = ret.release();\n API_END();\n}\n\nint LGBM_DatasetGetSubset(\n const DatasetHandle handle,\n const int32_t* used_row_indices,\n int32_t num_used_row_indices,\n const char* parameters,\n DatasetHandle* out) {\n API_BEGIN();\n auto param = Config::Str2Map(parameters);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n auto full_dataset = reinterpret_cast(handle);\n CHECK_GT(num_used_row_indices, 0);\n const int32_t lower = 0;\n const int32_t upper = full_dataset->num_data() - 1;\n CheckElementsIntervalClosed(used_row_indices, lower, upper, num_used_row_indices, \"Used indices of subset\");\n if (!std::is_sorted(used_row_indices, used_row_indices + num_used_row_indices)) {\n Log::Fatal(\"used_row_indices should be sorted in Subset\");\n }\n auto ret = std::unique_ptr(new Dataset(num_used_row_indices));\n ret->CopyFeatureMapperFrom(full_dataset);\n ret->CopySubrow(full_dataset, used_row_indices, num_used_row_indices, true);\n *out = ret.release();\n API_END();\n}\n\nint LGBM_DatasetSetFeatureNames(\n DatasetHandle handle,\n const char** feature_names,\n int num_feature_names) {\n API_BEGIN();\n auto dataset = reinterpret_cast(handle);\n std::vector feature_names_str;\n for (int i = 0; i < num_feature_names; ++i) {\n feature_names_str.emplace_back(feature_names[i]);\n }\n dataset->set_feature_names(feature_names_str);\n API_END();\n}\n\nint LGBM_DatasetGetFeatureNames(\n DatasetHandle handle,\n const int len,\n int* num_feature_names,\n const size_t buffer_len,\n size_t* out_buffer_len,\n char** feature_names) {\n API_BEGIN();\n *out_buffer_len = 0;\n auto dataset = reinterpret_cast(handle);\n auto inside_feature_name = dataset->feature_names();\n *num_feature_names = static_cast(inside_feature_name.size());\n for (int i = 0; i < *num_feature_names; ++i) {\n if (i < len) {\n std::memcpy(feature_names[i], inside_feature_name[i].c_str(), std::min(inside_feature_name[i].size() + 1, buffer_len));\n feature_names[i][buffer_len - 1] = '\\0';\n }\n *out_buffer_len = std::max(inside_feature_name[i].size() + 1, *out_buffer_len);\n }\n API_END();\n}\n\n#ifdef _MSC_VER\n #pragma warning(disable : 4702)\n#endif\nint LGBM_DatasetFree(DatasetHandle handle) {\n API_BEGIN();\n delete reinterpret_cast(handle);\n API_END();\n}\n\nint LGBM_DatasetSaveBinary(DatasetHandle handle,\n const char* filename) {\n API_BEGIN();\n auto dataset = reinterpret_cast(handle);\n dataset->SaveBinaryFile(filename);\n API_END();\n}\n\nint LGBM_DatasetSerializeReferenceToBinary(DatasetHandle handle,\n ByteBufferHandle* out,\n int32_t* out_len) {\n API_BEGIN();\n auto dataset = reinterpret_cast(handle);\n std::unique_ptr ret;\n ret.reset(new LightGBM::ByteBuffer());\n dataset->SerializeReference(ret.get());\n *out_len = static_cast(ret->GetSize());\n *out = ret.release();\n API_END();\n}\n\nint LGBM_DatasetDumpText(DatasetHandle handle,\n const char* filename) {\n API_BEGIN();\n auto dataset = reinterpret_cast(handle);\n dataset->DumpTextFile(filename);\n API_END();\n}\n\nint LGBM_DatasetSetField(DatasetHandle handle,\n const char* field_name,\n const void* field_data,\n int num_element,\n int type) {\n API_BEGIN();\n auto dataset = reinterpret_cast(handle);\n bool is_success = false;\n if (type == C_API_DTYPE_FLOAT32) {\n is_success = dataset->SetFloatField(field_name, reinterpret_cast(field_data), static_cast(num_element));\n } else if (type == C_API_DTYPE_INT32) {\n is_success = dataset->SetIntField(field_name, reinterpret_cast(field_data), static_cast(num_element));\n } else if (type == C_API_DTYPE_FLOAT64) {\n is_success = dataset->SetDoubleField(field_name, reinterpret_cast(field_data), static_cast(num_element));\n }\n if (!is_success) {\n Log::Fatal(\"Input data type error or field not found\");\n }\n API_END();\n}\n\nint LGBM_DatasetSetFieldFromArrow(DatasetHandle handle,\n const char* field_name,\n int64_t n_chunks,\n const struct ArrowArray* chunks,\n const struct ArrowSchema* schema) {\n API_BEGIN();\n auto dataset = reinterpret_cast(handle);\n ArrowChunkedArray ca(n_chunks, chunks, schema);\n auto is_success = dataset->SetFieldFromArrow(field_name, ca);\n if (!is_success) {\n Log::Fatal(\"Input field is not supported\");\n }\n API_END();\n}\n\nint LGBM_DatasetGetField(DatasetHandle handle,\n const char* field_name,\n int* out_len,\n const void** out_ptr,\n int* out_type) {\n API_BEGIN();\n auto dataset = reinterpret_cast(handle);\n bool is_success = false;\n if (dataset->GetFloatField(field_name, out_len, reinterpret_cast(out_ptr))) {\n *out_type = C_API_DTYPE_FLOAT32;\n is_success = true;\n } else if (dataset->GetIntField(field_name, out_len, reinterpret_cast(out_ptr))) {\n *out_type = C_API_DTYPE_INT32;\n is_success = true;\n } else if (dataset->GetDoubleField(field_name, out_len, reinterpret_cast(out_ptr))) {\n *out_type = C_API_DTYPE_FLOAT64;\n is_success = true;\n }\n if (!is_success) {\n Log::Fatal(\"Field not found\");\n }\n if (*out_ptr == nullptr) {\n *out_len = 0;\n }\n API_END();\n}\n\nint LGBM_DatasetUpdateParamChecking(const char* old_parameters, const char* new_parameters) {\n API_BEGIN();\n auto old_param = Config::Str2Map(old_parameters);\n Config old_config;\n old_config.Set(old_param);\n auto new_param = Config::Str2Map(new_parameters);\n Booster::CheckDatasetResetConfig(old_config, new_param);\n API_END();\n}\n\nint LGBM_DatasetGetNumData(DatasetHandle handle,\n int* out) {\n API_BEGIN();\n auto dataset = reinterpret_cast(handle);\n *out = dataset->num_data();\n API_END();\n}\n\nint LGBM_DatasetGetNumFeature(DatasetHandle handle,\n int* out) {\n API_BEGIN();\n auto dataset = reinterpret_cast(handle);\n *out = dataset->num_total_features();\n API_END();\n}\n\nint LGBM_DatasetGetFeatureNumBin(DatasetHandle handle,\n int feature,\n int* out) {\n API_BEGIN();\n auto dataset = reinterpret_cast(handle);\n int num_features = dataset->num_total_features();\n if (feature < 0 || feature >= num_features) {\n Log::Fatal(\"Tried to retrieve number of bins for feature index %d, \"\n \"but the valid feature indices are [0, %d].\", feature, num_features - 1);\n }\n int inner_idx = dataset->InnerFeatureIndex(feature);\n if (inner_idx >= 0) {\n *out = dataset->FeatureNumBin(inner_idx);\n } else {\n *out = 0;\n }\n API_END();\n}\n\nint LGBM_DatasetAddFeaturesFrom(DatasetHandle target,\n DatasetHandle source) {\n API_BEGIN();\n auto target_d = reinterpret_cast(target);\n auto source_d = reinterpret_cast(source);\n target_d->AddFeaturesFrom(source_d);\n API_END();\n}\n\n// ---- start of booster\n\nint LGBM_BoosterCreate(const DatasetHandle train_data,\n const char* parameters,\n BoosterHandle* out) {\n API_BEGIN();\n const Dataset* p_train_data = reinterpret_cast(train_data);\n auto ret = std::unique_ptr(new Booster(p_train_data, parameters));\n *out = ret.release();\n API_END();\n}\n\nint LGBM_BoosterCreateFromModelfile(\n const char* filename,\n int* out_num_iterations,\n BoosterHandle* out) {\n API_BEGIN();\n auto ret = std::unique_ptr(new Booster(filename));\n *out_num_iterations = ret->GetBoosting()->GetCurrentIteration();\n *out = ret.release();\n API_END();\n}\n\nint LGBM_BoosterLoadModelFromString(\n const char* model_str,\n int* out_num_iterations,\n BoosterHandle* out) {\n API_BEGIN();\n auto ret = std::unique_ptr(new Booster(nullptr));\n ret->LoadModelFromString(model_str);\n *out_num_iterations = ret->GetBoosting()->GetCurrentIteration();\n *out = ret.release();\n API_END();\n}\n\nint LGBM_BoosterGetLoadedParam(\n BoosterHandle handle,\n int64_t buffer_len,\n int64_t* out_len,\n char* out_str) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n std::string params = ref_booster->GetBoosting()->GetLoadedParam();\n *out_len = static_cast(params.size()) + 1;\n if (*out_len <= buffer_len) {\n std::memcpy(out_str, params.c_str(), *out_len);\n }\n API_END();\n}\n\n#ifdef _MSC_VER\n #pragma warning(disable : 4702)\n#endif\nint LGBM_BoosterFree(BoosterHandle handle) {\n API_BEGIN();\n delete reinterpret_cast(handle);\n API_END();\n}\n\nint LGBM_BoosterShuffleModels(BoosterHandle handle, int start_iter, int end_iter) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n ref_booster->ShuffleModels(start_iter, end_iter);\n API_END();\n}\n\nint LGBM_BoosterMerge(BoosterHandle handle,\n BoosterHandle other_handle) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n Booster* ref_other_booster = reinterpret_cast(other_handle);\n ref_booster->MergeFrom(ref_other_booster);\n API_END();\n}\n\nint LGBM_BoosterAddValidData(BoosterHandle handle,\n const DatasetHandle valid_data) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n const Dataset* p_dataset = reinterpret_cast(valid_data);\n ref_booster->AddValidData(p_dataset);\n API_END();\n}\n\nint LGBM_BoosterResetTrainingData(BoosterHandle handle,\n const DatasetHandle train_data) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n const Dataset* p_dataset = reinterpret_cast(train_data);\n ref_booster->ResetTrainingData(p_dataset);\n API_END();\n}\n\nint LGBM_BoosterResetParameter(BoosterHandle handle, const char* parameters) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n ref_booster->ResetConfig(parameters);\n API_END();\n}\n\nint LGBM_BoosterGetNumClasses(BoosterHandle handle, int* out_len) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n *out_len = ref_booster->GetBoosting()->NumberOfClasses();\n API_END();\n}\n\nint LGBM_BoosterGetLinear(BoosterHandle handle, int* out) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n if (ref_booster->GetBoosting()->IsLinear()) {\n *out = 1;\n } else {\n *out = 0;\n }\n API_END();\n}\n\nint LGBM_BoosterRefit(BoosterHandle handle, const int32_t* leaf_preds, int32_t nrow, int32_t ncol) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n ref_booster->Refit(leaf_preds, nrow, ncol);\n API_END();\n}\n\nint LGBM_BoosterUpdateOneIter(BoosterHandle handle, int* produced_empty_tree) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n if (ref_booster->TrainOneIter()) {\n *produced_empty_tree = 1;\n } else {\n *produced_empty_tree = 0;\n }\n API_END();\n}\n\nint LGBM_BoosterUpdateOneIterCustom(BoosterHandle handle,\n const float* grad,\n const float* hess,\n int* produced_empty_tree) {\n API_BEGIN();\n #ifdef SCORE_T_USE_DOUBLE\n (void) handle; // UNUSED VARIABLE\n (void) grad; // UNUSED VARIABLE\n (void) hess; // UNUSED VARIABLE\n (void) produced_empty_tree; // UNUSED VARIABLE\n Log::Fatal(\"Don't support custom loss function when SCORE_T_USE_DOUBLE is enabled\");\n #else\n Booster* ref_booster = reinterpret_cast(handle);\n if (ref_booster->TrainOneIter(grad, hess)) {\n *produced_empty_tree = 1;\n } else {\n *produced_empty_tree = 0;\n }\n #endif\n API_END();\n}\n\nint LGBM_BoosterRollbackOneIter(BoosterHandle handle) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n ref_booster->RollbackOneIter();\n API_END();\n}\n\nint LGBM_BoosterGetCurrentIteration(BoosterHandle handle, int* out_iteration) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n *out_iteration = ref_booster->GetBoosting()->GetCurrentIteration();\n API_END();\n}\n\nint LGBM_BoosterNumModelPerIteration(BoosterHandle handle, int* out_tree_per_iteration) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n *out_tree_per_iteration = ref_booster->GetBoosting()->NumModelPerIteration();\n API_END();\n}\n\nint LGBM_BoosterNumberOfTotalModel(BoosterHandle handle, int* out_models) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n *out_models = ref_booster->GetBoosting()->NumberOfTotalModel();\n API_END();\n}\n\nint LGBM_BoosterGetEvalCounts(BoosterHandle handle, int* out_len) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n *out_len = ref_booster->GetEvalCounts();\n API_END();\n}\n\nint LGBM_BoosterGetEvalNames(BoosterHandle handle,\n const int len,\n int* out_len,\n const size_t buffer_len,\n size_t* out_buffer_len,\n char** out_strs) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n *out_len = ref_booster->GetEvalNames(out_strs, len, buffer_len, out_buffer_len);\n API_END();\n}\n\nint LGBM_BoosterGetFeatureNames(BoosterHandle handle,\n const int len,\n int* out_len,\n const size_t buffer_len,\n size_t* out_buffer_len,\n char** out_strs) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n *out_len = ref_booster->GetFeatureNames(out_strs, len, buffer_len, out_buffer_len);\n API_END();\n}\n\nint LGBM_BoosterGetNumFeature(BoosterHandle handle, int* out_len) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n *out_len = ref_booster->GetBoosting()->MaxFeatureIdx() + 1;\n API_END();\n}\n\nint LGBM_BoosterGetEval(BoosterHandle handle,\n int data_idx,\n int* out_len,\n double* out_results) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n auto boosting = ref_booster->GetBoosting();\n auto result_buf = boosting->GetEvalAt(data_idx);\n *out_len = static_cast(result_buf.size());\n for (size_t i = 0; i < result_buf.size(); ++i) {\n (out_results)[i] = static_cast(result_buf[i]);\n }\n API_END();\n}\n\nint LGBM_BoosterGetNumPredict(BoosterHandle handle,\n int data_idx,\n int64_t* out_len) {\n API_BEGIN();\n auto boosting = reinterpret_cast(handle)->GetBoosting();\n *out_len = boosting->GetNumPredictAt(data_idx);\n API_END();\n}\n\nint LGBM_BoosterGetPredict(BoosterHandle handle,\n int data_idx,\n int64_t* out_len,\n double* out_result) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n ref_booster->GetPredictAt(data_idx, out_result, out_len);\n API_END();\n}\n\nint LGBM_BoosterPredictForFile(BoosterHandle handle,\n const char* data_filename,\n int data_has_header,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n const char* result_filename) {\n API_BEGIN();\n auto param = Config::Str2Map(parameter);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n Booster* ref_booster = reinterpret_cast(handle);\n ref_booster->Predict(start_iteration, num_iteration, predict_type, data_filename, data_has_header,\n config, result_filename);\n API_END();\n}\n\nint LGBM_BoosterCalcNumPredict(BoosterHandle handle,\n int num_row,\n int predict_type,\n int start_iteration,\n int num_iteration,\n int64_t* out_len) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n *out_len = static_cast(num_row) * ref_booster->GetBoosting()->NumPredictOneRow(start_iteration,\n num_iteration, predict_type == C_API_PREDICT_LEAF_INDEX, predict_type == C_API_PREDICT_CONTRIB);\n API_END();\n}\n\n// Naming: In future versions of LightGBM, public API named around `FastConfig` should be made named around\n// `SingleRowPredictor`, because it is specific to single row prediction, and doesn't actually hold only config.\n// For now this is kept as `FastConfig` for backwards compatibility.\n// At the same time, one should consider removing the old non-fast single row public API that stores its Predictor\n// in the Booster, because that will enable removing these Predictors from the Booster, and associated initialization\n// code.\nint LGBM_FastConfigFree(FastConfigHandle fastConfig) {\n API_BEGIN();\n delete reinterpret_cast(fastConfig);\n API_END();\n}\n\nint LGBM_BoosterPredictForCSR(BoosterHandle handle,\n const void* indptr,\n int indptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t nindptr,\n int64_t nelem,\n int64_t num_col,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int64_t* out_len,\n double* out_result) {\n API_BEGIN();\n if (num_col <= 0) {\n Log::Fatal(\"The number of columns should be greater than zero.\");\n } else if (num_col >= INT32_MAX) {\n Log::Fatal(\"The number of columns should be smaller than INT32_MAX.\");\n }\n auto param = Config::Str2Map(parameter);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n Booster* ref_booster = reinterpret_cast(handle);\n auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem);\n int nrow = static_cast(nindptr - 1);\n ref_booster->Predict(start_iteration, num_iteration, predict_type, nrow, static_cast(num_col), get_row_fun,\n config, out_result, out_len);\n API_END();\n}\n\nint LGBM_BoosterPredictSparseOutput(BoosterHandle handle,\n const void* indptr,\n int indptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t nindptr,\n int64_t nelem,\n int64_t num_col_or_row,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int matrix_type,\n int64_t* out_len,\n void** out_indptr,\n int32_t** out_indices,\n void** out_data) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n auto param = Config::Str2Map(parameter);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n if (matrix_type == C_API_MATRIX_TYPE_CSR) {\n if (num_col_or_row <= 0) {\n Log::Fatal(\"The number of columns should be greater than zero.\");\n } else if (num_col_or_row >= INT32_MAX) {\n Log::Fatal(\"The number of columns should be smaller than INT32_MAX.\");\n }\n auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem);\n int64_t nrow = nindptr - 1;\n ref_booster->PredictSparseCSR(start_iteration, num_iteration, predict_type, nrow, static_cast(num_col_or_row), get_row_fun,\n config, out_len, out_indptr, indptr_type, out_indices, out_data, data_type);\n } else if (matrix_type == C_API_MATRIX_TYPE_CSC) {\n int num_threads = OMP_NUM_THREADS();\n int ncol = static_cast(nindptr - 1);\n std::vector> iterators(num_threads, std::vector());\n for (int i = 0; i < num_threads; ++i) {\n for (int j = 0; j < ncol; ++j) {\n iterators[i].emplace_back(indptr, indptr_type, indices, data, data_type, nindptr, nelem, j);\n }\n }\n std::function>(int64_t row_idx)> get_row_fun =\n [&iterators, ncol](int64_t i) {\n std::vector> one_row;\n one_row.reserve(ncol);\n const int tid = omp_get_thread_num();\n for (int j = 0; j < ncol; ++j) {\n auto val = iterators[tid][j].Get(static_cast(i));\n if (std::fabs(val) > kZeroThreshold || std::isnan(val)) {\n one_row.emplace_back(j, val);\n }\n }\n return one_row;\n };\n ref_booster->PredictSparseCSC(start_iteration, num_iteration, predict_type, num_col_or_row, ncol, get_row_fun, config,\n out_len, out_indptr, indptr_type, out_indices, out_data, data_type);\n } else {\n Log::Fatal(\"Unknown matrix type in LGBM_BoosterPredictSparseOutput\");\n }\n API_END();\n}\n\nint LGBM_BoosterFreePredictSparse(void* indptr, int32_t* indices, void* data, int indptr_type, int data_type) {\n API_BEGIN();\n if (indptr_type == C_API_DTYPE_INT32) {\n delete[] reinterpret_cast(indptr);\n } else if (indptr_type == C_API_DTYPE_INT64) {\n delete[] reinterpret_cast(indptr);\n } else {\n Log::Fatal(\"Unknown indptr type in LGBM_BoosterFreePredictSparse\");\n }\n delete[] indices;\n if (data_type == C_API_DTYPE_FLOAT32) {\n delete[] reinterpret_cast(data);\n } else if (data_type == C_API_DTYPE_FLOAT64) {\n delete[] reinterpret_cast(data);\n } else {\n Log::Fatal(\"Unknown data type in LGBM_BoosterFreePredictSparse\");\n }\n API_END();\n}\n\nint LGBM_BoosterPredictForCSRSingleRow(BoosterHandle handle,\n const void* indptr,\n int indptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t nindptr,\n int64_t nelem,\n int64_t num_col,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int64_t* out_len,\n double* out_result) {\n API_BEGIN();\n if (num_col <= 0) {\n Log::Fatal(\"The number of columns should be greater than zero.\");\n } else if (num_col >= INT32_MAX) {\n Log::Fatal(\"The number of columns should be smaller than INT32_MAX.\");\n }\n auto param = Config::Str2Map(parameter);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n Booster* ref_booster = reinterpret_cast(handle);\n auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, data_type, nindptr, nelem);\n ref_booster->SetSingleRowPredictorInner(start_iteration, num_iteration, predict_type, config);\n ref_booster->PredictSingleRow(predict_type, static_cast(num_col), get_row_fun, config, out_result, out_len);\n API_END();\n}\n\nint LGBM_BoosterPredictForCSRSingleRowFastInit(BoosterHandle handle,\n const int predict_type,\n const int start_iteration,\n const int num_iteration,\n const int data_type,\n const int64_t num_col,\n const char* parameter,\n FastConfigHandle *out_fastConfig) {\n API_BEGIN();\n if (num_col <= 0) {\n Log::Fatal(\"The number of columns should be greater than zero.\");\n } else if (num_col >= INT32_MAX) {\n Log::Fatal(\"The number of columns should be smaller than INT32_MAX.\");\n }\n\n Booster* ref_booster = reinterpret_cast(handle);\n\n std::unique_ptr single_row_predictor =\n ref_booster->InitSingleRowPredictor(start_iteration, num_iteration, predict_type, data_type, static_cast(num_col), parameter);\n\n OMP_SET_NUM_THREADS(single_row_predictor->config.num_threads);\n\n *out_fastConfig = single_row_predictor.release();\n API_END();\n}\n\nint LGBM_BoosterPredictForCSRSingleRowFast(FastConfigHandle fastConfig_handle,\n const void* indptr,\n const int indptr_type,\n const int32_t* indices,\n const void* data,\n const int64_t nindptr,\n const int64_t nelem,\n int64_t* out_len,\n double* out_result) {\n API_BEGIN();\n SingleRowPredictor *single_row_predictor = reinterpret_cast(fastConfig_handle);\n auto get_row_fun = RowFunctionFromCSR(indptr, indptr_type, indices, data, single_row_predictor->data_type, nindptr, nelem);\n single_row_predictor->Predict(get_row_fun, out_result, out_len);\n API_END();\n}\n\n\nint LGBM_BoosterPredictForCSC(BoosterHandle handle,\n const void* col_ptr,\n int col_ptr_type,\n const int32_t* indices,\n const void* data,\n int data_type,\n int64_t ncol_ptr,\n int64_t nelem,\n int64_t num_row,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int64_t* out_len,\n double* out_result) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n auto param = Config::Str2Map(parameter);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n int num_threads = OMP_NUM_THREADS();\n int ncol = static_cast(ncol_ptr - 1);\n std::vector> iterators(num_threads, std::vector());\n for (int i = 0; i < num_threads; ++i) {\n for (int j = 0; j < ncol; ++j) {\n iterators[i].emplace_back(col_ptr, col_ptr_type, indices, data, data_type, ncol_ptr, nelem, j);\n }\n }\n std::function>(int row_idx)> get_row_fun =\n [&iterators, ncol](int i) {\n std::vector> one_row;\n one_row.reserve(ncol);\n const int tid = omp_get_thread_num();\n for (int j = 0; j < ncol; ++j) {\n auto val = iterators[tid][j].Get(i);\n if (std::fabs(val) > kZeroThreshold || std::isnan(val)) {\n one_row.emplace_back(j, val);\n }\n }\n return one_row;\n };\n ref_booster->Predict(start_iteration, num_iteration, predict_type, static_cast(num_row), ncol, get_row_fun, config,\n out_result, out_len);\n API_END();\n}\n\nint LGBM_BoosterValidateFeatureNames(BoosterHandle handle,\n const char** data_names,\n int data_num_features) {\n API_BEGIN();\n int booster_num_features;\n size_t out_buffer_len;\n LGBM_BoosterGetFeatureNames(handle, 0, &booster_num_features, 0, &out_buffer_len, nullptr);\n if (booster_num_features != data_num_features) {\n Log::Fatal(\"Model was trained on %d features, but got %d input features to predict.\", booster_num_features, data_num_features);\n }\n std::vector> tmp_names(booster_num_features, std::vector(out_buffer_len));\n std::vector booster_names = Vector2Ptr(&tmp_names);\n LGBM_BoosterGetFeatureNames(handle, data_num_features, &booster_num_features, out_buffer_len, &out_buffer_len, booster_names.data());\n for (int i = 0; i < booster_num_features; ++i) {\n if (strcmp(data_names[i], booster_names[i]) != 0) {\n Log::Fatal(\"Expected '%s' at position %d but found '%s'\", booster_names[i], i, data_names[i]);\n }\n }\n API_END();\n}\n\nint LGBM_BoosterPredictForMat(BoosterHandle handle,\n const void* data,\n int data_type,\n int32_t nrow,\n int32_t ncol,\n int is_row_major,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int64_t* out_len,\n double* out_result) {\n API_BEGIN();\n auto param = Config::Str2Map(parameter);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n Booster* ref_booster = reinterpret_cast(handle);\n auto get_row_fun = RowPairFunctionFromDenseMatrix(data, nrow, ncol, data_type, is_row_major);\n ref_booster->Predict(start_iteration, num_iteration, predict_type, nrow, ncol, get_row_fun,\n config, out_result, out_len);\n API_END();\n}\n\nint LGBM_BoosterPredictForMatSingleRow(BoosterHandle handle,\n const void* data,\n int data_type,\n int32_t ncol,\n int is_row_major,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int64_t* out_len,\n double* out_result) {\n API_BEGIN();\n auto param = Config::Str2Map(parameter);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n Booster* ref_booster = reinterpret_cast(handle);\n auto get_row_fun = RowPairFunctionFromDenseMatrix(data, 1, ncol, data_type, is_row_major);\n ref_booster->SetSingleRowPredictorInner(start_iteration, num_iteration, predict_type, config);\n ref_booster->PredictSingleRow(predict_type, ncol, get_row_fun, config, out_result, out_len);\n API_END();\n}\n\nint LGBM_BoosterPredictForMatSingleRowFastInit(BoosterHandle handle,\n const int predict_type,\n const int start_iteration,\n const int num_iteration,\n const int data_type,\n const int32_t ncol,\n const char* parameter,\n FastConfigHandle *out_fastConfig) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n\n std::unique_ptr single_row_predictor =\n ref_booster->InitSingleRowPredictor(predict_type, start_iteration, num_iteration, data_type, ncol, parameter);\n\n OMP_SET_NUM_THREADS(single_row_predictor->config.num_threads);\n\n *out_fastConfig = single_row_predictor.release();\n API_END();\n}\n\nint LGBM_BoosterPredictForMatSingleRowFast(FastConfigHandle fastConfig_handle,\n const void* data,\n int64_t* out_len,\n double* out_result) {\n API_BEGIN();\n SingleRowPredictor *single_row_predictor = reinterpret_cast(fastConfig_handle);\n // Single row in row-major format:\n auto get_row_fun = RowPairFunctionFromDenseMatrix(data, 1, single_row_predictor->num_cols, single_row_predictor->data_type, 1);\n single_row_predictor->Predict(get_row_fun, out_result, out_len);\n API_END();\n}\n\n\nint LGBM_BoosterPredictForMats(BoosterHandle handle,\n const void** data,\n int data_type,\n int32_t nrow,\n int32_t ncol,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int64_t* out_len,\n double* out_result) {\n API_BEGIN();\n auto param = Config::Str2Map(parameter);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n Booster* ref_booster = reinterpret_cast(handle);\n auto get_row_fun = RowPairFunctionFromDenseRows(data, ncol, data_type);\n ref_booster->Predict(start_iteration, num_iteration, predict_type, nrow, ncol, get_row_fun, config, out_result, out_len);\n API_END();\n}\n\nint LGBM_BoosterPredictForArrow(BoosterHandle handle,\n int64_t n_chunks,\n const struct ArrowArray* chunks,\n const struct ArrowSchema* schema,\n int predict_type,\n int start_iteration,\n int num_iteration,\n const char* parameter,\n int64_t* out_len,\n double* out_result) {\n API_BEGIN();\n\n // Apply the configuration\n auto param = Config::Str2Map(parameter);\n Config config;\n config.Set(param);\n OMP_SET_NUM_THREADS(config.num_threads);\n\n // Set up chunked array and iterators for all columns\n ArrowTable table(n_chunks, chunks, schema);\n std::vector> its;\n its.reserve(table.get_num_columns());\n for (int64_t j = 0; j < table.get_num_columns(); ++j) {\n its.emplace_back(table.get_column(j).begin());\n }\n\n // Build row function\n auto num_columns = table.get_num_columns();\n auto row_fn = [num_columns, &its] (int row_idx) {\n std::vector> result;\n result.reserve(num_columns);\n for (int64_t j = 0; j < num_columns; ++j) {\n result.emplace_back(static_cast(j), its[j][row_idx]);\n }\n return result;\n };\n\n // Run prediction\n Booster* ref_booster = reinterpret_cast(handle);\n ref_booster->Predict(start_iteration,\n num_iteration,\n predict_type,\n static_cast(table.get_num_rows()),\n static_cast(table.get_num_columns()),\n row_fn,\n config,\n out_result,\n out_len);\n API_END();\n}\n\nint LGBM_BoosterSaveModel(BoosterHandle handle,\n int start_iteration,\n int num_iteration,\n int feature_importance_type,\n const char* filename) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n ref_booster->SaveModelToFile(start_iteration, num_iteration,\n feature_importance_type, filename);\n API_END();\n}\n\nint LGBM_BoosterSaveModelToString(BoosterHandle handle,\n int start_iteration,\n int num_iteration,\n int feature_importance_type,\n int64_t buffer_len,\n int64_t* out_len,\n char* out_str) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n std::string model = ref_booster->SaveModelToString(\n start_iteration, num_iteration, feature_importance_type);\n *out_len = static_cast(model.size()) + 1;\n if (*out_len <= buffer_len) {\n std::memcpy(out_str, model.c_str(), *out_len);\n }\n API_END();\n}\n\nint LGBM_BoosterDumpModel(BoosterHandle handle,\n int start_iteration,\n int num_iteration,\n int feature_importance_type,\n int64_t buffer_len,\n int64_t* out_len,\n char* out_str) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n std::string model = ref_booster->DumpModel(start_iteration, num_iteration,\n feature_importance_type);\n *out_len = static_cast(model.size()) + 1;\n if (*out_len <= buffer_len) {\n std::memcpy(out_str, model.c_str(), *out_len);\n }\n API_END();\n}\n\nint LGBM_BoosterGetLeafValue(BoosterHandle handle,\n int tree_idx,\n int leaf_idx,\n double* out_val) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n *out_val = static_cast(ref_booster->GetLeafValue(tree_idx, leaf_idx));\n API_END();\n}\n\nint LGBM_BoosterSetLeafValue(BoosterHandle handle,\n int tree_idx,\n int leaf_idx,\n double val) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n ref_booster->SetLeafValue(tree_idx, leaf_idx, val);\n API_END();\n}\n\nint LGBM_BoosterFeatureImportance(BoosterHandle handle,\n int num_iteration,\n int importance_type,\n double* out_results) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n std::vector feature_importances = ref_booster->FeatureImportance(num_iteration, importance_type);\n for (size_t i = 0; i < feature_importances.size(); ++i) {\n (out_results)[i] = feature_importances[i];\n }\n API_END();\n}\n\nint LGBM_BoosterGetUpperBoundValue(BoosterHandle handle,\n double* out_results) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n double max_value = ref_booster->UpperBoundValue();\n *out_results = max_value;\n API_END();\n}\n\nint LGBM_BoosterGetLowerBoundValue(BoosterHandle handle,\n double* out_results) {\n API_BEGIN();\n Booster* ref_booster = reinterpret_cast(handle);\n double min_value = ref_booster->LowerBoundValue();\n *out_results = min_value;\n API_END();\n}\n\nint LGBM_NetworkInit(const char* machines,\n int local_listen_port,\n int listen_time_out,\n int num_machines) {\n API_BEGIN();\n Config config;\n config.machines = RemoveQuotationSymbol(std::string(machines));\n config.local_listen_port = local_listen_port;\n config.num_machines = num_machines;\n config.time_out = listen_time_out;\n if (num_machines > 1) {\n Network::Init(config);\n }\n API_END();\n}\n\nint LGBM_NetworkFree() {\n API_BEGIN();\n Network::Dispose();\n API_END();\n}\n\nint LGBM_NetworkInitWithFunctions(int num_machines, int rank,\n void* reduce_scatter_ext_fun,\n void* allgather_ext_fun) {\n API_BEGIN();\n if (num_machines > 1) {\n Network::Init(num_machines, rank, (ReduceScatterFunction)reduce_scatter_ext_fun, (AllgatherFunction)allgather_ext_fun);\n }\n API_END();\n}\n\nint LGBM_SetMaxThreads(int num_threads) {\n API_BEGIN();\n if (num_threads <= 0) {\n LGBM_MAX_NUM_THREADS = -1;\n } else {\n LGBM_MAX_NUM_THREADS = num_threads;\n }\n API_END();\n}\n\nint LGBM_GetMaxThreads(int* out) {\n API_BEGIN();\n *out = LGBM_MAX_NUM_THREADS;\n API_END();\n}\n\n\n// ---- start of some help functions\n\n\ntemplate\nstd::function(int row_idx)>\nRowFunctionFromDenseMatrix_helper(const void* data, int num_row, int num_col, int is_row_major) {\n const T* data_ptr = reinterpret_cast(data);\n if (is_row_major) {\n return [=] (int row_idx) {\n std::vector ret(num_col);\n auto tmp_ptr = data_ptr + static_cast(num_col) * row_idx;\n for (int i = 0; i < num_col; ++i) {\n ret[i] = static_cast(*(tmp_ptr + i));\n }\n return ret;\n };\n } else {\n return [=] (int row_idx) {\n std::vector ret(num_col);\n for (int i = 0; i < num_col; ++i) {\n ret[i] = static_cast(*(data_ptr + static_cast(num_row) * i + row_idx));\n }\n return ret;\n };\n }\n}\n\nstd::function(int row_idx)>\nRowFunctionFromDenseMatrix(const void* data, int num_row, int num_col, int data_type, int is_row_major) {\n if (data_type == C_API_DTYPE_FLOAT32) {\n return RowFunctionFromDenseMatrix_helper(data, num_row, num_col, is_row_major);\n } else if (data_type == C_API_DTYPE_FLOAT64) {\n return RowFunctionFromDenseMatrix_helper(data, num_row, num_col, is_row_major);\n }\n Log::Fatal(\"Unknown data type in RowFunctionFromDenseMatrix\");\n return nullptr;\n}\n\nstd::function>(int row_idx)>\nRowPairFunctionFromDenseMatrix(const void* data, int num_row, int num_col, int data_type, int is_row_major) {\n auto inner_function = RowFunctionFromDenseMatrix(data, num_row, num_col, data_type, is_row_major);\n if (inner_function != nullptr) {\n return [inner_function] (int row_idx) {\n auto raw_values = inner_function(row_idx);\n std::vector> ret;\n ret.reserve(raw_values.size());\n for (int i = 0; i < static_cast(raw_values.size()); ++i) {\n if (std::fabs(raw_values[i]) > kZeroThreshold || std::isnan(raw_values[i])) {\n ret.emplace_back(i, raw_values[i]);\n }\n }\n return ret;\n };\n }\n return nullptr;\n}\n\n// data is array of pointers to individual rows\nstd::function>(int row_idx)>\nRowPairFunctionFromDenseRows(const void** data, int num_col, int data_type) {\n return [=](int row_idx) {\n auto inner_function = RowFunctionFromDenseMatrix(data[row_idx], 1, num_col, data_type, /* is_row_major */ true);\n auto raw_values = inner_function(0);\n std::vector> ret;\n ret.reserve(raw_values.size());\n for (int i = 0; i < static_cast(raw_values.size()); ++i) {\n if (std::fabs(raw_values[i]) > kZeroThreshold || std::isnan(raw_values[i])) {\n ret.emplace_back(i, raw_values[i]);\n }\n }\n return ret;\n };\n}\n\ntemplate\nstd::function>(T idx)>\nRowFunctionFromCSR_helper(const void* indptr, const int32_t* indices, const void* data) {\n const T1* data_ptr = reinterpret_cast(data);\n const T2* ptr_indptr = reinterpret_cast(indptr);\n return [=] (T idx) {\n std::vector> ret;\n int64_t start = ptr_indptr[idx];\n int64_t end = ptr_indptr[idx + 1];\n if (end - start > 0) {\n ret.reserve(end - start);\n }\n for (int64_t i = start; i < end; ++i) {\n ret.emplace_back(indices[i], data_ptr[i]);\n }\n return ret;\n };\n}\n\ntemplate\nstd::function>(T idx)>\nRowFunctionFromCSR(const void* indptr, int indptr_type, const int32_t* indices, const void* data, int data_type, int64_t , int64_t ) {\n if (data_type == C_API_DTYPE_FLOAT32) {\n if (indptr_type == C_API_DTYPE_INT32) {\n return RowFunctionFromCSR_helper(indptr, indices, data);\n } else if (indptr_type == C_API_DTYPE_INT64) {\n return RowFunctionFromCSR_helper(indptr, indices, data);\n }\n } else if (data_type == C_API_DTYPE_FLOAT64) {\n if (indptr_type == C_API_DTYPE_INT32) {\n return RowFunctionFromCSR_helper(indptr, indices, data);\n } else if (indptr_type == C_API_DTYPE_INT64) {\n return RowFunctionFromCSR_helper(indptr, indices, data);\n }\n }\n Log::Fatal(\"Unknown data type in RowFunctionFromCSR\");\n return nullptr;\n}\n\n\n\ntemplate \nstd::function(int idx)> IterateFunctionFromCSC_helper(const void* col_ptr, const int32_t* indices, const void* data, int col_idx) {\n const T1* data_ptr = reinterpret_cast(data);\n const T2* ptr_col_ptr = reinterpret_cast(col_ptr);\n int64_t start = ptr_col_ptr[col_idx];\n int64_t end = ptr_col_ptr[col_idx + 1];\n return [=] (int offset) {\n int64_t i = static_cast(start + offset);\n if (i >= end) {\n return std::make_pair(-1, 0.0);\n }\n int idx = static_cast(indices[i]);\n double val = static_cast(data_ptr[i]);\n return std::make_pair(idx, val);\n };\n}\n\nstd::function(int idx)>\nIterateFunctionFromCSC(const void* col_ptr, int col_ptr_type, const int32_t* indices, const void* data, int data_type, int64_t ncol_ptr, int64_t , int col_idx) {\n CHECK(col_idx < ncol_ptr && col_idx >= 0);\n if (data_type == C_API_DTYPE_FLOAT32) {\n if (col_ptr_type == C_API_DTYPE_INT32) {\n return IterateFunctionFromCSC_helper(col_ptr, indices, data, col_idx);\n } else if (col_ptr_type == C_API_DTYPE_INT64) {\n return IterateFunctionFromCSC_helper(col_ptr, indices, data, col_idx);\n }\n } else if (data_type == C_API_DTYPE_FLOAT64) {\n if (col_ptr_type == C_API_DTYPE_INT32) {\n return IterateFunctionFromCSC_helper(col_ptr, indices, data, col_idx);\n } else if (col_ptr_type == C_API_DTYPE_INT64) {\n return IterateFunctionFromCSC_helper(col_ptr, indices, data, col_idx);\n }\n }\n Log::Fatal(\"Unknown data type in CSC matrix\");\n return nullptr;\n}\n\nCSC_RowIterator::CSC_RowIterator(const void* col_ptr, int col_ptr_type, const int32_t* indices,\n const void* data, int data_type, int64_t ncol_ptr, int64_t nelem, int col_idx) {\n iter_fun_ = IterateFunctionFromCSC(col_ptr, col_ptr_type, indices, data, data_type, ncol_ptr, nelem, col_idx);\n}\n\ndouble CSC_RowIterator::Get(int idx) {\n while (idx > cur_idx_ && !is_end_) {\n auto ret = iter_fun_(nonzero_idx_);\n if (ret.first < 0) {\n is_end_ = true;\n break;\n }\n cur_idx_ = ret.first;\n cur_val_ = ret.second;\n ++nonzero_idx_;\n }\n if (idx == cur_idx_) {\n return cur_val_;\n } else {\n return 0.0f;\n }\n}\n\nstd::pair CSC_RowIterator::NextNonZero() {\n if (!is_end_) {\n auto ret = iter_fun_(nonzero_idx_);\n ++nonzero_idx_;\n if (ret.first < 0) {\n is_end_ = true;\n }\n return ret;\n } else {\n return std::make_pair(-1, 0.0);\n }\n}\n" }, { "path": "src/cuda/cuda_algorithms.cu", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved.\n */\n\n#ifdef USE_CUDA\n\n#include \n#include \n\n#include \n\nnamespace LightGBM {\n\ntemplate \n__global__ void ShufflePrefixSumGlobalKernel(T* values, size_t len, T* block_prefix_sum_buffer) {\n __shared__ T shared_mem_buffer[WARPSIZE];\n const size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n T value = 0;\n if (index < len) {\n value = values[index];\n }\n const T prefix_sum_value = ShufflePrefixSum(value, shared_mem_buffer);\n values[index] = prefix_sum_value;\n if (threadIdx.x == blockDim.x - 1) {\n block_prefix_sum_buffer[blockIdx.x] = prefix_sum_value;\n }\n}\n\ntemplate \n__global__ void ShufflePrefixSumGlobalReduceBlockKernel(T* block_prefix_sum_buffer, int num_blocks) {\n __shared__ T shared_mem_buffer[WARPSIZE];\n const int num_blocks_per_thread = (num_blocks + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 2) / (GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1);\n int thread_block_start = threadIdx.x == 0 ? 0 : (threadIdx.x - 1) * num_blocks_per_thread;\n int thread_block_end = threadIdx.x == 0 ? 0 : min(thread_block_start + num_blocks_per_thread, num_blocks);\n T base = 0;\n for (int block_index = thread_block_start; block_index < thread_block_end; ++block_index) {\n base += block_prefix_sum_buffer[block_index];\n }\n base = ShufflePrefixSum(base, shared_mem_buffer);\n thread_block_start = threadIdx.x == blockDim.x - 1 ? 0 : threadIdx.x * num_blocks_per_thread;\n thread_block_end = threadIdx.x == blockDim.x - 1 ? 0 : min(thread_block_start + num_blocks_per_thread, num_blocks);\n for (int block_index = thread_block_start + 1; block_index < thread_block_end; ++block_index) {\n block_prefix_sum_buffer[block_index] += block_prefix_sum_buffer[block_index - 1];\n }\n for (int block_index = thread_block_start; block_index < thread_block_end; ++block_index) {\n block_prefix_sum_buffer[block_index] += base;\n }\n}\n\ntemplate \n__global__ void ShufflePrefixSumGlobalAddBase(size_t len, const T* block_prefix_sum_buffer, T* values) {\n const T base = blockIdx.x == 0 ? 0 : block_prefix_sum_buffer[blockIdx.x - 1];\n const size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n if (index < len) {\n values[index] += base;\n }\n}\n\ntemplate \nvoid ShufflePrefixSumGlobal(T* values, size_t len, T* block_prefix_sum_buffer) {\n const int num_blocks = (static_cast(len) + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE;\n ShufflePrefixSumGlobalKernel<<>>(values, len, block_prefix_sum_buffer);\n ShufflePrefixSumGlobalReduceBlockKernel<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(block_prefix_sum_buffer, num_blocks);\n ShufflePrefixSumGlobalAddBase<<>>(len, block_prefix_sum_buffer, values);\n}\n\ntemplate void ShufflePrefixSumGlobal(uint16_t* values, size_t len, uint16_t* block_prefix_sum_buffer);\ntemplate void ShufflePrefixSumGlobal(uint32_t* values, size_t len, uint32_t* block_prefix_sum_buffer);\ntemplate void ShufflePrefixSumGlobal(uint64_t* values, size_t len, uint64_t* block_prefix_sum_buffer);\n\n__global__ void BitonicArgSortItemsGlobalKernel(const double* scores,\n const int num_queries,\n const data_size_t* cuda_query_boundaries,\n data_size_t* out_indices) {\n const int query_index_start = static_cast(blockIdx.x) * BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE;\n const int query_index_end = min(query_index_start + BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE, num_queries);\n for (int query_index = query_index_start; query_index < query_index_end; ++query_index) {\n const data_size_t query_item_start = cuda_query_boundaries[query_index];\n const data_size_t query_item_end = cuda_query_boundaries[query_index + 1];\n const data_size_t num_items_in_query = query_item_end - query_item_start;\n BitonicArgSortDevice(scores + query_item_start,\n out_indices + query_item_start,\n num_items_in_query);\n __syncthreads();\n }\n}\n\nvoid BitonicArgSortItemsGlobal(\n const double* scores,\n const int num_queries,\n const data_size_t* cuda_query_boundaries,\n data_size_t* out_indices) {\n const int num_blocks = (num_queries + BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE - 1) / BITONIC_SORT_QUERY_ITEM_BLOCK_SIZE;\n BitonicArgSortItemsGlobalKernel<<>>(\n scores, num_queries, cuda_query_boundaries, out_indices);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\ntemplate \n__global__ void BlockReduceSum(T* block_buffer, const data_size_t num_blocks) {\n __shared__ T shared_buffer[WARPSIZE];\n T thread_sum = 0;\n for (data_size_t block_index = static_cast(threadIdx.x); block_index < num_blocks; block_index += static_cast(blockDim.x)) {\n thread_sum += block_buffer[block_index];\n }\n thread_sum = ShuffleReduceSum(thread_sum, shared_buffer, blockDim.x);\n if (threadIdx.x == 0) {\n block_buffer[0] = thread_sum;\n }\n}\n\ntemplate \n__global__ void ShuffleReduceSumGlobalKernel(const VAL_T* values, const data_size_t num_value, REDUCE_T* block_buffer) {\n __shared__ REDUCE_T shared_buffer[WARPSIZE];\n const data_size_t data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x);\n const REDUCE_T value = (data_index < num_value ? static_cast(values[data_index]) : 0.0f);\n const REDUCE_T reduce_value = ShuffleReduceSum(value, shared_buffer, blockDim.x);\n if (threadIdx.x == 0) {\n block_buffer[blockIdx.x] = reduce_value;\n }\n}\n\ntemplate \nvoid ShuffleReduceSumGlobal(const VAL_T* values, size_t n, REDUCE_T* block_buffer) {\n const data_size_t num_value = static_cast(n);\n const data_size_t num_blocks = (num_value + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE;\n ShuffleReduceSumGlobalKernel<<>>(values, num_value, block_buffer);\n BlockReduceSum<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(block_buffer, num_blocks);\n}\n\ntemplate void ShuffleReduceSumGlobal(const label_t* values, size_t n, double* block_buffer);\ntemplate void ShuffleReduceSumGlobal(const double* values, size_t n, double* block_buffer);\n\ntemplate \n__global__ void ShuffleReduceMinGlobalKernel(const VAL_T* values, const data_size_t num_value, REDUCE_T* block_buffer) {\n __shared__ REDUCE_T shared_buffer[WARPSIZE];\n const data_size_t data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x);\n const REDUCE_T value = (data_index < num_value ? static_cast(values[data_index]) : 0.0f);\n const REDUCE_T reduce_value = ShuffleReduceMin(value, shared_buffer, blockDim.x);\n if (threadIdx.x == 0) {\n block_buffer[blockIdx.x] = reduce_value;\n }\n}\n\ntemplate \n__global__ void ShuffleBlockReduceMin(T* block_buffer, const data_size_t num_blocks) {\n __shared__ T shared_buffer[WARPSIZE];\n T thread_min = 0;\n for (data_size_t block_index = static_cast(threadIdx.x); block_index < num_blocks; block_index += static_cast(blockDim.x)) {\n const T value = block_buffer[block_index];\n if (value < thread_min) {\n thread_min = value;\n }\n }\n thread_min = ShuffleReduceMin(thread_min, shared_buffer, blockDim.x);\n if (threadIdx.x == 0) {\n block_buffer[0] = thread_min;\n }\n}\n\ntemplate \nvoid ShuffleReduceMinGlobal(const VAL_T* values, size_t n, REDUCE_T* block_buffer) {\n const data_size_t num_value = static_cast(n);\n const data_size_t num_blocks = (num_value + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE;\n ShuffleReduceMinGlobalKernel<<>>(values, num_value, block_buffer);\n ShuffleBlockReduceMin<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(block_buffer, num_blocks);\n}\n\ntemplate void ShuffleReduceMinGlobal(const label_t* values, size_t n, double* block_buffer);\n\ntemplate \n__global__ void ShuffleReduceDotProdGlobalKernel(const VAL_T* values1, const VAL_T* values2, const data_size_t num_value, REDUCE_T* block_buffer) {\n __shared__ REDUCE_T shared_buffer[WARPSIZE];\n const data_size_t data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x);\n const REDUCE_T value1 = (data_index < num_value ? static_cast(values1[data_index]) : 0.0f);\n const REDUCE_T value2 = (data_index < num_value ? static_cast(values2[data_index]) : 0.0f);\n const REDUCE_T reduce_value = ShuffleReduceSum(value1 * value2, shared_buffer, blockDim.x);\n if (threadIdx.x == 0) {\n block_buffer[blockIdx.x] = reduce_value;\n }\n}\n\ntemplate \nvoid ShuffleReduceDotProdGlobal(const VAL_T* values1, const VAL_T* values2, size_t n, REDUCE_T* block_buffer) {\n const data_size_t num_value = static_cast(n);\n const data_size_t num_blocks = (num_value + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE;\n ShuffleReduceDotProdGlobalKernel<<>>(values1, values2, num_value, block_buffer);\n BlockReduceSum<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(block_buffer, num_blocks);\n}\n\ntemplate void ShuffleReduceDotProdGlobal(const label_t* values1, const label_t* values2, size_t n, double* block_buffer);\n\ntemplate \n__global__ void GlobalInclusiveArgPrefixSumKernel(\n const INDEX_T* sorted_indices, const VAL_T* in_values, REDUCE_T* out_values, REDUCE_T* block_buffer, data_size_t num_data) {\n __shared__ REDUCE_T shared_buffer[WARPSIZE];\n const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n REDUCE_T value = static_cast(data_index < num_data ? in_values[sorted_indices[data_index]] : 0);\n __syncthreads();\n value = ShufflePrefixSum(value, shared_buffer);\n if (data_index < num_data) {\n out_values[data_index] = value;\n }\n if (threadIdx.x == blockDim.x - 1) {\n block_buffer[blockIdx.x + 1] = value;\n }\n}\n\ntemplate \n__global__ void GlobalInclusivePrefixSumReduceBlockKernel(T* block_buffer, data_size_t num_blocks) {\n __shared__ T shared_buffer[WARPSIZE];\n T thread_sum = 0;\n const data_size_t num_blocks_per_thread = (num_blocks + static_cast(blockDim.x)) / static_cast(blockDim.x);\n const data_size_t thread_start_block_index = static_cast(threadIdx.x) * num_blocks_per_thread;\n const data_size_t thread_end_block_index = min(thread_start_block_index + num_blocks_per_thread, num_blocks + 1);\n for (data_size_t block_index = thread_start_block_index; block_index < thread_end_block_index; ++block_index) {\n thread_sum += block_buffer[block_index];\n }\n ShufflePrefixSumExclusive(thread_sum, shared_buffer);\n for (data_size_t block_index = thread_start_block_index; block_index < thread_end_block_index; ++block_index) {\n block_buffer[block_index] += thread_sum;\n }\n}\n\ntemplate \n__global__ void GlobalInclusivePrefixSumAddBlockBaseKernel(const T* block_buffer, T* values, data_size_t num_data) {\n const T block_sum_base = block_buffer[blockIdx.x];\n const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n if (data_index < num_data) {\n values[data_index] += block_sum_base;\n }\n}\n\ntemplate \nvoid GlobalInclusiveArgPrefixSum(const INDEX_T* sorted_indices, const VAL_T* in_values, REDUCE_T* out_values, REDUCE_T* block_buffer, size_t n) {\n const data_size_t num_data = static_cast(n);\n const data_size_t num_blocks = (num_data + GLOBAL_PREFIX_SUM_BLOCK_SIZE - 1) / GLOBAL_PREFIX_SUM_BLOCK_SIZE;\n GlobalInclusiveArgPrefixSumKernel<<>>(\n sorted_indices, in_values, out_values, block_buffer, num_data);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n GlobalInclusivePrefixSumReduceBlockKernel<<<1, GLOBAL_PREFIX_SUM_BLOCK_SIZE>>>(\n block_buffer, num_blocks);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n GlobalInclusivePrefixSumAddBlockBaseKernel<<>>(\n block_buffer, out_values, num_data);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\ntemplate void GlobalInclusiveArgPrefixSum(const data_size_t* sorted_indices, const label_t* in_values, double* out_values, double* block_buffer, size_t n);\n\ntemplate \n__global__ void BitonicArgSortGlobalKernel(const VAL_T* values, INDEX_T* indices, const int num_total_data) {\n const int thread_index = static_cast(threadIdx.x);\n const int low = static_cast(blockIdx.x * BITONIC_SORT_NUM_ELEMENTS);\n const bool outer_ascending = ASCENDING ? (blockIdx.x % 2 == 0) : (blockIdx.x % 2 == 1);\n const VAL_T* values_pointer = values + low;\n INDEX_T* indices_pointer = indices + low;\n const int num_data = min(BITONIC_SORT_NUM_ELEMENTS, num_total_data - low);\n __shared__ VAL_T shared_values[BITONIC_SORT_NUM_ELEMENTS];\n __shared__ INDEX_T shared_indices[BITONIC_SORT_NUM_ELEMENTS];\n if (thread_index < num_data) {\n shared_values[thread_index] = values_pointer[thread_index];\n shared_indices[thread_index] = static_cast(thread_index + blockIdx.x * blockDim.x);\n }\n __syncthreads();\n for (int depth = BITONIC_SORT_DEPTH - 1; depth >= 1; --depth) {\n const int segment_length = 1 << (BITONIC_SORT_DEPTH - depth);\n const int segment_index = thread_index / segment_length;\n const bool ascending = outer_ascending ? (segment_index % 2 == 0) : (segment_index % 2 == 1);\n const int num_total_segment = (num_data + segment_length - 1) / segment_length;\n {\n const int inner_depth = depth;\n const int inner_segment_length_half = 1 << (BITONIC_SORT_DEPTH - 1 - inner_depth);\n const int inner_segment_index_half = thread_index / inner_segment_length_half;\n const int offset = ((inner_segment_index_half >> 1) == num_total_segment - 1 && ascending == outer_ascending) ?\n (num_total_segment * segment_length - num_data) : 0;\n const int segment_start = segment_index * segment_length;\n if (inner_segment_index_half % 2 == 0) {\n if (thread_index >= offset + segment_start) {\n const int index_to_compare = thread_index + inner_segment_length_half - offset;\n const INDEX_T this_index = shared_indices[thread_index];\n const INDEX_T other_index = shared_indices[index_to_compare];\n const VAL_T this_value = shared_values[thread_index];\n const VAL_T other_value = shared_values[index_to_compare];\n if (index_to_compare < num_data && (this_value > other_value) == ascending) {\n shared_indices[thread_index] = other_index;\n shared_indices[index_to_compare] = this_index;\n shared_values[thread_index] = other_value;\n shared_values[index_to_compare] = this_value;\n }\n }\n }\n __syncthreads();\n }\n for (int inner_depth = depth + 1; inner_depth < BITONIC_SORT_DEPTH; ++inner_depth) {\n const int inner_segment_length_half = 1 << (BITONIC_SORT_DEPTH - 1 - inner_depth);\n const int inner_segment_index_half = thread_index / inner_segment_length_half;\n if (inner_segment_index_half % 2 == 0) {\n const int index_to_compare = thread_index + inner_segment_length_half;\n const INDEX_T this_index = shared_indices[thread_index];\n const INDEX_T other_index = shared_indices[index_to_compare];\n const VAL_T this_value = shared_values[thread_index];\n const VAL_T other_value = shared_values[index_to_compare];\n if (index_to_compare < num_data && (this_value > other_value) == ascending) {\n shared_indices[thread_index] = other_index;\n shared_indices[index_to_compare] = this_index;\n shared_values[thread_index] = other_value;\n shared_values[index_to_compare] = this_value;\n }\n }\n __syncthreads();\n }\n }\n if (thread_index < num_data) {\n indices_pointer[thread_index] = shared_indices[thread_index];\n }\n}\n\ntemplate \n__global__ void BitonicArgSortMergeKernel(const VAL_T* values, INDEX_T* indices, const int segment_length, const int len) {\n const int thread_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n const int segment_index = thread_index / segment_length;\n const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1);\n __shared__ VAL_T shared_values[BITONIC_SORT_NUM_ELEMENTS];\n __shared__ INDEX_T shared_indices[BITONIC_SORT_NUM_ELEMENTS];\n const int offset = static_cast(blockIdx.x * blockDim.x);\n const int local_len = min(BITONIC_SORT_NUM_ELEMENTS, len - offset);\n if (thread_index < len) {\n const INDEX_T index = indices[thread_index];\n shared_values[threadIdx.x] = values[index];\n shared_indices[threadIdx.x] = index;\n }\n __syncthreads();\n int half_segment_length = BITONIC_SORT_NUM_ELEMENTS / 2;\n while (half_segment_length >= 1) {\n const int half_segment_index = static_cast(threadIdx.x) / half_segment_length;\n if (half_segment_index % 2 == 0) {\n const int index_to_compare = static_cast(threadIdx.x) + half_segment_length;\n const INDEX_T this_index = shared_indices[threadIdx.x];\n const INDEX_T other_index = shared_indices[index_to_compare];\n const VAL_T this_value = shared_values[threadIdx.x];\n const VAL_T other_value = shared_values[index_to_compare];\n if (index_to_compare < local_len && ((this_value > other_value) == ascending)) {\n shared_indices[threadIdx.x] = other_index;\n shared_indices[index_to_compare] = this_index;\n shared_values[threadIdx.x] = other_value;\n shared_values[index_to_compare] = this_value;\n }\n }\n __syncthreads();\n half_segment_length >>= 1;\n }\n if (thread_index < len) {\n indices[thread_index] = shared_indices[threadIdx.x];\n }\n}\n\ntemplate \n__global__ void BitonicArgCompareKernel(const VAL_T* values, INDEX_T* indices, const int half_segment_length, const int outer_segment_length, const int len) {\n const int thread_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n const int segment_index = thread_index / outer_segment_length;\n const int half_segment_index = thread_index / half_segment_length;\n const bool ascending = ASCENDING ? (segment_index % 2 == 0) : (segment_index % 2 == 1);\n if (half_segment_index % 2 == 0) {\n const int num_total_segment = (len + outer_segment_length - 1) / outer_segment_length;\n if (BEGIN && (half_segment_index >> 1) == num_total_segment - 1 && ascending == ASCENDING) {\n const int offset = num_total_segment * outer_segment_length - len;\n const int segment_start = segment_index * outer_segment_length;\n if (thread_index >= offset + segment_start) {\n const int index_to_compare = thread_index + half_segment_length - offset;\n if (index_to_compare < len) {\n const INDEX_T this_index = indices[thread_index];\n const INDEX_T other_index = indices[index_to_compare];\n if ((values[this_index] > values[other_index]) == ascending) {\n indices[thread_index] = other_index;\n indices[index_to_compare] = this_index;\n }\n }\n }\n } else {\n const int index_to_compare = thread_index + half_segment_length;\n if (index_to_compare < len) {\n const INDEX_T this_index = indices[thread_index];\n const INDEX_T other_index = indices[index_to_compare];\n if ((values[this_index] > values[other_index]) == ascending) {\n indices[thread_index] = other_index;\n indices[index_to_compare] = this_index;\n }\n }\n }\n }\n}\n\ntemplate \nvoid BitonicArgSortGlobalHelper(const VAL_T* values, INDEX_T* indices, const size_t len) {\n int max_depth = 1;\n int len_to_shift = static_cast(len) - 1;\n while (len_to_shift > 0) {\n ++max_depth;\n len_to_shift >>= 1;\n }\n const int num_blocks = (static_cast(len) + BITONIC_SORT_NUM_ELEMENTS - 1) / BITONIC_SORT_NUM_ELEMENTS;\n BitonicArgSortGlobalKernel<<>>(values, indices, static_cast(len));\n SynchronizeCUDADevice(__FILE__, __LINE__);\n for (int depth = max_depth - 11; depth >= 1; --depth) {\n const int segment_length = (1 << (max_depth - depth));\n int half_segment_length = (segment_length >> 1);\n {\n BitonicArgCompareKernel<<>>(\n values, indices, half_segment_length, segment_length, static_cast(len));\n SynchronizeCUDADevice(__FILE__, __LINE__);\n half_segment_length >>= 1;\n }\n for (int inner_depth = depth + 1; inner_depth <= max_depth - 11; ++inner_depth) {\n BitonicArgCompareKernel<<>>(\n values, indices, half_segment_length, segment_length, static_cast(len));\n SynchronizeCUDADevice(__FILE__, __LINE__);\n half_segment_length >>= 1;\n }\n BitonicArgSortMergeKernel<<>>(\n values, indices, segment_length, static_cast(len));\n SynchronizeCUDADevice(__FILE__, __LINE__);\n }\n}\n\ntemplate <>\nvoid BitonicArgSortGlobal(const double* values, data_size_t* indices, const size_t len) {\n BitonicArgSortGlobalHelper(values, indices, len);\n}\n\ntemplate <>\nvoid BitonicArgSortGlobal(const double* values, data_size_t* indices, const size_t len) {\n BitonicArgSortGlobalHelper(values, indices, len);\n}\n\ntemplate <>\nvoid BitonicArgSortGlobal(const label_t* values, data_size_t* indices, const size_t len) {\n BitonicArgSortGlobalHelper(values, indices, len);\n}\n\ntemplate <>\nvoid BitonicArgSortGlobal(const data_size_t* values, int* indices, const size_t len) {\n BitonicArgSortGlobalHelper(values, indices, len);\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/cuda/cuda_utils.cpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifdef USE_CUDA\n\n#include \n#include \n\nnamespace LightGBM {\n\nvoid SynchronizeCUDADevice(const char* file, const int line) {\n gpuAssert(cudaDeviceSynchronize(), file, line);\n}\n\nvoid SynchronizeCUDAStream(cudaStream_t cuda_stream, const char* file, const int line) {\n gpuAssert(cudaStreamSynchronize(cuda_stream), file, line);\n}\n\nvoid PrintLastCUDAError() {\n const char* error_name = cudaGetErrorName(cudaGetLastError());\n Log::Fatal(error_name);\n}\n\nvoid SetCUDADevice(int gpu_device_id, const char* file, int line) {\n int cur_gpu_device_id = 0;\n CUDASUCCESS_OR_FATAL_OUTER(cudaGetDevice(&cur_gpu_device_id));\n if (cur_gpu_device_id != gpu_device_id) {\n CUDASUCCESS_OR_FATAL_OUTER(cudaSetDevice(gpu_device_id));\n }\n}\n\nint GetCUDADevice(const char* file, int line) {\n int cur_gpu_device_id = 0;\n CUDASUCCESS_OR_FATAL_OUTER(cudaGetDevice(&cur_gpu_device_id));\n return cur_gpu_device_id;\n}\n\ncudaStream_t CUDAStreamCreate() {\n cudaStream_t cuda_stream;\n CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_stream));\n return cuda_stream;\n}\n\nvoid CUDAStreamDestroy(cudaStream_t cuda_stream) {\n CUDASUCCESS_OR_FATAL(cudaStreamDestroy(cuda_stream));\n}\n\nvoid NCCLGroupStart() {\n NCCLCHECK(ncclGroupStart());\n}\n\nvoid NCCLGroupEnd() {\n NCCLCHECK(ncclGroupEnd());\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/io/bin.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"dense_bin.hpp\"\n#include \"multi_val_dense_bin.hpp\"\n#include \"multi_val_sparse_bin.hpp\"\n#include \"sparse_bin.hpp\"\n\nnamespace LightGBM {\n\nBinMapper::BinMapper(): num_bin_(1), is_trivial_(true), bin_type_(BinType::NumericalBin) {\n bin_upper_bound_.clear();\n bin_upper_bound_.push_back(std::numeric_limits::infinity());\n}\n\n// deep copy function for BinMapper\nBinMapper::BinMapper(const BinMapper& other) {\n num_bin_ = other.num_bin_;\n missing_type_ = other.missing_type_;\n is_trivial_ = other.is_trivial_;\n sparse_rate_ = other.sparse_rate_;\n bin_type_ = other.bin_type_;\n if (bin_type_ == BinType::NumericalBin) {\n bin_upper_bound_ = other.bin_upper_bound_;\n } else {\n bin_2_categorical_ = other.bin_2_categorical_;\n categorical_2_bin_ = other.categorical_2_bin_;\n }\n min_val_ = other.min_val_;\n max_val_ = other.max_val_;\n default_bin_ = other.default_bin_;\n most_freq_bin_ = other.most_freq_bin_;\n}\n\nBinMapper::BinMapper(const void* memory) {\n CopyFrom(reinterpret_cast(memory));\n}\n\nBinMapper::~BinMapper() {\n}\n\nbool NeedFilter(const std::vector& cnt_in_bin, int total_cnt, int filter_cnt, BinType bin_type) {\n if (bin_type == BinType::NumericalBin) {\n int sum_left = 0;\n for (size_t i = 0; i < cnt_in_bin.size() - 1; ++i) {\n sum_left += cnt_in_bin[i];\n if (sum_left >= filter_cnt && total_cnt - sum_left >= filter_cnt) {\n return false;\n }\n }\n } else {\n if (cnt_in_bin.size() <= 2) {\n for (size_t i = 0; i < cnt_in_bin.size() - 1; ++i) {\n int sum_left = cnt_in_bin[i];\n if (sum_left >= filter_cnt && total_cnt - sum_left >= filter_cnt) {\n return false;\n }\n }\n } else {\n return false;\n }\n }\n return true;\n}\n\nstd::vector GreedyFindBin(const double* distinct_values, const int* counts,\n int num_distinct_values, int max_bin,\n size_t total_cnt, int min_data_in_bin) {\n std::vector bin_upper_bound;\n CHECK_GT(max_bin, 0);\n if (num_distinct_values <= max_bin) {\n bin_upper_bound.clear();\n int cur_cnt_inbin = 0;\n for (int i = 0; i < num_distinct_values - 1; ++i) {\n cur_cnt_inbin += counts[i];\n if (cur_cnt_inbin >= min_data_in_bin) {\n auto val = Common::GetDoubleUpperBound((distinct_values[i] + distinct_values[i + 1]) / 2.0);\n if (bin_upper_bound.empty() || !Common::CheckDoubleEqualOrdered(bin_upper_bound.back(), val)) {\n bin_upper_bound.push_back(val);\n cur_cnt_inbin = 0;\n }\n }\n }\n cur_cnt_inbin += counts[num_distinct_values - 1];\n bin_upper_bound.push_back(std::numeric_limits::infinity());\n } else {\n if (min_data_in_bin > 0) {\n max_bin = std::min(max_bin, static_cast(total_cnt / min_data_in_bin));\n max_bin = std::max(max_bin, 1);\n }\n double mean_bin_size = static_cast(total_cnt) / max_bin;\n\n // mean size for one bin\n int rest_bin_cnt = max_bin;\n int rest_sample_cnt = static_cast(total_cnt);\n std::vector is_big_count_value(num_distinct_values, false);\n for (int i = 0; i < num_distinct_values; ++i) {\n if (counts[i] >= mean_bin_size) {\n is_big_count_value[i] = true;\n --rest_bin_cnt;\n rest_sample_cnt -= counts[i];\n }\n }\n mean_bin_size = static_cast(rest_sample_cnt) / rest_bin_cnt;\n std::vector upper_bounds(max_bin, std::numeric_limits::infinity());\n std::vector lower_bounds(max_bin, std::numeric_limits::infinity());\n\n int bin_cnt = 0;\n lower_bounds[bin_cnt] = distinct_values[0];\n int cur_cnt_inbin = 0;\n for (int i = 0; i < num_distinct_values - 1; ++i) {\n if (!is_big_count_value[i]) {\n rest_sample_cnt -= counts[i];\n }\n cur_cnt_inbin += counts[i];\n // need a new bin\n if (is_big_count_value[i] || cur_cnt_inbin >= mean_bin_size ||\n (is_big_count_value[i + 1] && cur_cnt_inbin >= std::max(1.0, mean_bin_size * 0.5f))) {\n upper_bounds[bin_cnt] = distinct_values[i];\n ++bin_cnt;\n lower_bounds[bin_cnt] = distinct_values[i + 1];\n if (bin_cnt >= max_bin - 1) {\n break;\n }\n cur_cnt_inbin = 0;\n if (!is_big_count_value[i]) {\n --rest_bin_cnt;\n mean_bin_size = rest_sample_cnt / static_cast(rest_bin_cnt);\n }\n }\n }\n ++bin_cnt;\n // update bin upper bound\n bin_upper_bound.clear();\n for (int i = 0; i < bin_cnt - 1; ++i) {\n auto val = Common::GetDoubleUpperBound((upper_bounds[i] + lower_bounds[i + 1]) / 2.0);\n if (bin_upper_bound.empty() || !Common::CheckDoubleEqualOrdered(bin_upper_bound.back(), val)) {\n bin_upper_bound.push_back(val);\n }\n }\n // last bin upper bound\n bin_upper_bound.push_back(std::numeric_limits::infinity());\n }\n return bin_upper_bound;\n}\n\nstd::vector FindBinWithPredefinedBin(const double* distinct_values, const int* counts,\n int num_distinct_values, int max_bin,\n size_t total_sample_cnt, int min_data_in_bin,\n const std::vector& forced_upper_bounds) {\n std::vector bin_upper_bound;\n\n // get number of positive and negative distinct values\n int left_cnt = -1;\n for (int i = 0; i < num_distinct_values; ++i) {\n if (distinct_values[i] > -kZeroThreshold) {\n left_cnt = i;\n break;\n }\n }\n if (left_cnt < 0) {\n left_cnt = num_distinct_values;\n }\n int right_start = -1;\n for (int i = left_cnt; i < num_distinct_values; ++i) {\n if (distinct_values[i] > kZeroThreshold) {\n right_start = i;\n break;\n }\n }\n\n // include zero bounds and infinity bound\n if (max_bin == 2) {\n if (left_cnt == 0) {\n bin_upper_bound.push_back(kZeroThreshold);\n } else {\n bin_upper_bound.push_back(-kZeroThreshold);\n }\n } else if (max_bin >= 3) {\n if (left_cnt > 0) {\n bin_upper_bound.push_back(-kZeroThreshold);\n }\n if (right_start >= 0) {\n bin_upper_bound.push_back(kZeroThreshold);\n }\n }\n bin_upper_bound.push_back(std::numeric_limits::infinity());\n\n // add forced bounds, excluding zeros since we have already added zero bounds\n int max_to_insert = max_bin - static_cast(bin_upper_bound.size());\n int num_inserted = 0;\n for (size_t i = 0; i < forced_upper_bounds.size(); ++i) {\n if (num_inserted >= max_to_insert) {\n break;\n }\n if (std::fabs(forced_upper_bounds[i]) > kZeroThreshold) {\n bin_upper_bound.push_back(forced_upper_bounds[i]);\n ++num_inserted;\n }\n }\n std::stable_sort(bin_upper_bound.begin(), bin_upper_bound.end());\n\n // find remaining bounds\n int free_bins = max_bin - static_cast(bin_upper_bound.size());\n std::vector bounds_to_add;\n int value_ind = 0;\n for (size_t i = 0; i < bin_upper_bound.size(); ++i) {\n int cnt_in_bin = 0;\n int distinct_cnt_in_bin = 0;\n int bin_start = value_ind;\n while ((value_ind < num_distinct_values) && (distinct_values[value_ind] < bin_upper_bound[i])) {\n cnt_in_bin += counts[value_ind];\n ++distinct_cnt_in_bin;\n ++value_ind;\n }\n int bins_remaining = max_bin - static_cast(bin_upper_bound.size()) - static_cast(bounds_to_add.size());\n int num_sub_bins = static_cast(std::lround((static_cast(cnt_in_bin) * free_bins / total_sample_cnt)));\n num_sub_bins = std::min(num_sub_bins, bins_remaining) + 1;\n if (i == bin_upper_bound.size() - 1) {\n num_sub_bins = bins_remaining + 1;\n }\n std::vector new_upper_bounds = GreedyFindBin(distinct_values + bin_start, counts + bin_start, distinct_cnt_in_bin,\n num_sub_bins, cnt_in_bin, min_data_in_bin);\n bounds_to_add.insert(bounds_to_add.end(), new_upper_bounds.begin(), new_upper_bounds.end() - 1); // last bound is infinity\n }\n bin_upper_bound.insert(bin_upper_bound.end(), bounds_to_add.begin(), bounds_to_add.end());\n std::stable_sort(bin_upper_bound.begin(), bin_upper_bound.end());\n CHECK_LE(bin_upper_bound.size(), static_cast(max_bin));\n return bin_upper_bound;\n}\n\nstd::vector FindBinWithZeroAsOneBin(const double* distinct_values, const int* counts, int num_distinct_values,\n int max_bin, size_t total_sample_cnt, int min_data_in_bin) {\n std::vector bin_upper_bound;\n int left_cnt_data = 0;\n int cnt_zero = 0;\n int right_cnt_data = 0;\n for (int i = 0; i < num_distinct_values; ++i) {\n if (distinct_values[i] <= -kZeroThreshold) {\n left_cnt_data += counts[i];\n } else if (distinct_values[i] > kZeroThreshold) {\n right_cnt_data += counts[i];\n } else {\n cnt_zero += counts[i];\n }\n }\n\n int left_cnt = -1;\n for (int i = 0; i < num_distinct_values; ++i) {\n if (distinct_values[i] > -kZeroThreshold) {\n left_cnt = i;\n break;\n }\n }\n\n if (left_cnt < 0) {\n left_cnt = num_distinct_values;\n }\n\n if ((left_cnt > 0) && (max_bin > 1)) {\n int left_max_bin = static_cast(static_cast(left_cnt_data) / (total_sample_cnt - cnt_zero) * (max_bin - 1));\n left_max_bin = std::max(1, left_max_bin);\n bin_upper_bound = GreedyFindBin(distinct_values, counts, left_cnt, left_max_bin, left_cnt_data, min_data_in_bin);\n if (bin_upper_bound.size() > 0) {\n bin_upper_bound.back() = -kZeroThreshold;\n }\n }\n\n int right_start = -1;\n for (int i = left_cnt; i < num_distinct_values; ++i) {\n if (distinct_values[i] > kZeroThreshold) {\n right_start = i;\n break;\n }\n }\n\n int right_max_bin = max_bin - 1 - static_cast(bin_upper_bound.size());\n if (right_start >= 0 && right_max_bin > 0) {\n auto right_bounds = GreedyFindBin(distinct_values + right_start, counts + right_start,\n num_distinct_values - right_start, right_max_bin, right_cnt_data, min_data_in_bin);\n bin_upper_bound.push_back(kZeroThreshold);\n bin_upper_bound.insert(bin_upper_bound.end(), right_bounds.begin(), right_bounds.end());\n } else {\n bin_upper_bound.push_back(std::numeric_limits::infinity());\n }\n CHECK_LE(bin_upper_bound.size(), static_cast(max_bin));\n return bin_upper_bound;\n}\n\nstd::vector FindBinWithZeroAsOneBin(const double* distinct_values, const int* counts, int num_distinct_values,\n int max_bin, size_t total_sample_cnt, int min_data_in_bin,\n const std::vector& forced_upper_bounds) {\n if (forced_upper_bounds.empty()) {\n return FindBinWithZeroAsOneBin(distinct_values, counts, num_distinct_values, max_bin, total_sample_cnt, min_data_in_bin);\n } else {\n return FindBinWithPredefinedBin(distinct_values, counts, num_distinct_values, max_bin, total_sample_cnt, min_data_in_bin,\n forced_upper_bounds);\n }\n}\n\nvoid BinMapper::FindBin(double* values, int num_sample_values, size_t total_sample_cnt,\n int max_bin, int min_data_in_bin, int min_split_data, bool pre_filter, BinType bin_type,\n bool use_missing, bool zero_as_missing,\n const std::vector& forced_upper_bounds) {\n int na_cnt = 0;\n int non_na_cnt = 0;\n for (int i = 0; i < num_sample_values; ++i) {\n if (!std::isnan(values[i])) {\n values[non_na_cnt++] = values[i];\n }\n }\n if (!use_missing) {\n missing_type_ = MissingType::None;\n } else if (zero_as_missing) {\n missing_type_ = MissingType::Zero;\n } else {\n if (non_na_cnt == num_sample_values) {\n missing_type_ = MissingType::None;\n } else {\n missing_type_ = MissingType::NaN;\n na_cnt = num_sample_values - non_na_cnt;\n }\n }\n num_sample_values = non_na_cnt;\n\n bin_type_ = bin_type;\n default_bin_ = 0;\n int zero_cnt = static_cast(total_sample_cnt - num_sample_values - na_cnt);\n // find distinct_values first\n std::vector distinct_values;\n std::vector counts; // count of data points for each distinct feature value.\n\n std::stable_sort(values, values + num_sample_values);\n\n // push zero in the front\n if (num_sample_values == 0 || (values[0] > 0.0f && zero_cnt > 0)) {\n distinct_values.push_back(0.0f);\n counts.push_back(zero_cnt);\n }\n\n if (num_sample_values > 0) {\n distinct_values.push_back(values[0]);\n counts.push_back(1);\n }\n\n for (int i = 1; i < num_sample_values; ++i) {\n if (!Common::CheckDoubleEqualOrdered(values[i - 1], values[i])) {\n if (values[i - 1] < 0.0f && values[i] > 0.0f) {\n distinct_values.push_back(0.0f);\n counts.push_back(zero_cnt);\n }\n distinct_values.push_back(values[i]);\n counts.push_back(1);\n } else {\n // use the large value\n distinct_values.back() = values[i];\n ++counts.back();\n }\n }\n\n // push zero in the back\n if (num_sample_values > 0 && values[num_sample_values - 1] < 0.0f && zero_cnt > 0) {\n distinct_values.push_back(0.0f);\n counts.push_back(zero_cnt);\n }\n min_val_ = distinct_values.front();\n max_val_ = distinct_values.back();\n std::vector cnt_in_bin; // count of data points in each bin.\n int num_distinct_values = static_cast(distinct_values.size());\n if (bin_type_ == BinType::NumericalBin) {\n if (missing_type_ == MissingType::Zero) {\n bin_upper_bound_ = FindBinWithZeroAsOneBin(distinct_values.data(), counts.data(), num_distinct_values, max_bin, total_sample_cnt,\n min_data_in_bin, forced_upper_bounds);\n if (bin_upper_bound_.size() == 2) {\n missing_type_ = MissingType::None;\n }\n } else if (missing_type_ == MissingType::None) {\n bin_upper_bound_ = FindBinWithZeroAsOneBin(distinct_values.data(), counts.data(), num_distinct_values, max_bin, total_sample_cnt,\n min_data_in_bin, forced_upper_bounds);\n } else {\n bin_upper_bound_ = FindBinWithZeroAsOneBin(distinct_values.data(), counts.data(), num_distinct_values, max_bin - 1, total_sample_cnt - na_cnt,\n min_data_in_bin, forced_upper_bounds);\n bin_upper_bound_.push_back(NaN);\n }\n num_bin_ = static_cast(bin_upper_bound_.size());\n {\n cnt_in_bin.resize(num_bin_, 0);\n int i_bin = 0;\n for (int i = 0; i < num_distinct_values; ++i) {\n while (distinct_values[i] > bin_upper_bound_[i_bin] && i_bin < num_bin_ - 1) {\n ++i_bin;\n }\n cnt_in_bin[i_bin] += counts[i];\n }\n if (missing_type_ == MissingType::NaN) {\n cnt_in_bin[num_bin_ - 1] = na_cnt;\n }\n }\n CHECK_LE(num_bin_, max_bin);\n } else {\n // convert to int type first\n std::vector distinct_values_int;\n std::vector counts_int;\n for (size_t i = 0; i < distinct_values.size(); ++i) {\n int val = static_cast(distinct_values[i]);\n if (val < 0) {\n na_cnt += counts[i];\n Log::Warning(\"Met negative value in categorical features, will convert it to NaN\");\n } else {\n if (distinct_values_int.empty() || val != distinct_values_int.back()) {\n distinct_values_int.push_back(val);\n counts_int.push_back(counts[i]);\n } else {\n counts_int.back() += counts[i];\n }\n }\n }\n int rest_cnt = static_cast(total_sample_cnt - na_cnt);\n if (rest_cnt > 0) {\n const int SPARSE_RATIO = 100;\n if (distinct_values_int.back() / SPARSE_RATIO > static_cast(distinct_values_int.size())) {\n Log::Warning(\"Met categorical feature which contains sparse values. \"\n \"Consider renumbering to consecutive integers started from zero\");\n }\n // sort by counts in descending order\n Common::SortForPair(&counts_int, &distinct_values_int, 0, true);\n // will ignore the categorical of small counts\n int cut_cnt = static_cast(\n Common::RoundInt((total_sample_cnt - na_cnt) * 0.99f));\n size_t cur_cat_idx = 0; // index of current category.\n categorical_2_bin_.clear();\n bin_2_categorical_.clear();\n int used_cnt = 0;\n int distinct_cnt = static_cast(distinct_values_int.size());\n if (na_cnt > 0) {\n ++distinct_cnt;\n }\n max_bin = std::min(distinct_cnt, max_bin);\n cnt_in_bin.clear();\n\n // Push the dummy bin for NaN\n bin_2_categorical_.push_back(-1);\n categorical_2_bin_[-1] = 0;\n cnt_in_bin.push_back(0);\n num_bin_ = 1;\n while (cur_cat_idx < distinct_values_int.size()\n && (used_cnt < cut_cnt || num_bin_ < max_bin)) {\n if (counts_int[cur_cat_idx] < min_data_in_bin && cur_cat_idx > 1) {\n break;\n }\n bin_2_categorical_.push_back(distinct_values_int[cur_cat_idx]);\n categorical_2_bin_[distinct_values_int[cur_cat_idx]] = static_cast(num_bin_);\n used_cnt += counts_int[cur_cat_idx];\n cnt_in_bin.push_back(counts_int[cur_cat_idx]);\n ++num_bin_;\n ++cur_cat_idx;\n }\n // Use MissingType::None to represent this bin contains all categoricals\n if (cur_cat_idx == distinct_values_int.size() && na_cnt == 0) {\n missing_type_ = MissingType::None;\n } else {\n missing_type_ = MissingType::NaN;\n }\n // fix count of NaN bin\n cnt_in_bin[0] = static_cast(total_sample_cnt - used_cnt);\n }\n }\n\n // check trivial(num_bin_ == 1) feature\n if (num_bin_ <= 1) {\n is_trivial_ = true;\n } else {\n is_trivial_ = false;\n }\n // check useless bin\n if (!is_trivial_ && pre_filter && NeedFilter(cnt_in_bin, static_cast(total_sample_cnt), min_split_data, bin_type_)) {\n is_trivial_ = true;\n }\n\n if (!is_trivial_) {\n default_bin_ = ValueToBin(0);\n most_freq_bin_ =\n static_cast(ArrayArgs::ArgMax(cnt_in_bin));\n const double max_sparse_rate =\n static_cast(cnt_in_bin[most_freq_bin_]) / total_sample_cnt;\n // When most_freq_bin_ != default_bin_, there are some additional data loading costs.\n // so use most_freq_bin_ = default_bin_ when there is not so sparse\n if (most_freq_bin_ != default_bin_ && max_sparse_rate < kSparseThreshold) {\n most_freq_bin_ = default_bin_;\n }\n sparse_rate_ =\n static_cast(cnt_in_bin[most_freq_bin_]) / total_sample_cnt;\n } else {\n sparse_rate_ = 1.0f;\n }\n}\n\nvoid BinMapper::CopyTo(char * buffer) const {\n std::memcpy(buffer, &num_bin_, sizeof(num_bin_));\n buffer += VirtualFileWriter::AlignedSize(sizeof(num_bin_));\n std::memcpy(buffer, &missing_type_, sizeof(missing_type_));\n buffer += VirtualFileWriter::AlignedSize(sizeof(missing_type_));\n std::memcpy(buffer, &is_trivial_, sizeof(is_trivial_));\n buffer += VirtualFileWriter::AlignedSize(sizeof(is_trivial_));\n std::memcpy(buffer, &sparse_rate_, sizeof(sparse_rate_));\n buffer += sizeof(sparse_rate_);\n std::memcpy(buffer, &bin_type_, sizeof(bin_type_));\n buffer += VirtualFileWriter::AlignedSize(sizeof(bin_type_));\n std::memcpy(buffer, &min_val_, sizeof(min_val_));\n buffer += sizeof(min_val_);\n std::memcpy(buffer, &max_val_, sizeof(max_val_));\n buffer += sizeof(max_val_);\n std::memcpy(buffer, &default_bin_, sizeof(default_bin_));\n buffer += VirtualFileWriter::AlignedSize(sizeof(default_bin_));\n std::memcpy(buffer, &most_freq_bin_, sizeof(most_freq_bin_));\n buffer += VirtualFileWriter::AlignedSize(sizeof(most_freq_bin_));\n if (bin_type_ == BinType::NumericalBin) {\n std::memcpy(buffer, bin_upper_bound_.data(), num_bin_ * sizeof(double));\n } else {\n std::memcpy(buffer, bin_2_categorical_.data(), num_bin_ * sizeof(int));\n }\n}\n\nvoid BinMapper::CopyFrom(const char * buffer) {\n std::memcpy(&num_bin_, buffer, sizeof(num_bin_));\n buffer += VirtualFileWriter::AlignedSize(sizeof(num_bin_));\n std::memcpy(&missing_type_, buffer, sizeof(missing_type_));\n buffer += VirtualFileWriter::AlignedSize(sizeof(missing_type_));\n std::memcpy(&is_trivial_, buffer, sizeof(is_trivial_));\n buffer += VirtualFileWriter::AlignedSize(sizeof(is_trivial_));\n std::memcpy(&sparse_rate_, buffer, sizeof(sparse_rate_));\n buffer += sizeof(sparse_rate_);\n std::memcpy(&bin_type_, buffer, sizeof(bin_type_));\n buffer += VirtualFileWriter::AlignedSize(sizeof(bin_type_));\n std::memcpy(&min_val_, buffer, sizeof(min_val_));\n buffer += sizeof(min_val_);\n std::memcpy(&max_val_, buffer, sizeof(max_val_));\n buffer += sizeof(max_val_);\n std::memcpy(&default_bin_, buffer, sizeof(default_bin_));\n buffer += VirtualFileWriter::AlignedSize(sizeof(default_bin_));\n std::memcpy(&most_freq_bin_, buffer, sizeof(most_freq_bin_));\n buffer += VirtualFileWriter::AlignedSize(sizeof(most_freq_bin_));\n if (bin_type_ == BinType::NumericalBin) {\n bin_upper_bound_ = std::vector(num_bin_);\n std::memcpy(bin_upper_bound_.data(), buffer, num_bin_ * sizeof(double));\n } else {\n bin_2_categorical_ = std::vector(num_bin_);\n std::memcpy(bin_2_categorical_.data(), buffer, num_bin_ * sizeof(int));\n categorical_2_bin_.clear();\n for (int i = 0; i < num_bin_; ++i) {\n categorical_2_bin_[bin_2_categorical_[i]] = static_cast(i);\n }\n }\n}\n\nvoid BinMapper::SaveBinaryToFile(BinaryWriter* writer) const {\n writer->AlignedWrite(&num_bin_, sizeof(num_bin_));\n writer->AlignedWrite(&missing_type_, sizeof(missing_type_));\n writer->AlignedWrite(&is_trivial_, sizeof(is_trivial_));\n writer->Write(&sparse_rate_, sizeof(sparse_rate_));\n writer->AlignedWrite(&bin_type_, sizeof(bin_type_));\n writer->Write(&min_val_, sizeof(min_val_));\n writer->Write(&max_val_, sizeof(max_val_));\n writer->AlignedWrite(&default_bin_, sizeof(default_bin_));\n writer->AlignedWrite(&most_freq_bin_, sizeof(most_freq_bin_));\n if (bin_type_ == BinType::NumericalBin) {\n writer->Write(bin_upper_bound_.data(), sizeof(double) * num_bin_);\n } else {\n writer->Write(bin_2_categorical_.data(), sizeof(int) * num_bin_);\n }\n}\n\nsize_t BinMapper::SizesInByte() const {\n size_t ret = VirtualFileWriter::AlignedSize(sizeof(num_bin_)) +\n VirtualFileWriter::AlignedSize(sizeof(missing_type_)) +\n VirtualFileWriter::AlignedSize(sizeof(is_trivial_)) +\n sizeof(sparse_rate_) +\n VirtualFileWriter::AlignedSize(sizeof(bin_type_)) +\n sizeof(min_val_) + sizeof(max_val_) +\n VirtualFileWriter::AlignedSize(sizeof(default_bin_)) +\n VirtualFileWriter::AlignedSize(sizeof(most_freq_bin_));\n if (bin_type_ == BinType::NumericalBin) {\n ret += sizeof(double) * num_bin_;\n } else {\n ret += sizeof(int) * num_bin_;\n }\n return ret;\n}\n\ntemplate class DenseBin;\ntemplate class DenseBin;\ntemplate class DenseBin;\ntemplate class DenseBin;\n\ntemplate class SparseBin;\ntemplate class SparseBin;\ntemplate class SparseBin;\n\ntemplate class MultiValDenseBin;\ntemplate class MultiValDenseBin;\ntemplate class MultiValDenseBin;\n\nBin* Bin::CreateDenseBin(data_size_t num_data, int num_bin) {\n if (num_bin <= 16) {\n return new DenseBin(num_data);\n } else if (num_bin <= 256) {\n return new DenseBin(num_data);\n } else if (num_bin <= 65536) {\n return new DenseBin(num_data);\n } else {\n return new DenseBin(num_data);\n }\n}\n\nBin* Bin::CreateSparseBin(data_size_t num_data, int num_bin) {\n if (num_bin <= 256) {\n return new SparseBin(num_data);\n } else if (num_bin <= 65536) {\n return new SparseBin(num_data);\n } else {\n return new SparseBin(num_data);\n }\n}\n\nMultiValBin* MultiValBin::CreateMultiValBin(data_size_t num_data, int num_bin, int num_feature,\n double sparse_rate, const std::vector& offsets) {\n if (sparse_rate >= multi_val_bin_sparse_threshold) {\n const double average_element_per_row = (1.0 - sparse_rate) * num_feature;\n return CreateMultiValSparseBin(num_data, num_bin,\n average_element_per_row);\n } else {\n return CreateMultiValDenseBin(num_data, num_bin, num_feature, offsets);\n }\n}\n\nMultiValBin* MultiValBin::CreateMultiValDenseBin(data_size_t num_data,\n int num_bin,\n int num_feature,\n const std::vector& offsets) {\n // calculate max bin of all features to select the int type in MultiValDenseBin\n int max_bin = 0;\n for (int i = 0; i < static_cast(offsets.size()) - 1; ++i) {\n int feature_bin = offsets[i + 1] - offsets[i];\n if (feature_bin > max_bin) {\n max_bin = feature_bin;\n }\n }\n if (max_bin <= 256) {\n return new MultiValDenseBin(num_data, num_bin, num_feature, offsets);\n } else if (max_bin <= 65536) {\n return new MultiValDenseBin(num_data, num_bin, num_feature, offsets);\n } else {\n return new MultiValDenseBin(num_data, num_bin, num_feature, offsets);\n }\n}\n\nMultiValBin* MultiValBin::CreateMultiValSparseBin(data_size_t num_data,\n int num_bin,\n double estimate_element_per_row) {\n size_t estimate_total_entries =\n static_cast(estimate_element_per_row * 1.1 * num_data);\n if (estimate_total_entries <= std::numeric_limits::max()) {\n if (num_bin <= 256) {\n return new MultiValSparseBin(\n num_data, num_bin, estimate_element_per_row);\n } else if (num_bin <= 65536) {\n return new MultiValSparseBin(\n num_data, num_bin, estimate_element_per_row);\n } else {\n return new MultiValSparseBin(\n num_data, num_bin, estimate_element_per_row);\n }\n } else if (estimate_total_entries <= std::numeric_limits::max()) {\n if (num_bin <= 256) {\n return new MultiValSparseBin(\n num_data, num_bin, estimate_element_per_row);\n } else if (num_bin <= 65536) {\n return new MultiValSparseBin(\n num_data, num_bin, estimate_element_per_row);\n } else {\n return new MultiValSparseBin(\n num_data, num_bin, estimate_element_per_row);\n }\n } else {\n if (num_bin <= 256) {\n return new MultiValSparseBin(\n num_data, num_bin, estimate_element_per_row);\n } else if (num_bin <= 65536) {\n return new MultiValSparseBin(\n num_data, num_bin, estimate_element_per_row);\n } else {\n return new MultiValSparseBin(\n num_data, num_bin, estimate_element_per_row);\n }\n }\n}\n\ntemplate <>\nconst void* DenseBin::GetColWiseData(\n uint8_t* bit_type,\n bool* is_sparse,\n std::vector* bin_iterator,\n const int /*num_threads*/) const {\n *is_sparse = false;\n *bit_type = 8;\n bin_iterator->clear();\n return reinterpret_cast(data_.data());\n}\n\ntemplate <>\nconst void* DenseBin::GetColWiseData(\n uint8_t* bit_type,\n bool* is_sparse,\n std::vector* bin_iterator,\n const int /*num_threads*/) const {\n *is_sparse = false;\n *bit_type = 16;\n bin_iterator->clear();\n return reinterpret_cast(data_.data());\n}\n\ntemplate <>\nconst void* DenseBin::GetColWiseData(\n uint8_t* bit_type,\n bool* is_sparse,\n std::vector* bin_iterator,\n const int /*num_threads*/) const {\n *is_sparse = false;\n *bit_type = 32;\n bin_iterator->clear();\n return reinterpret_cast(data_.data());\n}\n\ntemplate <>\nconst void* DenseBin::GetColWiseData(\n uint8_t* bit_type,\n bool* is_sparse,\n std::vector* bin_iterator,\n const int /*num_threads*/) const {\n *is_sparse = false;\n *bit_type = 4;\n bin_iterator->clear();\n return reinterpret_cast(data_.data());\n}\n\ntemplate <>\nconst void* DenseBin::GetColWiseData(\n uint8_t* bit_type,\n bool* is_sparse,\n BinIterator** bin_iterator) const {\n *is_sparse = false;\n *bit_type = 8;\n *bin_iterator = nullptr;\n return reinterpret_cast(data_.data());\n}\n\ntemplate <>\nconst void* DenseBin::GetColWiseData(\n uint8_t* bit_type,\n bool* is_sparse,\n BinIterator** bin_iterator) const {\n *is_sparse = false;\n *bit_type = 16;\n *bin_iterator = nullptr;\n return reinterpret_cast(data_.data());\n}\n\ntemplate <>\nconst void* DenseBin::GetColWiseData(\n uint8_t* bit_type,\n bool* is_sparse,\n BinIterator** bin_iterator) const {\n *is_sparse = false;\n *bit_type = 32;\n *bin_iterator = nullptr;\n return reinterpret_cast(data_.data());\n}\n\ntemplate <>\nconst void* DenseBin::GetColWiseData(\n uint8_t* bit_type,\n bool* is_sparse,\n BinIterator** bin_iterator) const {\n *is_sparse = false;\n *bit_type = 4;\n *bin_iterator = nullptr;\n return reinterpret_cast(data_.data());\n}\n\ntemplate <>\nconst void* SparseBin::GetColWiseData(\n uint8_t* bit_type,\n bool* is_sparse,\n std::vector* bin_iterator,\n const int num_threads) const {\n *is_sparse = true;\n *bit_type = 8;\n for (int thread_index = 0; thread_index < num_threads; ++thread_index) {\n bin_iterator->emplace_back(new SparseBinIterator(this, 0));\n }\n return nullptr;\n}\n\ntemplate <>\nconst void* SparseBin::GetColWiseData(\n uint8_t* bit_type,\n bool* is_sparse,\n std::vector* bin_iterator,\n const int num_threads) const {\n *is_sparse = true;\n *bit_type = 16;\n for (int thread_index = 0; thread_index < num_threads; ++thread_index) {\n bin_iterator->emplace_back(new SparseBinIterator(this, 0));\n }\n return nullptr;\n}\n\ntemplate <>\nconst void* SparseBin::GetColWiseData(\n uint8_t* bit_type,\n bool* is_sparse,\n std::vector* bin_iterator,\n const int num_threads) const {\n *is_sparse = true;\n *bit_type = 32;\n for (int thread_index = 0; thread_index < num_threads; ++thread_index) {\n bin_iterator->emplace_back(new SparseBinIterator(this, 0));\n }\n return nullptr;\n}\n\ntemplate <>\nconst void* SparseBin::GetColWiseData(\n uint8_t* bit_type,\n bool* is_sparse,\n BinIterator** bin_iterator) const {\n *is_sparse = true;\n *bit_type = 8;\n *bin_iterator = new SparseBinIterator(this, 0);\n return nullptr;\n}\n\ntemplate <>\nconst void* SparseBin::GetColWiseData(\n uint8_t* bit_type,\n bool* is_sparse,\n BinIterator** bin_iterator) const {\n *is_sparse = true;\n *bit_type = 16;\n *bin_iterator = new SparseBinIterator(this, 0);\n return nullptr;\n}\n\ntemplate <>\nconst void* SparseBin::GetColWiseData(\n uint8_t* bit_type,\n bool* is_sparse,\n BinIterator** bin_iterator) const {\n *is_sparse = true;\n *bit_type = 32;\n *bin_iterator = new SparseBinIterator(this, 0);\n return nullptr;\n}\n\n#ifdef USE_CUDA\ntemplate <>\nconst void* MultiValDenseBin::GetRowWiseData(uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const {\n const uint8_t* to_return = data_.data();\n *bit_type = 8;\n *total_size = static_cast(num_data_) * static_cast(num_feature_);\n CHECK_EQ(*total_size, data_.size());\n *is_sparse = false;\n *out_data_ptr = nullptr;\n *data_ptr_bit_type = 0;\n return to_return;\n}\n\ntemplate <>\nconst void* MultiValDenseBin::GetRowWiseData(uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const {\n const uint16_t* data_ptr = data_.data();\n const uint8_t* to_return = reinterpret_cast(data_ptr);\n *bit_type = 16;\n *total_size = static_cast(num_data_) * static_cast(num_feature_);\n CHECK_EQ(*total_size, data_.size());\n *is_sparse = false;\n *out_data_ptr = nullptr;\n *data_ptr_bit_type = 0;\n return to_return;\n}\n\ntemplate <>\nconst void* MultiValDenseBin::GetRowWiseData(uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const {\n const uint32_t* data_ptr = data_.data();\n const uint8_t* to_return = reinterpret_cast(data_ptr);\n *bit_type = 32;\n *total_size = static_cast(num_data_) * static_cast(num_feature_);\n CHECK_EQ(*total_size, data_.size());\n *is_sparse = false;\n *out_data_ptr = nullptr;\n *data_ptr_bit_type = 0;\n return to_return;\n}\n\ntemplate <>\nconst void* MultiValSparseBin::GetRowWiseData(\n uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const {\n const uint8_t* to_return = data_.data();\n *bit_type = 8;\n *total_size = data_.size();\n *is_sparse = true;\n *out_data_ptr = reinterpret_cast(row_ptr_.data());\n *data_ptr_bit_type = 16;\n return to_return;\n}\n\ntemplate <>\nconst void* MultiValSparseBin::GetRowWiseData(\n uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const {\n const uint8_t* to_return = reinterpret_cast(data_.data());\n *bit_type = 16;\n *total_size = data_.size();\n *is_sparse = true;\n *out_data_ptr = reinterpret_cast(row_ptr_.data());\n *data_ptr_bit_type = 16;\n return to_return;\n}\n\ntemplate <>\nconst void* MultiValSparseBin::GetRowWiseData(\n uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const {\n const uint8_t* to_return = reinterpret_cast(data_.data());\n *bit_type = 32;\n *total_size = data_.size();\n *is_sparse = true;\n *out_data_ptr = reinterpret_cast(row_ptr_.data());\n *data_ptr_bit_type = 16;\n return to_return;\n}\n\ntemplate <>\nconst void* MultiValSparseBin::GetRowWiseData(\n uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const {\n const uint8_t* to_return = data_.data();\n *bit_type = 8;\n *total_size = data_.size();\n *is_sparse = true;\n *out_data_ptr = reinterpret_cast(row_ptr_.data());\n *data_ptr_bit_type = 32;\n return to_return;\n}\n\ntemplate <>\nconst void* MultiValSparseBin::GetRowWiseData(\n uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const {\n const uint8_t* to_return = reinterpret_cast(data_.data());\n *bit_type = 16;\n *total_size = data_.size();\n *is_sparse = true;\n *out_data_ptr = reinterpret_cast(row_ptr_.data());\n *data_ptr_bit_type = 32;\n return to_return;\n}\n\ntemplate <>\nconst void* MultiValSparseBin::GetRowWiseData(\n uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const {\n const uint8_t* to_return = reinterpret_cast(data_.data());\n *bit_type = 32;\n *total_size = data_.size();\n *is_sparse = true;\n *out_data_ptr = reinterpret_cast(row_ptr_.data());\n *data_ptr_bit_type = 32;\n return to_return;\n}\n\ntemplate <>\nconst void* MultiValSparseBin::GetRowWiseData(\n uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const {\n const uint8_t* to_return = data_.data();\n *bit_type = 8;\n *total_size = data_.size();\n *is_sparse = true;\n *out_data_ptr = reinterpret_cast(row_ptr_.data());\n *data_ptr_bit_type = 64;\n return to_return;\n}\n\ntemplate <>\nconst void* MultiValSparseBin::GetRowWiseData(\n uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const {\n const uint8_t* to_return = reinterpret_cast(data_.data());\n *bit_type = 16;\n *total_size = data_.size();\n *is_sparse = true;\n *out_data_ptr = reinterpret_cast(row_ptr_.data());\n *data_ptr_bit_type = 64;\n return to_return;\n}\n\ntemplate <>\nconst void* MultiValSparseBin::GetRowWiseData(\n uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const {\n const uint8_t* to_return = reinterpret_cast(data_.data());\n *bit_type = 32;\n *total_size = data_.size();\n *is_sparse = true;\n *out_data_ptr = reinterpret_cast(row_ptr_.data());\n *data_ptr_bit_type = 64;\n return to_return;\n}\n\n#endif // USE_CUDA\n\n} // namespace LightGBM\n" }, { "path": "src/io/config.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nvoid Config::KV2Map(std::unordered_map>* params, const char* kv) {\n std::vector tmp_strs = Common::Split(kv, '=');\n if (tmp_strs.size() == 2 || tmp_strs.size() == 1) {\n std::string key = Common::RemoveQuotationSymbol(Common::Trim(tmp_strs[0]));\n std::string value = \"\";\n if (tmp_strs.size() == 2) {\n value = Common::RemoveQuotationSymbol(Common::Trim(tmp_strs[1]));\n }\n if (key.size() > 0) {\n params->operator[](key).emplace_back(value);\n }\n } else {\n Log::Warning(\"Unknown parameter %s\", kv);\n }\n}\n\nvoid GetFirstValueAsInt(const std::unordered_map>& params, std::string key, int* out) {\n const auto pair = params.find(key);\n if (pair != params.end()) {\n auto candidate = pair->second[0].c_str();\n if (!Common::AtoiAndCheck(candidate, out)) {\n Log::Fatal(\"Parameter %s should be of type int, got \\\"%s\\\"\", key.c_str(), candidate);\n }\n }\n}\n\nvoid Config::SetVerbosity(const std::unordered_map>& params) {\n int verbosity = 1;\n\n // if \"verbosity\" was found in params, prefer that to any other aliases\n const auto verbosity_iter = params.find(\"verbosity\");\n if (verbosity_iter != params.end()) {\n GetFirstValueAsInt(params, \"verbosity\", &verbosity);\n } else {\n // if \"verbose\" was found in params and \"verbosity\" was not, use that value\n const auto verbose_iter = params.find(\"verbose\");\n if (verbose_iter != params.end()) {\n GetFirstValueAsInt(params, \"verbose\", &verbosity);\n } else {\n // if \"verbosity\" and \"verbose\" were both missing from params, don't modify LightGBM's log level\n return;\n }\n }\n\n // otherwise, update LightGBM's log level based on the passed-in value\n if (verbosity < 0) {\n LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Fatal);\n } else if (verbosity == 0) {\n LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Warning);\n } else if (verbosity == 1) {\n LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Info);\n } else {\n LightGBM::Log::ResetLogLevel(LightGBM::LogLevel::Debug);\n }\n}\n\nvoid Config::KeepFirstValues(const std::unordered_map>& params, std::unordered_map* out) {\n for (auto pair = params.begin(); pair != params.end(); ++pair) {\n auto name = pair->first.c_str();\n auto values = pair->second;\n out->emplace(name, values[0]);\n for (size_t i = 1; i < pair->second.size(); ++i) {\n Log::Warning(\"%s is set=%s, %s=%s will be ignored. Current value: %s=%s\",\n name, values[0].c_str(),\n name, values[i].c_str(),\n name, values[0].c_str());\n }\n }\n}\n\nstd::unordered_map Config::Str2Map(const char* parameters) {\n std::unordered_map> all_params;\n std::unordered_map params;\n auto args = Common::Split(parameters, \" \\t\\n\\r\");\n for (auto arg : args) {\n KV2Map(&all_params, Common::Trim(arg).c_str());\n }\n SetVerbosity(all_params);\n KeepFirstValues(all_params, ¶ms);\n ParameterAlias::KeyAliasTransform(¶ms);\n return params;\n}\n\nvoid GetBoostingType(const std::unordered_map& params, std::string* boosting) {\n std::string value;\n if (Config::GetString(params, \"boosting\", &value)) {\n std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); });\n if (value == std::string(\"gbdt\") || value == std::string(\"gbrt\")) {\n *boosting = \"gbdt\";\n } else if (value == std::string(\"dart\")) {\n *boosting = \"dart\";\n } else if (value == std::string(\"goss\")) {\n *boosting = \"goss\";\n } else if (value == std::string(\"rf\") || value == std::string(\"random_forest\")) {\n *boosting = \"rf\";\n } else {\n Log::Fatal(\"Unknown boosting type %s\", value.c_str());\n }\n }\n}\n\nvoid GetDataSampleStrategy(const std::unordered_map& params, std::string* strategy) {\n std::string value;\n if (Config::GetString(params, \"data_sample_strategy\", &value)) {\n std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); });\n if (value == std::string(\"goss\")) {\n *strategy = \"goss\";\n } else if (value == std::string(\"bagging\")) {\n *strategy = \"bagging\";\n } else {\n Log::Fatal(\"Unknown sample strategy %s\", value.c_str());\n }\n }\n}\n\nvoid ParseMetrics(const std::string& value, std::vector* out_metric) {\n std::unordered_set metric_sets;\n out_metric->clear();\n std::vector metrics = Common::Split(value.c_str(), ',');\n for (auto& met : metrics) {\n auto type = ParseMetricAlias(met);\n if (metric_sets.count(type) <= 0) {\n out_metric->push_back(type);\n metric_sets.insert(type);\n }\n }\n}\n\nvoid GetObjectiveType(const std::unordered_map& params, std::string* objective) {\n std::string value;\n if (Config::GetString(params, \"objective\", &value)) {\n std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); });\n *objective = ParseObjectiveAlias(value);\n }\n}\n\nvoid GetMetricType(const std::unordered_map& params, const std::string& objective, std::vector* metric) {\n std::string value;\n if (Config::GetString(params, \"metric\", &value)) {\n std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); });\n ParseMetrics(value, metric);\n }\n // add names of objective function if not providing metric\n if (metric->empty() && value.size() == 0) {\n ParseMetrics(objective, metric);\n }\n}\n\nvoid GetTaskType(const std::unordered_map& params, TaskType* task) {\n std::string value;\n if (Config::GetString(params, \"task\", &value)) {\n std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); });\n if (value == std::string(\"train\") || value == std::string(\"training\")) {\n *task = TaskType::kTrain;\n } else if (value == std::string(\"predict\") || value == std::string(\"prediction\")\n || value == std::string(\"test\")) {\n *task = TaskType::kPredict;\n } else if (value == std::string(\"convert_model\")) {\n *task = TaskType::kConvertModel;\n } else if (value == std::string(\"refit\") || value == std::string(\"refit_tree\")) {\n *task = TaskType::KRefitTree;\n } else if (value == std::string(\"save_binary\")) {\n *task = TaskType::kSaveBinary;\n } else {\n Log::Fatal(\"Unknown task type %s\", value.c_str());\n }\n }\n}\n\nvoid GetDeviceType(const std::unordered_map& params, std::string* device_type) {\n std::string value;\n if (Config::GetString(params, \"device_type\", &value)) {\n std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); });\n if (value == std::string(\"cpu\")) {\n *device_type = \"cpu\";\n } else if (value == std::string(\"gpu\")) {\n *device_type = \"gpu\";\n } else if (value == std::string(\"cuda\")) {\n *device_type = \"cuda\";\n } else {\n Log::Fatal(\"Unknown device type %s\", value.c_str());\n }\n }\n}\n\nvoid GetTreeLearnerType(const std::unordered_map& params, std::string* tree_learner) {\n std::string value;\n if (Config::GetString(params, \"tree_learner\", &value)) {\n std::transform(value.begin(), value.end(), value.begin(), [](unsigned char c){ return std::tolower(c); });\n if (value == std::string(\"serial\")) {\n *tree_learner = \"serial\";\n } else if (value == std::string(\"feature\") || value == std::string(\"feature_parallel\")) {\n *tree_learner = \"feature\";\n } else if (value == std::string(\"data\") || value == std::string(\"data_parallel\")) {\n *tree_learner = \"data\";\n } else if (value == std::string(\"voting\") || value == std::string(\"voting_parallel\")) {\n *tree_learner = \"voting\";\n } else {\n Log::Fatal(\"Unknown tree learner type %s\", value.c_str());\n }\n }\n}\n\nvoid Config::GetAucMuWeights() {\n if (auc_mu_weights.empty()) {\n // equal weights for all classes\n auc_mu_weights_matrix = std::vector> (num_class, std::vector(num_class, 1));\n for (size_t i = 0; i < static_cast(num_class); ++i) {\n auc_mu_weights_matrix[i][i] = 0;\n }\n } else {\n auc_mu_weights_matrix = std::vector> (num_class, std::vector(num_class, 0));\n if (auc_mu_weights.size() != static_cast(num_class * num_class)) {\n Log::Fatal(\"auc_mu_weights must have %d elements, but found %zu\", num_class * num_class, auc_mu_weights.size());\n }\n for (size_t i = 0; i < static_cast(num_class); ++i) {\n for (size_t j = 0; j < static_cast(num_class); ++j) {\n if (i == j) {\n auc_mu_weights_matrix[i][j] = 0;\n if (std::fabs(auc_mu_weights[i * num_class + j]) > kZeroThreshold) {\n Log::Info(\"AUC-mu matrix must have zeros on diagonal. Overwriting value in position %zu of auc_mu_weights with 0.\", i * num_class + j);\n }\n } else {\n if (std::fabs(auc_mu_weights[i * num_class + j]) < kZeroThreshold) {\n Log::Fatal(\"AUC-mu matrix must have non-zero values for non-diagonal entries. Found zero value in position %zu of auc_mu_weights.\", i * num_class + j);\n }\n auc_mu_weights_matrix[i][j] = auc_mu_weights[i * num_class + j];\n }\n }\n }\n }\n}\n\nvoid Config::GetInteractionConstraints() {\n if (interaction_constraints == \"\") {\n interaction_constraints_vector = std::vector>();\n } else {\n interaction_constraints_vector = Common::StringToArrayofArrays(interaction_constraints, '[', ']', ',');\n }\n}\n\nvoid Config::Set(const std::unordered_map& params) {\n // generate seeds by seed.\n if (GetInt(params, \"seed\", &seed)) {\n Random rand(seed);\n int int_max = std::numeric_limits::max();\n data_random_seed = static_cast(rand.NextShort(0, int_max));\n bagging_seed = static_cast(rand.NextShort(0, int_max));\n drop_seed = static_cast(rand.NextShort(0, int_max));\n feature_fraction_seed = static_cast(rand.NextShort(0, int_max));\n objective_seed = static_cast(rand.NextShort(0, int_max));\n extra_seed = static_cast(rand.NextShort(0, int_max));\n }\n\n GetTaskType(params, &task);\n GetBoostingType(params, &boosting);\n GetDataSampleStrategy(params, &data_sample_strategy);\n GetObjectiveType(params, &objective);\n GetMetricType(params, objective, &metric);\n GetDeviceType(params, &device_type);\n if (device_type == std::string(\"cuda\")) {\n LGBM_config_::current_device = lgbm_device_cuda;\n }\n GetTreeLearnerType(params, &tree_learner);\n\n GetMembersFromString(params);\n\n GetAucMuWeights();\n\n GetInteractionConstraints();\n\n // sort eval_at\n std::sort(eval_at.begin(), eval_at.end());\n\n std::vector new_valid;\n for (size_t i = 0; i < valid.size(); ++i) {\n if (valid[i] != data) {\n // Only push the non-training data\n new_valid.push_back(valid[i]);\n } else {\n is_provide_training_metric = true;\n }\n }\n valid = new_valid;\n\n if ((task == TaskType::kSaveBinary) && !save_binary) {\n Log::Info(\"save_binary parameter set to true because task is save_binary\");\n save_binary = true;\n }\n\n // check for conflicts\n CheckParamConflict(params);\n}\n\nbool CheckMultiClassObjective(const std::string& objective) {\n return (objective == std::string(\"multiclass\") || objective == std::string(\"multiclassova\"));\n}\n\nvoid Config::CheckParamConflict(const std::unordered_map& params) {\n // check if objective, metric, and num_class match\n int num_class_check = num_class;\n bool objective_type_multiclass = CheckMultiClassObjective(objective) || (objective == std::string(\"custom\") && num_class_check > 1);\n\n if (objective_type_multiclass) {\n if (num_class_check <= 1) {\n Log::Fatal(\"Number of classes should be specified and greater than 1 for multiclass training\");\n }\n } else {\n if (task == TaskType::kTrain && num_class_check != 1) {\n Log::Fatal(\"Number of classes must be 1 for non-multiclass training\");\n }\n }\n for (std::string metric_type : metric) {\n bool metric_type_multiclass = (CheckMultiClassObjective(metric_type)\n || metric_type == std::string(\"multi_logloss\")\n || metric_type == std::string(\"multi_error\")\n || metric_type == std::string(\"auc_mu\")\n || (metric_type == std::string(\"custom\") && num_class_check > 1));\n if ((objective_type_multiclass && !metric_type_multiclass)\n || (!objective_type_multiclass && metric_type_multiclass)) {\n Log::Fatal(\"Multiclass objective and metrics don't match\");\n }\n }\n\n if (num_machines > 1) {\n is_parallel = true;\n } else {\n is_parallel = false;\n tree_learner = \"serial\";\n }\n\n bool is_single_tree_learner = tree_learner == std::string(\"serial\");\n\n if (is_single_tree_learner) {\n is_parallel = false;\n num_machines = 1;\n }\n\n if (is_single_tree_learner || tree_learner == std::string(\"feature\")) {\n is_data_based_parallel = false;\n } else if (tree_learner == std::string(\"data\")\n || tree_learner == std::string(\"voting\")) {\n is_data_based_parallel = true;\n if (histogram_pool_size >= 0\n && tree_learner == std::string(\"data\")) {\n Log::Warning(\"Histogram LRU queue was enabled (histogram_pool_size=%f).\\n\"\n \"Will disable this to reduce communication costs\",\n histogram_pool_size);\n // Change pool size to -1 (no limit) when using data parallel to reduce communication costs\n histogram_pool_size = -1;\n }\n }\n if (is_data_based_parallel) {\n if (!forcedsplits_filename.empty()) {\n Log::Fatal(\"Don't support forcedsplits in %s tree learner\",\n tree_learner.c_str());\n }\n }\n\n // max_depth defaults to -1, so max_depth>0 implies \"you explicitly overrode the default\"\n //\n // Changing max_depth while leaving num_leaves at its default (31) can lead to 2 undesirable situations:\n //\n // * (0 <= max_depth <= 4) it's not possible to produce a tree with 31 leaves\n // - this block reduces num_leaves to 2^max_depth\n // * (max_depth > 4) 31 leaves is less than a full depth-wise tree, which might lead to underfitting\n // - this block warns about that\n // ref: https://github.com/lightgbm-org/LightGBM/issues/2898#issuecomment-1002860601\n if (max_depth > 0 && (params.count(\"num_leaves\") == 0 || params.at(\"num_leaves\").empty())) {\n double full_num_leaves = std::pow(2, max_depth);\n if (full_num_leaves > num_leaves) {\n Log::Warning(\"Provided parameters constrain tree depth (max_depth=%d) without explicitly setting 'num_leaves'. \"\n \"This can lead to underfitting. To resolve this warning, pass 'num_leaves' (<=%.0f) in params. \"\n \"Alternatively, pass (max_depth=-1) and just use 'num_leaves' to constrain model complexity.\",\n max_depth,\n full_num_leaves);\n }\n\n if (full_num_leaves < num_leaves) {\n // Fits in an int, and is more restrictive than the current num_leaves\n num_leaves = static_cast(full_num_leaves);\n }\n }\n if (device_type == std::string(\"gpu\")) {\n // force col-wise for gpu version\n force_col_wise = true;\n force_row_wise = false;\n if (deterministic) {\n Log::Warning(\"Although \\\"deterministic\\\" is set, the results ran by GPU may be non-deterministic.\");\n }\n if (use_quantized_grad) {\n Log::Warning(\"Quantized training is not supported by GPU tree learner. Switch to full precision training.\");\n use_quantized_grad = false;\n }\n } else if (device_type == std::string(\"cuda\")) {\n // force row-wise for cuda version\n force_col_wise = false;\n force_row_wise = true;\n if (deterministic) {\n Log::Warning(\"Although \\\"deterministic\\\" is set, the results ran by GPU may be non-deterministic.\");\n }\n }\n // linear tree learner must be serial type and run on CPU device\n if (linear_tree) {\n if (device_type != std::string(\"cpu\") && device_type != std::string(\"gpu\")) {\n device_type = \"cpu\";\n Log::Warning(\"Linear tree learner only works with CPU and GPU. Falling back to CPU now.\");\n }\n if (tree_learner != std::string(\"serial\")) {\n tree_learner = \"serial\";\n Log::Warning(\"Linear tree learner must be serial.\");\n }\n if (zero_as_missing) {\n Log::Fatal(\"zero_as_missing must be false when fitting linear trees.\");\n }\n if (objective == std::string(\"regression_l1\")) {\n Log::Fatal(\"Cannot use regression_l1 objective when fitting linear trees.\");\n }\n }\n // min_data_in_leaf must be at least 2 if path smoothing is active. This is because when the split is calculated\n // the count is calculated using the proportion of hessian in the leaf which is rounded up to nearest int, so it can\n // be 1 when there is actually no data in the leaf. In rare cases this can cause a bug because with path smoothing the\n // calculated split gain can be positive even with zero gradient and hessian.\n if (path_smooth > kEpsilon && min_data_in_leaf < 2) {\n min_data_in_leaf = 2;\n Log::Warning(\"min_data_in_leaf has been increased to 2 because this is required when path smoothing is active.\");\n }\n if (is_parallel && (monotone_constraints_method == std::string(\"intermediate\") || monotone_constraints_method == std::string(\"advanced\"))) {\n // In distributed mode, local node doesn't have histograms on all features, cannot perform \"intermediate\" monotone constraints.\n Log::Warning(\"Cannot use \\\"intermediate\\\" or \\\"advanced\\\" monotone constraints in distributed learning, auto set to \\\"basic\\\" method.\");\n monotone_constraints_method = \"basic\";\n }\n if (feature_fraction_bynode != 1.0 && (monotone_constraints_method == std::string(\"intermediate\") || monotone_constraints_method == std::string(\"advanced\"))) {\n // \"intermediate\" monotone constraints need to recompute splits. If the features are sampled when computing the\n // split initially, then the sampling needs to be recorded or done once again, which is currently not supported\n Log::Warning(\"Cannot use \\\"intermediate\\\" or \\\"advanced\\\" monotone constraints with feature fraction different from 1, auto set monotone constraints to \\\"basic\\\" method.\");\n monotone_constraints_method = \"basic\";\n }\n if (max_depth > 0 && monotone_penalty >= max_depth) {\n Log::Warning(\"Monotone penalty greater than tree depth. Monotone features won't be used.\");\n }\n if (min_data_in_leaf <= 0 && min_sum_hessian_in_leaf <= kEpsilon) {\n Log::Warning(\n \"Cannot set both min_data_in_leaf and min_sum_hessian_in_leaf to 0. \"\n \"Will set min_data_in_leaf to 1.\");\n min_data_in_leaf = 1;\n }\n if (boosting == std::string(\"goss\")) {\n boosting = std::string(\"gbdt\");\n data_sample_strategy = std::string(\"goss\");\n Log::Warning(\"Found boosting=goss. For backwards compatibility reasons, LightGBM interprets this as boosting=gbdt, data_sample_strategy=goss.\"\n \"To suppress this warning, set data_sample_strategy=goss instead.\");\n }\n\n if (bagging_by_query && data_sample_strategy != std::string(\"bagging\")) {\n Log::Warning(\"bagging_by_query=true is only compatible with data_sample_strategy=bagging. Setting bagging_by_query=false.\");\n bagging_by_query = false;\n }\n}\n\nstd::string Config::ToString() const {\n std::stringstream str_buf;\n str_buf << \"[boosting: \" << boosting << \"]\\n\";\n str_buf << \"[objective: \" << objective << \"]\\n\";\n str_buf << \"[metric: \" << Common::Join(metric, \",\") << \"]\\n\";\n str_buf << \"[tree_learner: \" << tree_learner << \"]\\n\";\n str_buf << \"[device_type: \" << device_type << \"]\\n\";\n str_buf << SaveMembersToString();\n return str_buf.str();\n}\n\nconst std::string Config::DumpAliases() {\n auto map = Config::parameter2aliases();\n for (auto& pair : map) {\n std::sort(pair.second.begin(), pair.second.end(), SortAlias);\n }\n std::stringstream str_buf;\n str_buf << \"{\\n\";\n bool first = true;\n for (const auto& pair : map) {\n if (first) {\n str_buf << \" \\\"\";\n first = false;\n } else {\n str_buf << \" , \\\"\";\n }\n str_buf << pair.first << \"\\\": [\";\n if (pair.second.size() > 0) {\n str_buf << \"\\\"\" << CommonC::Join(pair.second, \"\\\", \\\"\") << \"\\\"\";\n }\n str_buf << \"]\\n\";\n }\n str_buf << \"}\\n\";\n return str_buf.str();\n}\n\n} // namespace LightGBM\n" }, { "path": "src/io/config_auto.cpp", "content": "/*!\n * Copyright (c) 2018-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2018-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n *\n * \\note\n * This file is auto generated by LightGBM\\.ci\\parameter-generator.py from LightGBM\\include\\LightGBM\\config.h file.\n */\n#include \n\n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\nconst std::unordered_map& Config::alias_table() {\n static std::unordered_map aliases({\n {\"config_file\", \"config\"},\n {\"task_type\", \"task\"},\n {\"objective_type\", \"objective\"},\n {\"app\", \"objective\"},\n {\"application\", \"objective\"},\n {\"loss\", \"objective\"},\n {\"boosting_type\", \"boosting\"},\n {\"boost\", \"boosting\"},\n {\"train\", \"data\"},\n {\"train_data\", \"data\"},\n {\"train_data_file\", \"data\"},\n {\"data_filename\", \"data\"},\n {\"test\", \"valid\"},\n {\"valid_data\", \"valid\"},\n {\"valid_data_file\", \"valid\"},\n {\"test_data\", \"valid\"},\n {\"test_data_file\", \"valid\"},\n {\"valid_filenames\", \"valid\"},\n {\"num_iteration\", \"num_iterations\"},\n {\"n_iter\", \"num_iterations\"},\n {\"num_tree\", \"num_iterations\"},\n {\"num_trees\", \"num_iterations\"},\n {\"num_round\", \"num_iterations\"},\n {\"num_rounds\", \"num_iterations\"},\n {\"nrounds\", \"num_iterations\"},\n {\"num_boost_round\", \"num_iterations\"},\n {\"n_estimators\", \"num_iterations\"},\n {\"max_iter\", \"num_iterations\"},\n {\"shrinkage_rate\", \"learning_rate\"},\n {\"eta\", \"learning_rate\"},\n {\"num_leaf\", \"num_leaves\"},\n {\"max_leaves\", \"num_leaves\"},\n {\"max_leaf\", \"num_leaves\"},\n {\"max_leaf_nodes\", \"num_leaves\"},\n {\"tree\", \"tree_learner\"},\n {\"tree_type\", \"tree_learner\"},\n {\"tree_learner_type\", \"tree_learner\"},\n {\"num_thread\", \"num_threads\"},\n {\"nthread\", \"num_threads\"},\n {\"nthreads\", \"num_threads\"},\n {\"n_jobs\", \"num_threads\"},\n {\"device\", \"device_type\"},\n {\"random_seed\", \"seed\"},\n {\"random_state\", \"seed\"},\n {\"hist_pool_size\", \"histogram_pool_size\"},\n {\"min_data_per_leaf\", \"min_data_in_leaf\"},\n {\"min_data\", \"min_data_in_leaf\"},\n {\"min_child_samples\", \"min_data_in_leaf\"},\n {\"min_samples_leaf\", \"min_data_in_leaf\"},\n {\"min_sum_hessian_per_leaf\", \"min_sum_hessian_in_leaf\"},\n {\"min_sum_hessian\", \"min_sum_hessian_in_leaf\"},\n {\"min_hessian\", \"min_sum_hessian_in_leaf\"},\n {\"min_child_weight\", \"min_sum_hessian_in_leaf\"},\n {\"sub_row\", \"bagging_fraction\"},\n {\"subsample\", \"bagging_fraction\"},\n {\"bagging\", \"bagging_fraction\"},\n {\"pos_sub_row\", \"pos_bagging_fraction\"},\n {\"pos_subsample\", \"pos_bagging_fraction\"},\n {\"pos_bagging\", \"pos_bagging_fraction\"},\n {\"neg_sub_row\", \"neg_bagging_fraction\"},\n {\"neg_subsample\", \"neg_bagging_fraction\"},\n {\"neg_bagging\", \"neg_bagging_fraction\"},\n {\"subsample_freq\", \"bagging_freq\"},\n {\"bagging_fraction_seed\", \"bagging_seed\"},\n {\"sub_feature\", \"feature_fraction\"},\n {\"colsample_bytree\", \"feature_fraction\"},\n {\"sub_feature_bynode\", \"feature_fraction_bynode\"},\n {\"colsample_bynode\", \"feature_fraction_bynode\"},\n {\"extra_tree\", \"extra_trees\"},\n {\"early_stopping_rounds\", \"early_stopping_round\"},\n {\"early_stopping\", \"early_stopping_round\"},\n {\"n_iter_no_change\", \"early_stopping_round\"},\n {\"max_tree_output\", \"max_delta_step\"},\n {\"max_leaf_output\", \"max_delta_step\"},\n {\"reg_alpha\", \"lambda_l1\"},\n {\"l1_regularization\", \"lambda_l1\"},\n {\"reg_lambda\", \"lambda_l2\"},\n {\"lambda\", \"lambda_l2\"},\n {\"l2_regularization\", \"lambda_l2\"},\n {\"min_split_gain\", \"min_gain_to_split\"},\n {\"rate_drop\", \"drop_rate\"},\n {\"topk\", \"top_k\"},\n {\"mc\", \"monotone_constraints\"},\n {\"monotone_constraint\", \"monotone_constraints\"},\n {\"monotonic_cst\", \"monotone_constraints\"},\n {\"monotone_constraining_method\", \"monotone_constraints_method\"},\n {\"mc_method\", \"monotone_constraints_method\"},\n {\"monotone_splits_penalty\", \"monotone_penalty\"},\n {\"ms_penalty\", \"monotone_penalty\"},\n {\"mc_penalty\", \"monotone_penalty\"},\n {\"feature_contrib\", \"feature_contri\"},\n {\"fc\", \"feature_contri\"},\n {\"fp\", \"feature_contri\"},\n {\"feature_penalty\", \"feature_contri\"},\n {\"fs\", \"forcedsplits_filename\"},\n {\"forced_splits_filename\", \"forcedsplits_filename\"},\n {\"forced_splits_file\", \"forcedsplits_filename\"},\n {\"forced_splits\", \"forcedsplits_filename\"},\n {\"verbose\", \"verbosity\"},\n {\"model_input\", \"input_model\"},\n {\"model_in\", \"input_model\"},\n {\"model_output\", \"output_model\"},\n {\"model_out\", \"output_model\"},\n {\"save_period\", \"snapshot_freq\"},\n {\"linear_trees\", \"linear_tree\"},\n {\"max_bins\", \"max_bin\"},\n {\"subsample_for_bin\", \"bin_construct_sample_cnt\"},\n {\"data_seed\", \"data_random_seed\"},\n {\"is_sparse\", \"is_enable_sparse\"},\n {\"enable_sparse\", \"is_enable_sparse\"},\n {\"sparse\", \"is_enable_sparse\"},\n {\"is_enable_bundle\", \"enable_bundle\"},\n {\"bundle\", \"enable_bundle\"},\n {\"is_pre_partition\", \"pre_partition\"},\n {\"two_round_loading\", \"two_round\"},\n {\"use_two_round_loading\", \"two_round\"},\n {\"has_header\", \"header\"},\n {\"label\", \"label_column\"},\n {\"weight\", \"weight_column\"},\n {\"group\", \"group_column\"},\n {\"group_id\", \"group_column\"},\n {\"query_column\", \"group_column\"},\n {\"query\", \"group_column\"},\n {\"query_id\", \"group_column\"},\n {\"ignore_feature\", \"ignore_column\"},\n {\"blacklist\", \"ignore_column\"},\n {\"cat_feature\", \"categorical_feature\"},\n {\"categorical_column\", \"categorical_feature\"},\n {\"cat_column\", \"categorical_feature\"},\n {\"categorical_features\", \"categorical_feature\"},\n {\"is_save_binary\", \"save_binary\"},\n {\"is_save_binary_file\", \"save_binary\"},\n {\"is_predict_raw_score\", \"predict_raw_score\"},\n {\"predict_rawscore\", \"predict_raw_score\"},\n {\"raw_score\", \"predict_raw_score\"},\n {\"is_predict_leaf_index\", \"predict_leaf_index\"},\n {\"leaf_index\", \"predict_leaf_index\"},\n {\"is_predict_contrib\", \"predict_contrib\"},\n {\"contrib\", \"predict_contrib\"},\n {\"predict_result\", \"output_result\"},\n {\"prediction_result\", \"output_result\"},\n {\"predict_name\", \"output_result\"},\n {\"prediction_name\", \"output_result\"},\n {\"pred_name\", \"output_result\"},\n {\"name_pred\", \"output_result\"},\n {\"convert_model_file\", \"convert_model\"},\n {\"num_classes\", \"num_class\"},\n {\"unbalance\", \"is_unbalance\"},\n {\"unbalanced_sets\", \"is_unbalance\"},\n {\"metrics\", \"metric\"},\n {\"metric_types\", \"metric\"},\n {\"output_freq\", \"metric_freq\"},\n {\"training_metric\", \"is_provide_training_metric\"},\n {\"is_training_metric\", \"is_provide_training_metric\"},\n {\"train_metric\", \"is_provide_training_metric\"},\n {\"ndcg_eval_at\", \"eval_at\"},\n {\"ndcg_at\", \"eval_at\"},\n {\"map_eval_at\", \"eval_at\"},\n {\"map_at\", \"eval_at\"},\n {\"num_machine\", \"num_machines\"},\n {\"local_port\", \"local_listen_port\"},\n {\"port\", \"local_listen_port\"},\n {\"machine_list_file\", \"machine_list_filename\"},\n {\"machine_list\", \"machine_list_filename\"},\n {\"mlist\", \"machine_list_filename\"},\n {\"workers\", \"machines\"},\n {\"nodes\", \"machines\"},\n });\n return aliases;\n}\n\nconst std::unordered_set& Config::parameter_set() {\n static std::unordered_set params({\n \"config\",\n \"task\",\n \"objective\",\n \"boosting\",\n \"data_sample_strategy\",\n \"data\",\n \"valid\",\n \"num_iterations\",\n \"learning_rate\",\n \"num_leaves\",\n \"tree_learner\",\n \"num_threads\",\n \"device_type\",\n \"seed\",\n \"deterministic\",\n \"force_col_wise\",\n \"force_row_wise\",\n \"histogram_pool_size\",\n \"max_depth\",\n \"min_data_in_leaf\",\n \"min_sum_hessian_in_leaf\",\n \"bagging_fraction\",\n \"pos_bagging_fraction\",\n \"neg_bagging_fraction\",\n \"bagging_freq\",\n \"bagging_seed\",\n \"bagging_by_query\",\n \"feature_fraction\",\n \"feature_fraction_bynode\",\n \"feature_fraction_seed\",\n \"extra_trees\",\n \"extra_seed\",\n \"early_stopping_round\",\n \"early_stopping_min_delta\",\n \"first_metric_only\",\n \"max_delta_step\",\n \"lambda_l1\",\n \"lambda_l2\",\n \"linear_lambda\",\n \"min_gain_to_split\",\n \"drop_rate\",\n \"max_drop\",\n \"skip_drop\",\n \"xgboost_dart_mode\",\n \"uniform_drop\",\n \"drop_seed\",\n \"top_rate\",\n \"other_rate\",\n \"min_data_per_group\",\n \"max_cat_threshold\",\n \"cat_l2\",\n \"cat_smooth\",\n \"max_cat_to_onehot\",\n \"top_k\",\n \"monotone_constraints\",\n \"monotone_constraints_method\",\n \"monotone_penalty\",\n \"feature_contri\",\n \"forcedsplits_filename\",\n \"refit_decay_rate\",\n \"cegb_tradeoff\",\n \"cegb_penalty_split\",\n \"cegb_penalty_feature_lazy\",\n \"cegb_penalty_feature_coupled\",\n \"path_smooth\",\n \"interaction_constraints\",\n \"verbosity\",\n \"input_model\",\n \"output_model\",\n \"saved_feature_importance_type\",\n \"snapshot_freq\",\n \"use_quantized_grad\",\n \"num_grad_quant_bins\",\n \"quant_train_renew_leaf\",\n \"stochastic_rounding\",\n \"linear_tree\",\n \"max_bin\",\n \"max_bin_by_feature\",\n \"min_data_in_bin\",\n \"bin_construct_sample_cnt\",\n \"data_random_seed\",\n \"is_enable_sparse\",\n \"enable_bundle\",\n \"use_missing\",\n \"zero_as_missing\",\n \"feature_pre_filter\",\n \"pre_partition\",\n \"two_round\",\n \"header\",\n \"label_column\",\n \"weight_column\",\n \"group_column\",\n \"ignore_column\",\n \"categorical_feature\",\n \"forcedbins_filename\",\n \"save_binary\",\n \"precise_float_parser\",\n \"parser_config_file\",\n \"start_iteration_predict\",\n \"num_iteration_predict\",\n \"predict_raw_score\",\n \"predict_leaf_index\",\n \"predict_contrib\",\n \"predict_disable_shape_check\",\n \"pred_early_stop\",\n \"pred_early_stop_freq\",\n \"pred_early_stop_margin\",\n \"output_result\",\n \"convert_model_language\",\n \"convert_model\",\n \"objective_seed\",\n \"num_class\",\n \"is_unbalance\",\n \"scale_pos_weight\",\n \"sigmoid\",\n \"boost_from_average\",\n \"reg_sqrt\",\n \"alpha\",\n \"fair_c\",\n \"poisson_max_delta_step\",\n \"tweedie_variance_power\",\n \"lambdarank_truncation_level\",\n \"lambdarank_norm\",\n \"label_gain\",\n \"lambdarank_position_bias_regularization\",\n \"metric\",\n \"metric_freq\",\n \"is_provide_training_metric\",\n \"eval_at\",\n \"multi_error_top_k\",\n \"auc_mu_weights\",\n \"num_machines\",\n \"local_listen_port\",\n \"time_out\",\n \"machine_list_filename\",\n \"machines\",\n \"gpu_platform_id\",\n \"gpu_device_id\",\n \"gpu_device_id_list\",\n \"gpu_use_dp\",\n \"num_gpu\",\n });\n return params;\n}\n\nvoid Config::GetMembersFromString(const std::unordered_map& params) {\n std::string tmp_str = \"\";\n GetString(params, \"data\", &data);\n\n if (GetString(params, \"valid\", &tmp_str)) {\n valid = Common::Split(tmp_str.c_str(), ',');\n }\n\n GetInt(params, \"num_iterations\", &num_iterations);\n CHECK_GE(num_iterations, 0);\n\n GetDouble(params, \"learning_rate\", &learning_rate);\n CHECK_GT(learning_rate, 0.0);\n\n GetInt(params, \"num_leaves\", &num_leaves);\n CHECK_GT(num_leaves, 1);\n CHECK_LE(num_leaves, 131072);\n\n GetInt(params, \"num_threads\", &num_threads);\n\n GetBool(params, \"deterministic\", &deterministic);\n\n GetBool(params, \"force_col_wise\", &force_col_wise);\n\n GetBool(params, \"force_row_wise\", &force_row_wise);\n\n GetDouble(params, \"histogram_pool_size\", &histogram_pool_size);\n\n GetInt(params, \"max_depth\", &max_depth);\n\n GetInt(params, \"min_data_in_leaf\", &min_data_in_leaf);\n CHECK_GE(min_data_in_leaf, 0);\n\n GetDouble(params, \"min_sum_hessian_in_leaf\", &min_sum_hessian_in_leaf);\n CHECK_GE(min_sum_hessian_in_leaf, 0.0);\n\n GetDouble(params, \"bagging_fraction\", &bagging_fraction);\n CHECK_GT(bagging_fraction, 0.0);\n CHECK_LE(bagging_fraction, 1.0);\n\n GetDouble(params, \"pos_bagging_fraction\", &pos_bagging_fraction);\n CHECK_GT(pos_bagging_fraction, 0.0);\n CHECK_LE(pos_bagging_fraction, 1.0);\n\n GetDouble(params, \"neg_bagging_fraction\", &neg_bagging_fraction);\n CHECK_GT(neg_bagging_fraction, 0.0);\n CHECK_LE(neg_bagging_fraction, 1.0);\n\n GetInt(params, \"bagging_freq\", &bagging_freq);\n\n GetInt(params, \"bagging_seed\", &bagging_seed);\n\n GetBool(params, \"bagging_by_query\", &bagging_by_query);\n\n GetDouble(params, \"feature_fraction\", &feature_fraction);\n CHECK_GT(feature_fraction, 0.0);\n CHECK_LE(feature_fraction, 1.0);\n\n GetDouble(params, \"feature_fraction_bynode\", &feature_fraction_bynode);\n CHECK_GT(feature_fraction_bynode, 0.0);\n CHECK_LE(feature_fraction_bynode, 1.0);\n\n GetInt(params, \"feature_fraction_seed\", &feature_fraction_seed);\n\n GetBool(params, \"extra_trees\", &extra_trees);\n\n GetInt(params, \"extra_seed\", &extra_seed);\n\n GetInt(params, \"early_stopping_round\", &early_stopping_round);\n\n GetDouble(params, \"early_stopping_min_delta\", &early_stopping_min_delta);\n CHECK_GE(early_stopping_min_delta, 0.0);\n\n GetBool(params, \"first_metric_only\", &first_metric_only);\n\n GetDouble(params, \"max_delta_step\", &max_delta_step);\n\n GetDouble(params, \"lambda_l1\", &lambda_l1);\n CHECK_GE(lambda_l1, 0.0);\n\n GetDouble(params, \"lambda_l2\", &lambda_l2);\n CHECK_GE(lambda_l2, 0.0);\n\n GetDouble(params, \"linear_lambda\", &linear_lambda);\n CHECK_GE(linear_lambda, 0.0);\n\n GetDouble(params, \"min_gain_to_split\", &min_gain_to_split);\n CHECK_GE(min_gain_to_split, 0.0);\n\n GetDouble(params, \"drop_rate\", &drop_rate);\n CHECK_GE(drop_rate, 0.0);\n CHECK_LE(drop_rate, 1.0);\n\n GetInt(params, \"max_drop\", &max_drop);\n\n GetDouble(params, \"skip_drop\", &skip_drop);\n CHECK_GE(skip_drop, 0.0);\n CHECK_LE(skip_drop, 1.0);\n\n GetBool(params, \"xgboost_dart_mode\", &xgboost_dart_mode);\n\n GetBool(params, \"uniform_drop\", &uniform_drop);\n\n GetInt(params, \"drop_seed\", &drop_seed);\n\n GetDouble(params, \"top_rate\", &top_rate);\n CHECK_GE(top_rate, 0.0);\n CHECK_LE(top_rate, 1.0);\n\n GetDouble(params, \"other_rate\", &other_rate);\n CHECK_GE(other_rate, 0.0);\n CHECK_LE(other_rate, 1.0);\n\n GetInt(params, \"min_data_per_group\", &min_data_per_group);\n CHECK_GT(min_data_per_group, 0);\n\n GetInt(params, \"max_cat_threshold\", &max_cat_threshold);\n CHECK_GT(max_cat_threshold, 0);\n\n GetDouble(params, \"cat_l2\", &cat_l2);\n CHECK_GE(cat_l2, 0.0);\n\n GetDouble(params, \"cat_smooth\", &cat_smooth);\n CHECK_GE(cat_smooth, 0.0);\n\n GetInt(params, \"max_cat_to_onehot\", &max_cat_to_onehot);\n CHECK_GT(max_cat_to_onehot, 0);\n\n GetInt(params, \"top_k\", &top_k);\n CHECK_GT(top_k, 0);\n\n if (GetString(params, \"monotone_constraints\", &tmp_str)) {\n monotone_constraints = Common::StringToArray(tmp_str, ',');\n }\n\n GetString(params, \"monotone_constraints_method\", &monotone_constraints_method);\n\n GetDouble(params, \"monotone_penalty\", &monotone_penalty);\n CHECK_GE(monotone_penalty, 0.0);\n\n if (GetString(params, \"feature_contri\", &tmp_str)) {\n feature_contri = Common::StringToArray(tmp_str, ',');\n }\n\n GetString(params, \"forcedsplits_filename\", &forcedsplits_filename);\n\n GetDouble(params, \"refit_decay_rate\", &refit_decay_rate);\n CHECK_GE(refit_decay_rate, 0.0);\n CHECK_LE(refit_decay_rate, 1.0);\n\n GetDouble(params, \"cegb_tradeoff\", &cegb_tradeoff);\n CHECK_GE(cegb_tradeoff, 0.0);\n\n GetDouble(params, \"cegb_penalty_split\", &cegb_penalty_split);\n CHECK_GE(cegb_penalty_split, 0.0);\n\n if (GetString(params, \"cegb_penalty_feature_lazy\", &tmp_str)) {\n cegb_penalty_feature_lazy = Common::StringToArray(tmp_str, ',');\n }\n\n if (GetString(params, \"cegb_penalty_feature_coupled\", &tmp_str)) {\n cegb_penalty_feature_coupled = Common::StringToArray(tmp_str, ',');\n }\n\n GetDouble(params, \"path_smooth\", &path_smooth);\n CHECK_GE(path_smooth, 0.0);\n\n GetString(params, \"interaction_constraints\", &interaction_constraints);\n\n GetInt(params, \"verbosity\", &verbosity);\n\n GetString(params, \"input_model\", &input_model);\n\n GetString(params, \"output_model\", &output_model);\n\n GetInt(params, \"saved_feature_importance_type\", &saved_feature_importance_type);\n\n GetInt(params, \"snapshot_freq\", &snapshot_freq);\n\n GetBool(params, \"use_quantized_grad\", &use_quantized_grad);\n\n GetInt(params, \"num_grad_quant_bins\", &num_grad_quant_bins);\n\n GetBool(params, \"quant_train_renew_leaf\", &quant_train_renew_leaf);\n\n GetBool(params, \"stochastic_rounding\", &stochastic_rounding);\n\n GetBool(params, \"linear_tree\", &linear_tree);\n\n GetInt(params, \"max_bin\", &max_bin);\n CHECK_GT(max_bin, 1);\n\n if (GetString(params, \"max_bin_by_feature\", &tmp_str)) {\n max_bin_by_feature = Common::StringToArray(tmp_str, ',');\n }\n\n GetInt(params, \"min_data_in_bin\", &min_data_in_bin);\n CHECK_GT(min_data_in_bin, 0);\n\n GetInt(params, \"bin_construct_sample_cnt\", &bin_construct_sample_cnt);\n CHECK_GT(bin_construct_sample_cnt, 0);\n\n GetInt(params, \"data_random_seed\", &data_random_seed);\n\n GetBool(params, \"is_enable_sparse\", &is_enable_sparse);\n\n GetBool(params, \"enable_bundle\", &enable_bundle);\n\n GetBool(params, \"use_missing\", &use_missing);\n\n GetBool(params, \"zero_as_missing\", &zero_as_missing);\n\n GetBool(params, \"feature_pre_filter\", &feature_pre_filter);\n\n GetBool(params, \"pre_partition\", &pre_partition);\n\n GetBool(params, \"two_round\", &two_round);\n\n GetBool(params, \"header\", &header);\n\n GetString(params, \"label_column\", &label_column);\n\n GetString(params, \"weight_column\", &weight_column);\n\n GetString(params, \"group_column\", &group_column);\n\n GetString(params, \"ignore_column\", &ignore_column);\n\n GetString(params, \"categorical_feature\", &categorical_feature);\n\n GetString(params, \"forcedbins_filename\", &forcedbins_filename);\n\n GetBool(params, \"save_binary\", &save_binary);\n\n GetBool(params, \"precise_float_parser\", &precise_float_parser);\n\n GetString(params, \"parser_config_file\", &parser_config_file);\n\n GetInt(params, \"start_iteration_predict\", &start_iteration_predict);\n\n GetInt(params, \"num_iteration_predict\", &num_iteration_predict);\n\n GetBool(params, \"predict_raw_score\", &predict_raw_score);\n\n GetBool(params, \"predict_leaf_index\", &predict_leaf_index);\n\n GetBool(params, \"predict_contrib\", &predict_contrib);\n\n GetBool(params, \"predict_disable_shape_check\", &predict_disable_shape_check);\n\n GetBool(params, \"pred_early_stop\", &pred_early_stop);\n\n GetInt(params, \"pred_early_stop_freq\", &pred_early_stop_freq);\n\n GetDouble(params, \"pred_early_stop_margin\", &pred_early_stop_margin);\n\n GetString(params, \"output_result\", &output_result);\n\n GetString(params, \"convert_model_language\", &convert_model_language);\n\n GetString(params, \"convert_model\", &convert_model);\n\n GetInt(params, \"objective_seed\", &objective_seed);\n\n GetInt(params, \"num_class\", &num_class);\n CHECK_GT(num_class, 0);\n\n GetBool(params, \"is_unbalance\", &is_unbalance);\n\n GetDouble(params, \"scale_pos_weight\", &scale_pos_weight);\n CHECK_GT(scale_pos_weight, 0.0);\n\n GetDouble(params, \"sigmoid\", &sigmoid);\n CHECK_GT(sigmoid, 0.0);\n\n GetBool(params, \"boost_from_average\", &boost_from_average);\n\n GetBool(params, \"reg_sqrt\", ®_sqrt);\n\n GetDouble(params, \"alpha\", &alpha);\n CHECK_GT(alpha, 0.0);\n\n GetDouble(params, \"fair_c\", &fair_c);\n CHECK_GT(fair_c, 0.0);\n\n GetDouble(params, \"poisson_max_delta_step\", &poisson_max_delta_step);\n CHECK_GT(poisson_max_delta_step, 0.0);\n\n GetDouble(params, \"tweedie_variance_power\", &tweedie_variance_power);\n CHECK_GE(tweedie_variance_power, 1.0);\n CHECK_LT(tweedie_variance_power, 2.0);\n\n GetInt(params, \"lambdarank_truncation_level\", &lambdarank_truncation_level);\n CHECK_GT(lambdarank_truncation_level, 0);\n\n GetBool(params, \"lambdarank_norm\", &lambdarank_norm);\n\n if (GetString(params, \"label_gain\", &tmp_str)) {\n label_gain = Common::StringToArray(tmp_str, ',');\n }\n\n GetDouble(params, \"lambdarank_position_bias_regularization\", &lambdarank_position_bias_regularization);\n CHECK_GE(lambdarank_position_bias_regularization, 0.0);\n\n GetInt(params, \"metric_freq\", &metric_freq);\n CHECK_GT(metric_freq, 0);\n\n GetBool(params, \"is_provide_training_metric\", &is_provide_training_metric);\n\n if (GetString(params, \"eval_at\", &tmp_str)) {\n eval_at = Common::StringToArray(tmp_str, ',');\n }\n\n GetInt(params, \"multi_error_top_k\", &multi_error_top_k);\n CHECK_GT(multi_error_top_k, 0);\n\n if (GetString(params, \"auc_mu_weights\", &tmp_str)) {\n auc_mu_weights = Common::StringToArray(tmp_str, ',');\n }\n\n GetInt(params, \"num_machines\", &num_machines);\n CHECK_GT(num_machines, 0);\n\n GetInt(params, \"local_listen_port\", &local_listen_port);\n CHECK_GT(local_listen_port, 0);\n\n GetInt(params, \"time_out\", &time_out);\n CHECK_GT(time_out, 0);\n\n GetString(params, \"machine_list_filename\", &machine_list_filename);\n\n GetString(params, \"machines\", &machines);\n\n GetInt(params, \"gpu_platform_id\", &gpu_platform_id);\n\n GetInt(params, \"gpu_device_id\", &gpu_device_id);\n\n GetString(params, \"gpu_device_id_list\", &gpu_device_id_list);\n\n GetBool(params, \"gpu_use_dp\", &gpu_use_dp);\n\n GetInt(params, \"num_gpu\", &num_gpu);\n CHECK_GT(num_gpu, 0);\n}\n\nstd::string Config::SaveMembersToString() const {\n std::stringstream str_buf;\n str_buf << \"[data_sample_strategy: \" << data_sample_strategy << \"]\\n\";\n str_buf << \"[data: \" << data << \"]\\n\";\n str_buf << \"[valid: \" << Common::Join(valid, \",\") << \"]\\n\";\n str_buf << \"[num_iterations: \" << num_iterations << \"]\\n\";\n str_buf << \"[learning_rate: \" << learning_rate << \"]\\n\";\n str_buf << \"[num_leaves: \" << num_leaves << \"]\\n\";\n str_buf << \"[num_threads: \" << num_threads << \"]\\n\";\n str_buf << \"[seed: \" << seed << \"]\\n\";\n str_buf << \"[deterministic: \" << deterministic << \"]\\n\";\n str_buf << \"[force_col_wise: \" << force_col_wise << \"]\\n\";\n str_buf << \"[force_row_wise: \" << force_row_wise << \"]\\n\";\n str_buf << \"[histogram_pool_size: \" << histogram_pool_size << \"]\\n\";\n str_buf << \"[max_depth: \" << max_depth << \"]\\n\";\n str_buf << \"[min_data_in_leaf: \" << min_data_in_leaf << \"]\\n\";\n str_buf << \"[min_sum_hessian_in_leaf: \" << min_sum_hessian_in_leaf << \"]\\n\";\n str_buf << \"[bagging_fraction: \" << bagging_fraction << \"]\\n\";\n str_buf << \"[pos_bagging_fraction: \" << pos_bagging_fraction << \"]\\n\";\n str_buf << \"[neg_bagging_fraction: \" << neg_bagging_fraction << \"]\\n\";\n str_buf << \"[bagging_freq: \" << bagging_freq << \"]\\n\";\n str_buf << \"[bagging_seed: \" << bagging_seed << \"]\\n\";\n str_buf << \"[bagging_by_query: \" << bagging_by_query << \"]\\n\";\n str_buf << \"[feature_fraction: \" << feature_fraction << \"]\\n\";\n str_buf << \"[feature_fraction_bynode: \" << feature_fraction_bynode << \"]\\n\";\n str_buf << \"[feature_fraction_seed: \" << feature_fraction_seed << \"]\\n\";\n str_buf << \"[extra_trees: \" << extra_trees << \"]\\n\";\n str_buf << \"[extra_seed: \" << extra_seed << \"]\\n\";\n str_buf << \"[early_stopping_round: \" << early_stopping_round << \"]\\n\";\n str_buf << \"[early_stopping_min_delta: \" << early_stopping_min_delta << \"]\\n\";\n str_buf << \"[first_metric_only: \" << first_metric_only << \"]\\n\";\n str_buf << \"[max_delta_step: \" << max_delta_step << \"]\\n\";\n str_buf << \"[lambda_l1: \" << lambda_l1 << \"]\\n\";\n str_buf << \"[lambda_l2: \" << lambda_l2 << \"]\\n\";\n str_buf << \"[linear_lambda: \" << linear_lambda << \"]\\n\";\n str_buf << \"[min_gain_to_split: \" << min_gain_to_split << \"]\\n\";\n str_buf << \"[drop_rate: \" << drop_rate << \"]\\n\";\n str_buf << \"[max_drop: \" << max_drop << \"]\\n\";\n str_buf << \"[skip_drop: \" << skip_drop << \"]\\n\";\n str_buf << \"[xgboost_dart_mode: \" << xgboost_dart_mode << \"]\\n\";\n str_buf << \"[uniform_drop: \" << uniform_drop << \"]\\n\";\n str_buf << \"[drop_seed: \" << drop_seed << \"]\\n\";\n str_buf << \"[top_rate: \" << top_rate << \"]\\n\";\n str_buf << \"[other_rate: \" << other_rate << \"]\\n\";\n str_buf << \"[min_data_per_group: \" << min_data_per_group << \"]\\n\";\n str_buf << \"[max_cat_threshold: \" << max_cat_threshold << \"]\\n\";\n str_buf << \"[cat_l2: \" << cat_l2 << \"]\\n\";\n str_buf << \"[cat_smooth: \" << cat_smooth << \"]\\n\";\n str_buf << \"[max_cat_to_onehot: \" << max_cat_to_onehot << \"]\\n\";\n str_buf << \"[top_k: \" << top_k << \"]\\n\";\n str_buf << \"[monotone_constraints: \" << Common::Join(Common::ArrayCast(monotone_constraints), \",\") << \"]\\n\";\n str_buf << \"[monotone_constraints_method: \" << monotone_constraints_method << \"]\\n\";\n str_buf << \"[monotone_penalty: \" << monotone_penalty << \"]\\n\";\n str_buf << \"[feature_contri: \" << Common::Join(feature_contri, \",\") << \"]\\n\";\n str_buf << \"[forcedsplits_filename: \" << forcedsplits_filename << \"]\\n\";\n str_buf << \"[refit_decay_rate: \" << refit_decay_rate << \"]\\n\";\n str_buf << \"[cegb_tradeoff: \" << cegb_tradeoff << \"]\\n\";\n str_buf << \"[cegb_penalty_split: \" << cegb_penalty_split << \"]\\n\";\n str_buf << \"[cegb_penalty_feature_lazy: \" << Common::Join(cegb_penalty_feature_lazy, \",\") << \"]\\n\";\n str_buf << \"[cegb_penalty_feature_coupled: \" << Common::Join(cegb_penalty_feature_coupled, \",\") << \"]\\n\";\n str_buf << \"[path_smooth: \" << path_smooth << \"]\\n\";\n str_buf << \"[interaction_constraints: \" << interaction_constraints << \"]\\n\";\n str_buf << \"[verbosity: \" << verbosity << \"]\\n\";\n str_buf << \"[saved_feature_importance_type: \" << saved_feature_importance_type << \"]\\n\";\n str_buf << \"[use_quantized_grad: \" << use_quantized_grad << \"]\\n\";\n str_buf << \"[num_grad_quant_bins: \" << num_grad_quant_bins << \"]\\n\";\n str_buf << \"[quant_train_renew_leaf: \" << quant_train_renew_leaf << \"]\\n\";\n str_buf << \"[stochastic_rounding: \" << stochastic_rounding << \"]\\n\";\n str_buf << \"[linear_tree: \" << linear_tree << \"]\\n\";\n str_buf << \"[max_bin: \" << max_bin << \"]\\n\";\n str_buf << \"[max_bin_by_feature: \" << Common::Join(max_bin_by_feature, \",\") << \"]\\n\";\n str_buf << \"[min_data_in_bin: \" << min_data_in_bin << \"]\\n\";\n str_buf << \"[bin_construct_sample_cnt: \" << bin_construct_sample_cnt << \"]\\n\";\n str_buf << \"[data_random_seed: \" << data_random_seed << \"]\\n\";\n str_buf << \"[is_enable_sparse: \" << is_enable_sparse << \"]\\n\";\n str_buf << \"[enable_bundle: \" << enable_bundle << \"]\\n\";\n str_buf << \"[use_missing: \" << use_missing << \"]\\n\";\n str_buf << \"[zero_as_missing: \" << zero_as_missing << \"]\\n\";\n str_buf << \"[feature_pre_filter: \" << feature_pre_filter << \"]\\n\";\n str_buf << \"[pre_partition: \" << pre_partition << \"]\\n\";\n str_buf << \"[two_round: \" << two_round << \"]\\n\";\n str_buf << \"[header: \" << header << \"]\\n\";\n str_buf << \"[label_column: \" << label_column << \"]\\n\";\n str_buf << \"[weight_column: \" << weight_column << \"]\\n\";\n str_buf << \"[group_column: \" << group_column << \"]\\n\";\n str_buf << \"[ignore_column: \" << ignore_column << \"]\\n\";\n str_buf << \"[categorical_feature: \" << categorical_feature << \"]\\n\";\n str_buf << \"[forcedbins_filename: \" << forcedbins_filename << \"]\\n\";\n str_buf << \"[precise_float_parser: \" << precise_float_parser << \"]\\n\";\n str_buf << \"[parser_config_file: \" << parser_config_file << \"]\\n\";\n str_buf << \"[objective_seed: \" << objective_seed << \"]\\n\";\n str_buf << \"[num_class: \" << num_class << \"]\\n\";\n str_buf << \"[is_unbalance: \" << is_unbalance << \"]\\n\";\n str_buf << \"[scale_pos_weight: \" << scale_pos_weight << \"]\\n\";\n str_buf << \"[sigmoid: \" << sigmoid << \"]\\n\";\n str_buf << \"[boost_from_average: \" << boost_from_average << \"]\\n\";\n str_buf << \"[reg_sqrt: \" << reg_sqrt << \"]\\n\";\n str_buf << \"[alpha: \" << alpha << \"]\\n\";\n str_buf << \"[fair_c: \" << fair_c << \"]\\n\";\n str_buf << \"[poisson_max_delta_step: \" << poisson_max_delta_step << \"]\\n\";\n str_buf << \"[tweedie_variance_power: \" << tweedie_variance_power << \"]\\n\";\n str_buf << \"[lambdarank_truncation_level: \" << lambdarank_truncation_level << \"]\\n\";\n str_buf << \"[lambdarank_norm: \" << lambdarank_norm << \"]\\n\";\n str_buf << \"[label_gain: \" << Common::Join(label_gain, \",\") << \"]\\n\";\n str_buf << \"[lambdarank_position_bias_regularization: \" << lambdarank_position_bias_regularization << \"]\\n\";\n str_buf << \"[eval_at: \" << Common::Join(eval_at, \",\") << \"]\\n\";\n str_buf << \"[multi_error_top_k: \" << multi_error_top_k << \"]\\n\";\n str_buf << \"[auc_mu_weights: \" << Common::Join(auc_mu_weights, \",\") << \"]\\n\";\n str_buf << \"[num_machines: \" << num_machines << \"]\\n\";\n str_buf << \"[local_listen_port: \" << local_listen_port << \"]\\n\";\n str_buf << \"[time_out: \" << time_out << \"]\\n\";\n str_buf << \"[machine_list_filename: \" << machine_list_filename << \"]\\n\";\n str_buf << \"[machines: \" << machines << \"]\\n\";\n str_buf << \"[gpu_platform_id: \" << gpu_platform_id << \"]\\n\";\n str_buf << \"[gpu_device_id: \" << gpu_device_id << \"]\\n\";\n str_buf << \"[gpu_device_id_list: \" << gpu_device_id_list << \"]\\n\";\n str_buf << \"[gpu_use_dp: \" << gpu_use_dp << \"]\\n\";\n str_buf << \"[num_gpu: \" << num_gpu << \"]\\n\";\n return str_buf.str();\n}\n\nconst std::unordered_map>& Config::parameter2aliases() {\n static std::unordered_map> map({\n {\"config\", {\"config_file\"}},\n {\"task\", {\"task_type\"}},\n {\"objective\", {\"objective_type\", \"app\", \"application\", \"loss\"}},\n {\"boosting\", {\"boosting_type\", \"boost\"}},\n {\"data_sample_strategy\", {}},\n {\"data\", {\"train\", \"train_data\", \"train_data_file\", \"data_filename\"}},\n {\"valid\", {\"test\", \"valid_data\", \"valid_data_file\", \"test_data\", \"test_data_file\", \"valid_filenames\"}},\n {\"num_iterations\", {\"num_iteration\", \"n_iter\", \"num_tree\", \"num_trees\", \"num_round\", \"num_rounds\", \"nrounds\", \"num_boost_round\", \"n_estimators\", \"max_iter\"}},\n {\"learning_rate\", {\"shrinkage_rate\", \"eta\"}},\n {\"num_leaves\", {\"num_leaf\", \"max_leaves\", \"max_leaf\", \"max_leaf_nodes\"}},\n {\"tree_learner\", {\"tree\", \"tree_type\", \"tree_learner_type\"}},\n {\"num_threads\", {\"num_thread\", \"nthread\", \"nthreads\", \"n_jobs\"}},\n {\"device_type\", {\"device\"}},\n {\"seed\", {\"random_seed\", \"random_state\"}},\n {\"deterministic\", {}},\n {\"force_col_wise\", {}},\n {\"force_row_wise\", {}},\n {\"histogram_pool_size\", {\"hist_pool_size\"}},\n {\"max_depth\", {}},\n {\"min_data_in_leaf\", {\"min_data_per_leaf\", \"min_data\", \"min_child_samples\", \"min_samples_leaf\"}},\n {\"min_sum_hessian_in_leaf\", {\"min_sum_hessian_per_leaf\", \"min_sum_hessian\", \"min_hessian\", \"min_child_weight\"}},\n {\"bagging_fraction\", {\"sub_row\", \"subsample\", \"bagging\"}},\n {\"pos_bagging_fraction\", {\"pos_sub_row\", \"pos_subsample\", \"pos_bagging\"}},\n {\"neg_bagging_fraction\", {\"neg_sub_row\", \"neg_subsample\", \"neg_bagging\"}},\n {\"bagging_freq\", {\"subsample_freq\"}},\n {\"bagging_seed\", {\"bagging_fraction_seed\"}},\n {\"bagging_by_query\", {}},\n {\"feature_fraction\", {\"sub_feature\", \"colsample_bytree\"}},\n {\"feature_fraction_bynode\", {\"sub_feature_bynode\", \"colsample_bynode\"}},\n {\"feature_fraction_seed\", {}},\n {\"extra_trees\", {\"extra_tree\"}},\n {\"extra_seed\", {}},\n {\"early_stopping_round\", {\"early_stopping_rounds\", \"early_stopping\", \"n_iter_no_change\"}},\n {\"early_stopping_min_delta\", {}},\n {\"first_metric_only\", {}},\n {\"max_delta_step\", {\"max_tree_output\", \"max_leaf_output\"}},\n {\"lambda_l1\", {\"reg_alpha\", \"l1_regularization\"}},\n {\"lambda_l2\", {\"reg_lambda\", \"lambda\", \"l2_regularization\"}},\n {\"linear_lambda\", {}},\n {\"min_gain_to_split\", {\"min_split_gain\"}},\n {\"drop_rate\", {\"rate_drop\"}},\n {\"max_drop\", {}},\n {\"skip_drop\", {}},\n {\"xgboost_dart_mode\", {}},\n {\"uniform_drop\", {}},\n {\"drop_seed\", {}},\n {\"top_rate\", {}},\n {\"other_rate\", {}},\n {\"min_data_per_group\", {}},\n {\"max_cat_threshold\", {}},\n {\"cat_l2\", {}},\n {\"cat_smooth\", {}},\n {\"max_cat_to_onehot\", {}},\n {\"top_k\", {\"topk\"}},\n {\"monotone_constraints\", {\"mc\", \"monotone_constraint\", \"monotonic_cst\"}},\n {\"monotone_constraints_method\", {\"monotone_constraining_method\", \"mc_method\"}},\n {\"monotone_penalty\", {\"monotone_splits_penalty\", \"ms_penalty\", \"mc_penalty\"}},\n {\"feature_contri\", {\"feature_contrib\", \"fc\", \"fp\", \"feature_penalty\"}},\n {\"forcedsplits_filename\", {\"fs\", \"forced_splits_filename\", \"forced_splits_file\", \"forced_splits\"}},\n {\"refit_decay_rate\", {}},\n {\"cegb_tradeoff\", {}},\n {\"cegb_penalty_split\", {}},\n {\"cegb_penalty_feature_lazy\", {}},\n {\"cegb_penalty_feature_coupled\", {}},\n {\"path_smooth\", {}},\n {\"interaction_constraints\", {}},\n {\"verbosity\", {\"verbose\"}},\n {\"input_model\", {\"model_input\", \"model_in\"}},\n {\"output_model\", {\"model_output\", \"model_out\"}},\n {\"saved_feature_importance_type\", {}},\n {\"snapshot_freq\", {\"save_period\"}},\n {\"use_quantized_grad\", {}},\n {\"num_grad_quant_bins\", {}},\n {\"quant_train_renew_leaf\", {}},\n {\"stochastic_rounding\", {}},\n {\"linear_tree\", {\"linear_trees\"}},\n {\"max_bin\", {\"max_bins\"}},\n {\"max_bin_by_feature\", {}},\n {\"min_data_in_bin\", {}},\n {\"bin_construct_sample_cnt\", {\"subsample_for_bin\"}},\n {\"data_random_seed\", {\"data_seed\"}},\n {\"is_enable_sparse\", {\"is_sparse\", \"enable_sparse\", \"sparse\"}},\n {\"enable_bundle\", {\"is_enable_bundle\", \"bundle\"}},\n {\"use_missing\", {}},\n {\"zero_as_missing\", {}},\n {\"feature_pre_filter\", {}},\n {\"pre_partition\", {\"is_pre_partition\"}},\n {\"two_round\", {\"two_round_loading\", \"use_two_round_loading\"}},\n {\"header\", {\"has_header\"}},\n {\"label_column\", {\"label\"}},\n {\"weight_column\", {\"weight\"}},\n {\"group_column\", {\"group\", \"group_id\", \"query_column\", \"query\", \"query_id\"}},\n {\"ignore_column\", {\"ignore_feature\", \"blacklist\"}},\n {\"categorical_feature\", {\"cat_feature\", \"categorical_column\", \"cat_column\", \"categorical_features\"}},\n {\"forcedbins_filename\", {}},\n {\"save_binary\", {\"is_save_binary\", \"is_save_binary_file\"}},\n {\"precise_float_parser\", {}},\n {\"parser_config_file\", {}},\n {\"start_iteration_predict\", {}},\n {\"num_iteration_predict\", {}},\n {\"predict_raw_score\", {\"is_predict_raw_score\", \"predict_rawscore\", \"raw_score\"}},\n {\"predict_leaf_index\", {\"is_predict_leaf_index\", \"leaf_index\"}},\n {\"predict_contrib\", {\"is_predict_contrib\", \"contrib\"}},\n {\"predict_disable_shape_check\", {}},\n {\"pred_early_stop\", {}},\n {\"pred_early_stop_freq\", {}},\n {\"pred_early_stop_margin\", {}},\n {\"output_result\", {\"predict_result\", \"prediction_result\", \"predict_name\", \"prediction_name\", \"pred_name\", \"name_pred\"}},\n {\"convert_model_language\", {}},\n {\"convert_model\", {\"convert_model_file\"}},\n {\"objective_seed\", {}},\n {\"num_class\", {\"num_classes\"}},\n {\"is_unbalance\", {\"unbalance\", \"unbalanced_sets\"}},\n {\"scale_pos_weight\", {}},\n {\"sigmoid\", {}},\n {\"boost_from_average\", {}},\n {\"reg_sqrt\", {}},\n {\"alpha\", {}},\n {\"fair_c\", {}},\n {\"poisson_max_delta_step\", {}},\n {\"tweedie_variance_power\", {}},\n {\"lambdarank_truncation_level\", {}},\n {\"lambdarank_norm\", {}},\n {\"label_gain\", {}},\n {\"lambdarank_position_bias_regularization\", {}},\n {\"metric\", {\"metrics\", \"metric_types\"}},\n {\"metric_freq\", {\"output_freq\"}},\n {\"is_provide_training_metric\", {\"training_metric\", \"is_training_metric\", \"train_metric\"}},\n {\"eval_at\", {\"ndcg_eval_at\", \"ndcg_at\", \"map_eval_at\", \"map_at\"}},\n {\"multi_error_top_k\", {}},\n {\"auc_mu_weights\", {}},\n {\"num_machines\", {\"num_machine\"}},\n {\"local_listen_port\", {\"local_port\", \"port\"}},\n {\"time_out\", {}},\n {\"machine_list_filename\", {\"machine_list_file\", \"machine_list\", \"mlist\"}},\n {\"machines\", {\"workers\", \"nodes\"}},\n {\"gpu_platform_id\", {}},\n {\"gpu_device_id\", {}},\n {\"gpu_device_id_list\", {}},\n {\"gpu_use_dp\", {}},\n {\"num_gpu\", {}},\n });\n return map;\n}\n\nconst std::unordered_map& Config::ParameterTypes() {\n static std::unordered_map map({\n {\"config\", \"string\"},\n {\"objective\", \"string\"},\n {\"boosting\", \"string\"},\n {\"data_sample_strategy\", \"string\"},\n {\"data\", \"string\"},\n {\"valid\", \"vector\"},\n {\"num_iterations\", \"int\"},\n {\"learning_rate\", \"double\"},\n {\"num_leaves\", \"int\"},\n {\"tree_learner\", \"string\"},\n {\"num_threads\", \"int\"},\n {\"device_type\", \"string\"},\n {\"seed\", \"int\"},\n {\"deterministic\", \"bool\"},\n {\"force_col_wise\", \"bool\"},\n {\"force_row_wise\", \"bool\"},\n {\"histogram_pool_size\", \"double\"},\n {\"max_depth\", \"int\"},\n {\"min_data_in_leaf\", \"int\"},\n {\"min_sum_hessian_in_leaf\", \"double\"},\n {\"bagging_fraction\", \"double\"},\n {\"pos_bagging_fraction\", \"double\"},\n {\"neg_bagging_fraction\", \"double\"},\n {\"bagging_freq\", \"int\"},\n {\"bagging_seed\", \"int\"},\n {\"bagging_by_query\", \"bool\"},\n {\"feature_fraction\", \"double\"},\n {\"feature_fraction_bynode\", \"double\"},\n {\"feature_fraction_seed\", \"int\"},\n {\"extra_trees\", \"bool\"},\n {\"extra_seed\", \"int\"},\n {\"early_stopping_round\", \"int\"},\n {\"early_stopping_min_delta\", \"double\"},\n {\"first_metric_only\", \"bool\"},\n {\"max_delta_step\", \"double\"},\n {\"lambda_l1\", \"double\"},\n {\"lambda_l2\", \"double\"},\n {\"linear_lambda\", \"double\"},\n {\"min_gain_to_split\", \"double\"},\n {\"drop_rate\", \"double\"},\n {\"max_drop\", \"int\"},\n {\"skip_drop\", \"double\"},\n {\"xgboost_dart_mode\", \"bool\"},\n {\"uniform_drop\", \"bool\"},\n {\"drop_seed\", \"int\"},\n {\"top_rate\", \"double\"},\n {\"other_rate\", \"double\"},\n {\"min_data_per_group\", \"int\"},\n {\"max_cat_threshold\", \"int\"},\n {\"cat_l2\", \"double\"},\n {\"cat_smooth\", \"double\"},\n {\"max_cat_to_onehot\", \"int\"},\n {\"top_k\", \"int\"},\n {\"monotone_constraints\", \"vector\"},\n {\"monotone_constraints_method\", \"string\"},\n {\"monotone_penalty\", \"double\"},\n {\"feature_contri\", \"vector\"},\n {\"forcedsplits_filename\", \"string\"},\n {\"refit_decay_rate\", \"double\"},\n {\"cegb_tradeoff\", \"double\"},\n {\"cegb_penalty_split\", \"double\"},\n {\"cegb_penalty_feature_lazy\", \"vector\"},\n {\"cegb_penalty_feature_coupled\", \"vector\"},\n {\"path_smooth\", \"double\"},\n {\"interaction_constraints\", \"vector>\"},\n {\"verbosity\", \"int\"},\n {\"input_model\", \"string\"},\n {\"output_model\", \"string\"},\n {\"saved_feature_importance_type\", \"int\"},\n {\"snapshot_freq\", \"int\"},\n {\"use_quantized_grad\", \"bool\"},\n {\"num_grad_quant_bins\", \"int\"},\n {\"quant_train_renew_leaf\", \"bool\"},\n {\"stochastic_rounding\", \"bool\"},\n {\"linear_tree\", \"bool\"},\n {\"max_bin\", \"int\"},\n {\"max_bin_by_feature\", \"vector\"},\n {\"min_data_in_bin\", \"int\"},\n {\"bin_construct_sample_cnt\", \"int\"},\n {\"data_random_seed\", \"int\"},\n {\"is_enable_sparse\", \"bool\"},\n {\"enable_bundle\", \"bool\"},\n {\"use_missing\", \"bool\"},\n {\"zero_as_missing\", \"bool\"},\n {\"feature_pre_filter\", \"bool\"},\n {\"pre_partition\", \"bool\"},\n {\"two_round\", \"bool\"},\n {\"header\", \"bool\"},\n {\"label_column\", \"string\"},\n {\"weight_column\", \"string\"},\n {\"group_column\", \"string\"},\n {\"ignore_column\", \"vector\"},\n {\"categorical_feature\", \"vector\"},\n {\"forcedbins_filename\", \"string\"},\n {\"save_binary\", \"bool\"},\n {\"precise_float_parser\", \"bool\"},\n {\"parser_config_file\", \"string\"},\n {\"start_iteration_predict\", \"int\"},\n {\"num_iteration_predict\", \"int\"},\n {\"predict_raw_score\", \"bool\"},\n {\"predict_leaf_index\", \"bool\"},\n {\"predict_contrib\", \"bool\"},\n {\"predict_disable_shape_check\", \"bool\"},\n {\"pred_early_stop\", \"bool\"},\n {\"pred_early_stop_freq\", \"int\"},\n {\"pred_early_stop_margin\", \"double\"},\n {\"output_result\", \"string\"},\n {\"convert_model_language\", \"string\"},\n {\"convert_model\", \"string\"},\n {\"objective_seed\", \"int\"},\n {\"num_class\", \"int\"},\n {\"is_unbalance\", \"bool\"},\n {\"scale_pos_weight\", \"double\"},\n {\"sigmoid\", \"double\"},\n {\"boost_from_average\", \"bool\"},\n {\"reg_sqrt\", \"bool\"},\n {\"alpha\", \"double\"},\n {\"fair_c\", \"double\"},\n {\"poisson_max_delta_step\", \"double\"},\n {\"tweedie_variance_power\", \"double\"},\n {\"lambdarank_truncation_level\", \"int\"},\n {\"lambdarank_norm\", \"bool\"},\n {\"label_gain\", \"vector\"},\n {\"lambdarank_position_bias_regularization\", \"double\"},\n {\"metric\", \"vector\"},\n {\"metric_freq\", \"int\"},\n {\"is_provide_training_metric\", \"bool\"},\n {\"eval_at\", \"vector\"},\n {\"multi_error_top_k\", \"int\"},\n {\"auc_mu_weights\", \"vector\"},\n {\"num_machines\", \"int\"},\n {\"local_listen_port\", \"int\"},\n {\"time_out\", \"int\"},\n {\"machine_list_filename\", \"string\"},\n {\"machines\", \"string\"},\n {\"gpu_platform_id\", \"int\"},\n {\"gpu_device_id\", \"int\"},\n {\"gpu_device_id_list\", \"string\"},\n {\"gpu_use_dp\", \"bool\"},\n {\"num_gpu\", \"int\"},\n });\n return map;\n}\n\n} // namespace LightGBM\n" }, { "path": "src/io/cuda/cuda_column_data.cpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifdef USE_CUDA\n\n#include \n\n#include \n#include \n\nnamespace LightGBM {\n\nCUDAColumnData::CUDAColumnData(const data_size_t num_data, const int gpu_device_id) {\n num_threads_ = OMP_NUM_THREADS();\n num_data_ = num_data;\n gpu_device_id_ = gpu_device_id >= 0 ? gpu_device_id : 0;\n SetCUDADevice(gpu_device_id_, __FILE__, __LINE__);\n data_by_column_.clear();\n}\n\nCUDAColumnData::~CUDAColumnData() {}\n\ntemplate \nvoid CUDAColumnData::InitOneColumnData(const void* in_column_data, BinIterator* bin_iterator, CUDAVector* out_column_data_pointer) {\n CUDAVector cuda_column_data;\n if (!IS_SPARSE) {\n if (IS_4BIT) {\n std::vector expanded_column_data(num_data_, 0);\n const BIN_TYPE* in_column_data_reintrepreted = reinterpret_cast(in_column_data);\n for (data_size_t i = 0; i < num_data_; ++i) {\n expanded_column_data[i] = static_cast((in_column_data_reintrepreted[i >> 1] >> ((i & 1) << 2)) & 0xf);\n }\n cuda_column_data.InitFromHostVector(expanded_column_data);\n } else {\n cuda_column_data.InitFromHostMemory(reinterpret_cast(in_column_data), static_cast(num_data_));\n }\n } else {\n // need to iterate bin iterator\n std::vector expanded_column_data(num_data_, 0);\n for (data_size_t i = 0; i < num_data_; ++i) {\n expanded_column_data[i] = static_cast(bin_iterator->RawGet(i));\n }\n cuda_column_data.InitFromHostVector(expanded_column_data);\n }\n out_column_data_pointer->MoveFrom(cuda_column_data, sizeof(BIN_TYPE) * cuda_column_data.Size());\n}\n\nvoid CUDAColumnData::Init(const int num_columns,\n const std::vector& column_data,\n const std::vector& column_bin_iterator,\n const std::vector& column_bit_type,\n const std::vector& feature_max_bin,\n const std::vector& feature_min_bin,\n const std::vector& feature_offset,\n const std::vector& feature_most_freq_bin,\n const std::vector& feature_default_bin,\n const std::vector& feature_missing_is_zero,\n const std::vector& feature_missing_is_na,\n const std::vector& feature_mfb_is_zero,\n const std::vector& feature_mfb_is_na,\n const std::vector& feature_to_column) {\n num_columns_ = num_columns;\n column_bit_type_ = column_bit_type;\n feature_max_bin_ = feature_max_bin;\n feature_min_bin_ = feature_min_bin;\n feature_offset_ = feature_offset;\n feature_most_freq_bin_ = feature_most_freq_bin;\n feature_default_bin_ = feature_default_bin;\n feature_missing_is_zero_ = feature_missing_is_zero;\n feature_missing_is_na_ = feature_missing_is_na;\n feature_mfb_is_zero_ = feature_mfb_is_zero;\n feature_mfb_is_na_ = feature_mfb_is_na;\n for (int column_index = 0; column_index < num_columns_; ++column_index) {\n data_by_column_.emplace_back(new CUDAVector());\n }\n OMP_INIT_EX();\n #pragma omp parallel num_threads(num_threads_)\n {\n SetCUDADevice(gpu_device_id_, __FILE__, __LINE__);\n #pragma omp for schedule(static)\n for (int column_index = 0; column_index < num_columns_; ++column_index) {\n OMP_LOOP_EX_BEGIN();\n const int8_t bit_type = column_bit_type[column_index];\n if (column_data[column_index] != nullptr) {\n // is dense column\n if (bit_type == 4) {\n column_bit_type_[column_index] = 8;\n InitOneColumnData(column_data[column_index], nullptr, data_by_column_[column_index].get());\n } else if (bit_type == 8) {\n InitOneColumnData(column_data[column_index], nullptr, data_by_column_[column_index].get());\n } else if (bit_type == 16) {\n InitOneColumnData(column_data[column_index], nullptr, data_by_column_[column_index].get());\n } else if (bit_type == 32) {\n InitOneColumnData(column_data[column_index], nullptr, data_by_column_[column_index].get());\n } else {\n Log::Fatal(\"Unknown column bit type %d\", bit_type);\n }\n } else {\n // is sparse column\n if (bit_type == 8) {\n InitOneColumnData(nullptr, column_bin_iterator[column_index], data_by_column_[column_index].get());\n } else if (bit_type == 16) {\n InitOneColumnData(nullptr, column_bin_iterator[column_index], data_by_column_[column_index].get());\n } else if (bit_type == 32) {\n InitOneColumnData(nullptr, column_bin_iterator[column_index], data_by_column_[column_index].get());\n } else {\n Log::Fatal(\"Unknown column bit type %d\", bit_type);\n }\n }\n OMP_LOOP_EX_END();\n }\n }\n OMP_THROW_EX();\n feature_to_column_ = feature_to_column;\n cuda_data_by_column_.InitFromHostVector(GetDataByColumnPointers(data_by_column_));\n InitColumnMetaInfo();\n}\n\nvoid CUDAColumnData::CopySubrow(\n const CUDAColumnData* full_set,\n const data_size_t* used_indices,\n const data_size_t num_used_indices) {\n num_threads_ = full_set->num_threads_;\n num_columns_ = full_set->num_columns_;\n column_bit_type_ = full_set->column_bit_type_;\n feature_min_bin_ = full_set->feature_min_bin_;\n feature_max_bin_ = full_set->feature_max_bin_;\n feature_offset_ = full_set->feature_offset_;\n feature_most_freq_bin_ = full_set->feature_most_freq_bin_;\n feature_default_bin_ = full_set->feature_default_bin_;\n feature_missing_is_zero_ = full_set->feature_missing_is_zero_;\n feature_missing_is_na_ = full_set->feature_missing_is_na_;\n feature_mfb_is_zero_ = full_set->feature_mfb_is_zero_;\n feature_mfb_is_na_ = full_set->feature_mfb_is_na_;\n feature_to_column_ = full_set->feature_to_column_;\n if (cuda_used_indices_.Size() == 0) {\n // initialize the subset cuda column data\n const size_t num_used_indices_size = static_cast(num_used_indices);\n cuda_used_indices_.Resize(num_used_indices_size);\n for (int column_index = 0; column_index < num_columns_; ++column_index) {\n data_by_column_.emplace_back(new CUDAVector());\n }\n OMP_INIT_EX();\n #pragma omp parallel num_threads(num_threads_)\n {\n SetCUDADevice(gpu_device_id_, __FILE__, __LINE__);\n #pragma omp for schedule(static)\n for (int column_index = 0; column_index < num_columns_; ++column_index) {\n OMP_LOOP_EX_BEGIN();\n const uint8_t bit_type = column_bit_type_[column_index];\n if (bit_type == 8) {\n CUDAVector column_data;\n column_data.Resize(num_used_indices_size);\n data_by_column_[column_index]->MoveFrom(column_data, sizeof(uint8_t) * column_data.Size());\n } else if (bit_type == 16) {\n CUDAVector column_data;\n column_data.Resize(num_used_indices_size);\n data_by_column_[column_index]->MoveFrom(column_data, sizeof(uint16_t) * column_data.Size());\n } else if (bit_type == 32) {\n CUDAVector column_data;\n column_data.Resize(num_used_indices_size);\n data_by_column_[column_index]->MoveFrom(column_data, sizeof(uint32_t) * column_data.Size());\n }\n OMP_LOOP_EX_END();\n }\n }\n OMP_THROW_EX();\n cuda_data_by_column_.InitFromHostVector(GetDataByColumnPointers(data_by_column_));\n InitColumnMetaInfo();\n cur_subset_buffer_size_ = num_used_indices;\n } else {\n if (num_used_indices > cur_subset_buffer_size_) {\n ResizeWhenCopySubrow(num_used_indices);\n cur_subset_buffer_size_ = num_used_indices;\n }\n }\n cuda_used_indices_.InitFromHostMemory(used_indices, static_cast(num_used_indices));\n num_used_indices_ = num_used_indices;\n LaunchCopySubrowKernel(full_set->cuda_data_by_column());\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\nvoid CUDAColumnData::ResizeWhenCopySubrow(const data_size_t num_used_indices) {\n const size_t num_used_indices_size = static_cast(num_used_indices);\n cuda_used_indices_.Resize(num_used_indices_size);\n OMP_INIT_EX();\n #pragma omp parallel num_threads(num_threads_)\n {\n SetCUDADevice(gpu_device_id_, __FILE__, __LINE__);\n #pragma omp for schedule(static)\n for (int column_index = 0; column_index < num_columns_; ++column_index) {\n OMP_LOOP_EX_BEGIN();\n const uint8_t bit_type = column_bit_type_[column_index];\n if (bit_type == 8) {\n data_by_column_[column_index]->Resize(sizeof(uint8_t) * num_used_indices_size);\n } else if (bit_type == 16) {\n data_by_column_[column_index]->Resize(sizeof(uint16_t) * num_used_indices_size);\n } else if (bit_type == 32) {\n data_by_column_[column_index]->Resize(sizeof(uint32_t) * num_used_indices_size);\n }\n OMP_LOOP_EX_END();\n }\n }\n OMP_THROW_EX();\n cuda_data_by_column_.InitFromHostVector(GetDataByColumnPointers(data_by_column_));\n}\n\nvoid CUDAColumnData::InitColumnMetaInfo() {\n cuda_column_bit_type_.InitFromHostVector(column_bit_type_);\n cuda_feature_max_bin_.InitFromHostVector(feature_max_bin_);\n cuda_feature_min_bin_.InitFromHostVector(feature_min_bin_);\n cuda_feature_offset_.InitFromHostVector(feature_offset_);\n cuda_feature_most_freq_bin_.InitFromHostVector(feature_most_freq_bin_);\n cuda_feature_default_bin_.InitFromHostVector(feature_default_bin_);\n cuda_feature_missing_is_zero_.InitFromHostVector(feature_missing_is_zero_);\n cuda_feature_missing_is_na_.InitFromHostVector(feature_missing_is_na_);\n cuda_feature_mfb_is_zero_.InitFromHostVector(feature_mfb_is_zero_);\n cuda_feature_mfb_is_na_.InitFromHostVector(feature_mfb_is_na_);\n cuda_feature_to_column_.InitFromHostVector(feature_to_column_);\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/io/cuda/cuda_column_data.cu", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n\n#ifdef USE_CUDA\n\n#include \n\n#define COPY_SUBROW_BLOCK_SIZE_COLUMN_DATA (1024)\n\nnamespace LightGBM {\n\n__global__ void CopySubrowKernel_ColumnData(\n uint8_t* const* in_cuda_data_by_column,\n const uint8_t* cuda_column_bit_type,\n const data_size_t* cuda_used_indices,\n const data_size_t num_used_indices,\n const int num_column,\n uint8_t** out_cuda_data_by_column) {\n const data_size_t local_data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n if (local_data_index < num_used_indices) {\n for (int column_index = 0; column_index < num_column; ++column_index) {\n const uint8_t* in_column_data = in_cuda_data_by_column[column_index];\n uint8_t* out_column_data = out_cuda_data_by_column[column_index];\n const uint8_t bit_type = cuda_column_bit_type[column_index];\n if (bit_type == 8) {\n const uint8_t* true_in_column_data = reinterpret_cast(in_column_data);\n uint8_t* true_out_column_data = reinterpret_cast(out_column_data);\n const data_size_t global_data_index = cuda_used_indices[local_data_index];\n true_out_column_data[local_data_index] = true_in_column_data[global_data_index];\n } else if (bit_type == 16) {\n const uint16_t* true_in_column_data = reinterpret_cast(in_column_data);\n uint16_t* true_out_column_data = reinterpret_cast(out_column_data);\n const data_size_t global_data_index = cuda_used_indices[local_data_index];\n true_out_column_data[local_data_index] = true_in_column_data[global_data_index];\n } else if (bit_type == 32) {\n const uint32_t* true_in_column_data = reinterpret_cast(in_column_data);\n uint32_t* true_out_column_data = reinterpret_cast(out_column_data);\n const data_size_t global_data_index = cuda_used_indices[local_data_index];\n true_out_column_data[local_data_index] = true_in_column_data[global_data_index];\n }\n }\n }\n}\n\nvoid CUDAColumnData::LaunchCopySubrowKernel(uint8_t* const* in_cuda_data_by_column) {\n const int num_blocks = (num_used_indices_ + COPY_SUBROW_BLOCK_SIZE_COLUMN_DATA - 1) / COPY_SUBROW_BLOCK_SIZE_COLUMN_DATA;\n CopySubrowKernel_ColumnData<<>>(\n in_cuda_data_by_column,\n cuda_column_bit_type_.RawData(),\n cuda_used_indices_.RawData(),\n num_used_indices_,\n num_columns_,\n cuda_data_by_column_.RawData());\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/io/cuda/cuda_metadata.cpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifdef USE_CUDA\n\n#include \n\n#include \n\nnamespace LightGBM {\n\nCUDAMetadata::CUDAMetadata(const int gpu_device_id) {\n if (gpu_device_id >= 0) {\n SetCUDADevice(gpu_device_id, __FILE__, __LINE__);\n } else {\n SetCUDADevice(0, __FILE__, __LINE__);\n }\n}\n\nCUDAMetadata::~CUDAMetadata() {}\n\nvoid CUDAMetadata::Init(const std::vector& label,\n const std::vector& weight,\n const std::vector& query_boundaries,\n const std::vector& query_weights,\n const std::vector& init_score) {\n if (label.size() == 0) {\n cuda_label_.Clear();\n } else {\n cuda_label_.InitFromHostVector(label);\n }\n if (weight.size() == 0) {\n cuda_weights_.Clear();\n } else {\n cuda_weights_.InitFromHostVector(weight);\n }\n if (query_boundaries.size() == 0) {\n cuda_query_boundaries_.Clear();\n } else {\n cuda_query_boundaries_.InitFromHostVector(query_boundaries);\n }\n if (query_weights.size() == 0) {\n cuda_query_weights_.Clear();\n } else {\n cuda_query_weights_.InitFromHostVector(query_weights);\n }\n if (init_score.size() == 0) {\n cuda_init_score_.Clear();\n } else {\n cuda_init_score_.InitFromHostVector(init_score);\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\nvoid CUDAMetadata::SetLabel(const label_t* label, data_size_t len) {\n cuda_label_.InitFromHostMemory(label, static_cast(len));\n}\n\nvoid CUDAMetadata::SetWeights(const label_t* weights, data_size_t len) {\n cuda_weights_.InitFromHostMemory(weights, static_cast(len));\n}\n\nvoid CUDAMetadata::SetQuery(const data_size_t* query_boundaries, const label_t* query_weights, data_size_t num_queries) {\n cuda_query_boundaries_.InitFromHostMemory(query_boundaries, static_cast(num_queries) + 1);\n if (query_weights != nullptr) {\n cuda_query_weights_.InitFromHostMemory(query_weights, static_cast(num_queries));\n }\n}\n\nvoid CUDAMetadata::SetInitScore(const double* init_score, data_size_t len) {\n cuda_init_score_.InitFromHostMemory(init_score, len);\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/io/cuda/cuda_row_data.cpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifdef USE_CUDA\n\n#include \n\n#include \n\nnamespace LightGBM {\n\nCUDARowData::CUDARowData(const Dataset* train_data,\n const TrainingShareStates* train_share_state,\n const int gpu_device_id,\n const bool gpu_use_dp):\ngpu_device_id_(gpu_device_id),\ngpu_use_dp_(gpu_use_dp) {\n num_threads_ = OMP_NUM_THREADS();\n num_data_ = train_data->num_data();\n const auto& feature_hist_offsets = train_share_state->feature_hist_offsets();\n if (gpu_use_dp_) {\n shared_hist_size_ = DP_SHARED_HIST_SIZE;\n } else {\n shared_hist_size_ = SP_SHARED_HIST_SIZE;\n }\n if (feature_hist_offsets.empty()) {\n num_total_bin_ = 0;\n } else {\n num_total_bin_ = static_cast(feature_hist_offsets.back());\n }\n num_feature_group_ = train_data->num_feature_groups();\n num_feature_ = train_data->num_features();\n if (gpu_device_id >= 0) {\n SetCUDADevice(gpu_device_id, __FILE__, __LINE__);\n } else {\n SetCUDADevice(0, __FILE__, __LINE__);\n }\n}\n\nCUDARowData::~CUDARowData() {}\n\nvoid CUDARowData::Init(const Dataset* train_data, TrainingShareStates* train_share_state) {\n if (num_feature_ == 0) {\n return;\n }\n DivideCUDAFeatureGroups(train_data, train_share_state);\n bit_type_ = 0;\n size_t total_size = 0;\n const void* host_row_ptr = nullptr;\n row_ptr_bit_type_ = 0;\n const void* host_data = train_share_state->GetRowWiseData(&bit_type_, &total_size, &is_sparse_, &host_row_ptr, &row_ptr_bit_type_);\n if (bit_type_ == 8) {\n if (!is_sparse_) {\n std::vector partitioned_data;\n GetDenseDataPartitioned(reinterpret_cast(host_data), &partitioned_data);\n cuda_data_uint8_t_.InitFromHostVector(partitioned_data);\n } else {\n if (row_ptr_bit_type_ == 16) {\n InitSparseData(\n reinterpret_cast(host_data),\n reinterpret_cast(host_row_ptr),\n &cuda_data_uint8_t_,\n &cuda_row_ptr_uint16_t_,\n &cuda_partition_ptr_uint16_t_);\n } else if (row_ptr_bit_type_ == 32) {\n InitSparseData(\n reinterpret_cast(host_data),\n reinterpret_cast(host_row_ptr),\n &cuda_data_uint8_t_,\n &cuda_row_ptr_uint32_t_,\n &cuda_partition_ptr_uint32_t_);\n } else if (row_ptr_bit_type_ == 64) {\n InitSparseData(\n reinterpret_cast(host_data),\n reinterpret_cast(host_row_ptr),\n &cuda_data_uint8_t_,\n &cuda_row_ptr_uint64_t_,\n &cuda_partition_ptr_uint64_t_);\n } else {\n Log::Fatal(\"Unknown data ptr bit type %d\", row_ptr_bit_type_);\n }\n }\n } else if (bit_type_ == 16) {\n if (!is_sparse_) {\n std::vector partitioned_data;\n GetDenseDataPartitioned(reinterpret_cast(host_data), &partitioned_data);\n cuda_data_uint16_t_.InitFromHostVector(partitioned_data);\n } else {\n if (row_ptr_bit_type_ == 16) {\n InitSparseData(\n reinterpret_cast(host_data),\n reinterpret_cast(host_row_ptr),\n &cuda_data_uint16_t_,\n &cuda_row_ptr_uint16_t_,\n &cuda_partition_ptr_uint16_t_);\n } else if (row_ptr_bit_type_ == 32) {\n InitSparseData(\n reinterpret_cast(host_data),\n reinterpret_cast(host_row_ptr),\n &cuda_data_uint16_t_,\n &cuda_row_ptr_uint32_t_,\n &cuda_partition_ptr_uint32_t_);\n } else if (row_ptr_bit_type_ == 64) {\n InitSparseData(\n reinterpret_cast(host_data),\n reinterpret_cast(host_row_ptr),\n &cuda_data_uint16_t_,\n &cuda_row_ptr_uint64_t_,\n &cuda_partition_ptr_uint64_t_);\n } else {\n Log::Fatal(\"Unknown data ptr bit type %d\", row_ptr_bit_type_);\n }\n }\n } else if (bit_type_ == 32) {\n if (!is_sparse_) {\n std::vector partitioned_data;\n GetDenseDataPartitioned(reinterpret_cast(host_data), &partitioned_data);\n cuda_data_uint32_t_.InitFromHostVector(partitioned_data);\n } else {\n if (row_ptr_bit_type_ == 16) {\n InitSparseData(\n reinterpret_cast(host_data),\n reinterpret_cast(host_row_ptr),\n &cuda_data_uint32_t_,\n &cuda_row_ptr_uint16_t_,\n &cuda_partition_ptr_uint16_t_);\n } else if (row_ptr_bit_type_ == 32) {\n InitSparseData(\n reinterpret_cast(host_data),\n reinterpret_cast(host_row_ptr),\n &cuda_data_uint32_t_,\n &cuda_row_ptr_uint32_t_,\n &cuda_partition_ptr_uint32_t_);\n } else if (row_ptr_bit_type_ == 64) {\n InitSparseData(\n reinterpret_cast(host_data),\n reinterpret_cast(host_row_ptr),\n &cuda_data_uint32_t_,\n &cuda_row_ptr_uint64_t_,\n &cuda_partition_ptr_uint64_t_);\n } else {\n Log::Fatal(\"Unknown data ptr bit type %d\", row_ptr_bit_type_);\n }\n }\n } else {\n Log::Fatal(\"Unknown bit type = %d\", bit_type_);\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\nvoid CUDARowData::DivideCUDAFeatureGroups(const Dataset* train_data, TrainingShareStates* share_state) {\n const uint32_t max_num_bin_per_partition = shared_hist_size_ / 2;\n const std::vector& column_hist_offsets = share_state->column_hist_offsets();\n std::vector feature_group_num_feature_offsets;\n int offsets = 0;\n int prev_group_index = -1;\n for (int feature_index = 0; feature_index < num_feature_; ++feature_index) {\n const int feature_group_index = train_data->Feature2Group(feature_index);\n if (prev_group_index == -1 || feature_group_index != prev_group_index) {\n feature_group_num_feature_offsets.emplace_back(offsets);\n prev_group_index = feature_group_index;\n }\n ++offsets;\n }\n CHECK_EQ(offsets, num_feature_);\n feature_group_num_feature_offsets.emplace_back(offsets);\n\n uint32_t start_hist_offset = 0;\n feature_partition_column_index_offsets_.clear();\n column_hist_offsets_.clear();\n partition_hist_offsets_.clear();\n feature_partition_column_index_offsets_.emplace_back(0);\n partition_hist_offsets_.emplace_back(0);\n const int num_feature_groups = train_data->num_feature_groups();\n int column_index = 0;\n num_feature_partitions_ = 0;\n large_bin_partitions_.clear();\n small_bin_partitions_.clear();\n for (int feature_group_index = 0; feature_group_index < num_feature_groups; ++feature_group_index) {\n if (!train_data->IsMultiGroup(feature_group_index)) {\n const uint32_t column_feature_hist_start = column_hist_offsets[column_index];\n const uint32_t column_feature_hist_end = column_hist_offsets[column_index + 1];\n const uint32_t num_bin_in_dense_group = column_feature_hist_end - column_feature_hist_start;\n\n // if one column has too many bins, use a separate partition for that column\n if (num_bin_in_dense_group > max_num_bin_per_partition) {\n feature_partition_column_index_offsets_.emplace_back(column_index + 1);\n start_hist_offset = column_feature_hist_end;\n partition_hist_offsets_.emplace_back(start_hist_offset);\n large_bin_partitions_.emplace_back(num_feature_partitions_);\n ++num_feature_partitions_;\n column_hist_offsets_.emplace_back(0);\n ++column_index;\n continue;\n }\n\n // try if adding this column exceed the maximum number per partition\n const uint32_t cur_hist_num_bin = column_feature_hist_end - start_hist_offset;\n if (cur_hist_num_bin > max_num_bin_per_partition) {\n feature_partition_column_index_offsets_.emplace_back(column_index);\n start_hist_offset = column_feature_hist_start;\n partition_hist_offsets_.emplace_back(start_hist_offset);\n small_bin_partitions_.emplace_back(num_feature_partitions_);\n ++num_feature_partitions_;\n }\n column_hist_offsets_.emplace_back(column_hist_offsets[column_index] - start_hist_offset);\n if (feature_group_index == num_feature_groups - 1) {\n feature_partition_column_index_offsets_.emplace_back(column_index + 1);\n partition_hist_offsets_.emplace_back(column_hist_offsets.back());\n small_bin_partitions_.emplace_back(num_feature_partitions_);\n ++num_feature_partitions_;\n }\n ++column_index;\n } else {\n const int group_feature_index_start = feature_group_num_feature_offsets[feature_group_index];\n const int num_feature_in_group = feature_group_num_feature_offsets[feature_group_index + 1] - group_feature_index_start;\n for (int sub_feature_index = 0; sub_feature_index < num_feature_in_group; ++sub_feature_index) {\n const int feature_index = group_feature_index_start + sub_feature_index;\n const uint32_t column_feature_hist_start = column_hist_offsets[column_index];\n const uint32_t column_feature_hist_end = column_hist_offsets[column_index + 1];\n const uint32_t num_bin_in_dense_group = column_feature_hist_end - column_feature_hist_start;\n\n // if one column has too many bins, use a separate partition for that column\n if (num_bin_in_dense_group > max_num_bin_per_partition) {\n feature_partition_column_index_offsets_.emplace_back(column_index + 1);\n start_hist_offset = column_feature_hist_end;\n partition_hist_offsets_.emplace_back(start_hist_offset);\n large_bin_partitions_.emplace_back(num_feature_partitions_);\n ++num_feature_partitions_;\n column_hist_offsets_.emplace_back(0);\n ++column_index;\n continue;\n }\n\n // try if adding this column exceed the maximum number per partition\n const uint32_t cur_hist_num_bin = column_feature_hist_end - start_hist_offset;\n if (cur_hist_num_bin > max_num_bin_per_partition) {\n feature_partition_column_index_offsets_.emplace_back(column_index);\n start_hist_offset = column_feature_hist_start;\n partition_hist_offsets_.emplace_back(start_hist_offset);\n small_bin_partitions_.emplace_back(num_feature_partitions_);\n ++num_feature_partitions_;\n }\n column_hist_offsets_.emplace_back(column_hist_offsets[column_index] - start_hist_offset);\n if (feature_group_index == num_feature_groups - 1 && sub_feature_index == num_feature_in_group - 1) {\n CHECK_EQ(feature_index, num_feature_ - 1);\n feature_partition_column_index_offsets_.emplace_back(column_index + 1);\n partition_hist_offsets_.emplace_back(column_hist_offsets.back());\n small_bin_partitions_.emplace_back(num_feature_partitions_);\n ++num_feature_partitions_;\n }\n ++column_index;\n }\n }\n }\n column_hist_offsets_.emplace_back(column_hist_offsets.back() - start_hist_offset);\n max_num_column_per_partition_ = 0;\n for (size_t i = 0; i < feature_partition_column_index_offsets_.size() - 1; ++i) {\n const int num_column = feature_partition_column_index_offsets_[i + 1] - feature_partition_column_index_offsets_[i];\n if (num_column > max_num_column_per_partition_) {\n max_num_column_per_partition_ = num_column;\n }\n }\n\n cuda_feature_partition_column_index_offsets_.InitFromHostVector(feature_partition_column_index_offsets_);\n cuda_column_hist_offsets_.InitFromHostVector(column_hist_offsets_);\n cuda_partition_hist_offsets_.InitFromHostVector(partition_hist_offsets_);\n}\n\ntemplate \nvoid CUDARowData::GetDenseDataPartitioned(const BIN_TYPE* row_wise_data, std::vector* partitioned_data) {\n const int num_total_columns = feature_partition_column_index_offsets_.back();\n partitioned_data->resize(static_cast(num_total_columns) * static_cast(num_data_), 0);\n BIN_TYPE* out_data = partitioned_data->data();\n Threading::For(0, num_data_, 512,\n [this, num_total_columns, row_wise_data, out_data] (int /*thread_index*/, data_size_t start, data_size_t end) {\n for (size_t i = 0; i < feature_partition_column_index_offsets_.size() - 1; ++i) {\n const int num_prev_columns = static_cast(feature_partition_column_index_offsets_[i]);\n const size_t offset = static_cast(num_data_) * static_cast(num_prev_columns);\n const int partition_column_start = feature_partition_column_index_offsets_[i];\n const int partition_column_end = feature_partition_column_index_offsets_[i + 1];\n const int num_columns_in_cur_partition = partition_column_end - partition_column_start;\n for (data_size_t data_index = start; data_index < end; ++data_index) {\n const size_t data_offset = offset + static_cast(data_index) * num_columns_in_cur_partition;\n const size_t read_data_offset = static_cast(data_index) * num_total_columns;\n for (int column_index = 0; column_index < num_columns_in_cur_partition; ++column_index) {\n const size_t true_column_index = read_data_offset + column_index + partition_column_start;\n const BIN_TYPE bin = row_wise_data[true_column_index];\n out_data[data_offset + column_index] = bin;\n }\n }\n }\n });\n}\n\ntemplate \nvoid CUDARowData::GetSparseDataPartitioned(\n const BIN_TYPE* row_wise_data,\n const DATA_PTR_TYPE* row_ptr,\n std::vector>* partitioned_data,\n std::vector>* partitioned_row_ptr,\n std::vector* partition_ptr) {\n const int num_partitions = static_cast(feature_partition_column_index_offsets_.size()) - 1;\n partitioned_data->resize(num_partitions);\n partitioned_row_ptr->resize(num_partitions);\n std::vector thread_max_elements_per_row(num_threads_, 0);\n Threading::For(0, num_partitions, 1,\n [partitioned_data, partitioned_row_ptr, row_ptr, row_wise_data, &thread_max_elements_per_row, this] (int thread_index, int start, int end) {\n for (int partition_index = start; partition_index < end; ++partition_index) {\n std::vector& data_for_this_partition = partitioned_data->at(partition_index);\n std::vector& row_ptr_for_this_partition = partitioned_row_ptr->at(partition_index);\n const int partition_hist_start = partition_hist_offsets_[partition_index];\n const int partition_hist_end = partition_hist_offsets_[partition_index + 1];\n DATA_PTR_TYPE offset = 0;\n row_ptr_for_this_partition.clear();\n data_for_this_partition.clear();\n row_ptr_for_this_partition.emplace_back(offset);\n for (data_size_t data_index = 0; data_index < num_data_; ++data_index) {\n const DATA_PTR_TYPE row_start = row_ptr[data_index];\n const DATA_PTR_TYPE row_end = row_ptr[data_index + 1];\n const BIN_TYPE* row_data_start = row_wise_data + row_start;\n const BIN_TYPE* row_data_end = row_wise_data + row_end;\n const size_t partition_start_in_row = std::lower_bound(row_data_start, row_data_end, partition_hist_start) - row_data_start;\n const size_t partition_end_in_row = std::lower_bound(row_data_start, row_data_end, partition_hist_end) - row_data_start;\n for (size_t pos = partition_start_in_row; pos < partition_end_in_row; ++pos) {\n const BIN_TYPE bin = row_data_start[pos];\n CHECK_GE(bin, static_cast(partition_hist_start));\n data_for_this_partition.emplace_back(bin - partition_hist_start);\n }\n CHECK_GE(partition_end_in_row, partition_start_in_row);\n const data_size_t num_elements_in_row = partition_end_in_row - partition_start_in_row;\n offset += static_cast(num_elements_in_row);\n row_ptr_for_this_partition.emplace_back(offset);\n if (num_elements_in_row > thread_max_elements_per_row[thread_index]) {\n thread_max_elements_per_row[thread_index] = num_elements_in_row;\n }\n }\n }\n });\n partition_ptr->clear();\n DATA_PTR_TYPE offset = 0;\n partition_ptr->emplace_back(offset);\n for (size_t i = 0; i < partitioned_row_ptr->size(); ++i) {\n offset += partitioned_row_ptr->at(i).back();\n partition_ptr->emplace_back(offset);\n }\n max_num_column_per_partition_ = 0;\n for (int thread_index = 0; thread_index < num_threads_; ++thread_index) {\n if (thread_max_elements_per_row[thread_index] > max_num_column_per_partition_) {\n max_num_column_per_partition_ = thread_max_elements_per_row[thread_index];\n }\n }\n}\n\ntemplate \nvoid CUDARowData::InitSparseData(const BIN_TYPE* host_data,\n const ROW_PTR_TYPE* host_row_ptr,\n CUDAVector* cuda_data,\n CUDAVector* cuda_row_ptr,\n CUDAVector* cuda_partition_ptr) {\n std::vector> partitioned_data;\n std::vector> partitioned_data_ptr;\n std::vector partition_ptr;\n GetSparseDataPartitioned(host_data, host_row_ptr, &partitioned_data, &partitioned_data_ptr, &partition_ptr);\n cuda_partition_ptr->InitFromHostVector(partition_ptr);\n cuda_data->Resize(partition_ptr.back());\n cuda_row_ptr->Resize((num_data_ + 1) * partitioned_data_ptr.size());\n for (size_t i = 0; i < partitioned_data.size(); ++i) {\n const std::vector& data_ptr_for_this_partition = partitioned_data_ptr[i];\n const std::vector& data_for_this_partition = partitioned_data[i];\n CopyFromHostToCUDADevice(cuda_data->RawData() + partition_ptr[i], data_for_this_partition.data(), data_for_this_partition.size(), __FILE__, __LINE__);\n CopyFromHostToCUDADevice(cuda_row_ptr->RawData() + i * (num_data_ + 1), data_ptr_for_this_partition.data(), data_ptr_for_this_partition.size(), __FILE__, __LINE__);\n }\n}\n\ntemplate \nconst BIN_TYPE* CUDARowData::GetBin() const {\n if (bit_type_ == 8) {\n return reinterpret_cast(cuda_data_uint8_t_.RawData());\n } else if (bit_type_ == 16) {\n return reinterpret_cast(cuda_data_uint16_t_.RawData());\n } else if (bit_type_ == 32) {\n return reinterpret_cast(cuda_data_uint32_t_.RawData());\n } else {\n Log::Fatal(\"Unknown bit_type %d for GetBin.\", bit_type_);\n }\n}\n\ntemplate const uint8_t* CUDARowData::GetBin() const;\n\ntemplate const uint16_t* CUDARowData::GetBin() const;\n\ntemplate const uint32_t* CUDARowData::GetBin() const;\n\ntemplate \nconst PTR_TYPE* CUDARowData::GetRowPtr() const {\n if (row_ptr_bit_type_ == 16) {\n return reinterpret_cast(cuda_row_ptr_uint16_t_.RawData());\n } else if (row_ptr_bit_type_ == 32) {\n return reinterpret_cast(cuda_row_ptr_uint32_t_.RawData());\n } else if (row_ptr_bit_type_ == 64) {\n return reinterpret_cast(cuda_row_ptr_uint64_t_.RawData());\n } else {\n Log::Fatal(\"Unknown row_ptr_bit_type = %d for GetRowPtr.\", row_ptr_bit_type_);\n }\n}\n\ntemplate const uint16_t* CUDARowData::GetRowPtr() const;\n\ntemplate const uint32_t* CUDARowData::GetRowPtr() const;\n\ntemplate const uint64_t* CUDARowData::GetRowPtr() const;\n\ntemplate \nconst PTR_TYPE* CUDARowData::GetPartitionPtr() const {\n if (row_ptr_bit_type_ == 16) {\n return reinterpret_cast(cuda_partition_ptr_uint16_t_.RawData());\n } else if (row_ptr_bit_type_ == 32) {\n return reinterpret_cast(cuda_partition_ptr_uint32_t_.RawData());\n } else if (row_ptr_bit_type_ == 64) {\n return reinterpret_cast(cuda_partition_ptr_uint64_t_.RawData());\n } else {\n Log::Fatal(\"Unknown row_ptr_bit_type = %d for GetPartitionPtr.\", row_ptr_bit_type_);\n }\n}\n\ntemplate const uint16_t* CUDARowData::GetPartitionPtr() const;\n\ntemplate const uint32_t* CUDARowData::GetPartitionPtr() const;\n\ntemplate const uint64_t* CUDARowData::GetPartitionPtr() const;\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/io/cuda/cuda_tree.cpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifdef USE_CUDA\n\n#include \n\nnamespace LightGBM {\n\nCUDATree::CUDATree(int max_leaves, bool track_branch_features, bool is_linear,\n const int gpu_device_id, const bool has_categorical_feature):\nTree(max_leaves, track_branch_features, is_linear),\nnum_threads_per_block_add_prediction_to_score_(1024) {\n is_cuda_tree_ = true;\n if (gpu_device_id >= 0) {\n SetCUDADevice(gpu_device_id, __FILE__, __LINE__);\n } else {\n SetCUDADevice(0, __FILE__, __LINE__);\n }\n if (has_categorical_feature) {\n cuda_cat_boundaries_.Resize(max_leaves);\n cuda_cat_boundaries_inner_.Resize(max_leaves);\n }\n InitCUDAMemory();\n}\n\nCUDATree::CUDATree(const Tree* host_tree):\n Tree(*host_tree),\n num_threads_per_block_add_prediction_to_score_(1024) {\n is_cuda_tree_ = true;\n InitCUDA();\n}\n\nCUDATree::~CUDATree() {\n gpuAssert(cudaStreamDestroy(cuda_stream_), __FILE__, __LINE__);\n}\n\nvoid CUDATree::InitCUDAMemory() {\n cuda_left_child_.Resize(static_cast(max_leaves_));\n cuda_right_child_.Resize(static_cast(max_leaves_));\n cuda_split_feature_inner_.Resize(static_cast(max_leaves_));\n cuda_split_feature_.Resize(static_cast(max_leaves_));\n cuda_leaf_depth_.Resize(static_cast(max_leaves_));\n cuda_leaf_parent_.Resize(static_cast(max_leaves_));\n cuda_threshold_in_bin_.Resize(static_cast(max_leaves_));\n cuda_threshold_.Resize(static_cast(max_leaves_));\n cuda_decision_type_.Resize(static_cast(max_leaves_));\n cuda_leaf_value_.Resize(static_cast(max_leaves_));\n cuda_internal_weight_.Resize(static_cast(max_leaves_));\n cuda_internal_value_.Resize(static_cast(max_leaves_));\n cuda_leaf_weight_.Resize(static_cast(max_leaves_));\n cuda_leaf_count_.Resize(static_cast(max_leaves_));\n cuda_internal_count_.Resize(static_cast(max_leaves_));\n cuda_split_gain_.Resize(static_cast(max_leaves_));\n SetCUDAMemory(cuda_leaf_value_.RawData(), 0.0f, 1, __FILE__, __LINE__);\n SetCUDAMemory(cuda_leaf_weight_.RawData(), 0.0f, 1, __FILE__, __LINE__);\n SetCUDAMemory(cuda_leaf_parent_.RawData(), -1, 1, __FILE__, __LINE__);\n CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_stream_));\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\nvoid CUDATree::InitCUDA() {\n cuda_left_child_.InitFromHostVector(left_child_);\n cuda_right_child_.InitFromHostVector(right_child_);\n cuda_split_feature_inner_.InitFromHostVector(split_feature_inner_);\n cuda_split_feature_.InitFromHostVector(split_feature_);\n cuda_threshold_in_bin_.InitFromHostVector(threshold_in_bin_);\n cuda_threshold_.InitFromHostVector(threshold_);\n cuda_leaf_depth_.InitFromHostVector(leaf_depth_);\n cuda_decision_type_.InitFromHostVector(decision_type_);\n cuda_internal_weight_.InitFromHostVector(internal_weight_);\n cuda_internal_value_.InitFromHostVector(internal_value_);\n cuda_internal_count_.InitFromHostVector(internal_count_);\n cuda_leaf_count_.InitFromHostVector(leaf_count_);\n cuda_split_gain_.InitFromHostVector(split_gain_);\n cuda_leaf_value_.InitFromHostVector(leaf_value_);\n cuda_leaf_weight_.InitFromHostVector(leaf_weight_);\n cuda_leaf_parent_.InitFromHostVector(leaf_parent_);\n CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_stream_));\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\nint CUDATree::Split(const int leaf_index,\n const int real_feature_index,\n const double real_threshold,\n const MissingType missing_type,\n const CUDASplitInfo* cuda_split_info) {\n LaunchSplitKernel(leaf_index, real_feature_index, real_threshold, missing_type, cuda_split_info);\n RecordBranchFeatures(leaf_index, num_leaves_, real_feature_index);\n ++num_leaves_;\n return num_leaves_ - 1;\n}\n\nint CUDATree::SplitCategorical(const int leaf_index,\n const int real_feature_index,\n const MissingType missing_type,\n const CUDASplitInfo* cuda_split_info,\n uint32_t* cuda_bitset,\n size_t cuda_bitset_len,\n uint32_t* cuda_bitset_inner,\n size_t cuda_bitset_inner_len) {\n LaunchSplitCategoricalKernel(leaf_index, real_feature_index,\n missing_type, cuda_split_info,\n cuda_bitset_len, cuda_bitset_inner_len);\n cuda_bitset_.PushBack(cuda_bitset, cuda_bitset_len);\n cuda_bitset_inner_.PushBack(cuda_bitset_inner, cuda_bitset_inner_len);\n ++num_leaves_;\n ++num_cat_;\n RecordBranchFeatures(leaf_index, num_leaves_, real_feature_index);\n return num_leaves_ - 1;\n}\n\nvoid CUDATree::RecordBranchFeatures(const int left_leaf_index,\n const int right_leaf_index,\n const int real_feature_index) {\n if (track_branch_features_) {\n branch_features_[right_leaf_index] = branch_features_[left_leaf_index];\n branch_features_[right_leaf_index].push_back(real_feature_index);\n branch_features_[left_leaf_index].push_back(real_feature_index);\n }\n}\n\nvoid CUDATree::AddPredictionToScore(const Dataset* data,\n data_size_t num_data,\n double* score) const {\n LaunchAddPredictionToScoreKernel(data, nullptr, num_data, score);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\nvoid CUDATree::AddPredictionToScore(const Dataset* data,\n const data_size_t* used_data_indices,\n data_size_t num_data, double* score) const {\n LaunchAddPredictionToScoreKernel(data, used_data_indices, num_data, score);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\ninline void CUDATree::Shrinkage(double rate) {\n Tree::Shrinkage(rate);\n LaunchShrinkageKernel(rate);\n}\n\ninline void CUDATree::AddBias(double val) {\n Tree::AddBias(val);\n LaunchAddBiasKernel(val);\n}\n\nvoid CUDATree::ToHost() {\n left_child_.resize(max_leaves_ - 1);\n right_child_.resize(max_leaves_ - 1);\n split_feature_inner_.resize(max_leaves_ - 1);\n split_feature_.resize(max_leaves_ - 1);\n threshold_in_bin_.resize(max_leaves_ - 1);\n threshold_.resize(max_leaves_ - 1);\n decision_type_.resize(max_leaves_ - 1, 0);\n split_gain_.resize(max_leaves_ - 1);\n leaf_parent_.resize(max_leaves_);\n leaf_value_.resize(max_leaves_);\n leaf_weight_.resize(max_leaves_);\n leaf_count_.resize(max_leaves_);\n internal_value_.resize(max_leaves_ - 1);\n internal_weight_.resize(max_leaves_ - 1);\n internal_count_.resize(max_leaves_ - 1);\n leaf_depth_.resize(max_leaves_);\n\n const size_t num_leaves_size = static_cast(num_leaves_);\n CopyFromCUDADeviceToHost(left_child_.data(), cuda_left_child_.RawData(), num_leaves_size - 1, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(right_child_.data(), cuda_right_child_.RawData(), num_leaves_size - 1, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(split_feature_inner_.data(), cuda_split_feature_inner_.RawData(), num_leaves_size - 1, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(split_feature_.data(), cuda_split_feature_.RawData(), num_leaves_size - 1, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(threshold_in_bin_.data(), cuda_threshold_in_bin_.RawData(), num_leaves_size - 1, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(threshold_.data(), cuda_threshold_.RawData(), num_leaves_size - 1, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(decision_type_.data(), cuda_decision_type_.RawData(), num_leaves_size - 1, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(split_gain_.data(), cuda_split_gain_.RawData(), num_leaves_size - 1, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(leaf_parent_.data(), cuda_leaf_parent_.RawData(), num_leaves_size - 1, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(leaf_value_.data(), cuda_leaf_value_.RawData(), num_leaves_size, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(leaf_weight_.data(), cuda_leaf_weight_.RawData(), num_leaves_size, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(leaf_count_.data(), cuda_leaf_count_.RawData(), num_leaves_size, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(internal_value_.data(), cuda_internal_value_.RawData(), num_leaves_size - 1, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(internal_weight_.data(), cuda_internal_weight_.RawData(), num_leaves_size - 1, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(internal_count_.data(), cuda_internal_count_.RawData(), num_leaves_size - 1, __FILE__, __LINE__);\n CopyFromCUDADeviceToHost(leaf_depth_.data(), cuda_leaf_depth_.RawData(), num_leaves_size, __FILE__, __LINE__);\n\n if (num_cat_ > 0) {\n cuda_cat_boundaries_inner_.Resize(num_cat_ + 1);\n cuda_cat_boundaries_.Resize(num_cat_ + 1);\n cat_boundaries_ = cuda_cat_boundaries_.ToHost();\n cat_boundaries_inner_ = cuda_cat_boundaries_inner_.ToHost();\n cat_threshold_ = cuda_bitset_.ToHost();\n cat_threshold_inner_ = cuda_bitset_inner_.ToHost();\n }\n\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\nvoid CUDATree::SyncLeafOutputFromHostToCUDA() {\n CopyFromHostToCUDADevice(cuda_leaf_value_.RawData(), leaf_value_.data(), leaf_value_.size(), __FILE__, __LINE__);\n}\n\nvoid CUDATree::SyncLeafOutputFromCUDAToHost() {\n CopyFromCUDADeviceToHost(leaf_value_.data(), cuda_leaf_value_.RawData(), leaf_value_.size(), __FILE__, __LINE__);\n}\n\nvoid CUDATree::AsConstantTree(double val, int count) {\n Tree::AsConstantTree(val, count);\n CopyFromHostToCUDADevice(cuda_leaf_value_.RawData(), &val, 1, __FILE__, __LINE__);\n CopyFromHostToCUDADevice(cuda_leaf_count_.RawData(), &count, 1, __FILE__, __LINE__);\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/io/cuda/cuda_tree.cu", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n\n#ifdef USE_CUDA\n\n#include \n\nnamespace LightGBM {\n\n__device__ void SetDecisionTypeCUDA(int8_t* decision_type, bool input, int8_t mask) {\n if (input) {\n (*decision_type) |= mask;\n } else {\n (*decision_type) &= (127 - mask);\n }\n}\n\n__device__ void SetMissingTypeCUDA(int8_t* decision_type, int8_t input) {\n (*decision_type) &= 3;\n (*decision_type) |= (input << 2);\n}\n\n__device__ bool GetDecisionTypeCUDA(int8_t decision_type, int8_t mask) {\n return (decision_type & mask) > 0;\n}\n\n__device__ int8_t GetMissingTypeCUDA(int8_t decision_type) {\n return (decision_type >> 2) & 3;\n}\n\n__device__ bool IsZeroCUDA(double fval) {\n return (fval >= -kZeroThreshold && fval <= kZeroThreshold);\n}\n\ntemplate\n__device__ bool FindInBitsetCUDA(const uint32_t* bits, int n, T pos) {\n int i1 = pos / 32;\n if (i1 >= n) {\n return false;\n }\n int i2 = pos % 32;\n return (bits[i1] >> i2) & 1;\n}\n\n__global__ void SplitKernel( // split information\n const int leaf_index,\n const int real_feature_index,\n const double real_threshold,\n const MissingType missing_type,\n const CUDASplitInfo* cuda_split_info,\n // tree structure\n const int num_leaves,\n int* leaf_parent,\n int* leaf_depth,\n int* left_child,\n int* right_child,\n int* split_feature_inner,\n int* split_feature,\n float* split_gain,\n double* internal_weight,\n double* internal_value,\n data_size_t* internal_count,\n double* leaf_weight,\n double* leaf_value,\n data_size_t* leaf_count,\n int8_t* decision_type,\n uint32_t* threshold_in_bin,\n double* threshold) {\n const int new_node_index = num_leaves - 1;\n const int thread_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n const int parent_index = leaf_parent[leaf_index];\n if (thread_index == 0) {\n if (parent_index >= 0) {\n // if cur node is left child\n if (left_child[parent_index] == ~leaf_index) {\n left_child[parent_index] = new_node_index;\n } else {\n right_child[parent_index] = new_node_index;\n }\n }\n left_child[new_node_index] = ~leaf_index;\n right_child[new_node_index] = ~num_leaves;\n leaf_parent[leaf_index] = new_node_index;\n leaf_parent[num_leaves] = new_node_index;\n } else if (thread_index == 1) {\n // add new node\n split_feature_inner[new_node_index] = cuda_split_info->inner_feature_index;\n } else if (thread_index == 2) {\n split_feature[new_node_index] = real_feature_index;\n } else if (thread_index == 3) {\n split_gain[new_node_index] = static_cast(cuda_split_info->gain);\n } else if (thread_index == 4) {\n // save current leaf value to internal node before change\n internal_weight[new_node_index] = cuda_split_info->left_sum_hessians + cuda_split_info->right_sum_hessians;\n leaf_weight[leaf_index] = cuda_split_info->left_sum_hessians;\n } else if (thread_index == 5) {\n internal_value[new_node_index] = leaf_value[leaf_index];\n leaf_value[leaf_index] = isnan(cuda_split_info->left_value) ? 0.0f : cuda_split_info->left_value;\n } else if (thread_index == 6) {\n internal_count[new_node_index] = cuda_split_info->left_count + cuda_split_info->right_count;\n } else if (thread_index == 7) {\n leaf_count[leaf_index] = cuda_split_info->left_count;\n } else if (thread_index == 8) {\n leaf_value[num_leaves] = isnan(cuda_split_info->right_value) ? 0.0f : cuda_split_info->right_value;\n } else if (thread_index == 9) {\n leaf_weight[num_leaves] = cuda_split_info->right_sum_hessians;\n } else if (thread_index == 10) {\n leaf_count[num_leaves] = cuda_split_info->right_count;\n } else if (thread_index == 11) {\n // update leaf depth\n leaf_depth[num_leaves] = leaf_depth[leaf_index] + 1;\n leaf_depth[leaf_index]++;\n } else if (thread_index == 12) {\n decision_type[new_node_index] = 0;\n SetDecisionTypeCUDA(&decision_type[new_node_index], false, kCategoricalMask);\n SetDecisionTypeCUDA(&decision_type[new_node_index], cuda_split_info->default_left, kDefaultLeftMask);\n SetMissingTypeCUDA(&decision_type[new_node_index], static_cast(missing_type));\n } else if (thread_index == 13) {\n threshold_in_bin[new_node_index] = cuda_split_info->threshold;\n } else if (thread_index == 14) {\n threshold[new_node_index] = real_threshold;\n }\n}\n\nvoid CUDATree::LaunchSplitKernel(const int leaf_index,\n const int real_feature_index,\n const double real_threshold,\n const MissingType missing_type,\n const CUDASplitInfo* cuda_split_info) {\n SplitKernel<<<3, 5, 0, cuda_stream_>>>(\n // split information\n leaf_index,\n real_feature_index,\n real_threshold,\n missing_type,\n cuda_split_info,\n // tree structure\n num_leaves_,\n cuda_leaf_parent_.RawData(),\n cuda_leaf_depth_.RawData(),\n cuda_left_child_.RawData(),\n cuda_right_child_.RawData(),\n cuda_split_feature_inner_.RawData(),\n cuda_split_feature_.RawData(),\n cuda_split_gain_.RawData(),\n cuda_internal_weight_.RawData(),\n cuda_internal_value_.RawData(),\n cuda_internal_count_.RawData(),\n cuda_leaf_weight_.RawData(),\n cuda_leaf_value_.RawData(),\n cuda_leaf_count_.RawData(),\n cuda_decision_type_.RawData(),\n cuda_threshold_in_bin_.RawData(),\n cuda_threshold_.RawData());\n}\n\n__global__ void SplitCategoricalKernel( // split information\n const int leaf_index,\n const int real_feature_index,\n const MissingType missing_type,\n const CUDASplitInfo* cuda_split_info,\n // tree structure\n const int num_leaves,\n int* leaf_parent,\n int* leaf_depth,\n int* left_child,\n int* right_child,\n int* split_feature_inner,\n int* split_feature,\n float* split_gain,\n double* internal_weight,\n double* internal_value,\n data_size_t* internal_count,\n double* leaf_weight,\n double* leaf_value,\n data_size_t* leaf_count,\n int8_t* decision_type,\n uint32_t* threshold_in_bin,\n double* threshold,\n size_t cuda_bitset_len,\n size_t cuda_bitset_inner_len,\n int num_cat,\n int* cuda_cat_boundaries,\n int* cuda_cat_boundaries_inner) {\n const int new_node_index = num_leaves - 1;\n const int thread_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n const int parent_index = leaf_parent[leaf_index];\n if (thread_index == 0) {\n if (parent_index >= 0) {\n // if cur node is left child\n if (left_child[parent_index] == ~leaf_index) {\n left_child[parent_index] = new_node_index;\n } else {\n right_child[parent_index] = new_node_index;\n }\n }\n left_child[new_node_index] = ~leaf_index;\n right_child[new_node_index] = ~num_leaves;\n leaf_parent[leaf_index] = new_node_index;\n leaf_parent[num_leaves] = new_node_index;\n } else if (thread_index == 1) {\n // add new node\n split_feature_inner[new_node_index] = cuda_split_info->inner_feature_index;\n } else if (thread_index == 2) {\n split_feature[new_node_index] = real_feature_index;\n } else if (thread_index == 3) {\n split_gain[new_node_index] = static_cast(cuda_split_info->gain);\n } else if (thread_index == 4) {\n // save current leaf value to internal node before change\n internal_weight[new_node_index] = cuda_split_info->left_sum_hessians + cuda_split_info->right_sum_hessians;\n leaf_weight[leaf_index] = cuda_split_info->left_sum_hessians;\n } else if (thread_index == 5) {\n internal_value[new_node_index] = leaf_value[leaf_index];\n leaf_value[leaf_index] = isnan(cuda_split_info->left_value) ? 0.0f : cuda_split_info->left_value;\n } else if (thread_index == 6) {\n internal_count[new_node_index] = cuda_split_info->left_count + cuda_split_info->right_count;\n } else if (thread_index == 7) {\n leaf_count[leaf_index] = cuda_split_info->left_count;\n } else if (thread_index == 8) {\n leaf_value[num_leaves] = isnan(cuda_split_info->right_value) ? 0.0f : cuda_split_info->right_value;\n } else if (thread_index == 9) {\n leaf_weight[num_leaves] = cuda_split_info->right_sum_hessians;\n } else if (thread_index == 10) {\n leaf_count[num_leaves] = cuda_split_info->right_count;\n } else if (thread_index == 11) {\n // update leaf depth\n leaf_depth[num_leaves] = leaf_depth[leaf_index] + 1;\n leaf_depth[leaf_index]++;\n } else if (thread_index == 12) {\n decision_type[new_node_index] = 0;\n SetDecisionTypeCUDA(&decision_type[new_node_index], true, kCategoricalMask);\n SetMissingTypeCUDA(&decision_type[new_node_index], static_cast(missing_type));\n } else if (thread_index == 13) {\n threshold_in_bin[new_node_index] = num_cat;\n } else if (thread_index == 14) {\n threshold[new_node_index] = num_cat;\n } else if (thread_index == 15) {\n if (num_cat == 0) {\n cuda_cat_boundaries[num_cat] = 0;\n }\n cuda_cat_boundaries[num_cat + 1] = cuda_cat_boundaries[num_cat] + cuda_bitset_len;\n } else if (thread_index == 16) {\n if (num_cat == 0) {\n cuda_cat_boundaries_inner[num_cat] = 0;\n }\n cuda_cat_boundaries_inner[num_cat + 1] = cuda_cat_boundaries_inner[num_cat] + cuda_bitset_inner_len;\n }\n}\n\nvoid CUDATree::LaunchSplitCategoricalKernel(const int leaf_index,\n const int real_feature_index,\n const MissingType missing_type,\n const CUDASplitInfo* cuda_split_info,\n size_t cuda_bitset_len,\n size_t cuda_bitset_inner_len) {\n SplitCategoricalKernel<<<3, 6, 0, cuda_stream_>>>(\n // split information\n leaf_index,\n real_feature_index,\n missing_type,\n cuda_split_info,\n // tree structure\n num_leaves_,\n cuda_leaf_parent_.RawData(),\n cuda_leaf_depth_.RawData(),\n cuda_left_child_.RawData(),\n cuda_right_child_.RawData(),\n cuda_split_feature_inner_.RawData(),\n cuda_split_feature_.RawData(),\n cuda_split_gain_.RawData(),\n cuda_internal_weight_.RawData(),\n cuda_internal_value_.RawData(),\n cuda_internal_count_.RawData(),\n cuda_leaf_weight_.RawData(),\n cuda_leaf_value_.RawData(),\n cuda_leaf_count_.RawData(),\n cuda_decision_type_.RawData(),\n cuda_threshold_in_bin_.RawData(),\n cuda_threshold_.RawData(),\n cuda_bitset_len,\n cuda_bitset_inner_len,\n num_cat_,\n cuda_cat_boundaries_.RawData(),\n cuda_cat_boundaries_inner_.RawData());\n}\n\n__global__ void ShrinkageKernel(const double rate, double* cuda_leaf_value, const int num_leaves) {\n const int leaf_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x);\n if (leaf_index < num_leaves) {\n cuda_leaf_value[leaf_index] *= rate;\n }\n}\n\nvoid CUDATree::LaunchShrinkageKernel(const double rate) {\n const int num_threads_per_block = 1024;\n const int num_blocks = (num_leaves_ + num_threads_per_block - 1) / num_threads_per_block;\n ShrinkageKernel<<>>(rate, cuda_leaf_value_.RawData(), num_leaves_);\n}\n\n__global__ void AddBiasKernel(const double val, double* cuda_leaf_value, const int num_leaves) {\n const int leaf_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x);\n if (leaf_index < num_leaves) {\n cuda_leaf_value[leaf_index] += val;\n }\n}\n\nvoid CUDATree::LaunchAddBiasKernel(const double val) {\n const int num_threads_per_block = 1024;\n const int num_blocks = (num_leaves_ + num_threads_per_block - 1) / num_threads_per_block;\n AddBiasKernel<<>>(val, cuda_leaf_value_.RawData(), num_leaves_);\n}\n\ntemplate \n__global__ void AddPredictionToScoreKernel(\n // dataset information\n const data_size_t num_data,\n uint8_t* const* cuda_data_by_column,\n const uint8_t* cuda_column_bit_type,\n const uint32_t* cuda_feature_min_bin,\n const uint32_t* cuda_feature_max_bin,\n const uint32_t* cuda_feature_offset,\n const uint32_t* cuda_feature_default_bin,\n const uint32_t* cuda_feature_most_freq_bin,\n const int* cuda_feature_to_column,\n const data_size_t* cuda_used_indices,\n // tree information\n const uint32_t* cuda_threshold_in_bin,\n const int8_t* cuda_decision_type,\n const int* cuda_split_feature_inner,\n const int* cuda_left_child,\n const int* cuda_right_child,\n const double* cuda_leaf_value,\n const uint32_t* cuda_bitset_inner,\n const int* cuda_cat_boundaries_inner,\n // output\n double* score) {\n const data_size_t inner_data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n if (inner_data_index < num_data) {\n const data_size_t data_index = USE_INDICES ? cuda_used_indices[inner_data_index] : inner_data_index;\n int node = 0;\n while (node >= 0) {\n const int split_feature_inner = cuda_split_feature_inner[node];\n const int column = cuda_feature_to_column[split_feature_inner];\n const uint32_t default_bin = cuda_feature_default_bin[split_feature_inner];\n const uint32_t most_freq_bin = cuda_feature_most_freq_bin[split_feature_inner];\n const uint32_t max_bin = cuda_feature_max_bin[split_feature_inner];\n const uint32_t min_bin = cuda_feature_min_bin[split_feature_inner];\n const uint32_t offset = cuda_feature_offset[split_feature_inner];\n const uint8_t column_bit_type = cuda_column_bit_type[column];\n uint32_t bin = 0;\n if (column_bit_type == 8) {\n bin = static_cast((reinterpret_cast(cuda_data_by_column[column]))[data_index]);\n } else if (column_bit_type == 16) {\n bin = static_cast((reinterpret_cast(cuda_data_by_column[column]))[data_index]);\n } else if (column_bit_type == 32) {\n bin = static_cast((reinterpret_cast(cuda_data_by_column[column]))[data_index]);\n }\n if (bin >= min_bin && bin <= max_bin) {\n bin = bin - min_bin + offset;\n } else {\n bin = most_freq_bin;\n }\n const int8_t decision_type = cuda_decision_type[node];\n if (GetDecisionTypeCUDA(decision_type, kCategoricalMask)) {\n int cat_idx = static_cast(cuda_threshold_in_bin[node]);\n if (FindInBitsetCUDA(cuda_bitset_inner + cuda_cat_boundaries_inner[cat_idx],\n cuda_cat_boundaries_inner[cat_idx + 1] - cuda_cat_boundaries_inner[cat_idx], bin)) {\n node = cuda_left_child[node];\n } else {\n node = cuda_right_child[node];\n }\n } else {\n const uint32_t threshold_in_bin = cuda_threshold_in_bin[node];\n const int8_t missing_type = GetMissingTypeCUDA(decision_type);\n const bool default_left = ((decision_type & kDefaultLeftMask) > 0);\n if ((missing_type == 1 && bin == default_bin) || (missing_type == 2 && bin == max_bin)) {\n if (default_left) {\n node = cuda_left_child[node];\n } else {\n node = cuda_right_child[node];\n }\n } else {\n if (bin <= threshold_in_bin) {\n node = cuda_left_child[node];\n } else {\n node = cuda_right_child[node];\n }\n }\n }\n }\n score[data_index] += cuda_leaf_value[~node];\n }\n}\n\nvoid CUDATree::LaunchAddPredictionToScoreKernel(\n const Dataset* data,\n const data_size_t* used_data_indices,\n data_size_t num_data,\n double* score) const {\n const CUDAColumnData* cuda_column_data = data->cuda_column_data();\n const int num_blocks = (num_data + num_threads_per_block_add_prediction_to_score_ - 1) / num_threads_per_block_add_prediction_to_score_;\n if (used_data_indices == nullptr) {\n AddPredictionToScoreKernel<<>>(\n // dataset information\n num_data,\n cuda_column_data->cuda_data_by_column(),\n cuda_column_data->cuda_column_bit_type(),\n cuda_column_data->cuda_feature_min_bin(),\n cuda_column_data->cuda_feature_max_bin(),\n cuda_column_data->cuda_feature_offset(),\n cuda_column_data->cuda_feature_default_bin(),\n cuda_column_data->cuda_feature_most_freq_bin(),\n cuda_column_data->cuda_feature_to_column(),\n nullptr,\n // tree information\n cuda_threshold_in_bin_.RawData(),\n cuda_decision_type_.RawData(),\n cuda_split_feature_inner_.RawData(),\n cuda_left_child_.RawData(),\n cuda_right_child_.RawData(),\n cuda_leaf_value_.RawData(),\n cuda_bitset_inner_.RawDataReadOnly(),\n cuda_cat_boundaries_inner_.RawDataReadOnly(),\n // output\n score);\n } else {\n AddPredictionToScoreKernel<<>>(\n // dataset information\n num_data,\n cuda_column_data->cuda_data_by_column(),\n cuda_column_data->cuda_column_bit_type(),\n cuda_column_data->cuda_feature_min_bin(),\n cuda_column_data->cuda_feature_max_bin(),\n cuda_column_data->cuda_feature_offset(),\n cuda_column_data->cuda_feature_default_bin(),\n cuda_column_data->cuda_feature_most_freq_bin(),\n cuda_column_data->cuda_feature_to_column(),\n used_data_indices,\n // tree information\n cuda_threshold_in_bin_.RawData(),\n cuda_decision_type_.RawData(),\n cuda_split_feature_inner_.RawData(),\n cuda_left_child_.RawData(),\n cuda_right_child_.RawData(),\n cuda_leaf_value_.RawData(),\n cuda_bitset_inner_.RawDataReadOnly(),\n cuda_cat_boundaries_inner_.RawDataReadOnly(),\n // output\n score);\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/io/dataset.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nconst int Dataset::kSerializedReferenceVersionLength = 2;\nconst char* Dataset::serialized_reference_version = \"v1\";\n\nconst char* Dataset::binary_file_token = \"______LightGBM_Binary_File_Token______\\n\";\nconst char* Dataset::binary_serialized_reference_token = \"______LightGBM_Binary_Serialized_Token______\\n\";\n\nDataset::Dataset() {\n data_filename_ = \"noname\";\n num_data_ = 0;\n is_finish_load_ = false;\n wait_for_manual_finish_ = false;\n has_raw_ = false;\n}\n\nDataset::Dataset(data_size_t num_data) {\n CHECK_GT(num_data, 0);\n data_filename_ = \"noname\";\n num_data_ = num_data;\n metadata_.Init(num_data_, NO_SPECIFIC, NO_SPECIFIC);\n is_finish_load_ = false;\n wait_for_manual_finish_ = false;\n group_bin_boundaries_.push_back(0);\n has_raw_ = false;\n}\n\nDataset::~Dataset() {}\n\nstd::vector> OneFeaturePerGroup(const std::vector& used_features) {\n std::vector> features_in_group;\n features_in_group.resize(used_features.size());\n for (size_t i = 0; i < used_features.size(); ++i) {\n features_in_group[i].emplace_back(used_features[i]);\n }\n return features_in_group;\n}\n\nint GetConflictCount(const std::vector& mark, const int* indices,\n int num_indices, data_size_t max_cnt) {\n int ret = 0;\n for (int i = 0; i < num_indices; ++i) {\n if (mark[indices[i]]) {\n ++ret;\n }\n if (ret > max_cnt) {\n return -1;\n }\n }\n return ret;\n}\n\nvoid MarkUsed(std::vector* mark, const int* indices,\n data_size_t num_indices) {\n auto& ref_mark = *mark;\n for (int i = 0; i < num_indices; ++i) {\n ref_mark[indices[i]] = true;\n }\n}\n\nstd::vector FixSampleIndices(const BinMapper* bin_mapper,\n int num_total_samples, int num_indices,\n const int* sample_indices,\n const double* sample_values) {\n std::vector ret;\n if (bin_mapper->GetDefaultBin() == bin_mapper->GetMostFreqBin()) {\n return ret;\n }\n int i = 0, j = 0;\n while (i < num_total_samples) {\n if (j < num_indices && sample_indices[j] < i) {\n ++j;\n } else if (j < num_indices && sample_indices[j] == i) {\n if (bin_mapper->ValueToBin(sample_values[j]) !=\n bin_mapper->GetMostFreqBin()) {\n ret.push_back(i);\n }\n ++i;\n } else {\n ret.push_back(i++);\n }\n }\n return ret;\n}\n\nstd::vector> FindGroups(\n const std::vector>& bin_mappers,\n const std::vector& find_order, int** sample_indices,\n const int* num_per_col, int num_sample_col, data_size_t total_sample_cnt,\n data_size_t num_data, bool is_use_gpu, bool is_sparse,\n std::vector* multi_val_group) {\n const int max_search_group = 100;\n const int max_bin_per_group = 256;\n const data_size_t single_val_max_conflict_cnt =\n static_cast(total_sample_cnt / 10000);\n multi_val_group->clear();\n\n Random rand(num_data);\n std::vector> features_in_group;\n std::vector> conflict_marks;\n std::vector group_used_row_cnt;\n std::vector group_total_data_cnt;\n std::vector group_num_bin;\n\n // first round: fill the single val group\n for (auto fidx : find_order) {\n bool is_filtered_feature = fidx >= num_sample_col;\n const data_size_t cur_non_zero_cnt =\n is_filtered_feature ? 0 : num_per_col[fidx];\n std::vector available_groups;\n for (int gid = 0; gid < static_cast(features_in_group.size()); ++gid) {\n auto cur_num_bin = group_num_bin[gid] + bin_mappers[fidx]->num_bin() +\n (bin_mappers[fidx]->GetMostFreqBin() == 0 ? -1 : 0);\n if (group_total_data_cnt[gid] + cur_non_zero_cnt <=\n total_sample_cnt + single_val_max_conflict_cnt) {\n if (!is_use_gpu || cur_num_bin <= max_bin_per_group) {\n available_groups.push_back(gid);\n }\n }\n }\n std::vector search_groups;\n if (!available_groups.empty()) {\n int last = static_cast(available_groups.size()) - 1;\n auto indices = rand.Sample(last, std::min(last, max_search_group - 1));\n // always push the last group\n search_groups.push_back(available_groups.back());\n for (auto idx : indices) {\n search_groups.push_back(available_groups[idx]);\n }\n }\n int best_gid = -1;\n int best_conflict_cnt = -1;\n for (auto gid : search_groups) {\n const data_size_t rest_max_cnt = single_val_max_conflict_cnt -\n group_total_data_cnt[gid] +\n group_used_row_cnt[gid];\n const data_size_t cnt =\n is_filtered_feature\n ? 0\n : GetConflictCount(conflict_marks[gid], sample_indices[fidx],\n num_per_col[fidx], rest_max_cnt);\n if (cnt >= 0 && cnt <= rest_max_cnt && cnt <= cur_non_zero_cnt / 2) {\n best_gid = gid;\n best_conflict_cnt = cnt;\n break;\n }\n }\n if (best_gid >= 0) {\n features_in_group[best_gid].push_back(fidx);\n group_total_data_cnt[best_gid] += cur_non_zero_cnt;\n group_used_row_cnt[best_gid] += cur_non_zero_cnt - best_conflict_cnt;\n if (!is_filtered_feature) {\n MarkUsed(&conflict_marks[best_gid], sample_indices[fidx],\n num_per_col[fidx]);\n }\n group_num_bin[best_gid] +=\n bin_mappers[fidx]->num_bin() +\n (bin_mappers[fidx]->GetDefaultBin() == 0 ? -1 : 0);\n } else {\n features_in_group.emplace_back();\n features_in_group.back().push_back(fidx);\n conflict_marks.emplace_back(total_sample_cnt, false);\n if (!is_filtered_feature) {\n MarkUsed(&(conflict_marks.back()), sample_indices[fidx],\n num_per_col[fidx]);\n }\n group_total_data_cnt.emplace_back(cur_non_zero_cnt);\n group_used_row_cnt.emplace_back(cur_non_zero_cnt);\n group_num_bin.push_back(\n 1 + bin_mappers[fidx]->num_bin() +\n (bin_mappers[fidx]->GetMostFreqBin() == 0 ? -1 : 0));\n }\n }\n if (!is_sparse) {\n multi_val_group->resize(features_in_group.size(), false);\n return features_in_group;\n }\n std::vector second_round_features;\n std::vector> features_in_group2;\n std::vector> conflict_marks2;\n\n const double dense_threshold = 0.4;\n for (int gid = 0; gid < static_cast(features_in_group.size()); ++gid) {\n const double dense_rate =\n static_cast(group_used_row_cnt[gid]) / total_sample_cnt;\n if (dense_rate >= dense_threshold) {\n features_in_group2.push_back(std::move(features_in_group[gid]));\n conflict_marks2.push_back(std::move(conflict_marks[gid]));\n } else {\n for (auto fidx : features_in_group[gid]) {\n second_round_features.push_back(fidx);\n }\n }\n }\n\n features_in_group = features_in_group2;\n conflict_marks = conflict_marks2;\n multi_val_group->resize(features_in_group.size(), false);\n if (!second_round_features.empty()) {\n features_in_group.emplace_back();\n conflict_marks.emplace_back(total_sample_cnt, false);\n bool is_multi_val = is_use_gpu ? true : false;\n int conflict_cnt = 0;\n for (auto fidx : second_round_features) {\n features_in_group.back().push_back(fidx);\n if (!is_multi_val) {\n const int rest_max_cnt = single_val_max_conflict_cnt - conflict_cnt;\n const auto cnt =\n GetConflictCount(conflict_marks.back(), sample_indices[fidx],\n num_per_col[fidx], rest_max_cnt);\n conflict_cnt += cnt;\n if (cnt < 0 || conflict_cnt > single_val_max_conflict_cnt) {\n is_multi_val = true;\n continue;\n }\n MarkUsed(&(conflict_marks.back()), sample_indices[fidx],\n num_per_col[fidx]);\n }\n }\n multi_val_group->push_back(is_multi_val);\n }\n return features_in_group;\n}\n\nstd::vector> FastFeatureBundling(\n const std::vector>& bin_mappers,\n int** sample_indices, double** sample_values, const int* num_per_col,\n int num_sample_col, data_size_t total_sample_cnt,\n const std::vector& used_features, data_size_t num_data,\n bool is_use_gpu, bool is_sparse, std::vector* multi_val_group) {\n Common::FunctionTimer fun_timer(\"Dataset::FastFeatureBundling\", global_timer);\n std::vector feature_non_zero_cnt;\n feature_non_zero_cnt.reserve(used_features.size());\n // put dense feature first\n for (auto fidx : used_features) {\n if (fidx < num_sample_col) {\n feature_non_zero_cnt.emplace_back(num_per_col[fidx]);\n } else {\n feature_non_zero_cnt.emplace_back(0);\n }\n }\n // sort by non zero cnt\n std::vector sorted_idx;\n sorted_idx.reserve(used_features.size());\n for (int i = 0; i < static_cast(used_features.size()); ++i) {\n sorted_idx.emplace_back(i);\n }\n // sort by non zero cnt, bigger first\n std::stable_sort(sorted_idx.begin(), sorted_idx.end(),\n [&feature_non_zero_cnt](int a, int b) {\n return feature_non_zero_cnt[a] > feature_non_zero_cnt[b];\n });\n\n std::vector feature_order_by_cnt;\n feature_order_by_cnt.reserve(sorted_idx.size());\n for (auto sidx : sorted_idx) {\n feature_order_by_cnt.push_back(used_features[sidx]);\n }\n\n std::vector> tmp_indices;\n std::vector tmp_num_per_col(num_sample_col, 0);\n for (auto fidx : used_features) {\n if (fidx >= num_sample_col) {\n continue;\n }\n auto ret = FixSampleIndices(\n bin_mappers[fidx].get(), static_cast(total_sample_cnt),\n num_per_col[fidx], sample_indices[fidx], sample_values[fidx]);\n if (!ret.empty()) {\n tmp_indices.push_back(ret);\n tmp_num_per_col[fidx] = static_cast(ret.size());\n sample_indices[fidx] = tmp_indices.back().data();\n } else {\n tmp_num_per_col[fidx] = num_per_col[fidx];\n }\n }\n std::vector group_is_multi_val, group_is_multi_val2;\n auto features_in_group =\n FindGroups(bin_mappers, used_features, sample_indices,\n tmp_num_per_col.data(), num_sample_col, total_sample_cnt,\n num_data, is_use_gpu, is_sparse, &group_is_multi_val);\n auto group2 =\n FindGroups(bin_mappers, feature_order_by_cnt, sample_indices,\n tmp_num_per_col.data(), num_sample_col, total_sample_cnt,\n num_data, is_use_gpu, is_sparse, &group_is_multi_val2);\n\n if (features_in_group.size() > group2.size()) {\n features_in_group = group2;\n group_is_multi_val = group_is_multi_val2;\n }\n // shuffle groups\n int num_group = static_cast(features_in_group.size());\n Random tmp_rand(num_data);\n for (int i = 0; i < num_group - 1; ++i) {\n int j = tmp_rand.NextShort(i + 1, num_group);\n std::swap(features_in_group[i], features_in_group[j]);\n // Using std::swap for vector will cause the wrong result.\n std::swap(group_is_multi_val[i], group_is_multi_val[j]);\n }\n *multi_val_group = group_is_multi_val;\n return features_in_group;\n}\n\nvoid Dataset::Construct(std::vector>* bin_mappers,\n int num_total_features,\n const std::vector>& forced_bins,\n int** sample_non_zero_indices,\n double** sample_values,\n const int* num_per_col,\n int num_sample_col,\n size_t total_sample_cnt,\n const Config& io_config) {\n num_total_features_ = num_total_features;\n CHECK_EQ(num_total_features_, static_cast(bin_mappers->size()));\n // get num_features\n std::vector used_features;\n auto& ref_bin_mappers = *bin_mappers;\n for (int i = 0; i < static_cast(bin_mappers->size()); ++i) {\n if (ref_bin_mappers[i] != nullptr && !ref_bin_mappers[i]->is_trivial()) {\n used_features.emplace_back(i);\n }\n }\n if (used_features.empty()) {\n Log::Warning(\n \"There are no meaningful features which satisfy the provided configuration. \"\n \"Decreasing Dataset parameters min_data_in_bin or min_data_in_leaf and re-constructing \"\n \"Dataset might resolve this warning.\");\n }\n auto features_in_group = OneFeaturePerGroup(used_features);\n\n auto is_sparse = io_config.is_enable_sparse;\n if (io_config.device_type == std::string(\"cuda\")) {\n LGBM_config_::current_device = lgbm_device_cuda;\n if ((io_config.device_type == std::string(\"cuda\")) && is_sparse) {\n Log::Warning(\"Using sparse features with CUDA is currently not supported.\");\n is_sparse = false;\n }\n }\n\n std::vector group_is_multi_val(used_features.size(), 0);\n if (io_config.enable_bundle && !used_features.empty()) {\n bool lgbm_is_gpu_used = io_config.device_type == std::string(\"gpu\") || io_config.device_type == std::string(\"cuda\");\n features_in_group = FastFeatureBundling(\n *bin_mappers, sample_non_zero_indices, sample_values, num_per_col,\n num_sample_col, static_cast(total_sample_cnt),\n used_features, num_data_, lgbm_is_gpu_used,\n is_sparse, &group_is_multi_val);\n }\n\n num_features_ = 0;\n for (const auto& fs : features_in_group) {\n num_features_ += static_cast(fs.size());\n }\n int cur_fidx = 0;\n used_feature_map_ = std::vector(num_total_features_, -1);\n num_groups_ = static_cast(features_in_group.size());\n real_feature_idx_.resize(num_features_);\n feature2group_.resize(num_features_);\n feature2subfeature_.resize(num_features_);\n feature_need_push_zeros_.clear();\n group_bin_boundaries_.clear();\n uint64_t num_total_bin = 0;\n group_bin_boundaries_.push_back(num_total_bin);\n group_feature_start_.resize(num_groups_);\n group_feature_cnt_.resize(num_groups_);\n for (int i = 0; i < num_groups_; ++i) {\n auto cur_features = features_in_group[i];\n int cur_cnt_features = static_cast(cur_features.size());\n group_feature_start_[i] = cur_fidx;\n group_feature_cnt_[i] = cur_cnt_features;\n // get bin_mappers\n std::vector> cur_bin_mappers;\n for (int j = 0; j < cur_cnt_features; ++j) {\n int real_fidx = cur_features[j];\n used_feature_map_[real_fidx] = cur_fidx;\n real_feature_idx_[cur_fidx] = real_fidx;\n feature2group_[cur_fidx] = i;\n feature2subfeature_[cur_fidx] = j;\n cur_bin_mappers.emplace_back(ref_bin_mappers[real_fidx].release());\n if (cur_bin_mappers.back()->GetDefaultBin() !=\n cur_bin_mappers.back()->GetMostFreqBin()) {\n feature_need_push_zeros_.push_back(cur_fidx);\n }\n ++cur_fidx;\n }\n feature_groups_.emplace_back(std::unique_ptr(\n new FeatureGroup(cur_cnt_features, group_is_multi_val[i], &cur_bin_mappers, num_data_, i)));\n num_total_bin += feature_groups_[i]->num_total_bin_;\n group_bin_boundaries_.push_back(num_total_bin);\n }\n if (!io_config.max_bin_by_feature.empty()) {\n CHECK_EQ(static_cast(num_total_features_),\n io_config.max_bin_by_feature.size());\n CHECK_GT(*(std::min_element(io_config.max_bin_by_feature.begin(),\n io_config.max_bin_by_feature.end())), 1);\n max_bin_by_feature_.resize(num_total_features_);\n max_bin_by_feature_.assign(io_config.max_bin_by_feature.begin(),\n io_config.max_bin_by_feature.end());\n }\n forced_bin_bounds_ = forced_bins;\n max_bin_ = io_config.max_bin;\n min_data_in_bin_ = io_config.min_data_in_bin;\n bin_construct_sample_cnt_ = io_config.bin_construct_sample_cnt;\n use_missing_ = io_config.use_missing;\n zero_as_missing_ = io_config.zero_as_missing;\n has_raw_ = false;\n if (io_config.linear_tree) {\n has_raw_ = true;\n }\n numeric_feature_map_ = std::vector(num_features_, -1);\n num_numeric_features_ = 0;\n for (int i = 0; i < num_features_; ++i) {\n if (FeatureBinMapper(i)->bin_type() == BinType::NumericalBin) {\n numeric_feature_map_[i] = num_numeric_features_;\n ++num_numeric_features_;\n }\n }\n device_type_ = io_config.device_type;\n gpu_device_id_ = io_config.gpu_device_id;\n}\n\nvoid Dataset::FinishLoad() {\n if (is_finish_load_) {\n return;\n }\n if (num_groups_ > 0) {\n for (int i = 0; i < num_groups_; ++i) {\n feature_groups_[i]->FinishLoad();\n }\n }\n metadata_.FinishLoad();\n\n #ifdef USE_CUDA\n if (device_type_ == std::string(\"cuda\")) {\n CreateCUDAColumnData();\n metadata_.CreateCUDAMetadata(gpu_device_id_);\n } else {\n cuda_column_data_.reset(nullptr);\n }\n #endif // USE_CUDA\n is_finish_load_ = true;\n}\n\nvoid PushDataToMultiValBin(\n data_size_t num_data, const std::vector most_freq_bins,\n const std::vector offsets,\n std::vector>>* iters,\n MultiValBin* ret) {\n Common::FunctionTimer fun_time(\"Dataset::PushDataToMultiValBin\",\n global_timer);\n if (ret->IsSparse()) {\n Threading::For(\n 0, num_data, 1024, [&](int tid, data_size_t start, data_size_t end) {\n std::vector cur_data;\n cur_data.reserve(most_freq_bins.size());\n for (size_t j = 0; j < most_freq_bins.size(); ++j) {\n (*iters)[tid][j]->Reset(start);\n }\n for (data_size_t i = start; i < end; ++i) {\n cur_data.clear();\n for (size_t j = 0; j < most_freq_bins.size(); ++j) {\n // for sparse multi value bin, we store the feature bin values with offset added\n auto cur_bin = (*iters)[tid][j]->Get(i);\n if (cur_bin == most_freq_bins[j]) {\n continue;\n }\n cur_bin += offsets[j];\n if (most_freq_bins[j] == 0) {\n cur_bin -= 1;\n }\n cur_data.push_back(cur_bin);\n }\n ret->PushOneRow(tid, i, cur_data);\n }\n });\n } else {\n Threading::For(\n 0, num_data, 1024, [&](int tid, data_size_t start, data_size_t end) {\n std::vector cur_data(most_freq_bins.size(), 0);\n for (size_t j = 0; j < most_freq_bins.size(); ++j) {\n (*iters)[tid][j]->Reset(start);\n }\n for (data_size_t i = start; i < end; ++i) {\n for (size_t j = 0; j < most_freq_bins.size(); ++j) {\n // for dense multi value bin, the feature bin values without offsets are used\n auto cur_bin = (*iters)[tid][j]->Get(i);\n cur_data[j] = cur_bin;\n }\n ret->PushOneRow(tid, i, cur_data);\n }\n });\n }\n}\n\nMultiValBin* Dataset::GetMultiBinFromSparseFeatures(const std::vector& offsets) const {\n Common::FunctionTimer fun_time(\"Dataset::GetMultiBinFromSparseFeatures\",\n global_timer);\n int multi_group_id = -1;\n for (int i = 0; i < num_groups_; ++i) {\n if (feature_groups_[i]->is_multi_val_) {\n if (multi_group_id < 0) {\n multi_group_id = i;\n } else {\n Log::Fatal(\"Bug. There should be only one multi-val group.\");\n }\n }\n }\n if (multi_group_id < 0) {\n return nullptr;\n }\n const int num_feature = feature_groups_[multi_group_id]->num_feature_;\n int num_threads = OMP_NUM_THREADS();\n\n std::vector>> iters(num_threads);\n std::vector most_freq_bins;\n double sum_sparse_rate = 0;\n for (int i = 0; i < num_feature; ++i) {\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1)\n for (int tid = 0; tid < num_threads; ++tid) {\n iters[tid].emplace_back(\n feature_groups_[multi_group_id]->SubFeatureIterator(i));\n }\n most_freq_bins.push_back(\n feature_groups_[multi_group_id]->bin_mappers_[i]->GetMostFreqBin());\n sum_sparse_rate +=\n feature_groups_[multi_group_id]->bin_mappers_[i]->sparse_rate();\n }\n sum_sparse_rate /= num_feature;\n Log::Debug(\"Dataset::GetMultiBinFromSparseFeatures: sparse rate %f\",\n sum_sparse_rate);\n std::unique_ptr ret;\n ret.reset(MultiValBin::CreateMultiValBin(num_data_, offsets.back(),\n num_feature, sum_sparse_rate, offsets));\n PushDataToMultiValBin(num_data_, most_freq_bins, offsets, &iters, ret.get());\n ret->FinishLoad();\n return ret.release();\n}\n\nMultiValBin* Dataset::GetMultiBinFromAllFeatures(const std::vector& offsets) const {\n Common::FunctionTimer fun_time(\"Dataset::GetMultiBinFromAllFeatures\",\n global_timer);\n int num_threads = OMP_NUM_THREADS();\n double sum_dense_ratio = 0;\n\n std::unique_ptr ret;\n std::vector>> iters(num_threads);\n std::vector most_freq_bins;\n int ncol = 0;\n for (int gid = 0; gid < num_groups_; ++gid) {\n if (feature_groups_[gid]->is_multi_val_) {\n ncol += feature_groups_[gid]->num_feature_;\n } else {\n ++ncol;\n }\n for (int fid = 0; fid < feature_groups_[gid]->num_feature_; ++fid) {\n const auto& bin_mapper = feature_groups_[gid]->bin_mappers_[fid];\n sum_dense_ratio += 1.0f - bin_mapper->sparse_rate();\n }\n }\n sum_dense_ratio /= ncol;\n for (int gid = 0; gid < num_groups_; ++gid) {\n if (feature_groups_[gid]->is_multi_val_) {\n for (int fid = 0; fid < feature_groups_[gid]->num_feature_; ++fid) {\n const auto& bin_mapper = feature_groups_[gid]->bin_mappers_[fid];\n most_freq_bins.push_back(bin_mapper->GetMostFreqBin());\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1)\n for (int tid = 0; tid < num_threads; ++tid) {\n iters[tid].emplace_back(\n feature_groups_[gid]->SubFeatureIterator(fid));\n }\n }\n } else {\n most_freq_bins.push_back(0);\n for (int tid = 0; tid < num_threads; ++tid) {\n iters[tid].emplace_back(feature_groups_[gid]->FeatureGroupIterator());\n }\n }\n }\n CHECK(static_cast(most_freq_bins.size()) == ncol);\n Log::Debug(\"Dataset::GetMultiBinFromAllFeatures: sparse rate %f\",\n 1.0 - sum_dense_ratio);\n ret.reset(MultiValBin::CreateMultiValBin(\n num_data_, offsets.back(), static_cast(most_freq_bins.size()),\n 1.0 - sum_dense_ratio, offsets));\n PushDataToMultiValBin(num_data_, most_freq_bins, offsets, &iters, ret.get());\n ret->FinishLoad();\n return ret.release();\n}\n\ntemplate \nTrainingShareStates* Dataset::GetShareStates(\n score_t* gradients, score_t* hessians,\n const std::vector& is_feature_used, bool is_constant_hessian,\n bool force_col_wise, bool force_row_wise,\n const int num_grad_quant_bins) const {\n Common::FunctionTimer fun_timer(\"Dataset::TestMultiThreadingMethod\",\n global_timer);\n if (force_col_wise && force_row_wise) {\n Log::Fatal(\n \"Cannot set both of `force_col_wise` and `force_row_wise` to `true` at \"\n \"the same time\");\n }\n if (num_groups_ <= 0) {\n TrainingShareStates* share_state = new TrainingShareStates();\n share_state->is_col_wise = true;\n share_state->is_constant_hessian = is_constant_hessian;\n return share_state;\n }\n if (force_col_wise) {\n TrainingShareStates* share_state = new TrainingShareStates();\n std::vector offsets;\n share_state->CalcBinOffsets(\n feature_groups_, &offsets, true);\n share_state->SetMultiValBin(GetMultiBinFromSparseFeatures(offsets),\n num_data_, feature_groups_, false, true, num_grad_quant_bins);\n share_state->is_col_wise = true;\n share_state->is_constant_hessian = is_constant_hessian;\n return share_state;\n } else if (force_row_wise) {\n TrainingShareStates* share_state = new TrainingShareStates();\n std::vector offsets;\n share_state->CalcBinOffsets(\n feature_groups_, &offsets, false);\n share_state->SetMultiValBin(GetMultiBinFromAllFeatures(offsets), num_data_,\n feature_groups_, false, false, num_grad_quant_bins);\n share_state->is_col_wise = false;\n share_state->is_constant_hessian = is_constant_hessian;\n return share_state;\n } else {\n std::unique_ptr sparse_bin;\n std::unique_ptr all_bin;\n std::unique_ptr col_wise_state;\n std::unique_ptr row_wise_state;\n col_wise_state.reset(new TrainingShareStates());\n row_wise_state.reset(new TrainingShareStates());\n\n std::chrono::duration col_wise_init_time, row_wise_init_time;\n auto start_time = std::chrono::steady_clock::now();\n std::vector col_wise_offsets;\n col_wise_state->CalcBinOffsets(feature_groups_, &col_wise_offsets, true);\n col_wise_state->SetMultiValBin(GetMultiBinFromSparseFeatures(col_wise_offsets), num_data_,\n feature_groups_, false, true, num_grad_quant_bins);\n col_wise_init_time = std::chrono::steady_clock::now() - start_time;\n\n start_time = std::chrono::steady_clock::now();\n std::vector row_wise_offsets;\n row_wise_state->CalcBinOffsets(feature_groups_, &row_wise_offsets, false);\n row_wise_state->SetMultiValBin(GetMultiBinFromAllFeatures(row_wise_offsets), num_data_,\n feature_groups_, false, false, num_grad_quant_bins);\n row_wise_init_time = std::chrono::steady_clock::now() - start_time;\n\n uint64_t max_total_bin = std::max(row_wise_state->num_hist_total_bin(),\n col_wise_state->num_hist_total_bin());\n std::vector>\n hist_data(max_total_bin * 2);\n\n Log::Debug(\n \"init for col-wise cost %f seconds, init for row-wise cost %f seconds\",\n col_wise_init_time * 1e-3, row_wise_init_time * 1e-3);\n\n col_wise_state->is_col_wise = true;\n col_wise_state->is_constant_hessian = is_constant_hessian;\n InitTrain(is_feature_used, col_wise_state.get());\n row_wise_state->is_col_wise = false;\n row_wise_state->is_constant_hessian = is_constant_hessian;\n InitTrain(is_feature_used, row_wise_state.get());\n std::chrono::duration col_wise_time, row_wise_time;\n start_time = std::chrono::steady_clock::now();\n ConstructHistograms(is_feature_used, nullptr, num_data_, gradients,\n hessians, gradients, hessians, col_wise_state.get(),\n hist_data.data());\n col_wise_time = std::chrono::steady_clock::now() - start_time;\n start_time = std::chrono::steady_clock::now();\n ConstructHistograms(is_feature_used, nullptr, num_data_, gradients,\n hessians, gradients, hessians, row_wise_state.get(),\n hist_data.data());\n row_wise_time = std::chrono::steady_clock::now() - start_time;\n\n if (col_wise_time < row_wise_time) {\n auto overhead_cost = row_wise_init_time + row_wise_time + col_wise_time;\n Log::Info(\n \"Auto-choosing col-wise multi-threading, the overhead of testing was \"\n \"%f seconds.\\n\"\n \"You can set `force_col_wise=true` to remove the overhead.\",\n overhead_cost * 1e-3);\n return col_wise_state.release();\n } else {\n auto overhead_cost = col_wise_init_time + row_wise_time + col_wise_time;\n Log::Info(\n \"Auto-choosing row-wise multi-threading, the overhead of testing was \"\n \"%f seconds.\\n\"\n \"You can set `force_row_wise=true` to remove the overhead.\\n\"\n \"And if memory is not enough, you can set `force_col_wise=true`.\",\n overhead_cost * 1e-3);\n if (row_wise_state->IsSparseRowwise()) {\n Log::Debug(\"Using Sparse Multi-Val Bin\");\n } else {\n Log::Debug(\"Using Dense Multi-Val Bin\");\n }\n return row_wise_state.release();\n }\n }\n}\n\ntemplate TrainingShareStates* Dataset::GetShareStates(\n score_t* gradients, score_t* hessians,\n const std::vector& is_feature_used, bool is_constant_hessian,\n bool force_col_wise, bool force_row_wise,\n const int num_grad_quant_bins) const;\n\ntemplate TrainingShareStates* Dataset::GetShareStates(\n score_t* gradients, score_t* hessians,\n const std::vector& is_feature_used, bool is_constant_hessian,\n bool force_col_wise, bool force_row_wise,\n const int num_grad_quant_bins) const;\n\ntemplate TrainingShareStates* Dataset::GetShareStates(\n score_t* gradients, score_t* hessians,\n const std::vector& is_feature_used, bool is_constant_hessian,\n bool force_col_wise, bool force_row_wise,\n const int num_grad_quant_bins) const;\n\nvoid Dataset::CopyFeatureMapperFrom(const Dataset* dataset) {\n feature_groups_.clear();\n num_features_ = dataset->num_features_;\n num_groups_ = dataset->num_groups_;\n has_raw_ = dataset->has_raw();\n // copy feature bin mapper data\n for (int i = 0; i < num_groups_; ++i) {\n feature_groups_.emplace_back(\n new FeatureGroup(*dataset->feature_groups_[i], num_data_));\n }\n feature_groups_.shrink_to_fit();\n used_feature_map_ = dataset->used_feature_map_;\n num_total_features_ = dataset->num_total_features_;\n feature_names_ = dataset->feature_names_;\n label_idx_ = dataset->label_idx_;\n real_feature_idx_ = dataset->real_feature_idx_;\n feature2group_ = dataset->feature2group_;\n feature2subfeature_ = dataset->feature2subfeature_;\n group_bin_boundaries_ = dataset->group_bin_boundaries_;\n group_feature_start_ = dataset->group_feature_start_;\n group_feature_cnt_ = dataset->group_feature_cnt_;\n forced_bin_bounds_ = dataset->forced_bin_bounds_;\n feature_need_push_zeros_ = dataset->feature_need_push_zeros_;\n max_bin_ = dataset->max_bin_;\n min_data_in_bin_ = dataset->min_data_in_bin_;\n bin_construct_sample_cnt_ = dataset->bin_construct_sample_cnt_;\n use_missing_ = dataset->use_missing_;\n zero_as_missing_ = dataset->zero_as_missing_;\n}\n\nvoid Dataset::CreateValid(const Dataset* dataset) {\n feature_groups_.clear();\n num_features_ = dataset->num_features_;\n num_groups_ = num_features_;\n max_bin_ = dataset->max_bin_;\n min_data_in_bin_ = dataset->min_data_in_bin_;\n bin_construct_sample_cnt_ = dataset->bin_construct_sample_cnt_;\n use_missing_ = dataset->use_missing_;\n zero_as_missing_ = dataset->zero_as_missing_;\n feature2group_.clear();\n feature2subfeature_.clear();\n has_raw_ = dataset->has_raw();\n numeric_feature_map_ = dataset->numeric_feature_map_;\n num_numeric_features_ = dataset->num_numeric_features_;\n // copy feature bin mapper data\n feature_need_push_zeros_.clear();\n group_bin_boundaries_.clear();\n uint64_t num_total_bin = 0;\n group_bin_boundaries_.push_back(num_total_bin);\n group_feature_start_.resize(num_groups_);\n group_feature_cnt_.resize(num_groups_);\n for (int i = 0; i < num_features_; ++i) {\n std::vector> bin_mappers;\n bin_mappers.emplace_back(new BinMapper(*(dataset->FeatureBinMapper(i))));\n if (bin_mappers.back()->GetDefaultBin() !=\n bin_mappers.back()->GetMostFreqBin()) {\n feature_need_push_zeros_.push_back(i);\n }\n feature_groups_.emplace_back(new FeatureGroup(&bin_mappers, num_data_));\n feature2group_.push_back(i);\n feature2subfeature_.push_back(0);\n num_total_bin += feature_groups_[i]->num_total_bin_;\n group_bin_boundaries_.push_back(num_total_bin);\n group_feature_start_[i] = i;\n group_feature_cnt_[i] = 1;\n }\n\n feature_groups_.shrink_to_fit();\n used_feature_map_ = dataset->used_feature_map_;\n num_total_features_ = dataset->num_total_features_;\n feature_names_ = dataset->feature_names_;\n label_idx_ = dataset->label_idx_;\n real_feature_idx_ = dataset->real_feature_idx_;\n forced_bin_bounds_ = dataset->forced_bin_bounds_;\n device_type_ = dataset->device_type_;\n gpu_device_id_ = dataset->gpu_device_id_;\n}\n\nvoid Dataset::ReSize(data_size_t num_data) {\n if (num_data_ != num_data) {\n num_data_ = num_data;\n OMP_INIT_EX();\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int group = 0; group < num_groups_; ++group) {\n OMP_LOOP_EX_BEGIN();\n feature_groups_[group]->ReSize(num_data_);\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n }\n}\n\n\nvoid Dataset::CopySubrowHostPart(const Dataset* fullset, const data_size_t* used_indices, data_size_t num_used_indices, bool need_meta_data) {\n CHECK_EQ(num_used_indices, num_data_);\n\n std::vector group_ids, subfeature_ids;\n group_ids.reserve(num_features_);\n subfeature_ids.reserve(num_features_);\n for (int group = 0; group < num_groups_; ++group) {\n if (fullset->IsMultiGroup(group)) {\n for (int sub_feature = 0; sub_feature <\n fullset->feature_groups_[group]->num_feature_; ++sub_feature) {\n group_ids.emplace_back(group);\n subfeature_ids.emplace_back(sub_feature);\n }\n } else {\n group_ids.emplace_back(group);\n subfeature_ids.emplace_back(-1);\n }\n }\n int num_copy_tasks = static_cast(group_ids.size());\n\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(dynamic)\n for (int task_id = 0; task_id < num_copy_tasks; ++task_id) {\n OMP_LOOP_EX_BEGIN();\n int group = group_ids[task_id];\n int subfeature = subfeature_ids[task_id];\n feature_groups_[group]->CopySubrowByCol(fullset->feature_groups_[group].get(),\n used_indices, num_used_indices, subfeature);\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n\n if (need_meta_data) {\n metadata_.Init(fullset->metadata_, used_indices, num_used_indices);\n }\n is_finish_load_ = true;\n numeric_feature_map_ = fullset->numeric_feature_map_;\n num_numeric_features_ = fullset->num_numeric_features_;\n if (has_raw_) {\n ResizeRaw(num_used_indices);\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int i = 0; i < num_used_indices; ++i) {\n for (int j = 0; j < num_numeric_features_; ++j) {\n raw_data_[j][i] = fullset->raw_data_[j][used_indices[i]];\n }\n }\n }\n}\n\nvoid Dataset::CopySubrow(const Dataset* fullset,\n const data_size_t* used_indices,\n data_size_t num_used_indices, bool need_meta_data) {\n CopySubrowHostPart(fullset, used_indices, num_used_indices, need_meta_data);\n\n // update CUDA storage for column data and metadata\n device_type_ = fullset->device_type_;\n gpu_device_id_ = fullset->gpu_device_id_;\n\n #ifdef USE_CUDA\n if (device_type_ == std::string(\"cuda\")) {\n if (cuda_column_data_ == nullptr) {\n cuda_column_data_.reset(new CUDAColumnData(num_used_indices, gpu_device_id_));\n metadata_.CreateCUDAMetadata(gpu_device_id_);\n }\n cuda_column_data_->CopySubrow(fullset->cuda_column_data(), used_indices, num_used_indices);\n }\n #endif // USE_CUDA\n}\n\nvoid Dataset::CopySubrowToDevice(const Dataset* fullset,\n const data_size_t* used_indices,\n data_size_t num_used_indices, bool need_meta_data,\n int gpu_device_id) {\n CopySubrowHostPart(fullset, used_indices, num_used_indices, need_meta_data);\n\n // update CUDA storage for column data and metadata\n device_type_ = fullset->device_type_;\n gpu_device_id_ = gpu_device_id;\n\n #ifdef USE_CUDA\n if (device_type_ == std::string(\"cuda\")) {\n if (cuda_column_data_ == nullptr) {\n cuda_column_data_.reset(new CUDAColumnData(num_used_indices, gpu_device_id_));\n metadata_.CreateCUDAMetadata(gpu_device_id_);\n }\n cuda_column_data_->CopySubrow(fullset->cuda_column_data(), used_indices, num_used_indices);\n }\n #endif // USE_CUDA\n}\n\nbool Dataset::SetFieldFromArrow(const char* field_name, const ArrowChunkedArray &ca) {\n std::string name(field_name);\n name = Common::Trim(name);\n if (name == std::string(\"label\") || name == std::string(\"target\")) {\n metadata_.SetLabel(ca);\n } else if (name == std::string(\"weight\") || name == std::string(\"weights\")) {\n metadata_.SetWeights(ca);\n } else if (name == std::string(\"init_score\")) {\n metadata_.SetInitScore(ca);\n } else if (name == std::string(\"query\") || name == std::string(\"group\")) {\n metadata_.SetQuery(ca);\n } else {\n return false;\n }\n return true;\n}\n\nbool Dataset::SetFloatField(const char* field_name, const float* field_data,\n data_size_t num_element) {\n std::string name(field_name);\n name = Common::Trim(name);\n if (name == std::string(\"label\") || name == std::string(\"target\")) {\n#ifdef LABEL_T_USE_DOUBLE\n Log::Fatal(\"Don't support LABEL_T_USE_DOUBLE\");\n#else\n metadata_.SetLabel(field_data, num_element);\n#endif\n } else if (name == std::string(\"weight\") || name == std::string(\"weights\")) {\n#ifdef LABEL_T_USE_DOUBLE\n Log::Fatal(\"Don't support LABEL_T_USE_DOUBLE\");\n#else\n metadata_.SetWeights(field_data, num_element);\n#endif\n } else {\n return false;\n }\n return true;\n}\n\nbool Dataset::SetDoubleField(const char* field_name, const double* field_data,\n data_size_t num_element) {\n std::string name(field_name);\n name = Common::Trim(name);\n if (name == std::string(\"init_score\")) {\n metadata_.SetInitScore(field_data, num_element);\n } else {\n return false;\n }\n return true;\n}\n\nbool Dataset::SetIntField(const char* field_name, const int* field_data,\n data_size_t num_element) {\n std::string name(field_name);\n name = Common::Trim(name);\n if (name == std::string(\"query\") || name == std::string(\"group\")) {\n metadata_.SetQuery(field_data, num_element);\n } else if (name == std::string(\"position\")) {\n metadata_.SetPosition(field_data, num_element);\n } else {\n return false;\n }\n return true;\n}\n\nbool Dataset::GetFloatField(const char* field_name, data_size_t* out_len,\n const float** out_ptr) {\n std::string name(field_name);\n name = Common::Trim(name);\n if (name == std::string(\"label\") || name == std::string(\"target\")) {\n#ifdef LABEL_T_USE_DOUBLE\n Log::Fatal(\"Don't support LABEL_T_USE_DOUBLE\");\n#else\n *out_ptr = metadata_.label();\n *out_len = num_data_;\n#endif\n } else if (name == std::string(\"weight\") || name == std::string(\"weights\")) {\n#ifdef LABEL_T_USE_DOUBLE\n Log::Fatal(\"Don't support LABEL_T_USE_DOUBLE\");\n#else\n *out_ptr = metadata_.weights();\n *out_len = num_data_;\n#endif\n } else {\n return false;\n }\n return true;\n}\n\nbool Dataset::GetDoubleField(const char* field_name, data_size_t* out_len,\n const double** out_ptr) {\n std::string name(field_name);\n name = Common::Trim(name);\n if (name == std::string(\"init_score\")) {\n *out_ptr = metadata_.init_score();\n *out_len = static_cast(metadata_.num_init_score());\n } else {\n return false;\n }\n return true;\n}\n\nbool Dataset::GetIntField(const char* field_name, data_size_t* out_len,\n const int** out_ptr) {\n std::string name(field_name);\n name = Common::Trim(name);\n if (name == std::string(\"query\") || name == std::string(\"group\")) {\n *out_ptr = metadata_.query_boundaries();\n *out_len = metadata_.num_queries() + 1;\n } else if (name == std::string(\"position\")) {\n *out_ptr = metadata_.positions();\n *out_len = num_data_;\n } else {\n return false;\n }\n return true;\n}\n\nvoid Dataset::SaveBinaryFile(const char* bin_filename) {\n if (bin_filename != nullptr && std::string(bin_filename) == data_filename_) {\n Log::Warning(\"Binary file %s already exists\", bin_filename);\n return;\n }\n // if not pass a filename, just append \".bin\" of original file\n std::string bin_filename_str(data_filename_);\n if (bin_filename == nullptr || bin_filename[0] == '\\0') {\n bin_filename_str.append(\".bin\");\n bin_filename = bin_filename_str.c_str();\n }\n bool is_file_existed = false;\n\n if (VirtualFileWriter::Exists(bin_filename)) {\n is_file_existed = true;\n Log::Warning(\"File %s exists, cannot save binary to it\", bin_filename);\n }\n\n if (!is_file_existed) {\n auto writer = VirtualFileWriter::Make(bin_filename);\n if (!writer->Init()) {\n Log::Fatal(\"Cannot write binary data to %s \", bin_filename);\n }\n Log::Info(\"Saving data to binary file %s\", bin_filename);\n size_t size_of_token = std::strlen(binary_file_token);\n writer->AlignedWrite(binary_file_token, size_of_token);\n\n // Write the basic header information for the dataset\n SerializeHeader(writer.get());\n\n // get size of meta data\n size_t size_of_metadata = metadata_.SizesInByte();\n writer->Write(&size_of_metadata, sizeof(size_of_metadata));\n // write meta data\n metadata_.SaveBinaryToFile(writer.get());\n\n // write feature data\n for (int i = 0; i < num_groups_; ++i) {\n // get size of feature\n size_t size_of_feature = feature_groups_[i]->SizesInByte();\n writer->Write(&size_of_feature, sizeof(size_of_feature));\n // write feature\n feature_groups_[i]->SerializeToBinary(writer.get());\n }\n\n // write raw data; use row-major order so we can read row-by-row\n if (has_raw_) {\n for (int i = 0; i < num_data_; ++i) {\n for (int j = 0; j < num_features_; ++j) {\n int feat_ind = numeric_feature_map_[j];\n if (feat_ind > -1) {\n writer->Write(&raw_data_[feat_ind][i], sizeof(float));\n }\n }\n }\n }\n }\n}\n\nvoid Dataset::SerializeReference(ByteBuffer* buffer) {\n Log::Info(\"Saving data reference to binary buffer\");\n\n // Calculate approximate size of output and reserve space\n size_t size_of_token = std::strlen(binary_serialized_reference_token);\n size_t initial_capacity = size_of_token + GetSerializedHeaderSize();\n // write feature group definitions\n for (int i = 0; i < num_groups_; ++i) {\n initial_capacity += feature_groups_[i]->SizesInByte(/* include_data */ false);\n }\n\n // Give a little extra just in case, to avoid unnecessary resizes\n buffer->Reserve(static_cast(1.1 * static_cast(initial_capacity)));\n\n // Write token that marks the data as binary reference, and the version\n buffer->AlignedWrite(binary_serialized_reference_token, size_of_token);\n buffer->AlignedWrite(serialized_reference_version, kSerializedReferenceVersionLength);\n\n // Write the basic definition of the overall dataset\n SerializeHeader(buffer);\n\n // write feature group definitions\n for (int i = 0; i < num_groups_; ++i) {\n // get size of feature\n size_t size_of_feature = feature_groups_[i]->SizesInByte(false);\n buffer->Write(&size_of_feature, sizeof(size_of_feature));\n // write feature\n feature_groups_[i]->SerializeToBinary(buffer, /* include_data */ false);\n }\n}\n\nsize_t Dataset::GetSerializedHeaderSize() {\n size_t size_of_header =\n VirtualFileWriter::AlignedSize(sizeof(num_data_)) +\n VirtualFileWriter::AlignedSize(sizeof(num_features_)) +\n VirtualFileWriter::AlignedSize(sizeof(num_total_features_)) +\n VirtualFileWriter::AlignedSize(sizeof(int) * num_total_features_) +\n VirtualFileWriter::AlignedSize(sizeof(label_idx_)) +\n VirtualFileWriter::AlignedSize(sizeof(num_groups_)) +\n 3 * VirtualFileWriter::AlignedSize(sizeof(int) * num_features_) +\n sizeof(uint64_t) * (num_groups_ + 1) +\n 2 * VirtualFileWriter::AlignedSize(sizeof(int) * num_groups_) +\n VirtualFileWriter::AlignedSize(sizeof(int32_t) * num_total_features_) +\n VirtualFileWriter::AlignedSize(sizeof(int)) * 3 +\n VirtualFileWriter::AlignedSize(sizeof(bool)) * 3;\n // size of feature names and forced bins\n for (int i = 0; i < num_total_features_; ++i) {\n size_of_header +=\n VirtualFileWriter::AlignedSize(feature_names_[i].size()) +\n VirtualFileWriter::AlignedSize(sizeof(int)) +\n forced_bin_bounds_[i].size() * sizeof(double) +\n VirtualFileWriter::AlignedSize(sizeof(int));\n }\n\n return size_of_header;\n}\n\nvoid Dataset::SerializeHeader(BinaryWriter* writer) {\n size_t size_of_header = GetSerializedHeaderSize();\n writer->Write(&size_of_header, sizeof(size_of_header));\n\n // write header\n writer->AlignedWrite(&num_data_, sizeof(num_data_));\n writer->AlignedWrite(&num_features_, sizeof(num_features_));\n writer->AlignedWrite(&num_total_features_, sizeof(num_total_features_));\n writer->AlignedWrite(&label_idx_, sizeof(label_idx_));\n writer->AlignedWrite(&max_bin_, sizeof(max_bin_));\n writer->AlignedWrite(&bin_construct_sample_cnt_,\n sizeof(bin_construct_sample_cnt_));\n writer->AlignedWrite(&min_data_in_bin_, sizeof(min_data_in_bin_));\n writer->AlignedWrite(&use_missing_, sizeof(use_missing_));\n writer->AlignedWrite(&zero_as_missing_, sizeof(zero_as_missing_));\n writer->AlignedWrite(&has_raw_, sizeof(has_raw_));\n writer->AlignedWrite(used_feature_map_.data(),\n sizeof(int) * num_total_features_);\n writer->AlignedWrite(&num_groups_, sizeof(num_groups_));\n writer->AlignedWrite(real_feature_idx_.data(), sizeof(int) * num_features_);\n writer->AlignedWrite(feature2group_.data(), sizeof(int) * num_features_);\n writer->AlignedWrite(feature2subfeature_.data(),\n sizeof(int) * num_features_);\n writer->Write(group_bin_boundaries_.data(),\n sizeof(uint64_t) * (num_groups_ + 1));\n writer->AlignedWrite(group_feature_start_.data(),\n sizeof(int) * num_groups_);\n writer->AlignedWrite(group_feature_cnt_.data(), sizeof(int) * num_groups_);\n if (max_bin_by_feature_.empty()) {\n ArrayArgs::Assign(&max_bin_by_feature_, -1, num_total_features_);\n }\n writer->AlignedWrite(max_bin_by_feature_.data(),\n sizeof(int32_t) * num_total_features_);\n if (ArrayArgs::CheckAll(max_bin_by_feature_, -1)) {\n max_bin_by_feature_.clear();\n }\n // write feature names\n for (int i = 0; i < num_total_features_; ++i) {\n int str_len = static_cast(feature_names_[i].size());\n writer->AlignedWrite(&str_len, sizeof(int));\n const char* c_str = feature_names_[i].c_str();\n writer->AlignedWrite(c_str, sizeof(char) * str_len);\n }\n // write forced bins\n for (int i = 0; i < num_total_features_; ++i) {\n int num_bounds = static_cast(forced_bin_bounds_[i].size());\n writer->AlignedWrite(&num_bounds, sizeof(int));\n\n for (size_t j = 0; j < forced_bin_bounds_[i].size(); ++j) {\n writer->Write(&forced_bin_bounds_[i][j], sizeof(double));\n }\n }\n}\n\nvoid Dataset::DumpTextFile(const char* text_filename) {\n FILE* file = NULL;\n#if _MSC_VER\n fopen_s(&file, text_filename, \"wt\");\n#else\n file = fopen(text_filename, \"wt\");\n#endif\n fprintf(file, \"num_features: %d\\n\", num_features_);\n fprintf(file, \"num_total_features: %d\\n\", num_total_features_);\n fprintf(file, \"num_groups: %d\\n\", num_groups_);\n fprintf(file, \"num_data: %d\\n\", num_data_);\n fprintf(file, \"feature_names: \");\n for (auto n : feature_names_) {\n fprintf(file, \"%s, \", n.c_str());\n }\n fprintf(file, \"\\nmax_bin_by_feature: \");\n for (auto i : max_bin_by_feature_) {\n fprintf(file, \"%d, \", i);\n }\n fprintf(file, \"\\n\");\n for (auto n : feature_names_) {\n fprintf(file, \"%s, \", n.c_str());\n }\n fprintf(file, \"\\nforced_bins: \");\n for (int i = 0; i < num_total_features_; ++i) {\n fprintf(file, \"\\nfeature %d: \", i);\n for (size_t j = 0; j < forced_bin_bounds_[i].size(); ++j) {\n fprintf(file, \"%lf, \", forced_bin_bounds_[i][j]);\n }\n }\n std::vector> iterators;\n iterators.reserve(num_features_);\n for (int j = 0; j < num_features_; ++j) {\n auto group_idx = feature2group_[j];\n auto sub_idx = feature2subfeature_[j];\n iterators.emplace_back(\n feature_groups_[group_idx]->SubFeatureIterator(sub_idx));\n }\n for (data_size_t i = 0; i < num_data_; ++i) {\n fprintf(file, \"\\n\");\n for (int j = 0; j < num_total_features_; ++j) {\n auto inner_feature_idx = used_feature_map_[j];\n if (inner_feature_idx < 0) {\n fprintf(file, \"NA, \");\n } else {\n fprintf(file, \"%d, \", iterators[inner_feature_idx]->Get(i));\n }\n }\n }\n fclose(file);\n}\n\nvoid Dataset::InitTrain(const std::vector& is_feature_used,\n TrainingShareStates* share_state) const {\n Common::FunctionTimer fun_time(\"Dataset::InitTrain\", global_timer);\n share_state->InitTrain(group_feature_start_,\n feature_groups_,\n is_feature_used);\n}\n\ntemplate \nvoid Dataset::ConstructHistogramsMultiVal(const data_size_t* data_indices,\n data_size_t num_data,\n const score_t* gradients,\n const score_t* hessians,\n TrainingShareStates* share_state,\n hist_t* hist_data) const {\n Common::FunctionTimer fun_time(\"Dataset::ConstructHistogramsMultiVal\",\n global_timer);\n share_state->ConstructHistograms(\n data_indices, num_data, gradients, hessians, hist_data);\n}\n\ntemplate \nvoid Dataset::ConstructHistogramsInner(\n const std::vector& is_feature_used, const data_size_t* data_indices,\n data_size_t num_data, const score_t* gradients, const score_t* hessians,\n score_t* ordered_gradients, score_t* ordered_hessians,\n TrainingShareStates* share_state, hist_t* hist_data) const {\n if (!share_state->is_col_wise) {\n return ConstructHistogramsMultiVal(\n data_indices, num_data, gradients, hessians, share_state, hist_data);\n }\n std::vector used_dense_group;\n int multi_val_groud_id = -1;\n used_dense_group.reserve(num_groups_);\n for (int group = 0; group < num_groups_; ++group) {\n const int f_start = group_feature_start_[group];\n const int f_cnt = group_feature_cnt_[group];\n bool is_group_used = false;\n for (int j = 0; j < f_cnt; ++j) {\n const int fidx = f_start + j;\n if (is_feature_used[fidx]) {\n is_group_used = true;\n break;\n }\n }\n if (is_group_used) {\n if (feature_groups_[group]->is_multi_val_) {\n multi_val_groud_id = group;\n } else {\n used_dense_group.push_back(group);\n }\n }\n }\n int num_used_dense_group = static_cast(used_dense_group.size());\n global_timer.Start(\"Dataset::dense_bin_histogram\");\n auto ptr_ordered_grad = gradients;\n auto ptr_ordered_hess = hessians;\n if (num_used_dense_group > 0) {\n if (USE_QUANT_GRAD) {\n int16_t* ordered_gradients_and_hessians = reinterpret_cast(ordered_gradients);\n const int16_t* gradients_and_hessians = reinterpret_cast(gradients);\n if (USE_INDICES) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024)\n for (data_size_t i = 0; i < num_data; ++i) {\n ordered_gradients_and_hessians[i] = gradients_and_hessians[data_indices[i]];\n }\n ptr_ordered_grad = reinterpret_cast(ordered_gradients);\n ptr_ordered_hess = nullptr;\n }\n } else {\n if (USE_INDICES) {\n if (USE_HESSIAN) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024)\n for (data_size_t i = 0; i < num_data; ++i) {\n ordered_gradients[i] = gradients[data_indices[i]];\n ordered_hessians[i] = hessians[data_indices[i]];\n }\n ptr_ordered_grad = ordered_gradients;\n ptr_ordered_hess = ordered_hessians;\n } else {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024)\n for (data_size_t i = 0; i < num_data; ++i) {\n ordered_gradients[i] = gradients[data_indices[i]];\n }\n ptr_ordered_grad = ordered_gradients;\n }\n }\n }\n OMP_INIT_EX();\n#pragma omp parallel for schedule(static) num_threads(share_state->num_threads)\n for (int gi = 0; gi < num_used_dense_group; ++gi) {\n OMP_LOOP_EX_BEGIN();\n int group = used_dense_group[gi];\n const int num_bin = feature_groups_[group]->num_total_bin_;\n if (USE_QUANT_GRAD) {\n if (HIST_BITS == 16) {\n auto data_ptr = reinterpret_cast(reinterpret_cast(hist_data) + group_bin_boundaries_[group]);\n std::memset(reinterpret_cast(data_ptr), 0,\n num_bin * kInt16HistEntrySize);\n if (USE_HESSIAN) {\n if (USE_INDICES) {\n feature_groups_[group]->bin_data_->ConstructHistogramInt16(\n data_indices, 0, num_data, ptr_ordered_grad, ptr_ordered_hess,\n data_ptr);\n } else {\n feature_groups_[group]->bin_data_->ConstructHistogramInt16(\n 0, num_data, ptr_ordered_grad, ptr_ordered_hess, data_ptr);\n }\n } else {\n if (USE_INDICES) {\n feature_groups_[group]->bin_data_->ConstructHistogramInt16(\n data_indices, 0, num_data, ptr_ordered_grad,\n data_ptr);\n } else {\n feature_groups_[group]->bin_data_->ConstructHistogramInt16(\n 0, num_data, ptr_ordered_grad, data_ptr);\n }\n }\n } else {\n auto data_ptr = hist_data + group_bin_boundaries_[group];\n std::memset(reinterpret_cast(data_ptr), 0,\n num_bin * kInt32HistEntrySize);\n if (USE_HESSIAN) {\n if (USE_INDICES) {\n feature_groups_[group]->bin_data_->ConstructHistogramInt32(\n data_indices, 0, num_data, ptr_ordered_grad, ptr_ordered_hess,\n data_ptr);\n } else {\n feature_groups_[group]->bin_data_->ConstructHistogramInt32(\n 0, num_data, ptr_ordered_grad, ptr_ordered_hess, data_ptr);\n }\n } else {\n if (USE_INDICES) {\n feature_groups_[group]->bin_data_->ConstructHistogramInt32(\n data_indices, 0, num_data, ptr_ordered_grad,\n data_ptr);\n } else {\n feature_groups_[group]->bin_data_->ConstructHistogramInt32(\n 0, num_data, ptr_ordered_grad, data_ptr);\n }\n }\n }\n } else {\n auto data_ptr = hist_data + group_bin_boundaries_[group] * 2;\n std::memset(reinterpret_cast(data_ptr), 0,\n num_bin * kHistEntrySize);\n if (USE_HESSIAN) {\n if (USE_INDICES) {\n feature_groups_[group]->bin_data_->ConstructHistogram(\n data_indices, 0, num_data, ptr_ordered_grad, ptr_ordered_hess,\n data_ptr);\n } else {\n feature_groups_[group]->bin_data_->ConstructHistogram(\n 0, num_data, ptr_ordered_grad, ptr_ordered_hess, data_ptr);\n }\n } else {\n if (USE_INDICES) {\n feature_groups_[group]->bin_data_->ConstructHistogram(\n data_indices, 0, num_data, ptr_ordered_grad, data_ptr);\n } else {\n feature_groups_[group]->bin_data_->ConstructHistogram(\n 0, num_data, ptr_ordered_grad, data_ptr);\n }\n auto cnt_dst = reinterpret_cast(data_ptr + 1);\n for (int i = 0; i < num_bin * 2; i += 2) {\n data_ptr[i + 1] = static_cast(cnt_dst[i]) * hessians[0];\n }\n }\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n }\n global_timer.Stop(\"Dataset::dense_bin_histogram\");\n if (multi_val_groud_id >= 0) {\n if (USE_QUANT_GRAD) {\n if (HIST_BITS == 32) {\n int32_t* hist_data_ptr = reinterpret_cast(hist_data);\n if (num_used_dense_group > 0) {\n ConstructHistogramsMultiVal(\n data_indices, num_data, ptr_ordered_grad, ptr_ordered_hess,\n share_state,\n reinterpret_cast(hist_data_ptr + group_bin_boundaries_[multi_val_groud_id] * 2));\n } else {\n ConstructHistogramsMultiVal(\n data_indices, num_data, gradients, hessians, share_state,\n reinterpret_cast(hist_data_ptr + group_bin_boundaries_[multi_val_groud_id] * 2));\n }\n } else if (HIST_BITS == 16) {\n int16_t* hist_data_ptr = reinterpret_cast(hist_data);\n if (num_used_dense_group > 0) {\n ConstructHistogramsMultiVal(\n data_indices, num_data, ptr_ordered_grad, ptr_ordered_hess,\n share_state,\n reinterpret_cast(hist_data_ptr + group_bin_boundaries_[multi_val_groud_id] * 2));\n } else {\n ConstructHistogramsMultiVal(\n data_indices, num_data, gradients, hessians, share_state,\n reinterpret_cast(hist_data_ptr + group_bin_boundaries_[multi_val_groud_id] * 2));\n }\n }\n } else {\n if (num_used_dense_group > 0) {\n ConstructHistogramsMultiVal(\n data_indices, num_data, ptr_ordered_grad, ptr_ordered_hess,\n share_state,\n hist_data + group_bin_boundaries_[multi_val_groud_id] * 2);\n } else {\n ConstructHistogramsMultiVal(\n data_indices, num_data, gradients, hessians, share_state,\n hist_data + group_bin_boundaries_[multi_val_groud_id] * 2);\n }\n }\n }\n}\n\n// explicitly initialize template methods, for cross module call\n#define CONSTRUCT_HISTOGRAMS_INNER_PARMA \\\n const std::vector& is_feature_used, const data_size_t* data_indices, \\\n data_size_t num_data, const score_t* gradients, const score_t* hessians, \\\n score_t* ordered_gradients, score_t* ordered_hessians, \\\n TrainingShareStates* share_state, hist_t* hist_data\n\n// explicitly initialize template methods, for cross module call\ntemplate void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const;\n\ntemplate void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const;\n\ntemplate void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const;\n\ntemplate void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const;\n\ntemplate void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const;\n\ntemplate void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const;\n\ntemplate void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const;\n\ntemplate void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const;\n\ntemplate void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const;\n\ntemplate void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const;\n\ntemplate void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const;\n\ntemplate void Dataset::ConstructHistogramsInner(CONSTRUCT_HISTOGRAMS_INNER_PARMA) const;\n\nvoid Dataset::FixHistogram(int feature_idx, double sum_gradient,\n double sum_hessian, hist_t* data) const {\n const int group = feature2group_[feature_idx];\n const int sub_feature = feature2subfeature_[feature_idx];\n const BinMapper* bin_mapper =\n feature_groups_[group]->bin_mappers_[sub_feature].get();\n const int most_freq_bin = bin_mapper->GetMostFreqBin();\n if (most_freq_bin > 0) {\n const int num_bin = bin_mapper->num_bin();\n GET_GRAD(data, most_freq_bin) = sum_gradient;\n GET_HESS(data, most_freq_bin) = sum_hessian;\n for (int i = 0; i < num_bin; ++i) {\n if (i != most_freq_bin) {\n GET_GRAD(data, most_freq_bin) -= GET_GRAD(data, i);\n GET_HESS(data, most_freq_bin) -= GET_HESS(data, i);\n }\n }\n }\n}\n\ntemplate \nvoid Dataset::FixHistogramInt(int feature_idx, int64_t int_sum_gradient_and_hessian, hist_t* data) const {\n const int group = feature2group_[feature_idx];\n const int sub_feature = feature2subfeature_[feature_idx];\n const BinMapper* bin_mapper =\n feature_groups_[group]->bin_mappers_[sub_feature].get();\n const int most_freq_bin = bin_mapper->GetMostFreqBin();\n PACKED_HIST_BIN_T* data_ptr = reinterpret_cast(data);\n PACKED_HIST_ACC_T int_sum_gradient_and_hessian_local = HIST_BITS_ACC == 16 ?\n ((static_cast(int_sum_gradient_and_hessian >> 32) << 16) |\n static_cast(int_sum_gradient_and_hessian & 0x0000ffff)) :\n int_sum_gradient_and_hessian;\n if (most_freq_bin > 0) {\n const int num_bin = bin_mapper->num_bin();\n if (HIST_BITS_BIN == HIST_BITS_ACC) {\n for (int i = 0; i < num_bin; ++i) {\n if (i != most_freq_bin) {\n int_sum_gradient_and_hessian_local -= data_ptr[i];\n }\n }\n data_ptr[most_freq_bin] = int_sum_gradient_and_hessian_local;\n } else {\n CHECK_EQ(HIST_BITS_ACC, 32);\n CHECK_EQ(HIST_BITS_BIN, 16);\n for (int i = 0; i < num_bin; ++i) {\n if (i != most_freq_bin) {\n const PACKED_HIST_BIN_T packed_hist = data_ptr[i];\n const PACKED_HIST_ACC_T packed_hist_acc = (static_cast(static_cast(packed_hist >> 16)) << 32) |\n static_cast(packed_hist & 0x0000ffff);\n int_sum_gradient_and_hessian_local -= packed_hist_acc;\n }\n }\n PACKED_HIST_BIN_T int_sum_gradient_and_hessian_local_bin =\n (static_cast(int_sum_gradient_and_hessian_local >> 32) << 16) | static_cast(int_sum_gradient_and_hessian_local & 0x0000ffff);\n data_ptr[most_freq_bin] = int_sum_gradient_and_hessian_local_bin;\n }\n }\n}\n\ntemplate void Dataset::FixHistogramInt(int feature_idx, int64_t int_sum_gradient_and_hessian, hist_t* data) const;\n\ntemplate void Dataset::FixHistogramInt(int feature_idx, int64_t int_sum_gradient_and_hessian, hist_t* data) const;\n\ntemplate \nvoid PushVector(std::vector* dest, const std::vector& src) {\n dest->reserve(dest->size() + src.size());\n for (auto i : src) {\n dest->push_back(i);\n }\n}\n\ntemplate \nvoid PushOffset(std::vector* dest, const std::vector& src,\n const T& offset) {\n dest->reserve(dest->size() + src.size());\n for (auto i : src) {\n dest->push_back(i + offset);\n }\n}\n\ntemplate \nvoid PushClearIfEmpty(std::vector* dest, const size_t dest_len,\n const std::vector& src, const size_t src_len,\n const T& deflt) {\n if (!dest->empty() && !src.empty()) {\n PushVector(dest, src);\n } else if (!dest->empty() && src.empty()) {\n for (size_t i = 0; i < src_len; ++i) {\n dest->push_back(deflt);\n }\n } else if (dest->empty() && !src.empty()) {\n for (size_t i = 0; i < dest_len; ++i) {\n dest->push_back(deflt);\n }\n PushVector(dest, src);\n }\n}\n\nvoid Dataset::AddFeaturesFrom(Dataset* other) {\n if (other->num_data_ != num_data_) {\n Log::Fatal(\n \"Cannot add features from other Dataset with a different number of \"\n \"rows\");\n }\n if (other->has_raw_ != has_raw_) {\n Log::Fatal(\"Can only add features from other Dataset if both or neither have raw data.\");\n }\n int mv_gid = -1;\n int other_mv_gid = -1;\n for (int i = 0; i < num_groups_; ++i) {\n if (IsMultiGroup(i)) {\n mv_gid = i;\n }\n }\n for (int i = 0; i < other->num_groups_; ++i) {\n if (other->IsMultiGroup(i)) {\n other_mv_gid = i;\n }\n }\n // Only one multi-val group, just simply merge\n if (mv_gid < 0 || other_mv_gid < 0) {\n PushVector(&feature2subfeature_, other->feature2subfeature_);\n PushVector(&group_feature_cnt_, other->group_feature_cnt_);\n feature_groups_.reserve(other->feature_groups_.size());\n for (auto& fg : other->feature_groups_) {\n const int cur_group_id = static_cast(feature_groups_.size());\n feature_groups_.emplace_back(new FeatureGroup(*fg, true, cur_group_id));\n }\n for (auto feature_idx : other->used_feature_map_) {\n if (feature_idx >= 0) {\n used_feature_map_.push_back(feature_idx + num_features_);\n } else {\n used_feature_map_.push_back(-1); // Unused feature.\n }\n }\n PushOffset(&real_feature_idx_, other->real_feature_idx_,\n num_total_features_);\n PushOffset(&feature2group_, other->feature2group_, num_groups_);\n auto bin_offset = group_bin_boundaries_.back();\n // Skip the leading 0 when copying group_bin_boundaries.\n for (auto i = other->group_bin_boundaries_.begin() + 1;\n i < other->group_bin_boundaries_.end(); ++i) {\n group_bin_boundaries_.push_back(*i + bin_offset);\n }\n PushOffset(&group_feature_start_, other->group_feature_start_,\n num_features_);\n num_groups_ += other->num_groups_;\n num_features_ += other->num_features_;\n } else {\n std::vector> features_in_group;\n for (int i = 0; i < num_groups_; ++i) {\n int f_start = group_feature_start_[i];\n int f_cnt = group_feature_cnt_[i];\n features_in_group.emplace_back();\n for (int j = 0; j < f_cnt; ++j) {\n const int real_fidx = real_feature_idx_[f_start + j];\n features_in_group.back().push_back(real_fidx);\n }\n }\n feature_groups_[mv_gid]->AddFeaturesFrom(\n other->feature_groups_[other_mv_gid].get(), mv_gid);\n for (int i = 0; i < other->num_groups_; ++i) {\n int f_start = other->group_feature_start_[i];\n int f_cnt = other->group_feature_cnt_[i];\n if (i == other_mv_gid) {\n for (int j = 0; j < f_cnt; ++j) {\n const int real_fidx = other->real_feature_idx_[f_start + j] + num_total_features_;\n features_in_group[mv_gid].push_back(real_fidx);\n }\n } else {\n features_in_group.emplace_back();\n for (int j = 0; j < f_cnt; ++j) {\n const int real_fidx = other->real_feature_idx_[f_start + j] + num_total_features_;\n features_in_group.back().push_back(real_fidx);\n }\n feature_groups_.emplace_back(\n new FeatureGroup(*other->feature_groups_[i], false, -1));\n }\n }\n // regenerate other fields\n num_groups_ += other->num_groups_ - 1;\n CHECK(num_groups_ == static_cast(features_in_group.size()));\n num_features_ += other->num_features_;\n int cur_fidx = 0;\n used_feature_map_ =\n std::vector(num_total_features_ + other->num_total_features_, -1);\n real_feature_idx_.resize(num_features_);\n feature2group_.resize(num_features_);\n feature2subfeature_.resize(num_features_);\n group_feature_start_.resize(num_groups_);\n group_feature_cnt_.resize(num_groups_);\n\n group_bin_boundaries_.clear();\n uint64_t num_total_bin = 0;\n group_bin_boundaries_.push_back(num_total_bin);\n for (int i = 0; i < num_groups_; ++i) {\n auto cur_features = features_in_group[i];\n int cur_cnt_features = static_cast(cur_features.size());\n group_feature_start_[i] = cur_fidx;\n group_feature_cnt_[i] = cur_cnt_features;\n for (int j = 0; j < cur_cnt_features; ++j) {\n int real_fidx = cur_features[j];\n used_feature_map_[real_fidx] = cur_fidx;\n real_feature_idx_[cur_fidx] = real_fidx;\n feature2group_[cur_fidx] = i;\n feature2subfeature_[cur_fidx] = j;\n ++cur_fidx;\n }\n num_total_bin += feature_groups_[i]->num_total_bin_;\n group_bin_boundaries_.push_back(num_total_bin);\n }\n }\n std::unordered_set feature_names_set;\n for (const auto& val : feature_names_) {\n feature_names_set.emplace(val);\n }\n for (const auto& val : other->feature_names_) {\n std::string new_name = val;\n int cnt = 2;\n while (feature_names_set.count(new_name)) {\n new_name = \"D\" + std::to_string(cnt) + \"_\" + val;\n ++cnt;\n }\n if (new_name != val) {\n Log::Warning(\n \"Find the same feature name (%s) in Dataset::AddFeaturesFrom, change \"\n \"its name to (%s)\",\n val.c_str(), new_name.c_str());\n }\n feature_names_set.emplace(new_name);\n feature_names_.push_back(new_name);\n }\n PushVector(&forced_bin_bounds_, other->forced_bin_bounds_);\n PushClearIfEmpty(&max_bin_by_feature_, num_total_features_,\n other->max_bin_by_feature_, other->num_total_features_, -1);\n num_total_features_ += other->num_total_features_;\n for (size_t i = 0; i < (other->numeric_feature_map_).size(); ++i) {\n int feat_ind = other->numeric_feature_map_[i];\n if (feat_ind > -1) {\n numeric_feature_map_.push_back(feat_ind + num_numeric_features_);\n } else {\n numeric_feature_map_.push_back(-1);\n }\n }\n num_numeric_features_ += other->num_numeric_features_;\n if (has_raw_) {\n for (int i = 0; i < other->num_numeric_features_; ++i) {\n raw_data_.push_back(other->raw_data_[i]);\n }\n }\n #ifdef USE_CUDA\n if (device_type_ == std::string(\"cuda\")) {\n CreateCUDAColumnData();\n } else {\n cuda_column_data_ = nullptr;\n }\n #endif // USE_CUDA\n}\n\nconst void* Dataset::GetColWiseData(\n const int feature_group_index,\n const int sub_feature_index,\n uint8_t* bit_type,\n bool* is_sparse,\n std::vector* bin_iterator,\n const int num_threads) const {\n return feature_groups_[feature_group_index]->GetColWiseData(sub_feature_index, bit_type, is_sparse, bin_iterator, num_threads);\n}\n\nconst void* Dataset::GetColWiseData(\n const int feature_group_index,\n const int sub_feature_index,\n uint8_t* bit_type,\n bool* is_sparse,\n BinIterator** bin_iterator) const {\n return feature_groups_[feature_group_index]->GetColWiseData(sub_feature_index, bit_type, is_sparse, bin_iterator);\n}\n\n#ifdef USE_CUDA\nvoid Dataset::CreateCUDAColumnData() {\n cuda_column_data_.reset(new CUDAColumnData(num_data_, gpu_device_id_));\n int num_columns = 0;\n std::vector column_data;\n std::vector column_bin_iterator;\n std::vector column_bit_type;\n int feature_index = 0;\n std::vector feature_to_column(num_features_, -1);\n std::vector feature_max_bins(num_features_, 0);\n std::vector feature_min_bins(num_features_, 0);\n std::vector feature_offsets(num_features_, 0);\n std::vector feature_most_freq_bins(num_features_, 0);\n std::vector feature_default_bin(num_features_, 0);\n std::vector feature_missing_is_zero(num_features_, 0);\n std::vector feature_missing_is_na(num_features_, 0);\n std::vector feature_mfb_is_zero(num_features_, 0);\n std::vector feature_mfb_is_na(num_features_, 0);\n for (int feature_group_index = 0; feature_group_index < num_groups_; ++feature_group_index) {\n if (feature_groups_[feature_group_index]->is_multi_val_) {\n for (int sub_feature_index = 0; sub_feature_index < feature_groups_[feature_group_index]->num_feature_; ++sub_feature_index) {\n uint8_t bit_type = 0;\n bool is_sparse = false;\n BinIterator* bin_iterator = nullptr;\n const void* one_column_data = GetColWiseData(feature_group_index,\n sub_feature_index,\n &bit_type,\n &is_sparse,\n &bin_iterator);\n column_data.emplace_back(one_column_data);\n column_bin_iterator.emplace_back(bin_iterator);\n column_bit_type.emplace_back(bit_type);\n feature_to_column[feature_index] = num_columns;\n ++num_columns;\n const BinMapper* feature_bin_mapper = FeatureBinMapper(feature_index);\n feature_max_bins[feature_index] = feature_max_bin(feature_index);\n feature_min_bins[feature_index] = feature_min_bin(feature_index);\n const uint32_t most_freq_bin = feature_bin_mapper->GetMostFreqBin();\n feature_offsets[feature_index] = static_cast(most_freq_bin == 0);\n feature_most_freq_bins[feature_index] = most_freq_bin;\n feature_default_bin[feature_index] = feature_bin_mapper->GetDefaultBin();\n if (feature_bin_mapper->missing_type() == MissingType::Zero) {\n feature_missing_is_zero[feature_index] = 1;\n feature_missing_is_na[feature_index] = 0;\n if (feature_default_bin[feature_index] == feature_most_freq_bins[feature_index]) {\n feature_mfb_is_zero[feature_index] = 1;\n } else {\n feature_mfb_is_zero[feature_index] = 0;\n }\n feature_mfb_is_na[feature_index] = 0;\n } else if (feature_bin_mapper->missing_type() == MissingType::NaN) {\n feature_missing_is_zero[feature_index] = 0;\n feature_missing_is_na[feature_index] = 1;\n feature_mfb_is_zero[feature_index] = 0;\n if (feature_most_freq_bins[feature_index] + feature_min_bins[feature_index] == feature_max_bins[feature_index] &&\n feature_most_freq_bins[feature_index] > 0) {\n feature_mfb_is_na[feature_index] = 1;\n } else {\n feature_mfb_is_na[feature_index] = 0;\n }\n } else {\n feature_missing_is_zero[feature_index] = 0;\n feature_missing_is_na[feature_index] = 0;\n feature_mfb_is_zero[feature_index] = 0;\n feature_mfb_is_na[feature_index] = 0;\n }\n ++feature_index;\n }\n } else {\n uint8_t bit_type = 0;\n bool is_sparse = false;\n BinIterator* bin_iterator = nullptr;\n const void* one_column_data = GetColWiseData(feature_group_index,\n -1,\n &bit_type,\n &is_sparse,\n &bin_iterator);\n column_data.emplace_back(one_column_data);\n column_bin_iterator.emplace_back(bin_iterator);\n column_bit_type.emplace_back(bit_type);\n for (int sub_feature_index = 0; sub_feature_index < feature_groups_[feature_group_index]->num_feature_; ++sub_feature_index) {\n feature_to_column[feature_index] = num_columns;\n const BinMapper* feature_bin_mapper = FeatureBinMapper(feature_index);\n feature_max_bins[feature_index] = feature_max_bin(feature_index);\n feature_min_bins[feature_index] = feature_min_bin(feature_index);\n const uint32_t most_freq_bin = feature_bin_mapper->GetMostFreqBin();\n feature_offsets[feature_index] = static_cast(most_freq_bin == 0);\n feature_most_freq_bins[feature_index] = most_freq_bin;\n feature_default_bin[feature_index] = feature_bin_mapper->GetDefaultBin();\n if (feature_bin_mapper->missing_type() == MissingType::Zero) {\n feature_missing_is_zero[feature_index] = 1;\n feature_missing_is_na[feature_index] = 0;\n if (feature_default_bin[feature_index] == feature_most_freq_bins[feature_index]) {\n feature_mfb_is_zero[feature_index] = 1;\n } else {\n feature_mfb_is_zero[feature_index] = 0;\n }\n feature_mfb_is_na[feature_index] = 0;\n } else if (feature_bin_mapper->missing_type() == MissingType::NaN) {\n feature_missing_is_zero[feature_index] = 0;\n feature_missing_is_na[feature_index] = 1;\n feature_mfb_is_zero[feature_index] = 0;\n if (feature_most_freq_bins[feature_index] + feature_min_bins[feature_index] == feature_max_bins[feature_index] &&\n feature_most_freq_bins[feature_index] > 0) {\n feature_mfb_is_na[feature_index] = 1;\n } else {\n feature_mfb_is_na[feature_index] = 0;\n }\n } else {\n feature_missing_is_zero[feature_index] = 0;\n feature_missing_is_na[feature_index] = 0;\n feature_mfb_is_zero[feature_index] = 0;\n feature_mfb_is_na[feature_index] = 0;\n }\n ++feature_index;\n }\n ++num_columns;\n }\n }\n cuda_column_data_->Init(num_columns,\n column_data,\n column_bin_iterator,\n column_bit_type,\n feature_max_bins,\n feature_min_bins,\n feature_offsets,\n feature_most_freq_bins,\n feature_default_bin,\n feature_missing_is_zero,\n feature_missing_is_na,\n feature_mfb_is_zero,\n feature_mfb_is_na,\n feature_to_column);\n}\n\n#endif // USE_CUDA\n\n} // namespace LightGBM\n" }, { "path": "src/io/dataset_loader.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nusing json11_internal_lightgbm::Json;\n\nDatasetLoader::DatasetLoader(const Config& io_config, const PredictFunction& predict_fun, int num_class, const char* filename)\n :config_(io_config), random_(config_.data_random_seed), predict_fun_(predict_fun), num_class_(num_class) {\n label_idx_ = 0;\n weight_idx_ = NO_SPECIFIC;\n group_idx_ = NO_SPECIFIC;\n SetHeader(filename);\n store_raw_ = false;\n if (io_config.linear_tree) {\n store_raw_ = true;\n }\n}\n\nDatasetLoader::~DatasetLoader() {\n}\n\nvoid DatasetLoader::SetHeader(const char* filename) {\n std::unordered_map name2idx;\n std::string name_prefix(\"name:\");\n if (filename != nullptr && CheckCanLoadFromBin(filename) == \"\") {\n TextReader text_reader(filename, config_.header);\n\n // get column names\n if (config_.header) {\n std::string first_line = text_reader.first_line();\n feature_names_ = Common::Split(first_line.c_str(), \"\\t,\");\n } else if (!config_.parser_config_file.empty()) {\n // support to get header from parser config, so could utilize following label name to id mapping logic.\n TextReader parser_config_reader(config_.parser_config_file.c_str(), false);\n parser_config_reader.ReadAllLines();\n std::string parser_config_str = parser_config_reader.JoinedLines();\n if (!parser_config_str.empty()) {\n std::string header_in_parser_config = Common::GetFromParserConfig(parser_config_str, \"header\");\n if (!header_in_parser_config.empty()) {\n Log::Info(\"Get raw column names from parser config.\");\n feature_names_ = Common::Split(header_in_parser_config.c_str(), \"\\t,\");\n }\n }\n }\n\n // load label idx first\n if (config_.label_column.size() > 0) {\n if (Common::StartsWith(config_.label_column, name_prefix)) {\n std::string name = config_.label_column.substr(name_prefix.size());\n label_idx_ = -1;\n for (int i = 0; i < static_cast(feature_names_.size()); ++i) {\n if (name == feature_names_[i]) {\n label_idx_ = i;\n break;\n }\n }\n if (label_idx_ >= 0) {\n Log::Info(\"Using column %s as label\", name.c_str());\n } else {\n Log::Fatal(\"Could not find label column %s in data file \\n\"\n \"or data file doesn't contain header\", name.c_str());\n }\n } else {\n if (!Common::AtoiAndCheck(config_.label_column.c_str(), &label_idx_)) {\n Log::Fatal(\"label_column is not a number,\\n\"\n \"if you want to use a column name,\\n\"\n \"please add the prefix \\\"name:\\\" to the column name\");\n }\n Log::Info(\"Using column number %d as label\", label_idx_);\n }\n }\n\n if (!config_.parser_config_file.empty()) {\n // if parser config file exists, feature names may be changed after customized parser applied.\n // clear here so could use default filled feature names during dataset construction.\n // may improve by saving real feature names defined in parser in the future.\n if (!feature_names_.empty()) {\n feature_names_.clear();\n }\n }\n\n if (!feature_names_.empty()) {\n // erase label column name\n feature_names_.erase(feature_names_.begin() + label_idx_);\n for (size_t i = 0; i < feature_names_.size(); ++i) {\n name2idx[feature_names_[i]] = static_cast(i);\n }\n }\n\n // load ignore columns\n if (config_.ignore_column.size() > 0) {\n if (Common::StartsWith(config_.ignore_column, name_prefix)) {\n std::string names = config_.ignore_column.substr(name_prefix.size());\n for (auto name : Common::Split(names.c_str(), ',')) {\n if (name2idx.count(name) > 0) {\n int tmp = name2idx[name];\n ignore_features_.emplace(tmp);\n } else {\n Log::Fatal(\"Could not find ignore column %s in data file\", name.c_str());\n }\n }\n } else {\n for (auto token : Common::Split(config_.ignore_column.c_str(), ',')) {\n int tmp = 0;\n if (!Common::AtoiAndCheck(token.c_str(), &tmp)) {\n Log::Fatal(\"ignore_column is not a number,\\n\"\n \"if you want to use a column name,\\n\"\n \"please add the prefix \\\"name:\\\" to the column name\");\n }\n ignore_features_.emplace(tmp);\n }\n }\n }\n // load weight idx\n if (config_.weight_column.size() > 0) {\n if (Common::StartsWith(config_.weight_column, name_prefix)) {\n std::string name = config_.weight_column.substr(name_prefix.size());\n if (name2idx.count(name) > 0) {\n weight_idx_ = name2idx[name];\n Log::Info(\"Using column %s as weight\", name.c_str());\n } else {\n Log::Fatal(\"Could not find weight column %s in data file\", name.c_str());\n }\n } else {\n if (!Common::AtoiAndCheck(config_.weight_column.c_str(), &weight_idx_)) {\n Log::Fatal(\"weight_column is not a number,\\n\"\n \"if you want to use a column name,\\n\"\n \"please add the prefix \\\"name:\\\" to the column name\");\n }\n Log::Info(\"Using column number %d as weight\", weight_idx_);\n }\n ignore_features_.emplace(weight_idx_);\n }\n // load group idx\n if (config_.group_column.size() > 0) {\n if (Common::StartsWith(config_.group_column, name_prefix)) {\n std::string name = config_.group_column.substr(name_prefix.size());\n if (name2idx.count(name) > 0) {\n group_idx_ = name2idx[name];\n Log::Info(\"Using column %s as group/query id\", name.c_str());\n } else {\n Log::Fatal(\"Could not find group/query column %s in data file\", name.c_str());\n }\n } else {\n if (!Common::AtoiAndCheck(config_.group_column.c_str(), &group_idx_)) {\n Log::Fatal(\"group_column is not a number,\\n\"\n \"if you want to use a column name,\\n\"\n \"please add the prefix \\\"name:\\\" to the column name\");\n }\n Log::Info(\"Using column number %d as group/query id\", group_idx_);\n }\n ignore_features_.emplace(group_idx_);\n }\n }\n if (config_.categorical_feature.size() > 0) {\n if (Common::StartsWith(config_.categorical_feature, name_prefix)) {\n std::string names = config_.categorical_feature.substr(name_prefix.size());\n for (auto name : Common::Split(names.c_str(), ',')) {\n if (name2idx.count(name) > 0) {\n int tmp = name2idx[name];\n categorical_features_.emplace(tmp);\n } else {\n Log::Fatal(\"Could not find categorical_feature %s in data file\", name.c_str());\n }\n }\n } else {\n for (auto token : Common::Split(config_.categorical_feature.c_str(), ',')) {\n int tmp = 0;\n if (!Common::AtoiAndCheck(token.c_str(), &tmp)) {\n Log::Fatal(\"categorical_feature is not a number,\\n\"\n \"if you want to use a column name,\\n\"\n \"please add the prefix \\\"name:\\\" to the column name\");\n }\n categorical_features_.emplace(tmp);\n }\n }\n }\n}\n\nvoid CheckSampleSize(size_t sample_cnt, size_t num_data) {\n if (static_cast(sample_cnt) / num_data < 0.2f &&\n sample_cnt < 100000) {\n Log::Warning(\n \"Using too small ``bin_construct_sample_cnt`` may encounter \"\n \"unexpected \"\n \"errors and poor accuracy.\");\n }\n}\n\nDataset* DatasetLoader::LoadFromFile(const char* filename, int rank, int num_machines) {\n // don't support query id in data file when using distributed training\n if (num_machines > 1 && !config_.pre_partition) {\n if (group_idx_ > 0) {\n Log::Fatal(\"Using a query id without pre-partitioning the data file is not supported for distributed training.\\n\"\n \"Please use an additional query file or pre-partition the data\");\n }\n }\n auto dataset = std::unique_ptr(new Dataset());\n if (store_raw_) {\n dataset->SetHasRaw(true);\n }\n data_size_t num_global_data = 0;\n std::vector used_data_indices;\n auto bin_filename = CheckCanLoadFromBin(filename);\n bool is_load_from_binary = false;\n if (bin_filename.size() == 0) {\n dataset->parser_config_str_ = Parser::GenerateParserConfigStr(filename, config_.parser_config_file.c_str(), config_.header, label_idx_);\n auto parser = std::unique_ptr(Parser::CreateParser(filename, config_.header, 0, label_idx_,\n config_.precise_float_parser, dataset->parser_config_str_));\n if (parser == nullptr) {\n Log::Fatal(\"Could not recognize data format of %s\", filename);\n }\n dataset->data_filename_ = filename;\n dataset->label_idx_ = label_idx_;\n dataset->metadata_.Init(filename);\n if (!config_.two_round) {\n // read data to memory\n auto text_data = LoadTextDataToMemory(filename, dataset->metadata_, rank, num_machines, &num_global_data, &used_data_indices);\n dataset->num_data_ = static_cast(text_data.size());\n // sample data\n auto sample_data = SampleTextDataFromMemory(text_data);\n CheckSampleSize(sample_data.size(),\n static_cast(dataset->num_data_));\n // construct feature bin mappers & clear sample data\n ConstructBinMappersFromTextData(rank, num_machines, sample_data, parser.get(), dataset.get());\n std::vector().swap(sample_data);\n if (dataset->has_raw()) {\n dataset->ResizeRaw(dataset->num_data_);\n }\n // initialize label\n dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_);\n // extract features\n ExtractFeaturesFromMemory(&text_data, parser.get(), dataset.get());\n text_data.clear();\n } else {\n // sample data from file\n auto sample_data = SampleTextDataFromFile(filename, dataset->metadata_, rank, num_machines, &num_global_data, &used_data_indices);\n if (used_data_indices.size() > 0) {\n dataset->num_data_ = static_cast(used_data_indices.size());\n } else {\n dataset->num_data_ = num_global_data;\n }\n CheckSampleSize(sample_data.size(),\n static_cast(dataset->num_data_));\n // construct feature bin mappers & clear sample data\n ConstructBinMappersFromTextData(rank, num_machines, sample_data, parser.get(), dataset.get());\n std::vector().swap(sample_data);\n if (dataset->has_raw()) {\n dataset->ResizeRaw(dataset->num_data_);\n }\n // initialize label\n dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_);\n Log::Info(\"Making second pass...\");\n // extract features\n ExtractFeaturesFromFile(filename, parser.get(), used_data_indices, dataset.get());\n }\n } else {\n // load data from binary file\n is_load_from_binary = true;\n Log::Info(\"Load from binary file %s\", bin_filename.c_str());\n dataset.reset(LoadFromBinFile(filename, bin_filename.c_str(), rank, num_machines, &num_global_data, &used_data_indices));\n\n // checks whether there's a initial score file when loaded from binary data files\n // the initial score file should with suffix \".bin.init\"\n dataset->metadata_.LoadInitialScore(bin_filename);\n\n dataset->device_type_ = config_.device_type;\n dataset->gpu_device_id_ = config_.gpu_device_id;\n #ifdef USE_CUDA\n if (config_.device_type == std::string(\"cuda\")) {\n dataset->CreateCUDAColumnData();\n dataset->metadata_.CreateCUDAMetadata(dataset->gpu_device_id_);\n } else {\n dataset->cuda_column_data_ = nullptr;\n }\n #endif // USE_CUDA\n }\n // check meta data\n dataset->metadata_.CheckOrPartition(num_global_data, used_data_indices);\n // need to check training data\n CheckDataset(dataset.get(), is_load_from_binary);\n\n return dataset.release();\n}\n\nDataset* DatasetLoader::LoadFromFileAlignWithOtherDataset(const char* filename, const Dataset* train_data) {\n data_size_t num_global_data = 0;\n std::vector used_data_indices;\n auto dataset = std::unique_ptr(new Dataset());\n if (store_raw_) {\n dataset->SetHasRaw(true);\n }\n auto bin_filename = CheckCanLoadFromBin(filename);\n if (bin_filename.size() == 0) {\n auto parser = std::unique_ptr(Parser::CreateParser(filename, config_.header, 0, label_idx_,\n config_.precise_float_parser, train_data->parser_config_str_));\n if (parser == nullptr) {\n Log::Fatal(\"Could not recognize data format of %s\", filename);\n }\n dataset->data_filename_ = filename;\n dataset->label_idx_ = label_idx_;\n dataset->metadata_.Init(filename);\n if (!config_.two_round) {\n // read data in memory\n auto text_data = LoadTextDataToMemory(filename, dataset->metadata_, 0, 1, &num_global_data, &used_data_indices);\n dataset->num_data_ = static_cast(text_data.size());\n // initialize label\n dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_);\n dataset->CreateValid(train_data);\n if (dataset->has_raw()) {\n dataset->ResizeRaw(dataset->num_data_);\n }\n // extract features\n ExtractFeaturesFromMemory(&text_data, parser.get(), dataset.get());\n text_data.clear();\n } else {\n TextReader text_reader(filename, config_.header);\n // Get number of lines of data file\n dataset->num_data_ = static_cast(text_reader.CountLine());\n num_global_data = dataset->num_data_;\n // initialize label\n dataset->metadata_.Init(dataset->num_data_, weight_idx_, group_idx_);\n dataset->CreateValid(train_data);\n if (dataset->has_raw()) {\n dataset->ResizeRaw(dataset->num_data_);\n }\n // extract features\n ExtractFeaturesFromFile(filename, parser.get(), used_data_indices, dataset.get());\n }\n } else {\n // load data from binary file\n dataset.reset(LoadFromBinFile(filename, bin_filename.c_str(), 0, 1, &num_global_data, &used_data_indices));\n // checks whether there's a initial score file when loaded from binary data files\n // the initial score file should with suffix \".bin.init\"\n dataset->metadata_.LoadInitialScore(bin_filename);\n }\n // not need to check validation data\n // check meta data\n dataset->metadata_.CheckOrPartition(num_global_data, used_data_indices);\n return dataset.release();\n}\n\nDataset* DatasetLoader::LoadFromSerializedReference(const char* binary_data, size_t buffer_size, data_size_t num_data, int32_t num_classes) {\n auto dataset = std::unique_ptr(new Dataset(num_data));\n\n auto mem_ptr = binary_data;\n\n // check token\n const size_t size_of_token = std::strlen(Dataset::binary_serialized_reference_token);\n size_t size_of_token_in_input = VirtualFileWriter::AlignedSize(sizeof(char) * size_of_token);\n if (buffer_size < size_of_token_in_input) {\n Log::Fatal(\"Binary definition file error: token has the wrong size\");\n }\n if (std::string(mem_ptr, size_of_token) != std::string(Dataset::binary_serialized_reference_token)) {\n Log::Fatal(\"Input file is not LightGBM binary reference file\");\n }\n mem_ptr += size_of_token_in_input;\n\n size_t size_of_version = VirtualFileWriter::AlignedSize(Dataset::kSerializedReferenceVersionLength);\n std::string version(mem_ptr, Dataset::kSerializedReferenceVersionLength);\n if (version != std::string(Dataset::serialized_reference_version)) {\n Log::Fatal(\"Unexpected version of serialized binary data: %s\", version.c_str());\n }\n mem_ptr += size_of_version;\n\n size_t size_of_header = *(reinterpret_cast(mem_ptr));\n mem_ptr += sizeof(size_t);\n\n LoadHeaderFromMemory(dataset.get(), mem_ptr);\n dataset->num_data_ = num_data; // update to the given num_data\n mem_ptr += size_of_header;\n\n // read feature group definitions\n for (int i = 0; i < dataset->num_groups_; ++i) {\n // read feature size\n const size_t size_of_feature = *(reinterpret_cast(mem_ptr));\n mem_ptr += sizeof(size_t);\n dataset->feature_groups_.emplace_back(std::unique_ptr(new FeatureGroup(mem_ptr, num_data, i)));\n mem_ptr += size_of_feature;\n }\n dataset->feature_groups_.shrink_to_fit();\n\n dataset->numeric_feature_map_ = std::vector(dataset->num_features_, false);\n dataset->num_numeric_features_ = 0;\n for (int i = 0; i < dataset->num_features_; ++i) {\n if (dataset->FeatureBinMapper(i)->bin_type() == BinType::CategoricalBin) {\n dataset->numeric_feature_map_[i] = -1;\n } else {\n dataset->numeric_feature_map_[i] = dataset->num_numeric_features_;\n ++dataset->num_numeric_features_;\n }\n }\n\n int has_weights = config_.weight_column.size() > 0;\n int has_init_scores = num_classes > 0;\n int has_queries = config_.group_column.size() > 0;\n dataset->metadata_.Init(num_data, has_weights, has_init_scores, has_queries, num_classes);\n\n Log::Info(\"Loaded reference dataset: %d features, %d num_data\", dataset->num_features_, num_data);\n\n return dataset.release();\n}\n\nDataset* DatasetLoader::LoadFromBinFile(const char* data_filename, const char* bin_filename,\n int rank, int num_machines, int* num_global_data,\n std::vector* used_data_indices) {\n auto dataset = std::unique_ptr(new Dataset());\n auto reader = VirtualFileReader::Make(bin_filename);\n dataset->data_filename_ = data_filename;\n if (!reader->Init()) {\n Log::Fatal(\"Could not read binary data from %s\", bin_filename);\n }\n\n // buffer to read binary file\n size_t buffer_size = 16 * 1024 * 1024;\n auto buffer = std::vector(buffer_size);\n\n // check token\n size_t size_of_token = std::strlen(Dataset::binary_file_token);\n size_t read_cnt = reader->Read(\n buffer.data(),\n VirtualFileWriter::AlignedSize(sizeof(char) * size_of_token));\n if (read_cnt < sizeof(char) * size_of_token) {\n Log::Fatal(\"Binary file error: token has the wrong size\");\n }\n if (std::string(buffer.data()) != std::string(Dataset::binary_file_token)) {\n Log::Fatal(\"Input file is not LightGBM binary file\");\n }\n\n // read size of header\n read_cnt = reader->Read(buffer.data(), sizeof(size_t));\n\n if (read_cnt != sizeof(size_t)) {\n Log::Fatal(\"Binary file error: header has the wrong size\");\n }\n\n size_t size_of_head = *(reinterpret_cast(buffer.data()));\n\n // re-allocate space if not enough\n if (size_of_head > buffer_size) {\n buffer_size = size_of_head;\n buffer.resize(buffer_size);\n }\n // read header\n read_cnt = reader->Read(buffer.data(), size_of_head);\n\n if (read_cnt != size_of_head) {\n Log::Fatal(\"Binary file error: header is incorrect\");\n }\n // get header\n const char* mem_ptr = buffer.data();\n LoadHeaderFromMemory(dataset.get(), mem_ptr);\n\n // read size of meta data\n read_cnt = reader->Read(buffer.data(), sizeof(size_t));\n\n if (read_cnt != sizeof(size_t)) {\n Log::Fatal(\"Binary file error: meta data has the wrong size\");\n }\n\n size_t size_of_metadata = *(reinterpret_cast(buffer.data()));\n\n // re-allocate space if not enough\n if (size_of_metadata > buffer_size) {\n buffer_size = size_of_metadata;\n buffer.resize(buffer_size);\n }\n // read meta data\n read_cnt = reader->Read(buffer.data(), size_of_metadata);\n\n if (read_cnt != size_of_metadata) {\n Log::Fatal(\"Binary file error: meta data is incorrect\");\n }\n // load meta data\n dataset->metadata_.LoadFromMemory(buffer.data());\n\n *num_global_data = dataset->num_data_;\n used_data_indices->clear();\n // sample local used data if need to partition\n if (num_machines > 1 && !config_.pre_partition) {\n const data_size_t* query_boundaries = dataset->metadata_.query_boundaries();\n if (query_boundaries == nullptr) {\n // if not contain query file, minimal sample unit is one record\n for (data_size_t i = 0; i < dataset->num_data_; ++i) {\n if (random_.NextShort(0, num_machines) == rank) {\n used_data_indices->push_back(i);\n }\n }\n } else {\n // if contain query file, minimal sample unit is one query\n data_size_t num_queries = dataset->metadata_.num_queries();\n data_size_t qid = -1;\n bool is_query_used = false;\n for (data_size_t i = 0; i < dataset->num_data_; ++i) {\n if (qid >= num_queries) {\n Log::Fatal(\"Current query exceeds the range of the query file,\\n\"\n \"please ensure the query file is correct\");\n }\n if (i >= query_boundaries[qid + 1]) {\n // if is new query\n is_query_used = false;\n if (random_.NextShort(0, num_machines) == rank) {\n is_query_used = true;\n }\n ++qid;\n }\n if (is_query_used) {\n used_data_indices->push_back(i);\n }\n }\n }\n dataset->num_data_ = static_cast((*used_data_indices).size());\n }\n dataset->metadata_.PartitionLabel(*used_data_indices);\n // read feature data\n for (int i = 0; i < dataset->num_groups_; ++i) {\n // read feature size\n read_cnt = reader->Read(buffer.data(), sizeof(size_t));\n if (read_cnt != sizeof(size_t)) {\n Log::Fatal(\"Binary file error: feature %d has the wrong size\", i);\n }\n size_t size_of_feature = *(reinterpret_cast(buffer.data()));\n // re-allocate space if not enough\n if (size_of_feature > buffer_size) {\n buffer_size = size_of_feature;\n buffer.resize(buffer_size);\n }\n\n read_cnt = reader->Read(buffer.data(), size_of_feature);\n\n if (read_cnt != size_of_feature) {\n Log::Fatal(\"Binary file error: feature %d is incorrect, read count: %zu\", i, read_cnt);\n }\n dataset->feature_groups_.emplace_back(std::unique_ptr(\n new FeatureGroup(buffer.data(),\n *num_global_data,\n *used_data_indices, i)));\n }\n dataset->feature_groups_.shrink_to_fit();\n\n // raw data\n dataset->numeric_feature_map_ = std::vector(dataset->num_features_, false);\n dataset->num_numeric_features_ = 0;\n for (int i = 0; i < dataset->num_features_; ++i) {\n if (dataset->FeatureBinMapper(i)->bin_type() == BinType::CategoricalBin) {\n dataset->numeric_feature_map_[i] = -1;\n } else {\n dataset->numeric_feature_map_[i] = dataset->num_numeric_features_;\n ++dataset->num_numeric_features_;\n }\n }\n if (dataset->has_raw()) {\n dataset->ResizeRaw(dataset->num_data());\n size_t row_size = dataset->num_numeric_features_ * sizeof(float);\n if (row_size > buffer_size) {\n buffer_size = row_size;\n buffer.resize(buffer_size);\n }\n for (int i = 0; i < dataset->num_data(); ++i) {\n read_cnt = reader->Read(buffer.data(), row_size);\n if (read_cnt != row_size) {\n Log::Fatal(\"Binary file error: row %d of raw data is incorrect, read count: %zu\", i, read_cnt);\n }\n mem_ptr = buffer.data();\n const float* tmp_ptr_raw_row = reinterpret_cast(mem_ptr);\n for (int j = 0; j < dataset->num_features(); ++j) {\n int feat_ind = dataset->numeric_feature_map_[j];\n if (feat_ind >= 0) {\n dataset->raw_data_[feat_ind][i] = tmp_ptr_raw_row[feat_ind];\n }\n }\n mem_ptr += row_size;\n }\n }\n\n dataset->is_finish_load_ = true;\n return dataset.release();\n}\n\nDataset* DatasetLoader::ConstructFromSampleData(double** sample_values,\n int** sample_indices,\n int num_col,\n const int* num_per_col,\n size_t total_sample_size,\n data_size_t num_local_data,\n int64_t num_dist_data) {\n CheckSampleSize(total_sample_size, static_cast(num_dist_data));\n int num_total_features = num_col;\n if (Network::num_machines() > 1) {\n num_total_features = Network::GlobalSyncUpByMax(num_total_features);\n }\n std::vector> bin_mappers(num_total_features);\n // fill feature_names_ if not header\n if (feature_names_.empty()) {\n for (int i = 0; i < num_col; ++i) {\n std::stringstream str_buf;\n str_buf << \"Column_\" << i;\n feature_names_.push_back(str_buf.str());\n }\n }\n if (!config_.max_bin_by_feature.empty()) {\n CHECK_EQ(static_cast(num_col), config_.max_bin_by_feature.size());\n CHECK_GT(*(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end())), 1);\n }\n\n // get forced split\n std::string forced_bins_path = config_.forcedbins_filename;\n std::vector> forced_bin_bounds = DatasetLoader::GetForcedBins(forced_bins_path, num_col, categorical_features_);\n\n const data_size_t filter_cnt = static_cast(\n static_cast(config_.min_data_in_leaf * total_sample_size) / num_dist_data);\n if (Network::num_machines() == 1) {\n // if only one machine, find bin locally\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided)\n for (int i = 0; i < num_col; ++i) {\n OMP_LOOP_EX_BEGIN();\n if (ignore_features_.count(i) > 0) {\n bin_mappers[i] = nullptr;\n continue;\n }\n BinType bin_type = BinType::NumericalBin;\n if (categorical_features_.count(i)) {\n bin_type = BinType::CategoricalBin;\n bool feat_is_unconstrained = ((config_.monotone_constraints.size() == 0) || (config_.monotone_constraints[i] == 0));\n if (!feat_is_unconstrained) {\n Log::Fatal(\"The output cannot be monotone with respect to categorical features\");\n }\n }\n bin_mappers[i].reset(new BinMapper());\n if (config_.max_bin_by_feature.empty()) {\n bin_mappers[i]->FindBin(sample_values[i], num_per_col[i], total_sample_size,\n config_.max_bin, config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter,\n bin_type, config_.use_missing, config_.zero_as_missing,\n forced_bin_bounds[i]);\n } else {\n bin_mappers[i]->FindBin(sample_values[i], num_per_col[i], total_sample_size,\n config_.max_bin_by_feature[i], config_.min_data_in_bin,\n filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing,\n config_.zero_as_missing, forced_bin_bounds[i]);\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n } else {\n // if have multi-machines, need to find bin distributed\n // different machines will find bin for different features\n int num_machines = Network::num_machines();\n int rank = Network::rank();\n // start and len will store the process feature indices for different machines\n // machine i will find bins for features in [ start[i], start[i] + len[i] )\n std::vector start(num_machines);\n std::vector len(num_machines);\n int step = (num_total_features + num_machines - 1) / num_machines;\n if (step < 1) {\n step = 1;\n }\n\n start[0] = 0;\n for (int i = 0; i < num_machines - 1; ++i) {\n len[i] = std::min(step, num_total_features - start[i]);\n start[i + 1] = start[i] + len[i];\n }\n len[num_machines - 1] = num_total_features - start[num_machines - 1];\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided)\n for (int i = 0; i < len[rank]; ++i) {\n OMP_LOOP_EX_BEGIN();\n if (ignore_features_.count(start[rank] + i) > 0) {\n continue;\n }\n BinType bin_type = BinType::NumericalBin;\n if (categorical_features_.count(start[rank] + i)) {\n bin_type = BinType::CategoricalBin;\n }\n bin_mappers[i].reset(new BinMapper());\n if (num_col <= start[rank] + i) {\n continue;\n }\n if (config_.max_bin_by_feature.empty()) {\n bin_mappers[i]->FindBin(sample_values[start[rank] + i], num_per_col[start[rank] + i],\n total_sample_size, config_.max_bin, config_.min_data_in_bin,\n filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, config_.zero_as_missing,\n forced_bin_bounds[i]);\n } else {\n bin_mappers[i]->FindBin(sample_values[start[rank] + i], num_per_col[start[rank] + i],\n total_sample_size, config_.max_bin_by_feature[start[rank] + i],\n config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing,\n config_.zero_as_missing, forced_bin_bounds[i]);\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n comm_size_t self_buf_size = 0;\n for (int i = 0; i < len[rank]; ++i) {\n if (ignore_features_.count(start[rank] + i) > 0) {\n continue;\n }\n self_buf_size += static_cast(bin_mappers[i]->SizesInByte());\n }\n std::vector input_buffer(self_buf_size);\n auto cp_ptr = input_buffer.data();\n for (int i = 0; i < len[rank]; ++i) {\n if (ignore_features_.count(start[rank] + i) > 0) {\n continue;\n }\n bin_mappers[i]->CopyTo(cp_ptr);\n cp_ptr += bin_mappers[i]->SizesInByte();\n // free\n bin_mappers[i].reset(nullptr);\n }\n std::vector size_len = Network::GlobalArray(self_buf_size);\n std::vector size_start(num_machines, 0);\n for (int i = 1; i < num_machines; ++i) {\n size_start[i] = size_start[i - 1] + size_len[i - 1];\n }\n comm_size_t total_buffer_size = size_start[num_machines - 1] + size_len[num_machines - 1];\n std::vector output_buffer(total_buffer_size);\n // gather global feature bin mappers\n Network::Allgather(input_buffer.data(), size_start.data(), size_len.data(), output_buffer.data(), total_buffer_size);\n cp_ptr = output_buffer.data();\n // restore features bins from buffer\n for (int i = 0; i < num_total_features; ++i) {\n if (ignore_features_.count(i) > 0) {\n bin_mappers[i] = nullptr;\n continue;\n }\n bin_mappers[i].reset(new BinMapper());\n bin_mappers[i]->CopyFrom(cp_ptr);\n cp_ptr += bin_mappers[i]->SizesInByte();\n }\n }\n CheckCategoricalFeatureNumBin(bin_mappers, config_.max_bin, config_.max_bin_by_feature);\n auto dataset = std::unique_ptr(new Dataset(num_local_data));\n dataset->Construct(&bin_mappers, num_total_features, forced_bin_bounds, sample_indices, sample_values, num_per_col, num_col, total_sample_size, config_);\n if (dataset->has_raw()) {\n dataset->ResizeRaw(num_local_data);\n }\n dataset->set_feature_names(feature_names_);\n return dataset.release();\n}\n\n\n// ---- private functions ----\n\nvoid DatasetLoader::LoadHeaderFromMemory(Dataset* dataset, const char* buffer) {\n // get header\n const char* mem_ptr = buffer;\n dataset->num_data_ = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->num_data_));\n dataset->num_features_ = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->num_features_));\n dataset->num_total_features_ = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->num_total_features_));\n dataset->label_idx_ = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->label_idx_));\n dataset->max_bin_ = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->max_bin_));\n dataset->bin_construct_sample_cnt_ = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->bin_construct_sample_cnt_));\n dataset->min_data_in_bin_ = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->min_data_in_bin_));\n dataset->use_missing_ = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->use_missing_));\n dataset->zero_as_missing_ = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->zero_as_missing_));\n dataset->has_raw_ = *(reinterpret_cast(mem_ptr));\n\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->has_raw_));\n const int* tmp_feature_map = reinterpret_cast(mem_ptr);\n dataset->used_feature_map_.clear();\n for (int i = 0; i < dataset->num_total_features_; ++i) {\n dataset->used_feature_map_.push_back(tmp_feature_map[i]);\n }\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * dataset->num_total_features_);\n // num_groups\n dataset->num_groups_ = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(dataset->num_groups_));\n // real_feature_idx_\n const int* tmp_ptr_real_feature_idx_ = reinterpret_cast(mem_ptr);\n dataset->real_feature_idx_.clear();\n for (int i = 0; i < dataset->num_features_; ++i) {\n dataset->real_feature_idx_.push_back(tmp_ptr_real_feature_idx_[i]);\n }\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * dataset->num_features_);\n // feature2group\n const int* tmp_ptr_feature2group = reinterpret_cast(mem_ptr);\n dataset->feature2group_.clear();\n for (int i = 0; i < dataset->num_features_; ++i) {\n dataset->feature2group_.push_back(tmp_ptr_feature2group[i]);\n }\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * dataset->num_features_);\n // feature2subfeature\n const int* tmp_ptr_feature2subfeature = reinterpret_cast(mem_ptr);\n dataset->feature2subfeature_.clear();\n for (int i = 0; i < dataset->num_features_; ++i) {\n dataset->feature2subfeature_.push_back(tmp_ptr_feature2subfeature[i]);\n }\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * dataset->num_features_);\n // group_bin_boundaries\n const uint64_t* tmp_ptr_group_bin_boundaries = reinterpret_cast(mem_ptr);\n dataset->group_bin_boundaries_.clear();\n for (int i = 0; i < dataset->num_groups_ + 1; ++i) {\n dataset->group_bin_boundaries_.push_back(tmp_ptr_group_bin_boundaries[i]);\n }\n mem_ptr += sizeof(uint64_t) * (dataset->num_groups_ + 1);\n\n // group_feature_start_\n const int* tmp_ptr_group_feature_start = reinterpret_cast(mem_ptr);\n dataset->group_feature_start_.clear();\n for (int i = 0; i < dataset->num_groups_; ++i) {\n dataset->group_feature_start_.push_back(tmp_ptr_group_feature_start[i]);\n }\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * (dataset->num_groups_));\n\n // group_feature_cnt_\n const int* tmp_ptr_group_feature_cnt = reinterpret_cast(mem_ptr);\n dataset->group_feature_cnt_.clear();\n for (int i = 0; i < dataset->num_groups_; ++i) {\n dataset->group_feature_cnt_.push_back(tmp_ptr_group_feature_cnt[i]);\n }\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int) * (dataset->num_groups_));\n\n if (!config_.max_bin_by_feature.empty()) {\n CHECK_EQ(static_cast(dataset->num_total_features_), config_.max_bin_by_feature.size());\n CHECK_GT(*(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end())), 1);\n dataset->max_bin_by_feature_.resize(dataset->num_total_features_);\n dataset->max_bin_by_feature_.assign(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end());\n } else {\n const int32_t* tmp_ptr_max_bin_by_feature = reinterpret_cast(mem_ptr);\n dataset->max_bin_by_feature_.clear();\n for (int i = 0; i < dataset->num_total_features_; ++i) {\n dataset->max_bin_by_feature_.push_back(tmp_ptr_max_bin_by_feature[i]);\n }\n }\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int32_t) * (dataset->num_total_features_));\n if (ArrayArgs::CheckAll(dataset->max_bin_by_feature_, -1)) {\n dataset->max_bin_by_feature_.clear();\n }\n\n // get feature names\n dataset->feature_names_.clear();\n for (int i = 0; i < dataset->num_total_features_; ++i) {\n int str_len = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int));\n std::stringstream str_buf;\n auto tmp_arr = reinterpret_cast(mem_ptr);\n for (int j = 0; j < str_len; ++j) {\n char tmp_char = tmp_arr[j];\n str_buf << tmp_char;\n }\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(char) * str_len);\n dataset->feature_names_.emplace_back(str_buf.str());\n }\n // get forced_bin_bounds_\n dataset->forced_bin_bounds_ = std::vector>(dataset->num_total_features_, std::vector());\n for (int i = 0; i < dataset->num_total_features_; ++i) {\n int num_bounds = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(int));\n dataset->forced_bin_bounds_[i] = std::vector();\n const double* tmp_ptr_forced_bounds =\n reinterpret_cast(mem_ptr);\n for (int j = 0; j < num_bounds; ++j) {\n double bound = tmp_ptr_forced_bounds[j];\n dataset->forced_bin_bounds_[i].push_back(bound);\n }\n mem_ptr += num_bounds * sizeof(double);\n }\n}\n\nvoid DatasetLoader::CheckDataset(const Dataset* dataset, bool is_load_from_binary) {\n if (dataset->num_data_ <= 0) {\n Log::Fatal(\"Data file %s is empty\", dataset->data_filename_.c_str());\n }\n if (dataset->feature_names_.size() != static_cast(dataset->num_total_features_)) {\n Log::Fatal(\"Size of feature name error, should be %d, got %d\", dataset->num_total_features_,\n static_cast(dataset->feature_names_.size()));\n }\n bool is_feature_order_by_group = true;\n int last_group = -1;\n int last_sub_feature = -1;\n // if features are ordered, not need to use hist_buf\n for (int i = 0; i < dataset->num_features_; ++i) {\n int group = dataset->feature2group_[i];\n int sub_feature = dataset->feature2subfeature_[i];\n if (group < last_group) {\n is_feature_order_by_group = false;\n } else if (group == last_group) {\n if (sub_feature <= last_sub_feature) {\n is_feature_order_by_group = false;\n break;\n }\n }\n last_group = group;\n last_sub_feature = sub_feature;\n }\n if (!is_feature_order_by_group) {\n Log::Fatal(\"Features in dataset should be ordered by group\");\n }\n\n if (is_load_from_binary) {\n if (dataset->max_bin_ != config_.max_bin) {\n Log::Fatal(\"Dataset was constructed with parameter max_bin=%d. It cannot be changed to %d when loading from binary file.\",\n dataset->max_bin_, config_.max_bin);\n }\n if (dataset->min_data_in_bin_ != config_.min_data_in_bin) {\n Log::Fatal(\"Dataset was constructed with parameter min_data_in_bin=%d. It cannot be changed to %d when loading from binary file.\",\n dataset->min_data_in_bin_, config_.min_data_in_bin);\n }\n if (dataset->use_missing_ != config_.use_missing) {\n Log::Fatal(\"Dataset was constructed with parameter use_missing=%d. It cannot be changed to %d when loading from binary file.\",\n dataset->use_missing_, config_.use_missing);\n }\n if (dataset->zero_as_missing_ != config_.zero_as_missing) {\n Log::Fatal(\"Dataset was constructed with parameter zero_as_missing=%d. It cannot be changed to %d when loading from binary file.\",\n dataset->zero_as_missing_, config_.zero_as_missing);\n }\n if (dataset->bin_construct_sample_cnt_ != config_.bin_construct_sample_cnt) {\n Log::Fatal(\"Dataset was constructed with parameter bin_construct_sample_cnt=%d. It cannot be changed to %d when loading from binary file.\",\n dataset->bin_construct_sample_cnt_, config_.bin_construct_sample_cnt);\n }\n if ((dataset->max_bin_by_feature_.size() != config_.max_bin_by_feature.size()) ||\n !std::equal(dataset->max_bin_by_feature_.begin(), dataset->max_bin_by_feature_.end(),\n config_.max_bin_by_feature.begin())) {\n Log::Fatal(\"Parameter max_bin_by_feature cannot be changed when loading from binary file.\");\n }\n\n if (config_.label_column != \"\") {\n Log::Warning(\"Parameter label_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.\");\n }\n if (config_.weight_column != \"\") {\n Log::Warning(\"Parameter weight_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.\");\n }\n if (config_.group_column != \"\") {\n Log::Warning(\"Parameter group_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.\");\n }\n if (config_.ignore_column != \"\") {\n Log::Warning(\"Parameter ignore_column works only in case of loading data directly from text file. It will be ignored when loading from binary file.\");\n }\n if (config_.two_round) {\n Log::Warning(\"Parameter two_round works only in case of loading data directly from text file. It will be ignored when loading from binary file.\");\n }\n if (config_.header) {\n Log::Warning(\"Parameter header works only in case of loading data directly from text file. It will be ignored when loading from binary file.\");\n }\n }\n}\n\nstd::vector DatasetLoader::LoadTextDataToMemory(const char* filename, const Metadata& metadata,\n int rank, int num_machines, int* num_global_data,\n std::vector* used_data_indices) {\n TextReader text_reader(filename, config_.header, config_.file_load_progress_interval_bytes);\n used_data_indices->clear();\n if (num_machines == 1 || config_.pre_partition) {\n // read all lines\n *num_global_data = text_reader.ReadAllLines();\n } else { // need partition data\n // get query data\n const data_size_t* query_boundaries = metadata.query_boundaries();\n\n if (query_boundaries == nullptr) {\n // if not contain query data, minimal sample unit is one record\n *num_global_data = text_reader.ReadAndFilterLines([this, rank, num_machines](data_size_t) {\n if (random_.NextShort(0, num_machines) == rank) {\n return true;\n } else {\n return false;\n }\n }, used_data_indices);\n } else {\n // if contain query data, minimal sample unit is one query\n data_size_t num_queries = metadata.num_queries();\n data_size_t qid = -1;\n bool is_query_used = false;\n *num_global_data = text_reader.ReadAndFilterLines(\n [this, rank, num_machines, &qid, &query_boundaries, &is_query_used, num_queries]\n (data_size_t line_idx) {\n if (qid >= num_queries) {\n Log::Fatal(\"Current query exceeds the range of the query file,\\n\"\n \"please ensure the query file is correct\");\n }\n if (line_idx >= query_boundaries[qid + 1]) {\n // if is new query\n is_query_used = false;\n if (random_.NextShort(0, num_machines) == rank) {\n is_query_used = true;\n }\n ++qid;\n }\n return is_query_used;\n }, used_data_indices);\n }\n }\n return std::move(text_reader.Lines());\n}\n\nstd::vector DatasetLoader::SampleTextDataFromMemory(const std::vector& data) {\n int sample_cnt = config_.bin_construct_sample_cnt;\n if (static_cast(sample_cnt) > data.size()) {\n sample_cnt = static_cast(data.size());\n }\n auto sample_indices = random_.Sample(static_cast(data.size()), sample_cnt);\n std::vector out(sample_indices.size());\n for (size_t i = 0; i < sample_indices.size(); ++i) {\n const size_t idx = sample_indices[i];\n out[i] = data[idx];\n }\n return out;\n}\n\nstd::vector DatasetLoader::SampleTextDataFromFile(const char* filename, const Metadata& metadata,\n int rank, int num_machines, int* num_global_data,\n std::vector* used_data_indices) {\n const data_size_t sample_cnt = static_cast(config_.bin_construct_sample_cnt);\n TextReader text_reader(filename, config_.header, config_.file_load_progress_interval_bytes);\n std::vector out_data;\n if (num_machines == 1 || config_.pre_partition) {\n *num_global_data = static_cast(text_reader.SampleFromFile(&random_, sample_cnt, &out_data));\n } else { // need partition data\n // get query data\n const data_size_t* query_boundaries = metadata.query_boundaries();\n if (query_boundaries == nullptr) {\n // if not contain query file, minimal sample unit is one record\n *num_global_data = text_reader.SampleAndFilterFromFile([this, rank, num_machines]\n (data_size_t) {\n if (random_.NextShort(0, num_machines) == rank) {\n return true;\n } else {\n return false;\n }\n }, used_data_indices, &random_, sample_cnt, &out_data);\n } else {\n // if contain query file, minimal sample unit is one query\n data_size_t num_queries = metadata.num_queries();\n data_size_t qid = -1;\n bool is_query_used = false;\n *num_global_data = text_reader.SampleAndFilterFromFile(\n [this, rank, num_machines, &qid, &query_boundaries, &is_query_used, num_queries]\n (data_size_t line_idx) {\n if (qid >= num_queries) {\n Log::Fatal(\"Query id exceeds the range of the query file, \"\n \"please ensure the query file is correct\");\n }\n if (line_idx >= query_boundaries[qid + 1]) {\n // if is new query\n is_query_used = false;\n if (random_.NextShort(0, num_machines) == rank) {\n is_query_used = true;\n }\n ++qid;\n }\n return is_query_used;\n }, used_data_indices, &random_, sample_cnt, &out_data);\n }\n }\n return out_data;\n}\n\nvoid DatasetLoader::ConstructBinMappersFromTextData(int rank, int num_machines,\n const std::vector& sample_data,\n const Parser* parser, Dataset* dataset) {\n auto t1 = std::chrono::high_resolution_clock::now();\n std::vector> sample_values;\n std::vector> sample_indices;\n std::vector> oneline_features;\n double label;\n for (int i = 0; i < static_cast(sample_data.size()); ++i) {\n oneline_features.clear();\n // parse features\n parser->ParseOneLine(sample_data[i].c_str(), &oneline_features, &label);\n for (std::pair& inner_data : oneline_features) {\n if (static_cast(inner_data.first) >= sample_values.size()) {\n sample_values.resize(inner_data.first + 1);\n sample_indices.resize(inner_data.first + 1);\n }\n if (std::fabs(inner_data.second) > kZeroThreshold || std::isnan(inner_data.second)) {\n sample_values[inner_data.first].emplace_back(inner_data.second);\n sample_indices[inner_data.first].emplace_back(i);\n }\n }\n }\n\n dataset->feature_groups_.clear();\n dataset->num_total_features_ = std::max(static_cast(sample_values.size()), parser->NumFeatures());\n if (num_machines > 1) {\n dataset->num_total_features_ = Network::GlobalSyncUpByMax(dataset->num_total_features_);\n }\n if (!feature_names_.empty()) {\n CHECK_EQ(dataset->num_total_features_, static_cast(feature_names_.size()));\n }\n\n if (!config_.max_bin_by_feature.empty()) {\n CHECK_EQ(static_cast(dataset->num_total_features_), config_.max_bin_by_feature.size());\n CHECK_GT(*(std::min_element(config_.max_bin_by_feature.begin(), config_.max_bin_by_feature.end())), 1);\n }\n\n // get forced split\n std::string forced_bins_path = config_.forcedbins_filename;\n std::vector> forced_bin_bounds = DatasetLoader::GetForcedBins(forced_bins_path,\n dataset->num_total_features_,\n categorical_features_);\n\n // check the range of label_idx, weight_idx and group_idx\n // skip label check if user input parser config file,\n // because label id is got from raw features while dataset features are consistent with customized parser.\n if (dataset->parser_config_str_.empty()) {\n CHECK(label_idx_ >= 0 && label_idx_ <= dataset->num_total_features_);\n }\n CHECK(weight_idx_ < 0 || weight_idx_ < dataset->num_total_features_);\n CHECK(group_idx_ < 0 || group_idx_ < dataset->num_total_features_);\n\n // fill feature_names_ if not header\n if (feature_names_.empty()) {\n for (int i = 0; i < dataset->num_total_features_; ++i) {\n std::stringstream str_buf;\n str_buf << \"Column_\" << i;\n feature_names_.push_back(str_buf.str());\n }\n }\n dataset->set_feature_names(feature_names_);\n std::vector> bin_mappers(dataset->num_total_features_);\n const data_size_t filter_cnt = static_cast(\n static_cast(config_.min_data_in_leaf* sample_data.size()) / dataset->num_data_);\n // start find bins\n if (num_machines == 1) {\n // if only one machine, find bin locally\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided)\n for (int i = 0; i < static_cast(sample_values.size()); ++i) {\n OMP_LOOP_EX_BEGIN();\n if (ignore_features_.count(i) > 0) {\n bin_mappers[i] = nullptr;\n continue;\n }\n BinType bin_type = BinType::NumericalBin;\n if (categorical_features_.count(i)) {\n bin_type = BinType::CategoricalBin;\n }\n bin_mappers[i].reset(new BinMapper());\n if (config_.max_bin_by_feature.empty()) {\n bin_mappers[i]->FindBin(sample_values[i].data(), static_cast(sample_values[i].size()),\n sample_data.size(), config_.max_bin, config_.min_data_in_bin,\n filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, config_.zero_as_missing,\n forced_bin_bounds[i]);\n } else {\n bin_mappers[i]->FindBin(sample_values[i].data(), static_cast(sample_values[i].size()),\n sample_data.size(), config_.max_bin_by_feature[i],\n config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing,\n config_.zero_as_missing, forced_bin_bounds[i]);\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n } else {\n // start and len will store the process feature indices for different machines\n // machine i will find bins for features in [ start[i], start[i] + len[i] )\n std::vector start(num_machines);\n std::vector len(num_machines);\n int step = (dataset->num_total_features_ + num_machines - 1) / num_machines;\n if (step < 1) {\n step = 1;\n }\n\n start[0] = 0;\n for (int i = 0; i < num_machines - 1; ++i) {\n len[i] = std::min(step, dataset->num_total_features_ - start[i]);\n start[i + 1] = start[i] + len[i];\n }\n len[num_machines - 1] = dataset->num_total_features_ - start[num_machines - 1];\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided)\n for (int i = 0; i < len[rank]; ++i) {\n OMP_LOOP_EX_BEGIN();\n if (ignore_features_.count(start[rank] + i) > 0) {\n continue;\n }\n BinType bin_type = BinType::NumericalBin;\n if (categorical_features_.count(start[rank] + i)) {\n bin_type = BinType::CategoricalBin;\n }\n bin_mappers[i].reset(new BinMapper());\n if (static_cast(sample_values.size()) <= start[rank] + i) {\n continue;\n }\n if (config_.max_bin_by_feature.empty()) {\n bin_mappers[i]->FindBin(sample_values[start[rank] + i].data(),\n static_cast(sample_values[start[rank] + i].size()),\n sample_data.size(), config_.max_bin, config_.min_data_in_bin,\n filter_cnt, config_.feature_pre_filter, bin_type, config_.use_missing, config_.zero_as_missing,\n forced_bin_bounds[i]);\n } else {\n bin_mappers[i]->FindBin(sample_values[start[rank] + i].data(),\n static_cast(sample_values[start[rank] + i].size()),\n sample_data.size(), config_.max_bin_by_feature[i],\n config_.min_data_in_bin, filter_cnt, config_.feature_pre_filter, bin_type,\n config_.use_missing, config_.zero_as_missing, forced_bin_bounds[i]);\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n comm_size_t self_buf_size = 0;\n for (int i = 0; i < len[rank]; ++i) {\n if (ignore_features_.count(start[rank] + i) > 0) {\n continue;\n }\n self_buf_size += static_cast(bin_mappers[i]->SizesInByte());\n }\n std::vector input_buffer(self_buf_size);\n auto cp_ptr = input_buffer.data();\n for (int i = 0; i < len[rank]; ++i) {\n if (ignore_features_.count(start[rank] + i) > 0) {\n continue;\n }\n bin_mappers[i]->CopyTo(cp_ptr);\n cp_ptr += bin_mappers[i]->SizesInByte();\n // free\n bin_mappers[i].reset(nullptr);\n }\n std::vector size_len = Network::GlobalArray(self_buf_size);\n std::vector size_start(num_machines, 0);\n for (int i = 1; i < num_machines; ++i) {\n size_start[i] = size_start[i - 1] + size_len[i - 1];\n }\n comm_size_t total_buffer_size = size_start[num_machines - 1] + size_len[num_machines - 1];\n std::vector output_buffer(total_buffer_size);\n // gather global feature bin mappers\n Network::Allgather(input_buffer.data(), size_start.data(), size_len.data(), output_buffer.data(), total_buffer_size);\n cp_ptr = output_buffer.data();\n // restore features bins from buffer\n for (int i = 0; i < dataset->num_total_features_; ++i) {\n if (ignore_features_.count(i) > 0) {\n bin_mappers[i] = nullptr;\n continue;\n }\n bin_mappers[i].reset(new BinMapper());\n bin_mappers[i]->CopyFrom(cp_ptr);\n cp_ptr += bin_mappers[i]->SizesInByte();\n }\n }\n CheckCategoricalFeatureNumBin(bin_mappers, config_.max_bin, config_.max_bin_by_feature);\n dataset->Construct(&bin_mappers, dataset->num_total_features_, forced_bin_bounds, Common::Vector2Ptr(&sample_indices).data(),\n Common::Vector2Ptr(&sample_values).data(),\n Common::VectorSize(sample_indices).data(), static_cast(sample_indices.size()), sample_data.size(), config_);\n if (dataset->has_raw()) {\n dataset->ResizeRaw(static_cast(sample_data.size()));\n }\n\n auto t2 = std::chrono::high_resolution_clock::now();\n Log::Info(\"Construct bin mappers from text data time %.2f seconds\",\n std::chrono::duration(t2 - t1) * 1e-3);\n}\n\n/*! \\brief Extract local features from memory */\nvoid DatasetLoader::ExtractFeaturesFromMemory(std::vector* text_data, const Parser* parser, Dataset* dataset) {\n std::vector> oneline_features;\n double tmp_label = 0.0f;\n auto& ref_text_data = *text_data;\n std::vector feature_row(dataset->num_features_);\n if (!predict_fun_) {\n OMP_INIT_EX();\n // if doesn't need to prediction with initial model\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(oneline_features) firstprivate(tmp_label, feature_row)\n for (data_size_t i = 0; i < dataset->num_data_; ++i) {\n OMP_LOOP_EX_BEGIN();\n const int tid = omp_get_thread_num();\n oneline_features.clear();\n // parser\n parser->ParseOneLine(ref_text_data[i].c_str(), &oneline_features, &tmp_label);\n // set label\n dataset->metadata_.SetLabelAt(i, static_cast(tmp_label));\n // free processed line:\n ref_text_data[i].clear();\n // shrink_to_fit will be very slow in linux, and seems not free memory, disable for now\n // text_reader_->Lines()[i].shrink_to_fit();\n std::vector is_feature_added(dataset->num_features_, false);\n // push data\n for (auto& inner_data : oneline_features) {\n if (inner_data.first >= dataset->num_total_features_) {\n continue;\n }\n int feature_idx = dataset->used_feature_map_[inner_data.first];\n if (feature_idx >= 0) {\n is_feature_added[feature_idx] = true;\n // if is used feature\n int group = dataset->feature2group_[feature_idx];\n int sub_feature = dataset->feature2subfeature_[feature_idx];\n dataset->feature_groups_[group]->PushData(tid, sub_feature, i, inner_data.second);\n if (dataset->has_raw()) {\n feature_row[feature_idx] = static_cast(inner_data.second);\n }\n } else {\n if (inner_data.first == weight_idx_) {\n dataset->metadata_.SetWeightAt(i, static_cast(inner_data.second));\n } else if (inner_data.first == group_idx_) {\n dataset->metadata_.SetQueryAt(i, static_cast(inner_data.second));\n }\n }\n }\n if (dataset->has_raw()) {\n for (size_t j = 0; j < feature_row.size(); ++j) {\n int feat_ind = dataset->numeric_feature_map_[j];\n if (feat_ind >= 0) {\n dataset->raw_data_[feat_ind][i] = feature_row[j];\n }\n }\n }\n dataset->FinishOneRow(tid, i, is_feature_added);\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n } else {\n OMP_INIT_EX();\n // if need to prediction with initial model\n std::vector init_score(static_cast(dataset->num_data_) * num_class_);\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(oneline_features) firstprivate(tmp_label, feature_row)\n for (data_size_t i = 0; i < dataset->num_data_; ++i) {\n OMP_LOOP_EX_BEGIN();\n const int tid = omp_get_thread_num();\n oneline_features.clear();\n // parser\n parser->ParseOneLine(ref_text_data[i].c_str(), &oneline_features, &tmp_label);\n // set initial score\n std::vector oneline_init_score(num_class_);\n predict_fun_(oneline_features, oneline_init_score.data());\n for (int k = 0; k < num_class_; ++k) {\n init_score[k * dataset->num_data_ + i] = static_cast(oneline_init_score[k]);\n }\n // set label\n dataset->metadata_.SetLabelAt(i, static_cast(tmp_label));\n // free processed line:\n ref_text_data[i].clear();\n // shrink_to_fit will be very slow in Linux, and seems not free memory, disable for now\n // text_reader_->Lines()[i].shrink_to_fit();\n // push data\n std::vector is_feature_added(dataset->num_features_, false);\n for (auto& inner_data : oneline_features) {\n if (inner_data.first >= dataset->num_total_features_) {\n continue;\n }\n int feature_idx = dataset->used_feature_map_[inner_data.first];\n if (feature_idx >= 0) {\n is_feature_added[feature_idx] = true;\n // if is used feature\n int group = dataset->feature2group_[feature_idx];\n int sub_feature = dataset->feature2subfeature_[feature_idx];\n dataset->feature_groups_[group]->PushData(tid, sub_feature, i, inner_data.second);\n if (dataset->has_raw()) {\n feature_row[feature_idx] = static_cast(inner_data.second);\n }\n } else {\n if (inner_data.first == weight_idx_) {\n dataset->metadata_.SetWeightAt(i, static_cast(inner_data.second));\n } else if (inner_data.first == group_idx_) {\n dataset->metadata_.SetQueryAt(i, static_cast(inner_data.second));\n }\n }\n }\n dataset->FinishOneRow(tid, i, is_feature_added);\n if (dataset->has_raw()) {\n for (size_t j = 0; j < feature_row.size(); ++j) {\n int feat_ind = dataset->numeric_feature_map_[j];\n if (feat_ind >= 0) {\n dataset->raw_data_[feat_ind][i] = feature_row[j];\n }\n }\n }\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n // metadata_ will manage space of init_score\n dataset->metadata_.SetInitScore(init_score.data(), dataset->num_data_ * num_class_);\n }\n dataset->FinishLoad();\n // text data can be free after loaded feature values\n text_data->clear();\n}\n\n/*! \\brief Extract local features from file */\nvoid DatasetLoader::ExtractFeaturesFromFile(const char* filename, const Parser* parser,\n const std::vector& used_data_indices, Dataset* dataset) {\n std::vector init_score;\n if (predict_fun_) {\n init_score = std::vector(static_cast(dataset->num_data_) * num_class_);\n }\n std::function&)> process_fun =\n [this, &init_score, &parser, &dataset]\n (data_size_t start_idx, const std::vector& lines) {\n std::vector> oneline_features;\n double tmp_label = 0.0f;\n std::vector feature_row(dataset->num_features_);\n OMP_INIT_EX();\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(oneline_features) firstprivate(tmp_label, feature_row)\n for (data_size_t i = 0; i < static_cast(lines.size()); ++i) {\n OMP_LOOP_EX_BEGIN();\n const int tid = omp_get_thread_num();\n oneline_features.clear();\n // parser\n parser->ParseOneLine(lines[i].c_str(), &oneline_features, &tmp_label);\n // set initial score\n if (!init_score.empty()) {\n std::vector oneline_init_score(num_class_);\n predict_fun_(oneline_features, oneline_init_score.data());\n for (int k = 0; k < num_class_; ++k) {\n init_score[k * dataset->num_data_ + start_idx + i] = static_cast(oneline_init_score[k]);\n }\n }\n // set label\n dataset->metadata_.SetLabelAt(start_idx + i, static_cast(tmp_label));\n std::vector is_feature_added(dataset->num_features_, false);\n // push data\n for (auto& inner_data : oneline_features) {\n if (inner_data.first >= dataset->num_total_features_) {\n continue;\n }\n int feature_idx = dataset->used_feature_map_[inner_data.first];\n if (feature_idx >= 0) {\n is_feature_added[feature_idx] = true;\n // if is used feature\n int group = dataset->feature2group_[feature_idx];\n int sub_feature = dataset->feature2subfeature_[feature_idx];\n dataset->feature_groups_[group]->PushData(tid, sub_feature, start_idx + i, inner_data.second);\n if (dataset->has_raw()) {\n feature_row[feature_idx] = static_cast(inner_data.second);\n }\n } else {\n if (inner_data.first == weight_idx_) {\n dataset->metadata_.SetWeightAt(start_idx + i, static_cast(inner_data.second));\n } else if (inner_data.first == group_idx_) {\n dataset->metadata_.SetQueryAt(start_idx + i, static_cast(inner_data.second));\n }\n }\n }\n if (dataset->has_raw()) {\n for (size_t j = 0; j < feature_row.size(); ++j) {\n int feat_ind = dataset->numeric_feature_map_[j];\n if (feat_ind >= 0) {\n dataset->raw_data_[feat_ind][i] = feature_row[j];\n }\n }\n }\n dataset->FinishOneRow(tid, i, is_feature_added);\n OMP_LOOP_EX_END();\n }\n OMP_THROW_EX();\n };\n TextReader text_reader(filename, config_.header, config_.file_load_progress_interval_bytes);\n if (!used_data_indices.empty()) {\n // only need part of data\n text_reader.ReadPartAndProcessParallel(used_data_indices, process_fun);\n } else {\n // need full data\n text_reader.ReadAllAndProcessParallel(process_fun);\n }\n\n // metadata_ will manage space of init_score\n if (!init_score.empty()) {\n dataset->metadata_.SetInitScore(init_score.data(), dataset->num_data_ * num_class_);\n }\n dataset->FinishLoad();\n}\n\n/*! \\brief Check can load from binary file */\nstd::string DatasetLoader::CheckCanLoadFromBin(const char* filename) {\n std::string bin_filename(filename);\n bin_filename.append(\".bin\");\n\n auto reader = VirtualFileReader::Make(bin_filename.c_str());\n\n if (!reader->Init()) {\n bin_filename = std::string(filename);\n reader = VirtualFileReader::Make(bin_filename.c_str());\n if (!reader->Init()) {\n Log::Fatal(\"Cannot open data file %s\", bin_filename.c_str());\n }\n }\n\n size_t buffer_size = 256;\n auto buffer = std::vector(buffer_size);\n // read size of token\n size_t size_of_token = std::strlen(Dataset::binary_file_token);\n size_t read_cnt = reader->Read(buffer.data(), size_of_token);\n if (read_cnt == size_of_token\n && std::string(buffer.data()) == std::string(Dataset::binary_file_token)) {\n return bin_filename;\n } else {\n return std::string();\n }\n}\n\nstd::vector> DatasetLoader::GetForcedBins(std::string forced_bins_path, int num_total_features,\n const std::unordered_set& categorical_features) {\n std::vector> forced_bins(num_total_features, std::vector());\n if (forced_bins_path != \"\") {\n std::ifstream forced_bins_stream(forced_bins_path.c_str());\n if (forced_bins_stream.fail()) {\n Log::Warning(\"Could not open %s. Will ignore.\", forced_bins_path.c_str());\n } else {\n std::stringstream buffer;\n buffer << forced_bins_stream.rdbuf();\n std::string err;\n Json forced_bins_json = Json::parse(buffer.str(), &err);\n CHECK(forced_bins_json.is_array());\n std::vector forced_bins_arr = forced_bins_json.array_items();\n for (size_t i = 0; i < forced_bins_arr.size(); ++i) {\n int feature_num = forced_bins_arr[i][\"feature\"].int_value();\n CHECK_LT(feature_num, num_total_features);\n if (categorical_features.count(feature_num)) {\n Log::Warning(\"Feature %d is categorical. Will ignore forced bins for this feature.\", feature_num);\n } else {\n std::vector bounds_arr = forced_bins_arr[i][\"bin_upper_bound\"].array_items();\n for (size_t j = 0; j < bounds_arr.size(); ++j) {\n forced_bins[feature_num].push_back(bounds_arr[j].number_value());\n }\n }\n }\n // remove duplicates\n for (int i = 0; i < num_total_features; ++i) {\n auto new_end = std::unique(forced_bins[i].begin(), forced_bins[i].end());\n forced_bins[i].erase(new_end, forced_bins[i].end());\n }\n }\n }\n return forced_bins;\n}\n\nvoid DatasetLoader::CheckCategoricalFeatureNumBin(\n const std::vector>& bin_mappers,\n const int max_bin, const std::vector& max_bin_by_feature) const {\n bool need_warning = false;\n if (bin_mappers.size() < 1024) {\n for (size_t i = 0; i < bin_mappers.size(); ++i) {\n const int max_bin_for_this_feature = max_bin_by_feature.empty() ? max_bin : max_bin_by_feature[i];\n if (bin_mappers[i] != nullptr && bin_mappers[i]->bin_type() == BinType::CategoricalBin && bin_mappers[i]->num_bin() > max_bin_for_this_feature) {\n need_warning = true;\n break;\n }\n }\n } else {\n const int num_threads = OMP_NUM_THREADS();\n std::vector thread_need_warning(num_threads, false);\n Threading::For(0, bin_mappers.size(), 1,\n [&bin_mappers, &thread_need_warning, &max_bin_by_feature, max_bin] (int thread_index, size_t start, size_t end) {\n for (size_t i = start; i < end; ++i) {\n thread_need_warning[thread_index] = false;\n const int max_bin_for_this_feature = max_bin_by_feature.empty() ? max_bin : max_bin_by_feature[i];\n if (bin_mappers[i] != nullptr && bin_mappers[i]->bin_type() == BinType::CategoricalBin && bin_mappers[i]->num_bin() > max_bin_for_this_feature) {\n thread_need_warning[thread_index] = true;\n break;\n }\n }\n });\n for (int thread_index = 0; thread_index < num_threads; ++thread_index) {\n if (thread_need_warning[thread_index]) {\n need_warning = true;\n break;\n }\n }\n }\n\n if (need_warning) {\n Log::Warning(\"Categorical features with more bins than the configured maximum bin number found.\");\n Log::Warning(\"For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.\");\n }\n}\n\n} // namespace LightGBM\n" }, { "path": "src/io/dense_bin.hpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n#ifndef LIGHTGBM_SRC_IO_DENSE_BIN_HPP_\n#define LIGHTGBM_SRC_IO_DENSE_BIN_HPP_\n\n#include \n#include \n\n#include \n#include \n#include \n\nnamespace LightGBM {\n\ntemplate \nclass DenseBin;\n\ntemplate \nclass DenseBinIterator : public BinIterator {\n public:\n explicit DenseBinIterator(const DenseBin* bin_data,\n uint32_t min_bin, uint32_t max_bin,\n uint32_t most_freq_bin)\n : bin_data_(bin_data),\n min_bin_(static_cast(min_bin)),\n max_bin_(static_cast(max_bin)),\n most_freq_bin_(static_cast(most_freq_bin)) {\n if (most_freq_bin_ == 0) {\n offset_ = 1;\n } else {\n offset_ = 0;\n }\n }\n inline uint32_t RawGet(data_size_t idx) override;\n inline uint32_t Get(data_size_t idx) override;\n inline void Reset(data_size_t) override {}\n\n private:\n const DenseBin* bin_data_;\n VAL_T min_bin_;\n VAL_T max_bin_;\n VAL_T most_freq_bin_;\n uint8_t offset_;\n};\n/*!\n * \\brief Used to store bins for dense feature\n * Use template to reduce memory cost\n */\ntemplate \nclass DenseBin : public Bin {\n public:\n friend DenseBinIterator;\n explicit DenseBin(data_size_t num_data)\n : num_data_(num_data) {\n if (IS_4BIT) {\n CHECK_EQ(sizeof(VAL_T), 1);\n data_.resize((num_data_ + 1) / 2, static_cast(0));\n buf_.resize((num_data_ + 1) / 2, static_cast(0));\n } else {\n data_.resize(num_data_, static_cast(0));\n }\n }\n\n ~DenseBin() {}\n\n void Push(int, data_size_t idx, uint32_t value) override {\n if (IS_4BIT) {\n const int i1 = idx >> 1;\n const int i2 = (idx & 1) << 2;\n const uint8_t val = static_cast(value) << i2;\n if (i2 == 0) {\n data_[i1] = val;\n } else {\n buf_[i1] = val;\n }\n } else {\n data_[idx] = static_cast(value);\n }\n }\n\n void ReSize(data_size_t num_data) override {\n if (num_data_ != num_data) {\n num_data_ = num_data;\n if (IS_4BIT) {\n data_.resize((num_data_ + 1) / 2, static_cast(0));\n } else {\n data_.resize(num_data_);\n }\n }\n }\n\n BinIterator* GetIterator(uint32_t min_bin, uint32_t max_bin,\n uint32_t most_freq_bin) const override;\n\n template \n void ConstructHistogramInner(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n const score_t* ordered_hessians,\n hist_t* out) const {\n data_size_t i = start;\n hist_t* grad = out;\n hist_t* hess = out + 1;\n hist_cnt_t* cnt = reinterpret_cast(hess);\n if (USE_PREFETCH) {\n const data_size_t pf_offset = 64 / sizeof(VAL_T);\n const data_size_t pf_end = end - pf_offset;\n for (; i < pf_end; ++i) {\n const auto idx = USE_INDICES ? data_indices[i] : i;\n const auto pf_idx =\n USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset;\n if (IS_4BIT) {\n PREFETCH_T0(data_.data() + (pf_idx >> 1));\n } else {\n PREFETCH_T0(data_.data() + pf_idx);\n }\n const auto ti = static_cast(data(idx)) << 1;\n if (USE_HESSIAN) {\n grad[ti] += ordered_gradients[i];\n hess[ti] += ordered_hessians[i];\n } else {\n grad[ti] += ordered_gradients[i];\n ++cnt[ti];\n }\n }\n }\n for (; i < end; ++i) {\n const auto idx = USE_INDICES ? data_indices[i] : i;\n const auto ti = static_cast(data(idx)) << 1;\n if (USE_HESSIAN) {\n grad[ti] += ordered_gradients[i];\n hess[ti] += ordered_hessians[i];\n } else {\n grad[ti] += ordered_gradients[i];\n ++cnt[ti];\n }\n }\n }\n\n void ConstructHistogram(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n const score_t* ordered_hessians,\n hist_t* out) const override {\n ConstructHistogramInner(\n data_indices, start, end, ordered_gradients, ordered_hessians, out);\n }\n\n void ConstructHistogram(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n const score_t* ordered_hessians,\n hist_t* out) const override {\n ConstructHistogramInner(\n nullptr, start, end, ordered_gradients, ordered_hessians, out);\n }\n\n void ConstructHistogram(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n hist_t* out) const override {\n ConstructHistogramInner(data_indices, start, end,\n ordered_gradients, nullptr, out);\n }\n\n void ConstructHistogram(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n hist_t* out) const override {\n ConstructHistogramInner(\n nullptr, start, end, ordered_gradients, nullptr, out);\n }\n\n\n template \n void ConstructHistogramIntInner(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n hist_t* out) const {\n data_size_t i = start;\n PACKED_HIST_T* out_ptr = reinterpret_cast(out);\n const int16_t* gradients_ptr = reinterpret_cast(ordered_gradients);\n const VAL_T* data_ptr_base = data_.data();\n if (USE_PREFETCH) {\n const data_size_t pf_offset = 64 / sizeof(VAL_T);\n const data_size_t pf_end = end - pf_offset;\n for (; i < pf_end; ++i) {\n const auto idx = USE_INDICES ? data_indices[i] : i;\n const auto pf_idx =\n USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset;\n if (IS_4BIT) {\n PREFETCH_T0(data_ptr_base + (pf_idx >> 1));\n } else {\n PREFETCH_T0(data_ptr_base + pf_idx);\n }\n const auto ti = static_cast(data(idx));\n const int16_t gradient_16 = gradients_ptr[i];\n if (USE_HESSIAN) {\n const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 :\n (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (gradient_16 & 0xff);\n out_ptr[ti] += gradient_packed;\n } else {\n const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 :\n (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (1);\n out_ptr[ti] += gradient_packed;\n }\n }\n }\n for (; i < end; ++i) {\n const auto idx = USE_INDICES ? data_indices[i] : i;\n const auto ti = static_cast(data(idx));\n const int16_t gradient_16 = gradients_ptr[i];\n if (USE_HESSIAN) {\n const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 :\n (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (gradient_16 & 0xff);\n out_ptr[ti] += gradient_packed;\n } else {\n const PACKED_HIST_T gradient_packed = HIST_BITS == 8 ? gradient_16 :\n (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (1);\n out_ptr[ti] += gradient_packed;\n }\n }\n }\n\n void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n const score_t* /*ordered_hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n data_indices, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt8(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n const score_t* /*ordered_hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n nullptr, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n data_indices, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt8(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n nullptr, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n const score_t* /*ordered_hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n data_indices, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt16(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n const score_t* /*ordered_hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n nullptr, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n data_indices, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt16(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n nullptr, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n const score_t* /*ordered_hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n data_indices, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt32(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n const score_t* /*ordered_hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n nullptr, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n data_indices, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt32(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n nullptr, start, end, ordered_gradients, out);\n }\n\n template \n data_size_t SplitInner(uint32_t min_bin, uint32_t max_bin,\n uint32_t default_bin, uint32_t most_freq_bin,\n bool default_left, uint32_t threshold,\n const data_size_t* data_indices, data_size_t cnt,\n data_size_t* lte_indices,\n data_size_t* gt_indices) const {\n auto th = static_cast(threshold + min_bin);\n auto t_zero_bin = static_cast(min_bin + default_bin);\n if (most_freq_bin == 0) {\n --th;\n --t_zero_bin;\n }\n const auto minb = static_cast(min_bin);\n const auto maxb = static_cast(max_bin);\n data_size_t lte_count = 0;\n data_size_t gt_count = 0;\n data_size_t* default_indices = gt_indices;\n data_size_t* default_count = >_count;\n data_size_t* missing_default_indices = gt_indices;\n data_size_t* missing_default_count = >_count;\n if (most_freq_bin <= threshold) {\n default_indices = lte_indices;\n default_count = <e_count;\n }\n if (MISS_IS_ZERO || MISS_IS_NA) {\n if (default_left) {\n missing_default_indices = lte_indices;\n missing_default_count = <e_count;\n }\n }\n if (min_bin < max_bin) {\n for (data_size_t i = 0; i < cnt; ++i) {\n const data_size_t idx = data_indices[i];\n const auto bin = data(idx);\n if ((MISS_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) ||\n (MISS_IS_NA && !MFB_IS_NA && bin == maxb)) {\n missing_default_indices[(*missing_default_count)++] = idx;\n } else if ((USE_MIN_BIN && (bin < minb || bin > maxb)) ||\n (!USE_MIN_BIN && bin == 0)) {\n if ((MISS_IS_NA && MFB_IS_NA) || (MISS_IS_ZERO && MFB_IS_ZERO)) {\n missing_default_indices[(*missing_default_count)++] = idx;\n } else {\n default_indices[(*default_count)++] = idx;\n }\n } else if (bin > th) {\n gt_indices[gt_count++] = idx;\n } else {\n lte_indices[lte_count++] = idx;\n }\n }\n } else {\n data_size_t* max_bin_indices = gt_indices;\n data_size_t* max_bin_count = >_count;\n if (maxb <= th) {\n max_bin_indices = lte_indices;\n max_bin_count = <e_count;\n }\n for (data_size_t i = 0; i < cnt; ++i) {\n const data_size_t idx = data_indices[i];\n const auto bin = data(idx);\n if (MISS_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) {\n missing_default_indices[(*missing_default_count)++] = idx;\n } else if (bin != maxb) {\n if ((MISS_IS_NA && MFB_IS_NA) || (MISS_IS_ZERO && MFB_IS_ZERO)) {\n missing_default_indices[(*missing_default_count)++] = idx;\n } else {\n default_indices[(*default_count)++] = idx;\n }\n } else {\n if (MISS_IS_NA && !MFB_IS_NA) {\n missing_default_indices[(*missing_default_count)++] = idx;\n } else {\n max_bin_indices[(*max_bin_count)++] = idx;\n }\n }\n }\n }\n return lte_count;\n }\n\n data_size_t Split(uint32_t min_bin, uint32_t max_bin, uint32_t default_bin,\n uint32_t most_freq_bin, MissingType missing_type,\n bool default_left, uint32_t threshold,\n const data_size_t* data_indices, data_size_t cnt,\n data_size_t* lte_indices,\n data_size_t* gt_indices) const override {\n#define ARGUMENTS \\\n min_bin, max_bin, default_bin, most_freq_bin, default_left, threshold, \\\n data_indices, cnt, lte_indices, gt_indices\n if (missing_type == MissingType::None) {\n return SplitInner(ARGUMENTS);\n } else if (missing_type == MissingType::Zero) {\n if (default_bin == most_freq_bin) {\n return SplitInner(ARGUMENTS);\n } else {\n return SplitInner(ARGUMENTS);\n }\n } else {\n if (max_bin == most_freq_bin + min_bin && most_freq_bin > 0) {\n return SplitInner(ARGUMENTS);\n } else {\n return SplitInner(ARGUMENTS);\n }\n }\n#undef ARGUMENTS\n }\n\n data_size_t Split(uint32_t max_bin, uint32_t default_bin,\n uint32_t most_freq_bin, MissingType missing_type,\n bool default_left, uint32_t threshold,\n const data_size_t* data_indices, data_size_t cnt,\n data_size_t* lte_indices,\n data_size_t* gt_indices) const override {\n#define ARGUMENTS \\\n 1, max_bin, default_bin, most_freq_bin, default_left, threshold, \\\n data_indices, cnt, lte_indices, gt_indices\n if (missing_type == MissingType::None) {\n return SplitInner(ARGUMENTS);\n } else if (missing_type == MissingType::Zero) {\n if (default_bin == most_freq_bin) {\n return SplitInner(ARGUMENTS);\n } else {\n return SplitInner(ARGUMENTS);\n }\n } else {\n if (max_bin == most_freq_bin + 1 && most_freq_bin > 0) {\n return SplitInner(ARGUMENTS);\n } else {\n return SplitInner(ARGUMENTS);\n }\n }\n#undef ARGUMENTS\n }\n\n template \n data_size_t SplitCategoricalInner(uint32_t min_bin, uint32_t max_bin,\n uint32_t most_freq_bin,\n const uint32_t* threshold,\n int num_threshold,\n const data_size_t* data_indices,\n data_size_t cnt, data_size_t* lte_indices,\n data_size_t* gt_indices) const {\n data_size_t lte_count = 0;\n data_size_t gt_count = 0;\n data_size_t* default_indices = gt_indices;\n data_size_t* default_count = >_count;\n int8_t offset = most_freq_bin == 0 ? 1 : 0;\n if (most_freq_bin > 0 &&\n Common::FindInBitset(threshold, num_threshold, most_freq_bin)) {\n default_indices = lte_indices;\n default_count = <e_count;\n }\n for (data_size_t i = 0; i < cnt; ++i) {\n const data_size_t idx = data_indices[i];\n const uint32_t bin = data(idx);\n if (USE_MIN_BIN && (bin < min_bin || bin > max_bin)) {\n default_indices[(*default_count)++] = idx;\n } else if (!USE_MIN_BIN && bin == 0) {\n default_indices[(*default_count)++] = idx;\n } else if (Common::FindInBitset(threshold, num_threshold,\n bin - min_bin + offset)) {\n lte_indices[lte_count++] = idx;\n } else {\n gt_indices[gt_count++] = idx;\n }\n }\n return lte_count;\n }\n\n data_size_t SplitCategorical(uint32_t min_bin, uint32_t max_bin,\n uint32_t most_freq_bin,\n const uint32_t* threshold, int num_threshold,\n const data_size_t* data_indices, data_size_t cnt,\n data_size_t* lte_indices,\n data_size_t* gt_indices) const override {\n return SplitCategoricalInner(min_bin, max_bin, most_freq_bin,\n threshold, num_threshold, data_indices,\n cnt, lte_indices, gt_indices);\n }\n\n data_size_t SplitCategorical(uint32_t max_bin, uint32_t most_freq_bin,\n const uint32_t* threshold, int num_threshold,\n const data_size_t* data_indices, data_size_t cnt,\n data_size_t* lte_indices,\n data_size_t* gt_indices) const override {\n return SplitCategoricalInner(1, max_bin, most_freq_bin, threshold,\n num_threshold, data_indices, cnt,\n lte_indices, gt_indices);\n }\n\n data_size_t num_data() const override { return num_data_; }\n\n void* get_data() override { return data_.data(); }\n\n void FinishLoad() override {\n if (IS_4BIT) {\n if (buf_.empty()) {\n return;\n }\n int len = (num_data_ + 1) / 2;\n for (int i = 0; i < len; ++i) {\n data_[i] |= buf_[i];\n }\n buf_.clear();\n }\n }\n\n void LoadFromMemory(\n const void* memory,\n const std::vector& local_used_indices) override {\n const VAL_T* mem_data = reinterpret_cast(memory);\n if (!local_used_indices.empty()) {\n if (IS_4BIT) {\n const data_size_t rest = num_data_ & 1;\n for (int i = 0; i < num_data_ - rest; i += 2) {\n // get old bins\n data_size_t idx = local_used_indices[i];\n const auto bin1 = static_cast(\n (mem_data[idx >> 1] >> ((idx & 1) << 2)) & 0xf);\n idx = local_used_indices[i + 1];\n const auto bin2 = static_cast(\n (mem_data[idx >> 1] >> ((idx & 1) << 2)) & 0xf);\n // add\n const int i1 = i >> 1;\n data_[i1] = (bin1 | (bin2 << 4));\n }\n if (rest) {\n data_size_t idx = local_used_indices[num_data_ - 1];\n data_[num_data_ >> 1] =\n (mem_data[idx >> 1] >> ((idx & 1) << 2)) & 0xf;\n }\n } else {\n for (int i = 0; i < num_data_; ++i) {\n data_[i] = mem_data[local_used_indices[i]];\n }\n }\n } else {\n for (size_t i = 0; i < data_.size(); ++i) {\n data_[i] = mem_data[i];\n }\n }\n }\n\n inline VAL_T data(data_size_t idx) const {\n if (IS_4BIT) {\n return (data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf;\n } else {\n return data_[idx];\n }\n }\n\n void CopySubrow(const Bin* full_bin, const data_size_t* used_indices,\n data_size_t num_used_indices) override {\n auto other_bin = dynamic_cast*>(full_bin);\n if (IS_4BIT) {\n const data_size_t rest = num_used_indices & 1;\n for (int i = 0; i < num_used_indices - rest; i += 2) {\n data_size_t idx = used_indices[i];\n const auto bin1 = static_cast(\n (other_bin->data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf);\n idx = used_indices[i + 1];\n const auto bin2 = static_cast(\n (other_bin->data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf);\n const int i1 = i >> 1;\n data_[i1] = (bin1 | (bin2 << 4));\n }\n if (rest) {\n data_size_t idx = used_indices[num_used_indices - 1];\n data_[num_used_indices >> 1] =\n (other_bin->data_[idx >> 1] >> ((idx & 1) << 2)) & 0xf;\n }\n } else {\n for (int i = 0; i < num_used_indices; ++i) {\n data_[i] = other_bin->data_[used_indices[i]];\n }\n }\n }\n\n void SaveBinaryToFile(BinaryWriter* writer) const override {\n writer->AlignedWrite(data_.data(), sizeof(VAL_T) * data_.size());\n }\n\n size_t SizesInByte() const override {\n return VirtualFileWriter::AlignedSize(sizeof(VAL_T) * data_.size());\n }\n\n DenseBin* Clone() override;\n\n const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const override;\n\n const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const override;\n\n private:\n data_size_t num_data_;\n#ifdef USE_CUDA\n std::vector> data_;\n#else\n std::vector> data_;\n#endif\n std::vector buf_;\n\n DenseBin(const DenseBin& other)\n : num_data_(other.num_data_), data_(other.data_) {}\n};\n\ntemplate \nDenseBin* DenseBin::Clone() {\n return new DenseBin(*this);\n}\n\ntemplate \nuint32_t DenseBinIterator::Get(data_size_t idx) {\n auto ret = bin_data_->data(idx);\n if (ret >= min_bin_ && ret <= max_bin_) {\n return ret - min_bin_ + offset_;\n } else {\n return most_freq_bin_;\n }\n}\n\ntemplate \ninline uint32_t DenseBinIterator::RawGet(data_size_t idx) {\n return bin_data_->data(idx);\n}\n\ntemplate \nBinIterator* DenseBin::GetIterator(\n uint32_t min_bin, uint32_t max_bin, uint32_t most_freq_bin) const {\n return new DenseBinIterator(this, min_bin, max_bin,\n most_freq_bin);\n}\n\n} // namespace LightGBM\n#endif // LIGHTGBM_SRC_IO_DENSE_BIN_HPP_\n" }, { "path": "src/io/file_io.cpp", "content": "/*!\n * Copyright (c) 2018-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2018-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nstruct LocalFile : VirtualFileReader, VirtualFileWriter {\n LocalFile(const std::string& filename, const std::string& mode)\n : filename_(filename), mode_(mode) {}\n virtual ~LocalFile() {\n if (file_ != NULL) {\n fclose(file_);\n }\n }\n\n bool Init() {\n if (file_ == NULL) {\n#if _MSC_VER\n fopen_s(&file_, filename_.c_str(), mode_.c_str());\n#else\n file_ = fopen(filename_.c_str(), mode_.c_str());\n#endif\n }\n return file_ != NULL;\n }\n\n bool Exists() const {\n LocalFile file(filename_, \"rb\");\n return file.Init();\n }\n\n size_t Read(void* buffer, size_t bytes) const {\n return fread(buffer, 1, bytes, file_);\n }\n\n size_t Write(const void* buffer, size_t bytes) {\n return fwrite(buffer, bytes, 1, file_) == 1 ? bytes : 0;\n }\n\n private:\n FILE* file_ = NULL;\n const std::string filename_;\n const std::string mode_;\n};\n\nstd::unique_ptr VirtualFileReader::Make(\n const std::string& filename) {\n return std::unique_ptr(new LocalFile(filename, \"rb\"));\n}\n\nstd::unique_ptr VirtualFileWriter::Make(\n const std::string& filename) {\n return std::unique_ptr(new LocalFile(filename, \"wb\"));\n}\n\nbool VirtualFileWriter::Exists(const std::string& filename) {\n LocalFile file(filename, \"rb\");\n return file.Exists();\n}\n\n} // namespace LightGBM\n" }, { "path": "src/io/json11.cpp", "content": "/* Copyright (c) 2013 Dropbox, Inc.\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to deal\n * in the Software without restriction, including without limitation the rights\n * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n * copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n * THE SOFTWARE.\n */\n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace json11_internal_lightgbm {\n\nstatic const int max_depth = 200;\n\nusing std::initializer_list;\nusing std::make_shared;\nusing std::map;\nusing std::string;\nusing std::vector;\n\nusing LightGBM::Log;\n\n/* Helper for representing null - just a do-nothing struct, plus comparison\n * operators so the helpers in JsonValue work. We can't use nullptr_t because\n * it may not be orderable.\n */\nstruct NullStruct {\n bool operator==(NullStruct) const { return true; }\n bool operator<(NullStruct) const { return false; }\n};\n\n/* * * * * * * * * * * * * * * * * * * *\n * Serialization\n */\n\nstatic void dump(NullStruct, string *out) { *out += \"null\"; }\n\nstatic void dump(double value, string *out) {\n if (std::isfinite(value)) {\n char buf[32];\n snprintf(buf, sizeof buf, \"%.17g\", value);\n *out += buf;\n } else {\n *out += \"null\";\n }\n}\n\nstatic void dump(int value, string *out) {\n char buf[32];\n snprintf(buf, sizeof buf, \"%d\", value);\n *out += buf;\n}\n\nstatic void dump(bool value, string *out) { *out += value ? \"true\" : \"false\"; }\n\nstatic void dump(const string &value, string *out) {\n *out += '\"';\n for (size_t i = 0; i < value.length(); i++) {\n const char ch = value[i];\n if (ch == '\\\\') {\n *out += \"\\\\\\\\\";\n } else if (ch == '\"') {\n *out += \"\\\\\\\"\";\n } else if (ch == '\\b') {\n *out += \"\\\\b\";\n } else if (ch == '\\f') {\n *out += \"\\\\f\";\n } else if (ch == '\\n') {\n *out += \"\\\\n\";\n } else if (ch == '\\r') {\n *out += \"\\\\r\";\n } else if (ch == '\\t') {\n *out += \"\\\\t\";\n } else if (static_cast(ch) <= 0x1f) {\n char buf[8];\n snprintf(buf, sizeof buf, \"\\\\u%04x\", ch);\n *out += buf;\n } else if (static_cast(ch) == 0xe2 &&\n static_cast(value[i + 1]) == 0x80 &&\n static_cast(value[i + 2]) == 0xa8) {\n *out += \"\\\\u2028\";\n i += 2;\n } else if (static_cast(ch) == 0xe2 &&\n static_cast(value[i + 1]) == 0x80 &&\n static_cast(value[i + 2]) == 0xa9) {\n *out += \"\\\\u2029\";\n i += 2;\n } else {\n *out += ch;\n }\n }\n *out += '\"';\n}\n\nstatic void dump(const Json::array &values, string *out) {\n bool first = true;\n *out += \"[\";\n for (const auto &value : values) {\n if (!first) *out += \", \";\n value.dump(out);\n first = false;\n }\n *out += \"]\";\n}\n\nstatic void dump(const Json::object &values, string *out) {\n bool first = true;\n *out += \"{\";\n for (const auto &kv : values) {\n if (!first) *out += \", \";\n dump(kv.first, out);\n *out += \": \";\n kv.second.dump(out);\n first = false;\n }\n *out += \"}\";\n}\n\nvoid Json::dump(string *out) const { m_ptr->dump(out); }\n\n/* * * * * * * * * * * * * * * * * * * *\n * Value wrappers\n */\n\ntemplate \nclass Value : public JsonValue {\n protected:\n // Constructors\n explicit Value(const T &value) : m_value(value) {}\n explicit Value(T &&value) : m_value(std::move(value)) {}\n\n // Get type tag\n Json::Type type() const override { return tag; }\n\n // Comparisons\n bool equals(const JsonValue *other) const override {\n return m_value == static_cast *>(other)->m_value;\n }\n bool less(const JsonValue *other) const override {\n return m_value < (static_cast *>(other)->m_value);\n }\n\n const T m_value;\n void dump(string *out) const override { json11_internal_lightgbm::dump(m_value, out); }\n};\n\nclass JsonDouble final : public Value {\n double number_value() const override { return m_value; }\n int int_value() const override { return static_cast(m_value); }\n bool equals(const JsonValue *other) const override {\n return m_value == other->number_value();\n }\n bool less(const JsonValue *other) const override {\n return m_value < other->number_value();\n }\n\n public:\n explicit JsonDouble(double value) : Value(value) {}\n};\n\nclass JsonInt final : public Value {\n double number_value() const override { return m_value; }\n int int_value() const override { return m_value; }\n bool equals(const JsonValue *other) const override {\n return m_value == other->number_value();\n }\n bool less(const JsonValue *other) const override {\n return m_value < other->number_value();\n }\n\n public:\n explicit JsonInt(int value) : Value(value) {}\n};\n\nclass JsonBoolean final : public Value {\n bool bool_value() const override { return m_value; }\n\n public:\n explicit JsonBoolean(bool value) : Value(value) {}\n};\n\nclass JsonString final : public Value {\n const string &string_value() const override { return m_value; }\n\n public:\n explicit JsonString(const string &value) : Value(value) {}\n explicit JsonString(string &&value) : Value(std::move(value)) {}\n};\n\nclass JsonArray final : public Value {\n const Json::array &array_items() const override { return m_value; }\n const Json &operator[](size_t i) const override;\n\n public:\n explicit JsonArray(const Json::array &value) : Value(value) {}\n explicit JsonArray(Json::array &&value) : Value(std::move(value)) {}\n};\n\nclass JsonObject final : public Value {\n const Json::object &object_items() const override { return m_value; }\n const Json &operator[](const string &key) const override;\n\n public:\n explicit JsonObject(const Json::object &value) : Value(value) {}\n explicit JsonObject(Json::object &&value) : Value(std::move(value)) {}\n};\n\nclass JsonNull final : public Value {\n public:\n JsonNull() : Value({}) {}\n};\n\n/* * * * * * * * * * * * * * * * * * * *\n * Static globals - static-init-safe\n */\nstruct Statics {\n const std::shared_ptr null = make_shared();\n const std::shared_ptr t = make_shared(true);\n const std::shared_ptr f = make_shared(false);\n const string empty_string;\n const vector empty_vector;\n const map empty_map;\n Statics() {}\n};\n\nstatic const Statics &statics() {\n static const Statics s{};\n return s;\n}\n\nstatic const Json &static_null() {\n // This has to be separate, not in Statics, because Json() accesses\n // statics().null.\n static const Json json_null;\n return json_null;\n}\n\n/* * * * * * * * * * * * * * * * * * * *\n * Constructors\n */\n\nJson::Json() noexcept : m_ptr(statics().null) {}\nJson::Json(std::nullptr_t) noexcept : m_ptr(statics().null) {}\nJson::Json(double value) : m_ptr(make_shared(value)) {}\nJson::Json(int value) : m_ptr(make_shared(value)) {}\nJson::Json(bool value) : m_ptr(value ? statics().t : statics().f) {}\nJson::Json(const string &value) : m_ptr(make_shared(value)) {}\nJson::Json(string &&value) : m_ptr(make_shared(std::move(value))) {}\nJson::Json(const char *value) : m_ptr(make_shared(value)) {}\nJson::Json(const Json::array &values) : m_ptr(make_shared(values)) {}\nJson::Json(Json::array &&values)\n : m_ptr(make_shared(std::move(values))) {}\nJson::Json(const Json::object &values)\n : m_ptr(make_shared(values)) {}\nJson::Json(Json::object &&values)\n : m_ptr(make_shared(std::move(values))) {}\n\n/* * * * * * * * * * * * * * * * * * * *\n * Accessors\n */\n\nJson::Type Json::type() const { return m_ptr->type(); }\ndouble Json::number_value() const { return m_ptr->number_value(); }\nint Json::int_value() const { return m_ptr->int_value(); }\nbool Json::bool_value() const { return m_ptr->bool_value(); }\nconst string &Json::string_value() const { return m_ptr->string_value(); }\nconst vector &Json::array_items() const { return m_ptr->array_items(); }\nconst map &Json::object_items() const {\n return m_ptr->object_items();\n}\nconst Json &Json::operator[](size_t i) const { return (*m_ptr)[i]; }\nconst Json &Json::operator[](const string &key) const { return (*m_ptr)[key]; }\n\ndouble JsonValue::number_value() const { return 0; }\nint JsonValue::int_value() const { return 0; }\nbool JsonValue::bool_value() const { return false; }\nconst string &JsonValue::string_value() const { return statics().empty_string; }\nconst vector &JsonValue::array_items() const {\n return statics().empty_vector;\n}\nconst map &JsonValue::object_items() const {\n return statics().empty_map;\n}\nconst Json &JsonValue::operator[](size_t) const { return static_null(); }\nconst Json &JsonValue::operator[](const string &) const {\n return static_null();\n}\n\nconst Json &JsonObject::operator[](const string &key) const {\n auto iter = m_value.find(key);\n return (iter == m_value.end()) ? static_null() : iter->second;\n}\nconst Json &JsonArray::operator[](size_t i) const {\n if (i >= m_value.size())\n return static_null();\n else\n return m_value[i];\n}\n\n/* * * * * * * * * * * * * * * * * * * *\n * Comparison\n */\n\nbool Json::operator==(const Json &other) const {\n if (m_ptr == other.m_ptr) return true;\n if (m_ptr->type() != other.m_ptr->type()) return false;\n\n return m_ptr->equals(other.m_ptr.get());\n}\n\nbool Json::operator<(const Json &other) const {\n if (m_ptr == other.m_ptr) return false;\n if (m_ptr->type() != other.m_ptr->type())\n return m_ptr->type() < other.m_ptr->type();\n\n return m_ptr->less(other.m_ptr.get());\n}\n\n/* * * * * * * * * * * * * * * * * * * *\n * Parsing\n */\n\n/* esc(c)\n *\n * Format char c suitable for printing in an error message.\n */\nstatic inline string esc(char c) {\n char buf[12];\n if (static_cast(c) >= 0x20 && static_cast(c) <= 0x7f) {\n snprintf(buf, sizeof buf, \"'%c' (%d)\", c, c);\n } else {\n snprintf(buf, sizeof buf, \"(%d)\", c);\n }\n return string(buf);\n}\n\ntemplate \nstatic inline bool in_range(T x, T lower, T upper) {\n return (x >= lower && x <= upper);\n}\n\nnamespace {\n/* JsonParser\n *\n * Object that tracks all state of an in-progress parse.\n */\nstruct JsonParser final {\n /* State\n */\n const char *str;\n const size_t str_len;\n size_t i;\n string *err;\n bool failed;\n const JsonParse strategy;\n\n /* fail(msg, err_ret = Json())\n *\n * Mark this parse as failed.\n */\n Json fail(string &&msg) { return fail(std::move(msg), Json()); }\n\n template \n T fail(string &&msg, const T err_ret) {\n if (!failed) *err = std::move(msg);\n failed = true;\n return err_ret;\n }\n\n /* consume_whitespace()\n *\n * Advance until the current character is non-whitespace.\n */\n void consume_whitespace() {\n while (str[i] == ' ' || str[i] == '\\r' || str[i] == '\\n' || str[i] == '\\t')\n i++;\n }\n\n /* consume_comment()\n *\n * Advance comments (c-style inline and multiline).\n */\n bool consume_comment() {\n bool comment_found = false;\n if (str[i] == '/') {\n i++;\n if (i == str_len)\n return fail(\"Unexpected end of input after start of comment\", false);\n if (str[i] == '/') { // inline comment\n i++;\n // advance until next line, or end of input\n while (i < str_len && str[i] != '\\n') {\n i++;\n }\n comment_found = true;\n } else if (str[i] == '*') { // multiline comment\n i++;\n if (i > str_len - 2)\n return fail(\"Unexpected end of input inside multi-line comment\",\n false);\n // advance until closing tokens\n while (!(str[i] == '*' && str[i + 1] == '/')) {\n i++;\n if (i > str_len - 2)\n return fail(\"Unexpected end of input inside multi-line comment\",\n false);\n }\n i += 2;\n comment_found = true;\n } else {\n return fail(\"Malformed comment\", false);\n }\n }\n return comment_found;\n }\n\n /* consume_garbage()\n *\n * Advance until the current character is non-whitespace and non-comment.\n */\n void consume_garbage() {\n consume_whitespace();\n if (strategy == JsonParse::COMMENTS) {\n bool comment_found = false;\n do {\n comment_found = consume_comment();\n if (failed) return;\n consume_whitespace();\n } while (comment_found);\n }\n }\n\n /* get_next_token()\n *\n * Return the next non-whitespace character. If the end of the input is\n * reached, flag an error and return 0.\n */\n char get_next_token() {\n consume_garbage();\n if (failed) return char{0};\n if (i == str_len) return fail(\"Unexpected end of input\", char{0});\n\n return str[i++];\n }\n\n /* encode_utf8(pt, out)\n *\n * Encode pt as UTF-8 and add it to out.\n */\n void encode_utf8(int64_t pt, string* out) {\n if (pt < 0) return;\n\n if (pt < 0x80) {\n *out += static_cast(pt);\n } else if (pt < 0x800) {\n *out += static_cast((pt >> 6) | 0xC0);\n *out += static_cast((pt & 0x3F) | 0x80);\n } else if (pt < 0x10000) {\n *out += static_cast((pt >> 12) | 0xE0);\n *out += static_cast(((pt >> 6) & 0x3F) | 0x80);\n *out += static_cast((pt & 0x3F) | 0x80);\n } else {\n *out += static_cast((pt >> 18) | 0xF0);\n *out += static_cast(((pt >> 12) & 0x3F) | 0x80);\n *out += static_cast(((pt >> 6) & 0x3F) | 0x80);\n *out += static_cast((pt & 0x3F) | 0x80);\n }\n }\n\n /* parse_string()\n *\n * Parse a string, starting at the current position.\n */\n string parse_string() {\n string out;\n int64_t last_escaped_codepoint = -1;\n while (true) {\n if (i == str_len) return fail(\"Unexpected end of input in string\", \"\");\n\n char ch = str[i++];\n\n if (ch == '\"') {\n encode_utf8(last_escaped_codepoint, &out);\n return out;\n }\n\n if (in_range(ch, 0, 0x1f))\n return fail(\"Unescaped \" + esc(ch) + \" in string\", \"\");\n\n // The usual case: non-escaped characters\n if (ch != '\\\\') {\n encode_utf8(last_escaped_codepoint, &out);\n last_escaped_codepoint = -1;\n out += ch;\n continue;\n }\n\n // Handle escapes\n if (i == str_len) return fail(\"Unexpected end of input in string\", \"\");\n\n ch = str[i++];\n\n if (ch == 'u') {\n // Extract 4-byte escape sequence\n string esc = string(str + i, 4);\n // Explicitly check length of the substring. The following loop\n // relies on std::string returning the terminating NUL when\n // accessing str[length]. Checking here reduces brittleness.\n if (esc.length() < 4) {\n return fail(\"Bad \\\\u escape: \" + esc, \"\");\n }\n for (size_t j = 0; j < 4; j++) {\n if (!in_range(esc[j], 'a', 'f') && !in_range(esc[j], 'A', 'F') &&\n !in_range(esc[j], '0', '9'))\n return fail(\"Bad \\\\u escape: \" + esc, \"\");\n }\n\n int64_t codepoint =\n static_cast(strtol(esc.data(), nullptr, 16));\n\n // JSON specifies that characters outside the BMP shall be encoded as a\n // pair of 4-hex-digit \\u escapes encoding their surrogate pair\n // components. Check whether we're in the middle of such a beast: the\n // previous codepoint was an escaped lead (high) surrogate, and this is\n // a trail (low) surrogate.\n if (in_range(last_escaped_codepoint, 0xD800, 0xDBFF) &&\n in_range(codepoint, 0xDC00, 0xDFFF)) {\n // Reassemble the two surrogate pairs into one astral-plane character,\n // per the UTF-16 algorithm.\n encode_utf8((((last_escaped_codepoint - 0xD800) << 10) |\n (codepoint - 0xDC00)) +\n 0x10000,\n &out);\n last_escaped_codepoint = -1;\n } else {\n encode_utf8(last_escaped_codepoint, &out);\n last_escaped_codepoint = codepoint;\n }\n\n i += 4;\n continue;\n }\n\n encode_utf8(last_escaped_codepoint, &out);\n last_escaped_codepoint = -1;\n\n if (ch == 'b') {\n out += '\\b';\n } else if (ch == 'f') {\n out += '\\f';\n } else if (ch == 'n') {\n out += '\\n';\n } else if (ch == 'r') {\n out += '\\r';\n } else if (ch == 't') {\n out += '\\t';\n } else if (ch == '\"' || ch == '\\\\' || ch == '/') {\n out += ch;\n } else {\n return fail(\"Invalid escape character \" + esc(ch), \"\");\n }\n }\n }\n\n /* parse_number()\n *\n * Parse a double.\n */\n Json parse_number() {\n size_t start_pos = i;\n\n if (str[i] == '-') i++;\n\n // Integer part\n if (str[i] == '0') {\n i++;\n if (in_range(str[i], '0', '9'))\n return fail(\"Leading 0s not permitted in numbers\");\n } else if (in_range(str[i], '1', '9')) {\n i++;\n while (in_range(str[i], '0', '9')) i++;\n } else {\n return fail(\"Invalid \" + esc(str[i]) + \" in number\");\n }\n\n if (str[i] != '.' && str[i] != 'e' && str[i] != 'E' &&\n (i - start_pos) <=\n static_cast(std::numeric_limits::digits10)) {\n return Json(std::atoi(str + start_pos));\n }\n\n // Decimal part\n if (str[i] == '.') {\n i++;\n if (!in_range(str[i], '0', '9'))\n return fail(\"At least one digit required in fractional part\");\n\n while (in_range(str[i], '0', '9')) i++;\n }\n\n // Exponent part\n if (str[i] == 'e' || str[i] == 'E') {\n i++;\n\n if (str[i] == '+' || str[i] == '-') i++;\n\n if (!in_range(str[i], '0', '9'))\n return fail(\"At least one digit required in exponent\");\n\n while (in_range(str[i], '0', '9')) i++;\n }\n\n return Json(std::strtod(str + start_pos, nullptr));\n }\n\n /* expect(str, res)\n *\n * Expect that 'str' starts at the character that was just read. If it does,\n * advance the input and return res. If not, flag an error.\n */\n Json expect(const string &expected, Json res) {\n CHECK_NE(i, 0)\n i--;\n auto substr = string(str + i, expected.length());\n if (substr == expected) {\n i += expected.length();\n return res;\n } else {\n return fail(\"Parse error: expected \" + expected + \", got \" + substr);\n }\n }\n\n /* parse_json()\n *\n * Parse a JSON object.\n */\n Json parse_json(int depth) {\n if (depth > max_depth) {\n return fail(\"Exceeded maximum nesting depth\");\n }\n\n char ch = get_next_token();\n if (failed) return Json();\n\n if (ch == '-' || (ch >= '0' && ch <= '9')) {\n i--;\n return parse_number();\n }\n\n if (ch == 't') return expect(\"true\", Json(true));\n\n if (ch == 'f') return expect(\"false\", Json(false));\n\n if (ch == 'n') return expect(\"null\", Json());\n\n if (ch == '\"') return Json(parse_string());\n\n if (ch == '{') {\n map data;\n ch = get_next_token();\n if (ch == '}') return Json(data);\n\n while (1) {\n if (ch != '\"') return fail(\"Expected '\\\"' in object, got \" + esc(ch));\n\n string key = parse_string();\n if (failed) return Json();\n\n ch = get_next_token();\n if (ch != ':') return fail(\"Expected ':' in object, got \" + esc(ch));\n\n data[std::move(key)] = parse_json(depth + 1);\n if (failed) return Json();\n\n ch = get_next_token();\n if (ch == '}') break;\n if (ch != ',') return fail(\"Expected ',' in object, got \" + esc(ch));\n\n ch = get_next_token();\n }\n return Json(data);\n }\n\n if (ch == '[') {\n vector data;\n ch = get_next_token();\n if (ch == ']') return Json(data);\n\n while (1) {\n i--;\n data.push_back(parse_json(depth + 1));\n if (failed) return Json();\n\n ch = get_next_token();\n if (ch == ']') break;\n if (ch != ',') return fail(\"Expected ',' in list, got \" + esc(ch));\n\n ch = get_next_token();\n (void)ch;\n }\n return Json(data);\n }\n\n return fail(\"Expected value, got \" + esc(ch));\n }\n};\n} // namespace\n\nJson Json::parse(const string &in, string *err, JsonParse strategy) {\n JsonParser parser{in.c_str(), in.size(), 0, err, false, strategy};\n Json result = parser.parse_json(0);\n\n // Check for any trailing garbage\n parser.consume_garbage();\n if (parser.failed) return Json();\n if (parser.i != in.size())\n return parser.fail(\"Unexpected trailing \" + esc(in[parser.i]));\n\n return result;\n}\n\n// Documented in json11.hpp\nvector Json::parse_multi(const string &in,\n std::string::size_type *parser_stop_pos,\n string *err, JsonParse strategy) {\n JsonParser parser{in.c_str(), in.size(), 0, err, false, strategy};\n *parser_stop_pos = 0;\n vector json_vec;\n while (parser.i != in.size() && !parser.failed) {\n json_vec.push_back(parser.parse_json(0));\n if (parser.failed) break;\n\n // Check for another object\n parser.consume_garbage();\n if (parser.failed) break;\n *parser_stop_pos = parser.i;\n }\n return json_vec;\n}\n\n/* * * * * * * * * * * * * * * * * * * *\n * Shape-checking\n */\n\nbool Json::has_shape(const shape &types, string *err) const {\n if (!is_object()) {\n *err = \"Expected JSON object, got \" + dump();\n return false;\n }\n\n for (auto &item : types) {\n if ((*this)[item.first].type() != item.second) {\n *err = \"Bad type for \" + item.first + \" in \" + dump();\n return false;\n }\n }\n\n return true;\n}\n\n} // namespace json11_internal_lightgbm\n" }, { "path": "src/io/metadata.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nMetadata::Metadata() {\n num_weights_ = 0;\n num_init_score_ = 0;\n num_data_ = 0;\n num_queries_ = 0;\n num_positions_ = 0;\n weight_load_from_file_ = false;\n position_load_from_file_ = false;\n query_load_from_file_ = false;\n init_score_load_from_file_ = false;\n #ifdef USE_CUDA\n cuda_metadata_ = nullptr;\n #endif // USE_CUDA\n}\n\nvoid Metadata::Init(const char* data_filename) {\n data_filename_ = data_filename;\n // for lambdarank, it needs query data for partition data in distributed learning\n LoadQueryBoundaries();\n LoadWeights();\n LoadPositions();\n CalculateQueryWeights();\n LoadInitialScore(data_filename_);\n}\n\nMetadata::~Metadata() {\n}\n\nvoid Metadata::Init(data_size_t num_data, int weight_idx, int query_idx) {\n num_data_ = num_data;\n label_ = std::vector(num_data_);\n if (weight_idx >= 0) {\n if (!weights_.empty()) {\n Log::Info(\"Using weights in data file, ignoring the additional weights file\");\n weights_.clear();\n }\n weights_ = std::vector(num_data_, 0.0f);\n num_weights_ = num_data_;\n weight_load_from_file_ = false;\n }\n if (query_idx >= 0) {\n if (!query_boundaries_.empty()) {\n Log::Info(\"Using query id in data file, ignoring the additional query file\");\n query_boundaries_.clear();\n }\n if (!query_weights_.empty()) {\n query_weights_.clear();\n }\n queries_ = std::vector(num_data_, 0);\n query_load_from_file_ = false;\n }\n}\n\nvoid Metadata::InitByReference(data_size_t num_data, const Metadata* reference) {\n int has_weights = reference->num_weights_ > 0;\n int has_init_scores = reference->num_init_score_ > 0;\n int has_queries = reference->num_queries_ > 0;\n int nclasses = reference->num_init_score_classes();\n Init(num_data, has_weights, has_init_scores, has_queries, nclasses);\n}\n\nvoid Metadata::Init(data_size_t num_data, int32_t has_weights, int32_t has_init_scores, int32_t has_queries, int32_t nclasses) {\n num_data_ = num_data;\n label_ = std::vector(num_data_);\n if (has_weights) {\n if (!weights_.empty()) {\n Log::Fatal(\"Calling Init() on Metadata weights that have already been initialized\");\n }\n weights_.resize(num_data_, 0.0f);\n num_weights_ = num_data_;\n weight_load_from_file_ = false;\n }\n if (has_init_scores) {\n if (!init_score_.empty()) {\n Log::Fatal(\"Calling Init() on Metadata initial scores that have already been initialized\");\n }\n num_init_score_ = static_cast(num_data) * nclasses;\n init_score_.resize(num_init_score_, 0);\n }\n if (has_queries) {\n if (!query_weights_.empty()) {\n Log::Fatal(\"Calling Init() on Metadata queries that have already been initialized\");\n }\n queries_.resize(num_data_, 0);\n query_load_from_file_ = false;\n }\n}\n\nvoid Metadata::Init(const Metadata& fullset, const data_size_t* used_indices, data_size_t num_used_indices) {\n num_data_ = num_used_indices;\n\n label_.resize(num_used_indices);\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_used_indices >= 1024)\n for (data_size_t i = 0; i < num_used_indices; ++i) {\n label_[i] = fullset.label_[used_indices[i]];\n }\n\n if (!fullset.weights_.empty()) {\n weights_ = std::vector(num_used_indices);\n num_weights_ = num_used_indices;\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_used_indices >= 1024)\n for (data_size_t i = 0; i < num_used_indices; ++i) {\n weights_[i] = fullset.weights_[used_indices[i]];\n }\n } else {\n num_weights_ = 0;\n }\n\n if (!fullset.init_score_.empty()) {\n int num_class = static_cast(fullset.num_init_score_ / fullset.num_data_);\n init_score_ = std::vector(static_cast(num_used_indices) * num_class);\n num_init_score_ = static_cast(num_used_indices) * num_class;\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int k = 0; k < num_class; ++k) {\n const size_t offset_dest = static_cast(k) * num_data_;\n const size_t offset_src = static_cast(k) * fullset.num_data_;\n for (data_size_t i = 0; i < num_used_indices; ++i) {\n init_score_[offset_dest + i] = fullset.init_score_[offset_src + used_indices[i]];\n }\n }\n } else {\n num_init_score_ = 0;\n }\n\n if (!fullset.query_boundaries_.empty()) {\n std::vector used_query;\n data_size_t data_idx = 0;\n for (data_size_t qid = 0; qid < num_queries_ && data_idx < num_used_indices; ++qid) {\n data_size_t start = fullset.query_boundaries_[qid];\n data_size_t end = fullset.query_boundaries_[qid + 1];\n data_size_t len = end - start;\n if (used_indices[data_idx] > start) {\n continue;\n } else if (used_indices[data_idx] == start) {\n if (num_used_indices >= data_idx + len && used_indices[data_idx + len - 1] == end - 1) {\n used_query.push_back(qid);\n data_idx += len;\n } else {\n Log::Fatal(\"Data partition error, data didn't match queries\");\n }\n } else {\n Log::Fatal(\"Data partition error, data didn't match queries\");\n }\n }\n query_boundaries_ = std::vector(used_query.size() + 1);\n num_queries_ = static_cast(used_query.size());\n query_boundaries_[0] = 0;\n for (data_size_t i = 0; i < num_queries_; ++i) {\n data_size_t qid = used_query[i];\n data_size_t len = fullset.query_boundaries_[qid + 1] - fullset.query_boundaries_[qid];\n query_boundaries_[i + 1] = query_boundaries_[i] + len;\n }\n } else {\n num_queries_ = 0;\n }\n}\n\nvoid Metadata::PartitionLabel(const std::vector& used_indices) {\n if (used_indices.empty()) {\n return;\n }\n auto old_label = label_;\n num_data_ = static_cast(used_indices.size());\n label_ = std::vector(num_data_);\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data_ >= 1024)\n for (data_size_t i = 0; i < num_data_; ++i) {\n label_[i] = old_label[used_indices[i]];\n }\n old_label.clear();\n}\n\nvoid Metadata::CalculateQueryBoundaries() {\n if (!queries_.empty()) {\n // need convert query_id to boundaries\n std::vector tmp_buffer;\n data_size_t last_qid = -1;\n data_size_t cur_cnt = 0;\n for (data_size_t i = 0; i < num_data_; ++i) {\n if (last_qid != queries_[i]) {\n if (cur_cnt > 0) {\n tmp_buffer.push_back(cur_cnt);\n }\n cur_cnt = 0;\n last_qid = queries_[i];\n }\n ++cur_cnt;\n }\n tmp_buffer.push_back(cur_cnt);\n query_boundaries_ = std::vector(tmp_buffer.size() + 1);\n num_queries_ = static_cast(tmp_buffer.size());\n query_boundaries_[0] = 0;\n for (size_t i = 0; i < tmp_buffer.size(); ++i) {\n query_boundaries_[i + 1] = query_boundaries_[i] + tmp_buffer[i];\n }\n CalculateQueryWeights();\n queries_.clear();\n }\n}\n\nvoid Metadata::CheckOrPartition(data_size_t num_all_data, const std::vector& used_data_indices) {\n if (used_data_indices.empty()) {\n CalculateQueryBoundaries();\n // check weights\n if (!weights_.empty() && num_weights_ != num_data_) {\n weights_.clear();\n num_weights_ = 0;\n Log::Fatal(\"Weights size doesn't match data size\");\n }\n\n // check positions\n if (!positions_.empty() && num_positions_ != num_data_) {\n Log::Fatal(\"Positions size (%i) doesn't match data size (%i)\", num_positions_, num_data_);\n positions_.clear();\n num_positions_ = 0;\n }\n\n // check query boundaries\n if (!query_boundaries_.empty() && query_boundaries_[num_queries_] != num_data_) {\n query_boundaries_.clear();\n num_queries_ = 0;\n Log::Fatal(\"Query size doesn't match data size\");\n }\n\n // contain initial score file\n if (!init_score_.empty() && (num_init_score_ % num_data_) != 0) {\n init_score_.clear();\n num_init_score_ = 0;\n Log::Fatal(\"Initial score size doesn't match data size\");\n }\n } else {\n if (!queries_.empty()) {\n Log::Fatal(\"Cannot used query_id for distributed training\");\n }\n data_size_t num_used_data = static_cast(used_data_indices.size());\n // check weights\n if (weight_load_from_file_) {\n if (weights_.size() > 0 && num_weights_ != num_all_data) {\n weights_.clear();\n num_weights_ = 0;\n Log::Fatal(\"Weights size doesn't match data size\");\n }\n // get local weights\n if (!weights_.empty()) {\n auto old_weights = weights_;\n num_weights_ = num_data_;\n weights_ = std::vector(num_data_);\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512)\n for (int i = 0; i < static_cast(used_data_indices.size()); ++i) {\n weights_[i] = old_weights[used_data_indices[i]];\n }\n old_weights.clear();\n }\n }\n // check positions\n if (position_load_from_file_) {\n if (positions_.size() > 0 && num_positions_ != num_all_data) {\n positions_.clear();\n num_positions_ = 0;\n Log::Fatal(\"Positions size (%i) doesn't match data size (%i)\", num_positions_, num_data_);\n }\n // get local positions\n if (!positions_.empty()) {\n auto old_positions = positions_;\n num_positions_ = num_data_;\n positions_ = std::vector(num_data_);\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512)\n for (int i = 0; i < static_cast(used_data_indices.size()); ++i) {\n positions_[i] = old_positions[used_data_indices[i]];\n }\n old_positions.clear();\n }\n }\n if (query_load_from_file_) {\n // check query boundaries\n if (!query_boundaries_.empty() && query_boundaries_[num_queries_] != num_all_data) {\n query_boundaries_.clear();\n num_queries_ = 0;\n Log::Fatal(\"Query size doesn't match data size\");\n }\n // get local query boundaries\n if (!query_boundaries_.empty()) {\n std::vector used_query;\n data_size_t data_idx = 0;\n for (data_size_t qid = 0; qid < num_queries_ && data_idx < num_used_data; ++qid) {\n data_size_t start = query_boundaries_[qid];\n data_size_t end = query_boundaries_[qid + 1];\n data_size_t len = end - start;\n if (used_data_indices[data_idx] > start) {\n continue;\n } else if (used_data_indices[data_idx] == start) {\n if (num_used_data >= data_idx + len && used_data_indices[data_idx + len - 1] == end - 1) {\n used_query.push_back(qid);\n data_idx += len;\n } else {\n Log::Fatal(\"Data partition error, data didn't match queries\");\n }\n } else {\n Log::Fatal(\"Data partition error, data didn't match queries\");\n }\n }\n auto old_query_boundaries = query_boundaries_;\n query_boundaries_ = std::vector(used_query.size() + 1);\n num_queries_ = static_cast(used_query.size());\n query_boundaries_[0] = 0;\n for (data_size_t i = 0; i < num_queries_; ++i) {\n data_size_t qid = used_query[i];\n data_size_t len = old_query_boundaries[qid + 1] - old_query_boundaries[qid];\n query_boundaries_[i + 1] = query_boundaries_[i] + len;\n }\n old_query_boundaries.clear();\n }\n }\n if (init_score_load_from_file_) {\n // contain initial score file\n if (!init_score_.empty() && (num_init_score_ % num_all_data) != 0) {\n init_score_.clear();\n num_init_score_ = 0;\n Log::Fatal(\"Initial score size doesn't match data size\");\n }\n\n // get local initial scores\n if (!init_score_.empty()) {\n auto old_scores = init_score_;\n int num_class = static_cast(num_init_score_ / num_all_data);\n num_init_score_ = static_cast(num_data_) * num_class;\n init_score_ = std::vector(num_init_score_);\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (int k = 0; k < num_class; ++k) {\n const size_t offset_dest = static_cast(k) * num_data_;\n const size_t offset_src = static_cast(k) * num_all_data;\n for (size_t i = 0; i < used_data_indices.size(); ++i) {\n init_score_[offset_dest + i] = old_scores[offset_src + used_data_indices[i]];\n }\n }\n old_scores.clear();\n }\n }\n // re-calculate query weight\n CalculateQueryWeights();\n }\n if (num_queries_ > 0) {\n Log::Debug(\"Number of queries in %s: %i. Average number of rows per query: %f.\",\n data_filename_.c_str(), static_cast(num_queries_), static_cast(num_data_) / num_queries_);\n }\n}\n\ntemplate \nvoid Metadata::SetInitScoresFromIterator(It first, It last) {\n std::lock_guard lock(mutex_);\n // Clear init scores on empty input\n if (last - first == 0) {\n init_score_.clear();\n num_init_score_ = 0;\n return;\n }\n if (((last - first) % num_data_) != 0) {\n Log::Fatal(\"Initial score size doesn't match data size\");\n }\n if (init_score_.empty()) {\n init_score_.resize(last - first);\n }\n num_init_score_ = last - first;\n\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_init_score_ >= 1024)\n for (int64_t i = 0; i < num_init_score_; ++i) {\n init_score_[i] = Common::AvoidInf(first[i]);\n }\n init_score_load_from_file_ = false;\n\n #ifdef USE_CUDA\n if (cuda_metadata_ != nullptr) {\n cuda_metadata_->SetInitScore(init_score_.data(), init_score_.size());\n }\n #endif // USE_CUDA\n}\n\nvoid Metadata::SetInitScore(const double* init_score, data_size_t len) {\n SetInitScoresFromIterator(init_score, init_score + len);\n}\n\nvoid Metadata::SetInitScore(const ArrowChunkedArray& array) {\n SetInitScoresFromIterator(array.begin(), array.end());\n}\n\nvoid Metadata::InsertInitScores(const double* init_scores, data_size_t start_index, data_size_t len, data_size_t source_size) {\n if (num_init_score_ <= 0) {\n Log::Fatal(\"Inserting initial score data into dataset with no initial scores\");\n }\n if (start_index + len > num_data_) {\n // Note that len here is row count, not num_init_score, so we compare against num_data\n Log::Fatal(\"Inserted initial score data is too large for dataset\");\n }\n if (init_score_.empty()) {\n init_score_.resize(num_init_score_);\n }\n\n int nclasses = num_init_score_classes();\n\n for (int32_t col = 0; col < nclasses; ++col) {\n int32_t dest_offset = num_data_ * col + start_index;\n // We need to use source_size here, because len might not equal size (due to a partially loaded dataset)\n int32_t source_offset = source_size * col;\n memcpy(init_score_.data() + dest_offset, init_scores + source_offset, sizeof(double) * len);\n }\n init_score_load_from_file_ = false;\n // CUDA is handled after all insertions are complete\n}\n\ntemplate \nvoid Metadata::SetLabelsFromIterator(It first, It last) {\n std::lock_guard lock(mutex_);\n if (num_data_ != last - first) {\n Log::Fatal(\"Length of labels differs from the length of #data\");\n }\n if (label_.empty()) {\n label_.resize(num_data_);\n }\n\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data_ >= 1024)\n for (data_size_t i = 0; i < num_data_; ++i) {\n label_[i] = Common::AvoidInf(first[i]);\n }\n\n #ifdef USE_CUDA\n if (cuda_metadata_ != nullptr) {\n cuda_metadata_->SetLabel(label_.data(), label_.size());\n }\n #endif // USE_CUDA\n}\n\nvoid Metadata::SetLabel(const label_t* label, data_size_t len) {\n if (label == nullptr) {\n Log::Fatal(\"label cannot be nullptr\");\n }\n SetLabelsFromIterator(label, label + len);\n}\n\nvoid Metadata::SetLabel(const ArrowChunkedArray& array) {\n SetLabelsFromIterator(array.begin(), array.end());\n}\n\nvoid Metadata::InsertLabels(const label_t* labels, data_size_t start_index, data_size_t len) {\n if (labels == nullptr) {\n Log::Fatal(\"label cannot be nullptr\");\n }\n if (start_index + len > num_data_) {\n Log::Fatal(\"Inserted label data is too large for dataset\");\n }\n if (label_.empty()) {\n label_.resize(num_data_);\n }\n\n memcpy(label_.data() + start_index, labels, sizeof(label_t) * len);\n\n // CUDA is handled after all insertions are complete\n}\n\ntemplate \nvoid Metadata::SetWeightsFromIterator(It first, It last) {\n std::lock_guard lock(mutex_);\n // Clear weights on empty input\n if (last - first == 0) {\n weights_.clear();\n num_weights_ = 0;\n return;\n }\n if (num_data_ != last - first) {\n Log::Fatal(\"Length of weights differs from the length of #data\");\n }\n if (weights_.empty()) {\n weights_.resize(num_data_);\n }\n num_weights_ = num_data_;\n\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_weights_ >= 1024)\n for (data_size_t i = 0; i < num_weights_; ++i) {\n weights_[i] = Common::AvoidInf(first[i]);\n }\n CalculateQueryWeights();\n weight_load_from_file_ = false;\n\n #ifdef USE_CUDA\n if (cuda_metadata_ != nullptr) {\n cuda_metadata_->SetWeights(weights_.data(), weights_.size());\n }\n #endif // USE_CUDA\n}\n\nvoid Metadata::SetWeights(const label_t* weights, data_size_t len) {\n SetWeightsFromIterator(weights, weights + len);\n}\n\nvoid Metadata::SetWeights(const ArrowChunkedArray& array) {\n SetWeightsFromIterator(array.begin(), array.end());\n}\n\nvoid Metadata::InsertWeights(const label_t* weights, data_size_t start_index, data_size_t len) {\n if (!weights) {\n Log::Fatal(\"Passed null weights\");\n }\n if (num_weights_ <= 0) {\n Log::Fatal(\"Inserting weight data into dataset with no weights\");\n }\n if (start_index + len > num_weights_) {\n Log::Fatal(\"Inserted weight data is too large for dataset\");\n }\n if (weights_.empty()) {\n weights_.resize(num_weights_);\n }\n\n memcpy(weights_.data() + start_index, weights, sizeof(label_t) * len);\n\n weight_load_from_file_ = false;\n // CUDA is handled after all insertions are complete\n}\n\ntemplate \nvoid Metadata::SetQueriesFromIterator(It first, It last) {\n std::lock_guard lock(mutex_);\n // Clear query boundaries on empty input\n if (last - first == 0) {\n query_boundaries_.clear();\n num_queries_ = 0;\n return;\n }\n\n data_size_t sum = 0;\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum)\n for (data_size_t i = 0; i < static_cast(last - first); ++i) {\n sum += first[i];\n }\n if (num_data_ != sum) {\n Log::Fatal(\"Sum of query counts (%i) differs from the length of #data (%i)\", num_data_, sum);\n }\n num_queries_ = last - first;\n\n query_boundaries_.resize(num_queries_ + 1);\n query_boundaries_[0] = 0;\n for (data_size_t i = 0; i < num_queries_; ++i) {\n query_boundaries_[i + 1] = query_boundaries_[i] + first[i];\n }\n CalculateQueryWeights();\n query_load_from_file_ = false;\n\n #ifdef USE_CUDA\n if (cuda_metadata_ != nullptr) {\n if (query_weights_.size() > 0) {\n CHECK_EQ(query_weights_.size(), static_cast(num_queries_));\n cuda_metadata_->SetQuery(query_boundaries_.data(), query_weights_.data(), num_queries_);\n } else {\n cuda_metadata_->SetQuery(query_boundaries_.data(), nullptr, num_queries_);\n }\n }\n #endif // USE_CUDA\n}\n\nvoid Metadata::SetQuery(const data_size_t* query, data_size_t len) {\n SetQueriesFromIterator(query, query + len);\n}\n\nvoid Metadata::SetQuery(const ArrowChunkedArray& array) {\n SetQueriesFromIterator(array.begin(), array.end());\n}\n\nvoid Metadata::SetPosition(const data_size_t* positions, data_size_t len) {\n std::lock_guard lock(mutex_);\n // save to nullptr\n if (positions == nullptr || len == 0) {\n positions_.clear();\n num_positions_ = 0;\n return;\n }\n #ifdef USE_CUDA\n Log::Fatal(\"Positions in learning to rank is not supported in CUDA version yet.\");\n #endif // USE_CUDA\n if (num_data_ != len) {\n Log::Fatal(\"Positions size (%i) doesn't match data size (%i)\", len, num_data_);\n }\n if (positions_.empty()) {\n positions_.resize(num_data_);\n } else {\n Log::Warning(\"Overwriting positions in dataset.\");\n }\n num_positions_ = num_data_;\n\n position_load_from_file_ = false;\n\n position_ids_.clear();\n std::unordered_map map_id2pos;\n for (data_size_t i = 0; i < num_positions_; ++i) {\n if (map_id2pos.count(positions[i]) == 0) {\n int pos = static_cast(map_id2pos.size());\n map_id2pos[positions[i]] = pos;\n position_ids_.push_back(std::to_string(positions[i]));\n }\n }\n\n Log::Debug(\"number of unique positions found = %ld\", position_ids_.size());\n\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_positions_ >= 1024)\n for (data_size_t i = 0; i < num_positions_; ++i) {\n positions_[i] = map_id2pos.at(positions[i]);\n }\n}\n\nvoid Metadata::InsertQueries(const data_size_t* queries, data_size_t start_index, data_size_t len) {\n if (!queries) {\n Log::Fatal(\"Passed null queries\");\n }\n if (queries_.size() <= 0) {\n Log::Fatal(\"Inserting query data into dataset with no queries\");\n }\n if (static_cast(start_index + len) > queries_.size()) {\n Log::Fatal(\"Inserted query data is too large for dataset\");\n }\n\n memcpy(queries_.data() + start_index, queries, sizeof(data_size_t) * len);\n\n query_load_from_file_ = false;\n // CUDA is handled after all insertions are complete\n}\n\nvoid Metadata::LoadWeights() {\n num_weights_ = 0;\n std::string weight_filename(data_filename_);\n // default weight file name\n weight_filename.append(\".weight\");\n TextReader reader(weight_filename.c_str(), false);\n reader.ReadAllLines();\n if (reader.Lines().empty()) {\n return;\n }\n Log::Info(\"Loading weights...\");\n num_weights_ = static_cast(reader.Lines().size());\n weights_ = std::vector(num_weights_);\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (data_size_t i = 0; i < num_weights_; ++i) {\n double tmp_weight = 0.0f;\n Common::Atof(reader.Lines()[i].c_str(), &tmp_weight);\n weights_[i] = Common::AvoidInf(static_cast(tmp_weight));\n }\n weight_load_from_file_ = true;\n}\n\nvoid Metadata::LoadPositions() {\n num_positions_ = 0;\n std::string position_filename(data_filename_);\n // default position file name\n position_filename.append(\".position\");\n TextReader reader(position_filename.c_str(), false);\n reader.ReadAllLines();\n if (reader.Lines().empty()) {\n return;\n }\n Log::Info(\"Loading positions from %s ...\", position_filename.c_str());\n num_positions_ = static_cast(reader.Lines().size());\n positions_ = std::vector(num_positions_);\n position_ids_ = std::vector();\n std::unordered_map map_id2pos;\n for (data_size_t i = 0; i < num_positions_; ++i) {\n std::string& line = reader.Lines()[i];\n if (map_id2pos.count(line) == 0) {\n map_id2pos[line] = static_cast(position_ids_.size());\n position_ids_.push_back(line);\n }\n positions_[i] = map_id2pos.at(line);\n }\n position_load_from_file_ = true;\n}\n\nvoid Metadata::LoadInitialScore(const std::string& data_filename) {\n num_init_score_ = 0;\n std::string init_score_filename(data_filename);\n init_score_filename = std::string(data_filename);\n // default init_score file name\n init_score_filename.append(\".init\");\n TextReader reader(init_score_filename.c_str(), false);\n reader.ReadAllLines();\n if (reader.Lines().empty()) {\n return;\n }\n Log::Info(\"Loading initial scores...\");\n\n // use first line to count number class\n int num_class = static_cast(Common::Split(reader.Lines()[0].c_str(), '\\t').size());\n data_size_t num_line = static_cast(reader.Lines().size());\n num_init_score_ = static_cast(num_line) * num_class;\n\n init_score_ = std::vector(num_init_score_);\n if (num_class == 1) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (data_size_t i = 0; i < num_line; ++i) {\n double tmp = 0.0f;\n Common::Atof(reader.Lines()[i].c_str(), &tmp);\n init_score_[i] = Common::AvoidInf(static_cast(tmp));\n }\n } else {\n std::vector oneline_init_score;\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (data_size_t i = 0; i < num_line; ++i) {\n double tmp = 0.0f;\n oneline_init_score = Common::Split(reader.Lines()[i].c_str(), '\\t');\n if (static_cast(oneline_init_score.size()) != num_class) {\n Log::Fatal(\"Invalid initial score file. Redundant or insufficient columns\");\n }\n for (int k = 0; k < num_class; ++k) {\n Common::Atof(oneline_init_score[k].c_str(), &tmp);\n init_score_[static_cast(k) * num_line + i] = Common::AvoidInf(static_cast(tmp));\n }\n }\n }\n init_score_load_from_file_ = true;\n}\n\nvoid Metadata::LoadQueryBoundaries() {\n num_queries_ = 0;\n std::string query_filename(data_filename_);\n // default query file name\n query_filename.append(\".query\");\n TextReader reader(query_filename.c_str(), false);\n reader.ReadAllLines();\n if (reader.Lines().empty()) {\n return;\n }\n Log::Info(\"Calculating query boundaries...\");\n query_boundaries_ = std::vector(reader.Lines().size() + 1);\n num_queries_ = static_cast(reader.Lines().size());\n query_boundaries_[0] = 0;\n for (size_t i = 0; i < reader.Lines().size(); ++i) {\n int tmp_cnt;\n Common::Atoi(reader.Lines()[i].c_str(), &tmp_cnt);\n query_boundaries_[i + 1] = query_boundaries_[i] + static_cast(tmp_cnt);\n }\n query_load_from_file_ = true;\n}\n\nvoid Metadata::CalculateQueryWeights() {\n if (weights_.size() == 0 || query_boundaries_.size() == 0) {\n return;\n }\n query_weights_.clear();\n Log::Info(\"Calculating query weights...\");\n query_weights_ = std::vector(num_queries_);\n for (data_size_t i = 0; i < num_queries_; ++i) {\n query_weights_[i] = 0.0f;\n for (data_size_t j = query_boundaries_[i]; j < query_boundaries_[i + 1]; ++j) {\n query_weights_[i] += weights_[j];\n }\n query_weights_[i] /= (query_boundaries_[i + 1] - query_boundaries_[i]);\n }\n}\n\nvoid Metadata::InsertAt(data_size_t start_index,\n data_size_t count,\n const float* labels,\n const float* weights,\n const double* init_scores,\n const int32_t* queries) {\n if (num_data_ < count + start_index) {\n Log::Fatal(\"Length of metadata is too long to append #data\");\n }\n InsertLabels(labels, start_index, count);\n if (weights) {\n InsertWeights(weights, start_index, count);\n }\n if (init_scores) {\n InsertInitScores(init_scores, start_index, count, count);\n }\n if (queries) {\n InsertQueries(queries, start_index, count);\n }\n}\n\nvoid Metadata::FinishLoad() {\n CalculateQueryBoundaries();\n}\n\n#ifdef USE_CUDA\nvoid Metadata::CreateCUDAMetadata(const int gpu_device_id) {\n cuda_metadata_.reset(new CUDAMetadata(gpu_device_id));\n cuda_metadata_->Init(label_, weights_, query_boundaries_, query_weights_, init_score_);\n}\n#endif // USE_CUDA\n\nvoid Metadata::LoadFromMemory(const void* memory) {\n const char* mem_ptr = reinterpret_cast(memory);\n\n num_data_ = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(num_data_));\n num_weights_ = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(num_weights_));\n num_queries_ = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(num_queries_));\n\n if (!label_.empty()) {\n label_.clear();\n }\n label_ = std::vector(num_data_);\n std::memcpy(label_.data(), mem_ptr, sizeof(label_t) * num_data_);\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(label_t) * num_data_);\n\n if (num_weights_ > 0) {\n if (!weights_.empty()) {\n weights_.clear();\n }\n weights_ = std::vector(num_weights_);\n std::memcpy(weights_.data(), mem_ptr, sizeof(label_t) * num_weights_);\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(label_t) * num_weights_);\n weight_load_from_file_ = true;\n }\n if (num_queries_ > 0) {\n if (!query_boundaries_.empty()) {\n query_boundaries_.clear();\n }\n query_boundaries_ = std::vector(num_queries_ + 1);\n std::memcpy(query_boundaries_.data(), mem_ptr, sizeof(data_size_t) * (num_queries_ + 1));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(data_size_t) *\n (num_queries_ + 1));\n query_load_from_file_ = true;\n }\n CalculateQueryWeights();\n}\n\nvoid Metadata::SaveBinaryToFile(BinaryWriter* writer) const {\n writer->AlignedWrite(&num_data_, sizeof(num_data_));\n writer->AlignedWrite(&num_weights_, sizeof(num_weights_));\n writer->AlignedWrite(&num_queries_, sizeof(num_queries_));\n writer->AlignedWrite(label_.data(), sizeof(label_t) * num_data_);\n if (!weights_.empty()) {\n writer->AlignedWrite(weights_.data(), sizeof(label_t) * num_weights_);\n }\n if (!query_boundaries_.empty()) {\n writer->AlignedWrite(query_boundaries_.data(),\n sizeof(data_size_t) * (num_queries_ + 1));\n }\n if (num_init_score_ > 0) {\n Log::Warning(\"Please note that `init_score` is not saved in binary file.\\n\"\n \"If you need it, please set it again after loading Dataset.\");\n }\n}\n\nsize_t Metadata::SizesInByte() const {\n size_t size = VirtualFileWriter::AlignedSize(sizeof(num_data_)) +\n VirtualFileWriter::AlignedSize(sizeof(num_weights_)) +\n VirtualFileWriter::AlignedSize(sizeof(num_queries_));\n size += VirtualFileWriter::AlignedSize(sizeof(label_t) * num_data_);\n if (!weights_.empty()) {\n size += VirtualFileWriter::AlignedSize(sizeof(label_t) * num_weights_);\n }\n if (!query_boundaries_.empty()) {\n size += VirtualFileWriter::AlignedSize(sizeof(data_size_t) *\n (num_queries_ + 1));\n }\n return size;\n}\n\n\n} // namespace LightGBM\n" }, { "path": "src/io/multi_val_dense_bin.hpp", "content": "/*!\n * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_IO_MULTI_VAL_DENSE_BIN_HPP_\n#define LIGHTGBM_SRC_IO_MULTI_VAL_DENSE_BIN_HPP_\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\ntemplate \nclass MultiValDenseBin : public MultiValBin {\n public:\n explicit MultiValDenseBin(data_size_t num_data, int num_bin, int num_feature,\n const std::vector& offsets)\n : num_data_(num_data), num_bin_(num_bin), num_feature_(num_feature),\n offsets_(offsets) {\n data_.resize(static_cast(num_data_) * num_feature_, static_cast(0));\n }\n\n ~MultiValDenseBin() {\n }\n\n data_size_t num_data() const override {\n return num_data_;\n }\n\n int num_bin() const override {\n return num_bin_;\n }\n\n double num_element_per_row() const override { return num_feature_; }\n\n const std::vector& offsets() const override { return offsets_; }\n\n void PushOneRow(int , data_size_t idx, const std::vector& values) override {\n auto start = RowPtr(idx);\n for (auto i = 0; i < num_feature_; ++i) {\n data_[start + i] = static_cast(values[i]);\n }\n }\n\n void FinishLoad() override {\n }\n\n bool IsSparse() override {\n return false;\n }\n\n template\n void ConstructHistogramInner(const data_size_t* data_indices, data_size_t start, data_size_t end,\n const score_t* gradients, const score_t* hessians, hist_t* out) const {\n data_size_t i = start;\n hist_t* grad = out;\n hist_t* hess = out + 1;\n\n if (USE_PREFETCH) {\n const data_size_t pf_offset = 32 / sizeof(VAL_T);\n const data_size_t pf_end = end - pf_offset;\n\n for (; i < pf_end; ++i) {\n const auto idx = USE_INDICES ? data_indices[i] : i;\n const auto pf_idx = USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset;\n if (!ORDERED) {\n PREFETCH_T0(gradients + pf_idx);\n PREFETCH_T0(hessians + pf_idx);\n }\n PREFETCH_T0(data_.data() + RowPtr(pf_idx));\n const auto j_start = RowPtr(idx);\n const VAL_T* data_ptr = data_.data() + j_start;\n const score_t gradient = ORDERED ? gradients[i] : gradients[idx];\n const score_t hessian = ORDERED ? hessians[i] : hessians[idx];\n for (int j = 0; j < num_feature_; ++j) {\n const uint32_t bin = static_cast(data_ptr[j]);\n const auto ti = (bin + offsets_[j]) << 1;\n grad[ti] += gradient;\n hess[ti] += hessian;\n }\n }\n }\n for (; i < end; ++i) {\n const auto idx = USE_INDICES ? data_indices[i] : i;\n const auto j_start = RowPtr(idx);\n const VAL_T* data_ptr = data_.data() + j_start;\n const score_t gradient = ORDERED ? gradients[i] : gradients[idx];\n const score_t hessian = ORDERED ? hessians[i] : hessians[idx];\n for (int j = 0; j < num_feature_; ++j) {\n const uint32_t bin = static_cast(data_ptr[j]);\n const auto ti = (bin + offsets_[j]) << 1;\n grad[ti] += gradient;\n hess[ti] += hessian;\n }\n }\n }\n\n void ConstructHistogram(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* gradients,\n const score_t* hessians, hist_t* out) const override {\n ConstructHistogramInner(data_indices, start, end,\n gradients, hessians, out);\n }\n\n void ConstructHistogram(data_size_t start, data_size_t end,\n const score_t* gradients, const score_t* hessians,\n hist_t* out) const override {\n ConstructHistogramInner(\n nullptr, start, end, gradients, hessians, out);\n }\n\n void ConstructHistogramOrdered(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* hessians,\n hist_t* out) const override {\n ConstructHistogramInner(data_indices, start, end,\n gradients, hessians, out);\n }\n\n template\n void ConstructHistogramIntInner(const data_size_t* data_indices, data_size_t start, data_size_t end,\n const score_t* gradients_and_hessians, hist_t* out) const {\n data_size_t i = start;\n const VAL_T* data_ptr_base = data_.data();\n const int16_t* gradients_and_hessians_ptr = reinterpret_cast(gradients_and_hessians);\n PACKED_HIST_T* out_ptr = reinterpret_cast(out);\n\n if (USE_PREFETCH) {\n const data_size_t pf_offset = 32 / sizeof(VAL_T);\n const data_size_t pf_end = end - pf_offset;\n\n for (; i < pf_end; ++i) {\n const auto idx = USE_INDICES ? data_indices[i] : i;\n const auto pf_idx = USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset;\n if (!ORDERED) {\n PREFETCH_T0(gradients_and_hessians_ptr + pf_idx);\n }\n PREFETCH_T0(data_ptr_base + RowPtr(pf_idx));\n const auto j_start = RowPtr(idx);\n const VAL_T* data_ptr = data_ptr_base + j_start;\n const int16_t gradient_16 = gradients_and_hessians_ptr[idx];\n const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 :\n ((static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) |\n static_cast(gradient_16 & 0xff));\n for (int j = 0; j < num_feature_; ++j) {\n const uint32_t bin = static_cast(data_ptr[j]);\n const auto ti = (bin + offsets_[j]);\n out_ptr[ti] += gradient_packed;\n }\n }\n }\n for (; i < end; ++i) {\n const auto idx = USE_INDICES ? data_indices[i] : i;\n const auto j_start = RowPtr(idx);\n const VAL_T* data_ptr = data_ptr_base + j_start;\n const int16_t gradient_16 = gradients_and_hessians_ptr[idx];\n const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 :\n ((static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) |\n static_cast(gradient_16 & 0xff));\n for (int j = 0; j < num_feature_; ++j) {\n const uint32_t bin = static_cast(data_ptr[j]);\n const auto ti = (bin + offsets_[j]);\n out_ptr[ti] += gradient_packed;\n }\n }\n }\n\n void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* gradients,\n const score_t* /*hessians*/, hist_t* out) const override {\n ConstructHistogramIntInner(data_indices, start, end,\n gradients, out);\n }\n\n void ConstructHistogramInt32(data_size_t start, data_size_t end,\n const score_t* gradients, const score_t* /*hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n nullptr, start, end, gradients, out);\n }\n\n void ConstructHistogramOrderedInt32(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* /*hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(data_indices, start, end,\n gradients, out);\n }\n\n void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* gradients,\n const score_t* /*hessians*/, hist_t* out) const override {\n ConstructHistogramIntInner(data_indices, start, end,\n gradients, out);\n }\n\n void ConstructHistogramInt16(data_size_t start, data_size_t end,\n const score_t* gradients, const score_t* /*hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n nullptr, start, end, gradients, out);\n }\n\n void ConstructHistogramOrderedInt16(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* /*hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(data_indices, start, end,\n gradients, out);\n }\n\n void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* gradients,\n const score_t* /*hessians*/, hist_t* out) const override {\n ConstructHistogramIntInner(data_indices, start, end,\n gradients, out);\n }\n\n void ConstructHistogramInt8(data_size_t start, data_size_t end,\n const score_t* gradients, const score_t* /*hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n nullptr, start, end, gradients, out);\n }\n\n void ConstructHistogramOrderedInt8(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* /*hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(data_indices, start, end,\n gradients, out);\n }\n\n MultiValBin* CreateLike(data_size_t num_data, int num_bin, int num_feature, double,\n const std::vector& offsets) const override {\n return new MultiValDenseBin(num_data, num_bin, num_feature, offsets);\n }\n\n void ReSize(data_size_t num_data, int num_bin, int num_feature,\n double, const std::vector& offsets) override {\n num_data_ = num_data;\n num_bin_ = num_bin;\n num_feature_ = num_feature;\n offsets_ = offsets;\n size_t new_size = static_cast(num_feature_) * num_data_;\n if (data_.size() < new_size) {\n data_.resize(new_size, 0);\n }\n }\n\n template \n void CopyInner(const MultiValBin* full_bin, const data_size_t* used_indices,\n data_size_t num_used_indices,\n const std::vector& used_feature_index) {\n const auto other_bin =\n reinterpret_cast*>(full_bin);\n if (SUBROW) {\n CHECK_EQ(num_data_, num_used_indices);\n }\n int n_block = 1;\n data_size_t block_size = num_data_;\n Threading::BlockInfo(num_data_, 1024, &n_block,\n &block_size);\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1)\n for (int tid = 0; tid < n_block; ++tid) {\n data_size_t start = tid * block_size;\n data_size_t end = std::min(num_data_, start + block_size);\n for (data_size_t i = start; i < end; ++i) {\n const auto j_start = RowPtr(i);\n const auto other_j_start =\n SUBROW ? other_bin->RowPtr(used_indices[i]) : other_bin->RowPtr(i);\n for (int j = 0; j < num_feature_; ++j) {\n if (SUBCOL) {\n if (other_bin->data_[other_j_start + used_feature_index[j]] > 0) {\n data_[j_start + j] = static_cast(\n other_bin->data_[other_j_start + used_feature_index[j]]);\n } else {\n data_[j_start + j] = 0;\n }\n } else {\n data_[j_start + j] =\n static_cast(other_bin->data_[other_j_start + j]);\n }\n }\n }\n }\n }\n\n\n void CopySubrow(const MultiValBin* full_bin, const data_size_t* used_indices,\n data_size_t num_used_indices) override {\n CopyInner(full_bin, used_indices, num_used_indices,\n std::vector());\n }\n\n void CopySubcol(const MultiValBin* full_bin,\n const std::vector& used_feature_index,\n const std::vector&,\n const std::vector&,\n const std::vector&) override {\n CopyInner(full_bin, nullptr, num_data_, used_feature_index);\n }\n\n void CopySubrowAndSubcol(const MultiValBin* full_bin,\n const data_size_t* used_indices,\n data_size_t num_used_indices,\n const std::vector& used_feature_index,\n const std::vector&,\n const std::vector&,\n const std::vector&) override {\n CopyInner(full_bin, used_indices, num_used_indices,\n used_feature_index);\n }\n\n inline size_t RowPtr(data_size_t idx) const {\n return static_cast(idx) * num_feature_;\n }\n\n MultiValDenseBin* Clone() override;\n\n #ifdef USE_CUDA\n const void* GetRowWiseData(uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const override;\n #endif // USE_CUDA\n\n private:\n data_size_t num_data_;\n int num_bin_;\n int num_feature_;\n std::vector offsets_;\n std::vector> data_;\n\n MultiValDenseBin(const MultiValDenseBin& other)\n : num_data_(other.num_data_), num_bin_(other.num_bin_), num_feature_(other.num_feature_),\n offsets_(other.offsets_), data_(other.data_) {\n }\n};\n\ntemplate\nMultiValDenseBin* MultiValDenseBin::Clone() {\n return new MultiValDenseBin(*this);\n}\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_SRC_IO_MULTI_VAL_DENSE_BIN_HPP_\n" }, { "path": "src/io/multi_val_sparse_bin.hpp", "content": "/*!\n * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_IO_MULTI_VAL_SPARSE_BIN_HPP_\n#define LIGHTGBM_SRC_IO_MULTI_VAL_SPARSE_BIN_HPP_\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\ntemplate \nclass MultiValSparseBin : public MultiValBin {\n public:\n explicit MultiValSparseBin(data_size_t num_data, int num_bin,\n double estimate_element_per_row)\n : num_data_(num_data),\n num_bin_(num_bin),\n estimate_element_per_row_(estimate_element_per_row) {\n row_ptr_.resize(num_data_ + 1, 0);\n INDEX_T estimate_num_data = static_cast(estimate_element_per_row_ * 1.1 * num_data_);\n int num_threads = OMP_NUM_THREADS();\n if (num_threads > 1) {\n t_data_.resize(num_threads - 1);\n for (size_t i = 0; i < t_data_.size(); ++i) {\n t_data_[i].resize(estimate_num_data / num_threads);\n }\n }\n t_size_.resize(num_threads, 0);\n data_.resize(estimate_num_data / num_threads);\n }\n\n ~MultiValSparseBin() {}\n\n data_size_t num_data() const override { return num_data_; }\n\n int num_bin() const override { return num_bin_; }\n\n double num_element_per_row() const override {\n return estimate_element_per_row_;\n }\n\n const std::vector& offsets() const override { return offsets_; }\n\n void PushOneRow(int tid, data_size_t idx,\n const std::vector& values) override {\n const int pre_alloc_size = 50;\n row_ptr_[idx + 1] = static_cast(values.size());\n if (tid == 0) {\n if (t_size_[tid] + row_ptr_[idx + 1] >\n static_cast(data_.size())) {\n data_.resize(t_size_[tid] + row_ptr_[idx + 1] * pre_alloc_size);\n }\n for (auto val : values) {\n data_[t_size_[tid]++] = static_cast(val);\n }\n } else {\n if (t_size_[tid] + row_ptr_[idx + 1] >\n static_cast(t_data_[tid - 1].size())) {\n t_data_[tid - 1].resize(t_size_[tid] +\n row_ptr_[idx + 1] * pre_alloc_size);\n }\n for (auto val : values) {\n t_data_[tid - 1][t_size_[tid]++] = static_cast(val);\n }\n }\n }\n\n void MergeData(const INDEX_T* sizes) {\n Common::FunctionTimer fun_time(\"MultiValSparseBin::MergeData\", global_timer);\n for (data_size_t i = 0; i < num_data_; ++i) {\n row_ptr_[i + 1] += row_ptr_[i];\n }\n if (t_data_.size() > 0) {\n std::vector offsets(1 + t_data_.size());\n offsets[0] = sizes[0];\n for (size_t tid = 0; tid < t_data_.size() - 1; ++tid) {\n offsets[tid + 1] = offsets[tid] + sizes[tid + 1];\n }\n data_.resize(row_ptr_[num_data_]);\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1)\n for (int tid = 0; tid < static_cast(t_data_.size()); ++tid) {\n std::copy_n(t_data_[tid].data(), sizes[tid + 1],\n data_.data() + offsets[tid]);\n }\n } else {\n data_.resize(row_ptr_[num_data_]);\n }\n }\n\n void FinishLoad() override {\n MergeData(t_size_.data());\n t_size_.clear();\n row_ptr_.shrink_to_fit();\n data_.shrink_to_fit();\n t_data_.clear();\n t_data_.shrink_to_fit();\n // update estimate_element_per_row_ by all data\n estimate_element_per_row_ =\n static_cast(row_ptr_[num_data_]) / num_data_;\n }\n\n bool IsSparse() override { return true; }\n\n template \n void ConstructHistogramInner(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* hessians, hist_t* out) const {\n data_size_t i = start;\n hist_t* grad = out;\n hist_t* hess = out + 1;\n const VAL_T* data_ptr = data_.data();\n if (USE_PREFETCH) {\n const data_size_t pf_offset = 32 / sizeof(VAL_T);\n const data_size_t pf_end = end - pf_offset;\n\n for (; i < pf_end; ++i) {\n const auto idx = USE_INDICES ? data_indices[i] : i;\n const auto pf_idx =\n USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset;\n if (!ORDERED) {\n PREFETCH_T0(gradients + pf_idx);\n PREFETCH_T0(hessians + pf_idx);\n }\n PREFETCH_T0(row_ptr_.data() + pf_idx);\n PREFETCH_T0(data_ptr + row_ptr_[pf_idx]);\n const auto j_start = RowPtr(idx);\n const auto j_end = RowPtr(idx + 1);\n const score_t gradient = ORDERED ? gradients[i] : gradients[idx];\n const score_t hessian = ORDERED ? hessians[i] : hessians[idx];\n for (auto j = j_start; j < j_end; ++j) {\n const auto ti = static_cast(data_ptr[j]) << 1;\n grad[ti] += gradient;\n hess[ti] += hessian;\n }\n }\n }\n for (; i < end; ++i) {\n const auto idx = USE_INDICES ? data_indices[i] : i;\n const auto j_start = RowPtr(idx);\n const auto j_end = RowPtr(idx + 1);\n const score_t gradient = ORDERED ? gradients[i] : gradients[idx];\n const score_t hessian = ORDERED ? hessians[i] : hessians[idx];\n for (auto j = j_start; j < j_end; ++j) {\n const auto ti = static_cast(data_ptr[j]) << 1;\n grad[ti] += gradient;\n hess[ti] += hessian;\n }\n }\n }\n\n void ConstructHistogram(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* gradients,\n const score_t* hessians, hist_t* out) const override {\n ConstructHistogramInner(data_indices, start, end,\n gradients, hessians, out);\n }\n\n void ConstructHistogram(data_size_t start, data_size_t end,\n const score_t* gradients, const score_t* hessians,\n hist_t* out) const override {\n ConstructHistogramInner(\n nullptr, start, end, gradients, hessians, out);\n }\n\n void ConstructHistogramOrdered(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* hessians,\n hist_t* out) const override {\n ConstructHistogramInner(data_indices, start, end,\n gradients, hessians, out);\n }\n\n template \n void ConstructHistogramIntInner(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* gradients_and_hessians, hist_t* out) const {\n data_size_t i = start;\n PACKED_HIST_T* out_ptr = reinterpret_cast(out);\n const int16_t* gradients_and_hessians_ptr = reinterpret_cast(gradients_and_hessians);\n const VAL_T* data_ptr = data_.data();\n const INDEX_T* row_ptr_base = row_ptr_.data();\n if (USE_PREFETCH) {\n const data_size_t pf_offset = 32 / sizeof(VAL_T);\n const data_size_t pf_end = end - pf_offset;\n\n for (; i < pf_end; ++i) {\n const auto idx = USE_INDICES ? data_indices[i] : i;\n const auto pf_idx =\n USE_INDICES ? data_indices[i + pf_offset] : i + pf_offset;\n if (!ORDERED) {\n PREFETCH_T0(gradients_and_hessians_ptr + pf_idx);\n }\n PREFETCH_T0(row_ptr_base + pf_idx);\n PREFETCH_T0(data_ptr + row_ptr_[pf_idx]);\n const auto j_start = RowPtr(idx);\n const auto j_end = RowPtr(idx + 1);\n const int16_t gradient_16 = ORDERED ? gradients_and_hessians_ptr[i] : gradients_and_hessians_ptr[idx];\n const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 :\n ((static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) |\n static_cast(gradient_16 & 0xff));\n for (auto j = j_start; j < j_end; ++j) {\n const auto ti = static_cast(data_ptr[j]);\n out_ptr[ti] += gradient_packed;\n }\n }\n }\n for (; i < end; ++i) {\n const auto idx = USE_INDICES ? data_indices[i] : i;\n const auto j_start = RowPtr(idx);\n const auto j_end = RowPtr(idx + 1);\n const int16_t gradient_16 = ORDERED ? gradients_and_hessians_ptr[i] : gradients_and_hessians_ptr[idx];\n const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 :\n ((static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) |\n static_cast(gradient_16 & 0xff));\n for (auto j = j_start; j < j_end; ++j) {\n const auto ti = static_cast(data_ptr[j]);\n out_ptr[ti] += gradient_packed;\n }\n }\n }\n\n void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* gradients,\n const score_t* /*hessians*/, hist_t* out) const override {\n ConstructHistogramIntInner(data_indices, start, end,\n gradients, out);\n }\n\n void ConstructHistogramInt32(data_size_t start, data_size_t end,\n const score_t* gradients, const score_t* /*hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n nullptr, start, end, gradients, out);\n }\n\n void ConstructHistogramOrderedInt32(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* /*hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(data_indices, start, end,\n gradients, out);\n }\n\n void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* gradients,\n const score_t* /*hessians*/, hist_t* out) const override {\n ConstructHistogramIntInner(data_indices, start, end,\n gradients, out);\n }\n\n void ConstructHistogramInt16(data_size_t start, data_size_t end,\n const score_t* gradients, const score_t* /*hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n nullptr, start, end, gradients, out);\n }\n\n void ConstructHistogramOrderedInt16(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* /*hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(data_indices, start, end,\n gradients, out);\n }\n\n void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* gradients,\n const score_t* /*hessians*/, hist_t* out) const override {\n ConstructHistogramIntInner(data_indices, start, end,\n gradients, out);\n }\n\n void ConstructHistogramInt8(data_size_t start, data_size_t end,\n const score_t* gradients, const score_t* /*hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(\n nullptr, start, end, gradients, out);\n }\n\n void ConstructHistogramOrderedInt8(const data_size_t* data_indices,\n data_size_t start, data_size_t end,\n const score_t* gradients,\n const score_t* /*hessians*/,\n hist_t* out) const override {\n ConstructHistogramIntInner(data_indices, start, end,\n gradients, out);\n }\n\n MultiValBin* CreateLike(data_size_t num_data, int num_bin, int,\n double estimate_element_per_row,\n const std::vector& /*offsets*/) const override {\n return new MultiValSparseBin(num_data, num_bin,\n estimate_element_per_row);\n }\n\n void ReSize(data_size_t num_data, int num_bin, int,\n double estimate_element_per_row, const std::vector& /*offsets*/) override {\n num_data_ = num_data;\n num_bin_ = num_bin;\n estimate_element_per_row_ = estimate_element_per_row;\n INDEX_T estimate_num_data =\n static_cast(estimate_element_per_row_ * 1.1 * num_data_);\n size_t npart = 1 + t_data_.size();\n INDEX_T avg_num_data = static_cast(estimate_num_data / npart);\n if (static_cast(data_.size()) < avg_num_data) {\n data_.resize(avg_num_data, 0);\n }\n for (size_t i = 0; i < t_data_.size(); ++i) {\n if (static_cast(t_data_[i].size()) < avg_num_data) {\n t_data_[i].resize(avg_num_data, 0);\n }\n }\n if (num_data_ + 1 > static_cast(row_ptr_.size())) {\n row_ptr_.resize(num_data_ + 1);\n }\n }\n\n template \n void CopyInner(const MultiValBin* full_bin, const data_size_t* used_indices,\n data_size_t num_used_indices,\n const std::vector& lower,\n const std::vector& upper,\n const std::vector& delta) {\n const auto other =\n reinterpret_cast*>(full_bin);\n if (SUBROW) {\n CHECK_EQ(num_data_, num_used_indices);\n }\n int n_block = 1;\n data_size_t block_size = num_data_;\n Threading::BlockInfo(static_cast(t_data_.size() + 1),\n num_data_, 1024, &n_block, &block_size);\n std::vector sizes(t_data_.size() + 1, 0);\n const int pre_alloc_size = 50;\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 1)\n for (int tid = 0; tid < n_block; ++tid) {\n data_size_t start = tid * block_size;\n data_size_t end = std::min(num_data_, start + block_size);\n auto& buf = (tid == 0) ? data_ : t_data_[tid - 1];\n INDEX_T size = 0;\n for (data_size_t i = start; i < end; ++i) {\n const auto j_start =\n SUBROW ? other->RowPtr(used_indices[i]) : other->RowPtr(i);\n const auto j_end =\n SUBROW ? other->RowPtr(used_indices[i] + 1) : other->RowPtr(i + 1);\n if (size + (j_end - j_start) > static_cast(buf.size())) {\n buf.resize(size + (j_end - j_start) * pre_alloc_size);\n }\n int k = 0;\n const auto pre_size = size;\n for (auto j = j_start; j < j_end; ++j) {\n const auto val = other->data_[j];\n if (SUBCOL) {\n while (val >= upper[k]) {\n ++k;\n }\n if (val >= lower[k]) {\n buf[size++] = static_cast(val - delta[k]);\n }\n } else {\n buf[size++] = val;\n }\n }\n row_ptr_[i + 1] = size - pre_size;\n }\n sizes[tid] = size;\n }\n MergeData(sizes.data());\n }\n\n void CopySubrow(const MultiValBin* full_bin, const data_size_t* used_indices,\n data_size_t num_used_indices) override {\n CopyInner(full_bin, used_indices, num_used_indices,\n std::vector(), std::vector(),\n std::vector());\n }\n\n void CopySubcol(const MultiValBin* full_bin, const std::vector&,\n const std::vector& lower,\n const std::vector& upper,\n const std::vector& delta) override {\n CopyInner(full_bin, nullptr, num_data_, lower, upper, delta);\n }\n\n void CopySubrowAndSubcol(const MultiValBin* full_bin,\n const data_size_t* used_indices,\n data_size_t num_used_indices,\n const std::vector&,\n const std::vector& lower,\n const std::vector& upper,\n const std::vector& delta) override {\n CopyInner(full_bin, used_indices, num_used_indices, lower,\n upper, delta);\n }\n\n inline INDEX_T RowPtr(data_size_t idx) const { return row_ptr_[idx]; }\n\n MultiValSparseBin* Clone() override;\n\n\n #ifdef USE_CUDA\n const void* GetRowWiseData(uint8_t* bit_type,\n size_t* total_size,\n bool* is_sparse,\n const void** out_data_ptr,\n uint8_t* data_ptr_bit_type) const override;\n #endif // USE_CUDA\n\n private:\n data_size_t num_data_;\n int num_bin_;\n double estimate_element_per_row_;\n std::vector> data_;\n std::vector>\n row_ptr_;\n std::vector>>\n t_data_;\n std::vector t_size_;\n std::vector offsets_;\n\n MultiValSparseBin(const MultiValSparseBin& other)\n : num_data_(other.num_data_),\n num_bin_(other.num_bin_),\n estimate_element_per_row_(other.estimate_element_per_row_),\n data_(other.data_),\n row_ptr_(other.row_ptr_) {}\n};\n\ntemplate \nMultiValSparseBin* MultiValSparseBin::Clone() {\n return new MultiValSparseBin(*this);\n}\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_SRC_IO_MULTI_VAL_SPARSE_BIN_HPP_\n" }, { "path": "src/io/parser.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \"parser.hpp\"\n\n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nvoid GetStatistic(const char* str, int* comma_cnt, int* tab_cnt, int* colon_cnt) {\n *comma_cnt = 0;\n *tab_cnt = 0;\n *colon_cnt = 0;\n for (int i = 0; str[i] != '\\0'; ++i) {\n if (str[i] == ',') {\n ++(*comma_cnt);\n } else if (str[i] == '\\t') {\n ++(*tab_cnt);\n } else if (str[i] == ':') {\n ++(*colon_cnt);\n }\n }\n}\n\nint GetLabelIdxForLibsvm(const std::string& str, int num_features, int label_idx) {\n if (num_features <= 0) {\n return label_idx;\n }\n auto str2 = Common::Trim(str);\n auto pos_space = str2.find_first_of(\" \\f\\n\\r\\t\\v\");\n auto pos_colon = str2.find_first_of(\":\");\n if (pos_space == std::string::npos || pos_space < pos_colon) {\n return label_idx;\n } else {\n return -1;\n }\n}\n\nint GetLabelIdxForTSV(const std::string& str, int num_features, int label_idx) {\n if (num_features <= 0) {\n return label_idx;\n }\n auto str2 = Common::Trim(str);\n auto tokens = Common::Split(str2.c_str(), '\\t');\n if (static_cast(tokens.size()) == num_features) {\n return -1;\n } else {\n return label_idx;\n }\n}\n\nint GetLabelIdxForCSV(const std::string& str, int num_features, int label_idx) {\n if (num_features <= 0) {\n return label_idx;\n }\n auto str2 = Common::Trim(str);\n auto tokens = Common::Split(str2.c_str(), ',');\n if (static_cast(tokens.size()) == num_features) {\n return -1;\n } else {\n return label_idx;\n }\n}\n\nenum DataType {\n INVALID,\n CSV,\n TSV,\n LIBSVM\n};\n\nvoid GetLine(std::stringstream* ss, std::string* line, const VirtualFileReader* reader, std::vector* buffer, size_t buffer_size) {\n std::getline(*ss, *line);\n while (ss->eof()) {\n size_t read_len = reader->Read(buffer->data(), buffer_size);\n if (read_len <= 0) {\n break;\n }\n ss->clear();\n ss->str(std::string(buffer->data(), read_len));\n std::string tmp;\n std::getline(*ss, tmp);\n *line += tmp;\n }\n}\n\nstd::vector ReadKLineFromFile(const char* filename, bool header, int k) {\n auto reader = VirtualFileReader::Make(filename);\n if (!reader->Init()) {\n Log::Fatal(\"Data file %s doesn't exist.\", filename);\n }\n std::vector ret;\n std::string cur_line;\n const size_t buffer_size = 1024 * 1024;\n auto buffer = std::vector(buffer_size);\n size_t read_len = reader->Read(buffer.data(), buffer_size);\n if (read_len <= 0) {\n Log::Fatal(\"Data file %s couldn't be read.\", filename);\n }\n std::string read_str = std::string(buffer.data(), read_len);\n std::stringstream tmp_file(read_str);\n if (header) {\n if (!tmp_file.eof()) {\n GetLine(&tmp_file, &cur_line, reader.get(), &buffer, buffer_size);\n }\n }\n for (int i = 0; i < k; ++i) {\n if (!tmp_file.eof()) {\n GetLine(&tmp_file, &cur_line, reader.get(), &buffer, buffer_size);\n cur_line = Common::Trim(cur_line);\n if (!cur_line.empty()) {\n ret.push_back(cur_line);\n }\n } else {\n break;\n }\n }\n if (ret.empty()) {\n Log::Fatal(\"Data file %s should have at least one line.\", filename);\n } else if (ret.size() == 1) {\n Log::Warning(\"Data file %s only has one line.\", filename);\n }\n return ret;\n}\n\nint GetNumColFromLIBSVMFile(const char* filename, bool header) {\n auto reader = VirtualFileReader::Make(filename);\n if (!reader->Init()) {\n Log::Fatal(\"Data file %s doesn't exist.\", filename);\n }\n std::vector ret;\n std::string cur_line;\n const size_t buffer_size = 1024 * 1024;\n auto buffer = std::vector(buffer_size);\n size_t read_len = reader->Read(buffer.data(), buffer_size);\n if (read_len <= 0) {\n Log::Fatal(\"Data file %s couldn't be read.\", filename);\n }\n std::string read_str = std::string(buffer.data(), read_len);\n std::stringstream tmp_file(read_str);\n if (header) {\n if (!tmp_file.eof()) {\n GetLine(&tmp_file, &cur_line, reader.get(), &buffer, buffer_size);\n }\n }\n int max_col_idx = 0;\n int max_line_idx = 0;\n const int stop_round = 1 << 7;\n const int max_line = 1 << 13;\n for (int i = 0; i < max_line; ++i) {\n if (!tmp_file.eof()) {\n GetLine(&tmp_file, &cur_line, reader.get(), &buffer, buffer_size);\n cur_line = Common::Trim(cur_line);\n auto colon_pos = cur_line.find_last_of(\":\");\n auto space_pos = cur_line.find_last_of(\" \\f\\t\\v\");\n auto sub_str = cur_line.substr(space_pos + 1, space_pos - colon_pos - 1);\n int cur_idx = 0;\n Common::Atoi(sub_str.c_str(), &cur_idx);\n if (cur_idx > max_col_idx) {\n max_col_idx = cur_idx;\n max_line_idx = i;\n }\n if (i - max_line_idx >= stop_round) {\n break;\n }\n } else {\n break;\n }\n }\n CHECK_GT(max_col_idx, 0);\n return max_col_idx;\n}\n\nDataType GetDataType(const char* filename, bool header,\n const std::vector& lines, int* num_col) {\n DataType type = DataType::INVALID;\n if (lines.empty()) {\n return type;\n }\n int comma_cnt = 0;\n int tab_cnt = 0;\n int colon_cnt = 0;\n GetStatistic(lines[0].c_str(), &comma_cnt, &tab_cnt, &colon_cnt);\n size_t num_lines = lines.size();\n if (num_lines == 1) {\n if (colon_cnt > 0) {\n type = DataType::LIBSVM;\n } else if (tab_cnt > 0) {\n type = DataType::TSV;\n } else if (comma_cnt > 0) {\n type = DataType::CSV;\n }\n } else {\n int comma_cnt2 = 0;\n int tab_cnt2 = 0;\n int colon_cnt2 = 0;\n GetStatistic(lines[1].c_str(), &comma_cnt2, &tab_cnt2, &colon_cnt2);\n if (colon_cnt > 0 || colon_cnt2 > 0) {\n type = DataType::LIBSVM;\n } else if (tab_cnt == tab_cnt2 && tab_cnt > 0) {\n type = DataType::TSV;\n } else if (comma_cnt == comma_cnt2 && comma_cnt > 0) {\n type = DataType::CSV;\n }\n if (type == DataType::TSV || type == DataType::CSV) {\n // valid the type\n for (size_t i = 2; i < num_lines; ++i) {\n GetStatistic(lines[i].c_str(), &comma_cnt2, &tab_cnt2, &colon_cnt2);\n if (type == DataType::TSV && tab_cnt2 != tab_cnt) {\n type = DataType::INVALID;\n break;\n } else if (type == DataType::CSV && comma_cnt != comma_cnt2) {\n type = DataType::INVALID;\n break;\n }\n }\n }\n }\n if (type == DataType::LIBSVM) {\n int max_col_idx = GetNumColFromLIBSVMFile(filename, header);\n *num_col = max_col_idx + 1;\n } else if (type == DataType::CSV) {\n *num_col = comma_cnt + 1;\n } else if (type == DataType::TSV) {\n *num_col = tab_cnt + 1;\n }\n return type;\n}\n\n// parser factory implementation.\nParserFactory& ParserFactory::getInstance() {\n static ParserFactory factory;\n return factory;\n}\n\nvoid ParserFactory::Register(std::string class_name, std::function m_objc) {\n if (m_objc) {\n object_map_.insert(\n std::map>::value_type(class_name, m_objc));\n }\n}\n\nParser* ParserFactory::getObject(std::string class_name, std::string config_str) {\n std::map>::const_iterator iter =\n object_map_.find(class_name);\n if (iter != object_map_.end()) {\n return iter->second(config_str);\n } else {\n Log::Fatal(\"Cannot find parser class '%s', please register first or check config format.\", class_name.c_str());\n return nullptr;\n }\n}\n\nParser* Parser::CreateParser(const char* filename, bool header, int num_features, int label_idx, bool precise_float_parser) {\n const int n_read_line = 32;\n auto lines = ReadKLineFromFile(filename, header, n_read_line);\n int num_col = 0;\n DataType type = GetDataType(filename, header, lines, &num_col);\n if (type == DataType::INVALID) {\n Log::Fatal(\"Unknown format of training data. Only CSV, TSV, and LibSVM (zero-based) formatted text files are supported.\");\n }\n std::unique_ptr ret;\n int output_label_index = -1;\n AtofFunc atof = precise_float_parser ? Common::AtofPrecise : Common::Atof;\n if (type == DataType::LIBSVM) {\n output_label_index = GetLabelIdxForLibsvm(lines[0], num_features, label_idx);\n ret.reset(new LibSVMParser(output_label_index, num_col, atof));\n } else if (type == DataType::TSV) {\n output_label_index = GetLabelIdxForTSV(lines[0], num_features, label_idx);\n ret.reset(new TSVParser(output_label_index, num_col, atof));\n } else if (type == DataType::CSV) {\n output_label_index = GetLabelIdxForCSV(lines[0], num_features, label_idx);\n ret.reset(new CSVParser(output_label_index, num_col, atof));\n }\n\n if (output_label_index < 0 && label_idx >= 0) {\n Log::Info(\"Data file %s doesn't contain a label column.\", filename);\n }\n return ret.release();\n}\n\nParser* Parser::CreateParser(const char* filename, bool header, int num_features, int label_idx, bool precise_float_parser, std::string parser_config_str) {\n // customized parser add-on.\n if (!parser_config_str.empty()) {\n std::unique_ptr ret;\n std::string class_name = Common::GetFromParserConfig(parser_config_str, \"className\");\n Log::Info(\"Custom parser class name: %s\", class_name.c_str());\n Parser* p = ParserFactory::getInstance().getObject(class_name, parser_config_str);\n ret.reset(p);\n return ret.release();\n }\n return CreateParser(filename, header, num_features, label_idx, precise_float_parser);\n}\n\nstd::string Parser::GenerateParserConfigStr(const char* filename, const char* parser_config_filename, bool header, int label_idx) {\n TextReader parser_config_reader(parser_config_filename, false);\n parser_config_reader.ReadAllLines();\n std::string parser_config_str = parser_config_reader.JoinedLines();\n if (!parser_config_str.empty()) {\n // save header to parser config in case needed.\n if (header && Common::GetFromParserConfig(parser_config_str, \"header\").empty()) {\n TextReader text_reader(filename, header);\n parser_config_str = Common::SaveToParserConfig(parser_config_str, \"header\", text_reader.first_line());\n }\n // save label id to parser config in case needed.\n if (Common::GetFromParserConfig(parser_config_str, \"labelId\").empty()) {\n parser_config_str = Common::SaveToParserConfig(parser_config_str, \"labelId\", std::to_string(label_idx));\n }\n }\n return parser_config_str;\n}\n} // namespace LightGBM\n" }, { "path": "src/io/parser.hpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_IO_PARSER_HPP_\n#define LIGHTGBM_SRC_IO_PARSER_HPP_\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n\nnamespace LightGBM {\n\nclass CSVParser: public Parser {\n public:\n explicit CSVParser(int label_idx, int total_columns, AtofFunc atof)\n :label_idx_(label_idx), total_columns_(total_columns), atof_(atof) {\n }\n inline void ParseOneLine(const char* str,\n std::vector>* out_features, double* out_label) const override {\n int idx = 0;\n double val = 0.0f;\n int offset = 0;\n *out_label = 0.0f;\n while (*str != '\\0') {\n str = atof_(str, &val);\n if (idx == label_idx_) {\n *out_label = val;\n offset = -1;\n } else if (std::fabs(val) > kZeroThreshold || std::isnan(val)) {\n out_features->emplace_back(idx + offset, val);\n }\n ++idx;\n if (*str == ',') {\n ++str;\n } else if (*str != '\\0') {\n Log::Fatal(\"Input format error when parsing as CSV\");\n }\n }\n }\n\n inline int NumFeatures() const override {\n return total_columns_ - (label_idx_ >= 0);\n }\n\n private:\n int label_idx_ = 0;\n int total_columns_ = -1;\n AtofFunc atof_;\n};\n\nclass TSVParser: public Parser {\n public:\n explicit TSVParser(int label_idx, int total_columns, AtofFunc atof)\n :label_idx_(label_idx), total_columns_(total_columns), atof_(atof) {\n }\n inline void ParseOneLine(const char* str,\n std::vector>* out_features, double* out_label) const override {\n int idx = 0;\n double val = 0.0f;\n int offset = 0;\n while (*str != '\\0') {\n str = atof_(str, &val);\n if (idx == label_idx_) {\n *out_label = val;\n offset = -1;\n } else if (std::fabs(val) > kZeroThreshold || std::isnan(val)) {\n out_features->emplace_back(idx + offset, val);\n }\n ++idx;\n if (*str == '\\t') {\n ++str;\n } else if (*str != '\\0') {\n Log::Fatal(\"Input format error when parsing as TSV\");\n }\n }\n }\n\n inline int NumFeatures() const override {\n return total_columns_ - (label_idx_ >= 0);\n }\n\n private:\n int label_idx_ = 0;\n int total_columns_ = -1;\n AtofFunc atof_;\n};\n\nclass LibSVMParser: public Parser {\n public:\n explicit LibSVMParser(int label_idx, int total_columns, AtofFunc atof)\n :label_idx_(label_idx), total_columns_(total_columns), atof_(atof) {\n if (label_idx > 0) {\n Log::Fatal(\"Label should be the first column in a LibSVM file\");\n }\n }\n inline void ParseOneLine(const char* str,\n std::vector>* out_features, double* out_label) const override {\n int idx = 0;\n double val = 0.0f;\n if (label_idx_ == 0) {\n str = atof_(str, &val);\n *out_label = val;\n str = Common::SkipSpaceAndTab(str);\n }\n while (*str != '\\0') {\n str = Common::Atoi(str, &idx);\n str = Common::SkipSpaceAndTab(str);\n if (*str == ':') {\n ++str;\n str = Common::Atof(str, &val);\n out_features->emplace_back(idx, val);\n } else {\n Log::Fatal(\"Input format error when parsing as LibSVM\");\n }\n str = Common::SkipSpaceAndTab(str);\n }\n }\n\n inline int NumFeatures() const override {\n return total_columns_;\n }\n\n private:\n int label_idx_ = 0;\n int total_columns_ = -1;\n AtofFunc atof_;\n};\n\n} // namespace LightGBM\n#endif // LIGHTGBM_SRC_IO_PARSER_HPP_\n" }, { "path": "src/io/sparse_bin.hpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n#ifndef LIGHTGBM_SRC_IO_SPARSE_BIN_HPP_\n#define LIGHTGBM_SRC_IO_SPARSE_BIN_HPP_\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\ntemplate \nclass SparseBin;\n\nconst size_t kNumFastIndex = 64;\n\ntemplate \nclass SparseBinIterator : public BinIterator {\n public:\n SparseBinIterator(const SparseBin* bin_data, uint32_t min_bin,\n uint32_t max_bin, uint32_t most_freq_bin)\n : bin_data_(bin_data),\n min_bin_(static_cast(min_bin)),\n max_bin_(static_cast(max_bin)),\n most_freq_bin_(static_cast(most_freq_bin)) {\n if (most_freq_bin_ == 0) {\n offset_ = 1;\n } else {\n offset_ = 0;\n }\n Reset(0);\n }\n SparseBinIterator(const SparseBin* bin_data, data_size_t start_idx)\n : bin_data_(bin_data) {\n Reset(start_idx);\n }\n\n inline uint32_t RawGet(data_size_t idx) override;\n inline VAL_T InnerRawGet(data_size_t idx);\n\n inline uint32_t Get(data_size_t idx) override {\n VAL_T ret = InnerRawGet(idx);\n if (ret >= min_bin_ && ret <= max_bin_) {\n return ret - min_bin_ + offset_;\n } else {\n return most_freq_bin_;\n }\n }\n\n inline void Reset(data_size_t idx) override;\n\n private:\n const SparseBin* bin_data_;\n data_size_t cur_pos_;\n data_size_t i_delta_;\n VAL_T min_bin_;\n VAL_T max_bin_;\n VAL_T most_freq_bin_;\n uint8_t offset_;\n};\n\ntemplate \nclass SparseBin : public Bin {\n public:\n friend class SparseBinIterator;\n\n explicit SparseBin(data_size_t num_data) : num_data_(num_data) {\n int num_threads = OMP_NUM_THREADS();\n push_buffers_.resize(num_threads);\n }\n\n ~SparseBin() {}\n\n void InitStreaming(uint32_t num_thread, int32_t omp_max_threads) override {\n // Each external thread needs its own set of OpenMP push buffers,\n // so allocate num_thread times the maximum number of OMP threads per external thread\n push_buffers_.resize(omp_max_threads * num_thread);\n };\n\n void ReSize(data_size_t num_data) override { num_data_ = num_data; }\n\n void Push(int tid, data_size_t idx, uint32_t value) override {\n auto cur_bin = static_cast(value);\n if (cur_bin != 0) {\n push_buffers_[tid].emplace_back(idx, cur_bin);\n }\n }\n\n BinIterator* GetIterator(uint32_t min_bin, uint32_t max_bin,\n uint32_t most_freq_bin) const override;\n\n#define ACC_GH(hist, i, g, h) \\\n const auto ti = static_cast(i) << 1; \\\n hist[ti] += g; \\\n hist[ti + 1] += h;\n\n void ConstructHistogram(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n const score_t* ordered_hessians,\n hist_t* out) const override {\n data_size_t i_delta, cur_pos;\n InitIndex(data_indices[start], &i_delta, &cur_pos);\n data_size_t i = start;\n for (;;) {\n if (cur_pos < data_indices[i]) {\n cur_pos += deltas_[++i_delta];\n if (i_delta >= num_vals_) {\n break;\n }\n } else if (cur_pos > data_indices[i]) {\n if (++i >= end) {\n break;\n }\n } else {\n const VAL_T bin = vals_[i_delta];\n ACC_GH(out, bin, ordered_gradients[i], ordered_hessians[i]);\n if (++i >= end) {\n break;\n }\n cur_pos += deltas_[++i_delta];\n if (i_delta >= num_vals_) {\n break;\n }\n }\n }\n }\n\n void ConstructHistogram(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n const score_t* ordered_hessians,\n hist_t* out) const override {\n data_size_t i_delta, cur_pos;\n InitIndex(start, &i_delta, &cur_pos);\n while (cur_pos < start && i_delta < num_vals_) {\n cur_pos += deltas_[++i_delta];\n }\n while (cur_pos < end && i_delta < num_vals_) {\n const VAL_T bin = vals_[i_delta];\n ACC_GH(out, bin, ordered_gradients[cur_pos], ordered_hessians[cur_pos]);\n cur_pos += deltas_[++i_delta];\n }\n }\n\n void ConstructHistogram(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n hist_t* out) const override {\n data_size_t i_delta, cur_pos;\n InitIndex(data_indices[start], &i_delta, &cur_pos);\n data_size_t i = start;\n hist_t* grad = out;\n hist_cnt_t* cnt = reinterpret_cast(out + 1);\n for (;;) {\n if (cur_pos < data_indices[i]) {\n cur_pos += deltas_[++i_delta];\n if (i_delta >= num_vals_) {\n break;\n }\n } else if (cur_pos > data_indices[i]) {\n if (++i >= end) {\n break;\n }\n } else {\n const uint32_t ti = static_cast(vals_[i_delta]) << 1;\n grad[ti] += ordered_gradients[i];\n ++cnt[ti];\n if (++i >= end) {\n break;\n }\n cur_pos += deltas_[++i_delta];\n if (i_delta >= num_vals_) {\n break;\n }\n }\n }\n }\n\n void ConstructHistogram(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n hist_t* out) const override {\n data_size_t i_delta, cur_pos;\n InitIndex(start, &i_delta, &cur_pos);\n hist_t* grad = out;\n hist_cnt_t* cnt = reinterpret_cast(out + 1);\n while (cur_pos < start && i_delta < num_vals_) {\n cur_pos += deltas_[++i_delta];\n }\n while (cur_pos < end && i_delta < num_vals_) {\n const uint32_t ti = static_cast(vals_[i_delta]) << 1;\n grad[ti] += ordered_gradients[cur_pos];\n ++cnt[ti];\n cur_pos += deltas_[++i_delta];\n }\n }\n#undef ACC_GH\n\n template \n void ConstructIntHistogramInner(data_size_t start, data_size_t end,\n const score_t* ordered_gradients_and_hessians,\n hist_t* out) const {\n data_size_t i_delta, cur_pos;\n InitIndex(start, &i_delta, &cur_pos);\n if (USE_HESSIAN) {\n PACKED_HIST_T* out_ptr = reinterpret_cast(out);\n const int16_t* gradients_and_hessians_ptr = reinterpret_cast(ordered_gradients_and_hessians);\n while (cur_pos < start && i_delta < num_vals_) {\n cur_pos += deltas_[++i_delta];\n }\n while (cur_pos < end && i_delta < num_vals_) {\n const VAL_T bin = vals_[i_delta];\n const int16_t gradient_16 = gradients_and_hessians_ptr[cur_pos];\n const PACKED_HIST_T gradient_64 = (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (gradient_16 & 0xff);\n out_ptr[bin] += gradient_64;\n cur_pos += deltas_[++i_delta];\n }\n } else {\n GRAD_HIST_T* grad = reinterpret_cast(out);\n HESS_HIST_T* cnt = reinterpret_cast(out) + 1;\n const int8_t* gradients_and_hessians_ptr = reinterpret_cast(ordered_gradients_and_hessians);\n while (cur_pos < start && i_delta < num_vals_) {\n cur_pos += deltas_[++i_delta];\n }\n while (cur_pos < end && i_delta < num_vals_) {\n const uint32_t ti = static_cast(vals_[i_delta]) << 1;\n grad[ti] += gradients_and_hessians_ptr[cur_pos];\n ++cnt[ti];\n cur_pos += deltas_[++i_delta];\n }\n }\n }\n\n template \n void ConstructIntHistogramInner(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients_and_hessians,\n hist_t* out) const {\n data_size_t i_delta, cur_pos;\n InitIndex(data_indices[start], &i_delta, &cur_pos);\n data_size_t i = start;\n if (USE_HESSIAN) {\n PACKED_HIST_T* out_ptr = reinterpret_cast(out);\n const int16_t* gradients_and_hessians_ptr = reinterpret_cast(ordered_gradients_and_hessians);\n for (;;) {\n if (cur_pos < data_indices[i]) {\n cur_pos += deltas_[++i_delta];\n if (i_delta >= num_vals_) {\n break;\n }\n } else if (cur_pos > data_indices[i]) {\n if (++i >= end) {\n break;\n }\n } else {\n const VAL_T bin = vals_[i_delta];\n const int16_t gradient_16 = gradients_and_hessians_ptr[i];\n const PACKED_HIST_T gradient_packed = (HIST_BITS == 8) ? gradient_16 :\n (static_cast(static_cast(gradient_16 >> 8)) << HIST_BITS) | (gradient_16 & 0xff);\n out_ptr[bin] += gradient_packed;\n if (++i >= end) {\n break;\n }\n cur_pos += deltas_[++i_delta];\n if (i_delta >= num_vals_) {\n break;\n }\n }\n }\n } else {\n GRAD_HIST_T* grad = reinterpret_cast(out);\n HESS_HIST_T* cnt = reinterpret_cast(out) + 1;\n const int8_t* gradients_and_hessians_ptr = reinterpret_cast(ordered_gradients_and_hessians);\n for (;;) {\n if (cur_pos < data_indices[i]) {\n cur_pos += deltas_[++i_delta];\n if (i_delta >= num_vals_) {\n break;\n }\n } else if (cur_pos > data_indices[i]) {\n if (++i >= end) {\n break;\n }\n } else {\n const uint32_t ti = static_cast(vals_[i_delta]) << 1;\n grad[ti] += gradients_and_hessians_ptr[i << 1];\n ++cnt[ti];\n if (++i >= end) {\n break;\n }\n cur_pos += deltas_[++i_delta];\n if (i_delta >= num_vals_) {\n break;\n }\n }\n }\n }\n }\n\n void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n const score_t* /*ordered_hessians*/,\n hist_t* out) const override {\n ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt32(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n const score_t* /*ordered_hessians*/,\n hist_t* out) const override {\n ConstructIntHistogramInner(start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt32(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n hist_t* out) const override {\n ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt32(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n hist_t* out) const override {\n ConstructIntHistogramInner(start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n const score_t* /*ordered_hessians*/,\n hist_t* out) const override {\n ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt16(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n const score_t* /*ordered_hessians*/,\n hist_t* out) const override {\n ConstructIntHistogramInner(start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt16(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n hist_t* out) const override {\n ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt16(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n hist_t* out) const override {\n ConstructIntHistogramInner(start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n const score_t* /*ordered_hessians*/,\n hist_t* out) const override {\n ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt8(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n const score_t* /*ordered_hessians*/,\n hist_t* out) const override {\n ConstructIntHistogramInner(start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt8(const data_size_t* data_indices, data_size_t start,\n data_size_t end, const score_t* ordered_gradients,\n hist_t* out) const override {\n ConstructIntHistogramInner(data_indices, start, end, ordered_gradients, out);\n }\n\n void ConstructHistogramInt8(data_size_t start, data_size_t end,\n const score_t* ordered_gradients,\n hist_t* out) const override {\n ConstructIntHistogramInner(start, end, ordered_gradients, out);\n }\n\n inline void NextNonzeroFast(data_size_t* i_delta,\n data_size_t* cur_pos) const {\n *cur_pos += deltas_[++(*i_delta)];\n if (*i_delta >= num_vals_) {\n *cur_pos = num_data_;\n }\n }\n\n inline bool NextNonzero(data_size_t* i_delta, data_size_t* cur_pos) const {\n *cur_pos += deltas_[++(*i_delta)];\n if (*i_delta < num_vals_) {\n return true;\n } else {\n *cur_pos = num_data_;\n return false;\n }\n }\n\n template \n data_size_t SplitInner(uint32_t min_bin, uint32_t max_bin,\n uint32_t default_bin, uint32_t most_freq_bin,\n bool default_left, uint32_t threshold,\n const data_size_t* data_indices, data_size_t cnt,\n data_size_t* lte_indices,\n data_size_t* gt_indices) const {\n auto th = static_cast(threshold + min_bin);\n auto t_zero_bin = static_cast(min_bin + default_bin);\n if (most_freq_bin == 0) {\n --th;\n --t_zero_bin;\n }\n const auto minb = static_cast(min_bin);\n const auto maxb = static_cast(max_bin);\n data_size_t lte_count = 0;\n data_size_t gt_count = 0;\n data_size_t* default_indices = gt_indices;\n data_size_t* default_count = >_count;\n data_size_t* missing_default_indices = gt_indices;\n data_size_t* missing_default_count = >_count;\n if (most_freq_bin <= threshold) {\n default_indices = lte_indices;\n default_count = <e_count;\n }\n if (MISS_IS_ZERO || MISS_IS_NA) {\n if (default_left) {\n missing_default_indices = lte_indices;\n missing_default_count = <e_count;\n }\n }\n SparseBinIterator iterator(this, data_indices[0]);\n if (min_bin < max_bin) {\n for (data_size_t i = 0; i < cnt; ++i) {\n const data_size_t idx = data_indices[i];\n const auto bin = iterator.InnerRawGet(idx);\n if ((MISS_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) ||\n (MISS_IS_NA && !MFB_IS_NA && bin == maxb)) {\n missing_default_indices[(*missing_default_count)++] = idx;\n } else if ((USE_MIN_BIN && (bin < minb || bin > maxb)) ||\n (!USE_MIN_BIN && bin == 0)) {\n if ((MISS_IS_NA && MFB_IS_NA) || (MISS_IS_ZERO && MFB_IS_ZERO)) {\n missing_default_indices[(*missing_default_count)++] = idx;\n } else {\n default_indices[(*default_count)++] = idx;\n }\n } else if (bin > th) {\n gt_indices[gt_count++] = idx;\n } else {\n lte_indices[lte_count++] = idx;\n }\n }\n } else {\n data_size_t* max_bin_indices = gt_indices;\n data_size_t* max_bin_count = >_count;\n if (maxb <= th) {\n max_bin_indices = lte_indices;\n max_bin_count = <e_count;\n }\n for (data_size_t i = 0; i < cnt; ++i) {\n const data_size_t idx = data_indices[i];\n const auto bin = iterator.InnerRawGet(idx);\n if (MISS_IS_ZERO && !MFB_IS_ZERO && bin == t_zero_bin) {\n missing_default_indices[(*missing_default_count)++] = idx;\n } else if (bin != maxb) {\n if ((MISS_IS_NA && MFB_IS_NA) || (MISS_IS_ZERO && MFB_IS_ZERO)) {\n missing_default_indices[(*missing_default_count)++] = idx;\n } else {\n default_indices[(*default_count)++] = idx;\n }\n } else {\n if (MISS_IS_NA && !MFB_IS_NA) {\n missing_default_indices[(*missing_default_count)++] = idx;\n } else {\n max_bin_indices[(*max_bin_count)++] = idx;\n }\n }\n }\n }\n return lte_count;\n }\n\n data_size_t Split(uint32_t min_bin, uint32_t max_bin, uint32_t default_bin,\n uint32_t most_freq_bin, MissingType missing_type,\n bool default_left, uint32_t threshold,\n const data_size_t* data_indices, data_size_t cnt,\n data_size_t* lte_indices,\n data_size_t* gt_indices) const override {\n#define ARGUMENTS \\\n min_bin, max_bin, default_bin, most_freq_bin, default_left, threshold, \\\n data_indices, cnt, lte_indices, gt_indices\n if (missing_type == MissingType::None) {\n return SplitInner(ARGUMENTS);\n } else if (missing_type == MissingType::Zero) {\n if (default_bin == most_freq_bin) {\n return SplitInner(ARGUMENTS);\n } else {\n return SplitInner(ARGUMENTS);\n }\n } else {\n if (max_bin == most_freq_bin + min_bin && most_freq_bin > 0) {\n return SplitInner(ARGUMENTS);\n } else {\n return SplitInner(ARGUMENTS);\n }\n }\n#undef ARGUMENTS\n }\n\n data_size_t Split(uint32_t max_bin, uint32_t default_bin,\n uint32_t most_freq_bin, MissingType missing_type,\n bool default_left, uint32_t threshold,\n const data_size_t* data_indices, data_size_t cnt,\n data_size_t* lte_indices,\n data_size_t* gt_indices) const override {\n#define ARGUMENTS \\\n 1, max_bin, default_bin, most_freq_bin, default_left, threshold, \\\n data_indices, cnt, lte_indices, gt_indices\n if (missing_type == MissingType::None) {\n return SplitInner(ARGUMENTS);\n } else if (missing_type == MissingType::Zero) {\n if (default_bin == most_freq_bin) {\n return SplitInner(ARGUMENTS);\n } else {\n return SplitInner(ARGUMENTS);\n }\n } else {\n if (max_bin == most_freq_bin + 1 && most_freq_bin > 0) {\n return SplitInner(ARGUMENTS);\n } else {\n return SplitInner(ARGUMENTS);\n }\n }\n#undef ARGUMENTS\n }\n template \n data_size_t SplitCategoricalInner(uint32_t min_bin, uint32_t max_bin,\n uint32_t most_freq_bin,\n const uint32_t* threshold,\n int num_threshold,\n const data_size_t* data_indices,\n data_size_t cnt, data_size_t* lte_indices,\n data_size_t* gt_indices) const {\n data_size_t lte_count = 0;\n data_size_t gt_count = 0;\n data_size_t* default_indices = gt_indices;\n data_size_t* default_count = >_count;\n SparseBinIterator iterator(this, data_indices[0]);\n int8_t offset = most_freq_bin == 0 ? 1 : 0;\n if (most_freq_bin > 0 && Common::FindInBitset(threshold, num_threshold, most_freq_bin)) {\n default_indices = lte_indices;\n default_count = <e_count;\n }\n for (data_size_t i = 0; i < cnt; ++i) {\n const data_size_t idx = data_indices[i];\n const uint32_t bin = iterator.RawGet(idx);\n if (USE_MIN_BIN && (bin < min_bin || bin > max_bin)) {\n default_indices[(*default_count)++] = idx;\n } else if (!USE_MIN_BIN && bin == 0) {\n default_indices[(*default_count)++] = idx;\n } else if (Common::FindInBitset(threshold, num_threshold,\n bin - min_bin + offset)) {\n lte_indices[lte_count++] = idx;\n } else {\n gt_indices[gt_count++] = idx;\n }\n }\n return lte_count;\n }\n\n data_size_t SplitCategorical(uint32_t min_bin, uint32_t max_bin,\n uint32_t most_freq_bin,\n const uint32_t* threshold, int num_threshold,\n const data_size_t* data_indices, data_size_t cnt,\n data_size_t* lte_indices,\n data_size_t* gt_indices) const override {\n return SplitCategoricalInner(min_bin, max_bin, most_freq_bin,\n threshold, num_threshold, data_indices,\n cnt, lte_indices, gt_indices);\n }\n\n data_size_t SplitCategorical(uint32_t max_bin, uint32_t most_freq_bin,\n const uint32_t* threshold, int num_threshold,\n const data_size_t* data_indices, data_size_t cnt,\n data_size_t* lte_indices,\n data_size_t* gt_indices) const override {\n return SplitCategoricalInner(1, max_bin, most_freq_bin, threshold,\n num_threshold, data_indices, cnt,\n lte_indices, gt_indices);\n }\n\n data_size_t num_data() const override { return num_data_; }\n\n void* get_data() override { return nullptr; }\n\n void FinishLoad() override {\n // get total non zero size\n size_t pair_cnt = 0;\n for (size_t i = 0; i < push_buffers_.size(); ++i) {\n pair_cnt += push_buffers_[i].size();\n }\n std::vector>& idx_val_pairs =\n push_buffers_[0];\n idx_val_pairs.reserve(pair_cnt);\n\n for (size_t i = 1; i < push_buffers_.size(); ++i) {\n idx_val_pairs.insert(idx_val_pairs.end(), push_buffers_[i].begin(),\n push_buffers_[i].end());\n push_buffers_[i].clear();\n push_buffers_[i].shrink_to_fit();\n }\n // sort by data index\n std::sort(idx_val_pairs.begin(), idx_val_pairs.end(),\n [](const std::pair& a,\n const std::pair& b) {\n return a.first < b.first;\n });\n // load delta array\n LoadFromPair(idx_val_pairs);\n }\n\n void LoadFromPair(\n const std::vector>& idx_val_pairs) {\n deltas_.clear();\n vals_.clear();\n deltas_.reserve(idx_val_pairs.size());\n vals_.reserve(idx_val_pairs.size());\n // transform to delta array\n data_size_t last_idx = 0;\n for (size_t i = 0; i < idx_val_pairs.size(); ++i) {\n const data_size_t cur_idx = idx_val_pairs[i].first;\n const VAL_T bin = idx_val_pairs[i].second;\n data_size_t cur_delta = cur_idx - last_idx;\n // disallow the multi-val in one row\n if (i > 0 && cur_delta == 0) {\n continue;\n }\n while (cur_delta >= 256) {\n deltas_.push_back(255);\n vals_.push_back(0);\n cur_delta -= 255;\n }\n deltas_.push_back(static_cast(cur_delta));\n vals_.push_back(bin);\n last_idx = cur_idx;\n }\n // avoid out of range\n deltas_.push_back(0);\n num_vals_ = static_cast(vals_.size());\n\n // reduce memory cost\n deltas_.shrink_to_fit();\n vals_.shrink_to_fit();\n\n // generate fast index\n GetFastIndex();\n }\n\n void GetFastIndex() {\n fast_index_.clear();\n // get shift cnt\n data_size_t mod_size = (num_data_ + kNumFastIndex - 1) / kNumFastIndex;\n data_size_t pow2_mod_size = 1;\n fast_index_shift_ = 0;\n while (pow2_mod_size < mod_size) {\n pow2_mod_size <<= 1;\n ++fast_index_shift_;\n }\n // build fast index\n data_size_t i_delta = -1;\n data_size_t cur_pos = 0;\n data_size_t next_threshold = 0;\n while (NextNonzero(&i_delta, &cur_pos)) {\n while (next_threshold <= cur_pos) {\n fast_index_.emplace_back(i_delta, cur_pos);\n next_threshold += pow2_mod_size;\n }\n }\n // avoid out of range\n while (next_threshold < num_data_) {\n fast_index_.emplace_back(num_vals_ - 1, cur_pos);\n next_threshold += pow2_mod_size;\n }\n fast_index_.shrink_to_fit();\n }\n\n void SaveBinaryToFile(BinaryWriter* writer) const override {\n writer->AlignedWrite(&num_vals_, sizeof(num_vals_));\n writer->AlignedWrite(deltas_.data(), sizeof(uint8_t) * (num_vals_ + 1));\n writer->AlignedWrite(vals_.data(), sizeof(VAL_T) * num_vals_);\n }\n\n size_t SizesInByte() const override {\n return VirtualFileWriter::AlignedSize(sizeof(num_vals_)) +\n VirtualFileWriter::AlignedSize(sizeof(uint8_t) * (num_vals_ + 1)) +\n VirtualFileWriter::AlignedSize(sizeof(VAL_T) * num_vals_);\n }\n\n void LoadFromMemory(\n const void* memory,\n const std::vector& local_used_indices) override {\n const char* mem_ptr = reinterpret_cast(memory);\n data_size_t tmp_num_vals = *(reinterpret_cast(mem_ptr));\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(tmp_num_vals));\n const uint8_t* tmp_delta = reinterpret_cast(mem_ptr);\n mem_ptr += VirtualFileWriter::AlignedSize(sizeof(uint8_t) * (tmp_num_vals + 1));\n const VAL_T* tmp_vals = reinterpret_cast(mem_ptr);\n\n deltas_.clear();\n vals_.clear();\n num_vals_ = tmp_num_vals;\n for (data_size_t i = 0; i < num_vals_; ++i) {\n deltas_.push_back(tmp_delta[i]);\n vals_.push_back(tmp_vals[i]);\n }\n deltas_.push_back(0);\n // reduce memory cost\n deltas_.shrink_to_fit();\n vals_.shrink_to_fit();\n\n if (local_used_indices.empty()) {\n // generate fast index\n GetFastIndex();\n } else {\n std::vector> tmp_pair;\n data_size_t cur_pos = 0;\n data_size_t j = -1;\n for (data_size_t i = 0;\n i < static_cast(local_used_indices.size()); ++i) {\n const data_size_t idx = local_used_indices[i];\n while (cur_pos < idx && j < num_vals_) {\n NextNonzero(&j, &cur_pos);\n }\n if (cur_pos == idx && j < num_vals_ && vals_[j] > 0) {\n // new row index is i\n tmp_pair.emplace_back(i, vals_[j]);\n }\n }\n LoadFromPair(tmp_pair);\n }\n }\n\n void CopySubrow(const Bin* full_bin, const data_size_t* used_indices,\n data_size_t num_used_indices) override {\n auto other_bin = dynamic_cast*>(full_bin);\n deltas_.clear();\n vals_.clear();\n data_size_t start = 0;\n if (num_used_indices > 0) {\n start = used_indices[0];\n }\n SparseBinIterator iterator(other_bin, start);\n // transform to delta array\n data_size_t last_idx = 0;\n for (data_size_t i = 0; i < num_used_indices; ++i) {\n auto bin = iterator.InnerRawGet(used_indices[i]);\n if (bin > 0) {\n data_size_t cur_delta = i - last_idx;\n while (cur_delta >= 256) {\n deltas_.push_back(255);\n vals_.push_back(0);\n cur_delta -= 255;\n }\n deltas_.push_back(static_cast(cur_delta));\n vals_.push_back(bin);\n last_idx = i;\n }\n }\n // avoid out of range\n deltas_.push_back(0);\n num_vals_ = static_cast(vals_.size());\n\n // reduce memory cost\n deltas_.shrink_to_fit();\n vals_.shrink_to_fit();\n\n // generate fast index\n GetFastIndex();\n }\n\n SparseBin* Clone() override;\n\n SparseBin(const SparseBin& other)\n : num_data_(other.num_data_),\n deltas_(other.deltas_),\n vals_(other.vals_),\n num_vals_(other.num_vals_),\n push_buffers_(other.push_buffers_),\n fast_index_(other.fast_index_),\n fast_index_shift_(other.fast_index_shift_) {}\n\n void InitIndex(data_size_t start_idx, data_size_t* i_delta,\n data_size_t* cur_pos) const {\n auto idx = start_idx >> fast_index_shift_;\n if (static_cast(idx) < fast_index_.size()) {\n const auto fast_pair = fast_index_[start_idx >> fast_index_shift_];\n *i_delta = fast_pair.first;\n *cur_pos = fast_pair.second;\n } else {\n *i_delta = -1;\n *cur_pos = 0;\n }\n }\n\n const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, std::vector* bin_iterator, const int num_threads) const override;\n\n const void* GetColWiseData(uint8_t* bit_type, bool* is_sparse, BinIterator** bin_iterator) const override;\n\n private:\n data_size_t num_data_;\n std::vector>\n deltas_;\n std::vector> vals_;\n data_size_t num_vals_;\n std::vector>> push_buffers_;\n std::vector> fast_index_;\n data_size_t fast_index_shift_;\n};\n\ntemplate \nSparseBin* SparseBin::Clone() {\n return new SparseBin(*this);\n}\n\ntemplate \ninline uint32_t SparseBinIterator::RawGet(data_size_t idx) {\n return InnerRawGet(idx);\n}\n\ntemplate \ninline VAL_T SparseBinIterator::InnerRawGet(data_size_t idx) {\n while (cur_pos_ < idx) {\n bin_data_->NextNonzeroFast(&i_delta_, &cur_pos_);\n }\n if (cur_pos_ == idx) {\n return bin_data_->vals_[i_delta_];\n } else {\n return 0;\n }\n}\n\ntemplate \ninline void SparseBinIterator::Reset(data_size_t start_idx) {\n bin_data_->InitIndex(start_idx, &i_delta_, &cur_pos_);\n}\n\ntemplate \nBinIterator* SparseBin::GetIterator(uint32_t min_bin, uint32_t max_bin,\n uint32_t most_freq_bin) const {\n return new SparseBinIterator(this, min_bin, max_bin, most_freq_bin);\n}\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_SRC_IO_SPARSE_BIN_HPP_\n" }, { "path": "src/io/train_share_states.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#include \n\n#include \n#include \n#include \n\nnamespace LightGBM {\n\nMultiValBinWrapper::MultiValBinWrapper(MultiValBin* bin, data_size_t num_data,\n const std::vector& feature_groups_contained, const int num_grad_quant_bins):\n feature_groups_contained_(feature_groups_contained) {\n num_threads_ = OMP_NUM_THREADS();\n num_data_ = num_data;\n multi_val_bin_.reset(bin);\n if (bin == nullptr) {\n return;\n }\n num_bin_ = bin->num_bin();\n num_bin_aligned_ = (num_bin_ + kAlignedSize - 1) / kAlignedSize * kAlignedSize;\n num_grad_quant_bins_ = num_grad_quant_bins;\n}\n\nvoid MultiValBinWrapper::InitTrain(const std::vector& group_feature_start,\n const std::vector>& feature_groups,\n const std::vector& is_feature_used,\n const data_size_t* bagging_use_indices,\n data_size_t bagging_indices_cnt) {\n is_use_subcol_ = false;\n if (multi_val_bin_ == nullptr) {\n return;\n }\n CopyMultiValBinSubset(group_feature_start, feature_groups,\n is_feature_used, bagging_use_indices, bagging_indices_cnt);\n const auto cur_multi_val_bin = (is_use_subcol_ || is_use_subrow_)\n ? multi_val_bin_subset_.get()\n : multi_val_bin_.get();\n if (cur_multi_val_bin != nullptr) {\n num_bin_ = cur_multi_val_bin->num_bin();\n num_bin_aligned_ = (num_bin_ + kAlignedSize - 1) / kAlignedSize * kAlignedSize;\n auto num_element_per_row = cur_multi_val_bin->num_element_per_row();\n min_block_size_ = std::min(static_cast(0.3f * num_bin_ /\n (num_element_per_row + kZeroThreshold)) + 1, 1024);\n min_block_size_ = std::max(min_block_size_, 32);\n }\n}\n\ntemplate \nvoid MultiValBinWrapper::HistMove(const std::vector>& hist_buf) {\n if (!is_use_subcol_ && INNER_HIST_BITS != 8) {\n return;\n }\n if (USE_QUANT_GRAD) {\n if (HIST_BITS == 32) {\n const int64_t* src = reinterpret_cast(hist_buf.data()) + hist_buf.size() / 2 -\n static_cast(num_bin_aligned_);\n #pragma omp parallel for schedule(static) num_threads(num_threads_)\n for (int i = 0; i < static_cast(hist_move_src_.size()); ++i) {\n std::copy_n(src + hist_move_src_[i] / 2, hist_move_size_[i] / 2,\n reinterpret_cast(origin_hist_data_) + hist_move_dest_[i] / 2);\n }\n } else if (HIST_BITS == 16) {\n if (is_use_subcol_) {\n const int32_t* src = reinterpret_cast(hist_buf.data()) + hist_buf.size() / 2 -\n static_cast(num_bin_aligned_);\n #pragma omp parallel for schedule(static) num_threads(num_threads_)\n for (int i = 0; i < static_cast(hist_move_src_.size()); ++i) {\n std::copy_n(src + hist_move_src_[i] / 2, hist_move_size_[i] / 2,\n reinterpret_cast(origin_hist_data_) + hist_move_dest_[i] / 2);\n }\n } else {\n CHECK_EQ(INNER_HIST_BITS, 8);\n const int32_t* src = reinterpret_cast(hist_buf.data()) + hist_buf.size() / 2;\n int32_t* orig_ptr = reinterpret_cast(origin_hist_data_);\n #pragma omp parallel for schedule(static) num_threads(num_threads_)\n for (int i = 0; i < num_bin_; ++i) {\n orig_ptr[i] = src[i];\n }\n }\n }\n } else {\n const hist_t* src = hist_buf.data() + hist_buf.size() -\n 2 * static_cast(num_bin_aligned_);\n #pragma omp parallel for schedule(static) num_threads(num_threads_)\n for (int i = 0; i < static_cast(hist_move_src_.size()); ++i) {\n std::copy_n(src + hist_move_src_[i], hist_move_size_[i],\n origin_hist_data_ + hist_move_dest_[i]);\n }\n }\n}\n\ntemplate void MultiValBinWrapper::HistMove(const std::vector>& hist_buf);\n\ntemplate void MultiValBinWrapper::HistMove(const std::vector>& hist_buf);\n\ntemplate void MultiValBinWrapper::HistMove(const std::vector>& hist_buf);\n\ntemplate void MultiValBinWrapper::HistMove(const std::vector>& hist_buf);\n\ntemplate void MultiValBinWrapper::HistMove(const std::vector>& hist_buf);\n\ntemplate void MultiValBinWrapper::HistMove(const std::vector>& hist_buf);\n\ntemplate \nvoid MultiValBinWrapper::HistMerge(std::vector>* hist_buf) {\n int n_bin_block = 1;\n int bin_block_size = num_bin_;\n Threading::BlockInfo(num_threads_, num_bin_, 512, &n_bin_block,\n &bin_block_size);\n if (USE_QUANT_GRAD) {\n if (HIST_BITS == 32) {\n int64_t* dst = reinterpret_cast(origin_hist_data_);\n if (is_use_subcol_) {\n dst = reinterpret_cast(hist_buf->data()) + hist_buf->size() / 2 - static_cast(num_bin_aligned_);\n }\n #pragma omp parallel for schedule(static, 1) num_threads(num_threads_)\n for (int t = 0; t < n_bin_block; ++t) {\n const int start = t * bin_block_size;\n const int end = std::min(start + bin_block_size, num_bin_);\n for (int tid = 1; tid < n_data_block_; ++tid) {\n auto src_ptr = reinterpret_cast(hist_buf->data()) + static_cast(num_bin_aligned_) * (tid - 1);\n for (int i = start; i < end; ++i) {\n dst[i] += src_ptr[i];\n }\n }\n }\n } else if (HIST_BITS == 16 && INNER_HIST_BITS == 16) {\n int32_t* dst = reinterpret_cast(origin_hist_data_);\n if (is_use_subcol_) {\n dst = reinterpret_cast(hist_buf->data()) + hist_buf->size() / 2 - static_cast(num_bin_aligned_);\n }\n #pragma omp parallel for schedule(static, 1) num_threads(num_threads_)\n for (int t = 0; t < n_bin_block; ++t) {\n const int start = t * bin_block_size;\n const int end = std::min(start + bin_block_size, num_bin_);\n for (int tid = 1; tid < n_data_block_; ++tid) {\n auto src_ptr = reinterpret_cast(hist_buf->data()) + static_cast(num_bin_aligned_) * (tid - 1);\n for (int i = start; i < end; ++i) {\n dst[i] += src_ptr[i];\n }\n }\n }\n } else if (HIST_BITS == 16 && INNER_HIST_BITS == 8) {\n int32_t* dst = reinterpret_cast(hist_buf->data()) + hist_buf->size() / 2;\n std::memset(reinterpret_cast(dst), 0, num_bin_ * kInt16HistBufferEntrySize);\n #pragma omp parallel for schedule(static, 1) num_threads(num_threads_)\n for (int t = 0; t < n_bin_block; ++t) {\n const int start = t * bin_block_size;\n const int end = std::min(start + bin_block_size, num_bin_);\n for (int tid = 0; tid < n_data_block_; ++tid) {\n auto src_ptr = reinterpret_cast(hist_buf->data()) + static_cast(num_bin_aligned_) * tid;\n for (int i = start; i < end; ++i) {\n const int16_t packed_hist = src_ptr[i];\n const int32_t packed_hist_int32 = (static_cast(static_cast(packed_hist >> 8)) << 16) | static_cast(packed_hist & 0x00ff);\n dst[i] += packed_hist_int32;\n }\n }\n }\n }\n } else {\n hist_t* dst = origin_hist_data_;\n if (is_use_subcol_) {\n dst = hist_buf->data() + hist_buf->size() - 2 * static_cast(num_bin_aligned_);\n }\n #pragma omp parallel for schedule(static, 1) num_threads(num_threads_)\n for (int t = 0; t < n_bin_block; ++t) {\n const int start = t * bin_block_size;\n const int end = std::min(start + bin_block_size, num_bin_);\n for (int tid = 1; tid < n_data_block_; ++tid) {\n auto src_ptr = hist_buf->data() + static_cast(num_bin_aligned_) * 2 * (tid - 1);\n for (int i = start * 2; i < end * 2; ++i) {\n dst[i] += src_ptr[i];\n }\n }\n }\n }\n}\n\ntemplate void MultiValBinWrapper::HistMerge(std::vector>* hist_buf);\n\ntemplate void MultiValBinWrapper::HistMerge(std::vector>* hist_buf);\n\ntemplate void MultiValBinWrapper::HistMerge(std::vector>* hist_buf);\n\ntemplate void MultiValBinWrapper::HistMerge(std::vector>* hist_buf);\n\ntemplate void MultiValBinWrapper::HistMerge(std::vector>* hist_buf);\n\ntemplate void MultiValBinWrapper::HistMerge(std::vector>* hist_buf);\n\nvoid MultiValBinWrapper::ResizeHistBuf(std::vector>* hist_buf,\n MultiValBin* sub_multi_val_bin,\n hist_t* origin_hist_data) {\n num_bin_ = sub_multi_val_bin->num_bin();\n num_bin_aligned_ = (num_bin_ + kAlignedSize - 1) / kAlignedSize * kAlignedSize;\n origin_hist_data_ = origin_hist_data;\n size_t new_buf_size = static_cast(n_data_block_) * static_cast(num_bin_aligned_) * 2;\n if (hist_buf->size() < new_buf_size) {\n hist_buf->resize(new_buf_size);\n }\n}\n\nvoid MultiValBinWrapper::CopyMultiValBinSubset(\n const std::vector& group_feature_start,\n const std::vector>& feature_groups,\n const std::vector& is_feature_used,\n const data_size_t* bagging_use_indices,\n data_size_t bagging_indices_cnt) {\n double sum_used_dense_ratio = 0.0;\n double sum_dense_ratio = 0.0;\n int num_used = 0;\n int total = 0;\n std::vector used_feature_index;\n for (int i : feature_groups_contained_) {\n int f_start = group_feature_start[i];\n if (feature_groups[i]->is_multi_val_) {\n for (int j = 0; j < feature_groups[i]->num_feature_; ++j) {\n const auto dense_rate =\n 1.0 - feature_groups[i]->bin_mappers_[j]->sparse_rate();\n if (is_feature_used[f_start + j]) {\n ++num_used;\n used_feature_index.push_back(total);\n sum_used_dense_ratio += dense_rate;\n }\n sum_dense_ratio += dense_rate;\n ++total;\n }\n } else {\n bool is_group_used = false;\n double dense_rate = 0;\n for (int j = 0; j < feature_groups[i]->num_feature_; ++j) {\n if (is_feature_used[f_start + j]) {\n is_group_used = true;\n }\n dense_rate += 1.0 - feature_groups[i]->bin_mappers_[j]->sparse_rate();\n }\n if (is_group_used) {\n ++num_used;\n used_feature_index.push_back(total);\n sum_used_dense_ratio += dense_rate;\n }\n sum_dense_ratio += dense_rate;\n ++total;\n }\n }\n const double k_subfeature_threshold = 0.6;\n if (sum_used_dense_ratio >= sum_dense_ratio * k_subfeature_threshold) {\n // only need to copy subset\n if (is_use_subrow_ && !is_subrow_copied_) {\n if (multi_val_bin_subset_ == nullptr) {\n multi_val_bin_subset_.reset(multi_val_bin_->CreateLike(\n bagging_indices_cnt, multi_val_bin_->num_bin(), total,\n multi_val_bin_->num_element_per_row(), multi_val_bin_->offsets()));\n } else {\n multi_val_bin_subset_->ReSize(\n bagging_indices_cnt, multi_val_bin_->num_bin(), total,\n multi_val_bin_->num_element_per_row(), multi_val_bin_->offsets());\n }\n multi_val_bin_subset_->CopySubrow(\n multi_val_bin_.get(), bagging_use_indices,\n bagging_indices_cnt);\n // avoid to copy subset many times\n is_subrow_copied_ = true;\n }\n } else {\n is_use_subcol_ = true;\n std::vector upper_bound;\n std::vector lower_bound;\n std::vector delta;\n std::vector offsets;\n hist_move_src_.clear();\n hist_move_dest_.clear();\n hist_move_size_.clear();\n\n const int offset = multi_val_bin_->IsSparse() ? 1 : 0;\n int num_total_bin = offset;\n int new_num_total_bin = offset;\n offsets.push_back(static_cast(new_num_total_bin));\n for (int i : feature_groups_contained_) {\n int f_start = group_feature_start[i];\n if (feature_groups[i]->is_multi_val_) {\n for (int j = 0; j < feature_groups[i]->num_feature_; ++j) {\n const auto& bin_mapper = feature_groups[i]->bin_mappers_[j];\n if (i == 0 && j == 0 && bin_mapper->GetMostFreqBin() > 0) {\n num_total_bin = 1;\n }\n int cur_num_bin = bin_mapper->num_bin();\n if (bin_mapper->GetMostFreqBin() == 0) {\n cur_num_bin -= offset;\n }\n num_total_bin += cur_num_bin;\n if (is_feature_used[f_start + j]) {\n new_num_total_bin += cur_num_bin;\n offsets.push_back(static_cast(new_num_total_bin));\n lower_bound.push_back(num_total_bin - cur_num_bin);\n upper_bound.push_back(num_total_bin);\n\n hist_move_src_.push_back(\n (new_num_total_bin - cur_num_bin) * 2);\n hist_move_dest_.push_back((num_total_bin - cur_num_bin) *\n 2);\n hist_move_size_.push_back(cur_num_bin * 2);\n delta.push_back(num_total_bin - new_num_total_bin);\n }\n }\n } else {\n bool is_group_used = false;\n for (int j = 0; j < feature_groups[i]->num_feature_; ++j) {\n if (is_feature_used[f_start + j]) {\n is_group_used = true;\n break;\n }\n }\n int cur_num_bin = feature_groups[i]->bin_offsets_.back() - offset;\n num_total_bin += cur_num_bin;\n if (is_group_used) {\n new_num_total_bin += cur_num_bin;\n offsets.push_back(static_cast(new_num_total_bin));\n lower_bound.push_back(num_total_bin - cur_num_bin);\n upper_bound.push_back(num_total_bin);\n\n hist_move_src_.push_back(\n (new_num_total_bin - cur_num_bin) * 2);\n hist_move_dest_.push_back((num_total_bin - cur_num_bin) *\n 2);\n hist_move_size_.push_back(cur_num_bin * 2);\n delta.push_back(num_total_bin - new_num_total_bin);\n }\n }\n }\n // avoid out of range\n lower_bound.push_back(num_total_bin);\n upper_bound.push_back(num_total_bin);\n data_size_t num_data = is_use_subrow_ ? bagging_indices_cnt : num_data_;\n if (multi_val_bin_subset_ == nullptr) {\n multi_val_bin_subset_.reset(multi_val_bin_->CreateLike(\n num_data, new_num_total_bin, num_used, sum_used_dense_ratio, offsets));\n } else {\n multi_val_bin_subset_->ReSize(num_data, new_num_total_bin,\n num_used, sum_used_dense_ratio, offsets);\n }\n if (is_use_subrow_) {\n multi_val_bin_subset_->CopySubrowAndSubcol(\n multi_val_bin_.get(), bagging_use_indices,\n bagging_indices_cnt, used_feature_index, lower_bound,\n upper_bound, delta);\n // may need to recopy subset\n is_subrow_copied_ = false;\n } else {\n multi_val_bin_subset_->CopySubcol(\n multi_val_bin_.get(), used_feature_index, lower_bound, upper_bound, delta);\n }\n }\n}\n\nvoid TrainingShareStates::CalcBinOffsets(const std::vector>& feature_groups,\n std::vector* offsets, bool in_is_col_wise) {\n offsets->clear();\n feature_hist_offsets_.clear();\n if (in_is_col_wise) {\n uint32_t cur_num_bin = 0;\n uint32_t hist_cur_num_bin = 0;\n for (int group = 0; group < static_cast(feature_groups.size()); ++group) {\n const std::unique_ptr& feature_group = feature_groups[group];\n if (feature_group->is_multi_val_) {\n if (feature_group->is_dense_multi_val_) {\n for (int i = 0; i < feature_group->num_feature_; ++i) {\n const std::unique_ptr& bin_mapper = feature_group->bin_mappers_[i];\n if (group == 0 && i == 0 && bin_mapper->GetMostFreqBin() > 0) {\n cur_num_bin += 1;\n hist_cur_num_bin += 1;\n }\n offsets->push_back(cur_num_bin);\n feature_hist_offsets_.push_back(hist_cur_num_bin);\n int num_bin = bin_mapper->num_bin();\n hist_cur_num_bin += num_bin;\n if (bin_mapper->GetMostFreqBin() == 0) {\n feature_hist_offsets_.back() += 1;\n }\n cur_num_bin += num_bin;\n }\n offsets->push_back(cur_num_bin);\n CHECK(cur_num_bin == feature_group->bin_offsets_.back());\n } else {\n cur_num_bin += 1;\n hist_cur_num_bin += 1;\n for (int i = 0; i < feature_group->num_feature_; ++i) {\n offsets->push_back(cur_num_bin);\n feature_hist_offsets_.push_back(hist_cur_num_bin);\n const std::unique_ptr& bin_mapper = feature_group->bin_mappers_[i];\n int num_bin = bin_mapper->num_bin();\n if (bin_mapper->GetMostFreqBin() == 0) {\n num_bin -= 1;\n }\n hist_cur_num_bin += num_bin;\n cur_num_bin += num_bin;\n }\n offsets->push_back(cur_num_bin);\n CHECK(cur_num_bin == feature_group->bin_offsets_.back());\n }\n } else {\n for (int i = 0; i < feature_group->num_feature_; ++i) {\n feature_hist_offsets_.push_back(hist_cur_num_bin + feature_group->bin_offsets_[i]);\n }\n hist_cur_num_bin += feature_group->bin_offsets_.back();\n }\n }\n feature_hist_offsets_.push_back(hist_cur_num_bin);\n num_hist_total_bin_ = static_cast(feature_hist_offsets_.back());\n } else {\n double sum_dense_ratio = 0.0f;\n int ncol = 0;\n for (int gid = 0; gid < static_cast(feature_groups.size()); ++gid) {\n if (feature_groups[gid]->is_multi_val_) {\n ncol += feature_groups[gid]->num_feature_;\n } else {\n ++ncol;\n }\n for (int fid = 0; fid < feature_groups[gid]->num_feature_; ++fid) {\n const auto& bin_mapper = feature_groups[gid]->bin_mappers_[fid];\n sum_dense_ratio += 1.0f - bin_mapper->sparse_rate();\n }\n }\n sum_dense_ratio /= ncol;\n const bool is_sparse_row_wise = (1.0f - sum_dense_ratio) >=\n MultiValBin::multi_val_bin_sparse_threshold ? 1 : 0;\n if (is_sparse_row_wise) {\n int cur_num_bin = 1;\n uint32_t hist_cur_num_bin = 1;\n for (int group = 0; group < static_cast(feature_groups.size()); ++group) {\n const std::unique_ptr& feature_group = feature_groups[group];\n if (feature_group->is_multi_val_) {\n for (int i = 0; i < feature_group->num_feature_; ++i) {\n offsets->push_back(cur_num_bin);\n feature_hist_offsets_.push_back(hist_cur_num_bin);\n const std::unique_ptr& bin_mapper = feature_group->bin_mappers_[i];\n int num_bin = bin_mapper->num_bin();\n if (bin_mapper->GetMostFreqBin() == 0) {\n num_bin -= 1;\n }\n cur_num_bin += num_bin;\n hist_cur_num_bin += num_bin;\n }\n } else {\n offsets->push_back(cur_num_bin);\n cur_num_bin += feature_group->bin_offsets_.back() - 1;\n for (int i = 0; i < feature_group->num_feature_; ++i) {\n feature_hist_offsets_.push_back(hist_cur_num_bin + feature_group->bin_offsets_[i] - 1);\n }\n hist_cur_num_bin += feature_group->bin_offsets_.back() - 1;\n }\n }\n offsets->push_back(cur_num_bin);\n feature_hist_offsets_.push_back(hist_cur_num_bin);\n } else {\n int cur_num_bin = 0;\n uint32_t hist_cur_num_bin = 0;\n for (int group = 0; group < static_cast(feature_groups.size()); ++group) {\n const std::unique_ptr& feature_group = feature_groups[group];\n if (feature_group->is_multi_val_) {\n for (int i = 0; i < feature_group->num_feature_; ++i) {\n const std::unique_ptr& bin_mapper = feature_group->bin_mappers_[i];\n if (group == 0 && i == 0 && bin_mapper->GetMostFreqBin() > 0) {\n cur_num_bin += 1;\n hist_cur_num_bin += 1;\n }\n offsets->push_back(cur_num_bin);\n feature_hist_offsets_.push_back(hist_cur_num_bin);\n int num_bin = bin_mapper->num_bin();\n cur_num_bin += num_bin;\n hist_cur_num_bin += num_bin;\n if (bin_mapper->GetMostFreqBin() == 0) {\n feature_hist_offsets_.back() += 1;\n }\n }\n } else {\n offsets->push_back(cur_num_bin);\n cur_num_bin += feature_group->bin_offsets_.back();\n for (int i = 0; i < feature_group->num_feature_; ++i) {\n feature_hist_offsets_.push_back(hist_cur_num_bin + feature_group->bin_offsets_[i]);\n }\n hist_cur_num_bin += feature_group->bin_offsets_.back();\n }\n }\n offsets->push_back(cur_num_bin);\n feature_hist_offsets_.push_back(hist_cur_num_bin);\n }\n num_hist_total_bin_ = static_cast(feature_hist_offsets_.back());\n }\n #ifdef USE_CUDA\n column_hist_offsets_ = *offsets;\n #endif // USE_CUDA\n}\n\nvoid TrainingShareStates::SetMultiValBin(MultiValBin* bin, data_size_t num_data,\n const std::vector>& feature_groups,\n bool dense_only, bool sparse_only, const int num_grad_quant_bins) {\n num_threads = OMP_NUM_THREADS();\n if (bin == nullptr) {\n return;\n }\n std::vector feature_groups_contained;\n for (int group = 0; group < static_cast(feature_groups.size()); ++group) {\n const auto& feature_group = feature_groups[group];\n if (feature_group->is_multi_val_) {\n if (!dense_only) {\n feature_groups_contained.push_back(group);\n }\n } else if (!sparse_only) {\n feature_groups_contained.push_back(group);\n }\n }\n num_total_bin_ += bin->num_bin();\n num_elements_per_row_ += bin->num_element_per_row();\n multi_val_bin_wrapper_.reset(new MultiValBinWrapper(\n bin, num_data, feature_groups_contained, num_grad_quant_bins));\n}\n\n} // namespace LightGBM\n" }, { "path": "src/io/tree.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nTree::Tree(int max_leaves, bool track_branch_features, bool is_linear)\n :max_leaves_(max_leaves), track_branch_features_(track_branch_features) {\n left_child_.resize(max_leaves_ - 1);\n right_child_.resize(max_leaves_ - 1);\n split_feature_inner_.resize(max_leaves_ - 1);\n split_feature_.resize(max_leaves_ - 1);\n threshold_in_bin_.resize(max_leaves_ - 1);\n threshold_.resize(max_leaves_ - 1);\n decision_type_.resize(max_leaves_ - 1, 0);\n split_gain_.resize(max_leaves_ - 1);\n leaf_parent_.resize(max_leaves_);\n leaf_value_.resize(max_leaves_);\n leaf_weight_.resize(max_leaves_);\n leaf_count_.resize(max_leaves_);\n internal_value_.resize(max_leaves_ - 1);\n internal_weight_.resize(max_leaves_ - 1);\n internal_count_.resize(max_leaves_ - 1);\n leaf_depth_.resize(max_leaves_);\n if (track_branch_features_) {\n branch_features_ = std::vector>(max_leaves_);\n }\n // root is in the depth 0\n leaf_depth_[0] = 0;\n num_leaves_ = 1;\n leaf_value_[0] = 0.0f;\n leaf_weight_[0] = 0.0f;\n leaf_parent_[0] = -1;\n shrinkage_ = 1.0f;\n num_cat_ = 0;\n cat_boundaries_.push_back(0);\n cat_boundaries_inner_.push_back(0);\n max_depth_ = -1;\n is_linear_ = is_linear;\n if (is_linear_) {\n leaf_coeff_.resize(max_leaves_);\n leaf_const_ = std::vector(max_leaves_, 0);\n leaf_features_.resize(max_leaves_);\n leaf_features_inner_.resize(max_leaves_);\n }\n #ifdef USE_CUDA\n is_cuda_tree_ = false;\n #endif // USE_CUDA\n}\n\nint Tree::Split(int leaf, int feature, int real_feature, uint32_t threshold_bin,\n double threshold_double, double left_value, double right_value,\n int left_cnt, int right_cnt, double left_weight, double right_weight, float gain,\n MissingType missing_type, bool default_left) {\n Split(leaf, feature, real_feature, left_value, right_value, left_cnt, right_cnt, left_weight, right_weight, gain);\n int new_node_idx = num_leaves_ - 1;\n decision_type_[new_node_idx] = 0;\n SetDecisionType(&decision_type_[new_node_idx], false, kCategoricalMask);\n SetDecisionType(&decision_type_[new_node_idx], default_left, kDefaultLeftMask);\n SetMissingType(&decision_type_[new_node_idx], static_cast(missing_type));\n threshold_in_bin_[new_node_idx] = threshold_bin;\n threshold_[new_node_idx] = threshold_double;\n ++num_leaves_;\n return num_leaves_ - 1;\n}\n\nint Tree::SplitCategorical(int leaf, int feature, int real_feature, const uint32_t* threshold_bin, int num_threshold_bin,\n const uint32_t* threshold, int num_threshold, double left_value, double right_value,\n data_size_t left_cnt, data_size_t right_cnt, double left_weight, double right_weight, float gain, MissingType missing_type) {\n Split(leaf, feature, real_feature, left_value, right_value, left_cnt, right_cnt, left_weight, right_weight, gain);\n int new_node_idx = num_leaves_ - 1;\n decision_type_[new_node_idx] = 0;\n SetDecisionType(&decision_type_[new_node_idx], true, kCategoricalMask);\n SetMissingType(&decision_type_[new_node_idx], static_cast(missing_type));\n threshold_in_bin_[new_node_idx] = num_cat_;\n threshold_[new_node_idx] = num_cat_;\n ++num_cat_;\n cat_boundaries_.push_back(cat_boundaries_.back() + num_threshold);\n for (int i = 0; i < num_threshold; ++i) {\n cat_threshold_.push_back(threshold[i]);\n }\n cat_boundaries_inner_.push_back(cat_boundaries_inner_.back() + num_threshold_bin);\n for (int i = 0; i < num_threshold_bin; ++i) {\n cat_threshold_inner_.push_back(threshold_bin[i]);\n }\n ++num_leaves_;\n return num_leaves_ - 1;\n}\n\n#define PredictionFun(niter, fidx_in_iter, start_pos, decision_fun, iter_idx, \\\n data_idx) \\\n std::vector> iter((niter)); \\\n for (int i = 0; i < (niter); ++i) { \\\n iter[i].reset(data->FeatureIterator((fidx_in_iter))); \\\n iter[i]->Reset((start_pos)); \\\n } \\\n for (data_size_t i = start; i < end; ++i) { \\\n int node = 0; \\\n while (node >= 0) { \\\n node = decision_fun(iter[(iter_idx)]->Get((data_idx)), node, \\\n default_bins[node], max_bins[node]); \\\n } \\\n score[(data_idx)] += static_cast(leaf_value_[~node]); \\\n }\\\n\n\n#define PredictionFunLinear(niter, fidx_in_iter, start_pos, decision_fun, \\\n iter_idx, data_idx) \\\n std::vector> iter((niter)); \\\n for (int i = 0; i < (niter); ++i) { \\\n iter[i].reset(data->FeatureIterator((fidx_in_iter))); \\\n iter[i]->Reset((start_pos)); \\\n } \\\n for (data_size_t i = start; i < end; ++i) { \\\n int node = 0; \\\n if (num_leaves_ > 1) { \\\n while (node >= 0) { \\\n node = decision_fun(iter[(iter_idx)]->Get((data_idx)), node, \\\n default_bins[node], max_bins[node]); \\\n } \\\n node = ~node; \\\n } \\\n double add_score = leaf_const_[node]; \\\n bool nan_found = false; \\\n const double* coeff_ptr = leaf_coeff_[node].data(); \\\n const float** data_ptr = feat_ptr[node].data(); \\\n for (size_t j = 0; j < leaf_features_inner_[node].size(); ++j) { \\\n float feat_val = data_ptr[j][(data_idx)]; \\\n if (std::isnan(feat_val)) { \\\n nan_found = true; \\\n break; \\\n } \\\n add_score += coeff_ptr[j] * feat_val; \\\n } \\\n if (nan_found) { \\\n score[(data_idx)] += leaf_value_[node]; \\\n } else { \\\n score[(data_idx)] += add_score; \\\n } \\\n}\\\n\n\nvoid Tree::AddPredictionToScore(const Dataset* data, data_size_t num_data, double* score) const {\n if (!is_linear_ && num_leaves_ <= 1) {\n if (leaf_value_[0] != 0.0f) {\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024)\n for (data_size_t i = 0; i < num_data; ++i) {\n score[i] += leaf_value_[0];\n }\n }\n return;\n }\n std::vector default_bins(num_leaves_ - 1);\n std::vector max_bins(num_leaves_ - 1);\n for (int i = 0; i < num_leaves_ - 1; ++i) {\n const int fidx = split_feature_inner_[i];\n auto bin_mapper = data->FeatureBinMapper(fidx);\n default_bins[i] = bin_mapper->GetDefaultBin();\n max_bins[i] = bin_mapper->num_bin() - 1;\n }\n if (is_linear_) {\n std::vector> feat_ptr(num_leaves_);\n for (int leaf_num = 0; leaf_num < num_leaves_; ++leaf_num) {\n for (int feat : leaf_features_inner_[leaf_num]) {\n feat_ptr[leaf_num].push_back(data->raw_index(feat));\n }\n }\n if (num_cat_ > 0) {\n if (data->num_features() > num_leaves_ - 1) {\n Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins, &feat_ptr]\n (int, data_size_t start, data_size_t end) {\n PredictionFunLinear(num_leaves_ - 1, split_feature_inner_[i], start, DecisionInner, node, i);\n });\n } else {\n Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins, &feat_ptr]\n (int, data_size_t start, data_size_t end) {\n PredictionFunLinear(data->num_features(), i, start, DecisionInner, split_feature_inner_[node], i);\n });\n }\n } else {\n if (data->num_features() > num_leaves_ - 1) {\n Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins, &feat_ptr]\n (int, data_size_t start, data_size_t end) {\n PredictionFunLinear(num_leaves_ - 1, split_feature_inner_[i], start, NumericalDecisionInner, node, i);\n });\n } else {\n Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins, &feat_ptr]\n (int, data_size_t start, data_size_t end) {\n PredictionFunLinear(data->num_features(), i, start, NumericalDecisionInner, split_feature_inner_[node], i);\n });\n }\n }\n } else {\n if (num_cat_ > 0) {\n if (data->num_features() > num_leaves_ - 1) {\n Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins]\n (int, data_size_t start, data_size_t end) {\n PredictionFun(num_leaves_ - 1, split_feature_inner_[i], start, DecisionInner, node, i);\n });\n } else {\n Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins]\n (int, data_size_t start, data_size_t end) {\n PredictionFun(data->num_features(), i, start, DecisionInner, split_feature_inner_[node], i);\n });\n }\n } else {\n if (data->num_features() > num_leaves_ - 1) {\n Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins]\n (int, data_size_t start, data_size_t end) {\n PredictionFun(num_leaves_ - 1, split_feature_inner_[i], start, NumericalDecisionInner, node, i);\n });\n } else {\n Threading::For(0, num_data, 512, [this, &data, score, &default_bins, &max_bins]\n (int, data_size_t start, data_size_t end) {\n PredictionFun(data->num_features(), i, start, NumericalDecisionInner, split_feature_inner_[node], i);\n });\n }\n }\n }\n}\n\nvoid Tree::AddPredictionToScore(const Dataset* data,\n const data_size_t* used_data_indices,\n data_size_t num_data, double* score) const {\n if (!is_linear_ && num_leaves_ <= 1) {\n if (leaf_value_[0] != 0.0f) {\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (num_data >= 1024)\n for (data_size_t i = 0; i < num_data; ++i) {\n score[used_data_indices[i]] += leaf_value_[0];\n }\n }\n return;\n }\n std::vector default_bins(num_leaves_ - 1);\n std::vector max_bins(num_leaves_ - 1);\n for (int i = 0; i < num_leaves_ - 1; ++i) {\n const int fidx = split_feature_inner_[i];\n auto bin_mapper = data->FeatureBinMapper(fidx);\n default_bins[i] = bin_mapper->GetDefaultBin();\n max_bins[i] = bin_mapper->num_bin() - 1;\n }\n if (is_linear_) {\n std::vector> feat_ptr(num_leaves_);\n for (int leaf_num = 0; leaf_num < num_leaves_; ++leaf_num) {\n for (int feat : leaf_features_inner_[leaf_num]) {\n feat_ptr[leaf_num].push_back(data->raw_index(feat));\n }\n }\n if (num_cat_ > 0) {\n if (data->num_features() > num_leaves_ - 1) {\n Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins, &feat_ptr]\n (int, data_size_t start, data_size_t end) {\n PredictionFunLinear(num_leaves_ - 1, split_feature_inner_[i], used_data_indices[start], DecisionInner,\n node, used_data_indices[i]);\n });\n } else {\n Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins, &feat_ptr]\n (int, data_size_t start, data_size_t end) {\n PredictionFunLinear(data->num_features(), i, used_data_indices[start], DecisionInner, split_feature_inner_[node], used_data_indices[i]);\n });\n }\n } else {\n if (data->num_features() > num_leaves_ - 1) {\n Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins, &feat_ptr]\n (int, data_size_t start, data_size_t end) {\n PredictionFunLinear(num_leaves_ - 1, split_feature_inner_[i], used_data_indices[start], NumericalDecisionInner,\n node, used_data_indices[i]);\n });\n } else {\n Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins, &feat_ptr]\n (int, data_size_t start, data_size_t end) {\n PredictionFunLinear(data->num_features(), i, used_data_indices[start], NumericalDecisionInner,\n split_feature_inner_[node], used_data_indices[i]);\n });\n }\n }\n } else {\n if (num_cat_ > 0) {\n if (data->num_features() > num_leaves_ - 1) {\n Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins]\n (int, data_size_t start, data_size_t end) {\n PredictionFun(num_leaves_ - 1, split_feature_inner_[i], used_data_indices[start], DecisionInner, node, used_data_indices[i]);\n });\n } else {\n Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins]\n (int, data_size_t start, data_size_t end) {\n PredictionFun(data->num_features(), i, used_data_indices[start], DecisionInner, split_feature_inner_[node], used_data_indices[i]);\n });\n }\n } else {\n if (data->num_features() > num_leaves_ - 1) {\n Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins]\n (int, data_size_t start, data_size_t end) {\n PredictionFun(num_leaves_ - 1, split_feature_inner_[i], used_data_indices[start], NumericalDecisionInner, node, used_data_indices[i]);\n });\n } else {\n Threading::For(0, num_data, 512, [this, &data, score, used_data_indices, &default_bins, &max_bins]\n (int, data_size_t start, data_size_t end) {\n PredictionFun(data->num_features(), i, used_data_indices[start], NumericalDecisionInner, split_feature_inner_[node], used_data_indices[i]);\n });\n }\n }\n }\n}\n\n#undef PredictionFun\n#undef PredictionFunLinear\n\ndouble Tree::GetUpperBoundValue() const {\n double upper_bound = leaf_value_[0];\n for (int i = 1; i < num_leaves_; ++i) {\n if (leaf_value_[i] > upper_bound) {\n upper_bound = leaf_value_[i];\n }\n }\n return upper_bound;\n}\n\ndouble Tree::GetLowerBoundValue() const {\n double lower_bound = leaf_value_[0];\n for (int i = 1; i < num_leaves_; ++i) {\n if (leaf_value_[i] < lower_bound) {\n lower_bound = leaf_value_[i];\n }\n }\n return lower_bound;\n}\n\nstd::string Tree::ToString() const {\n std::stringstream str_buf;\n Common::C_stringstream(str_buf);\n\n using CommonC::ArrayToString;\n\n str_buf << \"num_leaves=\" << num_leaves_ << '\\n';\n str_buf << \"num_cat=\" << num_cat_ << '\\n';\n str_buf << \"split_feature=\"\n << ArrayToString(split_feature_, num_leaves_ - 1) << '\\n';\n str_buf << \"split_gain=\"\n << ArrayToString(split_gain_, num_leaves_ - 1) << '\\n';\n str_buf << \"threshold=\"\n << ArrayToString(threshold_, num_leaves_ - 1) << '\\n';\n str_buf << \"decision_type=\"\n << ArrayToString(Common::ArrayCast(decision_type_), num_leaves_ - 1) << '\\n';\n str_buf << \"left_child=\"\n << ArrayToString(left_child_, num_leaves_ - 1) << '\\n';\n str_buf << \"right_child=\"\n << ArrayToString(right_child_, num_leaves_ - 1) << '\\n';\n str_buf << \"leaf_value=\"\n << ArrayToString(leaf_value_, num_leaves_) << '\\n';\n str_buf << \"leaf_weight=\"\n << ArrayToString(leaf_weight_, num_leaves_) << '\\n';\n str_buf << \"leaf_count=\"\n << ArrayToString(leaf_count_, num_leaves_) << '\\n';\n str_buf << \"internal_value=\"\n << ArrayToString(internal_value_, num_leaves_ - 1) << '\\n';\n str_buf << \"internal_weight=\"\n << ArrayToString(internal_weight_, num_leaves_ - 1) << '\\n';\n str_buf << \"internal_count=\"\n << ArrayToString(internal_count_, num_leaves_ - 1) << '\\n';\n if (num_cat_ > 0) {\n str_buf << \"cat_boundaries=\"\n << ArrayToString(cat_boundaries_, num_cat_ + 1) << '\\n';\n str_buf << \"cat_threshold=\"\n << ArrayToString(cat_threshold_, cat_threshold_.size()) << '\\n';\n }\n str_buf << \"is_linear=\" << is_linear_ << '\\n';\n\n if (is_linear_) {\n str_buf << \"leaf_const=\"\n << ArrayToString(leaf_const_, num_leaves_) << '\\n';\n std::vector num_feat(num_leaves_);\n for (int i = 0; i < num_leaves_; ++i) {\n num_feat[i] = static_cast(leaf_coeff_[i].size());\n }\n str_buf << \"num_features=\"\n << ArrayToString(num_feat, num_leaves_) << '\\n';\n str_buf << \"leaf_features=\";\n for (int i = 0; i < num_leaves_; ++i) {\n if (num_feat[i] > 0) {\n str_buf << ArrayToString(leaf_features_[i], leaf_features_[i].size()) << ' ';\n }\n str_buf << ' ';\n }\n str_buf << '\\n';\n str_buf << \"leaf_coeff=\";\n for (int i = 0; i < num_leaves_; ++i) {\n if (num_feat[i] > 0) {\n str_buf << ArrayToString(leaf_coeff_[i], leaf_coeff_[i].size()) << ' ';\n }\n str_buf << ' ';\n }\n str_buf << '\\n';\n }\n str_buf << \"shrinkage=\" << shrinkage_ << '\\n';\n str_buf << '\\n';\n\n return str_buf.str();\n}\n\nstd::string Tree::ToJSON() const {\n std::stringstream str_buf;\n Common::C_stringstream(str_buf);\n str_buf << std::setprecision(std::numeric_limits::digits10 + 2);\n str_buf << \"\\\"num_leaves\\\":\" << num_leaves_ << \",\" << '\\n';\n str_buf << \"\\\"num_cat\\\":\" << num_cat_ << \",\" << '\\n';\n str_buf << \"\\\"shrinkage\\\":\" << shrinkage_ << \",\" << '\\n';\n if (num_leaves_ == 1) {\n str_buf << \"\\\"tree_structure\\\":{\";\n str_buf << \"\\\"leaf_value\\\":\" << leaf_value_[0] << \", \" << '\\n';\n if (is_linear_) {\n str_buf << \"\\\"leaf_count\\\":\" << leaf_count_[0] << \", \" << '\\n';\n str_buf << LinearModelToJSON(0);\n } else {\n str_buf << \"\\\"leaf_count\\\":\" << leaf_count_[0];\n }\n str_buf << \"}\" << '\\n';\n } else {\n str_buf << \"\\\"tree_structure\\\":\" << NodeToJSON(0) << '\\n';\n }\n return str_buf.str();\n}\n\nstd::string Tree::LinearModelToJSON(int index) const {\n std::stringstream str_buf;\n Common::C_stringstream(str_buf);\n str_buf << std::setprecision(std::numeric_limits::digits10 + 2);\n str_buf << \"\\\"leaf_const\\\":\" << leaf_const_[index] << \",\" << \"\\n\";\n int num_features = static_cast(leaf_features_[index].size());\n if (num_features > 0) {\n str_buf << \"\\\"leaf_features\\\":[\";\n for (int i = 0; i < num_features - 1; ++i) {\n str_buf << leaf_features_[index][i] << \", \";\n }\n str_buf << leaf_features_[index][num_features - 1] << \"]\" << \", \" << \"\\n\";\n str_buf << \"\\\"leaf_coeff\\\":[\";\n for (int i = 0; i < num_features - 1; ++i) {\n str_buf << leaf_coeff_[index][i] << \", \";\n }\n str_buf << leaf_coeff_[index][num_features - 1] << \"]\" << \"\\n\";\n } else {\n str_buf << \"\\\"leaf_features\\\":[],\\n\";\n str_buf << \"\\\"leaf_coeff\\\":[]\\n\";\n }\n return str_buf.str();\n}\n\nstd::string Tree::NodeToJSON(int index) const {\n std::stringstream str_buf;\n Common::C_stringstream(str_buf);\n str_buf << std::setprecision(std::numeric_limits::digits10 + 2);\n if (index >= 0) {\n // non-leaf\n str_buf << \"{\" << '\\n';\n str_buf << \"\\\"split_index\\\":\" << index << \",\" << '\\n';\n str_buf << \"\\\"split_feature\\\":\" << split_feature_[index] << \",\" << '\\n';\n str_buf << \"\\\"split_gain\\\":\" << Common::AvoidInf(split_gain_[index]) << \",\" << '\\n';\n if (GetDecisionType(decision_type_[index], kCategoricalMask)) {\n int cat_idx = static_cast(threshold_[index]);\n std::vector cats;\n for (int i = cat_boundaries_[cat_idx]; i < cat_boundaries_[cat_idx + 1]; ++i) {\n for (int j = 0; j < 32; ++j) {\n int cat = (i - cat_boundaries_[cat_idx]) * 32 + j;\n if (Common::FindInBitset(cat_threshold_.data() + cat_boundaries_[cat_idx],\n cat_boundaries_[cat_idx + 1] - cat_boundaries_[cat_idx], cat)) {\n cats.push_back(cat);\n }\n }\n }\n str_buf << \"\\\"threshold\\\":\\\"\" << CommonC::Join(cats, \"||\") << \"\\\",\" << '\\n';\n str_buf << \"\\\"decision_type\\\":\\\"==\\\",\" << '\\n';\n } else {\n str_buf << \"\\\"threshold\\\":\" << Common::AvoidInf(threshold_[index]) << \",\" << '\\n';\n str_buf << \"\\\"decision_type\\\":\\\"<=\\\",\" << '\\n';\n }\n if (GetDecisionType(decision_type_[index], kDefaultLeftMask)) {\n str_buf << \"\\\"default_left\\\":true,\" << '\\n';\n } else {\n str_buf << \"\\\"default_left\\\":false,\" << '\\n';\n }\n uint8_t missing_type = GetMissingType(decision_type_[index]);\n if (missing_type == MissingType::None) {\n str_buf << \"\\\"missing_type\\\":\\\"None\\\",\" << '\\n';\n } else if (missing_type == MissingType::Zero) {\n str_buf << \"\\\"missing_type\\\":\\\"Zero\\\",\" << '\\n';\n } else {\n str_buf << \"\\\"missing_type\\\":\\\"NaN\\\",\" << '\\n';\n }\n str_buf << \"\\\"internal_value\\\":\" << internal_value_[index] << \",\" << '\\n';\n str_buf << \"\\\"internal_weight\\\":\" << internal_weight_[index] << \",\" << '\\n';\n str_buf << \"\\\"internal_count\\\":\" << internal_count_[index] << \",\" << '\\n';\n str_buf << \"\\\"left_child\\\":\" << NodeToJSON(left_child_[index]) << \",\" << '\\n';\n str_buf << \"\\\"right_child\\\":\" << NodeToJSON(right_child_[index]) << '\\n';\n str_buf << \"}\";\n } else {\n // leaf\n index = ~index;\n str_buf << \"{\" << '\\n';\n str_buf << \"\\\"leaf_index\\\":\" << index << \",\" << '\\n';\n str_buf << \"\\\"leaf_value\\\":\" << leaf_value_[index] << \",\" << '\\n';\n str_buf << \"\\\"leaf_weight\\\":\" << leaf_weight_[index] << \",\" << '\\n';\n if (is_linear_) {\n str_buf << \"\\\"leaf_count\\\":\" << leaf_count_[index] << \",\" << '\\n';\n str_buf << LinearModelToJSON(index);\n } else {\n str_buf << \"\\\"leaf_count\\\":\" << leaf_count_[index] << '\\n';\n }\n str_buf << \"}\";\n }\n return str_buf.str();\n}\n\nstd::string Tree::NumericalDecisionIfElse(int node) const {\n std::stringstream str_buf;\n Common::C_stringstream(str_buf);\n str_buf << std::setprecision(std::numeric_limits::digits10 + 2);\n uint8_t missing_type = GetMissingType(decision_type_[node]);\n bool default_left = GetDecisionType(decision_type_[node], kDefaultLeftMask);\n if (missing_type != MissingType::NaN) {\n str_buf << \"if (std::isnan(fval)) fval = 0.0;\";\n }\n if (missing_type == MissingType::Zero) {\n if (default_left) {\n str_buf << \"if (Tree::IsZero(fval)) {\";\n } else {\n str_buf << \"if (!Tree::IsZero(fval)) {\";\n }\n } else if (missing_type == MissingType::NaN) {\n if (default_left) {\n str_buf << \"if (std::isnan(fval)) {\";\n } else {\n str_buf << \"if (!std::isnan(fval)) {\";\n }\n } else {\n str_buf << \"if (fval <= \" << threshold_[node] << \") {\";\n }\n return str_buf.str();\n}\n\nstd::string Tree::CategoricalDecisionIfElse(int node) const {\n std::stringstream str_buf;\n Common::C_stringstream(str_buf);\n int cat_idx = static_cast(threshold_[node]);\n str_buf << \"if (std::isnan(fval)) { int_fval = -1; } else { int_fval = static_cast(fval); }\";\n str_buf << \"if (int_fval >= 0 && int_fval < 32 * (\";\n str_buf << cat_boundaries_[cat_idx + 1] - cat_boundaries_[cat_idx];\n str_buf << \") && (((cat_threshold[\" << cat_boundaries_[cat_idx];\n str_buf << \" + int_fval / 32] >> (int_fval & 31)) & 1))) {\";\n return str_buf.str();\n}\n\nstd::string Tree::ToIfElse(int index, bool predict_leaf_index) const {\n std::stringstream str_buf;\n Common::C_stringstream(str_buf);\n str_buf << \"double PredictTree\" << index;\n if (predict_leaf_index) {\n str_buf << \"Leaf\";\n }\n str_buf << \"(const double* arr) { \";\n if (num_leaves_ <= 1) {\n str_buf << \"return \" << leaf_value_[0] << \";\";\n } else {\n str_buf << \"const std::vector cat_threshold = {\";\n for (size_t i = 0; i < cat_threshold_.size(); ++i) {\n if (i != 0) {\n str_buf << \",\";\n }\n str_buf << cat_threshold_[i];\n }\n str_buf << \"};\";\n // use this for the missing value conversion\n str_buf << \"double fval = 0.0f; \";\n if (num_cat_ > 0) {\n str_buf << \"int int_fval = 0; \";\n }\n str_buf << NodeToIfElse(0, predict_leaf_index);\n }\n str_buf << \" }\" << '\\n';\n\n // Predict func by Map to ifelse\n str_buf << \"double PredictTree\" << index;\n if (predict_leaf_index) {\n str_buf << \"LeafByMap\";\n } else {\n str_buf << \"ByMap\";\n }\n str_buf << \"(const std::unordered_map& arr) { \";\n if (num_leaves_ <= 1) {\n str_buf << \"return \" << leaf_value_[0] << \";\";\n } else {\n str_buf << \"const std::vector cat_threshold = {\";\n for (size_t i = 0; i < cat_threshold_.size(); ++i) {\n if (i != 0) {\n str_buf << \",\";\n }\n str_buf << cat_threshold_[i];\n }\n str_buf << \"};\";\n // use this for the missing value conversion\n str_buf << \"double fval = 0.0f; \";\n if (num_cat_ > 0) {\n str_buf << \"int int_fval = 0; \";\n }\n str_buf << NodeToIfElseByMap(0, predict_leaf_index);\n }\n str_buf << \" }\" << '\\n';\n\n return str_buf.str();\n}\n\nstd::string Tree::NodeToIfElse(int index, bool predict_leaf_index) const {\n std::stringstream str_buf;\n Common::C_stringstream(str_buf);\n str_buf << std::setprecision(std::numeric_limits::digits10 + 2);\n if (index >= 0) {\n // non-leaf\n str_buf << \"fval = arr[\" << split_feature_[index] << \"];\";\n if (GetDecisionType(decision_type_[index], kCategoricalMask) == 0) {\n str_buf << NumericalDecisionIfElse(index);\n } else {\n str_buf << CategoricalDecisionIfElse(index);\n }\n // left subtree\n str_buf << NodeToIfElse(left_child_[index], predict_leaf_index);\n str_buf << \" } else { \";\n // right subtree\n str_buf << NodeToIfElse(right_child_[index], predict_leaf_index);\n str_buf << \" }\";\n } else {\n // leaf\n str_buf << \"return \";\n if (predict_leaf_index) {\n str_buf << ~index;\n } else {\n str_buf << leaf_value_[~index];\n }\n str_buf << \";\";\n }\n\n return str_buf.str();\n}\n\nstd::string Tree::NodeToIfElseByMap(int index, bool predict_leaf_index) const {\n std::stringstream str_buf;\n Common::C_stringstream(str_buf);\n str_buf << std::setprecision(std::numeric_limits::digits10 + 2);\n if (index >= 0) {\n // non-leaf\n str_buf << \"fval = arr.count(\" << split_feature_[index] << \") > 0 ? arr.at(\" << split_feature_[index] << \") : 0.0f;\";\n if (GetDecisionType(decision_type_[index], kCategoricalMask) == 0) {\n str_buf << NumericalDecisionIfElse(index);\n } else {\n str_buf << CategoricalDecisionIfElse(index);\n }\n // left subtree\n str_buf << NodeToIfElseByMap(left_child_[index], predict_leaf_index);\n str_buf << \" } else { \";\n // right subtree\n str_buf << NodeToIfElseByMap(right_child_[index], predict_leaf_index);\n str_buf << \" }\";\n } else {\n // leaf\n str_buf << \"return \";\n if (predict_leaf_index) {\n str_buf << ~index;\n } else {\n str_buf << leaf_value_[~index];\n }\n str_buf << \";\";\n }\n\n return str_buf.str();\n}\n\nTree::Tree(const char* str, size_t* used_len) {\n auto p = str;\n std::unordered_map key_vals;\n const int max_num_line = 22;\n int read_line = 0;\n while (read_line < max_num_line) {\n if (*p == '\\r' || *p == '\\n') break;\n auto start = p;\n while (*p != '=') ++p;\n std::string key(start, p - start);\n ++p;\n start = p;\n while (*p != '\\r' && *p != '\\n') ++p;\n key_vals[key] = std::string(start, p - start);\n ++read_line;\n if (*p == '\\r') ++p;\n if (*p == '\\n') ++p;\n }\n *used_len = p - str;\n\n if (key_vals.count(\"num_leaves\") <= 0) {\n Log::Fatal(\"Tree model should contain num_leaves field\");\n }\n\n Common::Atoi(key_vals[\"num_leaves\"].c_str(), &num_leaves_);\n\n if (key_vals.count(\"num_cat\") <= 0) {\n Log::Fatal(\"Tree model should contain num_cat field\");\n }\n\n Common::Atoi(key_vals[\"num_cat\"].c_str(), &num_cat_);\n\n if (key_vals.count(\"leaf_value\")) {\n leaf_value_ = CommonC::StringToArray(key_vals[\"leaf_value\"], num_leaves_);\n } else {\n Log::Fatal(\"Tree model string format error, should contain leaf_value field\");\n }\n\n if (key_vals.count(\"shrinkage\")) {\n CommonC::Atof(key_vals[\"shrinkage\"].c_str(), &shrinkage_);\n } else {\n shrinkage_ = 1.0f;\n }\n\n if (key_vals.count(\"is_linear\")) {\n int is_linear_int;\n Common::Atoi(key_vals[\"is_linear\"].c_str(), &is_linear_int);\n is_linear_ = static_cast(is_linear_int);\n } else {\n is_linear_ = false;\n }\n\n if (key_vals.count(\"leaf_count\")) {\n leaf_count_ = CommonC::StringToArrayFast(key_vals[\"leaf_count\"], num_leaves_);\n } else {\n leaf_count_.resize(num_leaves_);\n }\n\n #ifdef USE_CUDA\n is_cuda_tree_ = false;\n #endif // USE_CUDA\n\n if ((num_leaves_ <= 1) && !is_linear_) {\n return;\n }\n\n if (key_vals.count(\"left_child\")) {\n left_child_ = CommonC::StringToArrayFast(key_vals[\"left_child\"], num_leaves_ - 1);\n } else {\n Log::Fatal(\"Tree model string format error, should contain left_child field\");\n }\n\n if (key_vals.count(\"right_child\")) {\n right_child_ = CommonC::StringToArrayFast(key_vals[\"right_child\"], num_leaves_ - 1);\n } else {\n Log::Fatal(\"Tree model string format error, should contain right_child field\");\n }\n\n if (key_vals.count(\"split_feature\")) {\n split_feature_ = CommonC::StringToArrayFast(key_vals[\"split_feature\"], num_leaves_ - 1);\n } else {\n Log::Fatal(\"Tree model string format error, should contain split_feature field\");\n }\n\n if (key_vals.count(\"threshold\")) {\n threshold_ = CommonC::StringToArray(key_vals[\"threshold\"], num_leaves_ - 1);\n } else {\n Log::Fatal(\"Tree model string format error, should contain threshold field\");\n }\n\n if (key_vals.count(\"split_gain\")) {\n split_gain_ = CommonC::StringToArrayFast(key_vals[\"split_gain\"], num_leaves_ - 1);\n } else {\n split_gain_.resize(num_leaves_ - 1);\n }\n\n if (key_vals.count(\"internal_count\")) {\n internal_count_ = CommonC::StringToArrayFast(key_vals[\"internal_count\"], num_leaves_ - 1);\n } else {\n internal_count_.resize(num_leaves_ - 1);\n }\n\n if (key_vals.count(\"internal_value\")) {\n internal_value_ = CommonC::StringToArrayFast(key_vals[\"internal_value\"], num_leaves_ - 1);\n } else {\n internal_value_.resize(num_leaves_ - 1);\n }\n\n if (key_vals.count(\"internal_weight\")) {\n internal_weight_ = CommonC::StringToArrayFast(key_vals[\"internal_weight\"], num_leaves_ - 1);\n } else {\n internal_weight_.resize(num_leaves_ - 1);\n }\n\n if (key_vals.count(\"leaf_weight\")) {\n leaf_weight_ = CommonC::StringToArray(key_vals[\"leaf_weight\"], num_leaves_);\n } else {\n leaf_weight_.resize(num_leaves_);\n }\n\n if (key_vals.count(\"decision_type\")) {\n decision_type_ = CommonC::StringToArrayFast(key_vals[\"decision_type\"], num_leaves_ - 1);\n } else {\n decision_type_ = std::vector(num_leaves_ - 1, 0);\n }\n\n if (is_linear_) {\n if (key_vals.count(\"leaf_const\")) {\n leaf_const_ = Common::StringToArray(key_vals[\"leaf_const\"], num_leaves_);\n } else {\n leaf_const_.resize(num_leaves_);\n }\n std::vector num_feat;\n if (key_vals.count(\"num_features\")) {\n num_feat = Common::StringToArrayFast(key_vals[\"num_features\"], num_leaves_);\n }\n leaf_coeff_.resize(num_leaves_);\n leaf_features_.resize(num_leaves_);\n leaf_features_inner_.resize(num_leaves_);\n if (num_feat.size() > 0) {\n int total_num_feat = 0;\n for (size_t i = 0; i < num_feat.size(); ++i) {\n total_num_feat += num_feat[i];\n }\n std::vector all_leaf_features;\n if (key_vals.count(\"leaf_features\")) {\n all_leaf_features = Common::StringToArrayFast(key_vals[\"leaf_features\"], total_num_feat);\n }\n std::vector all_leaf_coeff;\n if (key_vals.count(\"leaf_coeff\")) {\n all_leaf_coeff = Common::StringToArray(key_vals[\"leaf_coeff\"], total_num_feat);\n }\n int sum_num_feat = 0;\n for (int i = 0; i < num_leaves_; ++i) {\n if (num_feat[i] > 0) {\n if (key_vals.count(\"leaf_features\")) {\n leaf_features_[i].assign(all_leaf_features.begin() + sum_num_feat, all_leaf_features.begin() + sum_num_feat + num_feat[i]);\n }\n if (key_vals.count(\"leaf_coeff\")) {\n leaf_coeff_[i].assign(all_leaf_coeff.begin() + sum_num_feat, all_leaf_coeff.begin() + sum_num_feat + num_feat[i]);\n }\n }\n sum_num_feat += num_feat[i];\n }\n }\n }\n\n if (num_cat_ > 0) {\n if (key_vals.count(\"cat_boundaries\")) {\n cat_boundaries_ = CommonC::StringToArrayFast(key_vals[\"cat_boundaries\"], num_cat_ + 1);\n } else {\n Log::Fatal(\"Tree model should contain cat_boundaries field.\");\n }\n\n if (key_vals.count(\"cat_threshold\")) {\n cat_threshold_ = CommonC::StringToArrayFast(key_vals[\"cat_threshold\"], cat_boundaries_.back());\n } else {\n Log::Fatal(\"Tree model should contain cat_threshold field\");\n }\n }\n max_depth_ = -1;\n}\n\nvoid Tree::ExtendPath(PathElement *unique_path, int unique_depth,\n double zero_fraction, double one_fraction, int feature_index) {\n unique_path[unique_depth].feature_index = feature_index;\n unique_path[unique_depth].zero_fraction = zero_fraction;\n unique_path[unique_depth].one_fraction = one_fraction;\n unique_path[unique_depth].pweight = (unique_depth == 0 ? 1 : 0);\n for (int i = unique_depth - 1; i >= 0; i--) {\n unique_path[i + 1].pweight += one_fraction*unique_path[i].pweight*(i + 1)\n / static_cast(unique_depth + 1);\n unique_path[i].pweight = zero_fraction*unique_path[i].pweight*(unique_depth - i)\n / static_cast(unique_depth + 1);\n }\n}\n\nvoid Tree::UnwindPath(PathElement *unique_path, int unique_depth, int path_index) {\n const double one_fraction = unique_path[path_index].one_fraction;\n const double zero_fraction = unique_path[path_index].zero_fraction;\n double next_one_portion = unique_path[unique_depth].pweight;\n\n for (int i = unique_depth - 1; i >= 0; --i) {\n if (one_fraction != 0) {\n const double tmp = unique_path[i].pweight;\n unique_path[i].pweight = next_one_portion*(unique_depth + 1)\n / static_cast((i + 1)*one_fraction);\n next_one_portion = tmp - unique_path[i].pweight*zero_fraction*(unique_depth - i)\n / static_cast(unique_depth + 1);\n } else {\n unique_path[i].pweight = (unique_path[i].pweight*(unique_depth + 1))\n / static_cast(zero_fraction*(unique_depth - i));\n }\n }\n\n for (int i = path_index; i < unique_depth; ++i) {\n unique_path[i].feature_index = unique_path[i + 1].feature_index;\n unique_path[i].zero_fraction = unique_path[i + 1].zero_fraction;\n unique_path[i].one_fraction = unique_path[i + 1].one_fraction;\n }\n}\n\ndouble Tree::UnwoundPathSum(const PathElement *unique_path, int unique_depth, int path_index) {\n const double one_fraction = unique_path[path_index].one_fraction;\n const double zero_fraction = unique_path[path_index].zero_fraction;\n double next_one_portion = unique_path[unique_depth].pweight;\n double total = 0;\n for (int i = unique_depth - 1; i >= 0; --i) {\n if (one_fraction != 0) {\n const double tmp = next_one_portion*(unique_depth + 1)\n / static_cast((i + 1)*one_fraction);\n total += tmp;\n next_one_portion = unique_path[i].pweight - tmp*zero_fraction*((unique_depth - i)\n / static_cast(unique_depth + 1));\n } else {\n total += (unique_path[i].pweight / zero_fraction) / ((unique_depth - i)\n / static_cast(unique_depth + 1));\n }\n }\n return total;\n}\n\n// recursive computation of SHAP values for a decision tree\nvoid Tree::TreeSHAP(const double *feature_values, double *phi,\n int node, int unique_depth,\n PathElement *parent_unique_path, double parent_zero_fraction,\n double parent_one_fraction, int parent_feature_index) const {\n // extend the unique path\n PathElement* unique_path = parent_unique_path + unique_depth;\n if (unique_depth > 0) {\n std::copy(parent_unique_path, parent_unique_path + unique_depth, unique_path);\n }\n ExtendPath(unique_path, unique_depth, parent_zero_fraction,\n parent_one_fraction, parent_feature_index);\n\n // leaf node\n if (node < 0) {\n for (int i = 1; i <= unique_depth; ++i) {\n const double w = UnwoundPathSum(unique_path, unique_depth, i);\n const PathElement &el = unique_path[i];\n phi[el.feature_index] += w*(el.one_fraction - el.zero_fraction)*leaf_value_[~node];\n }\n\n // internal node\n } else {\n const int hot_index = Decision(feature_values[split_feature_[node]], node);\n const int cold_index = (hot_index == left_child_[node] ? right_child_[node] : left_child_[node]);\n const double w = data_count(node);\n const double hot_zero_fraction = data_count(hot_index) / w;\n const double cold_zero_fraction = data_count(cold_index) / w;\n double incoming_zero_fraction = 1;\n double incoming_one_fraction = 1;\n\n // see if we have already split on this feature,\n // if so we undo that split so we can redo it for this node\n int path_index = 0;\n for (; path_index <= unique_depth; ++path_index) {\n if (unique_path[path_index].feature_index == split_feature_[node]) break;\n }\n if (path_index != unique_depth + 1) {\n incoming_zero_fraction = unique_path[path_index].zero_fraction;\n incoming_one_fraction = unique_path[path_index].one_fraction;\n UnwindPath(unique_path, unique_depth, path_index);\n unique_depth -= 1;\n }\n\n TreeSHAP(feature_values, phi, hot_index, unique_depth + 1, unique_path,\n hot_zero_fraction*incoming_zero_fraction, incoming_one_fraction, split_feature_[node]);\n\n TreeSHAP(feature_values, phi, cold_index, unique_depth + 1, unique_path,\n cold_zero_fraction*incoming_zero_fraction, 0, split_feature_[node]);\n }\n}\n\n// recursive sparse computation of SHAP values for a decision tree\nvoid Tree::TreeSHAPByMap(const std::unordered_map& feature_values, std::unordered_map* phi,\n int node, int unique_depth,\n PathElement *parent_unique_path, double parent_zero_fraction,\n double parent_one_fraction, int parent_feature_index) const {\n // extend the unique path\n PathElement* unique_path = parent_unique_path + unique_depth;\n if (unique_depth > 0) {\n std::copy(parent_unique_path, parent_unique_path + unique_depth, unique_path);\n }\n ExtendPath(unique_path, unique_depth, parent_zero_fraction,\n parent_one_fraction, parent_feature_index);\n\n // leaf node\n if (node < 0) {\n for (int i = 1; i <= unique_depth; ++i) {\n const double w = UnwoundPathSum(unique_path, unique_depth, i);\n const PathElement &el = unique_path[i];\n (*phi)[el.feature_index] += w*(el.one_fraction - el.zero_fraction)*leaf_value_[~node];\n }\n\n // internal node\n } else {\n const int hot_index = Decision(feature_values.count(split_feature_[node]) > 0 ? feature_values.at(split_feature_[node]) : 0.0f, node);\n const int cold_index = (hot_index == left_child_[node] ? right_child_[node] : left_child_[node]);\n const double w = data_count(node);\n const double hot_zero_fraction = data_count(hot_index) / w;\n const double cold_zero_fraction = data_count(cold_index) / w;\n double incoming_zero_fraction = 1;\n double incoming_one_fraction = 1;\n\n // see if we have already split on this feature,\n // if so we undo that split so we can redo it for this node\n int path_index = 0;\n for (; path_index <= unique_depth; ++path_index) {\n if (unique_path[path_index].feature_index == split_feature_[node]) break;\n }\n if (path_index != unique_depth + 1) {\n incoming_zero_fraction = unique_path[path_index].zero_fraction;\n incoming_one_fraction = unique_path[path_index].one_fraction;\n UnwindPath(unique_path, unique_depth, path_index);\n unique_depth -= 1;\n }\n\n TreeSHAPByMap(feature_values, phi, hot_index, unique_depth + 1, unique_path,\n hot_zero_fraction*incoming_zero_fraction, incoming_one_fraction, split_feature_[node]);\n\n TreeSHAPByMap(feature_values, phi, cold_index, unique_depth + 1, unique_path,\n cold_zero_fraction*incoming_zero_fraction, 0, split_feature_[node]);\n }\n}\n\ndouble Tree::ExpectedValue() const {\n if (num_leaves_ == 1) return LeafOutput(0);\n const double total_count = internal_count_[0];\n double exp_value = 0.0;\n for (int i = 0; i < num_leaves(); ++i) {\n exp_value += (leaf_count_[i] / total_count)*LeafOutput(i);\n }\n return exp_value;\n}\n\nvoid Tree::RecomputeMaxDepth() {\n if (num_leaves_ == 1) {\n max_depth_ = 0;\n } else {\n if (leaf_depth_.size() == 0) {\n RecomputeLeafDepths(0, 0);\n }\n max_depth_ = leaf_depth_[0];\n for (int i = 1; i < num_leaves(); ++i) {\n if (max_depth_ < leaf_depth_[i]) max_depth_ = leaf_depth_[i];\n }\n }\n}\n\n} // namespace LightGBM\n" }, { "path": "src/main.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n\n#include \n#include \n\n#ifdef USE_MPI\n #include \"network/linkers.h\"\n#endif\n\nint main(int argc, char** argv) {\n bool success = false;\n try {\n LightGBM::Application app(argc, argv);\n app.Run();\n\n#ifdef USE_MPI\n LightGBM::Linkers::MpiFinalizeIfIsParallel();\n#endif\n\n success = true;\n }\n catch (const std::exception& ex) {\n std::cerr << \"Met Exceptions:\" << std::endl;\n std::cerr << ex.what() << std::endl;\n }\n catch (const std::string& ex) {\n std::cerr << \"Met Exceptions:\" << std::endl;\n std::cerr << ex << std::endl;\n }\n catch (...) {\n std::cerr << \"Unknown Exceptions\" << std::endl;\n }\n\n if (!success) {\n#ifdef USE_MPI\n LightGBM::Linkers::MpiAbortIfIsParallel();\n#endif\n\n exit(-1);\n }\n}\n" }, { "path": "src/metric/binary_metric.hpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_METRIC_BINARY_METRIC_HPP_\n#define LIGHTGBM_SRC_METRIC_BINARY_METRIC_HPP_\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\n/*!\n* \\brief Metric for binary classification task.\n* Use static class \"PointWiseLossCalculator\" to calculate loss point-wise\n*/\ntemplate\nclass BinaryMetric: public Metric {\n public:\n explicit BinaryMetric(const Config&) {\n }\n\n virtual ~BinaryMetric() {\n }\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n name_.emplace_back(PointWiseLossCalculator::Name());\n\n num_data_ = num_data;\n // get label\n label_ = metadata.label();\n\n // get weights\n weights_ = metadata.weights();\n\n if (weights_ == nullptr) {\n sum_weights_ = static_cast(num_data_);\n } else {\n sum_weights_ = 0.0f;\n for (data_size_t i = 0; i < num_data; ++i) {\n sum_weights_ += weights_[i];\n }\n }\n }\n\n const std::vector& GetName() const override {\n return name_;\n }\n\n double factor_to_bigger_better() const override {\n return -1.0f;\n }\n\n std::vector Eval(const double* score, const ObjectiveFunction* objective) const override {\n double sum_loss = 0.0f;\n if (objective == nullptr) {\n if (weights_ == nullptr) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\n for (data_size_t i = 0; i < num_data_; ++i) {\n // add loss\n sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], score[i]);\n }\n } else {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\n for (data_size_t i = 0; i < num_data_; ++i) {\n // add loss\n sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], score[i]) * weights_[i];\n }\n }\n } else {\n if (weights_ == nullptr) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\n for (data_size_t i = 0; i < num_data_; ++i) {\n double prob = 0;\n objective->ConvertOutput(&score[i], &prob);\n // add loss\n sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], prob);\n }\n } else {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\n for (data_size_t i = 0; i < num_data_; ++i) {\n double prob = 0;\n objective->ConvertOutput(&score[i], &prob);\n // add loss\n sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], prob) * weights_[i];\n }\n }\n }\n double loss = sum_loss / sum_weights_;\n return std::vector(1, loss);\n }\n\n protected:\n /*! \\brief Number of data */\n data_size_t num_data_;\n /*! \\brief Pointer of label */\n const label_t* label_;\n /*! \\brief Pointer of weighs */\n const label_t* weights_;\n /*! \\brief Sum weights */\n double sum_weights_;\n /*! \\brief Name of test set */\n std::vector name_;\n};\n\n/*!\n* \\brief Log loss metric for binary classification task.\n*/\nclass BinaryLoglossMetric: public BinaryMetric {\n public:\n explicit BinaryLoglossMetric(const Config& config) :BinaryMetric(config) {}\n\n inline static double LossOnPoint(label_t label, double prob) {\n if (label <= 0) {\n if (1.0f - prob > kEpsilon) {\n return -std::log(1.0f - prob);\n }\n } else {\n if (prob > kEpsilon) {\n return -std::log(prob);\n }\n }\n return -std::log(kEpsilon);\n }\n\n inline static const char* Name() {\n return \"binary_logloss\";\n }\n};\n/*!\n* \\brief Error rate metric for binary classification task.\n*/\nclass BinaryErrorMetric: public BinaryMetric {\n public:\n explicit BinaryErrorMetric(const Config& config) :BinaryMetric(config) {}\n\n inline static double LossOnPoint(label_t label, double prob) {\n if (prob <= 0.5f) {\n return label > 0;\n } else {\n return label <= 0;\n }\n }\n\n inline static const char* Name() {\n return \"binary_error\";\n }\n};\n\n/*!\n* \\brief Auc Metric for binary classification task.\n*/\nclass AUCMetric: public Metric {\n public:\n explicit AUCMetric(const Config&) {\n }\n\n virtual ~AUCMetric() {\n }\n\n const std::vector& GetName() const override {\n return name_;\n }\n\n double factor_to_bigger_better() const override {\n return 1.0f;\n }\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n name_.emplace_back(\"auc\");\n\n num_data_ = num_data;\n // get label\n label_ = metadata.label();\n // get weights\n weights_ = metadata.weights();\n\n if (weights_ == nullptr) {\n sum_weights_ = static_cast(num_data_);\n } else {\n sum_weights_ = 0.0f;\n for (data_size_t i = 0; i < num_data; ++i) {\n sum_weights_ += weights_[i];\n }\n }\n }\n\n std::vector Eval(const double* score, const ObjectiveFunction*) const override {\n // get indices sorted by score, descent order\n std::vector sorted_idx;\n for (data_size_t i = 0; i < num_data_; ++i) {\n sorted_idx.emplace_back(i);\n }\n Common::ParallelSort(sorted_idx.begin(), sorted_idx.end(), [score](data_size_t a, data_size_t b) {return score[a] > score[b]; });\n // temp sum of positive label\n double cur_pos = 0.0f;\n // total sum of positive label\n double sum_pos = 0.0f;\n // accumulate of AUC\n double accum = 0.0f;\n // temp sum of negative label\n double cur_neg = 0.0f;\n double threshold = score[sorted_idx[0]];\n if (weights_ == nullptr) { // no weights\n for (data_size_t i = 0; i < num_data_; ++i) {\n const label_t cur_label = label_[sorted_idx[i]];\n const double cur_score = score[sorted_idx[i]];\n // new threshold\n if (cur_score != threshold) {\n threshold = cur_score;\n // accumulate\n accum += cur_neg*(cur_pos * 0.5f + sum_pos);\n sum_pos += cur_pos;\n // reset\n cur_neg = cur_pos = 0.0f;\n }\n cur_neg += (cur_label <= 0);\n cur_pos += (cur_label > 0);\n }\n } else { // has weights\n for (data_size_t i = 0; i < num_data_; ++i) {\n const label_t cur_label = label_[sorted_idx[i]];\n const double cur_score = score[sorted_idx[i]];\n const label_t cur_weight = weights_[sorted_idx[i]];\n // new threshold\n if (cur_score != threshold) {\n threshold = cur_score;\n // accumulate\n accum += cur_neg*(cur_pos * 0.5f + sum_pos);\n sum_pos += cur_pos;\n // reset\n cur_neg = cur_pos = 0.0f;\n }\n cur_neg += (cur_label <= 0)*cur_weight;\n cur_pos += (cur_label > 0)*cur_weight;\n }\n }\n accum += cur_neg*(cur_pos * 0.5f + sum_pos);\n sum_pos += cur_pos;\n double auc = 1.0f;\n if (sum_pos > 0.0f && sum_pos != sum_weights_) {\n auc = accum / (sum_pos *(sum_weights_ - sum_pos));\n }\n return std::vector(1, auc);\n }\n\n private:\n /*! \\brief Number of data */\n data_size_t num_data_;\n /*! \\brief Pointer of label */\n const label_t* label_;\n /*! \\brief Pointer of weighs */\n const label_t* weights_;\n /*! \\brief Sum weights */\n double sum_weights_;\n /*! \\brief Name of test set */\n std::vector name_;\n};\n\n\n/*!\n* \\brief Average Precision Metric for binary classification task.\n*/\nclass AveragePrecisionMetric: public Metric {\n public:\n explicit AveragePrecisionMetric(const Config&) {\n }\n\n virtual ~AveragePrecisionMetric() {\n }\n\n const std::vector& GetName() const override {\n return name_;\n }\n\n double factor_to_bigger_better() const override {\n return 1.0f;\n }\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n name_.emplace_back(\"average_precision\");\n\n num_data_ = num_data;\n // get label\n label_ = metadata.label();\n // get weights\n weights_ = metadata.weights();\n\n if (weights_ == nullptr) {\n sum_weights_ = static_cast(num_data_);\n } else {\n sum_weights_ = 0.0f;\n for (data_size_t i = 0; i < num_data; ++i) {\n sum_weights_ += weights_[i];\n }\n }\n }\n\n std::vector Eval(const double* score, const ObjectiveFunction*) const override {\n // get indices sorted by score, descending order\n std::vector sorted_idx;\n for (data_size_t i = 0; i < num_data_; ++i) {\n sorted_idx.emplace_back(i);\n }\n Common::ParallelSort(sorted_idx.begin(), sorted_idx.end(), [score](data_size_t a, data_size_t b) {return score[a] > score[b]; });\n // temp sum of positive label\n double cur_actual_pos = 0.0f;\n // total sum of positive label\n double sum_actual_pos = 0.0f;\n // total sum of predicted positive\n double sum_pred_pos = 0.0f;\n // accumulated precision\n double accum_prec = 1.0f;\n // accumulated pr-auc\n double accum = 0.0f;\n // temp sum of negative label\n double cur_neg = 0.0f;\n double threshold = score[sorted_idx[0]];\n if (weights_ == nullptr) { // no weights\n for (data_size_t i = 0; i < num_data_; ++i) {\n const label_t cur_label = label_[sorted_idx[i]];\n const double cur_score = score[sorted_idx[i]];\n // new threshold\n if (cur_score != threshold) {\n threshold = cur_score;\n // accumulate\n sum_actual_pos += cur_actual_pos;\n sum_pred_pos += cur_actual_pos + cur_neg;\n accum_prec = sum_actual_pos / sum_pred_pos;\n accum += cur_actual_pos * accum_prec;\n // reset\n cur_neg = cur_actual_pos = 0.0f;\n }\n cur_neg += (cur_label <= 0);\n cur_actual_pos += (cur_label > 0);\n }\n } else { // has weights\n for (data_size_t i = 0; i < num_data_; ++i) {\n const label_t cur_label = label_[sorted_idx[i]];\n const double cur_score = score[sorted_idx[i]];\n const label_t cur_weight = weights_[sorted_idx[i]];\n // new threshold\n if (cur_score != threshold) {\n threshold = cur_score;\n // accumulate\n sum_actual_pos += cur_actual_pos;\n sum_pred_pos += cur_actual_pos + cur_neg;\n accum_prec = sum_actual_pos / sum_pred_pos;\n accum += cur_actual_pos * accum_prec;\n // reset\n cur_neg = cur_actual_pos = 0.0f;\n }\n cur_neg += (cur_label <= 0) * cur_weight;\n cur_actual_pos += (cur_label > 0) * cur_weight;\n }\n }\n sum_actual_pos += cur_actual_pos;\n sum_pred_pos += cur_actual_pos + cur_neg;\n accum_prec = sum_actual_pos / sum_pred_pos;\n accum += cur_actual_pos * accum_prec;\n double ap = 1.0f;\n if (sum_actual_pos > 0.0f && sum_actual_pos != sum_weights_) {\n ap = accum / sum_actual_pos;\n }\n return std::vector(1, ap);\n }\n\n private:\n /*! \\brief Number of data */\n data_size_t num_data_;\n /*! \\brief Pointer of label */\n const label_t* label_;\n /*! \\brief Pointer of weighs */\n const label_t* weights_;\n /*! \\brief Sum weights */\n double sum_weights_;\n /*! \\brief Name of test set */\n std::vector name_;\n};\n\n} // namespace LightGBM\n#endif // LIGHTGBM_SRC_METRIC_BINARY_METRIC_HPP_\n" }, { "path": "src/metric/cuda/cuda_binary_metric.cpp", "content": "/*!\n * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifdef USE_CUDA\n\n#include \"cuda_binary_metric.hpp\"\n\n#include \n\nnamespace LightGBM {\n\ntemplate \nstd::vector CUDABinaryMetricInterface::Eval(const double* score, const ObjectiveFunction* objective) const {\n const double* score_convert = score;\n if (objective != nullptr && objective->NeedConvertOutputCUDA()) {\n this->score_convert_buffer_.Resize(static_cast(this->num_data_) * static_cast(this->num_class_));\n score_convert = objective->ConvertOutputCUDA(this->num_data_, score, this->score_convert_buffer_.RawData());\n }\n double sum_loss = 0.0, sum_weight = 0.0;\n this->LaunchEvalKernel(score_convert, &sum_loss, &sum_weight);\n const double eval_score = sum_loss / sum_weight;\n return std::vector{eval_score};\n}\n\nCUDABinaryLoglossMetric::CUDABinaryLoglossMetric(const Config& config):CUDABinaryMetricInterface(config) {}\n\nCUDABinaryErrorMetric::CUDABinaryErrorMetric(const Config& config):CUDABinaryMetricInterface(config) {}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/metric/cuda/cuda_binary_metric.hpp", "content": "/*!\n * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifndef LIGHTGBM_SRC_METRIC_CUDA_CUDA_BINARY_METRIC_HPP_\n#define LIGHTGBM_SRC_METRIC_CUDA_CUDA_BINARY_METRIC_HPP_\n\n#ifdef USE_CUDA\n\n#include \n#include \n\n#include \n\n#include \"cuda_regression_metric.hpp\"\n#include \"../binary_metric.hpp\"\n\nnamespace LightGBM {\n\ntemplate \nclass CUDABinaryMetricInterface: public CUDAPointwiseMetricInterface {\n public:\n explicit CUDABinaryMetricInterface(const Config& config): CUDAPointwiseMetricInterface(config) {}\n\n virtual ~CUDABinaryMetricInterface() {}\n\n std::vector Eval(const double* score, const ObjectiveFunction* objective) const override;\n};\n\nclass CUDABinaryLoglossMetric: public CUDABinaryMetricInterface {\n public:\n explicit CUDABinaryLoglossMetric(const Config& config);\n\n virtual ~CUDABinaryLoglossMetric() {}\n\n __device__ static double MetricOnPointCUDA(label_t label, double score, const double /*param*/) {\n // score should have been converted to probability\n if (label <= 0) {\n if (1.0f - score > kEpsilon) {\n return -log(1.0f - score);\n }\n } else {\n if (score > kEpsilon) {\n return -log(score);\n }\n }\n return -log(kEpsilon);\n }\n};\n\nclass CUDABinaryErrorMetric: public CUDABinaryMetricInterface {\n public:\n explicit CUDABinaryErrorMetric(const Config& config);\n\n virtual ~CUDABinaryErrorMetric() {}\n\n __device__ inline static double MetricOnPointCUDA(label_t label, double score, const double /*param*/) {\n if (score <= 0.5f) {\n return label > 0;\n } else {\n return label <= 0;\n }\n }\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_SRC_METRIC_CUDA_CUDA_BINARY_METRIC_HPP_\n" }, { "path": "src/metric/cuda/cuda_pointwise_metric.cpp", "content": "/*!\n * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifdef USE_CUDA\n\n#include \"cuda_binary_metric.hpp\"\n#include \"cuda_pointwise_metric.hpp\"\n#include \"cuda_regression_metric.hpp\"\n\nnamespace LightGBM {\n\ntemplate \nvoid CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data) {\n CUDAMetricInterface::Init(metadata, num_data);\n const int max_num_reduce_blocks = (this->num_data_ + NUM_DATA_PER_EVAL_THREAD - 1) / NUM_DATA_PER_EVAL_THREAD;\n if (this->cuda_weights_ == nullptr) {\n reduce_block_buffer_.Resize(max_num_reduce_blocks);\n } else {\n reduce_block_buffer_.Resize(max_num_reduce_blocks * 2);\n }\n const int max_num_reduce_blocks_inner = (max_num_reduce_blocks + NUM_DATA_PER_EVAL_THREAD - 1) / NUM_DATA_PER_EVAL_THREAD;\n if (this->cuda_weights_ == nullptr) {\n reduce_block_buffer_inner_.Resize(max_num_reduce_blocks_inner);\n } else {\n reduce_block_buffer_inner_.Resize(max_num_reduce_blocks_inner * 2);\n }\n}\n\n// Regression metrics\ntemplate void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data);\n\n// Binary metrics\ntemplate void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDAPointwiseMetricInterface::Init(const Metadata& metadata, data_size_t num_data);\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/metric/cuda/cuda_pointwise_metric.cu", "content": "/*!\n * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved.\n */\n\n#ifdef USE_CUDA\n\n#include \n#include \n\n#include \"cuda_binary_metric.hpp\"\n#include \"cuda_pointwise_metric.hpp\"\n#include \"cuda_regression_metric.hpp\"\n\nnamespace LightGBM {\n\ntemplate \n__global__ void EvalKernel(const data_size_t num_data, const label_t* labels, const label_t* weights,\n const double* scores, double* reduce_block_buffer, const double param) {\n __shared__ double shared_mem_buffer[WARPSIZE];\n const data_size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n double point_metric = 0.0;\n if (index < num_data) {\n point_metric = USE_WEIGHTS ?\n CUDA_METRIC::MetricOnPointCUDA(labels[index], scores[index], param) * weights[index] :\n CUDA_METRIC::MetricOnPointCUDA(labels[index], scores[index], param);\n }\n const double block_sum_point_metric = ShuffleReduceSum(point_metric, shared_mem_buffer, NUM_DATA_PER_EVAL_THREAD);\n if (threadIdx.x == 0) {\n reduce_block_buffer[blockIdx.x] = block_sum_point_metric;\n }\n if (USE_WEIGHTS) {\n double weight = 0.0;\n if (index < num_data) {\n weight = static_cast(weights[index]);\n const double block_sum_weight = ShuffleReduceSum(weight, shared_mem_buffer, NUM_DATA_PER_EVAL_THREAD);\n if (threadIdx.x == 0) {\n reduce_block_buffer[blockIdx.x + gridDim.x] = block_sum_weight;\n }\n }\n }\n}\n\ntemplate \nvoid CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const {\n const int num_blocks = (this->num_data_ + NUM_DATA_PER_EVAL_THREAD - 1) / NUM_DATA_PER_EVAL_THREAD;\n if (this->cuda_weights_ != nullptr) {\n EvalKernel<<>>(\n this->num_data_, this->cuda_labels_, this->cuda_weights_, score, reduce_block_buffer_.RawData(), GetParamFromConfig());\n } else {\n EvalKernel<<>>(\n this->num_data_, this->cuda_labels_, this->cuda_weights_, score, reduce_block_buffer_.RawData(), GetParamFromConfig());\n }\n ShuffleReduceSumGlobal(reduce_block_buffer_.RawData(), num_blocks, reduce_block_buffer_inner_.RawData());\n CopyFromCUDADeviceToHost(sum_loss, reduce_block_buffer_inner_.RawData(), 1, __FILE__, __LINE__);\n *sum_weight = static_cast(this->num_data_);\n if (this->cuda_weights_ != nullptr) {\n ShuffleReduceSumGlobal(reduce_block_buffer_.RawData() + num_blocks, num_blocks, reduce_block_buffer_inner_.RawData());\n CopyFromCUDADeviceToHost(sum_weight, reduce_block_buffer_inner_.RawData(), 1, __FILE__, __LINE__);\n }\n}\n\n// Regression metrics\ntemplate void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const;\ntemplate void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const;\ntemplate void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const;\ntemplate void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const;\ntemplate void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const;\ntemplate void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const;\ntemplate void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const;\ntemplate void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const;\ntemplate void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const;\ntemplate void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const;\ntemplate void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const;\n\n// Binary metrics\ntemplate void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const;\ntemplate void CUDAPointwiseMetricInterface::LaunchEvalKernel(const double* score, double* sum_loss, double* sum_weight) const;\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/metric/cuda/cuda_pointwise_metric.hpp", "content": "/*!\n * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifndef LIGHTGBM_SRC_METRIC_CUDA_CUDA_POINTWISE_METRIC_HPP_\n#define LIGHTGBM_SRC_METRIC_CUDA_CUDA_POINTWISE_METRIC_HPP_\n\n#ifdef USE_CUDA\n\n#include \n#include \n\n#include \n\n#define NUM_DATA_PER_EVAL_THREAD (1024)\n\nnamespace LightGBM {\n\ntemplate \nclass CUDAPointwiseMetricInterface: public CUDAMetricInterface {\n public:\n explicit CUDAPointwiseMetricInterface(const Config& config): CUDAMetricInterface(config), num_class_(config.num_class) {}\n\n virtual ~CUDAPointwiseMetricInterface() {}\n\n void Init(const Metadata& metadata, data_size_t num_data) override;\n\n protected:\n void LaunchEvalKernel(const double* score_convert, double* sum_loss, double* sum_weight) const;\n\n virtual double GetParamFromConfig() const { return 0.0; }\n\n mutable CUDAVector score_convert_buffer_;\n CUDAVector reduce_block_buffer_;\n CUDAVector reduce_block_buffer_inner_;\n const int num_class_;\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_SRC_METRIC_CUDA_CUDA_POINTWISE_METRIC_HPP_\n" }, { "path": "src/metric/cuda/cuda_regression_metric.cpp", "content": "/*!\n * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifdef USE_CUDA\n\n#include \n\n#include \"cuda_regression_metric.hpp\"\n\nnamespace LightGBM {\n\ntemplate \nstd::vector CUDARegressionMetricInterface::Eval(const double* score, const ObjectiveFunction* objective) const {\n const double* score_convert = score;\n if (objective != nullptr && objective->NeedConvertOutputCUDA()) {\n this->score_convert_buffer_.Resize(static_cast(this->num_data_) * static_cast(this->num_class_));\n score_convert = objective->ConvertOutputCUDA(this->num_data_, score, this->score_convert_buffer_.RawData());\n }\n double sum_loss = 0.0, sum_weight = 0.0;\n this->LaunchEvalKernel(score_convert, &sum_loss, &sum_weight);\n const double eval_score = this->AverageLoss(sum_loss, sum_weight);\n return std::vector{eval_score};\n}\n\nCUDARMSEMetric::CUDARMSEMetric(const Config& config): CUDARegressionMetricInterface(config) {}\n\nCUDAL2Metric::CUDAL2Metric(const Config& config): CUDARegressionMetricInterface(config) {}\n\nCUDAQuantileMetric::CUDAQuantileMetric(const Config& config): CUDARegressionMetricInterface(config), alpha_(config.alpha) {}\n\nCUDAL1Metric::CUDAL1Metric(const Config& config): CUDARegressionMetricInterface(config) {}\n\nCUDAHuberLossMetric::CUDAHuberLossMetric(const Config& config): CUDARegressionMetricInterface(config), alpha_(config.alpha) {}\n\nCUDAFairLossMetric::CUDAFairLossMetric(const Config& config): CUDARegressionMetricInterface(config) , fair_c_(config.fair_c) {}\n\nCUDAPoissonMetric::CUDAPoissonMetric(const Config& config): CUDARegressionMetricInterface(config) {}\n\nCUDAMAPEMetric::CUDAMAPEMetric(const Config& config): CUDARegressionMetricInterface(config) {}\n\nCUDAGammaMetric::CUDAGammaMetric(const Config& config): CUDARegressionMetricInterface(config) {}\n\nCUDAGammaDevianceMetric::CUDAGammaDevianceMetric(const Config& config): CUDARegressionMetricInterface(config) {}\n\nCUDATweedieMetric::CUDATweedieMetric(const Config& config): CUDARegressionMetricInterface(config) , tweedie_variance_power_(config.tweedie_variance_power) {}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/metric/cuda/cuda_regression_metric.hpp", "content": "/*!\n * Copyright (c) 2022-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2022-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifndef LIGHTGBM_SRC_METRIC_CUDA_CUDA_REGRESSION_METRIC_HPP_\n#define LIGHTGBM_SRC_METRIC_CUDA_CUDA_REGRESSION_METRIC_HPP_\n\n#ifdef USE_CUDA\n\n#include \n#include \n\n#include \n\n#include \"cuda_pointwise_metric.hpp\"\n#include \"../regression_metric.hpp\"\n\nnamespace LightGBM {\n\ntemplate \nclass CUDARegressionMetricInterface: public CUDAPointwiseMetricInterface {\n public:\n explicit CUDARegressionMetricInterface(const Config& config):\n CUDAPointwiseMetricInterface(config) {}\n\n virtual ~CUDARegressionMetricInterface() {}\n\n std::vector Eval(const double* score, const ObjectiveFunction* objective) const override;\n};\n\nclass CUDARMSEMetric: public CUDARegressionMetricInterface {\n public:\n explicit CUDARMSEMetric(const Config& config);\n\n virtual ~CUDARMSEMetric() {}\n\n __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) {\n return (score - label) * (score - label);\n }\n};\n\nclass CUDAL2Metric : public CUDARegressionMetricInterface {\n public:\n explicit CUDAL2Metric(const Config& config);\n\n virtual ~CUDAL2Metric() {}\n\n __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) {\n return (score - label) * (score - label);\n }\n};\n\nclass CUDAQuantileMetric : public CUDARegressionMetricInterface {\n public:\n explicit CUDAQuantileMetric(const Config& config);\n\n virtual ~CUDAQuantileMetric() {}\n\n __device__ inline static double MetricOnPointCUDA(label_t label, double score, double alpha) {\n double delta = label - score;\n if (delta < 0) {\n return (alpha - 1.0f) * delta;\n } else {\n return alpha * delta;\n }\n }\n\n double GetParamFromConfig() const override {\n return alpha_;\n }\n\n private:\n const double alpha_;\n};\n\nclass CUDAL1Metric : public CUDARegressionMetricInterface {\n public:\n explicit CUDAL1Metric(const Config& config);\n\n virtual ~CUDAL1Metric() {}\n\n __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) {\n return std::fabs(score - label);\n }\n};\n\nclass CUDAHuberLossMetric : public CUDARegressionMetricInterface {\n public:\n explicit CUDAHuberLossMetric(const Config& config);\n\n virtual ~CUDAHuberLossMetric() {}\n\n __device__ inline static double MetricOnPointCUDA(label_t label, double score, double alpha) {\n const double diff = score - label;\n if (std::abs(diff) <= alpha) {\n return 0.5f * diff * diff;\n } else {\n return alpha * (std::abs(diff) - 0.5f * alpha);\n }\n }\n\n double GetParamFromConfig() const override {\n return alpha_;\n }\n private:\n const double alpha_;\n};\n\nclass CUDAFairLossMetric : public CUDARegressionMetricInterface {\n public:\n explicit CUDAFairLossMetric(const Config& config);\n\n virtual ~CUDAFairLossMetric() {}\n\n __device__ inline static double MetricOnPointCUDA(label_t label, double score, double fair_c) {\n const double x = std::fabs(score - label);\n const double c = fair_c;\n return c * x - c * c * std::log1p(x / c);\n }\n\n double GetParamFromConfig() const override {\n return fair_c_;\n }\n\n private:\n const double fair_c_;\n};\n\nclass CUDAPoissonMetric : public CUDARegressionMetricInterface {\n public:\n explicit CUDAPoissonMetric(const Config& config);\n\n virtual ~CUDAPoissonMetric() {}\n\n __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) {\n const double eps = 1e-10f;\n if (score < eps) {\n score = eps;\n }\n return score - label * std::log(score);\n }\n};\n\nclass CUDAMAPEMetric : public CUDARegressionMetricInterface {\n public:\n explicit CUDAMAPEMetric(const Config& config);\n\n virtual ~CUDAMAPEMetric() {}\n\n __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) {\n return std::fabs((label - score)) / fmax(1.0f, std::fabs(label));\n }\n};\n\nclass CUDAGammaMetric : public CUDARegressionMetricInterface {\n public:\n explicit CUDAGammaMetric(const Config& config);\n\n virtual ~CUDAGammaMetric() {}\n\n __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) {\n const double psi = 1.0;\n const double theta = -1.0 / score;\n const double a = psi;\n const double b = -SafeLog(-theta);\n const double c = 1. / psi * SafeLog(label / psi) - SafeLog(label) - 0; // 0 = std::lgamma(1.0 / psi) = std::lgamma(1.0);\n return -((label * theta - b) / a + c);\n }\n};\n\nclass CUDAGammaDevianceMetric : public CUDARegressionMetricInterface {\n public:\n explicit CUDAGammaDevianceMetric(const Config& config);\n\n virtual ~CUDAGammaDevianceMetric() {}\n\n __device__ inline static double MetricOnPointCUDA(label_t label, double score, double /*alpha*/) {\n const double epsilon = 1.0e-9;\n const double tmp = label / (score + epsilon);\n return tmp - SafeLog(tmp) - 1;\n }\n};\n\nclass CUDATweedieMetric : public CUDARegressionMetricInterface {\n public:\n explicit CUDATweedieMetric(const Config& config);\n\n virtual ~CUDATweedieMetric() {}\n\n __device__ inline static double MetricOnPointCUDA(label_t label, double score, double tweedie_variance_power) {\n const double rho = tweedie_variance_power;\n const double eps = 1e-10f;\n if (score < eps) {\n score = eps;\n }\n const double a = label * std::exp((1 - rho) * std::log(score)) / (1 - rho);\n const double b = std::exp((2 - rho) * std::log(score)) / (2 - rho);\n return -a + b;\n }\n\n double GetParamFromConfig() const override {\n return tweedie_variance_power_;\n }\n\n private:\n const double tweedie_variance_power_;\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_SRC_METRIC_CUDA_CUDA_REGRESSION_METRIC_HPP_\n" }, { "path": "src/metric/dcg_calculator.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n#include \n\n#include \n#include \n#include \n\nnamespace LightGBM {\n\n/*! \\brief Declaration for some static members */\nstd::vector DCGCalculator::label_gain_;\nstd::vector DCGCalculator::discount_;\nconst data_size_t DCGCalculator::kMaxPosition = 10000;\n\n\nvoid DCGCalculator::DefaultEvalAt(std::vector* eval_at) {\n auto& ref_eval_at = *eval_at;\n if (ref_eval_at.empty()) {\n for (int i = 1; i <= 5; ++i) {\n ref_eval_at.push_back(i);\n }\n } else {\n for (size_t i = 0; i < eval_at->size(); ++i) {\n CHECK_GT(ref_eval_at[i], 0);\n }\n }\n}\n\nvoid DCGCalculator::DefaultLabelGain(std::vector* label_gain) {\n if (!label_gain->empty()) {\n return;\n }\n // label_gain = 2^i - 1, may overflow, so we use 31 here\n const int max_label = 31;\n label_gain->push_back(0.0f);\n for (int i = 1; i < max_label; ++i) {\n label_gain->push_back(static_cast((1 << i) - 1));\n }\n}\n\nvoid DCGCalculator::Init(const std::vector& input_label_gain) {\n label_gain_.resize(input_label_gain.size());\n for (size_t i = 0; i < input_label_gain.size(); ++i) {\n label_gain_[i] = static_cast(input_label_gain[i]);\n }\n discount_.resize(kMaxPosition);\n for (data_size_t i = 0; i < kMaxPosition; ++i) {\n discount_[i] = 1.0 / std::log2(2.0 + i);\n }\n}\n\ndouble DCGCalculator::CalMaxDCGAtK(data_size_t k, const label_t* label, data_size_t num_data) {\n double ret = 0.0f;\n // counts for all labels\n std::vector label_cnt(label_gain_.size(), 0);\n for (data_size_t i = 0; i < num_data; ++i) {\n ++label_cnt[static_cast(label[i])];\n }\n int top_label = static_cast(label_gain_.size()) - 1;\n\n if (k > num_data) {\n k = num_data;\n }\n // start from top label, and accumulate DCG\n for (data_size_t j = 0; j < k; ++j) {\n while (top_label > 0 && label_cnt[top_label] <= 0) {\n top_label -= 1;\n }\n if (top_label < 0) {\n break;\n }\n ret += discount_[j] * label_gain_[top_label];\n label_cnt[top_label] -= 1;\n }\n return ret;\n}\n\nvoid DCGCalculator::CalMaxDCG(const std::vector& ks,\n const label_t* label,\n data_size_t num_data,\n std::vector* out) {\n std::vector label_cnt(label_gain_.size(), 0);\n // counts for all labels\n for (data_size_t i = 0; i < num_data; ++i) {\n ++label_cnt[static_cast(label[i])];\n }\n double cur_result = 0.0f;\n data_size_t cur_left = 0;\n int top_label = static_cast(label_gain_.size()) - 1;\n // calculate k Max DCG by one pass\n for (size_t i = 0; i < ks.size(); ++i) {\n data_size_t cur_k = ks[i];\n if (cur_k > num_data) {\n cur_k = num_data;\n }\n for (data_size_t j = cur_left; j < cur_k; ++j) {\n while (top_label > 0 && label_cnt[top_label] <= 0) {\n top_label -= 1;\n }\n if (top_label < 0) {\n break;\n }\n cur_result += discount_[j] * label_gain_[top_label];\n label_cnt[top_label] -= 1;\n }\n (*out)[i] = cur_result;\n cur_left = cur_k;\n }\n}\n\nvoid DCGCalculator::CalDCG(const std::vector& ks, const label_t* label,\n const double * score, data_size_t num_data, std::vector* out) {\n // get sorted indices by score\n std::vector sorted_idx(num_data);\n for (data_size_t i = 0; i < num_data; ++i) {\n sorted_idx[i] = i;\n }\n std::stable_sort(sorted_idx.begin(), sorted_idx.end(),\n [score](data_size_t a, data_size_t b) {return score[a] > score[b]; });\n\n double cur_result = 0.0f;\n data_size_t cur_left = 0;\n // calculate multi dcg by one pass\n for (size_t i = 0; i < ks.size(); ++i) {\n data_size_t cur_k = ks[i];\n if (cur_k > num_data) {\n cur_k = num_data;\n }\n for (data_size_t j = cur_left; j < cur_k; ++j) {\n data_size_t idx = sorted_idx[j];\n cur_result += label_gain_[static_cast(label[idx])] * discount_[j];\n }\n (*out)[i] = cur_result;\n cur_left = cur_k;\n }\n}\n\nvoid DCGCalculator::CheckMetadata(const Metadata& metadata, data_size_t num_queries) {\n const data_size_t* query_boundaries = metadata.query_boundaries();\n if (num_queries > 0 && query_boundaries != nullptr) {\n for (data_size_t i = 0; i < num_queries; i++) {\n data_size_t num_rows = query_boundaries[i + 1] - query_boundaries[i];\n if (num_rows > kMaxPosition) {\n Log::Fatal(\"Number of rows %i exceeds upper limit of %i for a query\", static_cast(num_rows), static_cast(kMaxPosition));\n }\n }\n }\n}\n\n\nvoid DCGCalculator::CheckLabel(const label_t* label, data_size_t num_data) {\n for (data_size_t i = 0; i < num_data; ++i) {\n label_t delta = std::fabs(label[i] - static_cast(label[i]));\n if (delta > kEpsilon) {\n Log::Fatal(\"label should be int type (met %f) for ranking task,\\n\"\n \"for the gain of label, please set the label_gain parameter\", label[i]);\n }\n\n if (label[i] < 0) {\n Log::Fatal(\"Label should be non-negative (met %f) for ranking task\", label[i]);\n }\n\n if (static_cast(label[i]) >= label_gain_.size()) {\n Log::Fatal(\"Label %zu is not less than the number of label mappings (%zu)\", static_cast(label[i]), label_gain_.size());\n }\n }\n}\n\n} // namespace LightGBM\n" }, { "path": "src/metric/map_metric.hpp", "content": "/*!\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_METRIC_MAP_METRIC_HPP_\n#define LIGHTGBM_SRC_METRIC_MAP_METRIC_HPP_\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\nclass MapMetric:public Metric {\n public:\n explicit MapMetric(const Config& config) {\n // get eval position\n eval_at_ = config.eval_at;\n DCGCalculator::DefaultEvalAt(&eval_at_);\n }\n\n ~MapMetric() {\n }\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n for (auto k : eval_at_) {\n name_.emplace_back(std::string(\"map@\") + std::to_string(k));\n }\n num_data_ = num_data;\n // get label\n label_ = metadata.label();\n // get query boundaries\n query_boundaries_ = metadata.query_boundaries();\n if (query_boundaries_ == nullptr) {\n Log::Fatal(\"For MAP metric, there should be query information\");\n }\n num_queries_ = metadata.num_queries();\n Log::Info(\"Total groups: %d, total data: %d\", num_queries_, num_data_);\n // get query weights\n query_weights_ = metadata.query_weights();\n if (query_weights_ == nullptr) {\n sum_query_weights_ = static_cast(num_queries_);\n } else {\n sum_query_weights_ = 0.0f;\n for (data_size_t i = 0; i < num_queries_; ++i) {\n sum_query_weights_ += query_weights_[i];\n }\n }\n\n npos_per_query_.resize(num_queries_, 0);\n for (data_size_t i = 0; i < num_queries_; ++i) {\n for (data_size_t j = query_boundaries_[i]; j < query_boundaries_[i + 1]; ++j) {\n if (label_[j] > 0.5f) {\n ++npos_per_query_[i];\n }\n }\n }\n }\n\n const std::vector& GetName() const override {\n return name_;\n }\n\n double factor_to_bigger_better() const override {\n return 1.0f;\n }\n\n void CalMapAtK(std::vector ks, data_size_t npos, const label_t* label,\n const double* score, data_size_t num_data, std::vector* out) const {\n // get sorted indices by score\n std::vector sorted_idx;\n for (data_size_t i = 0; i < num_data; ++i) {\n sorted_idx.emplace_back(i);\n }\n std::stable_sort(sorted_idx.begin(), sorted_idx.end(),\n [score](data_size_t a, data_size_t b) {return score[a] > score[b]; });\n\n int num_hit = 0;\n double sum_ap = 0.0f;\n data_size_t cur_left = 0;\n for (size_t i = 0; i < ks.size(); ++i) {\n data_size_t cur_k = static_cast(ks[i]);\n if (cur_k > num_data) {\n cur_k = num_data;\n }\n for (data_size_t j = cur_left; j < cur_k; ++j) {\n data_size_t idx = sorted_idx[j];\n if (label[idx] > 0.5f) {\n ++num_hit;\n sum_ap += static_cast(num_hit) / (j + 1.0f);\n }\n }\n if (npos > 0) {\n (*out)[i] = sum_ap / std::min(npos, cur_k);\n } else {\n (*out)[i] = 1.0f;\n }\n cur_left = cur_k;\n }\n }\n std::vector Eval(const double* score, const ObjectiveFunction*) const override {\n // some buffers for multi-threading sum up\n int num_threads = OMP_NUM_THREADS();\n std::vector> result_buffer_;\n for (int i = 0; i < num_threads; ++i) {\n result_buffer_.emplace_back(eval_at_.size(), 0.0f);\n }\n std::vector tmp_map(eval_at_.size(), 0.0f);\n if (query_weights_ == nullptr) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) firstprivate(tmp_map)\n for (data_size_t i = 0; i < num_queries_; ++i) {\n const int tid = omp_get_thread_num();\n CalMapAtK(eval_at_, npos_per_query_[i], label_ + query_boundaries_[i],\n score + query_boundaries_[i], query_boundaries_[i + 1] - query_boundaries_[i], &tmp_map);\n for (size_t j = 0; j < eval_at_.size(); ++j) {\n result_buffer_[tid][j] += tmp_map[j];\n }\n }\n } else {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided) firstprivate(tmp_map)\n for (data_size_t i = 0; i < num_queries_; ++i) {\n const int tid = omp_get_thread_num();\n CalMapAtK(eval_at_, npos_per_query_[i], label_ + query_boundaries_[i],\n score + query_boundaries_[i], query_boundaries_[i + 1] - query_boundaries_[i], &tmp_map);\n for (size_t j = 0; j < eval_at_.size(); ++j) {\n result_buffer_[tid][j] += tmp_map[j] * query_weights_[i];\n }\n }\n }\n // Get final average MAP\n std::vector result(eval_at_.size(), 0.0f);\n for (size_t j = 0; j < result.size(); ++j) {\n for (int i = 0; i < num_threads; ++i) {\n result[j] += result_buffer_[i][j];\n }\n result[j] /= sum_query_weights_;\n }\n return result;\n }\n\n private:\n /*! \\brief Number of data */\n data_size_t num_data_;\n /*! \\brief Pointer of label */\n const label_t* label_;\n /*! \\brief Query boundaries information */\n const data_size_t* query_boundaries_;\n /*! \\brief Number of queries */\n data_size_t num_queries_;\n /*! \\brief Weights of queries */\n const label_t* query_weights_;\n /*! \\brief Sum weights of queries */\n double sum_query_weights_;\n /*! \\brief Evaluate position of Nmap */\n std::vector eval_at_;\n std::vector name_;\n std::vector npos_per_query_;\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_SRC_METRIC_MAP_METRIC_HPP_\n" }, { "path": "src/metric/metric.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n\n#include \n\n#include \"binary_metric.hpp\"\n#include \"map_metric.hpp\"\n#include \"multiclass_metric.hpp\"\n#include \"rank_metric.hpp\"\n#include \"regression_metric.hpp\"\n#include \"xentropy_metric.hpp\"\n\n#include \"cuda/cuda_binary_metric.hpp\"\n#include \"cuda/cuda_regression_metric.hpp\"\n\nnamespace LightGBM {\n\nMetric* Metric::CreateMetric(const std::string& type, const Config& config) {\n #ifdef USE_CUDA\n if (config.device_type == std::string(\"cuda\") && config.boosting == std::string(\"gbdt\")) {\n if (type == std::string(\"l2\")) {\n return new CUDAL2Metric(config);\n } else if (type == std::string(\"rmse\")) {\n return new CUDARMSEMetric(config);\n } else if (type == std::string(\"l1\")) {\n return new CUDAL1Metric(config);\n } else if (type == std::string(\"quantile\")) {\n return new CUDAQuantileMetric(config);\n } else if (type == std::string(\"huber\")) {\n return new CUDAHuberLossMetric(config);\n } else if (type == std::string(\"fair\")) {\n return new CUDAFairLossMetric(config);\n } else if (type == std::string(\"poisson\")) {\n return new CUDAPoissonMetric(config);\n } else if (type == std::string(\"binary_logloss\")) {\n return new CUDABinaryLoglossMetric(config);\n } else if (type == std::string(\"binary_error\")) {\n return new CUDABinaryErrorMetric(config);\n } else if (type == std::string(\"auc\")) {\n Log::Warning(\"Metric auc is not implemented in cuda version. Fall back to evaluation on CPU.\");\n return new AUCMetric(config);\n } else if (type == std::string(\"average_precision\")) {\n Log::Warning(\"Metric average_precision is not implemented in cuda version. Fall back to evaluation on CPU.\");\n return new AveragePrecisionMetric(config);\n } else if (type == std::string(\"auc_mu\")) {\n Log::Warning(\"Metric auc_mu is not implemented in cuda version. Fall back to evaluation on CPU.\");\n return new AucMuMetric(config);\n } else if (type == std::string(\"ndcg\")) {\n Log::Warning(\"Metric ndcg is not implemented in cuda version. Fall back to evaluation on CPU.\");\n return new NDCGMetric(config);\n } else if (type == std::string(\"map\")) {\n Log::Warning(\"Metric map is not implemented in cuda version. Fall back to evaluation on CPU.\");\n return new MapMetric(config);\n } else if (type == std::string(\"multi_logloss\")) {\n Log::Warning(\"Metric multi_logloss is not implemented in cuda version. Fall back to evaluation on CPU.\");\n return new MultiSoftmaxLoglossMetric(config);\n } else if (type == std::string(\"multi_error\")) {\n Log::Warning(\"Metric multi_error is not implemented in cuda version. Fall back to evaluation on CPU.\");\n return new MultiErrorMetric(config);\n } else if (type == std::string(\"cross_entropy\")) {\n Log::Warning(\"Metric cross_entropy is not implemented in cuda version. Fall back to evaluation on CPU.\");\n return new CrossEntropyMetric(config);\n } else if (type == std::string(\"cross_entropy_lambda\")) {\n Log::Warning(\"Metric cross_entropy_lambda is not implemented in cuda version. Fall back to evaluation on CPU.\");\n return new CrossEntropyLambdaMetric(config);\n } else if (type == std::string(\"kullback_leibler\")) {\n Log::Warning(\"Metric kullback_leibler is not implemented in cuda version. Fall back to evaluation on CPU.\");\n return new KullbackLeiblerDivergence(config);\n } else if (type == std::string(\"mape\")) {\n return new CUDAMAPEMetric(config);\n } else if (type == std::string(\"gamma\")) {\n return new CUDAGammaMetric(config);\n } else if (type == std::string(\"gamma_deviance\")) {\n return new CUDAGammaDevianceMetric(config);\n } else if (type == std::string(\"tweedie\")) {\n return new CUDATweedieMetric(config);\n } else if (type == std::string(\"r2\")) {\n Log::Warning(\"Metric r2 is not implemented in cuda version. Fall back to evaluation on CPU.\");\n return new R2Metric(config);\n }\n } else {\n #endif // USE_CUDA\n if (type == std::string(\"l2\")) {\n return new L2Metric(config);\n } else if (type == std::string(\"rmse\")) {\n return new RMSEMetric(config);\n } else if (type == std::string(\"l1\")) {\n return new L1Metric(config);\n } else if (type == std::string(\"quantile\")) {\n return new QuantileMetric(config);\n } else if (type == std::string(\"huber\")) {\n return new HuberLossMetric(config);\n } else if (type == std::string(\"fair\")) {\n return new FairLossMetric(config);\n } else if (type == std::string(\"poisson\")) {\n return new PoissonMetric(config);\n } else if (type == std::string(\"binary_logloss\")) {\n return new BinaryLoglossMetric(config);\n } else if (type == std::string(\"binary_error\")) {\n return new BinaryErrorMetric(config);\n } else if (type == std::string(\"auc\")) {\n return new AUCMetric(config);\n } else if (type == std::string(\"average_precision\")) {\n return new AveragePrecisionMetric(config);\n } else if (type == std::string(\"auc_mu\")) {\n return new AucMuMetric(config);\n } else if (type == std::string(\"ndcg\")) {\n return new NDCGMetric(config);\n } else if (type == std::string(\"map\")) {\n return new MapMetric(config);\n } else if (type == std::string(\"multi_logloss\")) {\n return new MultiSoftmaxLoglossMetric(config);\n } else if (type == std::string(\"multi_error\")) {\n return new MultiErrorMetric(config);\n } else if (type == std::string(\"cross_entropy\")) {\n return new CrossEntropyMetric(config);\n } else if (type == std::string(\"cross_entropy_lambda\")) {\n return new CrossEntropyLambdaMetric(config);\n } else if (type == std::string(\"kullback_leibler\")) {\n return new KullbackLeiblerDivergence(config);\n } else if (type == std::string(\"mape\")) {\n return new MAPEMetric(config);\n } else if (type == std::string(\"gamma\")) {\n return new GammaMetric(config);\n } else if (type == std::string(\"gamma_deviance\")) {\n return new GammaDevianceMetric(config);\n } else if (type == std::string(\"tweedie\")) {\n return new TweedieMetric(config);\n } else if (type == std::string(\"r2\")) {\n return new R2Metric(config);\n }\n #ifdef USE_CUDA\n }\n #endif // USE_CUDA\n return nullptr;\n}\n\n} // namespace LightGBM\n" }, { "path": "src/metric/multiclass_metric.hpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_METRIC_MULTICLASS_METRIC_HPP_\n#define LIGHTGBM_SRC_METRIC_MULTICLASS_METRIC_HPP_\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n/*!\n* \\brief Metric for multiclass task.\n* Use static class \"PointWiseLossCalculator\" to calculate loss point-wise\n*/\ntemplate\nclass MulticlassMetric: public Metric {\n public:\n explicit MulticlassMetric(const Config& config) :config_(config) {\n num_class_ = config.num_class;\n }\n\n virtual ~MulticlassMetric() {\n }\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n name_.emplace_back(PointWiseLossCalculator::Name(config_));\n num_data_ = num_data;\n // get label\n label_ = metadata.label();\n // get weights\n weights_ = metadata.weights();\n if (weights_ == nullptr) {\n sum_weights_ = static_cast(num_data_);\n } else {\n sum_weights_ = 0.0f;\n for (data_size_t i = 0; i < num_data_; ++i) {\n sum_weights_ += weights_[i];\n }\n }\n }\n\n const std::vector& GetName() const override {\n return name_;\n }\n\n double factor_to_bigger_better() const override {\n return -1.0f;\n }\n\n std::vector Eval(const double* score, const ObjectiveFunction* objective) const override {\n double sum_loss = 0.0;\n int num_tree_per_iteration = num_class_;\n int num_pred_per_row = num_class_;\n if (objective != nullptr) {\n num_tree_per_iteration = objective->NumModelPerIteration();\n num_pred_per_row = objective->NumPredictOneRow();\n }\n if (objective != nullptr) {\n if (weights_ == nullptr) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\n for (data_size_t i = 0; i < num_data_; ++i) {\n std::vector raw_score(num_tree_per_iteration);\n for (int k = 0; k < num_tree_per_iteration; ++k) {\n size_t idx = static_cast(num_data_) * k + i;\n raw_score[k] = static_cast(score[idx]);\n }\n std::vector rec(num_pred_per_row);\n objective->ConvertOutput(raw_score.data(), rec.data());\n // add loss\n sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], &rec, config_);\n }\n } else {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\n for (data_size_t i = 0; i < num_data_; ++i) {\n std::vector raw_score(num_tree_per_iteration);\n for (int k = 0; k < num_tree_per_iteration; ++k) {\n size_t idx = static_cast(num_data_) * k + i;\n raw_score[k] = static_cast(score[idx]);\n }\n std::vector rec(num_pred_per_row);\n objective->ConvertOutput(raw_score.data(), rec.data());\n // add loss\n sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], &rec, config_) * weights_[i];\n }\n }\n } else {\n if (weights_ == nullptr) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\n for (data_size_t i = 0; i < num_data_; ++i) {\n std::vector rec(num_tree_per_iteration);\n for (int k = 0; k < num_tree_per_iteration; ++k) {\n size_t idx = static_cast(num_data_) * k + i;\n rec[k] = static_cast(score[idx]);\n }\n // add loss\n sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], &rec, config_);\n }\n } else {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\n for (data_size_t i = 0; i < num_data_; ++i) {\n std::vector rec(num_tree_per_iteration);\n for (int k = 0; k < num_tree_per_iteration; ++k) {\n size_t idx = static_cast(num_data_) * k + i;\n rec[k] = static_cast(score[idx]);\n }\n // add loss\n sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], &rec, config_) * weights_[i];\n }\n }\n }\n double loss = sum_loss / sum_weights_;\n return std::vector(1, loss);\n }\n\n private:\n /*! \\brief Number of data */\n data_size_t num_data_;\n /*! \\brief Pointer of label */\n const label_t* label_;\n /*! \\brief Pointer of weighs */\n const label_t* weights_;\n /*! \\brief Sum weights */\n double sum_weights_;\n /*! \\brief Name of this test set */\n std::vector name_;\n int num_class_;\n /*! \\brief config parameters*/\n Config config_;\n};\n\n/*! \\brief top-k error for multiclass task; if k=1 (default) this is the usual multi-error */\nclass MultiErrorMetric: public MulticlassMetric {\n public:\n explicit MultiErrorMetric(const Config& config) :MulticlassMetric(config) {}\n\n inline static double LossOnPoint(label_t label, std::vector* score, const Config& config) {\n size_t k = static_cast(label);\n auto& ref_score = *score;\n int num_larger = 0;\n for (size_t i = 0; i < score->size(); ++i) {\n if (ref_score[i] >= ref_score[k]) ++num_larger;\n if (num_larger > config.multi_error_top_k) return 1.0f;\n }\n return 0.0f;\n }\n\n inline static const std::string Name(const Config& config) {\n if (config.multi_error_top_k == 1) {\n return \"multi_error\";\n } else {\n return \"multi_error@\" + std::to_string(config.multi_error_top_k);\n }\n }\n};\n\n/*! \\brief Logloss for multiclass task */\nclass MultiSoftmaxLoglossMetric: public MulticlassMetric {\n public:\n explicit MultiSoftmaxLoglossMetric(const Config& config) :MulticlassMetric(config) {}\n\n inline static double LossOnPoint(label_t label, std::vector* score, const Config&) {\n size_t k = static_cast(label);\n auto& ref_score = *score;\n if (ref_score[k] > kEpsilon) {\n return static_cast(-std::log(ref_score[k]));\n } else {\n return -std::log(kEpsilon);\n }\n }\n\n inline static const std::string Name(const Config&) {\n return \"multi_logloss\";\n }\n};\n\n/*! \\brief AUC mu for multiclass task*/\nclass AucMuMetric : public Metric {\n public:\n explicit AucMuMetric(const Config& config) : config_(config) {\n num_class_ = config.num_class;\n class_weights_ = config.auc_mu_weights_matrix;\n }\n\n virtual ~AucMuMetric() {}\n\n const std::vector& GetName() const override { return name_; }\n\n double factor_to_bigger_better() const override { return 1.0f; }\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n name_.emplace_back(\"auc_mu\");\n\n num_data_ = num_data;\n label_ = metadata.label();\n\n // get weights\n weights_ = metadata.weights();\n if (weights_ == nullptr) {\n sum_weights_ = static_cast(num_data_);\n } else {\n sum_weights_ = 0.0f;\n for (data_size_t i = 0; i < num_data_; ++i) {\n sum_weights_ += weights_[i];\n }\n }\n\n // sort the data indices by true class\n sorted_data_idx_ = std::vector(num_data_, 0);\n for (data_size_t i = 0; i < num_data_; ++i) {\n sorted_data_idx_[i] = i;\n }\n Common::ParallelSort(sorted_data_idx_.begin(), sorted_data_idx_.end(),\n [this](data_size_t a, data_size_t b) { return label_[a] < label_[b]; });\n\n // get size of each class\n class_sizes_ = std::vector(num_class_, 0);\n for (data_size_t i = 0; i < num_data_; ++i) {\n data_size_t curr_label = static_cast(label_[i]);\n ++class_sizes_[curr_label];\n }\n\n // get total weight of data in each class\n class_data_weights_ = std::vector(num_class_, 0);\n if (weights_ != nullptr) {\n for (data_size_t i = 0; i < num_data_; ++i) {\n data_size_t curr_label = static_cast(label_[i]);\n class_data_weights_[curr_label] += weights_[i];\n }\n }\n }\n\n std::vector Eval(const double* score, const ObjectiveFunction*) const override {\n // the notation follows that used in the paper introducing the auc-mu metric:\n // https://proceedings.mlr.press/v97/kleiman19a.html\n\n auto S = std::vector>(num_class_, std::vector(num_class_, 0));\n int i_start = 0;\n for (int i = 0; i < num_class_; ++i) {\n int j_start = i_start + class_sizes_[i];\n for (int j = i + 1; j < num_class_; ++j) {\n std::vector curr_v;\n for (int k = 0; k < num_class_; ++k) {\n curr_v.emplace_back(class_weights_[i][k] - class_weights_[j][k]);\n }\n double t1 = curr_v[i] - curr_v[j];\n // extract the data indices belonging to class i or j\n std::vector class_i_j_indices;\n class_i_j_indices.assign(sorted_data_idx_.begin() + i_start, sorted_data_idx_.begin() + i_start + class_sizes_[i]);\n class_i_j_indices.insert(class_i_j_indices.end(),\n sorted_data_idx_.begin() + j_start, sorted_data_idx_.begin() + j_start + class_sizes_[j]);\n // sort according to distance from separating hyperplane\n std::vector> dist;\n for (data_size_t k = 0; static_cast(k) < class_i_j_indices.size(); ++k) {\n data_size_t a = class_i_j_indices[k];\n double v_a = 0;\n for (int m = 0; m < num_class_; ++m) {\n v_a += curr_v[m] * score[num_data_ * m + a];\n }\n dist.push_back(std::pair(a, t1 * v_a));\n }\n Common::ParallelSort(dist.begin(), dist.end(),\n [this](std::pair a, std::pair b) {\n // if scores are equal, put j class first\n if (std::fabs(a.second - b.second) < kEpsilon) {\n return label_[a.first] > label_[b.first];\n } else if (a.second < b.second) {\n return true;\n } else {\n return false;\n }\n });\n // calculate AUC\n double num_j = 0;\n double last_j_dist = 0;\n double num_current_j = 0;\n if (weights_ == nullptr) {\n for (size_t k = 0; k < dist.size(); ++k) {\n data_size_t a = dist[k].first;\n double curr_dist = dist[k].second;\n if (label_[a] == i) {\n if (std::fabs(curr_dist - last_j_dist) < kEpsilon) {\n S[i][j] += num_j - 0.5 * num_current_j; // members of class j with same distance as a contribute 0.5\n } else {\n S[i][j] += num_j;\n }\n } else {\n ++num_j;\n if (std::fabs(curr_dist - last_j_dist) < kEpsilon) {\n ++num_current_j;\n } else {\n last_j_dist = dist[k].second;\n num_current_j = 1;\n }\n }\n }\n } else {\n for (size_t k = 0; k < dist.size(); ++k) {\n data_size_t a = dist[k].first;\n double curr_dist = dist[k].second;\n double curr_weight = weights_[a];\n if (label_[a] == i) {\n if (std::fabs(curr_dist - last_j_dist) < kEpsilon) {\n S[i][j] += curr_weight * (num_j - 0.5 * num_current_j); // members of class j with same distance as a contribute 0.5\n } else {\n S[i][j] += curr_weight * num_j;\n }\n } else {\n num_j += curr_weight;\n if (std::fabs(curr_dist - last_j_dist) < kEpsilon) {\n num_current_j += curr_weight;\n } else {\n last_j_dist = dist[k].second;\n num_current_j = curr_weight;\n }\n }\n }\n }\n j_start += class_sizes_[j];\n }\n i_start += class_sizes_[i];\n }\n double ans = 0;\n for (int i = 0; i < num_class_; ++i) {\n for (int j = i + 1; j < num_class_; ++j) {\n if (weights_ == nullptr) {\n ans += (S[i][j] / class_sizes_[i]) / class_sizes_[j];\n } else {\n ans += (S[i][j] / class_data_weights_[i]) / class_data_weights_[j];\n }\n }\n }\n ans = (2.0 * ans / num_class_) / (num_class_ - 1);\n return std::vector(1, ans);\n }\n\n private:\n /*! \\brief Number of data*/\n data_size_t num_data_;\n /*! \\brief Pointer to label*/\n const label_t* label_;\n /*! \\brief Name of this metric*/\n std::vector name_;\n /*! \\brief Number of classes*/\n int num_class_;\n /*! \\brief Class auc-mu weights*/\n std::vector> class_weights_;\n /*! \\brief Data weights */\n const label_t* weights_;\n /*! \\brief Sum of data weights */\n double sum_weights_;\n /*! \\brief Sum of data weights in each class*/\n std::vector class_data_weights_;\n /*! \\brief Number of data in each class*/\n std::vector class_sizes_;\n /*! \\brief config parameters*/\n Config config_;\n /*! \\brief index to data, sorted by true class*/\n std::vector sorted_data_idx_;\n};\n\n} // namespace LightGBM\n#endif // LIGHTGBM_SRC_METRIC_MULTICLASS_METRIC_HPP_\n" }, { "path": "src/metric/rank_metric.hpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_METRIC_RANK_METRIC_HPP_\n#define LIGHTGBM_SRC_METRIC_RANK_METRIC_HPP_\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n\nnamespace LightGBM {\n\nclass NDCGMetric:public Metric {\n public:\n explicit NDCGMetric(const Config& config) {\n // get eval position\n eval_at_ = config.eval_at;\n auto label_gain = config.label_gain;\n DCGCalculator::DefaultEvalAt(&eval_at_);\n DCGCalculator::DefaultLabelGain(&label_gain);\n // initialize DCG calculator\n DCGCalculator::Init(label_gain);\n }\n\n ~NDCGMetric() {\n }\n void Init(const Metadata& metadata, data_size_t num_data) override {\n for (auto k : eval_at_) {\n name_.emplace_back(std::string(\"ndcg@\") + std::to_string(k));\n }\n num_data_ = num_data;\n // get label\n label_ = metadata.label();\n num_queries_ = metadata.num_queries();\n DCGCalculator::CheckMetadata(metadata, num_queries_);\n DCGCalculator::CheckLabel(label_, num_data_);\n // get query boundaries\n query_boundaries_ = metadata.query_boundaries();\n if (query_boundaries_ == nullptr) {\n Log::Fatal(\"The NDCG metric requires query information\");\n }\n // get query weights\n query_weights_ = metadata.query_weights();\n if (query_weights_ == nullptr) {\n sum_query_weights_ = static_cast(num_queries_);\n } else {\n sum_query_weights_ = 0.0f;\n for (data_size_t i = 0; i < num_queries_; ++i) {\n sum_query_weights_ += query_weights_[i];\n }\n }\n inverse_max_dcgs_.resize(num_queries_);\n // cache the inverse max DCG for all queries, used to calculate NDCG\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (data_size_t i = 0; i < num_queries_; ++i) {\n inverse_max_dcgs_[i].resize(eval_at_.size(), 0.0f);\n DCGCalculator::CalMaxDCG(eval_at_, label_ + query_boundaries_[i],\n query_boundaries_[i + 1] - query_boundaries_[i],\n &inverse_max_dcgs_[i]);\n for (size_t j = 0; j < inverse_max_dcgs_[i].size(); ++j) {\n if (inverse_max_dcgs_[i][j] > 0.0f) {\n inverse_max_dcgs_[i][j] = 1.0f / inverse_max_dcgs_[i][j];\n } else {\n // marking negative for all negative queries.\n // if one meet this query, it's ndcg will be set as -1.\n inverse_max_dcgs_[i][j] = -1.0f;\n }\n }\n }\n }\n\n const std::vector& GetName() const override {\n return name_;\n }\n\n double factor_to_bigger_better() const override {\n return 1.0f;\n }\n\n std::vector Eval(const double* score, const ObjectiveFunction*) const override {\n int num_threads = OMP_NUM_THREADS();\n // some buffers for multi-threading sum up\n std::vector> result_buffer_;\n for (int i = 0; i < num_threads; ++i) {\n result_buffer_.emplace_back(eval_at_.size(), 0.0f);\n }\n std::vector tmp_dcg(eval_at_.size(), 0.0f);\n if (query_weights_ == nullptr) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) firstprivate(tmp_dcg)\n for (data_size_t i = 0; i < num_queries_; ++i) {\n const int tid = omp_get_thread_num();\n // if all doc in this query are all negative, let its NDCG=1\n if (inverse_max_dcgs_[i][0] <= 0.0f) {\n for (size_t j = 0; j < eval_at_.size(); ++j) {\n result_buffer_[tid][j] += 1.0f;\n }\n } else {\n // calculate DCG\n DCGCalculator::CalDCG(eval_at_, label_ + query_boundaries_[i],\n score + query_boundaries_[i],\n query_boundaries_[i + 1] - query_boundaries_[i], &tmp_dcg);\n // calculate NDCG\n for (size_t j = 0; j < eval_at_.size(); ++j) {\n result_buffer_[tid][j] += tmp_dcg[j] * inverse_max_dcgs_[i][j];\n }\n }\n }\n } else {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) firstprivate(tmp_dcg)\n for (data_size_t i = 0; i < num_queries_; ++i) {\n const int tid = omp_get_thread_num();\n // if all doc in this query are all negative, let its NDCG=1\n if (inverse_max_dcgs_[i][0] <= 0.0f) {\n for (size_t j = 0; j < eval_at_.size(); ++j) {\n result_buffer_[tid][j] += 1.0f;\n }\n } else {\n // calculate DCG\n DCGCalculator::CalDCG(eval_at_, label_ + query_boundaries_[i],\n score + query_boundaries_[i],\n query_boundaries_[i + 1] - query_boundaries_[i], &tmp_dcg);\n // calculate NDCG\n for (size_t j = 0; j < eval_at_.size(); ++j) {\n result_buffer_[tid][j] += tmp_dcg[j] * inverse_max_dcgs_[i][j] * query_weights_[i];\n }\n }\n }\n }\n // Get final average NDCG\n std::vector result(eval_at_.size(), 0.0f);\n for (size_t j = 0; j < result.size(); ++j) {\n for (int i = 0; i < num_threads; ++i) {\n result[j] += result_buffer_[i][j];\n }\n result[j] /= sum_query_weights_;\n }\n return result;\n }\n\n private:\n /*! \\brief Number of data */\n data_size_t num_data_;\n /*! \\brief Pointer of label */\n const label_t* label_;\n /*! \\brief Name of test set */\n std::vector name_;\n /*! \\brief Query boundaries information */\n const data_size_t* query_boundaries_;\n /*! \\brief Number of queries */\n data_size_t num_queries_;\n /*! \\brief Weights of queries */\n const label_t* query_weights_;\n /*! \\brief Sum weights of queries */\n double sum_query_weights_;\n /*! \\brief Evaluate position of NDCG */\n std::vector eval_at_;\n /*! \\brief Cache the inverse max dcg for all queries */\n std::vector> inverse_max_dcgs_;\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_SRC_METRIC_RANK_METRIC_HPP_\n" }, { "path": "src/metric/regression_metric.hpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_METRIC_REGRESSION_METRIC_HPP_\n#define LIGHTGBM_SRC_METRIC_REGRESSION_METRIC_HPP_\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n/*!\n* \\brief Metric for regression task.\n* Use static class \"PointWiseLossCalculator\" to calculate loss point-wise\n*/\ntemplate\nclass RegressionMetric: public Metric {\n public:\n explicit RegressionMetric(const Config& config) :config_(config) {\n }\n\n virtual ~RegressionMetric() {\n }\n\n const std::vector& GetName() const override {\n return name_;\n }\n\n double factor_to_bigger_better() const override {\n return -1.0f;\n }\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n name_.emplace_back(PointWiseLossCalculator::Name());\n num_data_ = num_data;\n // get label\n label_ = metadata.label();\n // get weights\n weights_ = metadata.weights();\n if (weights_ == nullptr) {\n sum_weights_ = static_cast(num_data_);\n } else {\n sum_weights_ = 0.0f;\n for (data_size_t i = 0; i < num_data_; ++i) {\n sum_weights_ += weights_[i];\n }\n }\n for (data_size_t i = 0; i < num_data_; ++i) {\n PointWiseLossCalculator::CheckLabel(label_[i]);\n }\n }\n\n std::vector Eval(const double* score, const ObjectiveFunction* objective) const override {\n double sum_loss = 0.0f;\n if (objective == nullptr) {\n if (weights_ == nullptr) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\n for (data_size_t i = 0; i < num_data_; ++i) {\n // add loss\n sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], score[i], config_);\n }\n } else {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\n for (data_size_t i = 0; i < num_data_; ++i) {\n // add loss\n sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], score[i], config_) * weights_[i];\n }\n }\n } else {\n if (weights_ == nullptr) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\n for (data_size_t i = 0; i < num_data_; ++i) {\n // add loss\n double t = 0;\n objective->ConvertOutput(&score[i], &t);\n sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], t, config_);\n }\n } else {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\n for (data_size_t i = 0; i < num_data_; ++i) {\n // add loss\n double t = 0;\n objective->ConvertOutput(&score[i], &t);\n sum_loss += PointWiseLossCalculator::LossOnPoint(label_[i], t, config_) * weights_[i];\n }\n }\n }\n double loss = PointWiseLossCalculator::AverageLoss(sum_loss, sum_weights_);\n return std::vector(1, loss);\n }\n\n inline static double AverageLoss(double sum_loss, double sum_weights) {\n return sum_loss / sum_weights;\n }\n\n inline static void CheckLabel(label_t) {\n }\n\n protected:\n /*! \\brief Number of data */\n data_size_t num_data_;\n /*! \\brief Pointer of label */\n const label_t* label_;\n /*! \\brief Pointer of weighs */\n const label_t* weights_;\n /*! \\brief Sum weights */\n double sum_weights_;\n /*! \\brief Name of this test set */\n Config config_;\n std::vector name_;\n};\n\n/*! \\brief RMSE loss for regression task */\nclass RMSEMetric: public RegressionMetric {\n public:\n explicit RMSEMetric(const Config& config) :RegressionMetric(config) {}\n\n inline static double LossOnPoint(label_t label, double score, const Config&) {\n return (score - label)*(score - label);\n }\n\n inline static double AverageLoss(double sum_loss, double sum_weights) {\n // need sqrt the result for RMSE loss\n return std::sqrt(sum_loss / sum_weights);\n }\n\n inline static const char* Name() {\n return \"rmse\";\n }\n};\n\n/*! \\brief L2 loss for regression task */\nclass L2Metric: public RegressionMetric {\n public:\n explicit L2Metric(const Config& config) :RegressionMetric(config) {}\n\n inline static double LossOnPoint(label_t label, double score, const Config&) {\n return (score - label)*(score - label);\n }\n\n inline static const char* Name() {\n return \"l2\";\n }\n};\n\n/*! \\brief Quantile loss for regression task */\nclass QuantileMetric : public RegressionMetric {\n public:\n explicit QuantileMetric(const Config& config) :RegressionMetric(config) {\n }\n\n inline static double LossOnPoint(label_t label, double score, const Config& config) {\n double delta = label - score;\n if (delta < 0) {\n return (config.alpha - 1.0f) * delta;\n } else {\n return config.alpha * delta;\n }\n }\n\n inline static const char* Name() {\n return \"quantile\";\n }\n};\n\n\n/*! \\brief L1 loss for regression task */\nclass L1Metric: public RegressionMetric {\n public:\n explicit L1Metric(const Config& config) :RegressionMetric(config) {}\n\n inline static double LossOnPoint(label_t label, double score, const Config&) {\n return std::fabs(score - label);\n }\n inline static const char* Name() {\n return \"l1\";\n }\n};\n\n/*! \\brief Huber loss for regression task */\nclass HuberLossMetric: public RegressionMetric {\n public:\n explicit HuberLossMetric(const Config& config) :RegressionMetric(config) {\n }\n\n inline static double LossOnPoint(label_t label, double score, const Config& config) {\n const double diff = score - label;\n if (std::abs(diff) <= config.alpha) {\n return 0.5f * diff * diff;\n } else {\n return config.alpha * (std::abs(diff) - 0.5f * config.alpha);\n }\n }\n\n inline static const char* Name() {\n return \"huber\";\n }\n};\n\n/*! \\brief Fair loss for regression task */\n// http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html\nclass FairLossMetric: public RegressionMetric {\n public:\n explicit FairLossMetric(const Config& config) :RegressionMetric(config) {\n }\n\n inline static double LossOnPoint(label_t label, double score, const Config& config) {\n const double x = std::fabs(score - label);\n const double c = config.fair_c;\n return c * x - c * c * std::log1p(x / c);\n }\n\n inline static const char* Name() {\n return \"fair\";\n }\n};\n\n/*! \\brief Poisson regression loss for regression task */\nclass PoissonMetric: public RegressionMetric {\n public:\n explicit PoissonMetric(const Config& config) :RegressionMetric(config) {\n }\n\n inline static double LossOnPoint(label_t label, double score, const Config&) {\n const double eps = 1e-10f;\n if (score < eps) {\n score = eps;\n }\n return score - label * std::log(score);\n }\n inline static const char* Name() {\n return \"poisson\";\n }\n};\n\n\n/*! \\brief MAPE regression loss for regression task */\nclass MAPEMetric : public RegressionMetric {\n public:\n explicit MAPEMetric(const Config& config) :RegressionMetric(config) {\n }\n\n inline static double LossOnPoint(label_t label, double score, const Config&) {\n return std::fabs((label - score)) / std::max(1.0f, std::fabs(label));\n }\n inline static const char* Name() {\n return \"mape\";\n }\n};\n\nclass GammaMetric : public RegressionMetric {\n public:\n explicit GammaMetric(const Config& config) :RegressionMetric(config) {\n }\n\n inline static double LossOnPoint(label_t label, double score, const Config&) {\n const double psi = 1.0;\n const double theta = -1.0 / score;\n const double a = psi;\n const double b = -Common::SafeLog(-theta);\n const double c = 1. / psi * Common::SafeLog(label / psi) - Common::SafeLog(label) - 0; // 0 = std::lgamma(1.0 / psi) = std::lgamma(1.0);\n return -((label * theta - b) / a + c);\n }\n inline static const char* Name() {\n return \"gamma\";\n }\n\n inline static void CheckLabel(label_t label) {\n CHECK_GT(label, 0);\n }\n};\n\n\nclass GammaDevianceMetric : public RegressionMetric {\n public:\n explicit GammaDevianceMetric(const Config& config) :RegressionMetric(config) {\n }\n\n inline static double LossOnPoint(label_t label, double score, const Config&) {\n const double epsilon = 1.0e-9;\n const double tmp = label / (score + epsilon);\n return tmp - Common::SafeLog(tmp) - 1;\n }\n inline static const char* Name() {\n return \"gamma_deviance\";\n }\n inline static double AverageLoss(double sum_loss, double) {\n return sum_loss * 2;\n }\n inline static void CheckLabel(label_t label) {\n CHECK_GT(label, 0);\n }\n};\n\nclass TweedieMetric : public RegressionMetric {\n public:\n explicit TweedieMetric(const Config& config) :RegressionMetric(config) {\n }\n\n inline static double LossOnPoint(label_t label, double score, const Config& config) {\n const double rho = config.tweedie_variance_power;\n const double eps = 1e-10f;\n if (score < eps) {\n score = eps;\n }\n const double a = label * std::exp((1 - rho) * std::log(score)) / (1 - rho);\n const double b = std::exp((2 - rho) * std::log(score)) / (2 - rho);\n return -a + b;\n }\n inline static const char* Name() {\n return \"tweedie\";\n }\n};\n\n\nclass R2Metric: public Metric {\n public:\n explicit R2Metric(const Config& config) :config_(config) {}\n const std::vector& GetName() const override {\n return name_;\n }\n\n double factor_to_bigger_better() const override {\n return 1.0f;\n }\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n name_.emplace_back(\"r2\");\n num_data_ = num_data;\n label_ = metadata.label();\n weights_ = metadata.weights();\n\n double sum_label = 0.0f;\n if (weights_ == nullptr) {\n sum_weights_ = static_cast(num_data_);\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_label)\n for (data_size_t i = 0; i < num_data_; ++i) {\n sum_label += label_[i];\n }\n } else {\n double local_sum_weights = 0.0f;\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:local_sum_weights, sum_label)\n for (data_size_t i = 0; i < num_data_; ++i) {\n local_sum_weights += weights_[i];\n sum_label += label_[i] * weights_[i];\n }\n sum_weights_ = local_sum_weights;\n }\n label_mean_ = sum_label / sum_weights_;\n\n total_sum_squares_ = 0.0f;\n double local_total_sum_squares = 0.0f;\n if (weights_ == nullptr) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:local_total_sum_squares)\n for (data_size_t i = 0; i < num_data_; ++i) {\n double diff = label_[i] - label_mean_;\n local_total_sum_squares += diff * diff;\n }\n } else {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:local_total_sum_squares)\n for (data_size_t i = 0; i < num_data_; ++i) {\n double diff = label_[i] - label_mean_;\n local_total_sum_squares += diff * diff * weights_[i];\n }\n }\n total_sum_squares_ = local_total_sum_squares;\n }\n\n std::vector Eval(const double* score, const ObjectiveFunction* objective) const override {\n double residual_sum_squares = 0.0f;\n if (objective == nullptr) {\n if (weights_ == nullptr) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:residual_sum_squares)\n for (data_size_t i = 0; i < num_data_; ++i) {\n double diff = label_[i] - score[i];\n residual_sum_squares += diff * diff;\n }\n } else {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:residual_sum_squares)\n for (data_size_t i = 0; i < num_data_; ++i) {\n double diff = label_[i] - score[i];\n residual_sum_squares += diff * diff * weights_[i];\n }\n }\n } else {\n if (weights_ == nullptr) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:residual_sum_squares)\n for (data_size_t i = 0; i < num_data_; ++i) {\n double t = 0;\n objective->ConvertOutput(&score[i], &t);\n double diff = label_[i] - t;\n residual_sum_squares += diff * diff;\n }\n } else {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:residual_sum_squares)\n for (data_size_t i = 0; i < num_data_; ++i) {\n double t = 0;\n objective->ConvertOutput(&score[i], &t);\n double diff = label_[i] - t;\n residual_sum_squares += diff * diff * weights_[i];\n }\n }\n }\n\n double r2 = 1.0 - (residual_sum_squares / total_sum_squares_);\n if (std::fabs(total_sum_squares_) < kZeroThreshold) {\n return std::vector(1, std::fabs(residual_sum_squares) < kZeroThreshold ? 1.0 : 0.0);\n }\n return std::vector(1, r2);\n }\n\n protected:\n data_size_t num_data_;\n const label_t* label_;\n const label_t* weights_;\n double sum_weights_;\n Config config_;\n std::vector name_;\n\n // Custom members for R2 calculation\n double label_mean_;\n double total_sum_squares_;\n};\n\n\n} // namespace LightGBM\n#endif // LIGHTGBM_SRC_METRIC_REGRESSION_METRIC_HPP_\n" }, { "path": "src/metric/xentropy_metric.hpp", "content": "/*!\r\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\r\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\r\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\r\n */\r\n#ifndef LIGHTGBM_SRC_METRIC_XENTROPY_METRIC_HPP_\r\n#define LIGHTGBM_SRC_METRIC_XENTROPY_METRIC_HPP_\r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\n/*\r\n * Implements three related metrics:\r\n *\r\n * (1) standard cross-entropy that can be used for continuous labels in [0, 1]\r\n * (2) \"intensity-weighted\" cross-entropy, also for continuous labels in [0, 1]\r\n * (3) Kullback-Leibler divergence, also for continuous labels in [0, 1]\r\n *\r\n * (3) adds an offset term to (1); the entropy of the label\r\n *\r\n * See xentropy_objective.hpp for further details.\r\n *\r\n */\r\n\r\nnamespace LightGBM {\r\n\r\n// label should be in interval [0, 1];\r\n// prob should be in interval (0, 1); prob is clipped if needed\r\ninline static double XentLoss(label_t label, double prob) {\r\n const double log_arg_epsilon = 1.0e-12;\r\n double a = label;\r\n if (prob > log_arg_epsilon) {\r\n a *= std::log(prob);\r\n } else {\r\n a *= std::log(log_arg_epsilon);\r\n }\r\n double b = 1.0f - label;\r\n if (1.0f - prob > log_arg_epsilon) {\r\n b *= std::log(1.0f - prob);\r\n } else {\r\n b *= std::log(log_arg_epsilon);\r\n }\r\n return - (a + b);\r\n}\r\n\r\n// hhat >(=) 0 assumed; and weight > 0 required; but not checked here\r\ninline static double XentLambdaLoss(label_t label, label_t weight, double hhat) {\r\n return XentLoss(label, 1.0f - std::exp(-weight * hhat));\r\n}\r\n\r\n// Computes the (negative) entropy for label p; p should be in interval [0, 1];\r\n// This is used to presum the KL-divergence offset term (to be _added_ to the cross-entropy loss).\r\n// NOTE: x*log(x) = 0 for x=0,1; so only add when in (0, 1); avoid log(0)*0\r\ninline static double YentLoss(double p) {\r\n double hp = 0.0;\r\n if (p > 0) hp += p * std::log(p);\r\n double q = 1.0f - p;\r\n if (q > 0) hp += q * std::log(q);\r\n return hp;\r\n}\r\n\r\n//\r\n// CrossEntropyMetric : \"xentropy\" : (optional) weights are used linearly\r\n//\r\nclass CrossEntropyMetric : public Metric {\r\n public:\r\n explicit CrossEntropyMetric(const Config&) {}\r\n virtual ~CrossEntropyMetric() {}\r\n\r\n void Init(const Metadata& metadata, data_size_t num_data) override {\r\n name_.emplace_back(\"cross_entropy\");\r\n num_data_ = num_data;\r\n label_ = metadata.label();\r\n weights_ = metadata.weights();\r\n\r\n CHECK_NOTNULL(label_);\r\n\r\n // ensure that labels are in interval [0, 1], interval ends included\r\n Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName()[0].c_str());\r\n Log::Info(\"[%s:%s]: (metric) labels passed interval [0, 1] check\", GetName()[0].c_str(), __func__);\r\n\r\n // check that weights are non-negative and sum is positive\r\n if (weights_ == nullptr) {\r\n sum_weights_ = static_cast(num_data_);\r\n } else {\r\n label_t minw;\r\n Common::ObtainMinMaxSum(weights_, num_data_, &minw, static_cast(nullptr), &sum_weights_);\r\n if (minw < 0.0f) {\r\n Log::Fatal(\"[%s:%s]: (metric) weights not allowed to be negative\", GetName()[0].c_str(), __func__);\r\n }\r\n }\r\n\r\n // check weight sum (may fail to be zero)\r\n if (sum_weights_ <= 0.0f) {\r\n Log::Fatal(\"[%s:%s]: sum-of-weights = %f is non-positive\", __func__, GetName()[0].c_str(), sum_weights_);\r\n }\r\n Log::Info(\"[%s:%s]: sum-of-weights = %f\", GetName()[0].c_str(), __func__, sum_weights_);\r\n }\r\n\r\n std::vector Eval(const double* score, const ObjectiveFunction* objective) const override {\r\n double sum_loss = 0.0f;\r\n if (objective == nullptr) {\r\n if (weights_ == nullptr) {\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n sum_loss += XentLoss(label_[i], score[i]); // NOTE: does not work unless score is a probability\r\n }\r\n } else {\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n sum_loss += XentLoss(label_[i], score[i]) * weights_[i]; // NOTE: does not work unless score is a probability\r\n }\r\n }\r\n } else {\r\n if (weights_ == nullptr) {\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n double p = 0;\r\n objective->ConvertOutput(&score[i], &p);\r\n sum_loss += XentLoss(label_[i], p);\r\n }\r\n } else {\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n double p = 0;\r\n objective->ConvertOutput(&score[i], &p);\r\n sum_loss += XentLoss(label_[i], p) * weights_[i];\r\n }\r\n }\r\n }\r\n double loss = sum_loss / sum_weights_;\r\n return std::vector(1, loss);\r\n }\r\n\r\n const std::vector& GetName() const override {\r\n return name_;\r\n }\r\n\r\n double factor_to_bigger_better() const override {\r\n return -1.0f; // negative means smaller loss is better, positive means larger loss is better\r\n }\r\n\r\n private:\r\n /*! \\brief Number of data points */\r\n data_size_t num_data_;\r\n /*! \\brief Pointer to label */\r\n const label_t* label_;\r\n /*! \\brief Pointer to weights */\r\n const label_t* weights_;\r\n /*! \\brief Sum of weights */\r\n double sum_weights_;\r\n /*! \\brief Name of this metric */\r\n std::vector name_;\r\n};\r\n\r\n//\r\n// CrossEntropyLambdaMetric : \"xentlambda\" : (optional) weights have a different meaning than for \"xentropy\"\r\n// ATTENTION: Supposed to be used when the objective also is \"xentlambda\"\r\n//\r\nclass CrossEntropyLambdaMetric : public Metric {\r\n public:\r\n explicit CrossEntropyLambdaMetric(const Config&) {}\r\n virtual ~CrossEntropyLambdaMetric() {}\r\n\r\n void Init(const Metadata& metadata, data_size_t num_data) override {\r\n name_.emplace_back(\"cross_entropy_lambda\");\r\n num_data_ = num_data;\r\n label_ = metadata.label();\r\n weights_ = metadata.weights();\r\n\r\n CHECK_NOTNULL(label_);\r\n Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName()[0].c_str());\r\n Log::Info(\"[%s:%s]: (metric) labels passed interval [0, 1] check\", GetName()[0].c_str(), __func__);\r\n\r\n // check all weights are strictly positive; throw error if not\r\n if (weights_ != nullptr) {\r\n label_t minw;\r\n Common::ObtainMinMaxSum(weights_, num_data_, &minw, static_cast(nullptr), static_cast(nullptr));\r\n if (minw <= 0.0f) {\r\n Log::Fatal(\"[%s:%s]: (metric) all weights must be positive\", GetName()[0].c_str(), __func__);\r\n }\r\n }\r\n }\r\n\r\n std::vector Eval(const double* score, const ObjectiveFunction* objective) const override {\r\n double sum_loss = 0.0f;\r\n if (objective == nullptr) {\r\n if (weights_ == nullptr) {\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n double hhat = std::log1p(std::exp(score[i])); // auto-convert\r\n sum_loss += XentLambdaLoss(label_[i], 1.0f, hhat);\r\n }\r\n } else {\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n double hhat = std::log1p(std::exp(score[i])); // auto-convert\r\n sum_loss += XentLambdaLoss(label_[i], weights_[i], hhat);\r\n }\r\n }\r\n } else {\r\n if (weights_ == nullptr) {\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n double hhat = 0;\r\n objective->ConvertOutput(&score[i], &hhat); // NOTE: this only works if objective = \"xentlambda\"\r\n sum_loss += XentLambdaLoss(label_[i], 1.0f, hhat);\r\n }\r\n } else {\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n double hhat = 0;\r\n objective->ConvertOutput(&score[i], &hhat); // NOTE: this only works if objective = \"xentlambda\"\r\n sum_loss += XentLambdaLoss(label_[i], weights_[i], hhat);\r\n }\r\n }\r\n }\r\n return std::vector(1, sum_loss / static_cast(num_data_));\r\n }\r\n\r\n const std::vector& GetName() const override {\r\n return name_;\r\n }\r\n\r\n double factor_to_bigger_better() const override {\r\n return -1.0f;\r\n }\r\n\r\n private:\r\n /*! \\brief Number of data points */\r\n data_size_t num_data_;\r\n /*! \\brief Pointer to label */\r\n const label_t* label_;\r\n /*! \\brief Pointer to weights */\r\n const label_t* weights_;\r\n /*! \\brief Name of this metric */\r\n std::vector name_;\r\n};\r\n\r\n//\r\n// KullbackLeiblerDivergence : \"kldiv\" : (optional) weights are used linearly\r\n//\r\nclass KullbackLeiblerDivergence : public Metric {\r\n public:\r\n explicit KullbackLeiblerDivergence(const Config&) {}\r\n virtual ~KullbackLeiblerDivergence() {}\r\n\r\n void Init(const Metadata& metadata, data_size_t num_data) override {\r\n name_.emplace_back(\"kullback_leibler\");\r\n num_data_ = num_data;\r\n label_ = metadata.label();\r\n weights_ = metadata.weights();\r\n\r\n CHECK_NOTNULL(label_);\r\n Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName()[0].c_str());\r\n Log::Info(\"[%s:%s]: (metric) labels passed interval [0, 1] check\", GetName()[0].c_str(), __func__);\r\n\r\n if (weights_ == nullptr) {\r\n sum_weights_ = static_cast(num_data_);\r\n } else {\r\n label_t minw;\r\n Common::ObtainMinMaxSum(weights_, num_data_, &minw, static_cast(nullptr), &sum_weights_);\r\n if (minw < 0.0f) {\r\n Log::Fatal(\"[%s:%s]: (metric) at least one weight is negative\", GetName()[0].c_str(), __func__);\r\n }\r\n }\r\n\r\n // check weight sum\r\n if (sum_weights_ <= 0.0f) {\r\n Log::Fatal(\"[%s:%s]: sum-of-weights = %f is non-positive\", GetName()[0].c_str(), __func__, sum_weights_);\r\n }\r\n\r\n Log::Info(\"[%s:%s]: sum-of-weights = %f\", GetName()[0].c_str(), __func__, sum_weights_);\r\n\r\n // evaluate offset term\r\n presum_label_entropy_ = 0.0f;\r\n if (weights_ == nullptr) {\r\n for (data_size_t i = 0; i < num_data; ++i) {\r\n presum_label_entropy_ += YentLoss(label_[i]);\r\n }\r\n } else {\r\n for (data_size_t i = 0; i < num_data; ++i) {\r\n presum_label_entropy_ += YentLoss(label_[i]) * weights_[i];\r\n }\r\n }\r\n presum_label_entropy_ /= sum_weights_;\r\n\r\n // communicate the value of the offset term to be added\r\n Log::Info(\"%s offset term = %f\", GetName()[0].c_str(), presum_label_entropy_);\r\n }\r\n\r\n std::vector Eval(const double* score, const ObjectiveFunction* objective) const override {\r\n double sum_loss = 0.0f;\r\n if (objective == nullptr) {\r\n if (weights_ == nullptr) {\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n sum_loss += XentLoss(label_[i], score[i]); // NOTE: does not work unless score is a probability\r\n }\r\n } else {\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n sum_loss += XentLoss(label_[i], score[i]) * weights_[i]; // NOTE: does not work unless score is a probability\r\n }\r\n }\r\n } else {\r\n if (weights_ == nullptr) {\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n double p = 0;\r\n objective->ConvertOutput(&score[i], &p);\r\n sum_loss += XentLoss(label_[i], p);\r\n }\r\n } else {\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:sum_loss)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n double p = 0;\r\n objective->ConvertOutput(&score[i], &p);\r\n sum_loss += XentLoss(label_[i], p) * weights_[i];\r\n }\r\n }\r\n }\r\n double loss = presum_label_entropy_ + sum_loss / sum_weights_;\r\n return std::vector(1, loss);\r\n }\r\n\r\n const std::vector& GetName() const override {\r\n return name_;\r\n }\r\n\r\n double factor_to_bigger_better() const override {\r\n return -1.0f;\r\n }\r\n\r\n private:\r\n /*! \\brief Number of data points */\r\n data_size_t num_data_;\r\n /*! \\brief Pointer to label */\r\n const label_t* label_;\r\n /*! \\brief Pointer to weights */\r\n const label_t* weights_;\r\n /*! \\brief Sum of weights */\r\n double sum_weights_;\r\n /*! \\brief Offset term to cross-entropy; precomputed during init */\r\n double presum_label_entropy_;\r\n /*! \\brief Name of this metric */\r\n std::vector name_;\r\n};\r\n\r\n} // end namespace LightGBM\r\n\r\n#endif // LIGHTGBM_SRC_METRIC_XENTROPY_METRIC_HPP_\r\n" }, { "path": "src/network/linker_topo.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n#include \n#include \n\n#include \n#include \n#include \n\nnamespace LightGBM {\n\n\nBruckMap::BruckMap() {\n k = 0;\n}\n\nBruckMap::BruckMap(int n) {\n k = n;\n // default set to -1\n for (int i = 0; i < n; ++i) {\n in_ranks.push_back(-1);\n out_ranks.push_back(-1);\n }\n}\n\nBruckMap BruckMap::Construct(int rank, int num_machines) {\n // distance at k-th communication, distance[k] = 2^k\n std::vector distance;\n int k = 0;\n for (k = 0; (1 << k) < num_machines; ++k) {\n distance.push_back(1 << k);\n }\n BruckMap bruckMap(k);\n for (int j = 0; j < k; ++j) {\n // set incoming rank at k-th communication\n const int in_rank = (rank + distance[j]) % num_machines;\n bruckMap.in_ranks[j] = in_rank;\n // set outgoing rank at k-th communication\n const int out_rank = (rank - distance[j] + num_machines) % num_machines;\n bruckMap.out_ranks[j] = out_rank;\n }\n return bruckMap;\n}\n\nRecursiveHalvingMap::RecursiveHalvingMap() {\n k = 0;\n}\n\nRecursiveHalvingMap::RecursiveHalvingMap(int in_k, RecursiveHalvingNodeType _type, bool _is_power_of_2) {\n type = _type;\n k = in_k;\n is_power_of_2 = _is_power_of_2;\n if (type != RecursiveHalvingNodeType::Other) {\n for (int i = 0; i < k; ++i) {\n // default set as -1\n ranks.push_back(-1);\n send_block_start.push_back(-1);\n send_block_len.push_back(-1);\n recv_block_start.push_back(-1);\n recv_block_len.push_back(-1);\n }\n }\n}\n\nRecursiveHalvingMap RecursiveHalvingMap::Construct(int rank, int num_machines) {\n // construct all recursive halving map for all machines\n int k = 0;\n while ((1 << k) <= num_machines) {\n ++k;\n }\n // let 1 << k <= num_machines\n --k;\n // distance of each communication\n std::vector distance;\n for (int i = 0; i < k; ++i) {\n distance.push_back(1 << (k - 1 - i));\n }\n\n if ((1 << k) == num_machines) {\n RecursiveHalvingMap rec_map(k, RecursiveHalvingNodeType::Normal, true);\n // if num_machines = 2^k, don't need to group machines\n for (int i = 0; i < k; ++i) {\n // communication direction, %2 == 0 is positive\n const int dir = ((rank / distance[i]) % 2 == 0) ? 1 : -1;\n // neighbor at k-th communication\n const int next_node_idx = rank + dir * distance[i];\n rec_map.ranks[i] = next_node_idx;\n // receive data block at k-th communication\n const int recv_block_start = rank / distance[i];\n rec_map.recv_block_start[i] = recv_block_start * distance[i];\n rec_map.recv_block_len[i] = distance[i];\n // send data block at k-th communication\n const int send_block_start = next_node_idx / distance[i];\n rec_map.send_block_start[i] = send_block_start * distance[i];\n rec_map.send_block_len[i] = distance[i];\n }\n return rec_map;\n } else {\n // if num_machines != 2^k, need to group machines\n\n int lower_power_of_2 = 1 << k;\n\n int rest = num_machines - lower_power_of_2;\n\n std::vector node_type(num_machines);\n for (int i = 0; i < num_machines; ++i) {\n node_type[i] = RecursiveHalvingNodeType::Normal;\n }\n // group, two machine in one group, total \"rest\" groups will have 2 machines.\n for (int i = 0; i < rest; ++i) {\n int right = num_machines - i * 2 - 1;\n int left = num_machines - i * 2 - 2;\n // let left machine as group leader\n node_type[left] = RecursiveHalvingNodeType::GroupLeader;\n node_type[right] = RecursiveHalvingNodeType::Other;\n }\n int group_cnt = 0;\n // cache block information for groups, group with 2 machines will have double block size\n std::vector group_block_start(lower_power_of_2);\n std::vector group_block_len(lower_power_of_2, 0);\n // convert from group to node leader\n std::vector group_to_node(lower_power_of_2);\n // convert from node to group\n std::vector node_to_group(num_machines);\n\n for (int i = 0; i < num_machines; ++i) {\n // meet new group\n if (node_type[i] == RecursiveHalvingNodeType::Normal || node_type[i] == RecursiveHalvingNodeType::GroupLeader) {\n group_to_node[group_cnt++] = i;\n }\n node_to_group[i] = group_cnt - 1;\n // add block len for this group\n group_block_len[group_cnt - 1]++;\n }\n // calculate the group block start\n group_block_start[0] = 0;\n for (int i = 1; i < lower_power_of_2; ++i) {\n group_block_start[i] = group_block_start[i - 1] + group_block_len[i - 1];\n }\n\n RecursiveHalvingMap rec_map(k, node_type[rank], false);\n if (node_type[rank] == RecursiveHalvingNodeType::Other) {\n rec_map.neighbor = rank - 1;\n // not need to construct\n return rec_map;\n }\n if (node_type[rank] == RecursiveHalvingNodeType::GroupLeader) {\n rec_map.neighbor = rank + 1;\n }\n const int cur_group_idx = node_to_group[rank];\n for (int i = 0; i < k; ++i) {\n const int dir = ((cur_group_idx / distance[i]) % 2 == 0) ? 1 : -1;\n const int next_node_idx = group_to_node[(cur_group_idx + dir * distance[i])];\n rec_map.ranks[i] = next_node_idx;\n // get receive block information\n const int recv_block_start = cur_group_idx / distance[i];\n rec_map.recv_block_start[i] = group_block_start[static_cast(recv_block_start) * distance[i]];\n int recv_block_len = 0;\n // accumulate block len\n for (int j = 0; j < distance[i]; ++j) {\n recv_block_len += group_block_len[recv_block_start * distance[i] + j];\n }\n rec_map.recv_block_len[i] = recv_block_len;\n // get send block information\n const int send_block_start = (cur_group_idx + dir * distance[i]) / distance[i];\n rec_map.send_block_start[i] = group_block_start[static_cast(send_block_start) * distance[i]];\n int send_block_len = 0;\n // accumulate block len\n for (int j = 0; j < distance[i]; ++j) {\n send_block_len += group_block_len[send_block_start * distance[i] + j];\n }\n rec_map.send_block_len[i] = send_block_len;\n }\n return rec_map;\n }\n}\n\n} // namespace LightGBM\n" }, { "path": "src/network/linkers.h", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_NETWORK_LINKERS_H_\n#define LIGHTGBM_SRC_NETWORK_LINKERS_H_\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#ifdef USE_SOCKET\n#include \"socket_wrapper.hpp\"\n#endif\n\n#ifdef USE_MPI\n#include \n#define MPI_SAFE_CALL(mpi_return) CHECK((mpi_return) == MPI_SUCCESS)\n#endif\n\nnamespace LightGBM {\n\n/*!\n* \\brief A network basic communication wrapper.\n* Will wrap low level communication methods, e.g. mpi, socket and so on.\n* This class will wrap all linkers to other machines if needs\n*/\nclass Linkers {\n public:\n Linkers() {\n is_init_ = false;\n }\n /*!\n * \\brief Constructor\n * \\param config Config of network settings\n */\n explicit Linkers(Config config);\n /*!\n * \\brief Destructor\n */\n ~Linkers();\n /*!\n * \\brief Recv data, blocking\n * \\param rank Which rank will send data to local machine\n * \\param data Pointer of receive data\n * \\param len Recv size, will block until receive len size of data\n */\n inline void Recv(int rank, char* data, int len) const;\n\n inline void Recv(int rank, char* data, int64_t len) const;\n\n /*!\n * \\brief Send data, blocking\n * \\param rank Which rank local machine will send to\n * \\param data Pointer of send data\n * \\param len Send size\n */\n inline void Send(int rank, char* data, int len) const;\n\n inline void Send(int rank, char* data, int64_t len) const;\n /*!\n * \\brief Send and Recv at same time, blocking\n * \\param send_rank\n * \\param send_data\n * \\param send_len\n * \\param recv_rank\n * \\param recv_data\n * \\param recv_len\n */\n inline void SendRecv(int send_rank, char* send_data, int send_len,\n int recv_rank, char* recv_data, int recv_len);\n\n inline void SendRecv(int send_rank, char* send_data, int64_t send_len,\n int recv_rank, char* recv_data, int64_t recv_len);\n /*!\n * \\brief Get rank of local machine\n */\n inline int rank();\n /*!\n * \\brief Get total number of machines\n */\n inline int num_machines();\n /*!\n * \\brief Get Bruck map of this network\n */\n inline const BruckMap& bruck_map();\n /*!\n * \\brief Get Recursive Halving map of this network\n */\n inline const RecursiveHalvingMap& recursive_halving_map();\n\n #ifdef USE_SOCKET\n /*!\n * \\brief Bind local listen to port\n * \\param port Local listen port\n */\n void TryBind(int port);\n /*!\n * \\brief Set socket to rank\n * \\param rank\n * \\param socket\n */\n void SetLinker(int rank, const TcpSocket& socket);\n /*!\n * \\brief Thread for listening\n * \\param incoming_cnt Number of incoming machines\n */\n void ListenThread(int incoming_cnt);\n /*!\n * \\brief Construct network topo\n */\n void Construct();\n /*!\n * \\brief Parser machines information from file\n * \\param machines\n * \\param filename\n */\n void ParseMachineList(const std::string& machines, const std::string& filename);\n /*!\n * \\brief Check one linker is connected or not\n * \\param rank\n * \\return True if linker is connected\n */\n bool CheckLinker(int rank);\n /*!\n * \\brief Print connected linkers\n */\n void PrintLinkers();\n\n #endif // USE_SOCKET\n\n #ifdef USE_MPI\n\n /*!\n * \\brief Check if MPI has been initialized\n */\n static bool IsMpiInitialized();\n\n /*!\n * \\brief Finalize the MPI session if it was initialized\n */\n static void MpiFinalizeIfIsParallel();\n\n /*!\n * \\brief Abort the MPI session if it was initialized (called in case there was a error that needs abrupt ending)\n */\n static void MpiAbortIfIsParallel();\n\n #endif\n\n private:\n /*! \\brief Rank of local machine */\n int rank_;\n /*! \\brief Total number machines */\n int num_machines_;\n /*! \\brief Bruck map */\n BruckMap bruck_map_;\n /*! \\brief Recursive Halving map */\n RecursiveHalvingMap recursive_halving_map_;\n\n std::chrono::duration network_time_;\n\n bool is_init_;\n\n #ifdef USE_SOCKET\n /*! \\brief use to store client ips */\n std::vector client_ips_;\n /*! \\brief use to store client ports */\n std::vector client_ports_;\n /*! \\brief time out for sockets, in minutes */\n int socket_timeout_;\n /*! \\brief Local listen ports */\n int local_listen_port_;\n /*! \\brief Linkers */\n std::vector> linkers_;\n /*! \\brief Local socket listener */\n std::unique_ptr listener_;\n #endif // USE_SOCKET\n};\n\n\ninline int Linkers::rank() {\n return rank_;\n}\n\ninline int Linkers::num_machines() {\n return num_machines_;\n}\n\ninline const BruckMap& Linkers::bruck_map() {\n return bruck_map_;\n}\n\ninline const RecursiveHalvingMap& Linkers::recursive_halving_map() {\n return recursive_halving_map_;\n}\n\ninline void Linkers::Recv(int rank, char* data, int64_t len) const {\n int64_t used = 0;\n do {\n int cur_size = static_cast(std::min(len - used, INT32_MAX));\n Recv(rank, data + used, cur_size);\n used += cur_size;\n } while (used < len);\n}\n\ninline void Linkers::Send(int rank, char* data, int64_t len) const {\n int64_t used = 0;\n do {\n int cur_size = static_cast(std::min(len - used, INT32_MAX));\n Send(rank, data + used, cur_size);\n used += cur_size;\n } while (used < len);\n}\n\ninline void Linkers::SendRecv(int send_rank, char* send_data, int64_t send_len,\n int recv_rank, char* recv_data, int64_t recv_len) {\n auto start_time = std::chrono::high_resolution_clock::now();\n std::thread send_worker(\n [this, send_rank, send_data, send_len]() {\n Send(send_rank, send_data, send_len);\n });\n Recv(recv_rank, recv_data, recv_len);\n send_worker.join();\n // wait for send complete\n auto end_time = std::chrono::high_resolution_clock::now();\n // output used time on each iteration\n network_time_ += std::chrono::duration(end_time - start_time);\n}\n\n#ifdef USE_SOCKET\n\ninline void Linkers::Recv(int rank, char* data, int len) const {\n int recv_cnt = 0;\n while (recv_cnt < len) {\n recv_cnt += linkers_[rank]->Recv(data + recv_cnt,\n // len - recv_cnt\n std::min(len - recv_cnt, SocketConfig::kMaxReceiveSize));\n }\n}\n\ninline void Linkers::Send(int rank, char* data, int len) const {\n if (len <= 0) {\n return;\n }\n int send_cnt = 0;\n while (send_cnt < len) {\n send_cnt += linkers_[rank]->Send(data + send_cnt, len - send_cnt);\n }\n}\n\ninline void Linkers::SendRecv(int send_rank, char* send_data, int send_len,\n int recv_rank, char* recv_data, int recv_len) {\n auto start_time = std::chrono::high_resolution_clock::now();\n if (send_len < SocketConfig::kSocketBufferSize) {\n // if buffer is enough, send will non-blocking\n Send(send_rank, send_data, send_len);\n Recv(recv_rank, recv_data, recv_len);\n } else {\n // if buffer is not enough, use another thread to send, since send will be blocking\n std::thread send_worker(\n [this, send_rank, send_data, send_len]() {\n Send(send_rank, send_data, send_len);\n });\n Recv(recv_rank, recv_data, recv_len);\n send_worker.join();\n }\n // wait for send complete\n auto end_time = std::chrono::high_resolution_clock::now();\n // output used time on each iteration\n network_time_ += std::chrono::duration(end_time - start_time);\n}\n\n#endif // USE_SOCKET\n\n#ifdef USE_MPI\n\ninline void Linkers::Recv(int rank, char* data, int len) const {\n MPI_Status status;\n int read_cnt = 0;\n while (read_cnt < len) {\n MPI_SAFE_CALL(MPI_Recv(data + read_cnt, len - read_cnt, MPI_BYTE, rank, MPI_ANY_TAG, MPI_COMM_WORLD, &status));\n int cur_cnt;\n MPI_SAFE_CALL(MPI_Get_count(&status, MPI_BYTE, &cur_cnt));\n read_cnt += cur_cnt;\n }\n}\n\ninline void Linkers::Send(int rank, char* data, int len) const {\n if (len <= 0) {\n return;\n }\n MPI_Status status;\n MPI_Request send_request;\n MPI_SAFE_CALL(MPI_Isend(data, len, MPI_BYTE, rank, 0, MPI_COMM_WORLD, &send_request));\n MPI_SAFE_CALL(MPI_Wait(&send_request, &status));\n}\n\ninline void Linkers::SendRecv(int send_rank, char* send_data, int send_len,\n int recv_rank, char* recv_data, int recv_len) {\n MPI_Request send_request;\n // send first, non-blocking\n MPI_SAFE_CALL(MPI_Isend(send_data, send_len, MPI_BYTE, send_rank, 0, MPI_COMM_WORLD, &send_request));\n // then receive, blocking\n MPI_Status status;\n int read_cnt = 0;\n while (read_cnt < recv_len) {\n MPI_SAFE_CALL(MPI_Recv(recv_data + read_cnt, recv_len - read_cnt, MPI_BYTE, recv_rank, 0, MPI_COMM_WORLD, &status));\n int cur_cnt;\n MPI_SAFE_CALL(MPI_Get_count(&status, MPI_BYTE, &cur_cnt));\n read_cnt += cur_cnt;\n }\n // wait for send complete\n MPI_SAFE_CALL(MPI_Wait(&send_request, &status));\n}\n\n#endif // USE_MPI\n} // namespace LightGBM\n#endif // LIGHTGBM_SRC_NETWORK_LINKERS_H_\n" }, { "path": "src/network/linkers_mpi.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifdef USE_MPI\n\n#include \"linkers.h\"\n\n#include \n\nnamespace LightGBM {\n\nLinkers::Linkers(Config) {\n is_init_ = false;\n int argc = 0;\n char**argv = nullptr;\n int flag = 0;\n MPI_SAFE_CALL(MPI_Initialized(&flag)); // test if MPI has been initialized\n if (!flag) { // if MPI not started, start it\n MPI_SAFE_CALL(MPI_Init_thread(&argc, &argv, MPI_THREAD_SERIALIZED, &flag));\n }\n MPI_SAFE_CALL(MPI_Comm_size(MPI_COMM_WORLD, &num_machines_));\n MPI_SAFE_CALL(MPI_Comm_rank(MPI_COMM_WORLD, &rank_));\n // wait for all client start up\n MPI_SAFE_CALL(MPI_Barrier(MPI_COMM_WORLD));\n bruck_map_ = BruckMap::Construct(rank_, num_machines_);\n recursive_halving_map_ = RecursiveHalvingMap::Construct(rank_, num_machines_);\n is_init_ = true;\n}\n\nLinkers::~Linkers() {\n // Don't call MPI_Finalize() here: If the destructor was called because only this node had an exception, calling MPI_Finalize() will cause all nodes to hang.\n // Instead we will handle finalize/abort for MPI in main().\n}\n\nbool Linkers::IsMpiInitialized() {\n int is_mpi_init;\n MPI_SAFE_CALL(MPI_Initialized(&is_mpi_init));\n return is_mpi_init;\n}\n\nvoid Linkers::MpiFinalizeIfIsParallel() {\n if (IsMpiInitialized()) {\n Log::Debug(\"Finalizing MPI session.\");\n MPI_SAFE_CALL(MPI_Finalize());\n }\n}\n\nvoid Linkers::MpiAbortIfIsParallel() {\n try {\n if (IsMpiInitialized()) {\n std::cerr << \"Aborting MPI communication.\" << std::endl << std::flush;\n MPI_SAFE_CALL(MPI_Abort(MPI_COMM_WORLD, -1));;\n }\n }\n catch (...) {\n std::cerr << \"Exception was raised before aborting MPI. Aborting process...\" << std::endl << std::flush;\n abort();\n }\n}\n\n} // namespace LightGBM\n#endif // USE_MPI\n" }, { "path": "src/network/linkers_socket.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifdef USE_SOCKET\n\n#include \n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"linkers.h\"\n\nnamespace LightGBM {\n\nLinkers::Linkers(Config config) {\n is_init_ = false;\n // start up socket\n TcpSocket::Startup();\n network_time_ = std::chrono::duration(0);\n num_machines_ = config.num_machines;\n local_listen_port_ = config.local_listen_port;\n socket_timeout_ = config.time_out;\n rank_ = -1;\n // parse clients from file\n ParseMachineList(config.machines, config.machine_list_filename);\n\n if (rank_ == -1) {\n // get ip list of local machine\n std::unordered_set local_ip_list = TcpSocket::GetLocalIpList();\n // get local rank\n for (size_t i = 0; i < client_ips_.size(); ++i) {\n if (local_ip_list.count(client_ips_[i]) > 0 && client_ports_[i] == local_listen_port_) {\n rank_ = static_cast(i);\n break;\n }\n }\n }\n if (rank_ == -1) {\n Log::Fatal(\"Machine list file doesn't contain the local machine\");\n }\n // construct listener\n listener_ = std::unique_ptr(new TcpSocket());\n TryBind(local_listen_port_);\n\n for (int i = 0; i < num_machines_; ++i) {\n linkers_.push_back(nullptr);\n }\n\n // construct communication topo\n bruck_map_ = BruckMap::Construct(rank_, num_machines_);\n recursive_halving_map_ = RecursiveHalvingMap::Construct(rank_, num_machines_);\n\n // construct linkers\n Construct();\n // free listener\n listener_->Close();\n is_init_ = true;\n}\n\nLinkers::~Linkers() {\n if (is_init_) {\n for (size_t i = 0; i < linkers_.size(); ++i) {\n if (linkers_[i] != nullptr) {\n linkers_[i]->Close();\n }\n }\n TcpSocket::Finalize();\n Log::Info(\"Finished linking network in %f seconds\", network_time_ * 1e-3);\n }\n}\n\nvoid Linkers::ParseMachineList(const std::string& machines, const std::string& filename) {\n std::vector lines;\n if (machines.empty()) {\n TextReader machine_list_reader(filename.c_str(), false);\n machine_list_reader.ReadAllLines();\n if (machine_list_reader.Lines().empty()) {\n Log::Fatal(\"Machine list file %s doesn't exist\", filename.c_str());\n }\n lines = machine_list_reader.Lines();\n } else {\n lines = Common::Split(machines.c_str(), ',');\n }\n for (auto& line : lines) {\n line = Common::Trim(line);\n if (line.find(\"rank=\") != std::string::npos) {\n std::vector str_after_split = Common::Split(line.c_str(), '=');\n Common::Atoi(str_after_split[1].c_str(), &rank_);\n continue;\n }\n std::vector str_after_split = Common::Split(line.c_str(), ' ');\n if (str_after_split.size() != 2) {\n str_after_split = Common::Split(line.c_str(), ':');\n if (str_after_split.size() != 2) {\n continue;\n }\n }\n if (client_ips_.size() >= static_cast(num_machines_)) {\n Log::Warning(\"machine_list size is larger than the parameter num_machines, ignoring redundant entries\");\n break;\n }\n str_after_split[0] = Common::Trim(str_after_split[0]);\n str_after_split[1] = Common::Trim(str_after_split[1]);\n client_ips_.push_back(str_after_split[0]);\n client_ports_.push_back(atoi(str_after_split[1].c_str()));\n }\n if (client_ips_.empty()) {\n Log::Fatal(\"Cannot find any ip and port.\\n\"\n \"Please check machine_list_filename or machines parameter\");\n }\n if (client_ips_.size() != static_cast(num_machines_)) {\n Log::Warning(\"World size is larger than the machine_list size, change world size to %zu\", client_ips_.size());\n num_machines_ = static_cast(client_ips_.size());\n }\n}\n\nvoid Linkers::TryBind(int port) {\n Log::Info(\"Trying to bind port %d...\", port);\n if (listener_->Bind(port)) {\n Log::Info(\"Binding port %d succeeded\", port);\n } else {\n Log::Fatal(\"Binding port %d failed\", port);\n }\n}\n\nvoid Linkers::SetLinker(int rank, const TcpSocket& socket) {\n linkers_[rank].reset(new TcpSocket(socket));\n // set timeout\n linkers_[rank]->SetTimeout(socket_timeout_ * 1000 * 60);\n}\n\nvoid Linkers::ListenThread(int incoming_cnt) {\n Log::Info(\"Listening...\");\n char buffer[100];\n int connected_cnt = 0;\n while (connected_cnt < incoming_cnt) {\n // accept incoming socket\n TcpSocket handler = listener_->Accept();\n if (handler.IsClosed()) {\n continue;\n }\n // receive rank\n int read_cnt = 0;\n int size_of_int = static_cast(sizeof(int));\n while (read_cnt < size_of_int) {\n int cur_read_cnt = handler.Recv(buffer + read_cnt, size_of_int - read_cnt);\n read_cnt += cur_read_cnt;\n }\n int* ptr_in_rank = reinterpret_cast(buffer);\n int in_rank = *ptr_in_rank;\n if (in_rank < 0 || in_rank >= num_machines_) {\n Log::Fatal(\"Invalid rank %d found during initialization of linkers. The world size is %d.\", in_rank, num_machines_);\n }\n // add new socket\n SetLinker(in_rank, handler);\n ++connected_cnt;\n }\n}\n\nvoid Linkers::Construct() {\n // save ranks that need to connect with\n std::unordered_map need_connect;\n for (int i = 0; i < num_machines_; ++i) {\n if (i != rank_) {\n need_connect[i] = 1;\n }\n }\n int incoming_cnt = 0;\n for (auto it = need_connect.begin(); it != need_connect.end(); ++it) {\n int machine_rank = it->first;\n if (machine_rank < rank_) {\n ++incoming_cnt;\n }\n }\n\n // start listener\n listener_->SetTimeout(socket_timeout_ * 1000 * 60);\n listener_->Listen(incoming_cnt);\n std::thread listen_thread(&Linkers::ListenThread, this, incoming_cnt);\n const int connect_fail_constant_factor = 20;\n const int connect_fail_retries_scale_factor = static_cast(num_machines_ / connect_fail_constant_factor);\n const int connect_fail_retry_cnt = std::max(connect_fail_constant_factor, connect_fail_retries_scale_factor);\n const int connect_fail_retry_first_delay_interval = 200; // 0.2 s\n const float connect_fail_retry_delay_factor = 1.3f;\n // start connect\n for (auto it = need_connect.begin(); it != need_connect.end(); ++it) {\n int out_rank = it->first;\n // let smaller rank connect to larger rank\n if (out_rank > rank_) {\n int connect_fail_delay_time = connect_fail_retry_first_delay_interval;\n for (int i = 0; i < connect_fail_retry_cnt; ++i) {\n TcpSocket cur_socket;\n if (cur_socket.Connect(client_ips_[out_rank].c_str(), client_ports_[out_rank])) {\n // send local rank\n cur_socket.Send(reinterpret_cast(&rank_), sizeof(rank_));\n SetLinker(out_rank, cur_socket);\n break;\n } else {\n Log::Warning(\"Connecting to rank %d failed, waiting for %d milliseconds\", out_rank, connect_fail_delay_time);\n cur_socket.Close();\n std::this_thread::sleep_for(std::chrono::milliseconds(connect_fail_delay_time));\n connect_fail_delay_time = static_cast(connect_fail_delay_time * connect_fail_retry_delay_factor);\n }\n }\n }\n }\n // wait for listener\n listen_thread.join();\n // print connected linkers\n PrintLinkers();\n}\n\nbool Linkers::CheckLinker(int rank) {\n if (linkers_[rank] == nullptr || linkers_[rank]->IsClosed()) {\n return false;\n }\n return true;\n}\n\nvoid Linkers::PrintLinkers() {\n for (int i = 0; i < num_machines_; ++i) {\n if (CheckLinker(i)) {\n Log::Info(\"Connected to rank %d\", i);\n }\n }\n}\n\n} // namespace LightGBM\n\n#endif // USE_SOCKET\n" }, { "path": "src/network/network.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#include \n\n#include \n\n#include \n#include \n#include \n#include \n#include \n\n#include \"linkers.h\"\n\nnamespace LightGBM {\n\n// static member definition\nTHREAD_LOCAL int Network::num_machines_ = 1;\nTHREAD_LOCAL int Network::rank_ = 0;\nTHREAD_LOCAL std::unique_ptr Network::linkers_;\nTHREAD_LOCAL BruckMap Network::bruck_map_;\nTHREAD_LOCAL RecursiveHalvingMap Network::recursive_halving_map_;\nTHREAD_LOCAL std::vector Network::block_start_;\nTHREAD_LOCAL std::vector Network::block_len_;\nTHREAD_LOCAL comm_size_t Network::buffer_size_ = 0;\nTHREAD_LOCAL std::vector Network::buffer_;\nTHREAD_LOCAL ReduceScatterFunction Network::reduce_scatter_ext_fun_ = nullptr;\nTHREAD_LOCAL AllgatherFunction Network::allgather_ext_fun_ = nullptr;\n\n\nvoid Network::Init(Config config) {\n if (config.num_machines > 1) {\n linkers_.reset(new Linkers(config));\n rank_ = linkers_->rank();\n num_machines_ = linkers_->num_machines();\n bruck_map_ = linkers_->bruck_map();\n recursive_halving_map_ = linkers_->recursive_halving_map();\n block_start_ = std::vector(num_machines_);\n block_len_ = std::vector(num_machines_);\n buffer_size_ = 1024 * 1024;\n buffer_.resize(buffer_size_);\n Log::Info(\"Local rank: %d, total number of machines: %d\", rank_, num_machines_);\n }\n}\n\nvoid Network::Init(int num_machines, int rank,\n ReduceScatterFunction reduce_scatter_ext_fun, AllgatherFunction allgather_ext_fun) {\n if (num_machines > 1) {\n rank_ = rank;\n num_machines_ = num_machines;\n block_start_ = std::vector(num_machines_);\n block_len_ = std::vector(num_machines_);\n buffer_size_ = 1024 * 1024;\n buffer_.resize(buffer_size_);\n reduce_scatter_ext_fun_ = reduce_scatter_ext_fun;\n allgather_ext_fun_ = allgather_ext_fun;\n Log::Info(\"Local rank: %d, total number of machines: %d\", rank_, num_machines_);\n }\n}\n\nvoid Network::Dispose() {\n num_machines_ = 1;\n rank_ = 0;\n linkers_.reset(new Linkers());\n reduce_scatter_ext_fun_ = nullptr;\n allgather_ext_fun_ = nullptr;\n}\n\nvoid Network::Allreduce(char* input, comm_size_t input_size, int type_size, char* output, const ReduceFunction& reducer) {\n if (num_machines_ <= 1) {\n Log::Fatal(\"Please initialize the network interface first\");\n }\n comm_size_t count = input_size / type_size;\n // if small package or small count , do it by all gather.(reduce the communication times.)\n if (count < num_machines_ || input_size < 4096) {\n AllreduceByAllGather(input, input_size, type_size, output, reducer);\n return;\n }\n // assign the blocks to every rank.\n comm_size_t step = (count + num_machines_ - 1) / num_machines_;\n if (step < 1) {\n step = 1;\n }\n block_start_[0] = 0;\n for (int i = 0; i < num_machines_ - 1; ++i) {\n block_len_[i] = std::min(step * type_size, input_size - block_start_[i]);\n block_start_[i + 1] = block_start_[i] + block_len_[i];\n }\n block_len_[num_machines_ - 1] = input_size - block_start_[num_machines_ - 1];\n // do reduce scatter\n ReduceScatter(input, input_size, type_size, block_start_.data(), block_len_.data(), output, input_size, reducer);\n // do all gather\n Allgather(output, block_start_.data(), block_len_.data(), output, input_size);\n}\n\nvoid Network::AllreduceByAllGather(char* input, comm_size_t input_size, int type_size, char* output, const ReduceFunction& reducer) {\n if (num_machines_ <= 1) {\n Log::Fatal(\"Please initialize the network interface first\");\n }\n // assign blocks\n comm_size_t all_size = input_size * num_machines_;\n block_start_[0] = 0;\n block_len_[0] = input_size;\n for (int i = 1; i < num_machines_; ++i) {\n block_start_[i] = block_start_[i - 1] + block_len_[i - 1];\n block_len_[i] = input_size;\n }\n // need use buffer here, since size of \"output\" is smaller than size after all gather\n if (input_size*num_machines_ > buffer_size_) {\n buffer_size_ = input_size*num_machines_;\n buffer_.resize(buffer_size_);\n }\n\n Allgather(input, block_start_.data(), block_len_.data(), buffer_.data(), all_size);\n for (int i = 1; i < num_machines_; ++i) {\n reducer(buffer_.data() + block_start_[i], buffer_.data() + block_start_[0], type_size, input_size);\n }\n // copy back\n std::memcpy(output, buffer_.data(), input_size);\n}\n\nvoid Network::Allgather(char* input, comm_size_t send_size, char* output) {\n if (num_machines_ <= 1) {\n Log::Fatal(\"Please initialize the network interface first\");\n return;\n }\n // assign blocks\n block_start_[0] = 0;\n block_len_[0] = send_size;\n for (int i = 1; i < num_machines_; ++i) {\n block_start_[i] = block_start_[i - 1] + block_len_[i - 1];\n block_len_[i] = send_size;\n }\n // start all gather\n Allgather(input, block_start_.data(), block_len_.data(), output, send_size * num_machines_);\n}\n\nvoid Network::Allgather(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size) {\n if (num_machines_ <= 1) {\n Log::Fatal(\"Please initialize the network interface first\");\n }\n if (allgather_ext_fun_ != nullptr) {\n return allgather_ext_fun_(input, block_len[rank_], block_start, block_len, num_machines_, output, all_size);\n }\n const comm_size_t kRingThreshold = 10 * 1024 * 1024; // 10MB\n const int kRingNodeThreshold = 64;\n if (all_size > kRingThreshold && num_machines_ < kRingNodeThreshold) {\n // when num_machines is small and data is large\n AllgatherRing(input, block_start, block_len, output, all_size);\n } else if (recursive_halving_map_.is_power_of_2) {\n AllgatherRecursiveDoubling(input, block_start, block_len, output, all_size);\n } else {\n AllgatherBruck(input, block_start, block_len, output, all_size);\n }\n}\n\nvoid Network::AllgatherBruck(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t all_size) {\n comm_size_t write_pos = 0;\n // use output as receive buffer\n std::memcpy(output, input, block_len[rank_]);\n write_pos += block_len[rank_];\n int accumulated_block = 1;\n for (int i = 0; i < bruck_map_.k; ++i) {\n // get current local block size\n int cur_block_size = std::min(1 << i, num_machines_ - accumulated_block);\n // get out rank\n int out_rank = bruck_map_.out_ranks[i];\n // get in rank\n int in_rank = bruck_map_.in_ranks[i];\n // get send information\n comm_size_t need_send_len = 0;\n // get recv information\n comm_size_t need_recv_len = 0;\n for (int j = 0; j < cur_block_size; ++j) {\n need_send_len += block_len[(rank_ + j) % num_machines_];\n need_recv_len += block_len[(rank_ + accumulated_block + j) % num_machines_];\n }\n // send and recv at same time\n linkers_->SendRecv(out_rank, output, need_send_len, in_rank, output + write_pos, need_recv_len);\n write_pos += need_recv_len;\n accumulated_block += cur_block_size;\n }\n // rotate in-place\n std::reverse(output, output + all_size);\n std::reverse(output, output + block_start[rank_]);\n std::reverse(output + block_start[rank_], output + all_size);\n}\n\nvoid Network::AllgatherRecursiveDoubling(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t) {\n // use output as receive buffer\n std::memcpy(output + block_start[rank_], input, block_len[rank_]);\n for (int i = 0; i < bruck_map_.k; ++i) {\n // get current local block size\n int cur_step = 1 << i;\n const int vgroup = rank_ / cur_step;\n const int vrank = vgroup * cur_step;\n int target = rank_ + cur_step;\n int target_vrank = (vgroup + 1) * cur_step;\n if (vgroup & 1) {\n target = rank_ - cur_step;\n target_vrank = (vgroup - 1) * cur_step;\n }\n // get send information\n comm_size_t need_send_len = 0;\n // get recv information\n comm_size_t need_recv_len = 0;\n for (int j = 0; j < cur_step; ++j) {\n need_send_len += block_len[(vrank + j)];\n need_recv_len += block_len[(target_vrank + j)];\n }\n // send and recv at same time\n linkers_->SendRecv(target, output + block_start[vrank], need_send_len,\n target, output + block_start[target_vrank], need_recv_len);\n }\n}\n\nvoid Network::AllgatherRing(char* input, const comm_size_t* block_start, const comm_size_t* block_len, char* output, comm_size_t) {\n // use output as receive buffer\n std::memcpy(output + block_start[rank_], input, block_len[rank_]);\n int out_rank = (rank_ + 1) % num_machines_;\n int in_rank = (rank_ - 1 + num_machines_) % num_machines_;\n int out_block = rank_;\n int in_block = in_rank;\n for (int i = 1; i < num_machines_; ++i) {\n // send and recv at same time\n linkers_->SendRecv(out_rank, output + block_start[out_block], block_len[out_block],\n in_rank, output + block_start[in_block], block_len[in_block]);\n out_block = (out_block - 1 + num_machines_) % num_machines_;\n in_block = (in_block - 1 + num_machines_) % num_machines_;\n }\n}\n\nvoid Network::ReduceScatter(char* input, comm_size_t input_size, int type_size,\n const comm_size_t* block_start, const comm_size_t* block_len, char* output,\n comm_size_t output_size, const ReduceFunction& reducer) {\n if (num_machines_ <= 1) {\n Log::Fatal(\"Please initialize the network interface first\");\n }\n if (reduce_scatter_ext_fun_ != nullptr) {\n return reduce_scatter_ext_fun_(input, input_size, type_size, block_start, block_len, num_machines_, output, output_size, reducer);\n }\n const comm_size_t kRingThreshold = 10 * 1024 * 1024; // 10MB\n if (recursive_halving_map_.is_power_of_2 || input_size < kRingThreshold) {\n ReduceScatterRecursiveHalving(input, input_size, type_size, block_start, block_len, output, output_size, reducer);\n } else {\n ReduceScatterRing(input, input_size, type_size, block_start, block_len, output, output_size, reducer);\n }\n}\n\nvoid Network::ReduceScatterRecursiveHalving(char* input, comm_size_t input_size, int type_size,\n const comm_size_t* block_start, const comm_size_t* block_len, char* output,\n comm_size_t, const ReduceFunction& reducer) {\n if (!recursive_halving_map_.is_power_of_2) {\n if (recursive_halving_map_.type == RecursiveHalvingNodeType::Other) {\n // send local data to neighbor first\n linkers_->Send(recursive_halving_map_.neighbor, input, input_size);\n } else if (recursive_halving_map_.type == RecursiveHalvingNodeType::GroupLeader) {\n // receive neighbor data first\n int need_recv_cnt = input_size;\n linkers_->Recv(recursive_halving_map_.neighbor, output, need_recv_cnt);\n // reduce\n reducer(output, input, type_size, input_size);\n }\n }\n if (recursive_halving_map_.type != RecursiveHalvingNodeType::Other) {\n for (int i = 0; i < recursive_halving_map_.k; ++i) {\n // get target\n int target = recursive_halving_map_.ranks[i];\n comm_size_t send_block_start = recursive_halving_map_.send_block_start[i];\n comm_size_t recv_block_start = recursive_halving_map_.recv_block_start[i];\n // get send information\n comm_size_t send_size = 0;\n for (int j = 0; j < recursive_halving_map_.send_block_len[i]; ++j) {\n send_size += block_len[send_block_start + j];\n }\n // get recv information\n comm_size_t need_recv_cnt = 0;\n for (int j = 0; j < recursive_halving_map_.recv_block_len[i]; ++j) {\n need_recv_cnt += block_len[recv_block_start + j];\n }\n // send and recv at same time\n linkers_->SendRecv(target, input + block_start[send_block_start], send_size, target, output, need_recv_cnt);\n // reduce\n reducer(output, input + block_start[recv_block_start], type_size, need_recv_cnt);\n }\n }\n if (!recursive_halving_map_.is_power_of_2) {\n if (recursive_halving_map_.type == RecursiveHalvingNodeType::GroupLeader) {\n // send result to neighbor\n linkers_->Send(recursive_halving_map_.neighbor,\n input + block_start[recursive_halving_map_.neighbor],\n block_len[recursive_halving_map_.neighbor]);\n } else if (recursive_halving_map_.type == RecursiveHalvingNodeType::Other) {\n // receive result from neighbor\n int need_recv_cnt = block_len[rank_];\n linkers_->Recv(recursive_halving_map_.neighbor, output, need_recv_cnt);\n return;\n }\n }\n // copy result\n std::memcpy(output, input + block_start[rank_], block_len[rank_]);\n}\n\nvoid Network::ReduceScatterRing(char* input, comm_size_t, int type_size,\n const comm_size_t* block_start, const comm_size_t* block_len, char* output,\n comm_size_t, const ReduceFunction& reducer) {\n const int out_rank = (rank_ + 1) % num_machines_;\n const int in_rank = (rank_ - 1 + num_machines_) % num_machines_;\n int out_block = in_rank;\n int in_block = (in_rank - 1 + num_machines_) % num_machines_;\n for (int i = 1; i < num_machines_; ++i) {\n linkers_->SendRecv(out_rank, input + block_start[out_block], block_len[out_block],\n in_rank, output, block_len[in_block]);\n reducer(output, input + block_start[in_block], type_size, block_len[in_block]);\n out_block = (out_block - 1 + num_machines_) % num_machines_;\n in_block = (in_block - 1 + num_machines_) % num_machines_;\n }\n std::memcpy(output, input + block_start[rank_], block_len[rank_]);\n}\n\nint Network::rank() {\n return rank_;\n}\n\nint Network::num_machines() {\n return num_machines_;\n}\n\n} // namespace LightGBM\n" }, { "path": "src/network/socket_wrapper.hpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_NETWORK_SOCKET_WRAPPER_HPP_\n#define LIGHTGBM_SRC_NETWORK_SOCKET_WRAPPER_HPP_\n#ifdef USE_SOCKET\n\n#include \n\n#include \n#include \n#include \n#include \n\n#if defined(_WIN32)\n\n#ifdef _MSC_VER\n#define NOMINMAX\n#endif\n\n#include \n#include \n#include \n\n#else\n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \n#include \n\n#endif // defined(_WIN32)\n\n#ifdef _MSC_VER\n#pragma comment(lib, \"Ws2_32.lib\")\n#pragma comment(lib, \"IPHLPAPI.lib\")\n#endif\n\nnamespace LightGBM {\n\n#ifndef _WIN32\n\ntypedef int SOCKET;\nconst int INVALID_SOCKET = -1;\n#define SOCKET_ERROR -1\n\n#endif\n\n#ifdef _WIN32\n// existence of inet_pton is checked in CMakeLists.txt and configure.win, then stored in WIN_HAS_INET_PTON\n#ifndef WIN_HAS_INET_PTON\ninline int inet_pton(int af, const char *src, void *dst) {\n struct sockaddr_storage ss;\n int size = sizeof(ss);\n char src_copy[INET6_ADDRSTRLEN + 1];\n\n ZeroMemory(&ss, sizeof(ss));\n /* stupid non-const API */\n strncpy(src_copy, src, INET6_ADDRSTRLEN + 1);\n src_copy[INET6_ADDRSTRLEN] = 0;\n\n if (WSAStringToAddress(src_copy, af, NULL, (struct sockaddr *)&ss, &size) == 0) {\n switch (af) {\n case AF_INET:\n *(struct in_addr *)dst = ((struct sockaddr_in *)&ss)->sin_addr;\n return 1;\n case AF_INET6:\n *(struct in6_addr *)dst = ((struct sockaddr_in6 *)&ss)->sin6_addr;\n return 1;\n }\n }\n return 0;\n}\n#endif\n#endif\n\n#define MALLOC(x) HeapAlloc(GetProcessHeap(), 0, (x))\n#define FREE(x) HeapFree(GetProcessHeap(), 0, (x))\n\nnamespace SocketConfig {\nconst int kSocketBufferSize = 100 * 1000;\nconst int kMaxReceiveSize = 100 * 1000;\nconst int kNoDelay = 1;\n}\n\nclass TcpSocket {\n public:\n TcpSocket() {\n sockfd_ = socket(AF_INET, SOCK_STREAM, IPPROTO_TCP);\n if (sockfd_ == INVALID_SOCKET) {\n Log::Fatal(\"Socket construction error\");\n return;\n }\n ConfigSocket();\n }\n\n explicit TcpSocket(SOCKET socket) {\n sockfd_ = socket;\n if (sockfd_ == INVALID_SOCKET) {\n Log::Fatal(\"Passed socket error\");\n return;\n }\n ConfigSocket();\n }\n\n TcpSocket(const TcpSocket &object) {\n sockfd_ = object.sockfd_;\n ConfigSocket();\n }\n ~TcpSocket() {\n }\n inline void SetTimeout(int timeout_ms) {\n#if defined(_WIN32)\n DWORD timeout = static_cast(timeout_ms);\n setsockopt(sockfd_, SOL_SOCKET, SO_RCVTIMEO, reinterpret_cast(&timeout), sizeof(timeout));\n#else\n struct timeval tv;\n tv.tv_sec = timeout_ms / 1000;\n tv.tv_usec = (timeout_ms % 1000) * 1000;\n setsockopt(sockfd_, SOL_SOCKET, SO_RCVTIMEO, &tv, sizeof(tv));\n#endif\n }\n inline void ConfigSocket() {\n if (sockfd_ == INVALID_SOCKET) {\n return;\n }\n\n if (setsockopt(sockfd_, SOL_SOCKET, SO_RCVBUF, reinterpret_cast(&SocketConfig::kSocketBufferSize), sizeof(SocketConfig::kSocketBufferSize)) != 0) {\n Log::Warning(\"Set SO_RCVBUF failed, please increase your net.core.rmem_max to 100k at least\");\n }\n\n if (setsockopt(sockfd_, SOL_SOCKET, SO_SNDBUF, reinterpret_cast(&SocketConfig::kSocketBufferSize), sizeof(SocketConfig::kSocketBufferSize)) != 0) {\n Log::Warning(\"Set SO_SNDBUF failed, please increase your net.core.wmem_max to 100k at least\");\n }\n if (setsockopt(sockfd_, IPPROTO_TCP, TCP_NODELAY, reinterpret_cast(&SocketConfig::kNoDelay), sizeof(SocketConfig::kNoDelay)) != 0) {\n Log::Warning(\"Set TCP_NODELAY failed\");\n }\n }\n\n inline static void Startup() {\n#if defined(_WIN32)\n WSADATA wsa_data;\n if (WSAStartup(MAKEWORD(2, 2), &wsa_data) == -1) {\n Log::Fatal(\"Socket error: WSAStartup error\");\n }\n if (LOBYTE(wsa_data.wVersion) != 2 || HIBYTE(wsa_data.wVersion) != 2) {\n WSACleanup();\n Log::Fatal(\"Socket error: Winsock.dll version error\");\n }\n#else\n#endif\n }\n inline static void Finalize() {\n#if defined(_WIN32)\n WSACleanup();\n#endif\n }\n\n inline static int GetLastError() {\n#if defined(_WIN32)\n return WSAGetLastError();\n#else\n return errno;\n#endif\n }\n\n\n\n#if defined(_WIN32)\n inline static std::unordered_set GetLocalIpList() {\n std::unordered_set ip_list;\n char buffer[512];\n // get hostName\n if (gethostname(buffer, sizeof(buffer)) == SOCKET_ERROR) {\n Log::Fatal(\"Error code %d, when getting local host name\", WSAGetLastError());\n }\n // push local ip\n PIP_ADAPTER_INFO pAdapterInfo;\n PIP_ADAPTER_INFO pAdapter = NULL;\n DWORD dwRetVal = 0;\n ULONG ulOutBufLen = sizeof(IP_ADAPTER_INFO);\n pAdapterInfo = reinterpret_cast(MALLOC(sizeof(IP_ADAPTER_INFO)));\n if (pAdapterInfo == NULL) {\n Log::Fatal(\"GetAdaptersinfo error: allocating memory\");\n }\n // Make an initial call to GetAdaptersInfo to get\n // the necessary size into the ulOutBufLen variable\n if (GetAdaptersInfo(pAdapterInfo, &ulOutBufLen) == ERROR_BUFFER_OVERFLOW) {\n FREE(pAdapterInfo);\n pAdapterInfo = reinterpret_cast(MALLOC(ulOutBufLen));\n if (pAdapterInfo == NULL) {\n Log::Fatal(\"GetAdaptersinfo error: allocating memory\");\n }\n }\n if ((dwRetVal = GetAdaptersInfo(pAdapterInfo, &ulOutBufLen)) == NO_ERROR) {\n pAdapter = pAdapterInfo;\n while (pAdapter) {\n ip_list.insert(pAdapter->IpAddressList.IpAddress.String);\n pAdapter = pAdapter->Next;\n }\n } else {\n Log::Fatal(\"GetAdaptersinfo error: code %d\", dwRetVal);\n }\n if (pAdapterInfo)\n FREE(pAdapterInfo);\n return ip_list;\n }\n#else\n inline static std::unordered_set GetLocalIpList() {\n std::unordered_set ip_list;\n struct ifaddrs * ifAddrStruct = NULL;\n struct ifaddrs * ifa = NULL;\n void * tmpAddrPtr = NULL;\n\n getifaddrs(&ifAddrStruct);\n\n for (ifa = ifAddrStruct; ifa != NULL; ifa = ifa->ifa_next) {\n if (!ifa->ifa_addr) {\n continue;\n }\n if (ifa->ifa_addr->sa_family == AF_INET) {\n // NOLINTNEXTLINE\n tmpAddrPtr = &((struct sockaddr_in *)ifa->ifa_addr)->sin_addr;\n char addressBuffer[INET_ADDRSTRLEN];\n inet_ntop(AF_INET, tmpAddrPtr, addressBuffer, INET_ADDRSTRLEN);\n ip_list.insert(std::string(addressBuffer));\n }\n }\n if (ifAddrStruct != NULL) freeifaddrs(ifAddrStruct);\n return ip_list;\n }\n#endif\n inline static sockaddr_in GetAddress(const char* url, int port) {\n sockaddr_in addr = sockaddr_in();\n std::memset(&addr, 0, sizeof(sockaddr_in));\n inet_pton(AF_INET, url, &addr.sin_addr);\n addr.sin_family = AF_INET;\n addr.sin_port = htons(static_cast(port));\n return addr;\n }\n\n inline bool Bind(int port) {\n sockaddr_in local_addr = GetAddress(\"0.0.0.0\", port);\n if (bind(sockfd_, reinterpret_cast(&local_addr), sizeof(sockaddr_in)) == 0) {\n return true;\n }\n return false;\n }\n\n inline bool Connect(const char *url, int port) {\n sockaddr_in server_addr = GetAddress(url, port);\n if (connect(sockfd_, reinterpret_cast(&server_addr), sizeof(sockaddr_in)) == 0) {\n return true;\n }\n return false;\n }\n\n inline void Listen(int backlog = 128) {\n listen(sockfd_, backlog);\n }\n\n inline TcpSocket Accept() {\n SOCKET newfd = accept(sockfd_, NULL, NULL);\n if (newfd == INVALID_SOCKET) {\n int err_code = GetLastError();\n#if defined(_WIN32)\n Log::Fatal(\"Socket accept error (code: %d)\", err_code);\n#else\n Log::Fatal(\"Socket accept error, %s (code: %d)\", std::strerror(err_code), err_code);\n#endif\n }\n return TcpSocket(newfd);\n }\n\n inline int Send(const char *buf_, int len, int flag = 0) {\n int cur_cnt = send(sockfd_, buf_, len, flag);\n if (cur_cnt == SOCKET_ERROR) {\n int err_code = GetLastError();\n#if defined(_WIN32)\n Log::Fatal(\"Socket send error (code: %d)\", err_code);\n#else\n Log::Fatal(\"Socket send error, %s (code: %d)\", std::strerror(err_code), err_code);\n#endif\n }\n return cur_cnt;\n }\n\n inline int Recv(char *buf_, int len, int flags = 0) {\n int cur_cnt = recv(sockfd_, buf_ , len , flags);\n if (cur_cnt == SOCKET_ERROR) {\n int err_code = GetLastError();\n#if defined(_WIN32)\n Log::Fatal(\"Socket recv error (code: %d)\", err_code);\n#else\n Log::Fatal(\"Socket recv error, %s (code: %d)\", std::strerror(err_code), err_code);\n#endif\n }\n return cur_cnt;\n }\n\n inline bool IsClosed() {\n return sockfd_ == INVALID_SOCKET;\n }\n\n inline void Close() {\n if (!IsClosed()) {\n#if defined(_WIN32)\n closesocket(sockfd_);\n#else\n close(sockfd_);\n#endif\n sockfd_ = INVALID_SOCKET;\n }\n }\n\n private:\n SOCKET sockfd_;\n};\n\n} // namespace LightGBM\n#endif // USE_SOCKET\n#endif // LIGHTGBM_SRC_NETWORK_SOCKET_WRAPPER_HPP_\n" }, { "path": "src/objective/binary_objective.hpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_OBJECTIVE_BINARY_OBJECTIVE_HPP_\n#define LIGHTGBM_SRC_OBJECTIVE_BINARY_OBJECTIVE_HPP_\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n/*!\n* \\brief Objective function for binary classification\n*/\nclass BinaryLogloss: public ObjectiveFunction {\n public:\n explicit BinaryLogloss(const Config& config,\n std::function is_pos = nullptr)\n : deterministic_(config.deterministic) {\n sigmoid_ = static_cast(config.sigmoid);\n if (sigmoid_ <= 0.0) {\n Log::Fatal(\"Sigmoid parameter %f should be greater than zero\", sigmoid_);\n }\n is_unbalance_ = config.is_unbalance;\n scale_pos_weight_ = static_cast(config.scale_pos_weight);\n if (is_unbalance_ && std::fabs(scale_pos_weight_ - 1.0f) > 1e-6) {\n Log::Fatal(\"Cannot set is_unbalance and scale_pos_weight at the same time\");\n }\n is_pos_ = is_pos;\n if (is_pos_ == nullptr) {\n is_pos_ = [](label_t label) { return label > 0; };\n }\n }\n\n explicit BinaryLogloss(const std::vector& strs)\n : deterministic_(false) {\n sigmoid_ = -1;\n for (auto str : strs) {\n auto tokens = Common::Split(str.c_str(), ':');\n if (tokens.size() == 2) {\n if (tokens[0] == std::string(\"sigmoid\")) {\n Common::Atof(tokens[1].c_str(), &sigmoid_);\n }\n }\n }\n if (sigmoid_ <= 0.0) {\n Log::Fatal(\"Sigmoid parameter %f should be greater than zero\", sigmoid_);\n }\n }\n\n ~BinaryLogloss() {}\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n num_data_ = num_data;\n label_ = metadata.label();\n weights_ = metadata.weights();\n data_size_t cnt_positive = 0;\n data_size_t cnt_negative = 0;\n // count for positive and negative samples\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:cnt_positive, cnt_negative)\n for (data_size_t i = 0; i < num_data_; ++i) {\n if (is_pos_(label_[i])) {\n ++cnt_positive;\n } else {\n ++cnt_negative;\n }\n }\n num_pos_data_ = cnt_positive;\n if (Network::num_machines() > 1) {\n cnt_positive = Network::GlobalSyncUpBySum(cnt_positive);\n cnt_negative = Network::GlobalSyncUpBySum(cnt_negative);\n }\n need_train_ = true;\n if (cnt_negative == 0 || cnt_positive == 0) {\n Log::Warning(\"Contains only one class\");\n // not need to boost.\n need_train_ = false;\n }\n Log::Info(\"Number of positive: %d, number of negative: %d\", cnt_positive, cnt_negative);\n // use -1 for negative class, and 1 for positive class\n label_val_[0] = -1;\n label_val_[1] = 1;\n // weight for label\n label_weights_[0] = 1.0f;\n label_weights_[1] = 1.0f;\n // if using unbalance, change the labels weight\n if (is_unbalance_ && cnt_positive > 0 && cnt_negative > 0) {\n if (cnt_positive > cnt_negative) {\n label_weights_[1] = 1.0f;\n label_weights_[0] = static_cast(cnt_positive) / cnt_negative;\n } else {\n label_weights_[1] = static_cast(cnt_negative) / cnt_positive;\n label_weights_[0] = 1.0f;\n }\n }\n label_weights_[1] *= scale_pos_weight_;\n }\n\n void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override {\n if (!need_train_) {\n return;\n }\n if (weights_ == nullptr) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (data_size_t i = 0; i < num_data_; ++i) {\n // get label and label weights\n const int is_pos = is_pos_(label_[i]);\n const int label = label_val_[is_pos];\n const double label_weight = label_weights_[is_pos];\n // calculate gradients and hessians\n const double response = -label * sigmoid_ / (1.0f + std::exp(label * sigmoid_ * score[i]));\n const double abs_response = fabs(response);\n gradients[i] = static_cast(response * label_weight);\n hessians[i] = static_cast(abs_response * (sigmoid_ - abs_response) * label_weight);\n }\n } else {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (data_size_t i = 0; i < num_data_; ++i) {\n // get label and label weights\n const int is_pos = is_pos_(label_[i]);\n const int label = label_val_[is_pos];\n const double label_weight = label_weights_[is_pos];\n // calculate gradients and hessians\n const double response = -label * sigmoid_ / (1.0f + std::exp(label * sigmoid_ * score[i]));\n const double abs_response = fabs(response);\n gradients[i] = static_cast(response * label_weight * weights_[i]);\n hessians[i] = static_cast(abs_response * (sigmoid_ - abs_response) * label_weight * weights_[i]);\n }\n }\n }\n\n // implement custom average to boost from (if enabled among options)\n double BoostFromScore(int) const override {\n double suml = 0.0f;\n double sumw = 0.0f;\n if (weights_ != nullptr) {\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml, sumw) if (!deterministic_)\n for (data_size_t i = 0; i < num_data_; ++i) {\n suml += is_pos_(label_[i]) * weights_[i];\n sumw += weights_[i];\n }\n } else {\n sumw = static_cast(num_data_);\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml) if (!deterministic_)\n for (data_size_t i = 0; i < num_data_; ++i) {\n suml += is_pos_(label_[i]);\n }\n }\n if (Network::num_machines() > 1) {\n suml = Network::GlobalSyncUpBySum(suml);\n sumw = Network::GlobalSyncUpBySum(sumw);\n }\n double pavg = suml / sumw;\n pavg = std::min(pavg, 1.0 - kEpsilon);\n pavg = std::max(pavg, kEpsilon);\n double initscore = std::log(pavg / (1.0f - pavg)) / sigmoid_;\n Log::Info(\"[%s:%s]: pavg=%f -> initscore=%f\", GetName(), __func__, pavg, initscore);\n return initscore;\n }\n\n bool ClassNeedTrain(int /*class_id*/) const override {\n return need_train_;\n }\n\n const char* GetName() const override {\n return \"binary\";\n }\n\n void ConvertOutput(const double* input, double* output) const override {\n output[0] = 1.0f / (1.0f + std::exp(-sigmoid_ * input[0]));\n }\n\n std::string ToString() const override {\n std::stringstream str_buf;\n str_buf << GetName() << \" \";\n str_buf << \"sigmoid:\" << sigmoid_;\n return str_buf.str();\n }\n\n bool SkipEmptyClass() const override { return true; }\n\n bool NeedAccuratePrediction() const override { return false; }\n\n data_size_t NumPositiveData() const override { return num_pos_data_; }\n\n protected:\n /*! \\brief Number of data */\n data_size_t num_data_;\n /*! \\brief Number of positive samples */\n data_size_t num_pos_data_;\n /*! \\brief Pointer of label */\n const label_t* label_;\n /*! \\brief True if using unbalance training */\n bool is_unbalance_;\n /*! \\brief Sigmoid parameter */\n double sigmoid_;\n /*! \\brief Values for positive and negative labels */\n int label_val_[2];\n /*! \\brief Weights for positive and negative labels */\n double label_weights_[2];\n /*! \\brief Weights for data */\n const label_t* weights_;\n double scale_pos_weight_;\n std::function is_pos_;\n bool need_train_;\n const bool deterministic_;\n};\n\n} // namespace LightGBM\n#endif // LIGHTGBM_SRC_OBJECTIVE_BINARY_OBJECTIVE_HPP_\n" }, { "path": "src/objective/cuda/cuda_binary_objective.cpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifdef USE_CUDA\n\n#include \"cuda_binary_objective.hpp\"\n\n#include \n#include \n\nnamespace LightGBM {\n\nCUDABinaryLogloss::CUDABinaryLogloss(const Config& config):\nCUDAObjectiveInterface(config), ova_class_id_(-1) {\n cuda_label_ = nullptr;\n cuda_weights_ = nullptr;\n}\n\nCUDABinaryLogloss::CUDABinaryLogloss(const Config& config, const int ova_class_id):\nCUDAObjectiveInterface(config), ova_class_id_(ova_class_id) {\n is_pos_ = [ova_class_id](label_t label) { return static_cast(label) == ova_class_id; };\n}\n\nCUDABinaryLogloss::CUDABinaryLogloss(const std::vector& strs): CUDAObjectiveInterface(strs) {}\n\nCUDABinaryLogloss::~CUDABinaryLogloss() {}\n\nvoid CUDABinaryLogloss::Init(const Metadata& metadata, data_size_t num_data) {\n CUDAObjectiveInterface::Init(metadata, num_data);\n if (ova_class_id_ == -1) {\n cuda_label_ = metadata.cuda_metadata()->cuda_label();\n cuda_ova_label_.Clear();\n } else {\n cuda_ova_label_.Resize(static_cast(num_data));\n CopyFromHostToCUDADevice(cuda_ova_label_.RawData(), metadata.cuda_metadata()->cuda_label(), static_cast(num_data), __FILE__, __LINE__);\n LaunchResetOVACUDALabelKernel();\n cuda_label_ = cuda_ova_label_.RawData();\n }\n cuda_weights_ = metadata.cuda_metadata()->cuda_weights();\n cuda_boost_from_score_.Resize(1);\n SetCUDAMemory(cuda_boost_from_score_.RawData(), 0, 1, __FILE__, __LINE__);\n cuda_sum_weights_.Resize(1);\n SetCUDAMemory(cuda_sum_weights_.RawData(), 0, 1, __FILE__, __LINE__);\n if (label_weights_[0] != 1.0f || label_weights_[1] != 1.0f) {\n cuda_label_weights_.Resize(2);\n CopyFromHostToCUDADevice(cuda_label_weights_.RawData(), label_weights_, 2, __FILE__, __LINE__);\n } else {\n cuda_label_weights_.Clear();\n }\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/objective/cuda/cuda_binary_objective.cu", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved.\n */\n\n#ifdef USE_CUDA\n\n#include \"cuda_binary_objective.hpp\"\n\n#include \n\n#include \n\nnamespace LightGBM {\n\ntemplate \n__global__ void BoostFromScoreKernel_1_BinaryLogloss(const label_t* cuda_labels, const data_size_t num_data, double* out_cuda_sum_labels,\n double* out_cuda_sum_weights, const label_t* cuda_weights) {\n __shared__ double shared_buffer[WARPSIZE];\n const uint32_t mask = 0xffffffff;\n const uint32_t warpLane = threadIdx.x % warpSize;\n const uint32_t warpID = threadIdx.x / warpSize;\n const uint32_t num_warp = blockDim.x / warpSize;\n const data_size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n double label_value = 0.0;\n double weight_value = 0.0;\n if (index < num_data) {\n if (USE_WEIGHT) {\n const label_t cuda_label = cuda_labels[index];\n const double sample_weight = cuda_weights[index];\n const label_t label = cuda_label > 0 ? 1 : 0;\n label_value = label * sample_weight;\n weight_value = sample_weight;\n } else {\n const label_t cuda_label = cuda_labels[index];\n label_value = cuda_label > 0 ? 1 : 0;\n }\n }\n for (uint32_t offset = warpSize / 2; offset >= 1; offset >>= 1) {\n label_value += __shfl_down_sync(mask, label_value, offset);\n }\n if (warpLane == 0) {\n shared_buffer[warpID] = label_value;\n }\n __syncthreads();\n if (warpID == 0) {\n label_value = (warpLane < num_warp ? shared_buffer[warpLane] : 0);\n for (uint32_t offset = warpSize / 2; offset >= 1; offset >>= 1) {\n label_value += __shfl_down_sync(mask, label_value, offset);\n }\n }\n __syncthreads();\n if (USE_WEIGHT) {\n for (uint32_t offset = warpSize / 2; offset >= 1; offset >>= 1) {\n weight_value += __shfl_down_sync(mask, weight_value, offset);\n }\n if (warpLane == 0) {\n shared_buffer[warpID] = weight_value;\n }\n __syncthreads();\n if (warpID == 0) {\n weight_value = (warpLane < num_warp ? shared_buffer[warpLane] : 0);\n for (uint32_t offset = warpSize / 2; offset >= 1; offset >>= 1) {\n weight_value += __shfl_down_sync(mask, weight_value, offset);\n }\n }\n __syncthreads();\n }\n if (threadIdx.x == 0) {\n atomicAdd_system(out_cuda_sum_labels, label_value);\n if (USE_WEIGHT) {\n atomicAdd_system(out_cuda_sum_weights, weight_value);\n }\n }\n}\n\ntemplate \n__global__ void BoostFromScoreKernel_2_BinaryLogloss(double* out_cuda_sum_labels, double* out_cuda_sum_weights,\n const data_size_t num_data, const double sigmoid) {\n const double suml = *out_cuda_sum_labels;\n const double sumw = USE_WEIGHT ? *out_cuda_sum_weights : static_cast(num_data);\n double pavg = suml / sumw;\n pavg = min(pavg, 1.0 - kEpsilon);\n pavg = max(pavg, kEpsilon);\n const double init_score = log(pavg / (1.0f - pavg)) / sigmoid;\n *out_cuda_sum_weights = pavg;\n *out_cuda_sum_labels = init_score;\n}\n\ndouble CUDABinaryLogloss::LaunchCalcInitScoreKernel(const int /*class_id*/) const {\n const int num_blocks = (num_data_ + CALC_INIT_SCORE_BLOCK_SIZE_BINARY - 1) / CALC_INIT_SCORE_BLOCK_SIZE_BINARY;\n SetCUDAMemory(cuda_boost_from_score_.RawData(), 0, 1, __FILE__, __LINE__);\n if (cuda_weights_ == nullptr) {\n BoostFromScoreKernel_1_BinaryLogloss<<>>\n (cuda_label_, num_data_, cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), cuda_weights_);\n } else {\n BoostFromScoreKernel_1_BinaryLogloss<<>>\n (cuda_label_, num_data_, cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), cuda_weights_);\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n if (cuda_weights_ == nullptr) {\n if (nccl_communicator_ == nullptr) {\n BoostFromScoreKernel_2_BinaryLogloss<<<1, 1>>>(cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), num_data_, sigmoid_);\n } else {\n NCCLAllReduce(cuda_boost_from_score_.RawData(), cuda_boost_from_score_.RawData(), 1, ncclFloat64, ncclSum, nccl_communicator_);\n const data_size_t global_num_data = NCCLAllReduce(num_data_, ncclInt32, ncclSum, nccl_communicator_);\n BoostFromScoreKernel_2_BinaryLogloss<<<1, 1>>>(cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), global_num_data, sigmoid_);\n }\n } else {\n if (nccl_communicator_ == nullptr) {\n BoostFromScoreKernel_2_BinaryLogloss<<<1, 1>>>(cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), num_data_, sigmoid_);\n } else {\n NCCLAllReduce(cuda_boost_from_score_.RawData(), cuda_boost_from_score_.RawData(), 1, ncclFloat64, ncclSum, nccl_communicator_);\n NCCLAllReduce(cuda_sum_weights_.RawData(), cuda_sum_weights_.RawData(), 1, ncclFloat64, ncclSum, nccl_communicator_);\n BoostFromScoreKernel_2_BinaryLogloss<<<1, 1>>>(cuda_boost_from_score_.RawData(), cuda_sum_weights_.RawData(), num_data_, sigmoid_);\n }\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n double boost_from_score = 0.0f;\n CopyFromCUDADeviceToHost(&boost_from_score, cuda_boost_from_score_.RawData(), 1, __FILE__, __LINE__);\n double pavg = 0.0f;\n CopyFromCUDADeviceToHost(&pavg, cuda_sum_weights_.RawData(), 1, __FILE__, __LINE__);\n // for some test cases in test_utilities.py which check the log output\n Log::Info(\"[%s:%s]: pavg=%f -> initscore=%f\", GetName(), \"BoostFromScore\", pavg, boost_from_score);\n return boost_from_score;\n}\n\ntemplate \n__global__ void GetGradientsKernel_BinaryLogloss(const double* cuda_scores, const label_t* cuda_labels,\n const double* cuda_label_weights, const label_t* cuda_weights,\n const double sigmoid, const data_size_t num_data,\n score_t* cuda_out_gradients, score_t* cuda_out_hessians) {\n const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x);\n if (data_index < num_data) {\n const label_t cuda_label = static_cast(cuda_labels[data_index]);\n const int label = cuda_label > 0 ? 1 : -1;\n const double response = -label * sigmoid / (1.0f + exp(label * sigmoid * cuda_scores[data_index]));\n const double abs_response = fabs(response);\n if (!USE_WEIGHT) {\n if (USE_LABEL_WEIGHT) {\n const double label_weight = cuda_label_weights[label];\n cuda_out_gradients[data_index] = static_cast(response * label_weight);\n cuda_out_hessians[data_index] = static_cast(abs_response * (sigmoid - abs_response) * label_weight);\n } else {\n cuda_out_gradients[data_index] = static_cast(response);\n cuda_out_hessians[data_index] = static_cast(abs_response * (sigmoid - abs_response));\n }\n } else {\n const double sample_weight = cuda_weights[data_index];\n if (USE_LABEL_WEIGHT) {\n const double label_weight = cuda_label_weights[label];\n cuda_out_gradients[data_index] = static_cast(response * label_weight * sample_weight);\n cuda_out_hessians[data_index] = static_cast(abs_response * (sigmoid - abs_response) * label_weight * sample_weight);\n } else {\n cuda_out_gradients[data_index] = static_cast(response * sample_weight);\n cuda_out_hessians[data_index] = static_cast(abs_response * (sigmoid - abs_response) * sample_weight);\n }\n }\n }\n}\n\n#define GetGradientsKernel_BinaryLogloss_ARGS \\\n scores, \\\n cuda_label_, \\\n cuda_label_weights_.RawData(), \\\n cuda_weights_, \\\n sigmoid_, \\\n num_data_, \\\n gradients, \\\n hessians\n\nvoid CUDABinaryLogloss::LaunchGetGradientsKernel(const double* scores, score_t* gradients, score_t* hessians) const {\n const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_BINARY - 1) / GET_GRADIENTS_BLOCK_SIZE_BINARY;\n if (cuda_label_weights_.Size() == 0) {\n if (cuda_weights_ == nullptr) {\n GetGradientsKernel_BinaryLogloss<<>>(GetGradientsKernel_BinaryLogloss_ARGS);\n } else {\n GetGradientsKernel_BinaryLogloss<<>>(GetGradientsKernel_BinaryLogloss_ARGS);\n }\n } else {\n if (cuda_weights_ == nullptr) {\n GetGradientsKernel_BinaryLogloss<<>>(GetGradientsKernel_BinaryLogloss_ARGS);\n } else {\n GetGradientsKernel_BinaryLogloss<<>>(GetGradientsKernel_BinaryLogloss_ARGS);\n }\n }\n}\n\n#undef GetGradientsKernel_BinaryLogloss_ARGS\n\n__global__ void ConvertOutputCUDAKernel_BinaryLogloss(const double sigmoid, const data_size_t num_data, const double* input, double* output) {\n const data_size_t data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x);\n if (data_index < num_data) {\n output[data_index] = 1.0f / (1.0f + exp(-sigmoid * input[data_index]));\n }\n}\n\nconst double* CUDABinaryLogloss::LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const {\n const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_BINARY - 1) / GET_GRADIENTS_BLOCK_SIZE_BINARY;\n ConvertOutputCUDAKernel_BinaryLogloss<<>>(sigmoid_, num_data, input, output);\n return output;\n}\n\n__global__ void ResetOVACUDALabelKernel(\n const int ova_class_id,\n const data_size_t num_data,\n label_t* cuda_label) {\n const data_size_t data_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n if (data_index < num_data) {\n const int int_label = static_cast(cuda_label[data_index]);\n cuda_label[data_index] = (int_label == ova_class_id ? 1.0f : 0.0f);\n }\n}\n\nvoid CUDABinaryLogloss::LaunchResetOVACUDALabelKernel() const {\n const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_BINARY - 1) / GET_GRADIENTS_BLOCK_SIZE_BINARY;\n ResetOVACUDALabelKernel<<>>(ova_class_id_, num_data_, cuda_ova_label_.RawData());\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/objective/cuda/cuda_binary_objective.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifndef LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_BINARY_OBJECTIVE_HPP_\n#define LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_BINARY_OBJECTIVE_HPP_\n\n#ifdef USE_CUDA\n\n#define GET_GRADIENTS_BLOCK_SIZE_BINARY (1024)\n#define CALC_INIT_SCORE_BLOCK_SIZE_BINARY (1024)\n\n#include \n\n#include \n#include \n\n#include \"../binary_objective.hpp\"\n\nnamespace LightGBM {\n\nclass CUDABinaryLogloss : public CUDAObjectiveInterface {\n public:\n explicit CUDABinaryLogloss(const Config& config);\n\n explicit CUDABinaryLogloss(const Config& config, const int ova_class_id);\n\n explicit CUDABinaryLogloss(const std::vector& strs);\n\n ~CUDABinaryLogloss();\n\n void Init(const Metadata& metadata, data_size_t num_data) override;\n\n bool NeedConvertOutputCUDA() const override { return true; }\n\n private:\n void LaunchGetGradientsKernel(const double* scores, score_t* gradients, score_t* hessians) const override;\n\n double LaunchCalcInitScoreKernel(const int class_id) const override;\n\n const double* LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const override;\n\n void LaunchResetOVACUDALabelKernel() const;\n\n // CUDA memory, held by other objects\n const label_t* cuda_label_;\n CUDAVector cuda_ova_label_;\n const label_t* cuda_weights_;\n\n // CUDA memory, held by this object\n CUDAVector cuda_boost_from_score_;\n CUDAVector cuda_sum_weights_;\n CUDAVector cuda_label_weights_;\n const int ova_class_id_ = -1;\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n\n#endif // LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_BINARY_OBJECTIVE_HPP_\n" }, { "path": "src/objective/cuda/cuda_multiclass_objective.cpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#ifdef USE_CUDA\n\n#include \"cuda_multiclass_objective.hpp\"\n\n#include \n#include \n\nnamespace LightGBM {\n\nCUDAMulticlassSoftmax::CUDAMulticlassSoftmax(const Config& config): CUDAObjectiveInterface(config) {}\n\nCUDAMulticlassSoftmax::CUDAMulticlassSoftmax(const std::vector& strs): CUDAObjectiveInterface(strs) {}\n\nCUDAMulticlassSoftmax::~CUDAMulticlassSoftmax() {}\n\nvoid CUDAMulticlassSoftmax::Init(const Metadata& metadata, data_size_t num_data) {\n CUDAObjectiveInterface::Init(metadata, num_data);\n cuda_softmax_buffer_.Resize(static_cast(num_data) * static_cast(num_class_));\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\n\nCUDAMulticlassOVA::CUDAMulticlassOVA(const Config& config): CUDAObjectiveInterface(config) {\n for (int i = 0; i < num_class_; ++i) {\n cuda_binary_loss_.emplace_back(new CUDABinaryLogloss(config, i));\n }\n}\n\nCUDAMulticlassOVA::CUDAMulticlassOVA(const std::vector& strs): CUDAObjectiveInterface(strs) {}\n\nCUDAMulticlassOVA::~CUDAMulticlassOVA() {}\n\nvoid CUDAMulticlassOVA::Init(const Metadata& metadata, data_size_t num_data) {\n MulticlassOVA::Init(metadata, num_data);\n for (int i = 0; i < num_class_; ++i) {\n cuda_binary_loss_[i]->Init(metadata, num_data);\n }\n}\n\nvoid CUDAMulticlassOVA::GetGradients(const double* score, score_t* gradients, score_t* hessians) const {\n for (int i = 0; i < num_class_; ++i) {\n int64_t offset = static_cast(num_data_) * i;\n cuda_binary_loss_[i]->GetGradients(score + offset, gradients + offset, hessians + offset);\n }\n}\n\nconst double* CUDAMulticlassOVA::ConvertOutputCUDA(const data_size_t num_data, const double* input, double* output) const {\n for (int i = 0; i < num_class_; ++i) {\n cuda_binary_loss_[i]->ConvertOutputCUDA(num_data, input + i * num_data, output + i * num_data);\n }\n return output;\n}\n\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/objective/cuda/cuda_rank_objective.cu", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved.\n */\n\n#ifdef USE_CUDA\n\n#include \"cuda_rank_objective.hpp\"\n\n#include \n#include \n#include \n\nnamespace LightGBM {\n\ntemplate \n__global__ void GetGradientsKernel_LambdarankNDCG(const double* cuda_scores, const label_t* cuda_labels, const data_size_t num_data,\n const data_size_t num_queries, const data_size_t* cuda_query_boundaries, const double* cuda_inverse_max_dcgs,\n const bool norm, const double sigmoid, const int truncation_level, const double* cuda_label_gain, const data_size_t num_rank_label,\n score_t* cuda_out_gradients, score_t* cuda_out_hessians) {\n __shared__ score_t shared_scores[MAX_ITEM_GREATER_THAN_1024 ? 2048 : 1024];\n __shared__ uint16_t shared_indices[MAX_ITEM_GREATER_THAN_1024 ? 2048 : 1024];\n __shared__ score_t shared_lambdas[MAX_ITEM_GREATER_THAN_1024 ? 2048 : 1024];\n __shared__ score_t shared_hessians[MAX_ITEM_GREATER_THAN_1024 ? 2048 : 1024];\n __shared__ double shared_label_gain[NUM_RANK_LABEL > 1024 ? 1 : NUM_RANK_LABEL];\n const double* label_gain_ptr = nullptr;\n if (NUM_RANK_LABEL <= 1024) {\n for (uint32_t i = threadIdx.x; i < num_rank_label; i += blockDim.x) {\n shared_label_gain[i] = cuda_label_gain[i];\n }\n __syncthreads();\n label_gain_ptr = shared_label_gain;\n } else {\n label_gain_ptr = cuda_label_gain;\n }\n const data_size_t query_index_start = static_cast(blockIdx.x) * NUM_QUERY_PER_BLOCK;\n const data_size_t query_index_end = min(query_index_start + NUM_QUERY_PER_BLOCK, num_queries);\n for (data_size_t query_index = query_index_start; query_index < query_index_end; ++query_index) {\n const double inverse_max_dcg = cuda_inverse_max_dcgs[query_index];\n const data_size_t query_start = cuda_query_boundaries[query_index];\n const data_size_t query_end = cuda_query_boundaries[query_index + 1];\n const data_size_t query_item_count = query_end - query_start;\n const double* cuda_scores_pointer = cuda_scores + query_start;\n score_t* cuda_out_gradients_pointer = cuda_out_gradients + query_start;\n score_t* cuda_out_hessians_pointer = cuda_out_hessians + query_start;\n const label_t* cuda_label_pointer = cuda_labels + query_start;\n if (threadIdx.x < query_item_count) {\n shared_scores[threadIdx.x] = cuda_scores_pointer[threadIdx.x];\n shared_indices[threadIdx.x] = static_cast(threadIdx.x);\n shared_lambdas[threadIdx.x] = 0.0f;\n shared_hessians[threadIdx.x] = 0.0f;\n } else {\n shared_scores[threadIdx.x] = kMinScore;\n shared_indices[threadIdx.x] = static_cast(threadIdx.x);\n }\n if (MAX_ITEM_GREATER_THAN_1024) {\n if (query_item_count > 1024) {\n const unsigned int threadIdx_x_plus_1024 = threadIdx.x + 1024;\n if (threadIdx_x_plus_1024 < query_item_count) {\n shared_scores[threadIdx_x_plus_1024] = cuda_scores_pointer[threadIdx_x_plus_1024];\n shared_indices[threadIdx_x_plus_1024] = static_cast(threadIdx_x_plus_1024);\n shared_lambdas[threadIdx_x_plus_1024] = 0.0f;\n shared_hessians[threadIdx_x_plus_1024] = 0.0f;\n } else {\n shared_scores[threadIdx_x_plus_1024] = kMinScore;\n shared_indices[threadIdx_x_plus_1024] = static_cast(threadIdx_x_plus_1024);\n }\n }\n }\n __syncthreads();\n if (MAX_ITEM_GREATER_THAN_1024) {\n if (query_item_count > 1024) {\n BitonicArgSort_2048(shared_scores, shared_indices);\n } else {\n BitonicArgSort_1024(shared_scores, shared_indices, static_cast(query_item_count));\n }\n } else {\n BitonicArgSort_1024(shared_scores, shared_indices, static_cast(query_item_count));\n }\n __syncthreads();\n // get best and worst score\n const double best_score = shared_scores[shared_indices[0]];\n data_size_t worst_idx = query_item_count - 1;\n if (worst_idx > 0 && shared_scores[shared_indices[worst_idx]] == kMinScore) {\n worst_idx -= 1;\n }\n const double worst_score = shared_scores[shared_indices[worst_idx]];\n __shared__ double sum_lambdas;\n if (threadIdx.x == 0) {\n sum_lambdas = 0.0f;\n }\n __syncthreads();\n // start accumulate lambdas by pairs that contain at least one document above truncation level\n const data_size_t num_items_i = min(query_item_count - 1, truncation_level);\n const data_size_t num_j_per_i = query_item_count - 1;\n const data_size_t s = num_j_per_i - num_items_i + 1;\n const data_size_t num_pairs = (num_j_per_i + s) * num_items_i / 2;\n double thread_sum_lambdas = 0.0f;\n for (data_size_t pair_index = static_cast(threadIdx.x); pair_index < num_pairs; pair_index += static_cast(blockDim.x)) {\n const double square = 2 * static_cast(pair_index) + s * s - s;\n const double sqrt_result = floor(sqrt(square));\n const data_size_t row_index = static_cast(floor(sqrt(square - sqrt_result)) + 1 - s);\n const data_size_t i = num_items_i - 1 - row_index;\n const data_size_t j = num_j_per_i - (pair_index - (2 * s + row_index - 1) * row_index / 2);\n if (cuda_label_pointer[shared_indices[i]] != cuda_label_pointer[shared_indices[j]] && shared_scores[shared_indices[j]] != kMinScore) {\n data_size_t high_rank, low_rank;\n if (cuda_label_pointer[shared_indices[i]] > cuda_label_pointer[shared_indices[j]]) {\n high_rank = i;\n low_rank = j;\n } else {\n high_rank = j;\n low_rank = i;\n }\n const data_size_t high = shared_indices[high_rank];\n const int high_label = static_cast(cuda_label_pointer[high]);\n const double high_score = shared_scores[high];\n const double high_label_gain = label_gain_ptr[high_label];\n const double high_discount = log2(2.0f + high_rank);\n const data_size_t low = shared_indices[low_rank];\n const int low_label = static_cast(cuda_label_pointer[low]);\n const double low_score = shared_scores[low];\n const double low_label_gain = label_gain_ptr[low_label];\n const double low_discount = log2(2.0f + low_rank);\n\n const double delta_score = high_score - low_score;\n\n // get dcg gap\n const double dcg_gap = high_label_gain - low_label_gain;\n // get discount of this pair\n const double paired_discount = fabs(high_discount - low_discount);\n // get delta NDCG\n double delta_pair_NDCG = dcg_gap * paired_discount * inverse_max_dcg;\n // regular the delta_pair_NDCG by score distance\n if (norm && best_score != worst_score) {\n delta_pair_NDCG /= (0.01f + fabs(delta_score));\n }\n // calculate lambda for this pair\n double p_lambda = 1.0f / (1.0f + exp(sigmoid * delta_score));\n double p_hessian = p_lambda * (1.0f - p_lambda);\n // update\n p_lambda *= -sigmoid * delta_pair_NDCG;\n p_hessian *= sigmoid * sigmoid * delta_pair_NDCG;\n atomicAdd_block(shared_lambdas + low, -static_cast(p_lambda));\n atomicAdd_block(shared_hessians + low, static_cast(p_hessian));\n atomicAdd_block(shared_lambdas + high, static_cast(p_lambda));\n atomicAdd_block(shared_hessians + high, static_cast(p_hessian));\n // lambda is negative, so use minus to accumulate\n thread_sum_lambdas -= 2 * p_lambda;\n }\n }\n atomicAdd_block(&sum_lambdas, thread_sum_lambdas);\n __syncthreads();\n if (norm && sum_lambdas > 0) {\n const double norm_factor = log2(1 + sum_lambdas) / sum_lambdas;\n if (threadIdx.x < static_cast(query_item_count)) {\n cuda_out_gradients_pointer[threadIdx.x] = static_cast(shared_lambdas[threadIdx.x] * norm_factor);\n cuda_out_hessians_pointer[threadIdx.x] = static_cast(shared_hessians[threadIdx.x] * norm_factor);\n }\n if (MAX_ITEM_GREATER_THAN_1024) {\n if (query_item_count > 1024) {\n const unsigned int threadIdx_x_plus_1024 = threadIdx.x + 1024;\n if (threadIdx_x_plus_1024 < static_cast(query_item_count)) {\n cuda_out_gradients_pointer[threadIdx_x_plus_1024] = static_cast(shared_lambdas[threadIdx_x_plus_1024] * norm_factor);\n cuda_out_hessians_pointer[threadIdx_x_plus_1024] = static_cast(shared_hessians[threadIdx_x_plus_1024] * norm_factor);\n }\n }\n }\n } else {\n if (threadIdx.x < static_cast(query_item_count)) {\n cuda_out_gradients_pointer[threadIdx.x] = static_cast(shared_lambdas[threadIdx.x]);\n cuda_out_hessians_pointer[threadIdx.x] = static_cast(shared_hessians[threadIdx.x]);\n }\n if (MAX_ITEM_GREATER_THAN_1024) {\n if (query_item_count > 1024) {\n const unsigned int threadIdx_x_plus_1024 = threadIdx.x + 1024;\n if (threadIdx_x_plus_1024 < static_cast(query_item_count)) {\n cuda_out_gradients_pointer[threadIdx_x_plus_1024] = static_cast(shared_lambdas[threadIdx_x_plus_1024]);\n cuda_out_hessians_pointer[threadIdx_x_plus_1024] = static_cast(shared_hessians[threadIdx_x_plus_1024]);\n }\n }\n }\n }\n __syncthreads();\n }\n}\n\ntemplate \n__global__ void GetGradientsKernel_LambdarankNDCG_Sorted(\n const double* cuda_scores, const int* cuda_item_indices_buffer, const label_t* cuda_labels, const data_size_t num_data,\n const data_size_t num_queries, const data_size_t* cuda_query_boundaries, const double* cuda_inverse_max_dcgs,\n const bool norm, const double sigmoid, const int truncation_level, const double* cuda_label_gain, const data_size_t num_rank_label,\n score_t* cuda_out_gradients, score_t* cuda_out_hessians) {\n __shared__ double shared_label_gain[NUM_RANK_LABEL > 1024 ? 1 : NUM_RANK_LABEL];\n const double* label_gain_ptr = nullptr;\n if (NUM_RANK_LABEL <= 1024) {\n for (uint32_t i = threadIdx.x; i < static_cast(num_rank_label); i += blockDim.x) {\n shared_label_gain[i] = cuda_label_gain[i];\n }\n __syncthreads();\n label_gain_ptr = shared_label_gain;\n } else {\n label_gain_ptr = cuda_label_gain;\n }\n const data_size_t query_index_start = static_cast(blockIdx.x) * NUM_QUERY_PER_BLOCK;\n const data_size_t query_index_end = min(query_index_start + NUM_QUERY_PER_BLOCK, num_queries);\n for (data_size_t query_index = query_index_start; query_index < query_index_end; ++query_index) {\n const double inverse_max_dcg = cuda_inverse_max_dcgs[query_index];\n const data_size_t query_start = cuda_query_boundaries[query_index];\n const data_size_t query_end = cuda_query_boundaries[query_index + 1];\n const data_size_t query_item_count = query_end - query_start;\n const double* cuda_scores_pointer = cuda_scores + query_start;\n const int* cuda_item_indices_buffer_pointer = cuda_item_indices_buffer + query_start;\n score_t* cuda_out_gradients_pointer = cuda_out_gradients + query_start;\n score_t* cuda_out_hessians_pointer = cuda_out_hessians + query_start;\n const label_t* cuda_label_pointer = cuda_labels + query_start;\n // get best and worst score\n const double best_score = cuda_scores_pointer[cuda_item_indices_buffer_pointer[0]];\n data_size_t worst_idx = query_item_count - 1;\n if (worst_idx > 0 && cuda_scores_pointer[cuda_item_indices_buffer_pointer[worst_idx]] == kMinScore) {\n worst_idx -= 1;\n }\n const double worst_score = cuda_scores_pointer[cuda_item_indices_buffer_pointer[worst_idx]];\n __shared__ double sum_lambdas;\n if (threadIdx.x == 0) {\n sum_lambdas = 0.0f;\n }\n for (int item_index = static_cast(threadIdx.x); item_index < query_item_count; item_index += static_cast(blockDim.x)) {\n cuda_out_gradients_pointer[item_index] = 0.0f;\n cuda_out_hessians_pointer[item_index] = 0.0f;\n }\n __syncthreads();\n // start accumulate lambdas by pairs that contain at least one document above truncation level\n const data_size_t num_items_i = min(query_item_count - 1, truncation_level);\n const data_size_t num_j_per_i = query_item_count - 1;\n const data_size_t s = num_j_per_i - num_items_i + 1;\n const data_size_t num_pairs = (num_j_per_i + s) * num_items_i / 2;\n double thread_sum_lambdas = 0.0f;\n for (data_size_t pair_index = static_cast(threadIdx.x); pair_index < num_pairs; pair_index += static_cast(blockDim.x)) {\n const double square = 2 * static_cast(pair_index) + s * s - s;\n const double sqrt_result = floor(sqrt(square));\n const data_size_t row_index = static_cast(floor(sqrt(square - sqrt_result)) + 1 - s);\n const data_size_t i = num_items_i - 1 - row_index;\n const data_size_t j = num_j_per_i - (pair_index - (2 * s + row_index - 1) * row_index / 2);\n if (j > i) {\n // skip pairs with the same labels\n if (cuda_label_pointer[cuda_item_indices_buffer_pointer[i]] != cuda_label_pointer[cuda_item_indices_buffer_pointer[j]] && cuda_scores_pointer[cuda_item_indices_buffer_pointer[j]] != kMinScore) {\n data_size_t high_rank, low_rank;\n if (cuda_label_pointer[cuda_item_indices_buffer_pointer[i]] > cuda_label_pointer[cuda_item_indices_buffer_pointer[j]]) {\n high_rank = i;\n low_rank = j;\n } else {\n high_rank = j;\n low_rank = i;\n }\n const data_size_t high = cuda_item_indices_buffer_pointer[high_rank];\n const int high_label = static_cast(cuda_label_pointer[high]);\n const double high_score = cuda_scores_pointer[high];\n const double high_label_gain = label_gain_ptr[high_label];\n const double high_discount = log2(2.0f + high_rank);\n const data_size_t low = cuda_item_indices_buffer_pointer[low_rank];\n const int low_label = static_cast(cuda_label_pointer[low]);\n const double low_score = cuda_scores_pointer[low];\n const double low_label_gain = label_gain_ptr[low_label];\n const double low_discount = log2(2.0f + low_rank);\n\n const double delta_score = high_score - low_score;\n\n // get dcg gap\n const double dcg_gap = high_label_gain - low_label_gain;\n // get discount of this pair\n const double paired_discount = fabs(high_discount - low_discount);\n // get delta NDCG\n double delta_pair_NDCG = dcg_gap * paired_discount * inverse_max_dcg;\n // regular the delta_pair_NDCG by score distance\n if (norm && best_score != worst_score) {\n delta_pair_NDCG /= (0.01f + fabs(delta_score));\n }\n // calculate lambda for this pair\n double p_lambda = 1.0f / (1.0f + exp(sigmoid * delta_score));\n double p_hessian = p_lambda * (1.0f - p_lambda);\n // update\n p_lambda *= -sigmoid * delta_pair_NDCG;\n p_hessian *= sigmoid * sigmoid * delta_pair_NDCG;\n atomicAdd_block(cuda_out_gradients_pointer + low, -static_cast(p_lambda));\n atomicAdd_block(cuda_out_hessians_pointer + low, static_cast(p_hessian));\n atomicAdd_block(cuda_out_gradients_pointer + high, static_cast(p_lambda));\n atomicAdd_block(cuda_out_hessians_pointer + high, static_cast(p_hessian));\n // lambda is negative, so use minus to accumulate\n thread_sum_lambdas -= 2 * p_lambda;\n }\n }\n }\n atomicAdd_block(&sum_lambdas, thread_sum_lambdas);\n __syncthreads();\n if (norm && sum_lambdas > 0) {\n const double norm_factor = log2(1 + sum_lambdas) / sum_lambdas;\n for (int item_index = static_cast(threadIdx.x); item_index < query_item_count; item_index += static_cast(blockDim.x)) {\n cuda_out_gradients_pointer[item_index] *= norm_factor;\n cuda_out_hessians_pointer[item_index] *= norm_factor;\n }\n }\n __syncthreads();\n }\n}\n\nvoid CUDALambdarankNDCG::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const {\n const int num_blocks = (num_queries_ + NUM_QUERY_PER_BLOCK - 1) / NUM_QUERY_PER_BLOCK;\n const data_size_t num_rank_label = static_cast(label_gain_.size());\n const int device_index = GetCUDADevice(__FILE__, __LINE__);\n cudaDeviceProp device_prop;\n CUDASUCCESS_OR_FATAL(cudaGetDeviceProperties(&device_prop, device_index));\n\n #define GetGradientsKernel_LambdarankNDCG_ARGS \\\n score, cuda_labels_, num_data_, \\\n num_queries_, cuda_query_boundaries_, cuda_inverse_max_dcgs_.RawData(), \\\n norm_, sigmoid_, truncation_level_, cuda_label_gain_.RawData(), num_rank_label, \\\n gradients, hessians\n\n #define GetGradientsKernel_LambdarankNDCG_Sorted_ARGS \\\n score, cuda_item_indices_buffer_.RawData(), cuda_labels_, num_data_, \\\n num_queries_, cuda_query_boundaries_, cuda_inverse_max_dcgs_.RawData(), \\\n norm_, sigmoid_, truncation_level_, cuda_label_gain_.RawData(), num_rank_label, \\\n gradients, hessians\n\n if (max_items_in_query_aligned_ <= 1024) {\n if (num_rank_label <= 32 && device_prop.warpSize == 32) {\n GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS);\n } else if (num_rank_label <= 64) {\n GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS);\n } else if (num_rank_label <= 128) {\n GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS);\n } else if (num_rank_label <= 256) {\n GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS);\n } else if (num_rank_label <= 512) {\n GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS);\n } else if (num_rank_label <= 1024) {\n GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS);\n } else {\n GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS);\n }\n } else if (max_items_in_query_aligned_ <= 2048) {\n if (num_rank_label <= 32 && device_prop.warpSize == 32) {\n GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS);\n } else if (num_rank_label <= 64) {\n GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS);\n } else if (num_rank_label <= 128) {\n GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS);\n } else if (num_rank_label <= 256) {\n GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS);\n } else if (num_rank_label <= 512) {\n GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS);\n } else if (num_rank_label <= 1024) {\n GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS);\n } else {\n GetGradientsKernel_LambdarankNDCG<<>>(GetGradientsKernel_LambdarankNDCG_ARGS);\n }\n } else {\n BitonicArgSortItemsGlobal(score, num_queries_, cuda_query_boundaries_, cuda_item_indices_buffer_.RawData());\n if (num_rank_label <= 32 && device_prop.warpSize == 32) {\n GetGradientsKernel_LambdarankNDCG_Sorted<32><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS);\n } else if (num_rank_label <= 64) {\n GetGradientsKernel_LambdarankNDCG_Sorted<64><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS);\n } else if (num_rank_label <= 128) {\n GetGradientsKernel_LambdarankNDCG_Sorted<128><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS);\n } else if (num_rank_label <= 256) {\n GetGradientsKernel_LambdarankNDCG_Sorted<256><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS);\n } else if (num_rank_label <= 512) {\n GetGradientsKernel_LambdarankNDCG_Sorted<512><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS);\n } else if (num_rank_label <= 1024) {\n GetGradientsKernel_LambdarankNDCG_Sorted<1024><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS);\n } else {\n GetGradientsKernel_LambdarankNDCG_Sorted<2048><<>>(GetGradientsKernel_LambdarankNDCG_Sorted_ARGS);\n }\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n\n #undef GetGradientsKernel_LambdarankNDCG_ARGS\n #undef GetGradientsKernel_LambdarankNDCG_Sorted_ARGS\n}\n\n\n__device__ __forceinline__ double CUDAPhi(const label_t l, double g) {\n return pow(2.0f, static_cast(l)) - g;\n}\n\ntemplate \n__global__ void GetGradientsKernel_RankXENDCG_SharedMemory(\n const double* cuda_scores,\n const label_t* cuda_labels,\n const double* cuda_item_rands,\n const data_size_t num_data,\n const data_size_t num_queries,\n const data_size_t* cuda_query_boundaries,\n score_t* cuda_out_gradients,\n score_t* cuda_out_hessians) {\n const data_size_t query_index_start = static_cast(blockIdx.x) * NUM_QUERY_PER_BLOCK;\n const data_size_t query_index_end = min(query_index_start + NUM_QUERY_PER_BLOCK, num_queries);\n for (data_size_t query_index = query_index_start; query_index < query_index_end; ++query_index) {\n const data_size_t item_index_start = cuda_query_boundaries[query_index];\n const data_size_t item_index_end = cuda_query_boundaries[query_index + 1];\n const data_size_t query_item_count = item_index_end - item_index_start;\n score_t* cuda_out_gradients_pointer = cuda_out_gradients + item_index_start;\n score_t* cuda_out_hessians_pointer = cuda_out_hessians + item_index_start;\n const label_t* cuda_labels_pointer = cuda_labels + item_index_start;\n const double* cuda_scores_pointer = cuda_scores + item_index_start;\n const double* cuda_item_rands_pointer = cuda_item_rands + item_index_start;\n const data_size_t block_reduce_size = query_item_count >= 1024 ? 1024 : query_item_count;\n __shared__ double shared_rho[SHARED_MEMORY_SIZE];\n // assert that warpSize == 32\n __shared__ double shared_buffer[1024 / WARPSIZE];\n __shared__ double shared_params[SHARED_MEMORY_SIZE];\n __shared__ score_t shared_lambdas[SHARED_MEMORY_SIZE];\n __shared__ double reduce_result;\n if (query_item_count <= 1) {\n for (data_size_t i = 0; i <= query_item_count; ++i) {\n cuda_out_gradients_pointer[i] = 0.0f;\n cuda_out_hessians_pointer[i] = 0.0f;\n }\n __syncthreads();\n } else {\n // compute softmax\n double thread_reduce_result = kMinScore;\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n const double rho = cuda_scores_pointer[i];\n shared_rho[i] = rho;\n if (rho > thread_reduce_result) {\n thread_reduce_result = rho;\n }\n }\n __syncthreads();\n thread_reduce_result = ShuffleReduceMax(thread_reduce_result, shared_buffer, block_reduce_size);\n if (threadIdx.x == 0) {\n reduce_result = thread_reduce_result;\n }\n __syncthreads();\n thread_reduce_result = 0.0f;\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n const double exp_value = exp(shared_rho[i] - reduce_result);\n shared_rho[i] = exp_value;\n thread_reduce_result += exp_value;\n }\n thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size);\n if (threadIdx.x == 0) {\n reduce_result = thread_reduce_result;\n }\n __syncthreads();\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n shared_rho[i] /= reduce_result;\n }\n __syncthreads();\n\n // compute params\n thread_reduce_result = 0.0f;\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n const double param_value = CUDAPhi(cuda_labels_pointer[i], cuda_item_rands_pointer[i]);\n shared_params[i] = param_value;\n thread_reduce_result += param_value;\n }\n thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size);\n if (threadIdx.x == 0) {\n reduce_result = thread_reduce_result;\n reduce_result = 1.0f / max(kEpsilon, reduce_result);\n }\n __syncthreads();\n const double inv_denominator = reduce_result;\n thread_reduce_result = 0.0f;\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n const double term = -shared_params[i] * inv_denominator + shared_rho[i];\n shared_lambdas[i] = static_cast(term);\n shared_params[i] = term / (1.0f - shared_rho[i]);\n thread_reduce_result += shared_params[i];\n }\n thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size);\n if (threadIdx.x == 0) {\n reduce_result = thread_reduce_result;\n }\n __syncthreads();\n const double sum_l1 = reduce_result;\n thread_reduce_result = 0.0f;\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n const double term = shared_rho[i] * (sum_l1 - shared_params[i]);\n shared_lambdas[i] += static_cast(term);\n shared_params[i] = term / (1.0f - shared_rho[i]);\n thread_reduce_result += shared_params[i];\n }\n thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size);\n if (threadIdx.x == 0) {\n reduce_result = thread_reduce_result;\n }\n __syncthreads();\n const double sum_l2 = reduce_result;\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n shared_lambdas[i] += static_cast(shared_rho[i] * (sum_l2 - shared_params[i]));\n cuda_out_hessians_pointer[i] = static_cast(shared_rho[i] * (1.0f - shared_rho[i]));\n }\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n cuda_out_gradients_pointer[i] = shared_lambdas[i];\n }\n __syncthreads();\n }\n }\n}\n\n__global__ void GetGradientsKernel_RankXENDCG_GlobalMemory(\n const double* cuda_scores,\n const label_t* cuda_labels,\n const double* cuda_item_rands,\n const data_size_t num_data,\n const data_size_t num_queries,\n const data_size_t* cuda_query_boundaries,\n double* cuda_params_buffer,\n score_t* cuda_out_gradients,\n score_t* cuda_out_hessians) {\n const data_size_t query_index_start = static_cast(blockIdx.x) * NUM_QUERY_PER_BLOCK;\n const data_size_t query_index_end = min(query_index_start + NUM_QUERY_PER_BLOCK, num_queries);\n for (data_size_t query_index = query_index_start; query_index < query_index_end; ++query_index) {\n const data_size_t item_index_start = cuda_query_boundaries[query_index];\n const data_size_t item_index_end = cuda_query_boundaries[query_index + 1];\n const data_size_t query_item_count = item_index_end - item_index_start;\n score_t* cuda_out_gradients_pointer = cuda_out_gradients + item_index_start;\n score_t* cuda_out_hessians_pointer = cuda_out_hessians + item_index_start;\n const label_t* cuda_labels_pointer = cuda_labels + item_index_start;\n const double* cuda_scores_pointer = cuda_scores + item_index_start;\n const double* cuda_item_rands_pointer = cuda_item_rands + item_index_start;\n double* cuda_params_buffer_pointer = cuda_params_buffer + item_index_start;\n const data_size_t block_reduce_size = query_item_count > 1024 ? 1024 : query_item_count;\n // assert that warpSize == 32, so we use buffer size 1024 / 32 = 32\n __shared__ double shared_buffer[1024 / WARPSIZE];\n __shared__ double reduce_result;\n if (query_item_count <= 1) {\n for (data_size_t i = 0; i <= query_item_count; ++i) {\n cuda_out_gradients_pointer[i] = 0.0f;\n cuda_out_hessians_pointer[i] = 0.0f;\n }\n __syncthreads();\n } else {\n // compute softmax\n double thread_reduce_result = kMinScore;\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n const double rho = cuda_scores_pointer[i];\n if (rho > thread_reduce_result) {\n thread_reduce_result = rho;\n }\n }\n __syncthreads();\n thread_reduce_result = ShuffleReduceMax(thread_reduce_result, shared_buffer, block_reduce_size);\n if (threadIdx.x == 0) {\n reduce_result = thread_reduce_result;\n }\n __syncthreads();\n thread_reduce_result = 0.0f;\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n const double exp_value = exp(cuda_scores_pointer[i] - reduce_result);\n cuda_out_hessians_pointer[i] = exp_value;\n thread_reduce_result += exp_value;\n }\n thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size);\n if (threadIdx.x == 0) {\n reduce_result = thread_reduce_result;\n }\n __syncthreads();\n // store probability into hessians\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n cuda_out_hessians_pointer[i] /= reduce_result;\n }\n __syncthreads();\n\n // compute params\n thread_reduce_result = 0.0f;\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n const double param_value = CUDAPhi(cuda_labels_pointer[i], cuda_item_rands_pointer[i]);\n cuda_params_buffer_pointer[i] = param_value;\n thread_reduce_result += param_value;\n }\n thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size);\n if (threadIdx.x == 0) {\n reduce_result = thread_reduce_result;\n reduce_result = 1.0f / max(kEpsilon, reduce_result);\n }\n __syncthreads();\n const double inv_denominator = reduce_result;\n thread_reduce_result = 0.0f;\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n const double term = -cuda_params_buffer_pointer[i] * inv_denominator + cuda_out_hessians_pointer[i];\n cuda_out_gradients_pointer[i] = static_cast(term);\n const double param = term / (1.0f - cuda_out_hessians_pointer[i]);\n cuda_params_buffer_pointer[i] = param;\n thread_reduce_result += param;\n }\n thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size);\n if (threadIdx.x == 0) {\n reduce_result = thread_reduce_result;\n }\n __syncthreads();\n const double sum_l1 = reduce_result;\n thread_reduce_result = 0.0f;\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n const double term = cuda_out_hessians_pointer[i] * (sum_l1 - cuda_params_buffer_pointer[i]);\n cuda_out_gradients_pointer[i] += static_cast(term);\n const double param = term / (1.0f - cuda_out_hessians_pointer[i]);\n cuda_params_buffer_pointer[i] = param;\n thread_reduce_result += param;\n }\n thread_reduce_result = ShuffleReduceSum(thread_reduce_result, shared_buffer, block_reduce_size);\n if (threadIdx.x == 0) {\n reduce_result = thread_reduce_result;\n }\n __syncthreads();\n const double sum_l2 = reduce_result;\n for (data_size_t i = static_cast(threadIdx.x); i < query_item_count; i += static_cast(blockDim.x)) {\n const double prob = cuda_out_hessians_pointer[i];\n cuda_out_gradients_pointer[i] += static_cast(prob * (sum_l2 - cuda_params_buffer_pointer[i]));\n cuda_out_hessians_pointer[i] = static_cast(prob * (1.0f - prob));\n }\n __syncthreads();\n }\n }\n}\n\nvoid CUDARankXENDCG::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const {\n GenerateItemRands();\n CopyFromHostToCUDADevice(cuda_item_rands_.RawData(), item_rands_.data(), item_rands_.size(), __FILE__, __LINE__);\n\n const int num_blocks = (num_queries_ + NUM_QUERY_PER_BLOCK - 1) / NUM_QUERY_PER_BLOCK;\n if (max_items_in_query_aligned_ <= 1024) {\n GetGradientsKernel_RankXENDCG_SharedMemory<1024><<>>(\n score,\n cuda_labels_,\n cuda_item_rands_.RawData(),\n num_data_,\n num_queries_,\n cuda_query_boundaries_,\n gradients,\n hessians);\n } else if (max_items_in_query_aligned_ <= 2 * 1024) {\n GetGradientsKernel_RankXENDCG_SharedMemory<2 * 1024><<>>(\n score,\n cuda_labels_,\n cuda_item_rands_.RawData(),\n num_data_,\n num_queries_,\n cuda_query_boundaries_,\n gradients,\n hessians);\n } else {\n GetGradientsKernel_RankXENDCG_GlobalMemory<<>>(\n score,\n cuda_labels_,\n cuda_item_rands_.RawData(),\n num_data_,\n num_queries_,\n cuda_query_boundaries_,\n cuda_params_buffer_.RawData(),\n gradients,\n hessians);\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/objective/cuda/cuda_rank_objective.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifndef LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_RANK_OBJECTIVE_HPP_\n#define LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_RANK_OBJECTIVE_HPP_\n\n#ifdef USE_CUDA\n\n#define NUM_QUERY_PER_BLOCK (10)\n\n#include \n#include \n\n#include \n#include \n#include \n\n#include \"../rank_objective.hpp\"\n\nnamespace LightGBM {\n\ntemplate \nclass CUDALambdaRankObjectiveInterface : public CUDAObjectiveInterface {\n public:\n explicit CUDALambdaRankObjectiveInterface(const Config& config): CUDAObjectiveInterface(config) {}\n\n explicit CUDALambdaRankObjectiveInterface(const std::vector& strs): CUDAObjectiveInterface(strs) {}\n\n ~CUDALambdaRankObjectiveInterface() {}\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n CUDAObjectiveInterface::Init(metadata, num_data);\n\n const int num_threads = OMP_NUM_THREADS();\n std::vector thread_max_num_items_in_query(num_threads);\n Threading::For(0, this->num_queries_, 1,\n [this, &thread_max_num_items_in_query] (int thread_index, data_size_t start, data_size_t end) {\n for (data_size_t query_index = start; query_index < end; ++query_index) {\n const data_size_t query_item_count = this->query_boundaries_[query_index + 1] - this->query_boundaries_[query_index];\n if (query_item_count > thread_max_num_items_in_query[thread_index]) {\n thread_max_num_items_in_query[thread_index] = query_item_count;\n }\n }\n });\n data_size_t max_items_in_query = 0;\n for (int thread_index = 0; thread_index < num_threads; ++thread_index) {\n if (thread_max_num_items_in_query[thread_index] > max_items_in_query) {\n max_items_in_query = thread_max_num_items_in_query[thread_index];\n }\n }\n max_items_in_query_aligned_ = 1;\n --max_items_in_query;\n while (max_items_in_query > 0) {\n max_items_in_query >>= 1;\n max_items_in_query_aligned_ <<= 1;\n }\n if (max_items_in_query_aligned_ > 2048) {\n cuda_item_indices_buffer_.Resize(static_cast(metadata.query_boundaries()[metadata.num_queries()]));\n }\n this->cuda_labels_ = metadata.cuda_metadata()->cuda_label();\n cuda_query_boundaries_ = metadata.cuda_metadata()->cuda_query_boundaries();\n }\n\n protected:\n // CUDA memory, held by this object\n CUDAVector cuda_item_indices_buffer_;\n\n // CUDA memory, held by other objects\n const data_size_t* cuda_query_boundaries_;\n\n // Host memory\n int max_items_in_query_aligned_;\n};\n\n\nclass CUDALambdarankNDCG: public CUDALambdaRankObjectiveInterface {\n public:\n explicit CUDALambdarankNDCG(const Config& config);\n\n explicit CUDALambdarankNDCG(const std::vector& strs);\n\n void Init(const Metadata& mdtadata, data_size_t num_data) override;\n\n ~CUDALambdarankNDCG();\n\n private:\n void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override;\n\n // CUDA memory, held by this object\n CUDAVector cuda_inverse_max_dcgs_;\n CUDAVector cuda_label_gain_;\n};\n\n\nclass CUDARankXENDCG : public CUDALambdaRankObjectiveInterface {\n public:\n explicit CUDARankXENDCG(const Config& config);\n\n explicit CUDARankXENDCG(const std::vector& strs);\n\n ~CUDARankXENDCG();\n\n void Init(const Metadata& metadata, data_size_t num_data) override;\n\n protected:\n void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const;\n\n void GenerateItemRands() const;\n\n mutable std::vector item_rands_;\n CUDAVector cuda_item_rands_;\n CUDAVector cuda_params_buffer_;\n};\n\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n#endif // LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_RANK_OBJECTIVE_HPP_\n" }, { "path": "src/objective/cuda/cuda_regression_objective.cpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifdef USE_CUDA\n\n#include \"cuda_regression_objective.hpp\"\n\n#include \n#include \n\nnamespace LightGBM {\n\nCUDARegressionL2loss::CUDARegressionL2loss(const Config& config):\nCUDARegressionObjectiveInterface(config) {}\n\nCUDARegressionL2loss::CUDARegressionL2loss(const std::vector& strs):\nCUDARegressionObjectiveInterface(strs) {}\n\nCUDARegressionL2loss::~CUDARegressionL2loss() {}\n\nvoid CUDARegressionL2loss::Init(const Metadata& metadata, data_size_t num_data) {\n CUDARegressionObjectiveInterface::Init(metadata, num_data);\n}\n\nCUDARegressionL1loss::CUDARegressionL1loss(const Config& config):\nCUDARegressionObjectiveInterface(config) {}\n\nCUDARegressionL1loss::CUDARegressionL1loss(const std::vector& strs):\nCUDARegressionObjectiveInterface(strs) {}\n\nCUDARegressionL1loss::~CUDARegressionL1loss() {}\n\nvoid CUDARegressionL1loss::Init(const Metadata& metadata, data_size_t num_data) {\n CUDARegressionObjectiveInterface::Init(metadata, num_data);\n cuda_data_indices_buffer_.Resize(static_cast(num_data));\n cuda_percentile_result_.Resize(1);\n if (cuda_weights_ != nullptr) {\n const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION + 1;\n cuda_weights_prefix_sum_.Resize(static_cast(num_data));\n cuda_weights_prefix_sum_buffer_.Resize(static_cast(num_blocks));\n cuda_weight_by_leaf_buffer_.Resize(static_cast(num_data));\n }\n cuda_residual_buffer_.Resize(static_cast(num_data));\n}\n\n\nCUDARegressionHuberLoss::CUDARegressionHuberLoss(const Config& config):\nCUDARegressionObjectiveInterface(config) {}\n\nCUDARegressionHuberLoss::CUDARegressionHuberLoss(const std::vector& strs):\nCUDARegressionObjectiveInterface(strs) {}\n\nCUDARegressionHuberLoss::~CUDARegressionHuberLoss() {}\n\n\nCUDARegressionFairLoss::CUDARegressionFairLoss(const Config& config):\nCUDARegressionObjectiveInterface(config) {}\n\nCUDARegressionFairLoss::CUDARegressionFairLoss(const std::vector& strs):\nCUDARegressionObjectiveInterface(strs) {}\n\nCUDARegressionFairLoss::~CUDARegressionFairLoss() {}\n\n\nCUDARegressionPoissonLoss::CUDARegressionPoissonLoss(const Config& config):\nCUDARegressionObjectiveInterface(config) {}\n\nCUDARegressionPoissonLoss::CUDARegressionPoissonLoss(const std::vector& strs):\nCUDARegressionObjectiveInterface(strs) {}\n\nCUDARegressionPoissonLoss::~CUDARegressionPoissonLoss() {}\n\nvoid CUDARegressionPoissonLoss::Init(const Metadata& metadata, data_size_t num_data) {\n CUDARegressionObjectiveInterface::Init(metadata, num_data);\n LaunchCheckLabelKernel();\n}\n\ndouble CUDARegressionPoissonLoss::LaunchCalcInitScoreKernel(const int class_id) const {\n return Common::SafeLog(CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(class_id));\n}\n\n\nCUDARegressionQuantileloss::CUDARegressionQuantileloss(const Config& config):\nCUDARegressionObjectiveInterface(config) {}\n\nCUDARegressionQuantileloss::CUDARegressionQuantileloss(const std::vector& strs):\nCUDARegressionObjectiveInterface(strs) {}\n\nCUDARegressionQuantileloss::~CUDARegressionQuantileloss() {}\n\nvoid CUDARegressionQuantileloss::Init(const Metadata& metadata, data_size_t num_data) {\n CUDARegressionObjectiveInterface::Init(metadata, num_data);\n cuda_data_indices_buffer_.Resize(static_cast(num_data));\n cuda_percentile_result_.Resize(1);\n if (cuda_weights_ != nullptr) {\n const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION + 1;\n cuda_weights_prefix_sum_.Resize(static_cast(num_data));\n cuda_weights_prefix_sum_buffer_.Resize(static_cast(num_blocks));\n cuda_weight_by_leaf_buffer_.Resize(static_cast(num_data));\n }\n cuda_residual_buffer_.Resize(static_cast(num_data));\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/objective/cuda/cuda_regression_objective.cu", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifdef USE_CUDA\n\n#include \"cuda_regression_objective.hpp\"\n#include \n\nnamespace LightGBM {\n\ntemplate \nvoid CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data) {\n CUDAObjectiveInterface::Init(metadata, num_data);\n const data_size_t num_get_gradients_blocks = (this->num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION;\n cuda_block_buffer_.Resize(static_cast(num_get_gradients_blocks));\n if (this->sqrt_) {\n cuda_trans_label_.Resize(this->trans_label_.size());\n CopyFromHostToCUDADevice(cuda_trans_label_.RawData(), this->trans_label_.data(), this->trans_label_.size(), __FILE__, __LINE__);\n this->cuda_labels_ = cuda_trans_label_.RawData();\n }\n}\n\ntemplate void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data);\ntemplate void CUDARegressionObjectiveInterface::Init(const Metadata& metadata, data_size_t num_data);\n\ntemplate \ndouble CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int /*class_id*/) const {\n double label_sum = 0.0f, weight_sum = 0.0f;\n if (this->cuda_weights_ == nullptr) {\n ShuffleReduceSumGlobal(this->cuda_labels_,\n static_cast(this->num_data_), cuda_block_buffer_.RawData());\n CopyFromCUDADeviceToHost(&label_sum, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__);\n weight_sum = static_cast(this->num_data_);\n } else {\n ShuffleReduceDotProdGlobal(this->cuda_labels_,\n this->cuda_weights_, static_cast(this->num_data_), cuda_block_buffer_.RawData());\n CopyFromCUDADeviceToHost(&label_sum, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__);\n ShuffleReduceSumGlobal(this->cuda_weights_,\n static_cast(this->num_data_), cuda_block_buffer_.RawData());\n CopyFromCUDADeviceToHost(&weight_sum, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__);\n }\n return label_sum / weight_sum;\n}\n\ntemplate double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const;\ntemplate double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const;\ntemplate double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const;\ntemplate double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const;\ntemplate double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const;\ntemplate double CUDARegressionObjectiveInterface::LaunchCalcInitScoreKernel(const int class_id) const;\n\n__global__ void ConvertOutputCUDAKernel_Regression(const bool sqrt, const data_size_t num_data, const double* input, double* output) {\n const int data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x);\n if (data_index < num_data) {\n if (sqrt) {\n const double sign = input[data_index] >= 0.0f ? 1 : -1;\n output[data_index] = sign * input[data_index] * input[data_index];\n } else {\n output[data_index] = input[data_index];\n }\n }\n}\n\nconst double* CUDARegressionL2loss::LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const {\n const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION;\n if (sqrt_) {\n ConvertOutputCUDAKernel_Regression<<>>(sqrt_, num_data, input, output);\n return output;\n } else {\n return input;\n }\n}\n\ntemplate \n__global__ void GetGradientsKernel_RegressionL2(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data,\n score_t* cuda_out_gradients, score_t* cuda_out_hessians) {\n const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x);\n if (data_index < num_data) {\n if (!USE_WEIGHT) {\n cuda_out_gradients[data_index] = static_cast(cuda_scores[data_index] - cuda_labels[data_index]);\n cuda_out_hessians[data_index] = 1.0f;\n } else {\n const score_t weight = static_cast(cuda_weights[data_index]);\n cuda_out_gradients[data_index] = static_cast(cuda_scores[data_index] - cuda_labels[data_index]) * weight;\n cuda_out_hessians[data_index] = weight;\n }\n }\n}\n\nvoid CUDARegressionL2loss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const {\n const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION;\n if (cuda_weights_ == nullptr) {\n GetGradientsKernel_RegressionL2<<>>(score, cuda_labels_, nullptr, num_data_, gradients, hessians);\n } else {\n GetGradientsKernel_RegressionL2<<>>(score, cuda_labels_, cuda_weights_, num_data_, gradients, hessians);\n }\n}\n\n\ndouble CUDARegressionL1loss::LaunchCalcInitScoreKernel(const int /*class_id*/) const {\n const double alpha = 0.5f;\n if (cuda_weights_ == nullptr) {\n PercentileGlobal(\n cuda_labels_, nullptr, cuda_data_indices_buffer_.RawData(), nullptr, nullptr, alpha, num_data_, cuda_percentile_result_.RawData());\n } else {\n PercentileGlobal(\n cuda_labels_, cuda_weights_, cuda_data_indices_buffer_.RawData(), cuda_weights_prefix_sum_.RawData(),\n cuda_weights_prefix_sum_buffer_.RawData(), alpha, num_data_, cuda_percentile_result_.RawData());\n }\n label_t percentile_result = 0.0f;\n CopyFromCUDADeviceToHost(&percentile_result, cuda_percentile_result_.RawData(), 1, __FILE__, __LINE__);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n return static_cast(percentile_result);\n}\n\ntemplate \n__global__ void GetGradientsKernel_RegressionL1(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data,\n score_t* cuda_out_gradients, score_t* cuda_out_hessians) {\n const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x);\n if (data_index < num_data) {\n if (!USE_WEIGHT) {\n const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]);\n cuda_out_gradients[data_index] = static_cast((diff > 0.0f) - (diff < 0.0f));\n cuda_out_hessians[data_index] = 1.0f;\n } else {\n const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]);\n const score_t weight = static_cast(cuda_weights[data_index]);\n cuda_out_gradients[data_index] = static_cast((diff > 0.0f) - (diff < 0.0f)) * weight;\n cuda_out_hessians[data_index] = weight;\n }\n }\n}\n\nvoid CUDARegressionL1loss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const {\n const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION;\n if (cuda_weights_ == nullptr) {\n GetGradientsKernel_RegressionL1<<>>(score, cuda_labels_, nullptr, num_data_, gradients, hessians);\n } else {\n GetGradientsKernel_RegressionL1<<>>(score, cuda_labels_, cuda_weights_, num_data_, gradients, hessians);\n }\n}\n\ntemplate \n__global__ void RenewTreeOutputCUDAKernel_RegressionL1(\n const double* score,\n const label_t* label,\n const label_t* weight,\n double* residual_buffer,\n label_t* weight_by_leaf,\n double* weight_prefix_sum_buffer,\n const data_size_t* data_indices_in_leaf,\n const data_size_t* num_data_in_leaf,\n const data_size_t* data_start_in_leaf,\n data_size_t* data_indices_buffer,\n double* leaf_value) {\n const int leaf_index = static_cast(blockIdx.x);\n const data_size_t data_start = data_start_in_leaf[leaf_index];\n const data_size_t num_data = num_data_in_leaf[leaf_index];\n data_size_t* data_indices_buffer_pointer = data_indices_buffer + data_start;\n const label_t* weight_by_leaf_pointer = weight_by_leaf + data_start;\n double* weight_prefix_sum_buffer_pointer = weight_prefix_sum_buffer + data_start;\n const double* residual_buffer_pointer = residual_buffer + data_start;\n const double alpha = 0.5f;\n for (data_size_t inner_data_index = data_start + static_cast(threadIdx.x);\n inner_data_index < data_start + num_data; inner_data_index += static_cast(blockDim.x)) {\n const data_size_t data_index = data_indices_in_leaf[inner_data_index];\n const label_t data_label = label[data_index];\n const double data_score = score[data_index];\n residual_buffer[inner_data_index] = static_cast(data_label) - data_score;\n if (USE_WEIGHT) {\n weight_by_leaf[inner_data_index] = weight[data_index];\n }\n }\n __syncthreads();\n const double renew_leaf_value = PercentileDevice(\n residual_buffer_pointer, weight_by_leaf_pointer, data_indices_buffer_pointer,\n weight_prefix_sum_buffer_pointer, alpha, num_data);\n if (threadIdx.x == 0) {\n leaf_value[leaf_index] = renew_leaf_value;\n }\n}\n\nvoid CUDARegressionL1loss::LaunchRenewTreeOutputCUDAKernel(\n const double* score,\n const data_size_t* data_indices_in_leaf,\n const data_size_t* num_data_in_leaf,\n const data_size_t* data_start_in_leaf,\n const int num_leaves,\n double* leaf_value) const {\n if (cuda_weights_ == nullptr) {\n RenewTreeOutputCUDAKernel_RegressionL1<<>>(\n score,\n cuda_labels_,\n cuda_weights_,\n cuda_residual_buffer_.RawData(),\n cuda_weight_by_leaf_buffer_.RawData(),\n cuda_weights_prefix_sum_.RawData(),\n data_indices_in_leaf,\n num_data_in_leaf,\n data_start_in_leaf,\n cuda_data_indices_buffer_.RawData(),\n leaf_value);\n } else {\n RenewTreeOutputCUDAKernel_RegressionL1<<>>(\n score,\n cuda_labels_,\n cuda_weights_,\n cuda_residual_buffer_.RawData(),\n cuda_weight_by_leaf_buffer_.RawData(),\n cuda_weights_prefix_sum_.RawData(),\n data_indices_in_leaf,\n num_data_in_leaf,\n data_start_in_leaf,\n cuda_data_indices_buffer_.RawData(),\n leaf_value);\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\n\ntemplate \n__global__ void GetGradientsKernel_Huber(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data,\n const double alpha, score_t* cuda_out_gradients, score_t* cuda_out_hessians) {\n const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x);\n if (data_index < num_data) {\n if (!USE_WEIGHT) {\n const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]);\n if (fabs(diff) <= alpha) {\n cuda_out_gradients[data_index] = static_cast(diff);\n } else {\n const score_t sign = static_cast((diff > 0.0f) - (diff < 0.0f));\n cuda_out_gradients[data_index] = static_cast(sign * alpha);\n }\n cuda_out_hessians[data_index] = 1.0f;\n } else {\n const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]);\n const score_t weight = static_cast(cuda_weights[data_index]);\n if (fabs(diff) <= alpha) {\n cuda_out_gradients[data_index] = static_cast(diff) * weight;\n } else {\n const score_t sign = static_cast((diff > 0.0f) - (diff < 0.0f));\n cuda_out_gradients[data_index] = static_cast(sign * alpha) * weight;\n }\n cuda_out_hessians[data_index] = weight;\n }\n }\n}\n\nvoid CUDARegressionHuberLoss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const {\n const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION;\n if (cuda_weights_ == nullptr) {\n GetGradientsKernel_Huber<<>>(score, cuda_labels_, nullptr, num_data_, alpha_, gradients, hessians);\n } else {\n GetGradientsKernel_Huber<<>>(score, cuda_labels_, cuda_weights_, num_data_, alpha_, gradients, hessians);\n }\n}\n\n\ntemplate \n__global__ void GetGradientsKernel_Fair(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data,\n const double c, score_t* cuda_out_gradients, score_t* cuda_out_hessians) {\n const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x);\n if (data_index < num_data) {\n if (!USE_WEIGHT) {\n const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]);\n cuda_out_gradients[data_index] = static_cast(c * diff / (fabs(diff) + c));\n cuda_out_hessians[data_index] = static_cast(c * c / ((fabs(diff) + c) * (fabs(diff) + c)));\n } else {\n const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]);\n const score_t weight = static_cast(cuda_weights[data_index]);\n cuda_out_gradients[data_index] = static_cast(c * diff / (fabs(diff) + c) * weight);\n cuda_out_hessians[data_index] = static_cast(c * c / ((fabs(diff) + c) * (fabs(diff) + c)) * weight);\n }\n }\n}\n\nvoid CUDARegressionFairLoss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const {\n const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION;\n if (cuda_weights_ == nullptr) {\n GetGradientsKernel_Fair<<>>(score, cuda_labels_, nullptr, num_data_, c_, gradients, hessians);\n } else {\n GetGradientsKernel_Fair<<>>(score, cuda_labels_, cuda_weights_, num_data_, c_, gradients, hessians);\n }\n}\n\nvoid CUDARegressionPoissonLoss::LaunchCheckLabelKernel() const {\n ShuffleReduceSumGlobal(cuda_labels_, static_cast(num_data_), cuda_block_buffer_.RawData());\n double label_sum = 0.0f;\n CopyFromCUDADeviceToHost(&label_sum, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__);\n\n ShuffleReduceMinGlobal(cuda_labels_, static_cast(num_data_), cuda_block_buffer_.RawData());\n double label_min = 0.0f;\n CopyFromCUDADeviceToHost(&label_min, cuda_block_buffer_.RawData(), 1, __FILE__, __LINE__);\n\n if (label_min < 0.0f) {\n Log::Fatal(\"[%s]: at least one target label is negative\", GetName());\n }\n if (label_sum == 0.0f) {\n Log::Fatal(\"[%s]: sum of labels is zero\", GetName());\n }\n}\n\ntemplate \n__global__ void GetGradientsKernel_Poisson(const double* cuda_scores, const label_t* cuda_labels, const label_t* cuda_weights, const data_size_t num_data,\n const double max_delta_step, score_t* cuda_out_gradients, score_t* cuda_out_hessians) {\n const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x);\n const double exp_max_delta_step = std::exp(max_delta_step);\n if (data_index < num_data) {\n if (!USE_WEIGHT) {\n const double exp_score = exp(cuda_scores[data_index]);\n cuda_out_gradients[data_index] = static_cast(exp_score - cuda_labels[data_index]);\n cuda_out_hessians[data_index] = static_cast(exp_score * exp_max_delta_step);\n } else {\n const double exp_score = exp(cuda_scores[data_index]);\n const score_t weight = static_cast(cuda_weights[data_index]);\n cuda_out_gradients[data_index] = static_cast((exp_score - cuda_labels[data_index]) * weight);\n cuda_out_hessians[data_index] = static_cast(exp_score * exp_max_delta_step * weight);\n }\n }\n}\n\nvoid CUDARegressionPoissonLoss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const {\n const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION;\n if (cuda_weights_ == nullptr) {\n GetGradientsKernel_Poisson<<>>(\n score, cuda_labels_, nullptr, num_data_, max_delta_step_, gradients, hessians);\n } else {\n GetGradientsKernel_Poisson<<>>(\n score, cuda_labels_, cuda_weights_, num_data_, max_delta_step_, gradients, hessians);\n }\n}\n\n__global__ void ConvertOutputCUDAKernel_Regression_Poisson(const data_size_t num_data, const double* input, double* output) {\n const int data_index = static_cast(blockIdx.x * blockDim.x + threadIdx.x);\n if (data_index < num_data) {\n output[data_index] = exp(input[data_index]);\n }\n}\n\nconst double* CUDARegressionPoissonLoss::LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const {\n const int num_blocks = (num_data + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION;\n ConvertOutputCUDAKernel_Regression_Poisson<<>>(num_data, input, output);\n return output;\n}\n\n\ndouble CUDARegressionQuantileloss::LaunchCalcInitScoreKernel(const int /*class_id*/) const {\n if (cuda_weights_ == nullptr) {\n PercentileGlobal(\n cuda_labels_, nullptr, cuda_data_indices_buffer_.RawData(), nullptr, nullptr, alpha_, num_data_, cuda_percentile_result_.RawData());\n } else {\n PercentileGlobal(\n cuda_labels_, cuda_weights_, cuda_data_indices_buffer_.RawData(), cuda_weights_prefix_sum_.RawData(),\n cuda_weights_prefix_sum_buffer_.RawData(), alpha_, num_data_, cuda_percentile_result_.RawData());\n }\n label_t percentile_result = 0.0f;\n CopyFromCUDADeviceToHost(&percentile_result, cuda_percentile_result_.RawData(), 1, __FILE__, __LINE__);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n return static_cast(percentile_result);\n}\n\ntemplate \n__global__ void RenewTreeOutputCUDAKernel_RegressionQuantile(\n const double* score,\n const label_t* label,\n const label_t* weight,\n double* residual_buffer,\n label_t* weight_by_leaf,\n double* weight_prefix_sum_buffer,\n const data_size_t* data_indices_in_leaf,\n const data_size_t* num_data_in_leaf,\n const data_size_t* data_start_in_leaf,\n data_size_t* data_indices_buffer,\n double* leaf_value,\n const double alpha) {\n const int leaf_index = static_cast(blockIdx.x);\n const data_size_t data_start = data_start_in_leaf[leaf_index];\n const data_size_t num_data = num_data_in_leaf[leaf_index];\n data_size_t* data_indices_buffer_pointer = data_indices_buffer + data_start;\n const label_t* weight_by_leaf_pointer = weight_by_leaf + data_start;\n double* weight_prefix_sum_buffer_pointer = weight_prefix_sum_buffer + data_start;\n const double* residual_buffer_pointer = residual_buffer + data_start;\n for (data_size_t inner_data_index = data_start + static_cast(threadIdx.x); inner_data_index < data_start + num_data; inner_data_index += static_cast(blockDim.x)) {\n const data_size_t data_index = data_indices_in_leaf[inner_data_index];\n const label_t data_label = label[data_index];\n const double data_score = score[data_index];\n residual_buffer[inner_data_index] = static_cast(data_label) - data_score;\n if (USE_WEIGHT) {\n weight_by_leaf[inner_data_index] = weight[data_index];\n }\n }\n __syncthreads();\n const double renew_leaf_value = PercentileDevice(\n residual_buffer_pointer, weight_by_leaf_pointer, data_indices_buffer_pointer,\n weight_prefix_sum_buffer_pointer, alpha, num_data);\n if (threadIdx.x == 0) {\n leaf_value[leaf_index] = renew_leaf_value;\n }\n}\n\nvoid CUDARegressionQuantileloss::LaunchRenewTreeOutputCUDAKernel(\n const double* score, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf,\n const data_size_t* data_start_in_leaf, const int num_leaves, double* leaf_value) const {\n if (cuda_weights_ == nullptr) {\n RenewTreeOutputCUDAKernel_RegressionQuantile<<>>(\n score,\n cuda_labels_,\n cuda_weights_,\n cuda_residual_buffer_.RawData(),\n cuda_weight_by_leaf_buffer_.RawData(),\n cuda_weights_prefix_sum_.RawData(),\n data_indices_in_leaf,\n num_data_in_leaf,\n data_start_in_leaf,\n cuda_data_indices_buffer_.RawData(),\n leaf_value,\n alpha_);\n } else {\n RenewTreeOutputCUDAKernel_RegressionQuantile<<>>(\n score,\n cuda_labels_,\n cuda_weights_,\n cuda_residual_buffer_.RawData(),\n cuda_weight_by_leaf_buffer_.RawData(),\n cuda_weights_prefix_sum_.RawData(),\n data_indices_in_leaf,\n num_data_in_leaf,\n data_start_in_leaf,\n cuda_data_indices_buffer_.RawData(),\n leaf_value,\n alpha_);\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n}\n\ntemplate \n__global__ void GetGradientsKernel_RegressionQuantile(const double* cuda_scores, const label_t* cuda_labels,\n const label_t* cuda_weights, const data_size_t num_data, const double alpha,\n score_t* cuda_out_gradients, score_t* cuda_out_hessians) {\n const data_size_t data_index = static_cast(blockDim.x * blockIdx.x + threadIdx.x);\n if (data_index < num_data) {\n if (!USE_WEIGHT) {\n const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]);\n if (diff >= 0.0f) {\n cuda_out_gradients[data_index] = (1.0f - alpha);\n } else {\n cuda_out_gradients[data_index] = -alpha;\n }\n cuda_out_hessians[data_index] = 1.0f;\n } else {\n const double diff = cuda_scores[data_index] - static_cast(cuda_labels[data_index]);\n const score_t weight = static_cast(cuda_weights[data_index]);\n if (diff >= 0.0f) {\n cuda_out_gradients[data_index] = (1.0f - alpha) * weight;\n } else {\n cuda_out_gradients[data_index] = -alpha * weight;\n }\n cuda_out_hessians[data_index] = weight;\n }\n }\n}\n\nvoid CUDARegressionQuantileloss::LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const {\n const int num_blocks = (num_data_ + GET_GRADIENTS_BLOCK_SIZE_REGRESSION - 1) / GET_GRADIENTS_BLOCK_SIZE_REGRESSION;\n if (cuda_weights_ == nullptr) {\n GetGradientsKernel_RegressionQuantile<<>>(score, cuda_labels_, nullptr, num_data_, alpha_, gradients, hessians);\n } else {\n GetGradientsKernel_RegressionQuantile<<>>(score, cuda_labels_, cuda_weights_, num_data_, alpha_, gradients, hessians);\n }\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/objective/cuda/cuda_regression_objective.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifndef LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_REGRESSION_OBJECTIVE_HPP_\n#define LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_REGRESSION_OBJECTIVE_HPP_\n\n#ifdef USE_CUDA\n\n#define GET_GRADIENTS_BLOCK_SIZE_REGRESSION (1024)\n\n#include \n\n#include \n#include \n\n#include \"../regression_objective.hpp\"\n\nnamespace LightGBM {\n\ntemplate \nclass CUDARegressionObjectiveInterface: public CUDAObjectiveInterface {\n public:\n explicit CUDARegressionObjectiveInterface(const Config& config): CUDAObjectiveInterface(config) {}\n\n explicit CUDARegressionObjectiveInterface(const std::vector& strs): CUDAObjectiveInterface(strs) {}\n\n void Init(const Metadata& metadata, data_size_t num_data) override;\n\n protected:\n double LaunchCalcInitScoreKernel(const int class_id) const override;\n\n CUDAVector cuda_block_buffer_;\n CUDAVector cuda_trans_label_;\n};\n\nclass CUDARegressionL2loss : public CUDARegressionObjectiveInterface {\n public:\n explicit CUDARegressionL2loss(const Config& config);\n\n explicit CUDARegressionL2loss(const std::vector& strs);\n\n ~CUDARegressionL2loss();\n\n void Init(const Metadata& metadata, data_size_t num_data) override;\n\n protected:\n void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override;\n\n const double* LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const override;\n\n bool NeedConvertOutputCUDA() const override { return sqrt_; }\n};\n\n\nclass CUDARegressionL1loss : public CUDARegressionObjectiveInterface {\n public:\n explicit CUDARegressionL1loss(const Config& config);\n\n explicit CUDARegressionL1loss(const std::vector& strs);\n\n ~CUDARegressionL1loss();\n\n void Init(const Metadata& metadata, data_size_t num_data) override;\n\n protected:\n CUDAVector cuda_data_indices_buffer_;\n CUDAVector cuda_weights_prefix_sum_;\n CUDAVector cuda_weights_prefix_sum_buffer_;\n CUDAVector cuda_residual_buffer_;\n CUDAVector cuda_weight_by_leaf_buffer_;\n CUDAVector cuda_percentile_result_;\n\n double LaunchCalcInitScoreKernel(const int class_id) const override;\n\n void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override;\n\n void LaunchRenewTreeOutputCUDAKernel(\n const double* score, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf,\n const data_size_t* data_start_in_leaf, const int num_leaves, double* leaf_value) const override;\n};\n\n\nclass CUDARegressionHuberLoss : public CUDARegressionObjectiveInterface {\n public:\n explicit CUDARegressionHuberLoss(const Config& config);\n\n explicit CUDARegressionHuberLoss(const std::vector& strs);\n\n ~CUDARegressionHuberLoss();\n\n private:\n void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override;\n};\n\n\n// http://research.microsoft.com/en-us/um/people/zhang/INRIA/Publis/Tutorial-Estim/node24.html\nclass CUDARegressionFairLoss : public CUDARegressionObjectiveInterface {\n public:\n explicit CUDARegressionFairLoss(const Config& config);\n\n explicit CUDARegressionFairLoss(const std::vector& strs);\n\n ~CUDARegressionFairLoss();\n\n private:\n void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override;\n};\n\n\nclass CUDARegressionPoissonLoss : public CUDARegressionObjectiveInterface {\n public:\n explicit CUDARegressionPoissonLoss(const Config& config);\n\n explicit CUDARegressionPoissonLoss(const std::vector& strs);\n\n ~CUDARegressionPoissonLoss();\n\n void Init(const Metadata& metadata, data_size_t num_data) override;\n\n private:\n void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override;\n\n const double* LaunchConvertOutputCUDAKernel(const data_size_t num_data, const double* input, double* output) const override;\n\n bool NeedConvertOutputCUDA() const override { return true; }\n\n double LaunchCalcInitScoreKernel(const int class_id) const override;\n\n void LaunchCheckLabelKernel() const;\n};\n\n\nclass CUDARegressionQuantileloss : public CUDARegressionObjectiveInterface {\n public:\n explicit CUDARegressionQuantileloss(const Config& config);\n\n explicit CUDARegressionQuantileloss(const std::vector& strs);\n\n ~CUDARegressionQuantileloss();\n\n void Init(const Metadata& metadata, data_size_t num_data) override;\n\n protected:\n void LaunchGetGradientsKernel(const double* score, score_t* gradients, score_t* hessians) const override;\n\n double LaunchCalcInitScoreKernel(const int class_id) const override;\n\n void LaunchRenewTreeOutputCUDAKernel(\n const double* score, const data_size_t* data_indices_in_leaf, const data_size_t* num_data_in_leaf,\n const data_size_t* data_start_in_leaf, const int num_leaves, double* leaf_value) const override;\n\n CUDAVector cuda_data_indices_buffer_;\n CUDAVector cuda_weights_prefix_sum_;\n CUDAVector cuda_weights_prefix_sum_buffer_;\n CUDAVector cuda_residual_buffer_;\n CUDAVector cuda_weight_by_leaf_buffer_;\n CUDAVector cuda_percentile_result_;\n};\n\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n#endif // LIGHTGBM_SRC_OBJECTIVE_CUDA_CUDA_REGRESSION_OBJECTIVE_HPP_\n" }, { "path": "src/objective/multiclass_objective.hpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n#ifndef LIGHTGBM_SRC_OBJECTIVE_MULTICLASS_OBJECTIVE_HPP_\n#define LIGHTGBM_SRC_OBJECTIVE_MULTICLASS_OBJECTIVE_HPP_\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n\n#include \"binary_objective.hpp\"\n\nnamespace LightGBM {\n/*!\n* \\brief Objective function for multiclass classification, use softmax as objective functions\n*/\nclass MulticlassSoftmax: public ObjectiveFunction {\n public:\n explicit MulticlassSoftmax(const Config& config) {\n num_class_ = config.num_class;\n // This factor is to rescale the redundant form of K-classification, to the non-redundant form.\n // In the traditional settings of K-classification, there is one redundant class, whose output is set to 0 (like the class 0 in binary classification).\n // This is from the Friedman GBDT paper.\n factor_ = static_cast(num_class_) / (num_class_ - 1.0f);\n }\n\n explicit MulticlassSoftmax(const std::vector& strs) {\n num_class_ = -1;\n for (auto str : strs) {\n auto tokens = Common::Split(str.c_str(), ':');\n if (tokens.size() == 2) {\n if (tokens[0] == std::string(\"num_class\")) {\n Common::Atoi(tokens[1].c_str(), &num_class_);\n }\n }\n }\n if (num_class_ < 0) {\n Log::Fatal(\"Objective should contain num_class field\");\n }\n factor_ = static_cast(num_class_) / (num_class_ - 1.0f);\n }\n\n ~MulticlassSoftmax() {\n }\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n num_data_ = num_data;\n label_ = metadata.label();\n weights_ = metadata.weights();\n label_int_.resize(num_data_);\n class_init_probs_.resize(num_class_, 0.0);\n double sum_weight = 0.0;\n for (int i = 0; i < num_data_; ++i) {\n label_int_[i] = static_cast(label_[i]);\n if (label_int_[i] < 0 || label_int_[i] >= num_class_) {\n Log::Fatal(\"Label must be in [0, %d), but found %d in label\", num_class_, label_int_[i]);\n }\n if (weights_ == nullptr) {\n class_init_probs_[label_int_[i]] += 1.0;\n } else {\n class_init_probs_[label_int_[i]] += weights_[i];\n sum_weight += weights_[i];\n }\n }\n if (weights_ == nullptr) {\n sum_weight = num_data_;\n }\n if (Network::num_machines() > 1) {\n sum_weight = Network::GlobalSyncUpBySum(sum_weight);\n for (int i = 0; i < num_class_; ++i) {\n class_init_probs_[i] = Network::GlobalSyncUpBySum(class_init_probs_[i]);\n }\n }\n for (int i = 0; i < num_class_; ++i) {\n class_init_probs_[i] /= sum_weight;\n }\n }\n\n void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override {\n if (weights_ == nullptr) {\n std::vector rec;\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(rec)\n for (data_size_t i = 0; i < num_data_; ++i) {\n rec.resize(num_class_);\n for (int k = 0; k < num_class_; ++k) {\n size_t idx = static_cast(num_data_) * k + i;\n rec[k] = static_cast(score[idx]);\n }\n Common::Softmax(&rec);\n for (int k = 0; k < num_class_; ++k) {\n auto p = rec[k];\n size_t idx = static_cast(num_data_) * k + i;\n if (label_int_[i] == k) {\n gradients[idx] = static_cast(p - 1.0f);\n } else {\n gradients[idx] = static_cast(p);\n }\n hessians[idx] = static_cast(factor_ * p * (1.0f - p));\n }\n }\n } else {\n std::vector rec;\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) private(rec)\n for (data_size_t i = 0; i < num_data_; ++i) {\n rec.resize(num_class_);\n for (int k = 0; k < num_class_; ++k) {\n size_t idx = static_cast(num_data_) * k + i;\n rec[k] = static_cast(score[idx]);\n }\n Common::Softmax(&rec);\n for (int k = 0; k < num_class_; ++k) {\n auto p = rec[k];\n size_t idx = static_cast(num_data_) * k + i;\n if (label_int_[i] == k) {\n gradients[idx] = static_cast((p - 1.0f) * weights_[i]);\n } else {\n gradients[idx] = static_cast(p * weights_[i]);\n }\n hessians[idx] = static_cast((factor_ * p * (1.0f - p))* weights_[i]);\n }\n }\n }\n }\n\n void ConvertOutput(const double* input, double* output) const override {\n Common::Softmax(input, output, num_class_);\n }\n\n const char* GetName() const override {\n return \"multiclass\";\n }\n\n std::string ToString() const override {\n std::stringstream str_buf;\n str_buf << GetName() << \" \";\n str_buf << \"num_class:\" << num_class_;\n return str_buf.str();\n }\n\n bool SkipEmptyClass() const override { return true; }\n\n int NumModelPerIteration() const override { return num_class_; }\n\n int NumPredictOneRow() const override { return num_class_; }\n\n bool NeedAccuratePrediction() const override { return false; }\n\n double BoostFromScore(int class_id) const override {\n return std::log(std::max(kEpsilon, class_init_probs_[class_id]));\n }\n\n bool ClassNeedTrain(int class_id) const override {\n if (std::fabs(class_init_probs_[class_id]) <= kEpsilon\n || std::fabs(class_init_probs_[class_id]) >= 1.0 - kEpsilon) {\n return false;\n } else {\n return true;\n }\n }\n\n protected:\n double factor_;\n /*! \\brief Number of data */\n data_size_t num_data_;\n /*! \\brief Number of classes */\n int num_class_;\n /*! \\brief Pointer of label */\n const label_t* label_;\n /*! \\brief Corresponding integers of label_ */\n std::vector label_int_;\n /*! \\brief Weights for data */\n const label_t* weights_;\n std::vector class_init_probs_;\n};\n\n/*!\n* \\brief Objective function for multiclass classification, use one-vs-all binary objective function\n*/\nclass MulticlassOVA: public ObjectiveFunction {\n public:\n explicit MulticlassOVA(const Config& config) {\n num_class_ = config.num_class;\n for (int i = 0; i < num_class_; ++i) {\n binary_loss_.emplace_back(\n new BinaryLogloss(config, [i](label_t label) { return static_cast(label) == i; }));\n }\n sigmoid_ = config.sigmoid;\n }\n\n explicit MulticlassOVA(const std::vector& strs) {\n num_class_ = -1;\n sigmoid_ = -1;\n for (auto str : strs) {\n auto tokens = Common::Split(str.c_str(), ':');\n if (tokens.size() == 2) {\n if (tokens[0] == std::string(\"num_class\")) {\n Common::Atoi(tokens[1].c_str(), &num_class_);\n } else if (tokens[0] == std::string(\"sigmoid\")) {\n Common::Atof(tokens[1].c_str(), &sigmoid_);\n }\n }\n }\n if (num_class_ < 0) {\n Log::Fatal(\"Objective should contain num_class field\");\n }\n if (sigmoid_ <= 0.0) {\n Log::Fatal(\"Sigmoid parameter %f should be greater than zero\", sigmoid_);\n }\n }\n\n ~MulticlassOVA() {\n }\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n num_data_ = num_data;\n for (int i = 0; i < num_class_; ++i) {\n binary_loss_[i]->Init(metadata, num_data);\n }\n }\n\n void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override {\n for (int i = 0; i < num_class_; ++i) {\n int64_t offset = static_cast(num_data_) * i;\n binary_loss_[i]->GetGradients(score + offset, gradients + offset, hessians + offset);\n }\n }\n\n const char* GetName() const override {\n return \"multiclassova\";\n }\n\n void ConvertOutput(const double* input, double* output) const override {\n for (int i = 0; i < num_class_; ++i) {\n output[i] = 1.0f / (1.0f + std::exp(-sigmoid_ * input[i]));\n }\n }\n\n std::string ToString() const override {\n std::stringstream str_buf;\n str_buf << GetName() << \" \";\n str_buf << \"num_class:\" << num_class_ << \" \";\n str_buf << \"sigmoid:\" << sigmoid_;\n return str_buf.str();\n }\n\n bool SkipEmptyClass() const override { return true; }\n\n int NumModelPerIteration() const override { return num_class_; }\n\n int NumPredictOneRow() const override { return num_class_; }\n\n bool NeedAccuratePrediction() const override { return false; }\n\n double BoostFromScore(int class_id) const override {\n return binary_loss_[class_id]->BoostFromScore(0);\n }\n\n bool ClassNeedTrain(int class_id) const override {\n return binary_loss_[class_id]->ClassNeedTrain(0);\n }\n\n protected:\n /*! \\brief Number of data */\n data_size_t num_data_;\n /*! \\brief Number of classes */\n int num_class_;\n std::vector> binary_loss_;\n double sigmoid_;\n};\n\n} // namespace LightGBM\n#endif // LIGHTGBM_SRC_OBJECTIVE_MULTICLASS_OBJECTIVE_HPP_\n" }, { "path": "src/objective/objective_function.cpp", "content": "/*!\n * Copyright (c) 2016-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2016-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\n */\n\n#include \n\n#include \n\n#include \"binary_objective.hpp\"\n#include \"multiclass_objective.hpp\"\n#include \"rank_objective.hpp\"\n#include \"regression_objective.hpp\"\n#include \"xentropy_objective.hpp\"\n\n#include \n\n#include \"cuda/cuda_binary_objective.hpp\"\n#include \"cuda/cuda_multiclass_objective.hpp\"\n#include \"cuda/cuda_rank_objective.hpp\"\n#include \"cuda/cuda_regression_objective.hpp\"\n\nnamespace LightGBM {\n\n#ifdef USE_CUDA\nObjectiveFunction* ObjectiveFunction::CreateObjectiveFunctionCUDA(const std::string& type, const Config& config) {\n if (type == std::string(\"regression\")) {\n return new CUDARegressionL2loss(config);\n } else if (type == std::string(\"regression_l1\")) {\n return new CUDARegressionL1loss(config);\n } else if (type == std::string(\"quantile\")) {\n return new CUDARegressionQuantileloss(config);\n } else if (type == std::string(\"huber\")) {\n return new CUDARegressionHuberLoss(config);\n } else if (type == std::string(\"fair\")) {\n return new CUDARegressionFairLoss(config);\n } else if (type == std::string(\"poisson\")) {\n return new CUDARegressionPoissonLoss(config);\n } else if (type == std::string(\"binary\")) {\n return new CUDABinaryLogloss(config);\n } else if (type == std::string(\"lambdarank\")) {\n return new CUDALambdarankNDCG(config);\n } else if (type == std::string(\"rank_xendcg\")) {\n return new CUDARankXENDCG(config);\n } else if (type == std::string(\"multiclass\")) {\n return new CUDAMulticlassSoftmax(config);\n } else if (type == std::string(\"multiclassova\")) {\n return new CUDAMulticlassOVA(config);\n } else if (type == std::string(\"cross_entropy\")) {\n Log::Warning(\"Objective cross_entropy is not implemented in cuda version. Fall back to boosting on CPU.\");\n return new CrossEntropy(config);\n } else if (type == std::string(\"cross_entropy_lambda\")) {\n Log::Warning(\"Objective cross_entropy_lambda is not implemented in cuda version. Fall back to boosting on CPU.\");\n return new CrossEntropyLambda(config);\n } else if (type == std::string(\"mape\")) {\n Log::Warning(\"Objective mape is not implemented in cuda version. Fall back to boosting on CPU.\");\n return new RegressionMAPELOSS(config);\n } else if (type == std::string(\"gamma\")) {\n Log::Warning(\"Objective gamma is not implemented in cuda version. Fall back to boosting on CPU.\");\n return new RegressionGammaLoss(config);\n } else if (type == std::string(\"tweedie\")) {\n Log::Warning(\"Objective tweedie is not implemented in cuda version. Fall back to boosting on CPU.\");\n return new RegressionTweedieLoss(config);\n } else if (type == std::string(\"custom\")) {\n Log::Warning(\"Using customized objective with cuda. This requires copying gradients from CPU to GPU, which can be slow.\");\n return nullptr;\n }\n}\n#endif // USE_CUDA\n\nObjectiveFunction* ObjectiveFunction::CreateObjectiveFunction(const std::string& type, const Config& config) {\n #ifdef USE_CUDA\n if (config.device_type == std::string(\"cuda\") &&\n config.data_sample_strategy != std::string(\"goss\") &&\n config.boosting != std::string(\"rf\")) {\n return CreateObjectiveFunctionCUDA(type, config);\n } else {\n #endif // USE_CUDA\n if (type == std::string(\"regression\")) {\n return new RegressionL2loss(config);\n } else if (type == std::string(\"regression_l1\")) {\n return new RegressionL1loss(config);\n } else if (type == std::string(\"quantile\")) {\n return new RegressionQuantileloss(config);\n } else if (type == std::string(\"huber\")) {\n return new RegressionHuberLoss(config);\n } else if (type == std::string(\"fair\")) {\n return new RegressionFairLoss(config);\n } else if (type == std::string(\"poisson\")) {\n return new RegressionPoissonLoss(config);\n } else if (type == std::string(\"binary\")) {\n return new BinaryLogloss(config);\n } else if (type == std::string(\"lambdarank\")) {\n return new LambdarankNDCG(config);\n } else if (type == std::string(\"rank_xendcg\")) {\n return new RankXENDCG(config);\n } else if (type == std::string(\"multiclass\")) {\n return new MulticlassSoftmax(config);\n } else if (type == std::string(\"multiclassova\")) {\n return new MulticlassOVA(config);\n } else if (type == std::string(\"cross_entropy\")) {\n return new CrossEntropy(config);\n } else if (type == std::string(\"cross_entropy_lambda\")) {\n return new CrossEntropyLambda(config);\n } else if (type == std::string(\"mape\")) {\n return new RegressionMAPELOSS(config);\n } else if (type == std::string(\"gamma\")) {\n return new RegressionGammaLoss(config);\n } else if (type == std::string(\"tweedie\")) {\n return new RegressionTweedieLoss(config);\n } else if (type == std::string(\"custom\")) {\n return nullptr;\n }\n #ifdef USE_CUDA\n }\n #endif // USE_CUDA\n Log::Fatal(\"Unknown objective type name: %s\", type.c_str());\n return nullptr;\n}\n\nObjectiveFunction* ObjectiveFunction::CreateObjectiveFunction(const std::string& str) {\n auto strs = Common::Split(str.c_str(), ' ');\n auto type = strs[0];\n if (type == std::string(\"regression\")) {\n return new RegressionL2loss(strs);\n } else if (type == std::string(\"regression_l1\")) {\n return new RegressionL1loss(strs);\n } else if (type == std::string(\"quantile\")) {\n return new RegressionQuantileloss(strs);\n } else if (type == std::string(\"huber\")) {\n return new RegressionHuberLoss(strs);\n } else if (type == std::string(\"fair\")) {\n return new RegressionFairLoss(strs);\n } else if (type == std::string(\"poisson\")) {\n return new RegressionPoissonLoss(strs);\n } else if (type == std::string(\"binary\")) {\n return new BinaryLogloss(strs);\n } else if (type == std::string(\"lambdarank\")) {\n return new LambdarankNDCG(strs);\n } else if (type == std::string(\"rank_xendcg\")) {\n return new RankXENDCG(strs);\n } else if (type == std::string(\"multiclass\")) {\n return new MulticlassSoftmax(strs);\n } else if (type == std::string(\"multiclassova\")) {\n return new MulticlassOVA(strs);\n } else if (type == std::string(\"cross_entropy\")) {\n return new CrossEntropy(strs);\n } else if (type == std::string(\"cross_entropy_lambda\")) {\n return new CrossEntropyLambda(strs);\n } else if (type == std::string(\"mape\")) {\n return new RegressionMAPELOSS(strs);\n } else if (type == std::string(\"gamma\")) {\n return new RegressionGammaLoss(strs);\n } else if (type == std::string(\"tweedie\")) {\n return new RegressionTweedieLoss(strs);\n } else if (type == std::string(\"custom\")) {\n return nullptr;\n }\n Log::Fatal(\"Unknown objective type name: %s\", type.c_str());\n return nullptr;\n}\n\n} // namespace LightGBM\n" }, { "path": "src/objective/rank_objective.hpp", "content": "/*!\n * Copyright (c) 2020-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2020-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n#ifndef LIGHTGBM_SRC_OBJECTIVE_RANK_OBJECTIVE_HPP_\n#define LIGHTGBM_SRC_OBJECTIVE_RANK_OBJECTIVE_HPP_\n\n#include \n#include \n\n#include \n#include \n#include \n#include \n#include \n#include \n#include \n\nnamespace LightGBM {\n\n/*!\n * \\brief Objective function for Ranking\n */\nclass RankingObjective : public ObjectiveFunction {\n public:\n explicit RankingObjective(const Config& config)\n : seed_(config.objective_seed) {\n learning_rate_ = config.learning_rate;\n position_bias_regularization_ = config.lambdarank_position_bias_regularization;\n }\n\n explicit RankingObjective(const std::vector&) : seed_(0) {}\n\n ~RankingObjective() {}\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n num_data_ = num_data;\n // get label\n label_ = metadata.label();\n // get weights\n weights_ = metadata.weights();\n // get positions\n positions_ = metadata.positions();\n // get position ids\n position_ids_ = metadata.position_ids();\n // get number of different position ids\n num_position_ids_ = static_cast(metadata.num_position_ids());\n // get boundaries\n query_boundaries_ = metadata.query_boundaries();\n if (query_boundaries_ == nullptr) {\n Log::Fatal(\"Ranking tasks require query information\");\n }\n num_queries_ = metadata.num_queries();\n // initialize position bias vectors\n pos_biases_.resize(num_position_ids_, 0.0);\n }\n\n void GetGradientsWithSampledQueries(const double* score, const data_size_t num_sampled_queries, const data_size_t* sampled_query_indices,\n score_t* gradients, score_t* hessians) const override {\n const data_size_t num_queries = (sampled_query_indices == nullptr ? num_queries_ : num_sampled_queries);\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(guided)\n for (data_size_t i = 0; i < num_queries; ++i) {\n const data_size_t query_index = (sampled_query_indices == nullptr ? i : sampled_query_indices[i]);\n const data_size_t start = query_boundaries_[query_index];\n const data_size_t cnt = query_boundaries_[query_index + 1] - query_boundaries_[query_index];\n std::vector score_adjusted;\n if (num_position_ids_ > 0) {\n for (data_size_t j = 0; j < cnt; ++j) {\n score_adjusted.push_back(score[start + j] + pos_biases_[positions_[start + j]]);\n }\n }\n GetGradientsForOneQuery(query_index, cnt, label_ + start, num_position_ids_ > 0 ? score_adjusted.data() : score + start,\n gradients + start, hessians + start);\n if (weights_ != nullptr) {\n for (data_size_t j = 0; j < cnt; ++j) {\n gradients[start + j] =\n static_cast(gradients[start + j] * weights_[start + j]);\n hessians[start + j] =\n static_cast(hessians[start + j] * weights_[start + j]);\n }\n }\n }\n if (num_position_ids_ > 0) {\n UpdatePositionBiasFactors(gradients, hessians);\n }\n }\n\n void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override {\n GetGradientsWithSampledQueries(score, num_queries_, nullptr, gradients, hessians);\n }\n\n virtual void GetGradientsForOneQuery(data_size_t query_id, data_size_t cnt,\n const label_t* label,\n const double* score, score_t* lambdas,\n score_t* hessians) const = 0;\n\n virtual void UpdatePositionBiasFactors(const score_t* /*lambdas*/, const score_t* /*hessians*/) const {}\n\n const char* GetName() const override = 0;\n\n std::string ToString() const override {\n std::stringstream str_buf;\n str_buf << GetName();\n return str_buf.str();\n }\n\n bool NeedAccuratePrediction() const override { return false; }\n\n protected:\n int seed_;\n data_size_t num_queries_;\n /*! \\brief Number of data */\n data_size_t num_data_;\n /*! \\brief Pointer of label */\n const label_t* label_;\n /*! \\brief Pointer of weights */\n const label_t* weights_;\n /*! \\brief Pointer of positions */\n const data_size_t* positions_;\n /*! \\brief Pointer of position IDs */\n const std::string* position_ids_;\n /*! \\brief Pointer of label */\n data_size_t num_position_ids_;\n /*! \\brief Query boundaries */\n const data_size_t* query_boundaries_;\n /*! \\brief Position bias factors */\n mutable std::vector pos_biases_;\n /*! \\brief Learning rate to update position bias factors */\n double learning_rate_;\n /*! \\brief Position bias regularization */\n double position_bias_regularization_;\n};\n\n/*!\n * \\brief Objective function for LambdaRank with NDCG\n */\nclass LambdarankNDCG : public RankingObjective {\n public:\n explicit LambdarankNDCG(const Config& config)\n : RankingObjective(config),\n sigmoid_(config.sigmoid),\n norm_(config.lambdarank_norm),\n truncation_level_(config.lambdarank_truncation_level) {\n label_gain_ = config.label_gain;\n // initialize DCG calculator\n DCGCalculator::DefaultLabelGain(&label_gain_);\n DCGCalculator::Init(label_gain_);\n sigmoid_table_.clear();\n inverse_max_dcgs_.clear();\n if (sigmoid_ <= 0.0) {\n Log::Fatal(\"Sigmoid param %f should be greater than zero\", sigmoid_);\n }\n }\n\n explicit LambdarankNDCG(const std::vector& strs)\n : RankingObjective(strs) {}\n\n ~LambdarankNDCG() {}\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n RankingObjective::Init(metadata, num_data);\n DCGCalculator::CheckMetadata(metadata, num_queries_);\n DCGCalculator::CheckLabel(label_, num_data_);\n inverse_max_dcgs_.resize(num_queries_);\n#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\n for (data_size_t i = 0; i < num_queries_; ++i) {\n inverse_max_dcgs_[i] = DCGCalculator::CalMaxDCGAtK(\n truncation_level_, label_ + query_boundaries_[i],\n query_boundaries_[i + 1] - query_boundaries_[i]);\n\n if (inverse_max_dcgs_[i] > 0.0) {\n inverse_max_dcgs_[i] = 1.0f / inverse_max_dcgs_[i];\n }\n }\n // construct Sigmoid table to speed up Sigmoid transform\n ConstructSigmoidTable();\n }\n\n inline void GetGradientsForOneQuery(data_size_t query_id, data_size_t cnt,\n const label_t* label, const double* score,\n score_t* lambdas,\n score_t* hessians) const override {\n // get max DCG on current query\n const double inverse_max_dcg = inverse_max_dcgs_[query_id];\n // initialize with zero\n for (data_size_t i = 0; i < cnt; ++i) {\n lambdas[i] = 0.0f;\n hessians[i] = 0.0f;\n }\n // get sorted indices for scores\n std::vector sorted_idx(cnt);\n for (data_size_t i = 0; i < cnt; ++i) {\n sorted_idx[i] = i;\n }\n std::stable_sort(\n sorted_idx.begin(), sorted_idx.end(),\n [score](data_size_t a, data_size_t b) { return score[a] > score[b]; });\n // get best and worst score\n const double best_score = score[sorted_idx[0]];\n data_size_t worst_idx = cnt - 1;\n if (worst_idx > 0 && score[sorted_idx[worst_idx]] == kMinScore) {\n worst_idx -= 1;\n }\n const double worst_score = score[sorted_idx[worst_idx]];\n double sum_lambdas = 0.0;\n // start accumulate lambdas by pairs that contain at least one document above truncation level\n for (data_size_t i = 0; i < cnt - 1 && i < truncation_level_; ++i) {\n if (score[sorted_idx[i]] == kMinScore) {\n continue;\n }\n for (data_size_t j = i + 1; j < cnt; ++j) {\n if (score[sorted_idx[j]] == kMinScore) {\n continue;\n }\n // skip pairs with the same labels\n if (label[sorted_idx[i]] == label[sorted_idx[j]]) {\n continue;\n }\n data_size_t high_rank, low_rank;\n if (label[sorted_idx[i]] > label[sorted_idx[j]]) {\n high_rank = i;\n low_rank = j;\n } else {\n high_rank = j;\n low_rank = i;\n }\n const data_size_t high = sorted_idx[high_rank];\n const int high_label = static_cast(label[high]);\n const double high_score = score[high];\n const double high_label_gain = label_gain_[high_label];\n const double high_discount = DCGCalculator::GetDiscount(high_rank);\n const data_size_t low = sorted_idx[low_rank];\n const int low_label = static_cast(label[low]);\n const double low_score = score[low];\n const double low_label_gain = label_gain_[low_label];\n const double low_discount = DCGCalculator::GetDiscount(low_rank);\n\n const double delta_score = high_score - low_score;\n\n // get dcg gap\n const double dcg_gap = high_label_gain - low_label_gain;\n // get discount of this pair\n const double paired_discount = fabs(high_discount - low_discount);\n // get delta NDCG\n double delta_pair_NDCG = dcg_gap * paired_discount * inverse_max_dcg;\n // regular the delta_pair_NDCG by score distance\n if (norm_ && best_score != worst_score) {\n delta_pair_NDCG /= (0.01f + fabs(delta_score));\n }\n // calculate lambda for this pair\n double p_lambda = GetSigmoid(delta_score);\n double p_hessian = p_lambda * (1.0f - p_lambda);\n // update\n p_lambda *= -sigmoid_ * delta_pair_NDCG;\n p_hessian *= sigmoid_ * sigmoid_ * delta_pair_NDCG;\n lambdas[low] -= static_cast(p_lambda);\n hessians[low] += static_cast(p_hessian);\n lambdas[high] += static_cast(p_lambda);\n hessians[high] += static_cast(p_hessian);\n // lambda is negative, so use minus to accumulate\n sum_lambdas -= 2 * p_lambda;\n }\n }\n if (norm_ && sum_lambdas > 0) {\n double norm_factor = std::log2(1 + sum_lambdas) / sum_lambdas;\n for (data_size_t i = 0; i < cnt; ++i) {\n lambdas[i] = static_cast(lambdas[i] * norm_factor);\n hessians[i] = static_cast(hessians[i] * norm_factor);\n }\n }\n }\n\n inline double GetSigmoid(double score) const {\n if (score <= min_sigmoid_input_) {\n // too small, use lower bound\n return sigmoid_table_[0];\n } else if (score >= max_sigmoid_input_) {\n // too large, use upper bound\n return sigmoid_table_[_sigmoid_bins - 1];\n } else {\n return sigmoid_table_[static_cast((score - min_sigmoid_input_) *\n sigmoid_table_idx_factor_)];\n }\n }\n\n void ConstructSigmoidTable() {\n // get boundary\n min_sigmoid_input_ = min_sigmoid_input_ / sigmoid_ / 2;\n max_sigmoid_input_ = -min_sigmoid_input_;\n sigmoid_table_.resize(_sigmoid_bins);\n // get score to bin factor\n sigmoid_table_idx_factor_ =\n _sigmoid_bins / (max_sigmoid_input_ - min_sigmoid_input_);\n // cache\n for (size_t i = 0; i < _sigmoid_bins; ++i) {\n const double score = i / sigmoid_table_idx_factor_ + min_sigmoid_input_;\n sigmoid_table_[i] = 1.0f / (1.0f + std::exp(score * sigmoid_));\n }\n }\n\n void UpdatePositionBiasFactors(const score_t* lambdas, const score_t* hessians) const override {\n /// get number of threads\n int num_threads = OMP_NUM_THREADS();\n // create per-thread buffers for first and second derivatives of utility w.r.t. position bias factors\n std::vector bias_first_derivatives(num_position_ids_ * num_threads, 0.0);\n std::vector bias_second_derivatives(num_position_ids_ * num_threads, 0.0);\n std::vector instance_counts(num_position_ids_ * num_threads, 0);\n #pragma omp parallel for schedule(guided) num_threads(num_threads)\n for (data_size_t i = 0; i < num_data_; i++) {\n // get thread ID\n const int tid = omp_get_thread_num();\n size_t offset = static_cast(positions_[i] + tid * num_position_ids_);\n // accumulate first derivatives of utility w.r.t. position bias factors, for each position\n bias_first_derivatives[offset] -= lambdas[i];\n // accumulate second derivatives of utility w.r.t. position bias factors, for each position\n bias_second_derivatives[offset] -= hessians[i];\n instance_counts[offset]++;\n }\n #pragma omp parallel for schedule(guided) num_threads(num_threads)\n for (data_size_t i = 0; i < num_position_ids_; i++) {\n double bias_first_derivative = 0.0;\n double bias_second_derivative = 0.0;\n int instance_count = 0;\n // aggregate derivatives from per-thread buffers\n for (int tid = 0; tid < num_threads; tid++) {\n size_t offset = static_cast(i + tid * num_position_ids_);\n bias_first_derivative += bias_first_derivatives[offset];\n bias_second_derivative += bias_second_derivatives[offset];\n instance_count += instance_counts[offset];\n }\n // L2 regularization on position bias factors\n bias_first_derivative -= pos_biases_[i] * position_bias_regularization_ * instance_count;\n bias_second_derivative -= position_bias_regularization_ * instance_count;\n // do Newton-Raphson step to update position bias factors\n pos_biases_[i] += learning_rate_ * bias_first_derivative / (std::abs(bias_second_derivative) + 0.001);\n }\n LogDebugPositionBiasFactors();\n }\n\n const char* GetName() const override { return \"lambdarank\"; }\n\n protected:\n void LogDebugPositionBiasFactors() const {\n std::stringstream message_stream;\n message_stream << std::setw(15) << \"position\"\n << std::setw(15) << \"bias_factor\"\n << std::endl;\n Log::Debug(message_stream.str().c_str());\n message_stream.str(\"\");\n for (int i = 0; i < num_position_ids_; ++i) {\n message_stream << std::setw(15) << position_ids_[i]\n << std::setw(15) << pos_biases_[i];\n Log::Debug(message_stream.str().c_str());\n message_stream.str(\"\");\n }\n }\n /*! \\brief Sigmoid param */\n double sigmoid_;\n /*! \\brief Normalize the lambdas or not */\n bool norm_;\n /*! \\brief Truncation position for max DCG */\n int truncation_level_;\n /*! \\brief Cache inverse max DCG, speed up calculation */\n std::vector inverse_max_dcgs_;\n /*! \\brief Cache result for sigmoid transform to speed up */\n std::vector sigmoid_table_;\n /*! \\brief Gains for labels */\n std::vector label_gain_;\n /*! \\brief Number of bins in simoid table */\n size_t _sigmoid_bins = 1024 * 1024;\n /*! \\brief Minimal input of sigmoid table */\n double min_sigmoid_input_ = -50;\n /*! \\brief Maximal input of Sigmoid table */\n double max_sigmoid_input_ = 50;\n /*! \\brief Factor that covert score to bin in Sigmoid table */\n double sigmoid_table_idx_factor_;\n};\n\n/*!\n * \\brief Implementation of the learning-to-rank objective function, XE_NDCG\n * [arxiv.org/abs/1911.09798].\n */\nclass RankXENDCG : public RankingObjective {\n public:\n explicit RankXENDCG(const Config& config) : RankingObjective(config) {}\n\n explicit RankXENDCG(const std::vector& strs)\n : RankingObjective(strs) {}\n\n ~RankXENDCG() {}\n\n void Init(const Metadata& metadata, data_size_t num_data) override {\n RankingObjective::Init(metadata, num_data);\n for (data_size_t i = 0; i < num_queries_; ++i) {\n rands_.emplace_back(seed_ + i);\n }\n }\n\n inline void GetGradientsForOneQuery(data_size_t query_id, data_size_t cnt,\n const label_t* label, const double* score,\n score_t* lambdas,\n score_t* hessians) const override {\n // Skip groups with too few items.\n if (cnt <= 1) {\n for (data_size_t i = 0; i < cnt; ++i) {\n lambdas[i] = 0.0f;\n hessians[i] = 0.0f;\n }\n return;\n }\n\n // Turn scores into a probability distribution using Softmax.\n std::vector rho(cnt, 0.0);\n Common::Softmax(score, rho.data(), cnt);\n\n // An auxiliary buffer of parameters used to form the ground-truth\n // distribution and compute the loss.\n std::vector params(cnt);\n\n double inv_denominator = 0;\n for (data_size_t i = 0; i < cnt; ++i) {\n params[i] = Phi(label[i], rands_[query_id].NextFloat());\n inv_denominator += params[i];\n }\n // sum_labels will always be positive number\n inv_denominator = 1. / std::max(kEpsilon, inv_denominator);\n\n // Approximate gradients and inverse Hessian.\n // First order terms.\n double sum_l1 = 0.0;\n for (data_size_t i = 0; i < cnt; ++i) {\n double term = -params[i] * inv_denominator + rho[i];\n lambdas[i] = static_cast(term);\n // Params will now store terms needed to compute second-order terms.\n params[i] = term / (1. - rho[i]);\n sum_l1 += params[i];\n }\n // Second order terms.\n double sum_l2 = 0.0;\n for (data_size_t i = 0; i < cnt; ++i) {\n double term = rho[i] * (sum_l1 - params[i]);\n lambdas[i] += static_cast(term);\n // Params will now store terms needed to compute third-order terms.\n params[i] = term / (1. - rho[i]);\n sum_l2 += params[i];\n }\n for (data_size_t i = 0; i < cnt; ++i) {\n lambdas[i] += static_cast(rho[i] * (sum_l2 - params[i]));\n hessians[i] = static_cast(rho[i] * (1.0 - rho[i]));\n }\n }\n\n double Phi(const label_t l, double g) const {\n return Common::Pow(2, static_cast(l)) - g;\n }\n\n const char* GetName() const override { return \"rank_xendcg\"; }\n\n protected:\n mutable std::vector rands_;\n};\n\n} // namespace LightGBM\n#endif // LIGHTGBM_SRC_OBJECTIVE_RANK_OBJECTIVE_HPP_\n" }, { "path": "src/objective/xentropy_objective.hpp", "content": "/*!\r\n * Copyright (c) 2017-2026 Microsoft Corporation. All rights reserved.\r\n * Copyright (c) 2017-2026 The LightGBM developers. All rights reserved.\r\n * Licensed under the MIT License. See LICENSE file in the project root for license information.\r\n */\r\n#ifndef LIGHTGBM_SRC_OBJECTIVE_XENTROPY_OBJECTIVE_HPP_\r\n#define LIGHTGBM_SRC_OBJECTIVE_XENTROPY_OBJECTIVE_HPP_\r\n\r\n#include \r\n#include \r\n#include \r\n\r\n#include \r\n#include \r\n#include \r\n#include \r\n#include \r\n\r\n/*\r\n * Implements gradients and Hessians for the following point losses.\r\n * Target y is anything in interval [0, 1].\r\n *\r\n * (1) CrossEntropy; \"xentropy\";\r\n *\r\n * loss(y, p, w) = { -(1-y)*log(1-p)-y*log(p) }*w,\r\n * with probability p = 1/(1+exp(-f)), where f is being boosted\r\n *\r\n * ConvertToOutput: f -> p\r\n *\r\n * (2) CrossEntropyLambda; \"xentlambda\"\r\n *\r\n * loss(y, p, w) = -(1-y)*log(1-p)-y*log(p),\r\n * with p = 1-exp(-lambda*w), lambda = log(1+exp(f)), f being boosted, and w > 0\r\n *\r\n * ConvertToOutput: f -> lambda\r\n *\r\n * (1) and (2) are the same if w=1; but outputs still differ.\r\n *\r\n */\r\n\r\nnamespace LightGBM {\r\n/*!\r\n* \\brief Objective function for cross-entropy (with optional linear weights)\r\n*/\r\nclass CrossEntropy: public ObjectiveFunction {\r\n public:\r\n explicit CrossEntropy(const Config& config)\r\n : deterministic_(config.deterministic) {}\r\n\r\n explicit CrossEntropy(const std::vector&)\r\n : deterministic_(false) {\r\n }\r\n\r\n ~CrossEntropy() {}\r\n\r\n void Init(const Metadata& metadata, data_size_t num_data) override {\r\n num_data_ = num_data;\r\n label_ = metadata.label();\r\n weights_ = metadata.weights();\r\n\r\n CHECK_NOTNULL(label_);\r\n Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName());\r\n Log::Info(\"[%s:%s]: (objective) labels passed interval [0, 1] check\", GetName(), __func__);\r\n\r\n if (weights_ != nullptr) {\r\n label_t minw;\r\n double sumw;\r\n Common::ObtainMinMaxSum(weights_, num_data_, &minw, static_cast(nullptr), &sumw);\r\n if (minw < 0.0f) {\r\n Log::Fatal(\"[%s]: at least one weight is negative\", GetName());\r\n }\r\n if (sumw == 0.0f) {\r\n Log::Fatal(\"[%s]: sum of weights is zero\", GetName());\r\n }\r\n }\r\n }\r\n\r\n void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override {\r\n // z = expit(score) = 1 / (1 + exp(-score))\r\n // gradient = z - label = expit(score) - label\r\n // Numerically more stable, see http://fa.bianp.net/blog/2019/evaluate_logistic/\r\n // if score < 0:\r\n // exp_tmp = exp(score)\r\n // return ((1 - label) * exp_tmp - label) / (1 + exp_tmp)\r\n // else:\r\n // exp_tmp = exp(-score)\r\n // return ((1 - label) - label * exp_tmp) / (1 + exp_tmp)\r\n // Note that optimal speed would be achieved, at the cost of precision, by\r\n // return expit(score) - y_true\r\n // i.e. no \"if else\" and an own inline implementation of expit.\r\n // The case distinction score < 0 in the stable implementation does not\r\n // provide significant better precision apart from protecting overflow of exp(..).\r\n // The branch (if else), however, can incur runtime costs of up to 30%.\r\n // Instead, we help branch prediction by almost always ending in the first if clause\r\n // and making the second branch (else) a bit simpler. This has the exact same\r\n // precision but is faster than the stable implementation.\r\n // As branching criteria, we use the same cutoff as in log1pexp, see link above.\r\n // Note that the maximal value to get gradient = -1 with label = 1 is -37.439198610162731\r\n // (based on mpmath), and scipy.special.logit(np.finfo(float).eps) ~ -36.04365.\r\n if (weights_ == nullptr) {\r\n // compute pointwise gradients and Hessians with implied unit weights\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n if (score[i] > -37.0) {\r\n const double exp_tmp = std::exp(-score[i]);\r\n gradients[i] = static_cast(((1.0f - label_[i]) - label_[i] * exp_tmp) / (1.0f + exp_tmp));\r\n hessians[i] = static_cast(exp_tmp / ((1 + exp_tmp) * (1 + exp_tmp)));\r\n } else {\r\n const double exp_tmp = std::exp(score[i]);\r\n gradients[i] = static_cast(exp_tmp - label_[i]);\r\n hessians[i] = static_cast(exp_tmp);\r\n }\r\n }\r\n } else {\r\n // compute pointwise gradients and Hessians with given weights\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n if (score[i] > -37.0) {\r\n const double exp_tmp = std::exp(-score[i]);\r\n gradients[i] = static_cast(((1.0f - label_[i]) - label_[i] * exp_tmp) / (1.0f + exp_tmp) * weights_[i]);\r\n hessians[i] = static_cast(exp_tmp / ((1 + exp_tmp) * (1 + exp_tmp)) * weights_[i]);\r\n } else {\r\n const double exp_tmp = std::exp(score[i]);\r\n gradients[i] = static_cast((exp_tmp - label_[i]) * weights_[i]);\r\n hessians[i] = static_cast(exp_tmp * weights_[i]);\r\n }\r\n }\r\n }\r\n }\r\n\r\n const char* GetName() const override {\r\n return \"cross_entropy\";\r\n }\r\n\r\n // convert score to a probability\r\n void ConvertOutput(const double* input, double* output) const override {\r\n output[0] = 1.0f / (1.0f + std::exp(-input[0]));\r\n }\r\n\r\n std::string ToString() const override {\r\n std::stringstream str_buf;\r\n str_buf << GetName();\r\n return str_buf.str();\r\n }\r\n\r\n // implement custom average to boost from (if enabled among options)\r\n double BoostFromScore(int) const override {\r\n double suml = 0.0f;\r\n double sumw = 0.0f;\r\n if (weights_ != nullptr) {\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml, sumw) if (!deterministic_)\r\n\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n suml += static_cast(label_[i]) * weights_[i];\r\n sumw += weights_[i];\r\n }\r\n } else {\r\n sumw = static_cast(num_data_);\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml) if (!deterministic_)\r\n\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n suml += label_[i];\r\n }\r\n }\r\n double pavg = suml / sumw;\r\n pavg = std::min(pavg, 1.0 - kEpsilon);\r\n pavg = std::max(pavg, kEpsilon);\r\n double initscore = std::log(pavg / (1.0f - pavg));\r\n Log::Info(\"[%s:%s]: pavg = %f -> initscore = %f\", GetName(), __func__, pavg, initscore);\r\n return initscore;\r\n }\r\n\r\n private:\r\n /*! \\brief Number of data points */\r\n data_size_t num_data_;\r\n /*! \\brief Pointer for label */\r\n const label_t* label_;\r\n /*! \\brief Weights for data */\r\n const label_t* weights_;\r\n const bool deterministic_;\r\n};\r\n\r\n/*!\r\n* \\brief Objective function for alternative parameterization of cross-entropy (see top of file for explanation)\r\n*/\r\nclass CrossEntropyLambda: public ObjectiveFunction {\r\n public:\r\n explicit CrossEntropyLambda(const Config& config)\r\n : deterministic_(config.deterministic) {\r\n min_weight_ = max_weight_ = 0.0f;\r\n }\r\n\r\n explicit CrossEntropyLambda(const std::vector&)\r\n : deterministic_(false) {}\r\n\r\n ~CrossEntropyLambda() {}\r\n\r\n void Init(const Metadata& metadata, data_size_t num_data) override {\r\n num_data_ = num_data;\r\n label_ = metadata.label();\r\n weights_ = metadata.weights();\r\n\r\n CHECK_NOTNULL(label_);\r\n Common::CheckElementsIntervalClosed(label_, 0.0f, 1.0f, num_data_, GetName());\r\n Log::Info(\"[%s:%s]: (objective) labels passed interval [0, 1] check\", GetName(), __func__);\r\n\r\n if (weights_ != nullptr) {\r\n Common::ObtainMinMaxSum(weights_, num_data_, &min_weight_, &max_weight_, static_cast(nullptr));\r\n if (min_weight_ <= 0.0f) {\r\n Log::Fatal(\"[%s]: at least one weight is non-positive\", GetName());\r\n }\r\n\r\n // Issue an info statement about this ratio\r\n double weight_ratio = max_weight_ / min_weight_;\r\n Log::Info(\"[%s:%s]: min, max weights = %f, %f; ratio = %f\",\r\n GetName(), __func__,\r\n min_weight_, max_weight_,\r\n weight_ratio);\r\n } else {\r\n // all weights are implied to be unity; no need to do anything\r\n }\r\n }\r\n\r\n void GetGradients(const double* score, score_t* gradients, score_t* hessians) const override {\r\n if (weights_ == nullptr) {\r\n // compute pointwise gradients and Hessians with implied unit weights; exactly equivalent to CrossEntropy with unit weights\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n const double z = 1.0f / (1.0f + std::exp(-score[i]));\r\n gradients[i] = static_cast(z - label_[i]);\r\n hessians[i] = static_cast(z * (1.0f - z));\r\n }\r\n } else {\r\n // compute pointwise gradients and Hessians with given weights\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static)\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n const double w = weights_[i];\r\n const double y = label_[i];\r\n const double epf = std::exp(score[i]);\r\n const double hhat = std::log1p(epf);\r\n const double z = 1.0f - std::exp(-w*hhat);\r\n const double enf = 1.0f / epf; // = std::exp(-score[i]);\r\n gradients[i] = static_cast((1.0f - y / z) * w / (1.0f + enf));\r\n const double c = 1.0f / (1.0f - z);\r\n double d = 1.0f + epf;\r\n const double a = w * epf / (d * d);\r\n d = c - 1.0f;\r\n const double b = (c / (d * d) ) * (1.0f + w * epf - c);\r\n hessians[i] = static_cast(a * (1.0f + y * b));\r\n }\r\n }\r\n }\r\n\r\n const char* GetName() const override {\r\n return \"cross_entropy_lambda\";\r\n }\r\n\r\n //\r\n // ATTENTION: the function output is the \"normalized exponential parameter\" lambda > 0, not the probability\r\n //\r\n // If this code would read: output[0] = 1.0f / (1.0f + std::exp(-input[0]));\r\n // The output would still not be the probability unless the weights are unity.\r\n //\r\n // Let z = 1 / (1 + exp(-f)), then prob(z) = 1-(1-z)^w, where w is the weight for the specific point.\r\n //\r\n\r\n void ConvertOutput(const double* input, double* output) const override {\r\n output[0] = std::log1p(std::exp(input[0]));\r\n }\r\n\r\n std::string ToString() const override {\r\n std::stringstream str_buf;\r\n str_buf << GetName();\r\n return str_buf.str();\r\n }\r\n\r\n double BoostFromScore(int) const override {\r\n double suml = 0.0f;\r\n double sumw = 0.0f;\r\n if (weights_ != nullptr) {\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml, sumw) if (!deterministic_)\r\n\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n suml += static_cast(label_[i]) * weights_[i];\r\n sumw += weights_[i];\r\n }\r\n } else {\r\n sumw = static_cast(num_data_);\r\n #pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static) reduction(+:suml) if (!deterministic_)\r\n\r\n for (data_size_t i = 0; i < num_data_; ++i) {\r\n suml += label_[i];\r\n }\r\n }\r\n double havg = suml / sumw;\r\n double initscore = std::log(std::expm1(havg));\r\n Log::Info(\"[%s:%s]: havg = %f -> initscore = %f\", GetName(), __func__, havg, initscore);\r\n return initscore;\r\n }\r\n\r\n private:\r\n /*! \\brief Number of data points */\r\n data_size_t num_data_;\r\n /*! \\brief Pointer for label */\r\n const label_t* label_;\r\n /*! \\brief Weights for data */\r\n const label_t* weights_;\r\n /*! \\brief Minimum weight found during init */\r\n label_t min_weight_;\r\n /*! \\brief Maximum weight found during init */\r\n label_t max_weight_;\r\n const bool deterministic_;\r\n};\r\n\r\n} // end namespace LightGBM\r\n\r\n#endif // LIGHTGBM_SRC_OBJECTIVE_XENTROPY_OBJECTIVE_HPP_\r\n" }, { "path": "src/treelearner/cost_effective_gradient_boosting.hpp", "content": "/*!\n * Copyright (c) 2019-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2019-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n#ifndef LIGHTGBM_SRC_TREELEARNER_COST_EFFECTIVE_GRADIENT_BOOSTING_HPP_\n#define LIGHTGBM_SRC_TREELEARNER_COST_EFFECTIVE_GRADIENT_BOOSTING_HPP_\n\n#include \n#include \n#include \n#include \n#include \n\n#include \n\n#include \"data_partition.hpp\"\n#include \"serial_tree_learner.h\"\n#include \"split_info.hpp\"\n\nnamespace LightGBM {\n\nclass CostEfficientGradientBoosting {\n public:\n explicit CostEfficientGradientBoosting(const SerialTreeLearner* tree_learner)\n : init_(false), tree_learner_(tree_learner) {}\n static bool IsEnable(const Config* config) {\n if (config->cegb_tradeoff >= 1.0f && config->cegb_penalty_split <= 0.0f &&\n config->cegb_penalty_feature_coupled.empty() &&\n config->cegb_penalty_feature_lazy.empty()) {\n return false;\n } else {\n return true;\n }\n }\n\n void Init() {\n auto train_data = tree_learner_->train_data_;\n if (!init_) {\n splits_per_leaf_.resize(\n static_cast(tree_learner_->config_->num_leaves) *\n train_data->num_features());\n is_feature_used_in_split_.clear();\n is_feature_used_in_split_.resize(train_data->num_features());\n }\n\n if (!tree_learner_->config_->cegb_penalty_feature_coupled.empty() &&\n tree_learner_->config_->cegb_penalty_feature_coupled.size() !=\n static_cast(train_data->num_total_features())) {\n Log::Fatal(\n \"cegb_penalty_feature_coupled should be the same size as feature \"\n \"number.\");\n }\n if (!tree_learner_->config_->cegb_penalty_feature_lazy.empty()) {\n if (tree_learner_->config_->cegb_penalty_feature_lazy.size() !=\n static_cast(train_data->num_total_features())) {\n Log::Fatal(\n \"cegb_penalty_feature_lazy should be the same size as feature \"\n \"number.\");\n }\n if (!init_) {\n feature_used_in_data_ = Common::EmptyBitset(train_data->num_features() *\n tree_learner_->num_data_);\n }\n }\n init_ = true;\n }\n\n void BeforeTrain() {\n // clear the splits in splits_per_leaf_\n Threading::For(0, splits_per_leaf_.size(), 1024,\n [this] (int /*thread_index*/, size_t start, size_t end) {\n for (size_t i = start; i < end; ++i) {\n splits_per_leaf_[i].Reset();\n }\n });\n }\n\n double DeltaGain(int feature_index, int real_fidx, int leaf_index,\n int num_data_in_leaf, SplitInfo split_info) {\n auto config = tree_learner_->config_;\n double delta =\n config->cegb_tradeoff * config->cegb_penalty_split * num_data_in_leaf;\n if (!config->cegb_penalty_feature_coupled.empty() &&\n !is_feature_used_in_split_[feature_index]) {\n delta += config->cegb_tradeoff *\n config->cegb_penalty_feature_coupled[real_fidx];\n }\n if (!config->cegb_penalty_feature_lazy.empty()) {\n delta += config->cegb_tradeoff *\n CalculateOndemandCosts(feature_index, real_fidx, leaf_index);\n }\n splits_per_leaf_[static_cast(leaf_index) *\n tree_learner_->train_data_->num_features() +\n feature_index] = split_info;\n return delta;\n }\n\n void UpdateLeafBestSplits(Tree* tree, int best_leaf,\n const SplitInfo* best_split_info,\n std::vector* best_split_per_leaf) {\n auto config = tree_learner_->config_;\n auto train_data = tree_learner_->train_data_;\n const int inner_feature_index =\n train_data->InnerFeatureIndex(best_split_info->feature);\n auto& ref_best_split_per_leaf = *best_split_per_leaf;\n if (!config->cegb_penalty_feature_coupled.empty() &&\n !is_feature_used_in_split_[inner_feature_index]) {\n is_feature_used_in_split_[inner_feature_index] = true;\n for (int i = 0; i < tree->num_leaves(); ++i) {\n if (i == best_leaf) continue;\n auto split = &splits_per_leaf_[static_cast(i) *\n train_data->num_features() +\n inner_feature_index];\n split->gain +=\n config->cegb_tradeoff *\n config->cegb_penalty_feature_coupled[best_split_info->feature];\n // Avoid to update the leaf that cannot split\n if (ref_best_split_per_leaf[i].gain > kMinScore &&\n *split > ref_best_split_per_leaf[i]) {\n ref_best_split_per_leaf[i] = *split;\n }\n }\n }\n if (!config->cegb_penalty_feature_lazy.empty()) {\n data_size_t cnt_leaf_data = 0;\n auto tmp_idx = tree_learner_->data_partition_->GetIndexOnLeaf(\n best_leaf, &cnt_leaf_data);\n for (data_size_t i_input = 0; i_input < cnt_leaf_data; ++i_input) {\n int real_idx = tmp_idx[i_input];\n Common::InsertBitset(\n &feature_used_in_data_,\n train_data->num_data() * inner_feature_index + real_idx);\n }\n }\n }\n\n private:\n double CalculateOndemandCosts(int feature_index, int real_fidx,\n int leaf_index) const {\n if (tree_learner_->config_->cegb_penalty_feature_lazy.empty()) {\n return 0.0f;\n }\n auto train_data = tree_learner_->train_data_;\n double penalty =\n tree_learner_->config_->cegb_penalty_feature_lazy[real_fidx];\n\n double total = 0.0f;\n data_size_t cnt_leaf_data = 0;\n auto tmp_idx = tree_learner_->data_partition_->GetIndexOnLeaf(\n leaf_index, &cnt_leaf_data);\n\n for (data_size_t i_input = 0; i_input < cnt_leaf_data; ++i_input) {\n int real_idx = tmp_idx[i_input];\n if (Common::FindInBitset(\n feature_used_in_data_.data(),\n train_data->num_data() * train_data->num_features(),\n train_data->num_data() * feature_index + real_idx)) {\n continue;\n }\n total += penalty;\n }\n return total;\n }\n bool init_;\n const SerialTreeLearner* tree_learner_;\n std::vector splits_per_leaf_;\n std::vector is_feature_used_in_split_;\n std::vector feature_used_in_data_;\n};\n\n} // namespace LightGBM\n\n#endif // LIGHTGBM_SRC_TREELEARNER_COST_EFFECTIVE_GRADIENT_BOOSTING_HPP_\n" }, { "path": "src/treelearner/cuda/cuda_best_split_finder.cpp", "content": "/*!\n * Copyright (c) 2021 Microsoft Corporation. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifdef USE_CUDA\n\n#include \n#include \n\n#include \"cuda_best_split_finder.hpp\"\n#include \"cuda_leaf_splits.hpp\"\n\nnamespace LightGBM {\n\nCUDABestSplitFinder::CUDABestSplitFinder(\n const hist_t* cuda_hist,\n const Dataset* train_data,\n const std::vector& feature_hist_offsets,\n const bool select_features_by_node,\n const Config* config):\n num_features_(train_data->num_features()),\n num_leaves_(config->num_leaves),\n feature_hist_offsets_(feature_hist_offsets),\n lambda_l1_(config->lambda_l1),\n lambda_l2_(config->lambda_l2),\n min_data_in_leaf_(config->min_data_in_leaf),\n min_sum_hessian_in_leaf_(config->min_sum_hessian_in_leaf),\n min_gain_to_split_(config->min_gain_to_split),\n cat_smooth_(config->cat_smooth),\n cat_l2_(config->cat_l2),\n max_cat_threshold_(config->max_cat_threshold),\n min_data_per_group_(config->min_data_per_group),\n max_cat_to_onehot_(config->max_cat_to_onehot),\n extra_trees_(config->extra_trees),\n extra_seed_(config->extra_seed),\n use_smoothing_(config->path_smooth > 0),\n path_smooth_(config->path_smooth),\n num_total_bin_(feature_hist_offsets.empty() ? 0 : static_cast(feature_hist_offsets.back())),\n select_features_by_node_(select_features_by_node),\n cuda_hist_(cuda_hist) {\n InitFeatureMetaInfo(train_data);\n if (has_categorical_feature_ && config->use_quantized_grad) {\n Log::Fatal(\"Quantized training on GPU with categorical features is not supported yet.\");\n }\n}\n\nCUDABestSplitFinder::~CUDABestSplitFinder() {\n gpuAssert(cudaStreamDestroy(cuda_streams_[0]), __FILE__, __LINE__);\n gpuAssert(cudaStreamDestroy(cuda_streams_[1]), __FILE__, __LINE__);\n cuda_streams_.clear();\n cuda_streams_.shrink_to_fit();\n}\n\nvoid CUDABestSplitFinder::InitFeatureMetaInfo(const Dataset* train_data) {\n feature_missing_type_.resize(num_features_);\n feature_mfb_offsets_.resize(num_features_);\n feature_default_bins_.resize(num_features_);\n feature_num_bins_.resize(num_features_);\n max_num_bin_in_feature_ = 0;\n has_categorical_feature_ = false;\n max_num_categorical_bin_ = 0;\n is_categorical_.resize(train_data->num_features(), 0);\n for (int inner_feature_index = 0; inner_feature_index < num_features_; ++inner_feature_index) {\n const BinMapper* bin_mapper = train_data->FeatureBinMapper(inner_feature_index);\n if (bin_mapper->bin_type() == BinType::CategoricalBin) {\n has_categorical_feature_ = true;\n is_categorical_[inner_feature_index] = 1;\n if (bin_mapper->num_bin() > max_num_categorical_bin_) {\n max_num_categorical_bin_ = bin_mapper->num_bin();\n }\n }\n const MissingType missing_type = bin_mapper->missing_type();\n feature_missing_type_[inner_feature_index] = missing_type;\n feature_mfb_offsets_[inner_feature_index] = static_cast(bin_mapper->GetMostFreqBin() == 0);\n feature_default_bins_[inner_feature_index] = bin_mapper->GetDefaultBin();\n feature_num_bins_[inner_feature_index] = static_cast(bin_mapper->num_bin());\n const int num_bin_hist = bin_mapper->num_bin() - feature_mfb_offsets_[inner_feature_index];\n if (num_bin_hist > max_num_bin_in_feature_) {\n max_num_bin_in_feature_ = num_bin_hist;\n }\n }\n if (max_num_bin_in_feature_ > NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER) {\n use_global_memory_ = true;\n } else {\n use_global_memory_ = false;\n }\n}\n\nvoid CUDABestSplitFinder::Init() {\n InitCUDAFeatureMetaInfo();\n cuda_streams_.resize(2);\n CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_streams_[0]));\n CUDASUCCESS_OR_FATAL(cudaStreamCreate(&cuda_streams_[1]));\n cuda_best_split_info_buffer_.Resize(8);\n if (use_global_memory_) {\n cuda_feature_hist_grad_buffer_.Resize(static_cast(num_total_bin_));\n cuda_feature_hist_hess_buffer_.Resize(static_cast(num_total_bin_));\n if (has_categorical_feature_) {\n cuda_feature_hist_stat_buffer_.Resize(static_cast(num_total_bin_));\n cuda_feature_hist_index_buffer_.Resize(static_cast(num_total_bin_));\n }\n }\n\n if (select_features_by_node_) {\n is_feature_used_by_smaller_node_.Resize(num_features_);\n is_feature_used_by_larger_node_.Resize(num_features_);\n }\n}\n\nvoid CUDABestSplitFinder::InitCUDAFeatureMetaInfo() {\n cuda_is_feature_used_bytree_.Resize(static_cast(num_features_));\n\n // initialize split find task information (a split find task is one pass through the histogram of a feature)\n num_tasks_ = 0;\n for (int inner_feature_index = 0; inner_feature_index < num_features_; ++inner_feature_index) {\n const uint32_t num_bin = feature_num_bins_[inner_feature_index];\n const MissingType missing_type = feature_missing_type_[inner_feature_index];\n if (num_bin > 2 && missing_type != MissingType::None && !is_categorical_[inner_feature_index]) {\n num_tasks_ += 2;\n } else {\n ++num_tasks_;\n }\n }\n split_find_tasks_.resize(num_tasks_);\n split_find_tasks_.shrink_to_fit();\n int cur_task_index = 0;\n for (int inner_feature_index = 0; inner_feature_index < num_features_; ++inner_feature_index) {\n const uint32_t num_bin = feature_num_bins_[inner_feature_index];\n const MissingType missing_type = feature_missing_type_[inner_feature_index];\n if (num_bin > 2 && missing_type != MissingType::None && !is_categorical_[inner_feature_index]) {\n if (missing_type == MissingType::Zero) {\n SplitFindTask* new_task = &split_find_tasks_[cur_task_index];\n new_task->reverse = false;\n new_task->skip_default_bin = true;\n new_task->na_as_missing = false;\n new_task->inner_feature_index = inner_feature_index;\n new_task->assume_out_default_left = false;\n new_task->is_categorical = false;\n uint32_t num_bin = feature_num_bins_[inner_feature_index];\n new_task->is_one_hot = false;\n new_task->hist_offset = feature_hist_offsets_[inner_feature_index];\n new_task->mfb_offset = feature_mfb_offsets_[inner_feature_index];\n new_task->default_bin = feature_default_bins_[inner_feature_index];\n new_task->num_bin = num_bin;\n ++cur_task_index;\n\n new_task = &split_find_tasks_[cur_task_index];\n new_task->reverse = true;\n new_task->skip_default_bin = true;\n new_task->na_as_missing = false;\n new_task->inner_feature_index = inner_feature_index;\n new_task->assume_out_default_left = true;\n new_task->is_categorical = false;\n num_bin = feature_num_bins_[inner_feature_index];\n new_task->is_one_hot = false;\n new_task->hist_offset = feature_hist_offsets_[inner_feature_index];\n new_task->default_bin = feature_default_bins_[inner_feature_index];\n new_task->mfb_offset = feature_mfb_offsets_[inner_feature_index];\n new_task->num_bin = num_bin;\n ++cur_task_index;\n } else {\n SplitFindTask* new_task = &split_find_tasks_[cur_task_index];\n new_task->reverse = false;\n new_task->skip_default_bin = false;\n new_task->na_as_missing = true;\n new_task->inner_feature_index = inner_feature_index;\n new_task->assume_out_default_left = false;\n new_task->is_categorical = false;\n uint32_t num_bin = feature_num_bins_[inner_feature_index];\n new_task->is_one_hot = false;\n new_task->hist_offset = feature_hist_offsets_[inner_feature_index];\n new_task->mfb_offset = feature_mfb_offsets_[inner_feature_index];\n new_task->default_bin = feature_default_bins_[inner_feature_index];\n new_task->num_bin = num_bin;\n ++cur_task_index;\n\n new_task = &split_find_tasks_[cur_task_index];\n new_task->reverse = true;\n new_task->skip_default_bin = false;\n new_task->na_as_missing = true;\n new_task->inner_feature_index = inner_feature_index;\n new_task->assume_out_default_left = true;\n new_task->is_categorical = false;\n num_bin = feature_num_bins_[inner_feature_index];\n new_task->is_one_hot = false;\n new_task->hist_offset = feature_hist_offsets_[inner_feature_index];\n new_task->mfb_offset = feature_mfb_offsets_[inner_feature_index];\n new_task->default_bin = feature_default_bins_[inner_feature_index];\n new_task->num_bin = num_bin;\n ++cur_task_index;\n }\n } else {\n SplitFindTask& new_task = split_find_tasks_[cur_task_index];\n const uint32_t num_bin = feature_num_bins_[inner_feature_index];\n if (is_categorical_[inner_feature_index]) {\n new_task.reverse = false;\n new_task.is_categorical = true;\n new_task.is_one_hot = (static_cast(num_bin) <= max_cat_to_onehot_);\n } else {\n new_task.reverse = true;\n new_task.is_categorical = false;\n new_task.is_one_hot = false;\n }\n new_task.skip_default_bin = false;\n new_task.na_as_missing = false;\n new_task.inner_feature_index = inner_feature_index;\n if (missing_type != MissingType::NaN && !is_categorical_[inner_feature_index]) {\n new_task.assume_out_default_left = true;\n } else {\n new_task.assume_out_default_left = false;\n }\n new_task.hist_offset = feature_hist_offsets_[inner_feature_index];\n new_task.mfb_offset = feature_mfb_offsets_[inner_feature_index];\n new_task.default_bin = feature_default_bins_[inner_feature_index];\n new_task.num_bin = num_bin;\n ++cur_task_index;\n }\n }\n CHECK_EQ(cur_task_index, static_cast(split_find_tasks_.size()));\n\n if (extra_trees_) {\n cuda_randoms_.Resize(num_tasks_ * 2);\n LaunchInitCUDARandomKernel();\n }\n\n const int num_task_blocks = (num_tasks_ + NUM_TASKS_PER_SYNC_BLOCK - 1) / NUM_TASKS_PER_SYNC_BLOCK;\n const size_t cuda_best_leaf_split_info_buffer_size = static_cast(num_task_blocks) * static_cast(num_leaves_);\n\n cuda_leaf_best_split_info_.Resize(cuda_best_leaf_split_info_buffer_size);\n\n cuda_split_find_tasks_.Resize(num_tasks_);\n CopyFromHostToCUDADevice(cuda_split_find_tasks_.RawData(),\n split_find_tasks_.data(),\n split_find_tasks_.size(),\n __FILE__,\n __LINE__);\n\n const size_t output_buffer_size = 2 * static_cast(num_tasks_);\n cuda_best_split_info_.Resize(output_buffer_size);\n\n max_num_categories_in_split_ = std::min(max_cat_threshold_, max_num_categorical_bin_ / 2);\n cuda_cat_threshold_feature_.Resize(max_num_categories_in_split_ * output_buffer_size);\n cuda_cat_threshold_real_feature_.Resize(max_num_categories_in_split_ * output_buffer_size);\n cuda_cat_threshold_leaf_.Resize(max_num_categories_in_split_ * cuda_best_leaf_split_info_buffer_size);\n cuda_cat_threshold_real_leaf_.Resize(max_num_categories_in_split_ * cuda_best_leaf_split_info_buffer_size);\n AllocateCatVectors(cuda_leaf_best_split_info_.RawData(), cuda_cat_threshold_leaf_.RawData(), cuda_cat_threshold_real_leaf_.RawData(), cuda_best_leaf_split_info_buffer_size);\n AllocateCatVectors(cuda_best_split_info_.RawData(), cuda_cat_threshold_feature_.RawData(), cuda_cat_threshold_real_feature_.RawData(), output_buffer_size);\n}\n\nvoid CUDABestSplitFinder::ResetTrainingData(\n const hist_t* cuda_hist,\n const Dataset* train_data,\n const std::vector& feature_hist_offsets) {\n cuda_hist_ = cuda_hist;\n num_features_ = train_data->num_features();\n feature_hist_offsets_ = feature_hist_offsets;\n InitFeatureMetaInfo(train_data);\n cuda_is_feature_used_bytree_.Clear();\n cuda_best_split_info_.Clear();\n InitCUDAFeatureMetaInfo();\n}\n\nvoid CUDABestSplitFinder::ResetConfig(const Config* config, const hist_t* cuda_hist) {\n num_leaves_ = config->num_leaves;\n lambda_l1_ = config->lambda_l1;\n lambda_l2_ = config->lambda_l2;\n min_data_in_leaf_ = config->min_data_in_leaf;\n min_sum_hessian_in_leaf_ = config->min_sum_hessian_in_leaf;\n min_gain_to_split_ = config->min_gain_to_split;\n cat_smooth_ = config->cat_smooth;\n cat_l2_ = config->cat_l2;\n max_cat_threshold_ = config->max_cat_threshold;\n min_data_per_group_ = config->min_data_per_group;\n max_cat_to_onehot_ = config->max_cat_to_onehot;\n extra_trees_ = config->extra_trees;\n extra_seed_ = config->extra_seed;\n use_smoothing_ = (config->path_smooth > 0.0f);\n path_smooth_ = config->path_smooth;\n cuda_hist_ = cuda_hist;\n\n const int num_task_blocks = (num_tasks_ + NUM_TASKS_PER_SYNC_BLOCK - 1) / NUM_TASKS_PER_SYNC_BLOCK;\n size_t cuda_best_leaf_split_info_buffer_size = static_cast(num_task_blocks) * static_cast(num_leaves_);\n cuda_leaf_best_split_info_.Resize(cuda_best_leaf_split_info_buffer_size);\n max_num_categories_in_split_ = std::min(max_cat_threshold_, max_num_categorical_bin_ / 2);\n size_t total_cat_threshold_size = max_num_categories_in_split_ * cuda_best_leaf_split_info_buffer_size;\n cuda_cat_threshold_leaf_.Resize(total_cat_threshold_size);\n cuda_cat_threshold_real_leaf_.Resize(total_cat_threshold_size);\n AllocateCatVectors(cuda_leaf_best_split_info_.RawData(), cuda_cat_threshold_leaf_.RawData(), cuda_cat_threshold_real_leaf_.RawData(), cuda_best_leaf_split_info_buffer_size);\n\n cuda_best_leaf_split_info_buffer_size = 2 * static_cast(num_tasks_);\n total_cat_threshold_size = max_num_categories_in_split_ * cuda_best_leaf_split_info_buffer_size;\n cuda_cat_threshold_feature_.Resize(total_cat_threshold_size);\n cuda_cat_threshold_real_feature_.Resize(total_cat_threshold_size);\n AllocateCatVectors(cuda_best_split_info_.RawData(), cuda_cat_threshold_feature_.RawData(), cuda_cat_threshold_real_feature_.RawData(), cuda_best_leaf_split_info_buffer_size);\n}\n\nvoid CUDABestSplitFinder::BeforeTrain(const std::vector& is_feature_used_bytree) {\n CopyFromHostToCUDADevice(cuda_is_feature_used_bytree_.RawData(),\n is_feature_used_bytree.data(),\n is_feature_used_bytree.size(), __FILE__, __LINE__);\n}\n\nvoid CUDABestSplitFinder::FindBestSplitsForLeaf(\n const CUDALeafSplitsStruct* smaller_leaf_splits,\n const CUDALeafSplitsStruct* larger_leaf_splits,\n const int smaller_leaf_index,\n const int larger_leaf_index,\n const data_size_t num_data_in_smaller_leaf,\n const data_size_t num_data_in_larger_leaf,\n const double sum_hessians_in_smaller_leaf,\n const double sum_hessians_in_larger_leaf,\n const score_t* grad_scale,\n const score_t* hess_scale,\n const uint8_t smaller_num_bits_in_histogram_bins,\n const uint8_t larger_num_bits_in_histogram_bins) {\n const bool is_smaller_leaf_valid = (num_data_in_smaller_leaf > min_data_in_leaf_ &&\n sum_hessians_in_smaller_leaf > min_sum_hessian_in_leaf_);\n const bool is_larger_leaf_valid = (num_data_in_larger_leaf > min_data_in_leaf_ &&\n sum_hessians_in_larger_leaf > min_sum_hessian_in_leaf_ && larger_leaf_index >= 0);\n if (grad_scale != nullptr && hess_scale != nullptr) {\n LaunchFindBestSplitsDiscretizedForLeafKernel(smaller_leaf_splits, larger_leaf_splits,\n smaller_leaf_index, larger_leaf_index, is_smaller_leaf_valid, is_larger_leaf_valid,\n grad_scale, hess_scale, smaller_num_bits_in_histogram_bins, larger_num_bits_in_histogram_bins, num_data_in_smaller_leaf, num_data_in_larger_leaf);\n } else {\n LaunchFindBestSplitsForLeafKernel(smaller_leaf_splits, larger_leaf_splits,\n smaller_leaf_index, larger_leaf_index, is_smaller_leaf_valid, is_larger_leaf_valid, num_data_in_smaller_leaf, num_data_in_larger_leaf);\n }\n global_timer.Start(\"CUDABestSplitFinder::LaunchSyncBestSplitForLeafKernel\");\n LaunchSyncBestSplitForLeafKernel(smaller_leaf_index, larger_leaf_index, is_smaller_leaf_valid, is_larger_leaf_valid);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n global_timer.Stop(\"CUDABestSplitFinder::LaunchSyncBestSplitForLeafKernel\");\n}\n\nconst CUDASplitInfo* CUDABestSplitFinder::FindBestFromAllSplits(\n const int cur_num_leaves,\n const int smaller_leaf_index,\n const int larger_leaf_index,\n int* smaller_leaf_best_split_feature,\n uint32_t* smaller_leaf_best_split_threshold,\n uint8_t* smaller_leaf_best_split_default_left,\n int* larger_leaf_best_split_feature,\n uint32_t* larger_leaf_best_split_threshold,\n uint8_t* larger_leaf_best_split_default_left,\n int* best_leaf_index,\n int* num_cat_threshold) {\n LaunchFindBestFromAllSplitsKernel(\n cur_num_leaves,\n smaller_leaf_index,\n larger_leaf_index,\n smaller_leaf_best_split_feature,\n smaller_leaf_best_split_threshold,\n smaller_leaf_best_split_default_left,\n larger_leaf_best_split_feature,\n larger_leaf_best_split_threshold,\n larger_leaf_best_split_default_left,\n best_leaf_index,\n num_cat_threshold);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n return cuda_leaf_best_split_info_.RawData() + (*best_leaf_index);\n}\n\nvoid CUDABestSplitFinder::AllocateCatVectors(CUDASplitInfo* cuda_split_infos, uint32_t* cat_threshold_vec, int* cat_threshold_real_vec, size_t len) {\n LaunchAllocateCatVectorsKernel(cuda_split_infos, cat_threshold_vec, cat_threshold_real_vec, len);\n}\n\nvoid CUDABestSplitFinder::SetUsedFeatureByNode(const std::vector& is_feature_used_by_smaller_node,\n const std::vector& is_feature_used_by_larger_node) {\n if (select_features_by_node_) {\n CopyFromHostToCUDADevice(is_feature_used_by_smaller_node_.RawData(),\n is_feature_used_by_smaller_node.data(), is_feature_used_by_smaller_node.size(), __FILE__, __LINE__);\n CopyFromHostToCUDADevice(is_feature_used_by_larger_node_.RawData(),\n is_feature_used_by_larger_node.data(), is_feature_used_by_larger_node.size(), __FILE__, __LINE__);\n }\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/treelearner/cuda/cuda_best_split_finder.cu", "content": "/*!\n * Copyright (c) 2021 Microsoft Corporation. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved.\n */\n\n#ifdef USE_CUDA\n\n#include \"cuda_best_split_finder.hpp\"\n\n#include \n#include \n\n#include \n#include \n\nnamespace LightGBM {\n\n__device__ void ReduceBestGainWarp(double gain, bool found, uint32_t thread_index, double* out_gain, bool* out_found, uint32_t* out_thread_index) {\n const uint32_t mask = 0xffffffff;\n const uint32_t warpLane = threadIdx.x % warpSize;\n for (uint32_t offset = warpSize / 2; offset > 0; offset >>= 1) {\n const bool other_found = __shfl_down_sync(mask, found, offset);\n const double other_gain = __shfl_down_sync(mask, gain, offset);\n const uint32_t other_thread_index = __shfl_down_sync(mask, thread_index, offset);\n if ((other_found && found && other_gain > gain) || (!found && other_found)) {\n found = other_found;\n gain = other_gain;\n thread_index = other_thread_index;\n }\n }\n if (warpLane == 0) {\n *out_gain = gain;\n *out_found = found;\n *out_thread_index = thread_index;\n }\n}\n\n__device__ uint32_t ReduceBestGainBlock(double gain, bool found, uint32_t thread_index) {\n const uint32_t mask = 0xffffffff;\n for (uint32_t offset = warpSize / 2; offset > 0; offset >>= 1) {\n const bool other_found = __shfl_down_sync(mask, found, offset);\n const double other_gain = __shfl_down_sync(mask, gain, offset);\n const uint32_t other_thread_index = __shfl_down_sync(mask, thread_index, offset);\n if ((other_found && found && other_gain > gain) || (!found && other_found)) {\n found = other_found;\n gain = other_gain;\n thread_index = other_thread_index;\n }\n }\n return thread_index;\n}\n\n__device__ uint32_t ReduceBestGain(double gain, bool found, uint32_t thread_index,\n double* shared_gain_buffer, bool* shared_found_buffer, uint32_t* shared_thread_index_buffer) {\n const uint32_t warpID = threadIdx.x / warpSize;\n const uint32_t warpLane = threadIdx.x % warpSize;\n const uint32_t num_warp = blockDim.x / warpSize;\n ReduceBestGainWarp(gain, found, thread_index, shared_gain_buffer + warpID, shared_found_buffer + warpID, shared_thread_index_buffer + warpID);\n __syncthreads();\n if (warpID == 0) {\n gain = warpLane < num_warp ? shared_gain_buffer[warpLane] : kMinScore;\n found = warpLane < num_warp ? shared_found_buffer[warpLane] : false;\n thread_index = warpLane < num_warp ? shared_thread_index_buffer[warpLane] : 0;\n thread_index = ReduceBestGainBlock(gain, found, thread_index);\n }\n return thread_index;\n}\n\n__device__ void ReduceBestGainForLeaves(double* gain, int* leaves, int cuda_cur_num_leaves) {\n const unsigned int tid = threadIdx.x;\n for (unsigned int s = 1; s < cuda_cur_num_leaves; s *= 2) {\n if (tid % (2 * s) == 0 && (tid + s) < cuda_cur_num_leaves) {\n const uint32_t tid_s = tid + s;\n if ((leaves[tid] == -1 && leaves[tid_s] != -1) || (leaves[tid] != -1 && leaves[tid_s] != -1 && gain[tid_s] > gain[tid])) {\n gain[tid] = gain[tid_s];\n leaves[tid] = leaves[tid_s];\n }\n }\n __syncthreads();\n }\n}\n\n__device__ void ReduceBestGainForLeavesWarp(double gain, int leaf_index, double* out_gain, int* out_leaf_index) {\n const uint32_t mask = 0xffffffff;\n const uint32_t warpLane = threadIdx.x % warpSize;\n for (uint32_t offset = warpSize / 2; offset > 0; offset >>= 1) {\n const int other_leaf_index = __shfl_down_sync(mask, leaf_index, offset);\n const double other_gain = __shfl_down_sync(mask, gain, offset);\n if ((leaf_index != -1 && other_leaf_index != -1 && other_gain > gain) || (leaf_index == -1 && other_leaf_index != -1)) {\n gain = other_gain;\n leaf_index = other_leaf_index;\n }\n }\n if (warpLane == 0) {\n *out_gain = gain;\n *out_leaf_index = leaf_index;\n }\n}\n\n__device__ int ReduceBestGainForLeavesBlock(double gain, int leaf_index) {\n const uint32_t mask = 0xffffffff;\n for (uint32_t offset = warpSize / 2; offset > 0; offset >>= 1) {\n const int other_leaf_index = __shfl_down_sync(mask, leaf_index, offset);\n const double other_gain = __shfl_down_sync(mask, gain, offset);\n if ((leaf_index != -1 && other_leaf_index != -1 && other_gain > gain) || (leaf_index == -1 && other_leaf_index != -1)) {\n gain = other_gain;\n leaf_index = other_leaf_index;\n }\n }\n return leaf_index;\n}\n\n__device__ int ReduceBestGainForLeaves(double gain, int leaf_index, double* shared_gain_buffer, int* shared_leaf_index_buffer) {\n const uint32_t warpID = threadIdx.x / warpSize;\n const uint32_t warpLane = threadIdx.x % warpSize;\n const uint32_t num_warp = blockDim.x / warpSize;\n ReduceBestGainForLeavesWarp(gain, leaf_index, shared_gain_buffer + warpID, shared_leaf_index_buffer + warpID);\n __syncthreads();\n if (warpID == 0) {\n gain = warpLane < num_warp ? shared_gain_buffer[warpLane] : kMinScore;\n leaf_index = warpLane < num_warp ? shared_leaf_index_buffer[warpLane] : -1;\n leaf_index = ReduceBestGainForLeavesBlock(gain, leaf_index);\n }\n return leaf_index;\n}\n\ntemplate \n__device__ void FindBestSplitsForLeafKernelInner(\n // input feature information\n const hist_t* feature_hist_ptr,\n // input task information\n const SplitFindTask* task,\n CUDARandom* cuda_random,\n // input config parameter values\n const double lambda_l1,\n const double lambda_l2,\n const double path_smooth,\n const data_size_t min_data_in_leaf,\n const double min_sum_hessian_in_leaf,\n const double min_gain_to_split,\n // input parent node information\n const double parent_gain,\n const double sum_gradients,\n const double sum_hessians,\n const data_size_t num_data,\n const double parent_output,\n // output parameters\n CUDASplitInfo* cuda_best_split_info) {\n const double cnt_factor = num_data / sum_hessians;\n const double min_gain_shift = parent_gain + min_gain_to_split;\n\n cuda_best_split_info->is_valid = false;\n\n hist_t local_grad_hist = 0.0f;\n hist_t local_hess_hist = 0.0f;\n double local_gain = 0.0f;\n bool threshold_found = false;\n uint32_t threshold_value = 0;\n __shared__ int rand_threshold;\n if (USE_RAND && threadIdx.x == 0) {\n if (task->num_bin - 2 > 0) {\n rand_threshold = cuda_random->NextInt(0, task->num_bin - 2);\n }\n }\n __shared__ uint32_t best_thread_index;\n __shared__ double shared_double_buffer[WARPSIZE];\n __shared__ bool shared_bool_buffer[WARPSIZE];\n __shared__ uint32_t shared_int_buffer[WARPSIZE];\n const unsigned int threadIdx_x = threadIdx.x;\n const bool skip_sum = REVERSE ?\n (task->skip_default_bin && (task->num_bin - 1 - threadIdx_x) == static_cast(task->default_bin)) :\n (task->skip_default_bin && (threadIdx_x + task->mfb_offset) == static_cast(task->default_bin));\n const uint32_t feature_num_bin_minus_offset = task->num_bin - task->mfb_offset;\n if (!REVERSE) {\n if (task->na_as_missing && task->mfb_offset == 1) {\n if (threadIdx_x < static_cast(task->num_bin) && threadIdx_x > 0) {\n const unsigned int bin_offset = (threadIdx_x - 1) << 1;\n local_grad_hist = feature_hist_ptr[bin_offset];\n local_hess_hist = feature_hist_ptr[bin_offset + 1];\n }\n } else {\n if (threadIdx_x < feature_num_bin_minus_offset && !skip_sum) {\n const unsigned int bin_offset = threadIdx_x << 1;\n local_grad_hist = feature_hist_ptr[bin_offset];\n local_hess_hist = feature_hist_ptr[bin_offset + 1];\n }\n }\n } else {\n if (threadIdx_x >= static_cast(task->na_as_missing) &&\n threadIdx_x < feature_num_bin_minus_offset && !skip_sum) {\n const unsigned int read_index = feature_num_bin_minus_offset - 1 - threadIdx_x;\n const unsigned int bin_offset = read_index << 1;\n local_grad_hist = feature_hist_ptr[bin_offset];\n local_hess_hist = feature_hist_ptr[bin_offset + 1];\n }\n }\n __syncthreads();\n if (!REVERSE && task->na_as_missing && task->mfb_offset == 1) {\n const hist_t sum_gradients_non_default = ShuffleReduceSum(local_grad_hist, shared_double_buffer, blockDim.x);\n __syncthreads();\n const hist_t sum_hessians_non_default = ShuffleReduceSum(local_hess_hist, shared_double_buffer, blockDim.x);\n if (threadIdx_x == 0) {\n local_grad_hist += (sum_gradients - sum_gradients_non_default);\n local_hess_hist += (sum_hessians - sum_hessians_non_default);\n }\n }\n if (threadIdx_x == 0) {\n local_hess_hist += kEpsilon;\n }\n local_gain = kMinScore;\n local_grad_hist = ShufflePrefixSum(local_grad_hist, shared_double_buffer);\n __syncthreads();\n local_hess_hist = ShufflePrefixSum(local_hess_hist, shared_double_buffer);\n if (REVERSE) {\n if (threadIdx_x >= static_cast(task->na_as_missing) && threadIdx_x <= task->num_bin - 2 && !skip_sum) {\n const double sum_right_gradient = local_grad_hist;\n const double sum_right_hessian = local_hess_hist;\n const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor));\n const double sum_left_gradient = sum_gradients - sum_right_gradient;\n const double sum_left_hessian = sum_hessians - sum_right_hessian;\n const data_size_t left_count = num_data - right_count;\n if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf &&\n sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf &&\n (!USE_RAND || static_cast(task->num_bin - 2 - threadIdx_x) == rand_threshold)) {\n double current_gain = CUDALeafSplits::GetSplitGains(\n sum_left_gradient, sum_left_hessian, sum_right_gradient,\n sum_right_hessian, lambda_l1,\n lambda_l2, path_smooth, left_count, right_count, parent_output);\n // gain with split is worse than without split\n if (current_gain > min_gain_shift) {\n local_gain = current_gain - min_gain_shift;\n threshold_value = static_cast(task->num_bin - 2 - threadIdx_x);\n threshold_found = true;\n }\n }\n }\n } else {\n const uint32_t end = (task->na_as_missing && task->mfb_offset == 1) ? static_cast(task->num_bin - 2) : feature_num_bin_minus_offset - 2;\n if (threadIdx_x <= end && !skip_sum) {\n const double sum_left_gradient = local_grad_hist;\n const double sum_left_hessian = local_hess_hist;\n const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor));\n const double sum_right_gradient = sum_gradients - sum_left_gradient;\n const double sum_right_hessian = sum_hessians - sum_left_hessian;\n const data_size_t right_count = num_data - left_count;\n if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf &&\n sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf &&\n (!USE_RAND || static_cast(threadIdx_x + task->mfb_offset) == rand_threshold)) {\n double current_gain = CUDALeafSplits::GetSplitGains(\n sum_left_gradient, sum_left_hessian, sum_right_gradient,\n sum_right_hessian, lambda_l1,\n lambda_l2, path_smooth, left_count, right_count, parent_output);\n // gain with split is worse than without split\n if (current_gain > min_gain_shift) {\n local_gain = current_gain - min_gain_shift;\n threshold_value = (task->na_as_missing && task->mfb_offset == 1) ?\n static_cast(threadIdx_x) :\n static_cast(threadIdx_x + task->mfb_offset);\n threshold_found = true;\n }\n }\n }\n }\n __syncthreads();\n const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_double_buffer, shared_bool_buffer, shared_int_buffer);\n if (threadIdx_x == 0) {\n best_thread_index = result;\n }\n __syncthreads();\n if (threshold_found && threadIdx_x == best_thread_index) {\n cuda_best_split_info->is_valid = true;\n cuda_best_split_info->threshold = threshold_value;\n cuda_best_split_info->gain = local_gain;\n cuda_best_split_info->default_left = task->assume_out_default_left;\n if (REVERSE) {\n const double sum_right_gradient = local_grad_hist;\n const double sum_right_hessian = local_hess_hist - kEpsilon;\n const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor));\n const double sum_left_gradient = sum_gradients - sum_right_gradient;\n const double sum_left_hessian = sum_hessians - sum_right_hessian - kEpsilon;\n const data_size_t left_count = num_data - right_count;\n const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output);\n const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output);\n cuda_best_split_info->left_sum_gradients = sum_left_gradient;\n cuda_best_split_info->left_sum_hessians = sum_left_hessian;\n cuda_best_split_info->left_count = left_count;\n cuda_best_split_info->right_sum_gradients = sum_right_gradient;\n cuda_best_split_info->right_sum_hessians = sum_right_hessian;\n cuda_best_split_info->right_count = right_count;\n cuda_best_split_info->left_value = left_output;\n cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, lambda_l2, left_output);\n cuda_best_split_info->right_value = right_output;\n cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, lambda_l2, right_output);\n } else {\n const double sum_left_gradient = local_grad_hist;\n const double sum_left_hessian = local_hess_hist - kEpsilon;\n const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor));\n const double sum_right_gradient = sum_gradients - sum_left_gradient;\n const double sum_right_hessian = sum_hessians - sum_left_hessian - kEpsilon;\n const data_size_t right_count = num_data - left_count;\n const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output);\n const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output);\n cuda_best_split_info->left_sum_gradients = sum_left_gradient;\n cuda_best_split_info->left_sum_hessians = sum_left_hessian;\n cuda_best_split_info->left_count = left_count;\n cuda_best_split_info->right_sum_gradients = sum_right_gradient;\n cuda_best_split_info->right_sum_hessians = sum_right_hessian;\n cuda_best_split_info->right_count = right_count;\n cuda_best_split_info->left_value = left_output;\n cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, lambda_l2, left_output);\n cuda_best_split_info->right_value = right_output;\n cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, lambda_l2, right_output);\n }\n }\n}\n\ntemplate \n__device__ void FindBestSplitsDiscretizedForLeafKernelInner(\n // input feature information\n const BIN_HIST_TYPE* feature_hist_ptr,\n // input task information\n const SplitFindTask* task,\n CUDARandom* cuda_random,\n // input config parameter values\n const double lambda_l1,\n const double lambda_l2,\n const double path_smooth,\n const data_size_t min_data_in_leaf,\n const double min_sum_hessian_in_leaf,\n const double min_gain_to_split,\n // input parent node information\n const double parent_gain,\n const int64_t sum_gradients_hessians,\n const data_size_t num_data,\n const double parent_output,\n // gradient scale\n const double grad_scale,\n const double hess_scale,\n // output parameters\n CUDASplitInfo* cuda_best_split_info) {\n const double sum_hessians = static_cast(sum_gradients_hessians & 0x00000000ffffffff) * hess_scale;\n const double cnt_factor = num_data / sum_hessians;\n const double min_gain_shift = parent_gain + min_gain_to_split;\n\n cuda_best_split_info->is_valid = false;\n\n ACC_HIST_TYPE local_grad_hess_hist = 0;\n double local_gain = 0.0f;\n bool threshold_found = false;\n uint32_t threshold_value = 0;\n __shared__ int rand_threshold;\n if (USE_RAND && threadIdx.x == 0) {\n if (task->num_bin - 2 > 0) {\n rand_threshold = cuda_random->NextInt(0, task->num_bin - 2);\n }\n }\n __shared__ uint32_t best_thread_index;\n __shared__ double shared_double_buffer[WARPSIZE];\n __shared__ bool shared_bool_buffer[WARPSIZE];\n __shared__ uint32_t shared_int_buffer[2 * WARPSIZE]; // need 2 * WARPSIZE since the actual ACC_HIST_TYPE could be long int\n const unsigned int threadIdx_x = threadIdx.x;\n const bool skip_sum = REVERSE ?\n (task->skip_default_bin && (task->num_bin - 1 - threadIdx_x) == static_cast(task->default_bin)) :\n (task->skip_default_bin && (threadIdx_x + task->mfb_offset) == static_cast(task->default_bin));\n const uint32_t feature_num_bin_minus_offset = task->num_bin - task->mfb_offset;\n if (!REVERSE) {\n if (threadIdx_x < feature_num_bin_minus_offset && !skip_sum) {\n const unsigned int bin_offset = threadIdx_x;\n if (USE_16BIT_BIN_HIST && !USE_16BIT_ACC_HIST) {\n const int32_t local_grad_hess_hist_int32 = feature_hist_ptr[bin_offset];\n local_grad_hess_hist = (static_cast(static_cast(local_grad_hess_hist_int32 >> 16)) << 32) | (static_cast(local_grad_hess_hist_int32 & 0x0000ffff));\n } else {\n local_grad_hess_hist = feature_hist_ptr[bin_offset];\n }\n }\n } else {\n if (threadIdx_x >= static_cast(task->na_as_missing) &&\n threadIdx_x < feature_num_bin_minus_offset && !skip_sum) {\n const unsigned int read_index = feature_num_bin_minus_offset - 1 - threadIdx_x;\n if (USE_16BIT_BIN_HIST && !USE_16BIT_ACC_HIST) {\n const int32_t local_grad_hess_hist_int32 = feature_hist_ptr[read_index];\n local_grad_hess_hist = (static_cast(static_cast(local_grad_hess_hist_int32 >> 16)) << 32) | (static_cast(local_grad_hess_hist_int32 & 0x0000ffff));\n } else {\n local_grad_hess_hist = feature_hist_ptr[read_index];\n }\n }\n }\n __syncthreads();\n local_gain = kMinScore;\n local_grad_hess_hist = ShufflePrefixSum(local_grad_hess_hist, reinterpret_cast(shared_int_buffer));\n double sum_left_gradient = 0.0f;\n double sum_left_hessian = 0.0f;\n double sum_right_gradient = 0.0f;\n double sum_right_hessian = 0.0f;\n data_size_t left_count = 0;\n data_size_t right_count = 0;\n int64_t sum_left_gradient_hessian = 0;\n int64_t sum_right_gradient_hessian = 0;\n if (REVERSE) {\n if (threadIdx_x >= static_cast(task->na_as_missing) && threadIdx_x <= task->num_bin - 2 && !skip_sum) {\n sum_right_gradient_hessian = USE_16BIT_ACC_HIST ?\n (static_cast(static_cast(local_grad_hess_hist >> 16)) << 32) | static_cast(local_grad_hess_hist & 0x0000ffff) :\n local_grad_hess_hist;\n sum_right_gradient = static_cast(static_cast((sum_right_gradient_hessian & 0xffffffff00000000) >> 32)) * grad_scale;\n sum_right_hessian = static_cast(static_cast(sum_right_gradient_hessian & 0x00000000ffffffff)) * hess_scale;\n right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor));\n sum_left_gradient_hessian = sum_gradients_hessians - sum_right_gradient_hessian;\n sum_left_gradient = static_cast(static_cast((sum_left_gradient_hessian & 0xffffffff00000000)>> 32)) * grad_scale;\n sum_left_hessian = static_cast(static_cast(sum_left_gradient_hessian & 0x00000000ffffffff)) * hess_scale;\n left_count = num_data - right_count;\n if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf &&\n sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf &&\n (!USE_RAND || static_cast(task->num_bin - 2 - threadIdx_x) == rand_threshold)) {\n double current_gain = CUDALeafSplits::GetSplitGains(\n sum_left_gradient, sum_left_hessian + kEpsilon, sum_right_gradient,\n sum_right_hessian + kEpsilon, lambda_l1,\n lambda_l2, path_smooth, left_count, right_count, parent_output);\n // gain with split is worse than without split\n if (current_gain > min_gain_shift) {\n local_gain = current_gain - min_gain_shift;\n threshold_value = static_cast(task->num_bin - 2 - threadIdx_x);\n threshold_found = true;\n }\n }\n }\n } else {\n if (threadIdx_x <= feature_num_bin_minus_offset - 2 && !skip_sum) {\n sum_left_gradient_hessian = USE_16BIT_ACC_HIST ?\n (static_cast(static_cast(local_grad_hess_hist >> 16)) << 32) | static_cast(local_grad_hess_hist & 0x0000ffff) :\n local_grad_hess_hist;\n sum_left_gradient = static_cast(static_cast((sum_left_gradient_hessian & 0xffffffff00000000) >> 32)) * grad_scale;\n sum_left_hessian = static_cast(static_cast(sum_left_gradient_hessian & 0x00000000ffffffff)) * hess_scale;\n left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor));\n sum_right_gradient_hessian = sum_gradients_hessians - sum_left_gradient_hessian;\n sum_right_gradient = static_cast(static_cast((sum_right_gradient_hessian & 0xffffffff00000000) >> 32)) * grad_scale;\n sum_right_hessian = static_cast(static_cast(sum_right_gradient_hessian & 0x00000000ffffffff)) * hess_scale;\n right_count = num_data - left_count;\n if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf &&\n sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf &&\n (!USE_RAND || static_cast(threadIdx_x + task->mfb_offset) == rand_threshold)) {\n double current_gain = CUDALeafSplits::GetSplitGains(\n sum_left_gradient, sum_left_hessian + kEpsilon, sum_right_gradient,\n sum_right_hessian + kEpsilon, lambda_l1,\n lambda_l2, path_smooth, left_count, right_count, parent_output);\n // gain with split is worse than without split\n if (current_gain > min_gain_shift) {\n local_gain = current_gain - min_gain_shift;\n threshold_value = static_cast(threadIdx_x + task->mfb_offset);\n threshold_found = true;\n }\n }\n }\n }\n __syncthreads();\n const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_double_buffer, shared_bool_buffer, shared_int_buffer);\n if (threadIdx_x == 0) {\n best_thread_index = result;\n }\n __syncthreads();\n if (threshold_found && threadIdx_x == best_thread_index) {\n cuda_best_split_info->is_valid = true;\n cuda_best_split_info->threshold = threshold_value;\n cuda_best_split_info->gain = local_gain;\n cuda_best_split_info->default_left = task->assume_out_default_left;\n const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output);\n const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output);\n cuda_best_split_info->left_sum_gradients = sum_left_gradient;\n cuda_best_split_info->left_sum_hessians = sum_left_hessian;\n cuda_best_split_info->left_sum_of_gradients_hessians = sum_left_gradient_hessian;\n cuda_best_split_info->left_count = left_count;\n cuda_best_split_info->right_sum_gradients = sum_right_gradient;\n cuda_best_split_info->right_sum_hessians = sum_right_hessian;\n cuda_best_split_info->right_sum_of_gradients_hessians = sum_right_gradient_hessian;\n cuda_best_split_info->right_count = right_count;\n cuda_best_split_info->left_value = left_output;\n cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, lambda_l2, left_output);\n cuda_best_split_info->right_value = right_output;\n cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, lambda_l2, right_output);\n }\n}\n\ntemplate \n__device__ void FindBestSplitsForLeafKernelCategoricalInner(\n // input feature information\n const hist_t* feature_hist_ptr,\n // input task information\n const SplitFindTask* task,\n CUDARandom* cuda_random,\n // input config parameter values\n const double lambda_l1,\n const double lambda_l2,\n const double path_smooth,\n const data_size_t min_data_in_leaf,\n const double min_sum_hessian_in_leaf,\n const double min_gain_to_split,\n const double cat_smooth,\n const double cat_l2,\n const int max_cat_threshold,\n const int min_data_per_group,\n // input parent node information\n const double parent_gain,\n const double sum_gradients,\n const double sum_hessians,\n const data_size_t num_data,\n const double parent_output,\n // output parameters\n CUDASplitInfo* cuda_best_split_info) {\n __shared__ double shared_gain_buffer[WARPSIZE];\n __shared__ bool shared_found_buffer[WARPSIZE];\n __shared__ uint32_t shared_thread_index_buffer[WARPSIZE];\n __shared__ uint32_t best_thread_index;\n const double cnt_factor = num_data / sum_hessians;\n const double min_gain_shift = parent_gain + min_gain_to_split;\n double l2 = lambda_l2;\n\n double local_gain = min_gain_shift;\n bool threshold_found = false;\n\n cuda_best_split_info->is_valid = false;\n\n const int bin_start = 1 - task->mfb_offset;\n const int bin_end = task->num_bin - task->mfb_offset;\n const int threadIdx_x = static_cast(threadIdx.x);\n\n __shared__ int rand_threshold;\n\n if (task->is_one_hot) {\n if (USE_RAND && threadIdx.x == 0) {\n rand_threshold = 0;\n if (bin_end > bin_start) {\n rand_threshold = cuda_random->NextInt(bin_start, bin_end);\n }\n }\n __syncthreads();\n if (threadIdx_x >= bin_start && threadIdx_x < bin_end) {\n const int bin_offset = (threadIdx_x << 1);\n const hist_t grad = feature_hist_ptr[bin_offset];\n const hist_t hess = feature_hist_ptr[bin_offset + 1];\n data_size_t cnt =\n static_cast(__double2int_rn(hess * cnt_factor));\n if (cnt >= min_data_in_leaf && hess >= min_sum_hessian_in_leaf) {\n const data_size_t other_count = num_data - cnt;\n if (other_count >= min_data_in_leaf) {\n const double sum_other_hessian = sum_hessians - hess - kEpsilon;\n if (sum_other_hessian >= min_sum_hessian_in_leaf && (!USE_RAND || static_cast(threadIdx_x) == rand_threshold)) {\n const double sum_other_gradient = sum_gradients - grad;\n double current_gain = CUDALeafSplits::GetSplitGains(\n sum_other_gradient, sum_other_hessian, grad,\n hess + kEpsilon, lambda_l1,\n l2, path_smooth, other_count, cnt, parent_output);\n if (current_gain > min_gain_shift) {\n local_gain = current_gain;\n threshold_found = true;\n }\n }\n }\n }\n }\n __syncthreads();\n const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer);\n if (threadIdx_x == 0) {\n best_thread_index = result;\n }\n __syncthreads();\n if (threshold_found && threadIdx_x == best_thread_index) {\n cuda_best_split_info->is_valid = true;\n cuda_best_split_info->num_cat_threshold = 1;\n cuda_best_split_info->gain = local_gain - min_gain_shift;\n *(cuda_best_split_info->cat_threshold) = static_cast(threadIdx_x + task->mfb_offset);\n cuda_best_split_info->default_left = false;\n const int bin_offset = (threadIdx_x << 1);\n const hist_t sum_left_gradient = feature_hist_ptr[bin_offset];\n const hist_t sum_left_hessian = feature_hist_ptr[bin_offset + 1];\n const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor));\n const double sum_right_gradient = sum_gradients - sum_left_gradient;\n const double sum_right_hessian = sum_hessians - sum_left_hessian;\n const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor));\n const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, l2, path_smooth, left_count, parent_output);\n const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, l2, path_smooth, right_count, parent_output);\n cuda_best_split_info->left_sum_gradients = sum_left_gradient;\n cuda_best_split_info->left_sum_hessians = sum_left_hessian;\n cuda_best_split_info->left_count = left_count;\n cuda_best_split_info->right_sum_gradients = sum_right_gradient;\n cuda_best_split_info->right_sum_hessians = sum_right_hessian;\n cuda_best_split_info->right_count = right_count;\n cuda_best_split_info->left_value = left_output;\n cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, l2, left_output);\n cuda_best_split_info->right_value = right_output;\n cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, l2, right_output);\n }\n } else {\n __shared__ double shared_value_buffer[NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER];\n __shared__ int16_t shared_index_buffer[NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER];\n __shared__ uint16_t shared_mem_buffer_uint16[WARPSIZE];\n __shared__ double shared_mem_buffer_double[WARPSIZE];\n __shared__ int used_bin;\n l2 += cat_l2;\n uint16_t is_valid_bin = 0;\n int best_dir = 0;\n double best_sum_left_gradient = 0.0f;\n double best_sum_left_hessian = 0.0f;\n if (threadIdx_x >= bin_start && threadIdx_x < bin_end) {\n const int bin_offset = (threadIdx_x << 1);\n const double hess = feature_hist_ptr[bin_offset + 1];\n if (__double2int_rn(hess * cnt_factor) >= cat_smooth) {\n const double grad = feature_hist_ptr[bin_offset];\n shared_value_buffer[threadIdx_x] = grad / (hess + cat_smooth);\n is_valid_bin = 1;\n } else {\n shared_value_buffer[threadIdx_x] = kMaxScore;\n }\n } else {\n shared_value_buffer[threadIdx_x] = kMaxScore;\n }\n shared_index_buffer[threadIdx_x] = threadIdx_x;\n __syncthreads();\n const int local_used_bin = ShuffleReduceSum(is_valid_bin, shared_mem_buffer_uint16, blockDim.x);\n if (threadIdx_x == 0) {\n used_bin = local_used_bin;\n }\n __syncthreads();\n BitonicArgSort_1024(shared_value_buffer, shared_index_buffer, bin_end);\n __syncthreads();\n const int max_num_cat = min(max_cat_threshold, (used_bin + 1) / 2);\n\n if (USE_RAND) {\n rand_threshold = 0;\n const int max_threshold = max(min(max_num_cat, used_bin) - 1, 0);\n if (max_threshold > 0) {\n rand_threshold = cuda_random->NextInt(0, max_threshold);\n }\n }\n\n // left to right\n double grad = 0.0f;\n double hess = 0.0f;\n if (threadIdx_x < used_bin && threadIdx_x < max_num_cat) {\n const int bin_offset = (shared_index_buffer[threadIdx_x] << 1);\n grad = feature_hist_ptr[bin_offset];\n hess = feature_hist_ptr[bin_offset + 1];\n }\n if (threadIdx_x == 0) {\n hess += kEpsilon;\n }\n __syncthreads();\n double sum_left_gradient = ShufflePrefixSum(grad, shared_mem_buffer_double);\n __syncthreads();\n double sum_left_hessian = ShufflePrefixSum(hess, shared_mem_buffer_double);\n if (threadIdx_x < used_bin && threadIdx_x < max_num_cat) {\n const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor));\n const double sum_right_gradient = sum_gradients - sum_left_gradient;\n const double sum_right_hessian = sum_hessians - sum_left_hessian;\n const data_size_t right_count = num_data - left_count;\n if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf &&\n sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf &&\n (!USE_RAND || threadIdx_x == static_cast(rand_threshold))) {\n double current_gain = CUDALeafSplits::GetSplitGains(\n sum_left_gradient, sum_left_hessian, sum_right_gradient,\n sum_right_hessian, lambda_l1,\n l2, path_smooth, left_count, right_count, parent_output);\n // gain with split is worse than without split\n if (current_gain > local_gain) {\n local_gain = current_gain;\n threshold_found = true;\n best_dir = 1;\n best_sum_left_gradient = sum_left_gradient;\n best_sum_left_hessian = sum_left_hessian;\n }\n }\n }\n __syncthreads();\n\n // right to left\n grad = 0.0f;\n hess = 0.0f;\n if (threadIdx_x < used_bin && threadIdx_x < max_num_cat) {\n const int bin_offset = (shared_index_buffer[used_bin - 1 - threadIdx_x] << 1);\n grad = feature_hist_ptr[bin_offset];\n hess = feature_hist_ptr[bin_offset + 1];\n }\n if (threadIdx_x == 0) {\n hess += kEpsilon;\n }\n __syncthreads();\n sum_left_gradient = ShufflePrefixSum(grad, shared_mem_buffer_double);\n __syncthreads();\n sum_left_hessian = ShufflePrefixSum(hess, shared_mem_buffer_double);\n if (threadIdx_x < used_bin && threadIdx_x < max_num_cat) {\n const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor));\n const double sum_right_gradient = sum_gradients - sum_left_gradient;\n const double sum_right_hessian = sum_hessians - sum_left_hessian;\n const data_size_t right_count = num_data - left_count;\n if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf &&\n sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf &&\n (!USE_RAND || threadIdx_x == static_cast(rand_threshold))) {\n double current_gain = CUDALeafSplits::GetSplitGains(\n sum_left_gradient, sum_left_hessian, sum_right_gradient,\n sum_right_hessian, lambda_l1,\n l2, path_smooth, left_count, right_count, parent_output);\n // gain with split is worse than without split\n if (current_gain > local_gain) {\n local_gain = current_gain;\n threshold_found = true;\n best_dir = -1;\n best_sum_left_gradient = sum_left_gradient;\n best_sum_left_hessian = sum_left_hessian;\n }\n }\n }\n __syncthreads();\n\n const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer);\n if (threadIdx_x == 0) {\n best_thread_index = result;\n }\n __syncthreads();\n if (threshold_found && threadIdx_x == best_thread_index) {\n cuda_best_split_info->is_valid = true;\n cuda_best_split_info->num_cat_threshold = threadIdx_x + 1;\n cuda_best_split_info->gain = local_gain - min_gain_shift;\n if (best_dir == 1) {\n for (int i = 0; i < threadIdx_x + 1; ++i) {\n (cuda_best_split_info->cat_threshold)[i] = shared_index_buffer[i] + task->mfb_offset;\n }\n } else {\n for (int i = 0; i < threadIdx_x + 1; ++i) {\n (cuda_best_split_info->cat_threshold)[i] = shared_index_buffer[used_bin - 1 - i] + task->mfb_offset;\n }\n }\n cuda_best_split_info->default_left = false;\n const hist_t sum_left_gradient = best_sum_left_gradient;\n const hist_t sum_left_hessian = best_sum_left_hessian;\n const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor));\n const double sum_right_gradient = sum_gradients - sum_left_gradient;\n const double sum_right_hessian = sum_hessians - sum_left_hessian;\n const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor));\n const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, l2, path_smooth, left_count, parent_output);\n const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, l2, path_smooth, right_count, parent_output);\n cuda_best_split_info->left_sum_gradients = sum_left_gradient;\n cuda_best_split_info->left_sum_hessians = sum_left_hessian;\n cuda_best_split_info->left_count = left_count;\n cuda_best_split_info->right_sum_gradients = sum_right_gradient;\n cuda_best_split_info->right_sum_hessians = sum_right_hessian;\n cuda_best_split_info->right_count = right_count;\n cuda_best_split_info->left_value = left_output;\n cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, l2, left_output);\n cuda_best_split_info->right_value = right_output;\n cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, l2, right_output);\n }\n }\n}\n\ntemplate \n__global__ void FindBestSplitsForLeafKernel(\n // input feature information\n const int8_t* is_feature_used_bytree,\n // input task information\n const int num_tasks,\n const SplitFindTask* tasks,\n CUDARandom* cuda_randoms,\n // input leaf information\n const CUDALeafSplitsStruct* smaller_leaf_splits,\n const CUDALeafSplitsStruct* larger_leaf_splits,\n // input config parameter values\n const data_size_t min_data_in_leaf,\n const double min_sum_hessian_in_leaf,\n const double min_gain_to_split,\n const double lambda_l1,\n const double lambda_l2,\n const double path_smooth,\n const double cat_smooth,\n const double cat_l2,\n const int max_cat_threshold,\n const int min_data_per_group,\n // output\n CUDASplitInfo* cuda_best_split_info,\n // global num data in leaf\n const data_size_t global_num_data_in_smaller_leaf,\n const data_size_t global_num_data_in_larger_leaf) {\n const unsigned int task_index = blockIdx.x;\n const SplitFindTask* task = tasks + task_index;\n const int inner_feature_index = task->inner_feature_index;\n const double parent_gain = IS_LARGER ? larger_leaf_splits->gain : smaller_leaf_splits->gain;\n const double sum_gradients = IS_LARGER ? larger_leaf_splits->sum_of_gradients : smaller_leaf_splits->sum_of_gradients;\n const double sum_hessians = (IS_LARGER ? larger_leaf_splits->sum_of_hessians : smaller_leaf_splits->sum_of_hessians) + 2 * kEpsilon;\n const data_size_t num_data = IS_LARGER ? global_num_data_in_larger_leaf : global_num_data_in_smaller_leaf;\n const double parent_output = IS_LARGER ? larger_leaf_splits->leaf_value : smaller_leaf_splits->leaf_value;\n const unsigned int output_offset = IS_LARGER ? (task_index + num_tasks) : task_index;\n CUDASplitInfo* out = cuda_best_split_info + output_offset;\n CUDARandom* cuda_random = USE_RAND ?\n (IS_LARGER ? cuda_randoms + task_index * 2 + 1 : cuda_randoms + task_index * 2) : nullptr;\n if (is_feature_used_bytree[inner_feature_index]) {\n const hist_t* hist_ptr = (IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset * 2;\n if (task->is_categorical) {\n FindBestSplitsForLeafKernelCategoricalInner(\n // input feature information\n hist_ptr,\n // input task information\n task,\n cuda_random,\n // input config parameter values\n lambda_l1,\n lambda_l2,\n path_smooth,\n min_data_in_leaf,\n min_sum_hessian_in_leaf,\n min_gain_to_split,\n cat_smooth,\n cat_l2,\n max_cat_threshold,\n min_data_per_group,\n // input parent node information\n parent_gain,\n sum_gradients,\n sum_hessians,\n num_data,\n parent_output,\n // output parameters\n out);\n } else {\n if (!task->reverse) {\n FindBestSplitsForLeafKernelInner(\n // input feature information\n hist_ptr,\n // input task information\n task,\n cuda_random,\n // input config parameter values\n lambda_l1,\n lambda_l2,\n path_smooth,\n min_data_in_leaf,\n min_sum_hessian_in_leaf,\n min_gain_to_split,\n // input parent node information\n parent_gain,\n sum_gradients,\n sum_hessians,\n num_data,\n parent_output,\n // output parameters\n out);\n } else {\n FindBestSplitsForLeafKernelInner(\n // input feature information\n hist_ptr,\n // input task information\n task,\n cuda_random,\n // input config parameter values\n lambda_l1,\n lambda_l2,\n path_smooth,\n min_data_in_leaf,\n min_sum_hessian_in_leaf,\n min_gain_to_split,\n // input parent node information\n parent_gain,\n sum_gradients,\n sum_hessians,\n num_data,\n parent_output,\n // output parameters\n out);\n }\n }\n } else {\n out->is_valid = false;\n }\n}\n\n\ntemplate \n__global__ void FindBestSplitsDiscretizedForLeafKernel(\n // input feature information\n const int8_t* is_feature_used_bytree,\n // input task information\n const int num_tasks,\n const SplitFindTask* tasks,\n CUDARandom* cuda_randoms,\n // input leaf information\n const CUDALeafSplitsStruct* smaller_leaf_splits,\n const CUDALeafSplitsStruct* larger_leaf_splits,\n const uint8_t smaller_leaf_num_bits_in_histogram_bin,\n const uint8_t larger_leaf_num_bits_in_histogram_bin,\n // input config parameter values\n const data_size_t min_data_in_leaf,\n const double min_sum_hessian_in_leaf,\n const double min_gain_to_split,\n const double lambda_l1,\n const double lambda_l2,\n const double path_smooth,\n const double cat_smooth,\n const double cat_l2,\n const int max_cat_threshold,\n const int min_data_per_group,\n const int max_cat_to_onehot,\n // gradient scale\n const score_t* grad_scale,\n const score_t* hess_scale,\n // output\n CUDASplitInfo* cuda_best_split_info,\n // global num data in leaf\n const data_size_t global_num_data_in_smaller_leaf,\n const data_size_t global_num_data_in_larger_leaf) {\n const unsigned int task_index = blockIdx.x;\n const SplitFindTask* task = tasks + task_index;\n const int inner_feature_index = task->inner_feature_index;\n const double parent_gain = IS_LARGER ? larger_leaf_splits->gain : smaller_leaf_splits->gain;\n const int64_t sum_gradients_hessians = IS_LARGER ? larger_leaf_splits->sum_of_gradients_hessians : smaller_leaf_splits->sum_of_gradients_hessians;\n const data_size_t num_data = IS_LARGER ? global_num_data_in_larger_leaf : global_num_data_in_smaller_leaf;\n const double parent_output = IS_LARGER ? larger_leaf_splits->leaf_value : smaller_leaf_splits->leaf_value;\n const unsigned int output_offset = IS_LARGER ? (task_index + num_tasks) : task_index;\n CUDASplitInfo* out = cuda_best_split_info + output_offset;\n CUDARandom* cuda_random = USE_RAND ?\n (IS_LARGER ? cuda_randoms + task_index * 2 + 1 : cuda_randoms + task_index * 2) : nullptr;\n const bool use_16bit_bin = IS_LARGER ? (larger_leaf_num_bits_in_histogram_bin <= 16) : (smaller_leaf_num_bits_in_histogram_bin <= 16);\n if (is_feature_used_bytree[inner_feature_index]) {\n if (task->is_categorical) {\n __threadfence(); // ensure store issued before trap\n#if defined(USE_ROCM)\n __builtin_trap();\n#else\n asm(\"trap;\");\n#endif\n } else {\n if (!task->reverse) {\n if (use_16bit_bin) {\n const int32_t* hist_ptr =\n reinterpret_cast(IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset;\n FindBestSplitsDiscretizedForLeafKernelInner(\n // input feature information\n hist_ptr,\n // input task information\n task,\n cuda_random,\n // input config parameter values\n lambda_l1,\n lambda_l2,\n path_smooth,\n min_data_in_leaf,\n min_sum_hessian_in_leaf,\n min_gain_to_split,\n // input parent node information\n parent_gain,\n sum_gradients_hessians,\n num_data,\n parent_output,\n // gradient scale\n *grad_scale,\n *hess_scale,\n // output parameters\n out);\n } else {\n const int32_t* hist_ptr =\n reinterpret_cast(IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset;\n FindBestSplitsDiscretizedForLeafKernelInner(\n // input feature information\n hist_ptr,\n // input task information\n task,\n cuda_random,\n // input config parameter values\n lambda_l1,\n lambda_l2,\n path_smooth,\n min_data_in_leaf,\n min_sum_hessian_in_leaf,\n min_gain_to_split,\n // input parent node information\n parent_gain,\n sum_gradients_hessians,\n num_data,\n parent_output,\n // gradient scale\n *grad_scale,\n *hess_scale,\n // output parameters\n out);\n }\n } else {\n if (use_16bit_bin) {\n const int32_t* hist_ptr =\n reinterpret_cast(IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset;\n FindBestSplitsDiscretizedForLeafKernelInner(\n // input feature information\n hist_ptr,\n // input task information\n task,\n cuda_random,\n // input config parameter values\n lambda_l1,\n lambda_l2,\n path_smooth,\n min_data_in_leaf,\n min_sum_hessian_in_leaf,\n min_gain_to_split,\n // input parent node information\n parent_gain,\n sum_gradients_hessians,\n num_data,\n parent_output,\n // gradient scale\n *grad_scale,\n *hess_scale,\n // output parameters\n out);\n } else {\n const int32_t* hist_ptr =\n reinterpret_cast(IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + task->hist_offset;\n FindBestSplitsDiscretizedForLeafKernelInner(\n // input feature information\n hist_ptr,\n // input task information\n task,\n cuda_random,\n // input config parameter values\n lambda_l1,\n lambda_l2,\n path_smooth,\n min_data_in_leaf,\n min_sum_hessian_in_leaf,\n min_gain_to_split,\n // input parent node information\n parent_gain,\n sum_gradients_hessians,\n num_data,\n parent_output,\n // gradient scale\n *grad_scale,\n *hess_scale,\n // output parameters\n out);\n }\n }\n }\n } else {\n out->is_valid = false;\n }\n}\n\ntemplate \n__device__ void FindBestSplitsForLeafKernelInner_GlobalMemory(\n // input feature information\n const hist_t* feature_hist_ptr,\n // input task information\n const SplitFindTask* task,\n CUDARandom* cuda_random,\n // input config parameter values\n const double lambda_l1,\n const double lambda_l2,\n const double path_smooth,\n const data_size_t min_data_in_leaf,\n const double min_sum_hessian_in_leaf,\n const double min_gain_to_split,\n // input parent node information\n const double parent_gain,\n const double sum_gradients,\n const double sum_hessians,\n const data_size_t num_data,\n const double parent_output,\n // output parameters\n CUDASplitInfo* cuda_best_split_info,\n // buffer\n hist_t* hist_grad_buffer_ptr,\n hist_t* hist_hess_buffer_ptr) {\n const double cnt_factor = num_data / sum_hessians;\n const double min_gain_shift = parent_gain + min_gain_to_split;\n\n cuda_best_split_info->is_valid = false;\n double local_gain = 0.0f;\n bool threshold_found = false;\n uint32_t threshold_value = 0;\n __shared__ int rand_threshold;\n if (USE_RAND && threadIdx.x == 0) {\n if (task->num_bin - 2 > 0) {\n rand_threshold = cuda_random->NextInt(0, task->num_bin - 2);\n }\n }\n __shared__ uint32_t best_thread_index;\n __shared__ double shared_double_buffer[WARPSIZE];\n __shared__ bool shared_found_buffer[WARPSIZE];\n __shared__ uint32_t shared_thread_index_buffer[WARPSIZE];\n const unsigned int threadIdx_x = threadIdx.x;\n const uint32_t feature_num_bin_minus_offset = task->num_bin - task->mfb_offset;\n if (!REVERSE) {\n if (task->na_as_missing && task->mfb_offset == 1) {\n uint32_t bin_start = threadIdx_x > 0 ? threadIdx_x : blockDim.x;\n hist_t thread_sum_gradients = 0.0f;\n hist_t thread_sum_hessians = 0.0f;\n for (unsigned int bin = bin_start; bin < static_cast(task->num_bin); bin += blockDim.x) {\n const unsigned int bin_offset = (bin - 1) << 1;\n const hist_t grad = feature_hist_ptr[bin_offset];\n const hist_t hess = feature_hist_ptr[bin_offset + 1];\n hist_grad_buffer_ptr[bin] = grad;\n hist_hess_buffer_ptr[bin] = hess;\n thread_sum_gradients += grad;\n thread_sum_hessians += hess;\n }\n const hist_t sum_gradients_non_default = ShuffleReduceSum(thread_sum_gradients, shared_double_buffer, blockDim.x);\n __syncthreads();\n const hist_t sum_hessians_non_default = ShuffleReduceSum(thread_sum_hessians, shared_double_buffer, blockDim.x);\n if (threadIdx_x == 0) {\n hist_grad_buffer_ptr[0] = sum_gradients - sum_gradients_non_default;\n hist_hess_buffer_ptr[0] = sum_hessians - sum_hessians_non_default;\n }\n } else {\n for (unsigned int bin = threadIdx_x; bin < feature_num_bin_minus_offset; bin += blockDim.x) {\n const bool skip_sum =\n (task->skip_default_bin && (bin + task->mfb_offset) == static_cast(task->default_bin));\n if (!skip_sum) {\n const unsigned int bin_offset = bin << 1;\n hist_grad_buffer_ptr[bin] = feature_hist_ptr[bin_offset];\n hist_hess_buffer_ptr[bin] = feature_hist_ptr[bin_offset + 1];\n } else {\n hist_grad_buffer_ptr[bin] = 0.0f;\n hist_hess_buffer_ptr[bin] = 0.0f;\n }\n }\n }\n } else {\n for (unsigned int bin = threadIdx_x; bin < feature_num_bin_minus_offset; bin += blockDim.x) {\n const bool skip_sum = bin >= static_cast(task->na_as_missing) &&\n (task->skip_default_bin && (task->num_bin - 1 - bin) == static_cast(task->default_bin));\n if (!skip_sum) {\n const unsigned int read_index = feature_num_bin_minus_offset - 1 - bin;\n const unsigned int bin_offset = read_index << 1;\n hist_grad_buffer_ptr[bin] = feature_hist_ptr[bin_offset];\n hist_hess_buffer_ptr[bin] = feature_hist_ptr[bin_offset + 1];\n } else {\n hist_grad_buffer_ptr[bin] = 0.0f;\n hist_hess_buffer_ptr[bin] = 0.0f;\n }\n }\n }\n __syncthreads();\n if (threadIdx_x == 0) {\n hist_hess_buffer_ptr[0] += kEpsilon;\n }\n local_gain = kMinScore;\n GlobalMemoryPrefixSum(hist_grad_buffer_ptr, static_cast(feature_num_bin_minus_offset));\n __syncthreads();\n GlobalMemoryPrefixSum(hist_hess_buffer_ptr, static_cast(feature_num_bin_minus_offset));\n if (REVERSE) {\n for (unsigned int bin = threadIdx_x; bin < feature_num_bin_minus_offset; bin += blockDim.x) {\n const bool skip_sum = (bin >= static_cast(task->na_as_missing) &&\n (task->skip_default_bin && (task->num_bin - 1 - bin) == static_cast(task->default_bin)));\n if (!skip_sum) {\n const double sum_right_gradient = hist_grad_buffer_ptr[bin];\n const double sum_right_hessian = hist_hess_buffer_ptr[bin];\n const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor));\n const double sum_left_gradient = sum_gradients - sum_right_gradient;\n const double sum_left_hessian = sum_hessians - sum_right_hessian;\n const data_size_t left_count = num_data - right_count;\n if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf &&\n sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf &&\n (!USE_RAND || static_cast(task->num_bin - 2 - bin) == rand_threshold)) {\n double current_gain = CUDALeafSplits::GetSplitGains(\n sum_left_gradient, sum_left_hessian, sum_right_gradient,\n sum_right_hessian, lambda_l1,\n lambda_l2, path_smooth, left_count, right_count, parent_output);\n // gain with split is worse than without split\n if (current_gain > min_gain_shift) {\n local_gain = current_gain - min_gain_shift;\n threshold_value = static_cast(task->num_bin - 2 - bin);\n threshold_found = true;\n }\n }\n }\n }\n } else {\n const uint32_t end = (task->na_as_missing && task->mfb_offset == 1) ? static_cast(task->num_bin - 2) : feature_num_bin_minus_offset - 2;\n for (unsigned int bin = threadIdx_x; bin <= end; bin += blockDim.x) {\n const bool skip_sum =\n (task->skip_default_bin && (bin + task->mfb_offset) == static_cast(task->default_bin));\n if (!skip_sum) {\n const double sum_left_gradient = hist_grad_buffer_ptr[bin];\n const double sum_left_hessian = hist_hess_buffer_ptr[bin];\n const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor));\n const double sum_right_gradient = sum_gradients - sum_left_gradient;\n const double sum_right_hessian = sum_hessians - sum_left_hessian;\n const data_size_t right_count = num_data - left_count;\n if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf &&\n sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf &&\n (!USE_RAND || static_cast(bin + task->mfb_offset) == rand_threshold)) {\n double current_gain = CUDALeafSplits::GetSplitGains(\n sum_left_gradient, sum_left_hessian, sum_right_gradient,\n sum_right_hessian, lambda_l1,\n lambda_l2, path_smooth, left_count, right_count, parent_output);\n // gain with split is worse than without split\n if (current_gain > min_gain_shift) {\n local_gain = current_gain - min_gain_shift;\n threshold_value = (task->na_as_missing && task->mfb_offset == 1) ?\n bin : static_cast(bin + task->mfb_offset);\n threshold_found = true;\n }\n }\n }\n }\n }\n __syncthreads();\n const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_double_buffer, shared_found_buffer, shared_thread_index_buffer);\n if (threadIdx_x == 0) {\n best_thread_index = result;\n }\n __syncthreads();\n if (threshold_found && threadIdx_x == best_thread_index) {\n cuda_best_split_info->is_valid = true;\n cuda_best_split_info->threshold = threshold_value;\n cuda_best_split_info->gain = local_gain;\n cuda_best_split_info->default_left = task->assume_out_default_left;\n if (REVERSE) {\n const unsigned int best_bin = static_cast(task->num_bin - 2 - threshold_value);\n const double sum_right_gradient = hist_grad_buffer_ptr[best_bin];\n const double sum_right_hessian = hist_hess_buffer_ptr[best_bin] - kEpsilon;\n const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor));\n const double sum_left_gradient = sum_gradients - sum_right_gradient;\n const double sum_left_hessian = sum_hessians - sum_right_hessian - kEpsilon;\n const data_size_t left_count = num_data - right_count;\n const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output);\n const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output);\n cuda_best_split_info->left_sum_gradients = sum_left_gradient;\n cuda_best_split_info->left_sum_hessians = sum_left_hessian;\n cuda_best_split_info->left_count = left_count;\n cuda_best_split_info->right_sum_gradients = sum_right_gradient;\n cuda_best_split_info->right_sum_hessians = sum_right_hessian;\n cuda_best_split_info->right_count = right_count;\n cuda_best_split_info->left_value = left_output;\n cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, lambda_l2, left_output);\n cuda_best_split_info->right_value = right_output;\n cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, lambda_l2, right_output);\n } else {\n const unsigned int best_bin = (task->na_as_missing && task->mfb_offset == 1) ?\n threshold_value : static_cast(threshold_value - task->mfb_offset);\n const double sum_left_gradient = hist_grad_buffer_ptr[best_bin];\n const double sum_left_hessian = hist_hess_buffer_ptr[best_bin] - kEpsilon;\n const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor));\n const double sum_right_gradient = sum_gradients - sum_left_gradient;\n const double sum_right_hessian = sum_hessians - sum_left_hessian - kEpsilon;\n const data_size_t right_count = num_data - left_count;\n const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, lambda_l2, path_smooth, left_count, parent_output);\n const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, lambda_l2, path_smooth, right_count, parent_output);\n cuda_best_split_info->left_sum_gradients = sum_left_gradient;\n cuda_best_split_info->left_sum_hessians = sum_left_hessian;\n cuda_best_split_info->left_count = left_count;\n cuda_best_split_info->right_sum_gradients = sum_right_gradient;\n cuda_best_split_info->right_sum_hessians = sum_right_hessian;\n cuda_best_split_info->right_count = right_count;\n cuda_best_split_info->left_value = left_output;\n cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, lambda_l2, left_output);\n cuda_best_split_info->right_value = right_output;\n cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, lambda_l2, right_output);\n }\n }\n}\n\ntemplate \n__device__ void FindBestSplitsForLeafKernelCategoricalInner_GlobalMemory(\n // input feature information\n const hist_t* feature_hist_ptr,\n // input task information\n const SplitFindTask* task,\n CUDARandom* cuda_random,\n // input config parameter values\n const double lambda_l1,\n const double lambda_l2,\n const double path_smooth,\n const data_size_t min_data_in_leaf,\n const double min_sum_hessian_in_leaf,\n const double min_gain_to_split,\n const double cat_smooth,\n const double cat_l2,\n const int max_cat_threshold,\n const int min_data_per_group,\n // input parent node information\n const double parent_gain,\n const double sum_gradients,\n const double sum_hessians,\n const data_size_t num_data,\n const double parent_output,\n // buffer\n hist_t* hist_grad_buffer_ptr,\n hist_t* hist_hess_buffer_ptr,\n hist_t* hist_stat_buffer_ptr,\n data_size_t* hist_index_buffer_ptr,\n // output parameters\n CUDASplitInfo* cuda_best_split_info) {\n __shared__ double shared_gain_buffer[WARPSIZE];\n __shared__ bool shared_found_buffer[WARPSIZE];\n __shared__ uint32_t shared_thread_index_buffer[WARPSIZE];\n __shared__ uint32_t best_thread_index;\n const double cnt_factor = num_data / sum_hessians;\n const double min_gain_shift = parent_gain + min_gain_to_split;\n double l2 = lambda_l2;\n\n double local_gain = kMinScore;\n bool threshold_found = false;\n\n cuda_best_split_info->is_valid = false;\n\n __shared__ int rand_threshold;\n\n const int bin_start = 1 - task->mfb_offset;\n const int bin_end = task->num_bin - task->mfb_offset;\n int best_threshold = -1;\n const int threadIdx_x = static_cast(threadIdx.x);\n if (task->is_one_hot) {\n if (USE_RAND && threadIdx.x == 0) {\n rand_threshold = 0;\n if (bin_end > bin_start) {\n rand_threshold = cuda_random->NextInt(bin_start, bin_end);\n }\n }\n __syncthreads();\n for (int bin = bin_start + threadIdx_x; bin < bin_end; bin += static_cast(blockDim.x)) {\n const int bin_offset = (bin << 1);\n const hist_t grad = feature_hist_ptr[bin_offset];\n const hist_t hess = feature_hist_ptr[bin_offset + 1];\n data_size_t cnt =\n static_cast(__double2int_rn(hess * cnt_factor));\n if (cnt >= min_data_in_leaf && hess >= min_sum_hessian_in_leaf) {\n const data_size_t other_count = num_data - cnt;\n if (other_count >= min_data_in_leaf) {\n const double sum_other_hessian = sum_hessians - hess - kEpsilon;\n if (sum_other_hessian >= min_sum_hessian_in_leaf && (!USE_RAND || bin == rand_threshold)) {\n const double sum_other_gradient = sum_gradients - grad;\n double current_gain = CUDALeafSplits::GetSplitGains(\n sum_other_gradient, sum_other_hessian, grad,\n hess + kEpsilon, lambda_l1,\n l2, path_smooth, other_count, cnt, parent_output);\n if (current_gain > min_gain_shift) {\n best_threshold = bin;\n local_gain = current_gain - min_gain_shift;\n threshold_found = true;\n }\n }\n }\n }\n }\n __syncthreads();\n const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer);\n if (threadIdx_x == 0) {\n best_thread_index = result;\n }\n __syncthreads();\n if (threshold_found && threadIdx_x == best_thread_index) {\n cuda_best_split_info->is_valid = true;\n cuda_best_split_info->num_cat_threshold = 1;\n cuda_best_split_info->cat_threshold = new uint32_t[1];\n *(cuda_best_split_info->cat_threshold) = static_cast(best_threshold);\n cuda_best_split_info->default_left = false;\n const int bin_offset = (best_threshold << 1);\n const hist_t sum_left_gradient = feature_hist_ptr[bin_offset];\n const hist_t sum_left_hessian = feature_hist_ptr[bin_offset + 1];\n const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor));\n const double sum_right_gradient = sum_gradients - sum_left_gradient;\n const double sum_right_hessian = sum_hessians - sum_left_hessian;\n const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor));\n const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, l2, path_smooth, left_count, parent_output);\n const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, l2, path_smooth, right_count, parent_output);\n cuda_best_split_info->left_sum_gradients = sum_left_gradient;\n cuda_best_split_info->left_sum_hessians = sum_left_hessian;\n cuda_best_split_info->left_count = left_count;\n cuda_best_split_info->right_sum_gradients = sum_right_gradient;\n cuda_best_split_info->right_sum_hessians = sum_right_hessian;\n cuda_best_split_info->right_count = right_count;\n cuda_best_split_info->left_value = left_output;\n cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, l2, left_output);\n cuda_best_split_info->right_value = right_output;\n cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, l2, right_output);\n }\n } else {\n __shared__ uint16_t shared_mem_buffer_uint16[WARPSIZE];\n __shared__ int used_bin;\n l2 += cat_l2;\n uint16_t is_valid_bin = 0;\n int best_dir = 0;\n double best_sum_left_gradient = 0.0f;\n double best_sum_left_hessian = 0.0f;\n for (int bin = 0; bin < bin_end; bin += static_cast(blockDim.x)) {\n if (bin >= bin_start) {\n const int bin_offset = (bin << 1);\n const double hess = feature_hist_ptr[bin_offset + 1];\n if (__double2int_rn(hess * cnt_factor) >= cat_smooth) {\n const double grad = feature_hist_ptr[bin_offset];\n hist_stat_buffer_ptr[bin] = grad / (hess + cat_smooth);\n hist_index_buffer_ptr[bin] = threadIdx_x;\n is_valid_bin = 1;\n } else {\n hist_stat_buffer_ptr[bin] = kMaxScore;\n hist_index_buffer_ptr[bin] = -1;\n }\n }\n }\n __syncthreads();\n const int local_used_bin = ShuffleReduceSum(is_valid_bin, shared_mem_buffer_uint16, blockDim.x);\n if (threadIdx_x == 0) {\n used_bin = local_used_bin;\n }\n __syncthreads();\n BitonicArgSortDevice(\n hist_stat_buffer_ptr, hist_index_buffer_ptr, task->num_bin - task->mfb_offset);\n const int max_num_cat = min(max_cat_threshold, (used_bin + 1) / 2);\n if (USE_RAND) {\n rand_threshold = 0;\n const int max_threshold = max(min(max_num_cat, used_bin) - 1, 0);\n if (max_threshold > 0) {\n rand_threshold = cuda_random->NextInt(0, max_threshold);\n }\n }\n __syncthreads();\n\n // left to right\n for (int bin = static_cast(threadIdx_x); bin < used_bin && bin < max_num_cat; bin += static_cast(blockDim.x)) {\n const int bin_offset = (hist_index_buffer_ptr[bin] << 1);\n hist_grad_buffer_ptr[bin] = feature_hist_ptr[bin_offset];\n hist_hess_buffer_ptr[bin] = feature_hist_ptr[bin_offset + 1];\n }\n if (threadIdx_x == 0) {\n hist_hess_buffer_ptr[0] += kEpsilon;\n }\n __syncthreads();\n GlobalMemoryPrefixSum(hist_grad_buffer_ptr, static_cast(bin_end));\n __syncthreads();\n GlobalMemoryPrefixSum(hist_hess_buffer_ptr, static_cast(bin_end));\n for (int bin = static_cast(threadIdx_x); bin < used_bin && bin < max_num_cat; bin += static_cast(blockDim.x)) {\n const double sum_left_gradient = hist_grad_buffer_ptr[bin];\n const double sum_left_hessian = hist_hess_buffer_ptr[bin];\n const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor));\n const double sum_right_gradient = sum_gradients - sum_left_gradient;\n const double sum_right_hessian = sum_hessians - sum_left_hessian;\n const data_size_t right_count = num_data - left_count;\n if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf &&\n sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf) {\n double current_gain = CUDALeafSplits::GetSplitGains(\n sum_left_gradient, sum_left_hessian, sum_right_gradient,\n sum_right_hessian, lambda_l1,\n l2, path_smooth, left_count, right_count, parent_output);\n // gain with split is worse than without split\n if (current_gain > min_gain_shift) {\n local_gain = current_gain - min_gain_shift;\n threshold_found = true;\n best_dir = 1;\n best_sum_left_gradient = sum_left_gradient;\n best_sum_left_hessian = sum_left_hessian;\n best_threshold = bin;\n }\n }\n }\n __syncthreads();\n\n // right to left\n for (int bin = static_cast(threadIdx_x); bin < used_bin && bin < max_num_cat; bin += static_cast(blockDim.x)) {\n const int bin_offset = (hist_index_buffer_ptr[used_bin - 1 - bin] << 1);\n hist_grad_buffer_ptr[bin] = feature_hist_ptr[bin_offset];\n hist_hess_buffer_ptr[bin] = feature_hist_ptr[bin_offset + 1];\n }\n if (threadIdx_x == 0) {\n hist_hess_buffer_ptr[0] += kEpsilon;\n }\n __syncthreads();\n GlobalMemoryPrefixSum(hist_grad_buffer_ptr, static_cast(bin_end));\n __syncthreads();\n GlobalMemoryPrefixSum(hist_hess_buffer_ptr, static_cast(bin_end));\n for (int bin = static_cast(threadIdx_x); bin < used_bin && bin < max_num_cat; bin += static_cast(blockDim.x)) {\n const double sum_left_gradient = hist_grad_buffer_ptr[bin];\n const double sum_left_hessian = hist_hess_buffer_ptr[bin];\n const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor));\n const double sum_right_gradient = sum_gradients - sum_left_gradient;\n const double sum_right_hessian = sum_hessians - sum_left_hessian;\n const data_size_t right_count = num_data - left_count;\n if (sum_left_hessian >= min_sum_hessian_in_leaf && left_count >= min_data_in_leaf &&\n sum_right_hessian >= min_sum_hessian_in_leaf && right_count >= min_data_in_leaf) {\n double current_gain = CUDALeafSplits::GetSplitGains(\n sum_left_gradient, sum_left_hessian, sum_right_gradient,\n sum_right_hessian, lambda_l1,\n l2, path_smooth, left_count, right_count, parent_output);\n // gain with split is worse than without split\n if (current_gain > min_gain_shift) {\n local_gain = current_gain - min_gain_shift;\n threshold_found = true;\n best_dir = -1;\n best_sum_left_gradient = sum_left_gradient;\n best_sum_left_hessian = sum_left_hessian;\n best_threshold = bin;\n }\n }\n }\n __syncthreads();\n\n const uint32_t result = ReduceBestGain(local_gain, threshold_found, threadIdx_x, shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer);\n if (threadIdx_x == 0) {\n best_thread_index = result;\n }\n __syncthreads();\n if (threshold_found && threadIdx_x == best_thread_index) {\n cuda_best_split_info->is_valid = true;\n cuda_best_split_info->num_cat_threshold = best_threshold + 1;\n cuda_best_split_info->cat_threshold = new uint32_t[best_threshold + 1];\n cuda_best_split_info->gain = local_gain;\n if (best_dir == 1) {\n for (int i = 0; i < best_threshold + 1; ++i) {\n (cuda_best_split_info->cat_threshold)[i] = hist_index_buffer_ptr[i] + task->mfb_offset;\n }\n } else {\n for (int i = 0; i < best_threshold + 1; ++i) {\n (cuda_best_split_info->cat_threshold)[i] = hist_index_buffer_ptr[used_bin - 1 - i] + task->mfb_offset;\n }\n }\n cuda_best_split_info->default_left = false;\n const hist_t sum_left_gradient = best_sum_left_gradient;\n const hist_t sum_left_hessian = best_sum_left_hessian;\n const data_size_t left_count = static_cast(__double2int_rn(sum_left_hessian * cnt_factor));\n const double sum_right_gradient = sum_gradients - sum_left_gradient;\n const double sum_right_hessian = sum_hessians - sum_left_hessian;\n const data_size_t right_count = static_cast(__double2int_rn(sum_right_hessian * cnt_factor));\n const double left_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, l2, path_smooth, left_count, parent_output);\n const double right_output = CUDALeafSplits::CalculateSplittedLeafOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, l2, path_smooth, right_count, parent_output);\n cuda_best_split_info->left_sum_gradients = sum_left_gradient;\n cuda_best_split_info->left_sum_hessians = sum_left_hessian;\n cuda_best_split_info->left_count = left_count;\n cuda_best_split_info->right_sum_gradients = sum_right_gradient;\n cuda_best_split_info->right_sum_hessians = sum_right_hessian;\n cuda_best_split_info->right_count = right_count;\n cuda_best_split_info->left_value = left_output;\n cuda_best_split_info->left_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_left_gradient,\n sum_left_hessian, lambda_l1, l2, left_output);\n cuda_best_split_info->right_value = right_output;\n cuda_best_split_info->right_gain = CUDALeafSplits::GetLeafGainGivenOutput(sum_right_gradient,\n sum_right_hessian, lambda_l1, l2, right_output);\n }\n }\n}\n\ntemplate \n__global__ void FindBestSplitsForLeafKernel_GlobalMemory(\n // input feature information\n const int8_t* is_feature_used_bytree,\n // input task information\n const int num_tasks,\n const SplitFindTask* tasks,\n CUDARandom* cuda_randoms,\n // input leaf information\n const CUDALeafSplitsStruct* smaller_leaf_splits,\n const CUDALeafSplitsStruct* larger_leaf_splits,\n // input config parameter values\n const data_size_t min_data_in_leaf,\n const double min_sum_hessian_in_leaf,\n const double min_gain_to_split,\n const double lambda_l1,\n const double lambda_l2,\n const double path_smooth,\n const double cat_smooth,\n const double cat_l2,\n const int max_cat_threshold,\n const int min_data_per_group,\n // output\n CUDASplitInfo* cuda_best_split_info,\n // global num data in leaf\n const data_size_t global_num_data_in_smaller_leaf,\n const data_size_t global_num_data_in_larger_leaf,\n // buffer\n hist_t* feature_hist_grad_buffer,\n hist_t* feature_hist_hess_buffer,\n hist_t* feature_hist_stat_buffer,\n data_size_t* feature_hist_index_buffer) {\n const unsigned int task_index = blockIdx.x;\n const SplitFindTask* task = tasks + task_index;\n const double parent_gain = IS_LARGER ? larger_leaf_splits->gain : smaller_leaf_splits->gain;\n const double sum_gradients = IS_LARGER ? larger_leaf_splits->sum_of_gradients : smaller_leaf_splits->sum_of_gradients;\n const double sum_hessians = (IS_LARGER ? larger_leaf_splits->sum_of_hessians : smaller_leaf_splits->sum_of_hessians) + 2 * kEpsilon;\n const data_size_t num_data = IS_LARGER ? global_num_data_in_larger_leaf : global_num_data_in_smaller_leaf;\n const double parent_output = IS_LARGER ? larger_leaf_splits->leaf_value : smaller_leaf_splits->leaf_value;\n const unsigned int output_offset = IS_LARGER ? (task_index + num_tasks) : task_index;\n CUDASplitInfo* out = cuda_best_split_info + output_offset;\n CUDARandom* cuda_random = USE_RAND ?\n (IS_LARGER ? cuda_randoms + task_index * 2 + 1: cuda_randoms + task_index * 2) : nullptr;\n if (is_feature_used_bytree[task->inner_feature_index]) {\n const uint32_t hist_offset = task->hist_offset;\n const hist_t* hist_ptr = (IS_LARGER ? larger_leaf_splits->hist_in_leaf : smaller_leaf_splits->hist_in_leaf) + hist_offset * 2;\n hist_t* hist_grad_buffer_ptr = feature_hist_grad_buffer + hist_offset * 2;\n hist_t* hist_hess_buffer_ptr = feature_hist_hess_buffer + hist_offset * 2;\n hist_t* hist_stat_buffer_ptr = feature_hist_stat_buffer + hist_offset * 2;\n data_size_t* hist_index_buffer_ptr = feature_hist_index_buffer + hist_offset * 2;\n if (task->is_categorical) {\n FindBestSplitsForLeafKernelCategoricalInner_GlobalMemory(\n // input feature information\n hist_ptr,\n // input task information\n task,\n cuda_random,\n // input config parameter values\n lambda_l1,\n lambda_l2,\n path_smooth,\n min_data_in_leaf,\n min_sum_hessian_in_leaf,\n min_gain_to_split,\n cat_smooth,\n cat_l2,\n max_cat_threshold,\n min_data_per_group,\n // input parent node information\n parent_gain,\n sum_gradients,\n sum_hessians,\n num_data,\n parent_output,\n // buffer\n hist_grad_buffer_ptr,\n hist_hess_buffer_ptr,\n hist_stat_buffer_ptr,\n hist_index_buffer_ptr,\n // output parameters\n out);\n } else {\n if (!task->reverse) {\n FindBestSplitsForLeafKernelInner_GlobalMemory(\n // input feature information\n hist_ptr,\n // input task information\n task,\n cuda_random,\n // input config parameter values\n lambda_l1,\n lambda_l2,\n path_smooth,\n min_data_in_leaf,\n min_sum_hessian_in_leaf,\n min_gain_to_split,\n // input parent node information\n parent_gain,\n sum_gradients,\n sum_hessians,\n num_data,\n parent_output,\n // output parameters\n out,\n // buffer\n hist_grad_buffer_ptr,\n hist_hess_buffer_ptr);\n } else {\n FindBestSplitsForLeafKernelInner_GlobalMemory(\n // input feature information\n hist_ptr,\n // input task information\n task,\n cuda_random,\n // input config parameter values\n lambda_l1,\n lambda_l2,\n path_smooth,\n min_data_in_leaf,\n min_sum_hessian_in_leaf,\n min_gain_to_split,\n // input parent node information\n parent_gain,\n sum_gradients,\n sum_hessians,\n num_data,\n parent_output,\n // output parameters\n out,\n // buffer\n hist_grad_buffer_ptr,\n hist_hess_buffer_ptr);\n }\n }\n } else {\n out->is_valid = false;\n }\n}\n\n#define LaunchFindBestSplitsForLeafKernel_PARAMS \\\n const CUDALeafSplitsStruct* smaller_leaf_splits, \\\n const CUDALeafSplitsStruct* larger_leaf_splits, \\\n const int smaller_leaf_index, \\\n const int larger_leaf_index, \\\n const bool is_smaller_leaf_valid, \\\n const bool is_larger_leaf_valid, \\\n const data_size_t global_num_data_in_smaller_leaf, \\\n const data_size_t global_num_data_in_larger_leaf\n\n#define LaunchFindBestSplitsForLeafKernel_ARGS \\\n smaller_leaf_splits, \\\n larger_leaf_splits, \\\n smaller_leaf_index, \\\n larger_leaf_index, \\\n is_smaller_leaf_valid, \\\n is_larger_leaf_valid, \\\n global_num_data_in_smaller_leaf, \\\n global_num_data_in_larger_leaf\n\n#define FindBestSplitsForLeafKernel_ARGS \\\n num_tasks_, \\\n cuda_split_find_tasks_.RawData(), \\\n cuda_randoms_.RawData(), \\\n smaller_leaf_splits, \\\n larger_leaf_splits, \\\n min_data_in_leaf_, \\\n min_sum_hessian_in_leaf_, \\\n min_gain_to_split_, \\\n lambda_l1_, \\\n lambda_l2_, \\\n path_smooth_, \\\n cat_smooth_, \\\n cat_l2_, \\\n max_cat_threshold_, \\\n min_data_per_group_, \\\n cuda_best_split_info_.RawData(), \\\n global_num_data_in_smaller_leaf, \\\n global_num_data_in_larger_leaf\n\n#define GlobalMemory_Buffer_ARGS \\\n cuda_feature_hist_grad_buffer_.RawData(), \\\n cuda_feature_hist_hess_buffer_.RawData(), \\\n cuda_feature_hist_stat_buffer_.RawData(), \\\n cuda_feature_hist_index_buffer_.RawData()\n\nvoid CUDABestSplitFinder::LaunchFindBestSplitsForLeafKernel(LaunchFindBestSplitsForLeafKernel_PARAMS) {\n if (!is_smaller_leaf_valid && !is_larger_leaf_valid) {\n return;\n }\n if (!extra_trees_) {\n LaunchFindBestSplitsForLeafKernelInner0(LaunchFindBestSplitsForLeafKernel_ARGS);\n } else {\n LaunchFindBestSplitsForLeafKernelInner0(LaunchFindBestSplitsForLeafKernel_ARGS);\n }\n}\n\ntemplate \nvoid CUDABestSplitFinder::LaunchFindBestSplitsForLeafKernelInner0(LaunchFindBestSplitsForLeafKernel_PARAMS) {\n if (lambda_l1_ <= 0.0f) {\n LaunchFindBestSplitsForLeafKernelInner1(LaunchFindBestSplitsForLeafKernel_ARGS);\n } else {\n LaunchFindBestSplitsForLeafKernelInner1(LaunchFindBestSplitsForLeafKernel_ARGS);\n }\n}\n\ntemplate \nvoid CUDABestSplitFinder::LaunchFindBestSplitsForLeafKernelInner1(LaunchFindBestSplitsForLeafKernel_PARAMS) {\n if (!use_smoothing_) {\n LaunchFindBestSplitsForLeafKernelInner2(LaunchFindBestSplitsForLeafKernel_ARGS);\n } else {\n LaunchFindBestSplitsForLeafKernelInner2(LaunchFindBestSplitsForLeafKernel_ARGS);\n }\n}\n\ntemplate \nvoid CUDABestSplitFinder::LaunchFindBestSplitsForLeafKernelInner2(LaunchFindBestSplitsForLeafKernel_PARAMS) {\n const int8_t* is_feature_used_by_smaller_node = cuda_is_feature_used_bytree_.RawData();\n const int8_t* is_feature_used_by_larger_node = cuda_is_feature_used_bytree_.RawData();\n if (select_features_by_node_) {\n is_feature_used_by_smaller_node = is_feature_used_by_smaller_node_.RawData();\n is_feature_used_by_larger_node = is_feature_used_by_larger_node_.RawData();\n }\n if (!use_global_memory_) {\n if (is_smaller_leaf_valid) {\n FindBestSplitsForLeafKernel\n <<>>\n (is_feature_used_by_smaller_node, FindBestSplitsForLeafKernel_ARGS);\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n if (is_larger_leaf_valid) {\n FindBestSplitsForLeafKernel\n <<>>\n (is_feature_used_by_larger_node, FindBestSplitsForLeafKernel_ARGS);\n }\n } else {\n if (is_smaller_leaf_valid) {\n FindBestSplitsForLeafKernel_GlobalMemory\n <<>>\n (is_feature_used_by_smaller_node, FindBestSplitsForLeafKernel_ARGS, GlobalMemory_Buffer_ARGS);\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n if (is_larger_leaf_valid) {\n FindBestSplitsForLeafKernel_GlobalMemory\n <<>>\n (is_feature_used_by_larger_node, FindBestSplitsForLeafKernel_ARGS, GlobalMemory_Buffer_ARGS);\n }\n }\n}\n\n#undef LaunchFindBestSplitsForLeafKernel_PARAMS\n#undef FindBestSplitsForLeafKernel_ARGS\n#undef GlobalMemory_Buffer_ARGS\n\n\n#define LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS \\\n const CUDALeafSplitsStruct* smaller_leaf_splits, \\\n const CUDALeafSplitsStruct* larger_leaf_splits, \\\n const int smaller_leaf_index, \\\n const int larger_leaf_index, \\\n const bool is_smaller_leaf_valid, \\\n const bool is_larger_leaf_valid, \\\n const score_t* grad_scale, \\\n const score_t* hess_scale, \\\n const uint8_t smaller_num_bits_in_histogram_bins, \\\n const uint8_t larger_num_bits_in_histogram_bins, \\\n const data_size_t global_num_data_in_smaller_leaf, \\\n const data_size_t global_num_data_in_larger_leaf\n\n#define LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS \\\n smaller_leaf_splits, \\\n larger_leaf_splits, \\\n smaller_leaf_index, \\\n larger_leaf_index, \\\n is_smaller_leaf_valid, \\\n is_larger_leaf_valid, \\\n grad_scale, \\\n hess_scale, \\\n smaller_num_bits_in_histogram_bins, \\\n larger_num_bits_in_histogram_bins, \\\n global_num_data_in_smaller_leaf, \\\n global_num_data_in_larger_leaf\n\n#define FindBestSplitsDiscretizedForLeafKernel_ARGS \\\n cuda_is_feature_used_bytree_.RawData(), \\\n num_tasks_, \\\n cuda_split_find_tasks_.RawData(), \\\n cuda_randoms_.RawData(), \\\n smaller_leaf_splits, \\\n larger_leaf_splits, \\\n smaller_num_bits_in_histogram_bins, \\\n larger_num_bits_in_histogram_bins, \\\n min_data_in_leaf_, \\\n min_sum_hessian_in_leaf_, \\\n min_gain_to_split_, \\\n lambda_l1_, \\\n lambda_l2_, \\\n path_smooth_, \\\n cat_smooth_, \\\n cat_l2_, \\\n max_cat_threshold_, \\\n min_data_per_group_, \\\n max_cat_to_onehot_, \\\n grad_scale, \\\n hess_scale, \\\n cuda_best_split_info_.RawData(), \\\n global_num_data_in_smaller_leaf, \\\n global_num_data_in_larger_leaf\n\nvoid CUDABestSplitFinder::LaunchFindBestSplitsDiscretizedForLeafKernel(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS) {\n if (!is_smaller_leaf_valid && !is_larger_leaf_valid) {\n return;\n }\n if (!extra_trees_) {\n LaunchFindBestSplitsDiscretizedForLeafKernelInner0(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS);\n } else {\n LaunchFindBestSplitsDiscretizedForLeafKernelInner0(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS);\n }\n}\n\ntemplate \nvoid CUDABestSplitFinder::LaunchFindBestSplitsDiscretizedForLeafKernelInner0(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS) {\n if (lambda_l1_ <= 0.0f) {\n LaunchFindBestSplitsDiscretizedForLeafKernelInner1(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS);\n } else {\n LaunchFindBestSplitsDiscretizedForLeafKernelInner1(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS);\n }\n}\n\ntemplate \nvoid CUDABestSplitFinder::LaunchFindBestSplitsDiscretizedForLeafKernelInner1(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS) {\n if (!use_smoothing_) {\n LaunchFindBestSplitsDiscretizedForLeafKernelInner2(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS);\n } else {\n LaunchFindBestSplitsDiscretizedForLeafKernelInner2(LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS);\n }\n}\n\ntemplate \nvoid CUDABestSplitFinder::LaunchFindBestSplitsDiscretizedForLeafKernelInner2(LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS) {\n if (!use_global_memory_) {\n if (is_smaller_leaf_valid) {\n FindBestSplitsDiscretizedForLeafKernel\n <<>>\n (FindBestSplitsDiscretizedForLeafKernel_ARGS);\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n if (is_larger_leaf_valid) {\n FindBestSplitsDiscretizedForLeafKernel\n <<>>\n (FindBestSplitsDiscretizedForLeafKernel_ARGS);\n }\n } else {\n // TODO(shiyu1994)\n }\n}\n\n#undef LaunchFindBestSplitsDiscretizedForLeafKernel_PARAMS\n#undef LaunchFindBestSplitsDiscretizedForLeafKernel_ARGS\n#undef FindBestSplitsDiscretizedForLeafKernel_ARGS\n\n\n__device__ void ReduceBestSplit(bool* found, double* gain, uint32_t* shared_read_index,\n uint32_t num_features_aligned) {\n const uint32_t threadIdx_x = threadIdx.x;\n for (unsigned int s = 1; s < num_features_aligned; s <<= 1) {\n if (threadIdx_x % (2 * s) == 0 && (threadIdx_x + s) < num_features_aligned) {\n const uint32_t pos_to_compare = threadIdx_x + s;\n if ((!found[threadIdx_x] && found[pos_to_compare]) ||\n (found[threadIdx_x] && found[pos_to_compare] && gain[threadIdx_x] < gain[pos_to_compare])) {\n found[threadIdx_x] = found[pos_to_compare];\n gain[threadIdx_x] = gain[pos_to_compare];\n shared_read_index[threadIdx_x] = shared_read_index[pos_to_compare];\n }\n }\n __syncthreads();\n }\n}\n\n__global__ void SyncBestSplitForLeafKernel(const int smaller_leaf_index, const int larger_leaf_index,\n CUDASplitInfo* cuda_leaf_best_split_info,\n // input parameters\n const SplitFindTask* tasks,\n const CUDASplitInfo* cuda_best_split_info,\n const int num_tasks,\n const int num_tasks_aligned,\n const int num_blocks_per_leaf,\n const bool larger_only,\n const int num_leaves) {\n __shared__ double shared_gain_buffer[WARPSIZE];\n __shared__ bool shared_found_buffer[WARPSIZE];\n __shared__ uint32_t shared_thread_index_buffer[WARPSIZE];\n const uint32_t threadIdx_x = threadIdx.x;\n const uint32_t blockIdx_x = blockIdx.x;\n\n bool best_found = false;\n double best_gain = kMinScore;\n uint32_t shared_read_index = 0;\n\n const bool is_smaller = (blockIdx_x < static_cast(num_blocks_per_leaf) && !larger_only);\n const uint32_t leaf_block_index = (is_smaller || larger_only) ? blockIdx_x : (blockIdx_x - static_cast(num_blocks_per_leaf));\n const int task_index = static_cast(leaf_block_index * blockDim.x + threadIdx_x);\n const uint32_t read_index = is_smaller ? static_cast(task_index) : static_cast(task_index + num_tasks);\n if (task_index < num_tasks) {\n best_found = cuda_best_split_info[read_index].is_valid;\n best_gain = cuda_best_split_info[read_index].gain;\n shared_read_index = read_index;\n } else {\n best_found = false;\n }\n\n __syncthreads();\n const uint32_t best_read_index = ReduceBestGain(best_gain, best_found, shared_read_index,\n shared_gain_buffer, shared_found_buffer, shared_thread_index_buffer);\n if (threadIdx.x == 0) {\n const int leaf_index_ref = is_smaller ? smaller_leaf_index : larger_leaf_index;\n const unsigned buffer_write_pos = static_cast(leaf_index_ref) + leaf_block_index * num_leaves;\n CUDASplitInfo* cuda_split_info = cuda_leaf_best_split_info + buffer_write_pos;\n const CUDASplitInfo* best_split_info = cuda_best_split_info + best_read_index;\n if (best_split_info->is_valid) {\n *cuda_split_info = *best_split_info;\n cuda_split_info->inner_feature_index = is_smaller ? tasks[best_read_index].inner_feature_index :\n tasks[static_cast(best_read_index) - num_tasks].inner_feature_index;\n cuda_split_info->is_valid = true;\n } else {\n cuda_split_info->gain = kMinScore;\n cuda_split_info->is_valid = false;\n }\n }\n}\n\n__global__ void SyncBestSplitForLeafKernelAllBlocks(\n const int smaller_leaf_index,\n const int larger_leaf_index,\n const unsigned int num_blocks_per_leaf,\n const int num_leaves,\n CUDASplitInfo* cuda_leaf_best_split_info,\n const bool larger_only) {\n if (!larger_only) {\n if (blockIdx.x == 0) {\n CUDASplitInfo* smaller_leaf_split_info = cuda_leaf_best_split_info + smaller_leaf_index;\n for (unsigned int block_index = 1; block_index < num_blocks_per_leaf; ++block_index) {\n const unsigned int leaf_read_pos = static_cast(smaller_leaf_index) + block_index * static_cast(num_leaves);\n const CUDASplitInfo* other_split_info = cuda_leaf_best_split_info + leaf_read_pos;\n if ((other_split_info->is_valid && smaller_leaf_split_info->is_valid &&\n other_split_info->gain > smaller_leaf_split_info->gain) ||\n (!smaller_leaf_split_info->is_valid && other_split_info->is_valid)) {\n *smaller_leaf_split_info = *other_split_info;\n }\n }\n }\n }\n if (larger_leaf_index >= 0) {\n if (blockIdx.x == 1 || larger_only) {\n CUDASplitInfo* larger_leaf_split_info = cuda_leaf_best_split_info + larger_leaf_index;\n for (unsigned int block_index = 1; block_index < num_blocks_per_leaf; ++block_index) {\n const unsigned int leaf_read_pos = static_cast(larger_leaf_index) + block_index * static_cast(num_leaves);\n const CUDASplitInfo* other_split_info = cuda_leaf_best_split_info + leaf_read_pos;\n if ((other_split_info->is_valid && larger_leaf_split_info->is_valid &&\n other_split_info->gain > larger_leaf_split_info->gain) ||\n (!larger_leaf_split_info->is_valid && other_split_info->is_valid)) {\n *larger_leaf_split_info = *other_split_info;\n }\n }\n }\n }\n}\n\n__global__ void SetInvalidLeafSplitInfoKernel(\n CUDASplitInfo* cuda_leaf_best_split_info,\n const bool is_smaller_leaf_valid,\n const bool is_larger_leaf_valid,\n const int smaller_leaf_index,\n const int larger_leaf_index) {\n if (!is_smaller_leaf_valid) {\n cuda_leaf_best_split_info[smaller_leaf_index].is_valid = false;\n }\n if (!is_larger_leaf_valid && larger_leaf_index >= 0) {\n cuda_leaf_best_split_info[larger_leaf_index].is_valid = false;\n }\n}\n\nvoid CUDABestSplitFinder::LaunchSyncBestSplitForLeafKernel(\n const int host_smaller_leaf_index,\n const int host_larger_leaf_index,\n const bool is_smaller_leaf_valid,\n const bool is_larger_leaf_valid) {\n if (!is_smaller_leaf_valid || !is_larger_leaf_valid) {\n SetInvalidLeafSplitInfoKernel<<<1, 1>>>(\n cuda_leaf_best_split_info_.RawData(),\n is_smaller_leaf_valid, is_larger_leaf_valid,\n host_smaller_leaf_index, host_larger_leaf_index);\n }\n if (!is_smaller_leaf_valid && !is_larger_leaf_valid) {\n return;\n }\n int num_tasks = num_tasks_;\n int num_tasks_aligned = 1;\n num_tasks -= 1;\n while (num_tasks > 0) {\n num_tasks_aligned <<= 1;\n num_tasks >>= 1;\n }\n const int num_blocks_per_leaf = (num_tasks_ + NUM_TASKS_PER_SYNC_BLOCK - 1) / NUM_TASKS_PER_SYNC_BLOCK;\n if (host_larger_leaf_index >= 0 && is_smaller_leaf_valid && is_larger_leaf_valid) {\n SyncBestSplitForLeafKernel<<>>(\n host_smaller_leaf_index,\n host_larger_leaf_index,\n cuda_leaf_best_split_info_.RawData(),\n cuda_split_find_tasks_.RawData(),\n cuda_best_split_info_.RawData(),\n num_tasks_,\n num_tasks_aligned,\n num_blocks_per_leaf,\n false,\n num_leaves_);\n if (num_blocks_per_leaf > 1) {\n SyncBestSplitForLeafKernelAllBlocks<<<1, 1, 0, cuda_streams_[0]>>>(\n host_smaller_leaf_index,\n host_larger_leaf_index,\n num_blocks_per_leaf,\n num_leaves_,\n cuda_leaf_best_split_info_.RawData(),\n false);\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n SyncBestSplitForLeafKernel<<>>(\n host_smaller_leaf_index,\n host_larger_leaf_index,\n cuda_leaf_best_split_info_.RawData(),\n cuda_split_find_tasks_.RawData(),\n cuda_best_split_info_.RawData(),\n num_tasks_,\n num_tasks_aligned,\n num_blocks_per_leaf,\n true,\n num_leaves_);\n if (num_blocks_per_leaf > 1) {\n SyncBestSplitForLeafKernelAllBlocks<<<1, 1, 0, cuda_streams_[1]>>>(\n host_smaller_leaf_index,\n host_larger_leaf_index,\n num_blocks_per_leaf,\n num_leaves_,\n cuda_leaf_best_split_info_.RawData(),\n true);\n }\n } else {\n const bool larger_only = (!is_smaller_leaf_valid && is_larger_leaf_valid);\n SyncBestSplitForLeafKernel<<>>(\n host_smaller_leaf_index,\n host_larger_leaf_index,\n cuda_leaf_best_split_info_.RawData(),\n cuda_split_find_tasks_.RawData(),\n cuda_best_split_info_.RawData(),\n num_tasks_,\n num_tasks_aligned,\n num_blocks_per_leaf,\n larger_only,\n num_leaves_);\n if (num_blocks_per_leaf > 1) {\n SynchronizeCUDADevice(__FILE__, __LINE__);\n SyncBestSplitForLeafKernelAllBlocks<<<1, 1>>>(\n host_smaller_leaf_index,\n host_larger_leaf_index,\n num_blocks_per_leaf,\n num_leaves_,\n cuda_leaf_best_split_info_.RawData(),\n larger_only);\n }\n }\n}\n\n__global__ void FindBestFromAllSplitsKernel(const int cur_num_leaves,\n CUDASplitInfo* cuda_leaf_best_split_info,\n int* cuda_best_split_info_buffer) {\n __shared__ double gain_shared_buffer[WARPSIZE];\n __shared__ int leaf_index_shared_buffer[WARPSIZE];\n double thread_best_gain = kMinScore;\n int thread_best_leaf_index = -1;\n const int threadIdx_x = static_cast(threadIdx.x);\n for (int leaf_index = threadIdx_x; leaf_index < cur_num_leaves; leaf_index += static_cast(blockDim.x)) {\n const double leaf_best_gain = cuda_leaf_best_split_info[leaf_index].gain;\n if (cuda_leaf_best_split_info[leaf_index].is_valid && leaf_best_gain > thread_best_gain) {\n thread_best_gain = leaf_best_gain;\n thread_best_leaf_index = leaf_index;\n }\n }\n const int best_leaf_index = ReduceBestGainForLeaves(thread_best_gain, thread_best_leaf_index, gain_shared_buffer, leaf_index_shared_buffer);\n if (threadIdx_x == 0) {\n cuda_best_split_info_buffer[6] = best_leaf_index;\n if (best_leaf_index != -1) {\n cuda_leaf_best_split_info[best_leaf_index].is_valid = false;\n cuda_leaf_best_split_info[cur_num_leaves].is_valid = false;\n cuda_best_split_info_buffer[7] = cuda_leaf_best_split_info[best_leaf_index].num_cat_threshold;\n }\n }\n}\n\n__global__ void PrepareLeafBestSplitInfo(const int smaller_leaf_index, const int larger_leaf_index,\n int* cuda_best_split_info_buffer,\n const CUDASplitInfo* cuda_leaf_best_split_info) {\n const unsigned int threadIdx_x = blockIdx.x;\n if (threadIdx_x == 0) {\n cuda_best_split_info_buffer[0] = cuda_leaf_best_split_info[smaller_leaf_index].inner_feature_index;\n } else if (threadIdx_x == 1) {\n cuda_best_split_info_buffer[1] = cuda_leaf_best_split_info[smaller_leaf_index].threshold;\n } else if (threadIdx_x == 2) {\n cuda_best_split_info_buffer[2] = cuda_leaf_best_split_info[smaller_leaf_index].default_left;\n }\n if (larger_leaf_index >= 0) {\n if (threadIdx_x == 3) {\n cuda_best_split_info_buffer[3] = cuda_leaf_best_split_info[larger_leaf_index].inner_feature_index;\n } else if (threadIdx_x == 4) {\n cuda_best_split_info_buffer[4] = cuda_leaf_best_split_info[larger_leaf_index].threshold;\n } else if (threadIdx_x == 5) {\n cuda_best_split_info_buffer[5] = cuda_leaf_best_split_info[larger_leaf_index].default_left;\n }\n }\n}\n\nvoid CUDABestSplitFinder::LaunchFindBestFromAllSplitsKernel(\n const int cur_num_leaves,\n const int smaller_leaf_index, const int larger_leaf_index,\n int* smaller_leaf_best_split_feature,\n uint32_t* smaller_leaf_best_split_threshold,\n uint8_t* smaller_leaf_best_split_default_left,\n int* larger_leaf_best_split_feature,\n uint32_t* larger_leaf_best_split_threshold,\n uint8_t* larger_leaf_best_split_default_left,\n int* best_leaf_index,\n int* num_cat_threshold) {\n FindBestFromAllSplitsKernel<<<1, NUM_THREADS_FIND_BEST_LEAF, 0, cuda_streams_[1]>>>(cur_num_leaves,\n cuda_leaf_best_split_info_.RawData(),\n cuda_best_split_info_buffer_.RawData());\n PrepareLeafBestSplitInfo<<<6, 1, 0, cuda_streams_[0]>>>(smaller_leaf_index, larger_leaf_index,\n cuda_best_split_info_buffer_.RawData(),\n cuda_leaf_best_split_info_.RawData());\n std::vector host_leaf_best_split_info_buffer(8, 0);\n SynchronizeCUDADevice(__FILE__, __LINE__);\n CopyFromCUDADeviceToHost(host_leaf_best_split_info_buffer.data(), cuda_best_split_info_buffer_.RawData(), 8, __FILE__, __LINE__);\n *smaller_leaf_best_split_feature = host_leaf_best_split_info_buffer[0];\n *smaller_leaf_best_split_threshold = static_cast(host_leaf_best_split_info_buffer[1]);\n *smaller_leaf_best_split_default_left = static_cast(host_leaf_best_split_info_buffer[2]);\n if (larger_leaf_index >= 0) {\n *larger_leaf_best_split_feature = host_leaf_best_split_info_buffer[3];\n *larger_leaf_best_split_threshold = static_cast(host_leaf_best_split_info_buffer[4]);\n *larger_leaf_best_split_default_left = static_cast(host_leaf_best_split_info_buffer[5]);\n }\n *best_leaf_index = host_leaf_best_split_info_buffer[6];\n *num_cat_threshold = host_leaf_best_split_info_buffer[7];\n}\n\n__global__ void AllocateCatVectorsKernel(\n CUDASplitInfo* cuda_split_infos, size_t len,\n const int max_num_categories_in_split,\n const bool has_categorical_feature,\n uint32_t* cat_threshold_vec,\n int* cat_threshold_real_vec) {\n const size_t i = threadIdx.x + blockIdx.x * blockDim.x;\n if (i < len) {\n if (has_categorical_feature) {\n cuda_split_infos[i].cat_threshold = cat_threshold_vec + i * max_num_categories_in_split;\n cuda_split_infos[i].cat_threshold_real = cat_threshold_real_vec + i * max_num_categories_in_split;\n cuda_split_infos[i].num_cat_threshold = 0;\n } else {\n cuda_split_infos[i].cat_threshold = nullptr;\n cuda_split_infos[i].cat_threshold_real = nullptr;\n cuda_split_infos[i].num_cat_threshold = 0;\n }\n }\n}\n\nvoid CUDABestSplitFinder::LaunchAllocateCatVectorsKernel(\n CUDASplitInfo* cuda_split_infos, uint32_t* cat_threshold_vec, int* cat_threshold_real_vec, size_t len) {\n const int num_blocks = (static_cast(len) + NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER - 1) / NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER;\n AllocateCatVectorsKernel<<>>(\n cuda_split_infos, len, max_num_categories_in_split_, has_categorical_feature_, cat_threshold_vec, cat_threshold_real_vec);\n}\n\n__global__ void InitCUDARandomKernel(\n const int seed,\n const int num_tasks,\n CUDARandom* cuda_randoms) {\n const int task_index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n if (task_index < num_tasks) {\n cuda_randoms[task_index].SetSeed(seed + task_index);\n }\n}\n\nvoid CUDABestSplitFinder::LaunchInitCUDARandomKernel() {\n const int num_blocks = (static_cast(cuda_randoms_.Size()) +\n NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER - 1) / NUM_THREADS_PER_BLOCK_BEST_SPLIT_FINDER;\n InitCUDARandomKernel<<>>(extra_seed_,\n static_cast(cuda_randoms_.Size()), cuda_randoms_.RawData());\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/treelearner/cuda/cuda_gradient_discretizer.cu", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifdef USE_CUDA\n\n#include \n\n#include \n\n#include \"cuda_gradient_discretizer.hpp\"\n\nnamespace LightGBM {\n\n__global__ void ReduceMinMaxKernel(\n const data_size_t num_data,\n const score_t* input_gradients,\n const score_t* input_hessians,\n score_t* grad_min_block_buffer,\n score_t* grad_max_block_buffer,\n score_t* hess_min_block_buffer,\n score_t* hess_max_block_buffer) {\n __shared__ score_t shared_mem_buffer[WARPSIZE];\n const data_size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n score_t grad_max_val = kMinScore;\n score_t grad_min_val = kMaxScore;\n score_t hess_max_val = kMinScore;\n score_t hess_min_val = kMaxScore;\n if (index < num_data) {\n grad_max_val = input_gradients[index];\n grad_min_val = input_gradients[index];\n hess_max_val = input_hessians[index];\n hess_min_val = input_hessians[index];\n }\n grad_min_val = ShuffleReduceMin(grad_min_val, shared_mem_buffer, blockDim.x);\n __syncthreads();\n grad_max_val = ShuffleReduceMax(grad_max_val, shared_mem_buffer, blockDim.x);\n __syncthreads();\n hess_min_val = ShuffleReduceMin(hess_min_val, shared_mem_buffer, blockDim.x);\n __syncthreads();\n hess_max_val = ShuffleReduceMax(hess_max_val, shared_mem_buffer, blockDim.x);\n if (threadIdx.x == 0) {\n grad_min_block_buffer[blockIdx.x] = grad_min_val;\n grad_max_block_buffer[blockIdx.x] = grad_max_val;\n hess_min_block_buffer[blockIdx.x] = hess_min_val;\n hess_max_block_buffer[blockIdx.x] = hess_max_val;\n }\n}\n\n__global__ void ReduceBlockMinMaxKernel(\n const int num_blocks,\n const int grad_discretize_bins,\n score_t* grad_min_block_buffer,\n score_t* grad_max_block_buffer,\n score_t* hess_min_block_buffer,\n score_t* hess_max_block_buffer) {\n __shared__ score_t shared_mem_buffer[WARPSIZE];\n score_t grad_max_val = kMinScore;\n score_t grad_min_val = kMaxScore;\n score_t hess_max_val = kMinScore;\n score_t hess_min_val = kMaxScore;\n for (int block_index = static_cast(threadIdx.x); block_index < num_blocks; block_index += static_cast(blockDim.x)) {\n grad_min_val = min(grad_min_val, grad_min_block_buffer[block_index]);\n grad_max_val = max(grad_max_val, grad_max_block_buffer[block_index]);\n hess_min_val = min(hess_min_val, hess_min_block_buffer[block_index]);\n hess_max_val = max(hess_max_val, hess_max_block_buffer[block_index]);\n }\n grad_min_val = ShuffleReduceMin(grad_min_val, shared_mem_buffer, blockDim.x);\n __syncthreads();\n grad_max_val = ShuffleReduceMax(grad_max_val, shared_mem_buffer, blockDim.x);\n __syncthreads();\n hess_max_val = ShuffleReduceMax(hess_max_val, shared_mem_buffer, blockDim.x);\n __syncthreads();\n hess_max_val = ShuffleReduceMax(hess_max_val, shared_mem_buffer, blockDim.x);\n if (threadIdx.x == 0) {\n const score_t grad_abs_max = max(fabs(grad_min_val), fabs(grad_max_val));\n const score_t hess_abs_max = max(fabs(hess_min_val), fabs(hess_max_val));\n grad_min_block_buffer[0] = 1.0f / (grad_abs_max / (grad_discretize_bins / 2));\n grad_max_block_buffer[0] = (grad_abs_max / (grad_discretize_bins / 2));\n hess_min_block_buffer[0] = 1.0f / (hess_abs_max / (grad_discretize_bins));\n hess_max_block_buffer[0] = (hess_abs_max / (grad_discretize_bins));\n }\n}\n\ntemplate \n__global__ void DiscretizeGradientsKernel(\n const data_size_t num_data,\n const score_t* input_gradients,\n const score_t* input_hessians,\n const score_t* grad_scale_ptr,\n const score_t* hess_scale_ptr,\n const int iter,\n const int* random_values_use_start,\n const score_t* gradient_random_values,\n const score_t* hessian_random_values,\n const int grad_discretize_bins,\n int8_t* output_gradients_and_hessians) {\n const int start = random_values_use_start[iter];\n const data_size_t index = static_cast(threadIdx.x + blockIdx.x * blockDim.x);\n const score_t grad_scale = *grad_scale_ptr;\n const score_t hess_scale = *hess_scale_ptr;\n int16_t* output_gradients_and_hessians_ptr = reinterpret_cast(output_gradients_and_hessians);\n if (index < num_data) {\n if (STOCHASTIC_ROUNDING) {\n const data_size_t index_offset = (index + start) % num_data;\n const score_t gradient = input_gradients[index];\n const score_t hessian = input_hessians[index];\n const score_t gradient_random_value = gradient_random_values[index_offset];\n const score_t hessian_random_value = hessian_random_values[index_offset];\n output_gradients_and_hessians_ptr[2 * index + 1] = gradient > 0.0f ?\n static_cast(gradient * grad_scale + gradient_random_value) :\n static_cast(gradient * grad_scale - gradient_random_value);\n output_gradients_and_hessians_ptr[2 * index] = static_cast(hessian * hess_scale + hessian_random_value);\n } else {\n const score_t gradient = input_gradients[index];\n const score_t hessian = input_hessians[index];\n output_gradients_and_hessians_ptr[2 * index + 1] = gradient > 0.0f ?\n static_cast(gradient * grad_scale + 0.5) :\n static_cast(gradient * grad_scale - 0.5);\n output_gradients_and_hessians_ptr[2 * index] = static_cast(hessian * hess_scale + 0.5);\n }\n }\n}\n\nvoid CUDAGradientDiscretizer::DiscretizeGradients(\n const data_size_t num_data,\n const score_t* input_gradients,\n const score_t* input_hessians) {\n ReduceMinMaxKernel<<>>(\n num_data, input_gradients, input_hessians,\n grad_min_block_buffer_.RawData(),\n grad_max_block_buffer_.RawData(),\n hess_min_block_buffer_.RawData(),\n hess_max_block_buffer_.RawData());\n SynchronizeCUDADevice(__FILE__, __LINE__);\n ReduceBlockMinMaxKernel<<<1, CUDA_GRADIENT_DISCRETIZER_BLOCK_SIZE>>>(\n num_reduce_blocks_,\n num_grad_quant_bins_,\n grad_min_block_buffer_.RawData(),\n grad_max_block_buffer_.RawData(),\n hess_min_block_buffer_.RawData(),\n hess_max_block_buffer_.RawData());\n SynchronizeCUDADevice(__FILE__, __LINE__);\n\n if (nccl_communicator_ != nullptr) {\n SynchronizeCUDADevice(__FILE__, __LINE__);\n cudaStream_t cuda_stream = CUDAStreamCreate();\n NCCLGroupStart();\n NCCLAllReduce(grad_min_block_buffer_.RawDataReadOnly(), grad_min_block_buffer_.RawData(), 1, ncclFloat32, ncclMin, nccl_communicator_, cuda_stream);\n NCCLAllReduce(hess_min_block_buffer_.RawDataReadOnly(), hess_min_block_buffer_.RawData(), 1, ncclFloat32, ncclMin, nccl_communicator_, cuda_stream);\n NCCLAllReduce(grad_max_block_buffer_.RawDataReadOnly(), grad_max_block_buffer_.RawData(), 1, ncclFloat32, ncclMax, nccl_communicator_, cuda_stream);\n NCCLAllReduce(hess_max_block_buffer_.RawDataReadOnly(), hess_max_block_buffer_.RawData(), 1, ncclFloat32, ncclMax, nccl_communicator_, cuda_stream);\n NCCLGroupEnd();\n SynchronizeCUDAStream(cuda_stream, __FILE__, __LINE__);\n CUDAStreamDestroy(cuda_stream);\n }\n\n #define DiscretizeGradientsKernel_ARGS \\\n num_data, \\\n input_gradients, \\\n input_hessians, \\\n grad_min_block_buffer_.RawData(), \\\n hess_min_block_buffer_.RawData(), \\\n iter_, \\\n random_values_use_start_.RawData(), \\\n gradient_random_values_.RawData(), \\\n hessian_random_values_.RawData(), \\\n num_grad_quant_bins_, \\\n discretized_gradients_and_hessians_.RawData()\n\n if (stochastic_rounding_) {\n DiscretizeGradientsKernel<<>>(DiscretizeGradientsKernel_ARGS);\n } else {\n DiscretizeGradientsKernel<<>>(DiscretizeGradientsKernel_ARGS);\n }\n SynchronizeCUDADevice(__FILE__, __LINE__);\n ++iter_;\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "src/treelearner/cuda/cuda_gradient_discretizer.hpp", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n */\n\n#ifndef LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_GRADIENT_DISCRETIZER_HPP_\n#define LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_GRADIENT_DISCRETIZER_HPP_\n\n#ifdef USE_CUDA\n\n#include \n#include \n#include \n#include \n\n#include \n#include \n#include \n\n#include \"cuda_leaf_splits.hpp\"\n#include \"../gradient_discretizer.hpp\"\n\nnamespace LightGBM {\n\n#define CUDA_GRADIENT_DISCRETIZER_BLOCK_SIZE (1024)\n\nclass CUDAGradientDiscretizer: public GradientDiscretizer, public NCCLInfo {\n public:\n CUDAGradientDiscretizer(int num_grad_quant_bins, int num_trees, int random_seed, bool is_constant_hessian, bool stochastic_roudning):\n GradientDiscretizer(num_grad_quant_bins, num_trees, random_seed, is_constant_hessian, stochastic_roudning) {\n }\n\n ~CUDAGradientDiscretizer() {}\n\n void DiscretizeGradients(\n const data_size_t num_data,\n const score_t* input_gradients,\n const score_t* input_hessians) override;\n\n const int8_t* discretized_gradients_and_hessians() const override { return discretized_gradients_and_hessians_.RawData(); }\n\n double grad_scale() const override {\n Log::Fatal(\"grad_scale() of CUDAGradientDiscretizer should not be called.\");\n return 0.0;\n }\n\n double hess_scale() const override {\n Log::Fatal(\"hess_scale() of CUDAGradientDiscretizer should not be called.\");\n return 0.0;\n }\n\n const score_t* grad_scale_ptr() const { return grad_max_block_buffer_.RawData(); }\n\n const score_t* hess_scale_ptr() const { return hess_max_block_buffer_.RawData(); }\n\n void Init(const data_size_t num_data, const int num_leaves,\n const int num_features, const Dataset* train_data) override {\n GradientDiscretizer::Init(num_data, num_leaves, num_features, train_data);\n discretized_gradients_and_hessians_.Resize(num_data * 2);\n num_reduce_blocks_ = (num_data + CUDA_GRADIENT_DISCRETIZER_BLOCK_SIZE - 1) / CUDA_GRADIENT_DISCRETIZER_BLOCK_SIZE;\n grad_min_block_buffer_.Resize(num_reduce_blocks_);\n grad_max_block_buffer_.Resize(num_reduce_blocks_);\n hess_min_block_buffer_.Resize(num_reduce_blocks_);\n hess_max_block_buffer_.Resize(num_reduce_blocks_);\n random_values_use_start_.Resize(num_trees_);\n gradient_random_values_.Resize(num_data);\n hessian_random_values_.Resize(num_data);\n\n std::vector gradient_random_values(num_data, 0.0f);\n std::vector hessian_random_values(num_data, 0.0f);\n std::vector random_values_use_start(num_trees_, 0);\n\n const int num_threads = OMP_NUM_THREADS();\n\n std::mt19937 random_values_use_start_eng = std::mt19937(random_seed_);\n std::uniform_int_distribution random_values_use_start_dist = std::uniform_int_distribution(0, num_data);\n for (int tree_index = 0; tree_index < num_trees_; ++tree_index) {\n random_values_use_start[tree_index] = random_values_use_start_dist(random_values_use_start_eng);\n }\n\n int num_blocks = 0;\n data_size_t block_size = 0;\n Threading::BlockInfo(num_data, 512, &num_blocks, &block_size);\n #pragma omp parallel for schedule(static, 1) num_threads(num_threads)\n for (int thread_id = 0; thread_id < num_blocks; ++thread_id) {\n const data_size_t start = thread_id * block_size;\n const data_size_t end = std::min(start + block_size, num_data);\n std::mt19937 gradient_random_values_eng(random_seed_ + thread_id);\n std::uniform_real_distribution gradient_random_values_dist(0.0f, 1.0f);\n std::mt19937 hessian_random_values_eng(random_seed_ + thread_id + num_threads);\n std::uniform_real_distribution hessian_random_values_dist(0.0f, 1.0f);\n for (data_size_t i = start; i < end; ++i) {\n gradient_random_values[i] = gradient_random_values_dist(gradient_random_values_eng);\n hessian_random_values[i] = hessian_random_values_dist(hessian_random_values_eng);\n }\n }\n\n CopyFromHostToCUDADevice(gradient_random_values_.RawData(), gradient_random_values.data(), gradient_random_values.size(), __FILE__, __LINE__);\n CopyFromHostToCUDADevice(hessian_random_values_.RawData(), hessian_random_values.data(), hessian_random_values.size(), __FILE__, __LINE__);\n CopyFromHostToCUDADevice(random_values_use_start_.RawData(), random_values_use_start.data(), random_values_use_start.size(), __FILE__, __LINE__);\n iter_ = 0;\n }\n\n protected:\n mutable CUDAVector discretized_gradients_and_hessians_;\n mutable CUDAVector grad_min_block_buffer_;\n mutable CUDAVector grad_max_block_buffer_;\n mutable CUDAVector hess_min_block_buffer_;\n mutable CUDAVector hess_max_block_buffer_;\n CUDAVector random_values_use_start_;\n CUDAVector gradient_random_values_;\n CUDAVector hessian_random_values_;\n int num_reduce_blocks_;\n};\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n#endif // LIGHTGBM_SRC_TREELEARNER_CUDA_CUDA_GRADIENT_DISCRETIZER_HPP_\n" }, { "path": "src/treelearner/cuda/cuda_histogram_constructor.cu", "content": "/*!\n * Copyright (c) 2021-2026 Microsoft Corporation. All rights reserved.\n * Copyright (c) 2021-2026 The LightGBM developers. All rights reserved.\n * Licensed under the MIT License. See LICENSE file in the project root for\n * license information.\n * Modifications Copyright(C) 2023 Advanced Micro Devices, Inc. All rights reserved.\n */\n\n#ifdef USE_CUDA\n\n#include \"cuda_histogram_constructor.hpp\"\n\n#include \n#include \n\n#include \n\nnamespace LightGBM {\n\ntemplate \n__global__ void CUDAConstructHistogramDenseKernel(\n const CUDALeafSplitsStruct* smaller_leaf_splits,\n const score_t* cuda_gradients,\n const score_t* cuda_hessians,\n const BIN_TYPE* data,\n const uint32_t* column_hist_offsets,\n const uint32_t* column_hist_offsets_full,\n const int* feature_partition_column_index_offsets,\n const data_size_t num_data) {\n const int dim_y = static_cast(gridDim.y * blockDim.y);\n const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf;\n const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y;\n const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf;\n __shared__ HIST_TYPE shared_hist[SHARED_HIST_SIZE];\n const unsigned int num_threads_per_block = blockDim.x * blockDim.y;\n const int partition_column_start = feature_partition_column_index_offsets[blockIdx.x];\n const int partition_column_end = feature_partition_column_index_offsets[blockIdx.x + 1];\n const BIN_TYPE* data_ptr = data + static_cast(partition_column_start) * num_data;\n const int num_columns_in_partition = partition_column_end - partition_column_start;\n const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x];\n const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1];\n const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start) << 1;\n const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n shared_hist[i] = 0.0f;\n }\n __syncthreads();\n const unsigned int blockIdx_y = blockIdx.y;\n const data_size_t block_start = (static_cast(blockIdx_y) * blockDim.y) * num_data_per_thread;\n const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start;\n data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y)));\n const int column_index = static_cast(threadIdx.x) + partition_column_start;\n if (threadIdx.x < static_cast(num_columns_in_partition)) {\n HIST_TYPE* shared_hist_ptr = shared_hist + (column_hist_offsets[column_index] << 1);\n for (data_size_t inner_data_index = static_cast(threadIdx.y); inner_data_index < block_num_data; inner_data_index += blockDim.y) {\n const data_size_t data_index = data_indices_ref_this_block[inner_data_index];\n const score_t grad = cuda_gradients[data_index];\n const score_t hess = cuda_hessians[data_index];\n const uint32_t bin = static_cast(data_ptr[static_cast(data_index) * num_columns_in_partition + threadIdx.x]);\n const uint32_t pos = bin << 1;\n HIST_TYPE* pos_ptr = shared_hist_ptr + pos;\n atomicAdd_block(pos_ptr, grad);\n atomicAdd_block(pos_ptr + 1, hess);\n }\n }\n __syncthreads();\n hist_t* feature_histogram_ptr = smaller_leaf_splits->hist_in_leaf + (partition_hist_start << 1);\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n atomicAdd_system(feature_histogram_ptr + i, shared_hist[i]);\n }\n}\n\ntemplate \n__global__ void CUDAConstructHistogramSparseKernel(\n const CUDALeafSplitsStruct* smaller_leaf_splits,\n const score_t* cuda_gradients,\n const score_t* cuda_hessians,\n const BIN_TYPE* data,\n const DATA_PTR_TYPE* row_ptr,\n const DATA_PTR_TYPE* partition_ptr,\n const uint32_t* column_hist_offsets_full,\n const data_size_t num_data) {\n const int dim_y = static_cast(gridDim.y * blockDim.y);\n const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf;\n const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y;\n const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf;\n __shared__ HIST_TYPE shared_hist[SHARED_HIST_SIZE];\n const unsigned int num_threads_per_block = blockDim.x * blockDim.y;\n const DATA_PTR_TYPE* block_row_ptr = row_ptr + static_cast(blockIdx.x) * (num_data + 1);\n const BIN_TYPE* data_ptr = data + partition_ptr[blockIdx.x];\n const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x];\n const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1];\n const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start) << 1;\n const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n shared_hist[i] = 0.0f;\n }\n __syncthreads();\n const unsigned int threadIdx_y = threadIdx.y;\n const unsigned int blockIdx_y = blockIdx.y;\n const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread;\n const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start;\n data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y)));\n const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y;\n const data_size_t remainder = block_num_data % blockDim.y;\n const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder);\n data_size_t inner_data_index = static_cast(threadIdx_y);\n for (data_size_t i = 0; i < num_iteration_this; ++i) {\n const data_size_t data_index = data_indices_ref_this_block[inner_data_index];\n const DATA_PTR_TYPE row_start = block_row_ptr[data_index];\n const DATA_PTR_TYPE row_end = block_row_ptr[data_index + 1];\n const DATA_PTR_TYPE row_size = row_end - row_start;\n if (threadIdx.x < row_size) {\n const score_t grad = cuda_gradients[data_index];\n const score_t hess = cuda_hessians[data_index];\n const uint32_t bin = static_cast(data_ptr[row_start + threadIdx.x]);\n const uint32_t pos = bin << 1;\n HIST_TYPE* pos_ptr = shared_hist + pos;\n atomicAdd_block(pos_ptr, grad);\n atomicAdd_block(pos_ptr + 1, hess);\n }\n inner_data_index += blockDim.y;\n }\n __syncthreads();\n hist_t* feature_histogram_ptr = smaller_leaf_splits->hist_in_leaf + (partition_hist_start << 1);\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n atomicAdd_system(feature_histogram_ptr + i, shared_hist[i]);\n }\n}\n\ntemplate \n__global__ void CUDAConstructHistogramDenseKernel_GlobalMemory(\n const CUDALeafSplitsStruct* smaller_leaf_splits,\n const score_t* cuda_gradients,\n const score_t* cuda_hessians,\n const BIN_TYPE* data,\n const uint32_t* column_hist_offsets,\n const uint32_t* column_hist_offsets_full,\n const int* feature_partition_column_index_offsets,\n const data_size_t num_data,\n HIST_TYPE* global_hist_buffer) {\n const int dim_y = static_cast(gridDim.y * blockDim.y);\n const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf;\n const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y;\n const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf;\n const unsigned int num_threads_per_block = blockDim.x * blockDim.y;\n const int partition_column_start = feature_partition_column_index_offsets[blockIdx.x];\n const int partition_column_end = feature_partition_column_index_offsets[blockIdx.x + 1];\n const BIN_TYPE* data_ptr = data + static_cast(partition_column_start) * num_data;\n const int num_columns_in_partition = partition_column_end - partition_column_start;\n const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x];\n const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1];\n const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start) << 1;\n const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x;\n const int num_total_bin = column_hist_offsets_full[gridDim.x];\n HIST_TYPE* shared_hist = global_hist_buffer + (blockIdx.y * num_total_bin + partition_hist_start) * 2;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n shared_hist[i] = 0.0f;\n }\n __syncthreads();\n const unsigned int threadIdx_y = threadIdx.y;\n const unsigned int blockIdx_y = blockIdx.y;\n const data_size_t block_start = (static_cast(blockIdx_y) * blockDim.y) * num_data_per_thread;\n const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start;\n data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y)));\n const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y;\n const data_size_t remainder = block_num_data % blockDim.y;\n const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder);\n data_size_t inner_data_index = static_cast(threadIdx_y);\n const int column_index = static_cast(threadIdx.x) + partition_column_start;\n if (threadIdx.x < static_cast(num_columns_in_partition)) {\n HIST_TYPE* shared_hist_ptr = shared_hist + (column_hist_offsets[column_index] << 1);\n for (data_size_t i = 0; i < num_iteration_this; ++i) {\n const data_size_t data_index = data_indices_ref_this_block[inner_data_index];\n const score_t grad = cuda_gradients[data_index];\n const score_t hess = cuda_hessians[data_index];\n const uint32_t bin = static_cast(data_ptr[static_cast(data_index) * num_columns_in_partition + threadIdx.x]);\n const uint32_t pos = bin << 1;\n HIST_TYPE* pos_ptr = shared_hist_ptr + pos;\n atomicAdd_block(pos_ptr, grad);\n atomicAdd_block(pos_ptr + 1, hess);\n inner_data_index += blockDim.y;\n }\n }\n __syncthreads();\n hist_t* feature_histogram_ptr = smaller_leaf_splits->hist_in_leaf + (partition_hist_start << 1);\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n atomicAdd_system(feature_histogram_ptr + i, shared_hist[i]);\n }\n}\n\ntemplate \n__global__ void CUDAConstructHistogramSparseKernel_GlobalMemory(\n const CUDALeafSplitsStruct* smaller_leaf_splits,\n const score_t* cuda_gradients,\n const score_t* cuda_hessians,\n const BIN_TYPE* data,\n const DATA_PTR_TYPE* row_ptr,\n const DATA_PTR_TYPE* partition_ptr,\n const uint32_t* column_hist_offsets_full,\n const data_size_t num_data,\n HIST_TYPE* global_hist_buffer) {\n const int dim_y = static_cast(gridDim.y * blockDim.y);\n const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf;\n const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y;\n const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf;\n const unsigned int num_threads_per_block = blockDim.x * blockDim.y;\n const DATA_PTR_TYPE* block_row_ptr = row_ptr + static_cast(blockIdx.x) * (num_data + 1);\n const BIN_TYPE* data_ptr = data + partition_ptr[blockIdx.x];\n const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x];\n const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1];\n const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start) << 1;\n const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x;\n const int num_total_bin = column_hist_offsets_full[gridDim.x];\n HIST_TYPE* shared_hist = global_hist_buffer + (blockIdx.y * num_total_bin + partition_hist_start) * 2;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n shared_hist[i] = 0.0f;\n }\n __syncthreads();\n const unsigned int threadIdx_y = threadIdx.y;\n const unsigned int blockIdx_y = blockIdx.y;\n const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread;\n const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start;\n data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y)));\n const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y;\n const data_size_t remainder = block_num_data % blockDim.y;\n const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder);\n data_size_t inner_data_index = static_cast(threadIdx_y);\n for (data_size_t i = 0; i < num_iteration_this; ++i) {\n const data_size_t data_index = data_indices_ref_this_block[inner_data_index];\n const DATA_PTR_TYPE row_start = block_row_ptr[data_index];\n const DATA_PTR_TYPE row_end = block_row_ptr[data_index + 1];\n const DATA_PTR_TYPE row_size = row_end - row_start;\n if (threadIdx.x < row_size) {\n const score_t grad = cuda_gradients[data_index];\n const score_t hess = cuda_hessians[data_index];\n const uint32_t bin = static_cast(data_ptr[row_start + threadIdx.x]);\n const uint32_t pos = bin << 1;\n HIST_TYPE* pos_ptr = shared_hist + pos;\n atomicAdd_block(pos_ptr, grad);\n atomicAdd_block(pos_ptr + 1, hess);\n }\n inner_data_index += blockDim.y;\n }\n __syncthreads();\n hist_t* feature_histogram_ptr = smaller_leaf_splits->hist_in_leaf + (partition_hist_start << 1);\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n atomicAdd_system(feature_histogram_ptr + i, shared_hist[i]);\n }\n}\n\ntemplate \n__global__ void CUDAConstructDiscretizedHistogramDenseKernel(\n const CUDALeafSplitsStruct* smaller_leaf_splits,\n const int32_t* cuda_gradients_and_hessians,\n const BIN_TYPE* data,\n const uint32_t* column_hist_offsets,\n const uint32_t* column_hist_offsets_full,\n const int* feature_partition_column_index_offsets,\n const data_size_t num_data) {\n const int dim_y = static_cast(gridDim.y * blockDim.y);\n const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf;\n const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y;\n const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf;\n __shared__ int16_t shared_hist[SHARED_HIST_SIZE];\n int32_t* shared_hist_packed = reinterpret_cast(shared_hist);\n const unsigned int num_threads_per_block = blockDim.x * blockDim.y;\n const int partition_column_start = feature_partition_column_index_offsets[blockIdx.x];\n const int partition_column_end = feature_partition_column_index_offsets[blockIdx.x + 1];\n const BIN_TYPE* data_ptr = data + partition_column_start * num_data;\n const int num_columns_in_partition = partition_column_end - partition_column_start;\n const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x];\n const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1];\n const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start);\n const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n shared_hist_packed[i] = 0;\n }\n __syncthreads();\n const unsigned int threadIdx_y = threadIdx.y;\n const unsigned int blockIdx_y = blockIdx.y;\n const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread;\n const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start;\n data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y)));\n const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y;\n const data_size_t remainder = block_num_data % blockDim.y;\n const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder);\n data_size_t inner_data_index = static_cast(threadIdx_y);\n const int column_index = static_cast(threadIdx.x) + partition_column_start;\n if (threadIdx.x < static_cast(num_columns_in_partition)) {\n int32_t* shared_hist_ptr = shared_hist_packed + (column_hist_offsets[column_index]);\n for (data_size_t i = 0; i < num_iteration_this; ++i) {\n const data_size_t data_index = data_indices_ref_this_block[inner_data_index];\n const int32_t grad_and_hess = cuda_gradients_and_hessians[data_index];\n const uint32_t bin = static_cast(data_ptr[data_index * num_columns_in_partition + threadIdx.x]);\n int32_t* pos_ptr = shared_hist_ptr + bin;\n atomicAdd_block(pos_ptr, grad_and_hess);\n inner_data_index += blockDim.y;\n }\n }\n __syncthreads();\n if (USE_16BIT_HIST) {\n int32_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n const int32_t packed_grad_hess = shared_hist_packed[i];\n atomicAdd_system(feature_histogram_ptr + i, packed_grad_hess);\n }\n } else {\n atomic_add_long_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n const int32_t packed_grad_hess = shared_hist_packed[i];\n const int64_t packed_grad_hess_int64 = (static_cast(static_cast(packed_grad_hess >> 16)) << 32) | (static_cast(packed_grad_hess & 0x0000ffff));\n atomicAdd_system(feature_histogram_ptr + i, (atomic_add_long_t)(packed_grad_hess_int64));\n }\n }\n}\n\ntemplate \n__global__ void CUDAConstructDiscretizedHistogramSparseKernel(\n const CUDALeafSplitsStruct* smaller_leaf_splits,\n const int32_t* cuda_gradients_and_hessians,\n const BIN_TYPE* data,\n const DATA_PTR_TYPE* row_ptr,\n const DATA_PTR_TYPE* partition_ptr,\n const uint32_t* column_hist_offsets_full,\n const data_size_t num_data) {\n const int dim_y = static_cast(gridDim.y * blockDim.y);\n const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf;\n const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y;\n const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf;\n __shared__ int16_t shared_hist[SHARED_HIST_SIZE];\n int32_t* shared_hist_packed = reinterpret_cast(shared_hist);\n const unsigned int num_threads_per_block = blockDim.x * blockDim.y;\n const DATA_PTR_TYPE* block_row_ptr = row_ptr + blockIdx.x * (num_data + 1);\n const BIN_TYPE* data_ptr = data + partition_ptr[blockIdx.x];\n const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x];\n const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1];\n const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start);\n const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n shared_hist_packed[i] = 0.0f;\n }\n __syncthreads();\n const unsigned int threadIdx_y = threadIdx.y;\n const unsigned int blockIdx_y = blockIdx.y;\n const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread;\n const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start;\n data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y)));\n const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y;\n const data_size_t remainder = block_num_data % blockDim.y;\n const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder);\n data_size_t inner_data_index = static_cast(threadIdx_y);\n for (data_size_t i = 0; i < num_iteration_this; ++i) {\n const data_size_t data_index = data_indices_ref_this_block[inner_data_index];\n const DATA_PTR_TYPE row_start = block_row_ptr[data_index];\n const DATA_PTR_TYPE row_end = block_row_ptr[data_index + 1];\n const DATA_PTR_TYPE row_size = row_end - row_start;\n if (threadIdx.x < row_size) {\n const int32_t grad_and_hess = cuda_gradients_and_hessians[data_index];\n const uint32_t bin = static_cast(data_ptr[row_start + threadIdx.x]);\n int32_t* pos_ptr = shared_hist_packed + bin;\n atomicAdd_block(pos_ptr, grad_and_hess);\n }\n inner_data_index += blockDim.y;\n }\n __syncthreads();\n if (USE_16BIT_HIST) {\n int32_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n const int32_t packed_grad_hess = shared_hist_packed[i];\n atomicAdd_system(feature_histogram_ptr + i, packed_grad_hess);\n }\n } else {\n atomic_add_long_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n const int32_t packed_grad_hess = shared_hist_packed[i];\n const int64_t packed_grad_hess_int64 = (static_cast(static_cast(packed_grad_hess >> 16)) << 32) | (static_cast(packed_grad_hess & 0x0000ffff));\n atomicAdd_system(feature_histogram_ptr + i, (atomic_add_long_t)(packed_grad_hess_int64));\n }\n }\n}\n\ntemplate \n__global__ void CUDAConstructDiscretizedHistogramDenseKernel_GlobalMemory(\n const CUDALeafSplitsStruct* smaller_leaf_splits,\n const int32_t* cuda_gradients_and_hessians,\n const BIN_TYPE* data,\n const uint32_t* column_hist_offsets,\n const uint32_t* column_hist_offsets_full,\n const int* feature_partition_column_index_offsets,\n const data_size_t num_data,\n int32_t* global_hist_buffer) {\n const int dim_y = static_cast(gridDim.y * blockDim.y);\n const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf;\n const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y;\n const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf;\n const unsigned int num_threads_per_block = blockDim.x * blockDim.y;\n const int partition_column_start = feature_partition_column_index_offsets[blockIdx.x];\n const int partition_column_end = feature_partition_column_index_offsets[blockIdx.x + 1];\n const BIN_TYPE* data_ptr = data + partition_column_start * num_data;\n const int num_columns_in_partition = partition_column_end - partition_column_start;\n const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x];\n const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1];\n const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start);\n const int num_total_bin = column_hist_offsets_full[gridDim.x];\n int32_t* shared_hist_packed = global_hist_buffer + (blockIdx.y * num_total_bin + partition_column_start);\n const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n shared_hist_packed[i] = 0;\n }\n __syncthreads();\n const unsigned int threadIdx_y = threadIdx.y;\n const unsigned int blockIdx_y = blockIdx.y;\n const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread;\n const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start;\n data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y)));\n const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y;\n const data_size_t remainder = block_num_data % blockDim.y;\n const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder);\n data_size_t inner_data_index = static_cast(threadIdx_y);\n const int column_index = static_cast(threadIdx.x) + partition_column_start;\n if (threadIdx.x < static_cast(num_columns_in_partition)) {\n int32_t* shared_hist_ptr = shared_hist_packed + (column_hist_offsets[column_index]);\n for (data_size_t i = 0; i < num_iteration_this; ++i) {\n const data_size_t data_index = data_indices_ref_this_block[inner_data_index];\n const int32_t grad_and_hess = cuda_gradients_and_hessians[data_index];\n const uint32_t bin = static_cast(data_ptr[data_index * num_columns_in_partition + threadIdx.x]);\n int32_t* pos_ptr = shared_hist_ptr + bin;\n atomicAdd_block(pos_ptr, grad_and_hess);\n inner_data_index += blockDim.y;\n }\n }\n __syncthreads();\n if (USE_16BIT_HIST) {\n int32_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n const int32_t packed_grad_hess = shared_hist_packed[i];\n atomicAdd_system(feature_histogram_ptr + i, packed_grad_hess);\n }\n } else {\n atomic_add_long_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n const int32_t packed_grad_hess = shared_hist_packed[i];\n const int64_t packed_grad_hess_int64 = (static_cast(static_cast(packed_grad_hess >> 16)) << 32) | (static_cast(packed_grad_hess & 0x0000ffff));\n atomicAdd_system(feature_histogram_ptr + i, (atomic_add_long_t)(packed_grad_hess_int64));\n }\n }\n}\n\ntemplate \n__global__ void CUDAConstructDiscretizedHistogramSparseKernel_GlobalMemory(\n const CUDALeafSplitsStruct* smaller_leaf_splits,\n const int32_t* cuda_gradients_and_hessians,\n const BIN_TYPE* data,\n const DATA_PTR_TYPE* row_ptr,\n const DATA_PTR_TYPE* partition_ptr,\n const uint32_t* column_hist_offsets_full,\n const data_size_t num_data,\n int32_t* global_hist_buffer) {\n const int dim_y = static_cast(gridDim.y * blockDim.y);\n const data_size_t num_data_in_smaller_leaf = smaller_leaf_splits->num_data_in_leaf;\n const data_size_t num_data_per_thread = (num_data_in_smaller_leaf + dim_y - 1) / dim_y;\n const data_size_t* data_indices_ref = smaller_leaf_splits->data_indices_in_leaf;\n const int num_total_bin = column_hist_offsets_full[gridDim.x];\n const unsigned int num_threads_per_block = blockDim.x * blockDim.y;\n const DATA_PTR_TYPE* block_row_ptr = row_ptr + blockIdx.x * (num_data + 1);\n const BIN_TYPE* data_ptr = data + partition_ptr[blockIdx.x];\n const uint32_t partition_hist_start = column_hist_offsets_full[blockIdx.x];\n const uint32_t partition_hist_end = column_hist_offsets_full[blockIdx.x + 1];\n const uint32_t num_items_in_partition = (partition_hist_end - partition_hist_start);\n const unsigned int thread_idx = threadIdx.x + threadIdx.y * blockDim.x;\n int32_t* shared_hist_packed = global_hist_buffer + (blockIdx.y * num_total_bin + partition_hist_start);\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n shared_hist_packed[i] = 0.0f;\n }\n __syncthreads();\n const unsigned int threadIdx_y = threadIdx.y;\n const unsigned int blockIdx_y = blockIdx.y;\n const data_size_t block_start = (blockIdx_y * blockDim.y) * num_data_per_thread;\n const data_size_t* data_indices_ref_this_block = data_indices_ref + block_start;\n data_size_t block_num_data = max(0, min(num_data_in_smaller_leaf - block_start, num_data_per_thread * static_cast(blockDim.y)));\n const data_size_t num_iteration_total = (block_num_data + blockDim.y - 1) / blockDim.y;\n const data_size_t remainder = block_num_data % blockDim.y;\n const data_size_t num_iteration_this = remainder == 0 ? num_iteration_total : num_iteration_total - static_cast(threadIdx_y >= remainder);\n data_size_t inner_data_index = static_cast(threadIdx_y);\n for (data_size_t i = 0; i < num_iteration_this; ++i) {\n const data_size_t data_index = data_indices_ref_this_block[inner_data_index];\n const DATA_PTR_TYPE row_start = block_row_ptr[data_index];\n const DATA_PTR_TYPE row_end = block_row_ptr[data_index + 1];\n const DATA_PTR_TYPE row_size = row_end - row_start;\n if (threadIdx.x < row_size) {\n const int32_t grad_and_hess = cuda_gradients_and_hessians[data_index];\n const uint32_t bin = static_cast(data_ptr[row_start + threadIdx.x]);\n int32_t* pos_ptr = shared_hist_packed + bin;\n atomicAdd_block(pos_ptr, grad_and_hess);\n }\n inner_data_index += blockDim.y;\n }\n __syncthreads();\n if (USE_16BIT_HIST) {\n int32_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n const int32_t packed_grad_hess = shared_hist_packed[i];\n atomicAdd_system(feature_histogram_ptr + i, packed_grad_hess);\n }\n } else {\n atomic_add_long_t* feature_histogram_ptr = reinterpret_cast(smaller_leaf_splits->hist_in_leaf) + partition_hist_start;\n for (unsigned int i = thread_idx; i < num_items_in_partition; i += num_threads_per_block) {\n const int32_t packed_grad_hess = shared_hist_packed[i];\n const int64_t packed_grad_hess_int64 = (static_cast(static_cast(packed_grad_hess >> 16)) << 32) | (static_cast(packed_grad_hess & 0x0000ffff));\n atomicAdd_system(feature_histogram_ptr + i, (atomic_add_long_t)(packed_grad_hess_int64));\n }\n }\n}\n\nvoid CUDAHistogramConstructor::LaunchConstructHistogramKernel(\n const CUDALeafSplitsStruct* cuda_smaller_leaf_splits,\n const data_size_t num_data_in_smaller_leaf,\n const uint8_t num_bits_in_histogram_bins) {\n if (cuda_row_data_->shared_hist_size() == DP_SHARED_HIST_SIZE && gpu_use_dp_) {\n LaunchConstructHistogramKernelInner(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins);\n } else if (cuda_row_data_->shared_hist_size() == SP_SHARED_HIST_SIZE && !gpu_use_dp_) {\n LaunchConstructHistogramKernelInner(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins);\n } else {\n Log::Fatal(\"Unknown shared histogram size %d\", cuda_row_data_->shared_hist_size());\n }\n}\n\ntemplate \nvoid CUDAHistogramConstructor::LaunchConstructHistogramKernelInner(\n const CUDALeafSplitsStruct* cuda_smaller_leaf_splits,\n const data_size_t num_data_in_smaller_leaf,\n const uint8_t num_bits_in_histogram_bins) {\n if (cuda_row_data_->bit_type() == 8) {\n LaunchConstructHistogramKernelInner0(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins);\n } else if (cuda_row_data_->bit_type() == 16) {\n LaunchConstructHistogramKernelInner0(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins);\n } else if (cuda_row_data_->bit_type() == 32) {\n LaunchConstructHistogramKernelInner0(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins);\n } else {\n Log::Fatal(\"Unknown bit_type = %d\", cuda_row_data_->bit_type());\n }\n}\n\ntemplate \nvoid CUDAHistogramConstructor::LaunchConstructHistogramKernelInner0(\n const CUDALeafSplitsStruct* cuda_smaller_leaf_splits,\n const data_size_t num_data_in_smaller_leaf,\n const uint8_t num_bits_in_histogram_bins) {\n if (cuda_row_data_->row_ptr_bit_type() == 16) {\n LaunchConstructHistogramKernelInner1(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins);\n } else if (cuda_row_data_->row_ptr_bit_type() == 32) {\n LaunchConstructHistogramKernelInner1(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins);\n } else if (cuda_row_data_->row_ptr_bit_type() == 64) {\n LaunchConstructHistogramKernelInner1(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins);\n } else {\n if (!cuda_row_data_->is_sparse()) {\n LaunchConstructHistogramKernelInner1(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins);\n } else {\n Log::Fatal(\"Unknown row_ptr_bit_type = %d\", cuda_row_data_->row_ptr_bit_type());\n }\n }\n}\n\ntemplate \nvoid CUDAHistogramConstructor::LaunchConstructHistogramKernelInner1(\n const CUDALeafSplitsStruct* cuda_smaller_leaf_splits,\n const data_size_t num_data_in_smaller_leaf,\n const uint8_t num_bits_in_histogram_bins) {\n if (cuda_row_data_->NumLargeBinPartition() == 0) {\n LaunchConstructHistogramKernelInner2(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins);\n } else {\n LaunchConstructHistogramKernelInner2(cuda_smaller_leaf_splits, num_data_in_smaller_leaf, num_bits_in_histogram_bins);\n }\n}\n\ntemplate \nvoid CUDAHistogramConstructor::LaunchConstructHistogramKernelInner2(\n const CUDALeafSplitsStruct* cuda_smaller_leaf_splits,\n const data_size_t num_data_in_smaller_leaf,\n const uint8_t num_bits_in_histogram_bins) {\n int grid_dim_x = 0;\n int grid_dim_y = 0;\n int block_dim_x = 0;\n int block_dim_y = 0;\n CalcConstructHistogramKernelDim(&grid_dim_x, &grid_dim_y, &block_dim_x, &block_dim_y, num_data_in_smaller_leaf);\n dim3 grid_dim(grid_dim_x, grid_dim_y);\n dim3 block_dim(block_dim_x, block_dim_y);\n if (use_quantized_grad_) {\n if (USE_GLOBAL_MEM_BUFFER) {\n if (cuda_row_data_->is_sparse()) {\n if (num_bits_in_histogram_bins <= 16) {\n CUDAConstructDiscretizedHistogramSparseKernel_GlobalMemory<<>>(\n cuda_smaller_leaf_splits,\n reinterpret_cast(cuda_gradients_),\n cuda_row_data_->GetBin(),\n cuda_row_data_->GetRowPtr(),\n cuda_row_data_->GetPartitionPtr(),\n cuda_row_data_->cuda_partition_hist_offsets(),\n num_data_,\n reinterpret_cast(cuda_hist_buffer_.RawData()));\n } else {\n CUDAConstructDiscretizedHistogramSparseKernel_GlobalMemory<<>>(\n cuda_smaller_leaf_splits,\n reinterpret_cast(cuda_gradients_),\n cuda_row_data_->GetBin(),\n cuda_row_data_->GetRowPtr(),\n cuda_row_data_->GetPartitionPtr(),\n cuda_row_data_->cuda_partition_hist_offsets(),\n num_data_,\n reinterpret_cast(cuda_hist_buffer_.RawData()));\n }\n } else {\n if (num_bits_in_histogram_bins <= 16) {\n CUDAConstructDiscretizedHistogramDenseKernel_GlobalMemory<<>>(\n cuda_smaller_leaf_splits,\n reinterpret_cast(cuda_gradients_),\n cuda_row_data_->GetBin(),\n cuda_row_data_->cuda_column_hist_offsets(),\n cuda_row_data_->cuda_partition_hist_offsets(),\n cuda_row_data_->cuda_feature_partition_column_index_offsets(),\n num_data_,\n reinterpret_cast(cuda_hist_buffer_.RawData()));\n } else {\n CUDAConstructDiscretizedHistogramDenseKernel_GlobalMemory<<>>(\n cuda_smaller_leaf_splits,\n reinterpret_cast(cuda_gradients_),\n cuda_row_data_->GetBin(),\n cuda_row_data_->cuda_column_hist_offsets(),\n cuda_row_data_->cuda_partition_hist_offsets(),\n cuda_row_data_->cuda_feature_partition_column_index_offsets(),\n num_data_,\n reinterpret_cast(cuda_hist_buffer_.RawData()));\n }\n }\n } else {\n if (cuda_row_data_->is_sparse()) {\n if (num_bits_in_histogram_bins <= 16) {\n CUDAConstructDiscretizedHistogramSparseKernel<<>>(\n cuda_smaller_leaf_splits,\n reinterpret_cast(cuda_gradients_),\n cuda_row_data_->GetBin(),\n cuda_row_data_->GetRowPtr(),\n cuda_row_data_->GetPartitionPtr(),\n cuda_row_data_->cuda_partition_hist_offsets(),\n num_data_);\n } else {\n CUDAConstructDiscretizedHistogramSparseKernel<<>>(\n cuda_smaller_leaf_splits,\n reinterpret_cast(cuda_gradients_),\n cuda_row_data_->GetBin(),\n cuda_row_data_->GetRowPtr(),\n cuda_row_data_->GetPartitionPtr(),\n cuda_row_data_->cuda_partition_hist_offsets(),\n num_data_);\n }\n } else {\n if (num_bits_in_histogram_bins <= 16) {\n CUDAConstructDiscretizedHistogramDenseKernel<<>>(\n cuda_smaller_leaf_splits,\n reinterpret_cast(cuda_gradients_),\n cuda_row_data_->GetBin(),\n cuda_row_data_->cuda_column_hist_offsets(),\n cuda_row_data_->cuda_partition_hist_offsets(),\n cuda_row_data_->cuda_feature_partition_column_index_offsets(),\n num_data_);\n } else {\n CUDAConstructDiscretizedHistogramDenseKernel<<>>(\n cuda_smaller_leaf_splits,\n reinterpret_cast(cuda_gradients_),\n cuda_row_data_->GetBin(),\n cuda_row_data_->cuda_column_hist_offsets(),\n cuda_row_data_->cuda_partition_hist_offsets(),\n cuda_row_data_->cuda_feature_partition_column_index_offsets(),\n num_data_);\n }\n }\n }\n } else {\n if (!USE_GLOBAL_MEM_BUFFER) {\n if (cuda_row_data_->is_sparse()) {\n CUDAConstructHistogramSparseKernel<<>>(\n cuda_smaller_leaf_splits,\n cuda_gradients_, cuda_hessians_,\n cuda_row_data_->GetBin(),\n cuda_row_data_->GetRowPtr(),\n cuda_row_data_->GetPartitionPtr(),\n cuda_row_data_->cuda_partition_hist_offsets(),\n num_data_);\n } else {\n CUDAConstructHistogramDenseKernel<<>>(\n cuda_smaller_leaf_splits,\n cuda_gradients_, cuda_hessians_,\n cuda_row_data_->GetBin(),\n cuda_row_data_->cuda_column_hist_offsets(),\n cuda_row_data_->cuda_partition_hist_offsets(),\n cuda_row_data_->cuda_feature_partition_column_index_offsets(),\n num_data_);\n }\n } else {\n if (cuda_row_data_->is_sparse()) {\n CUDAConstructHistogramSparseKernel_GlobalMemory<<>>(\n cuda_smaller_leaf_splits,\n cuda_gradients_, cuda_hessians_,\n cuda_row_data_->GetBin(),\n cuda_row_data_->GetRowPtr(),\n cuda_row_data_->GetPartitionPtr(),\n cuda_row_data_->cuda_partition_hist_offsets(),\n num_data_,\n reinterpret_cast(cuda_hist_buffer_.RawData()));\n } else {\n CUDAConstructHistogramDenseKernel_GlobalMemory<<>>(\n cuda_smaller_leaf_splits,\n cuda_gradients_, cuda_hessians_,\n cuda_row_data_->GetBin(),\n cuda_row_data_->cuda_column_hist_offsets(),\n cuda_row_data_->cuda_partition_hist_offsets(),\n cuda_row_data_->cuda_feature_partition_column_index_offsets(),\n num_data_,\n reinterpret_cast(cuda_hist_buffer_.RawData()));\n }\n }\n }\n}\n\n__global__ void SubtractHistogramKernel(\n const int num_total_bin,\n const CUDALeafSplitsStruct* cuda_smaller_leaf_splits,\n const CUDALeafSplitsStruct* cuda_larger_leaf_splits) {\n const unsigned int global_thread_index = threadIdx.x + blockIdx.x * blockDim.x;\n const int cuda_larger_leaf_index = cuda_larger_leaf_splits->leaf_index;\n if (cuda_larger_leaf_index >= 0) {\n const hist_t* smaller_leaf_hist = cuda_smaller_leaf_splits->hist_in_leaf;\n hist_t* larger_leaf_hist = cuda_larger_leaf_splits->hist_in_leaf;\n if (global_thread_index < 2 * num_total_bin) {\n larger_leaf_hist[global_thread_index] -= smaller_leaf_hist[global_thread_index];\n }\n }\n}\n\n__global__ void FixHistogramKernel(\n const uint32_t* cuda_feature_num_bins,\n const uint32_t* cuda_feature_hist_offsets,\n const uint32_t* cuda_feature_most_freq_bins,\n const int* cuda_need_fix_histogram_features,\n const uint32_t* cuda_need_fix_histogram_features_num_bin_aligned,\n const CUDALeafSplitsStruct* cuda_smaller_leaf_splits) {\n __shared__ hist_t shared_mem_buffer[WARPSIZE];\n const unsigned int blockIdx_x = blockIdx.x;\n const int feature_index = cuda_need_fix_histogram_features[blockIdx_x];\n const uint32_t num_bin_aligned = cuda_need_fix_histogram_features_num_bin_aligned[blockIdx_x];\n const uint32_t feature_hist_offset = cuda_feature_hist_offsets[feature_index];\n const uint32_t most_freq_bin = cuda_feature_most_freq_bins[feature_index];\n const double leaf_sum_gradients = cuda_smaller_leaf_splits->sum_of_gradients;\n const double leaf_sum_hessians = cuda_smaller_leaf_splits->sum_of_hessians;\n hist_t* feature_hist = cuda_smaller_leaf_splits->hist_in_leaf + feature_hist_offset * 2;\n const unsigned int threadIdx_x = threadIdx.x;\n const uint32_t num_bin = cuda_feature_num_bins[feature_index];\n const uint32_t hist_pos = threadIdx_x << 1;\n const hist_t bin_gradient = (threadIdx_x < num_bin && threadIdx_x != most_freq_bin) ? feature_hist[hist_pos] : 0.0f;\n const hist_t bin_hessian = (threadIdx_x < num_bin && threadIdx_x != most_freq_bin) ? feature_hist[hist_pos + 1] : 0.0f;\n const hist_t sum_gradient = ShuffleReduceSum(bin_gradient, shared_mem_buffer, num_bin_aligned);\n const hist_t sum_hessian = ShuffleReduceSum(bin_hessian, shared_mem_buffer, num_bin_aligned);\n if (threadIdx_x == 0) {\n feature_hist[most_freq_bin << 1] = leaf_sum_gradients - sum_gradient;\n feature_hist[(most_freq_bin << 1) + 1] = leaf_sum_hessians - sum_hessian;\n }\n}\n\ntemplate \n__global__ void SubtractHistogramDiscretizedKernel(\n const int num_total_bin,\n const CUDALeafSplitsStruct* cuda_smaller_leaf_splits,\n const CUDALeafSplitsStruct* cuda_larger_leaf_splits,\n hist_t* num_bit_change_buffer) {\n const unsigned int global_thread_index = threadIdx.x + blockIdx.x * blockDim.x;\n const int cuda_larger_leaf_index_ref = cuda_larger_leaf_splits->leaf_index;\n if (cuda_larger_leaf_index_ref >= 0) {\n if (PARENT_USE_16BIT_HIST) {\n const int32_t* smaller_leaf_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf);\n int32_t* larger_leaf_hist = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf);\n if (global_thread_index < num_total_bin) {\n larger_leaf_hist[global_thread_index] -= smaller_leaf_hist[global_thread_index];\n }\n } else if (LARGER_USE_16BIT_HIST) {\n int32_t* buffer = reinterpret_cast(num_bit_change_buffer);\n const int32_t* smaller_leaf_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf);\n int64_t* larger_leaf_hist = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf);\n if (global_thread_index < num_total_bin) {\n const int64_t parent_hist_item = larger_leaf_hist[global_thread_index];\n const int32_t smaller_hist_item = smaller_leaf_hist[global_thread_index];\n const int64_t smaller_hist_item_int64 = (static_cast(static_cast(smaller_hist_item >> 16)) << 32) |\n static_cast(smaller_hist_item & 0x0000ffff);\n const int64_t larger_hist_item = parent_hist_item - smaller_hist_item_int64;\n buffer[global_thread_index] = static_cast(static_cast(larger_hist_item >> 32) << 16) |\n static_cast(larger_hist_item & 0x000000000000ffff);\n }\n } else if (SMALLER_USE_16BIT_HIST) {\n const int32_t* smaller_leaf_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf);\n int64_t* larger_leaf_hist = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf);\n if (global_thread_index < num_total_bin) {\n const int64_t parent_hist_item = larger_leaf_hist[global_thread_index];\n const int32_t smaller_hist_item = smaller_leaf_hist[global_thread_index];\n const int64_t smaller_hist_item_int64 = (static_cast(static_cast(smaller_hist_item >> 16)) << 32) |\n static_cast(smaller_hist_item & 0x0000ffff);\n const int64_t larger_hist_item = parent_hist_item - smaller_hist_item_int64;\n larger_leaf_hist[global_thread_index] = larger_hist_item;\n }\n } else {\n const int64_t* smaller_leaf_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf);\n int64_t* larger_leaf_hist = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf);\n if (global_thread_index < num_total_bin) {\n larger_leaf_hist[global_thread_index] -= smaller_leaf_hist[global_thread_index];\n }\n }\n }\n}\n\n__global__ void CopyChangedNumBitHistogram(\n const int num_total_bin,\n const CUDALeafSplitsStruct* cuda_larger_leaf_splits,\n hist_t* num_bit_change_buffer) {\n int32_t* hist_dst = reinterpret_cast(cuda_larger_leaf_splits->hist_in_leaf);\n const int32_t* hist_src = reinterpret_cast(num_bit_change_buffer);\n const unsigned int global_thread_index = threadIdx.x + blockIdx.x * blockDim.x;\n if (global_thread_index < static_cast(num_total_bin)) {\n hist_dst[global_thread_index] = hist_src[global_thread_index];\n }\n}\n\ntemplate \n__global__ void FixHistogramDiscretizedKernel(\n const uint32_t* cuda_feature_num_bins,\n const uint32_t* cuda_feature_hist_offsets,\n const uint32_t* cuda_feature_most_freq_bins,\n const int* cuda_need_fix_histogram_features,\n const uint32_t* cuda_need_fix_histogram_features_num_bin_aligned,\n const CUDALeafSplitsStruct* cuda_smaller_leaf_splits) {\n __shared__ int64_t shared_mem_buffer[WARPSIZE];\n const unsigned int blockIdx_x = blockIdx.x;\n const int feature_index = cuda_need_fix_histogram_features[blockIdx_x];\n const uint32_t num_bin_aligned = cuda_need_fix_histogram_features_num_bin_aligned[blockIdx_x];\n const uint32_t feature_hist_offset = cuda_feature_hist_offsets[feature_index];\n const uint32_t most_freq_bin = cuda_feature_most_freq_bins[feature_index];\n if (USE_16BIT_HIST) {\n const int64_t leaf_sum_gradients_hessians_int64 = cuda_smaller_leaf_splits->sum_of_gradients_hessians;\n const int32_t leaf_sum_gradients_hessians =\n (static_cast(leaf_sum_gradients_hessians_int64 >> 32) << 16) | static_cast(leaf_sum_gradients_hessians_int64 & 0x000000000000ffff);\n int32_t* feature_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf) + feature_hist_offset;\n const unsigned int threadIdx_x = threadIdx.x;\n const uint32_t num_bin = cuda_feature_num_bins[feature_index];\n const int32_t bin_gradient_hessian = (threadIdx_x < num_bin && threadIdx_x != most_freq_bin) ? feature_hist[threadIdx_x] : 0;\n const int32_t sum_gradient_hessian = ShuffleReduceSum(\n bin_gradient_hessian,\n reinterpret_cast(shared_mem_buffer),\n num_bin_aligned);\n if (threadIdx_x == 0) {\n feature_hist[most_freq_bin] = leaf_sum_gradients_hessians - sum_gradient_hessian;\n }\n } else {\n const int64_t leaf_sum_gradients_hessians = cuda_smaller_leaf_splits->sum_of_gradients_hessians;\n int64_t* feature_hist = reinterpret_cast(cuda_smaller_leaf_splits->hist_in_leaf) + feature_hist_offset;\n const unsigned int threadIdx_x = threadIdx.x;\n const uint32_t num_bin = cuda_feature_num_bins[feature_index];\n const int64_t bin_gradient_hessian = (threadIdx_x < num_bin && threadIdx_x != most_freq_bin) ? feature_hist[threadIdx_x] : 0;\n const int64_t sum_gradient_hessian = ShuffleReduceSum(bin_gradient_hessian, shared_mem_buffer, num_bin_aligned);\n if (threadIdx_x == 0) {\n feature_hist[most_freq_bin] = leaf_sum_gradients_hessians - sum_gradient_hessian;\n }\n }\n}\n\nvoid CUDAHistogramConstructor::LaunchSubtractHistogramKernel(\n const CUDALeafSplitsStruct* cuda_smaller_leaf_splits,\n const CUDALeafSplitsStruct* cuda_larger_leaf_splits,\n const bool use_discretized_grad,\n const uint8_t parent_num_bits_in_histogram_bins,\n const uint8_t smaller_num_bits_in_histogram_bins,\n const uint8_t larger_num_bits_in_histogram_bins) {\n if (!use_discretized_grad) {\n const int num_subtract_threads = 2 * num_total_bin_;\n const int num_subtract_blocks = (num_subtract_threads + SUBTRACT_BLOCK_SIZE - 1) / SUBTRACT_BLOCK_SIZE;\n global_timer.Start(\"CUDAHistogramConstructor::FixHistogramKernel\");\n if (need_fix_histogram_features_.size() > 0) {\n FixHistogramKernel<<>>(\n cuda_feature_num_bins_.RawData(),\n cuda_feature_hist_offsets_.RawData(),\n cuda_feature_most_freq_bins_.RawData(),\n cuda_need_fix_histogram_features_.RawData(),\n cuda_need_fix_histogram_features_num_bin_aligned_.RawData(),\n cuda_smaller_leaf_splits);\n }\n global_timer.Stop(\"CUDAHistogramConstructor::FixHistogramKernel\");\n global_timer.Start(\"CUDAHistogramConstructor::SubtractHistogramKernel\");\n SubtractHistogramKernel<<>>(\n num_total_bin_,\n cuda_smaller_leaf_splits,\n cuda_larger_leaf_splits);\n global_timer.Stop(\"CUDAHistogramConstructor::SubtractHistogramKernel\");\n } else {\n const int num_subtract_threads = num_total_bin_;\n const int num_subtract_blocks = (num_subtract_threads + SUBTRACT_BLOCK_SIZE - 1) / SUBTRACT_BLOCK_SIZE;\n global_timer.Start(\"CUDAHistogramConstructor::FixHistogramDiscretizedKernel\");\n if (need_fix_histogram_features_.size() > 0) {\n if (smaller_num_bits_in_histogram_bins <= 16) {\n FixHistogramDiscretizedKernel<<>>(\n cuda_feature_num_bins_.RawData(),\n cuda_feature_hist_offsets_.RawData(),\n cuda_feature_most_freq_bins_.RawData(),\n cuda_need_fix_histogram_features_.RawData(),\n cuda_need_fix_histogram_features_num_bin_aligned_.RawData(),\n cuda_smaller_leaf_splits);\n } else {\n FixHistogramDiscretizedKernel<<>>(\n cuda_feature_num_bins_.RawData(),\n cuda_feature_hist_offsets_.RawData(),\n cuda_feature_most_freq_bins_.RawData(),\n cuda_need_fix_histogram_features_.RawData(),\n cuda_need_fix_histogram_features_num_bin_aligned_.RawData(),\n cuda_smaller_leaf_splits);\n }\n }\n global_timer.Stop(\"CUDAHistogramConstructor::FixHistogramDiscretizedKernel\");\n global_timer.Start(\"CUDAHistogramConstructor::SubtractHistogramDiscretizedKernel\");\n if (parent_num_bits_in_histogram_bins <= 16) {\n CHECK_LE(smaller_num_bits_in_histogram_bins, 16);\n CHECK_LE(larger_num_bits_in_histogram_bins, 16);\n SubtractHistogramDiscretizedKernel<<>>(\n num_total_bin_,\n cuda_smaller_leaf_splits,\n cuda_larger_leaf_splits,\n hist_buffer_for_num_bit_change_.RawData());\n } else if (larger_num_bits_in_histogram_bins <= 16) {\n CHECK_LE(smaller_num_bits_in_histogram_bins, 16);\n SubtractHistogramDiscretizedKernel<<>>(\n num_total_bin_,\n cuda_smaller_leaf_splits,\n cuda_larger_leaf_splits,\n hist_buffer_for_num_bit_change_.RawData());\n CopyChangedNumBitHistogram<<>>(\n num_total_bin_,\n cuda_larger_leaf_splits,\n hist_buffer_for_num_bit_change_.RawData());\n } else if (smaller_num_bits_in_histogram_bins <= 16) {\n SubtractHistogramDiscretizedKernel<<>>(\n num_total_bin_,\n cuda_smaller_leaf_splits,\n cuda_larger_leaf_splits,\n hist_buffer_for_num_bit_change_.RawData());\n } else {\n SubtractHistogramDiscretizedKernel<<>>(\n num_total_bin_,\n cuda_smaller_leaf_splits,\n cuda_larger_leaf_splits,\n hist_buffer_for_num_bit_change_.RawData());\n }\n global_timer.Stop(\"CUDAHistogramConstructor::SubtractHistogramDiscretizedKernel\");\n }\n}\n\n} // namespace LightGBM\n\n#endif // USE_CUDA\n" }, { "path": "tests/python_package_test/__init__.py", "content": "" } ]